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8 Research Methodology in Clinical Child and Adolescent Psychology

Jonathan S. Comer, Florida International University

Laura J. Bry Department of Psychology Florida International University Miami, FL, USA

• Published: 07 November 2018
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To continue to move the field of clinical child and adolescent psychology forward, researchers must systematically rely on research strategies that achieve favorable balances between scientific rigor and clinical relevance. This chapter presents an overview of modern methods and considerations that maximize both rigor and relevance in the evaluation of child and adolescent treatments. This research methodology chapter is organized around the four stages of a clinical trial: (a) planning a clinical trial; (b) conducting a clinical trial; (c) analyzing trial outcomes, and (d) reporting results. Sample selection, random assignment, control condition selection, treatment integrity, missing data, clinical significance, treatment mechanisms, and consolidated standards for communicating study findings to the scientific community are addressed. Collectively, the methods and design considerations detail modern research strategies for the continually evolving science of clinical child and adolescent psychology.

Children’s mental health problems impose a staggering public health burden. For example, roughly 40% of adolescents in the United States have reportedly suffered from a mental disorder in the past year (Kessler, Avenevoli, Costello, et al., 2012), and these disorders are associated with enormous individual, family, and societal costs. Youth mental disorders are associated with complex comorbid presentations (Kessler, Avenevoli, McLaughlin, et al., 2012); elevated substance use (Kendall & Kessler, 2002; Wu, Goodwin, Comer, Hoven, & Cohen, 2010 ); and medical comorbidities ( Merikangas et al., 2015 ). When left untreated, they persist into adulthood, during which time they are associated with family dysfunction; disability in major life roles ( Merikangas et al., 2007 ); poorer educational attainment ( Breslau, Lane, Sampson, & Kessler, 2008 ); criminality; suicide ( Nock & Kessler, 2006 ); and overall reduced health-related quality of life ( Comer et al., 2011 ).

Despite these daunting statistics, recent years have witnessed very promising advances in the development and evaluation of evidence-based interventions for the broad range of children’s mental health problems ( Kendall, 2012 ; Ollendick & King, 2004 ). Evaluations of therapeutic efficacy and effectiveness have evolved from a historical reliance on simply professional introspection and retrospective case histories to modern reliance on complex multimethod experimental investigations and well-controlled randomized trials across well-defined and increasingly generalizable samples.

However, much still remains to be learned about the treatment of child and adolescent mental health problems, and this should not be surprising. After all, whereas many sciences have been progressing for centuries (e.g., physics, chemistry, biology), it has been only relatively recently that empiricism and the scientific method have been applied systematically to clinical child and adolescent psychology ( Comer & Kendall, 2013b ). At this relatively early stage in the science of clinical child and adolescent psychology, most of the research is still ahead of us. As we face the challenge of optimally informing best practices in youth mental health care with data, the prepared investigator must be familiar with the portfolio of modern research strategies for conducting clinical evaluations of treatment methods—a set of “directions” so to speak for getting from “here” to “there” (see Comer & Kendall, 2013b ). Just as with any travel directions, where there may be many acceptable ways to get to the same destination (e.g., the scenic way, the quick way, the cheap way), for each testable question in clinical child and adolescent psychology, there are many methods that can be used to reveal meaningful information, each with limitations and strengths.

To continue to move the field of clinical child and adolescent psychology forward, investigators must systematically rely on research strategy “routes” that achieve favorable balances between scientific rigor and clinical relevance ( Comer & Kendall, 2013b ). This necessitates careful considerations regarding the trade-offs between internal validity (which is typically linked with rigor ) and external validity (which is typically linked with relevance ). Internal validity pertains to the extent to which the independent variable, rather than an extraneous influence, accounts for variance in the dependent variable. The more rigorous and tightly controlled a study design, the more persuasively the study is able to rule out the possibility that variables beyond the independent variable might be accounting for variance in the dependent variable. External validity, on the other hand, pertains to the extent to which study results generalize to people, settings, times, measures, and characteristics other than those included in a particular study. Accordingly, design decisions focusing on internal validity and aiming to improve interpretative conclusions typically have the consequence of reducing the external validity and generalizability of findings to broadly relevant settings and vice versa. With this in mind, we present an overview of modern methods and considerations that maximize both rigor and relevance in the evaluation of child and adolescent treatments.

Planning a Clinical Trial

When planning a clinical evaluation to examine the efficacy or effectiveness of treatment for child and adolescent mental health problems, six sets of considerations are essential: (a) design considerations; (b) control condition considerations; (c) independent variable considerations; (d) dependent variable considerations; (e) assessment point considerations; and (f) sample and setting considerations.

Design Considerations in Clinical Child and Adolescent Psychology

Broadly speaking, the development and evaluation of novel therapeutic interventions occur through a sequence of three progressive stages ( Rounsaville, Carroll, & Onken, 2001 ). Stage 1 encapsulates an iterative development process, combining previous research, clinical expertise, and consultation with experts. An experimental intervention is tested, preliminarily, on a small number of subjects of the population for which the treatment is intended. This idiographic approach provides initial evidence to show the relationship between treatment and symptoms on an individual basis. Stage 1 designs also address issues related to intervention feasibility and acceptability and provide opportunities for intervention refinement or tweaking before progressing to Stage 2, large-scale evaluation. In Stage 2, tightly controlled, systematic, and rigorous evaluations with high internal validity establish broad efficacy of the intervention by looking at nomothetic patterns. Stage 3 designs evaluate intervention effectiveness, prioritizing external validity, generalizability to a wider range of patients, and transportability across clinical settings and practitioners.

Depending on research goals and the stage of the intervention, a range of study designs is available to evaluate an experimental treatment. Selecting a study design involves finding a balance between one’s research question and goals and the limitations associated with each design option. We now turn our attention to leading design options available to investigators, including single-case and multiple-baseline experimental designs, the randomized controlled trial (RCT), and sequenced treatment designs.

Single-case and multiple-baseline designs.

Systematic research designs encompassing a single individual or a small sample of subjects are useful for informing our understanding of individual behavior change and revealing a signal of “how,” “why,” and “when” treatment-related changes may occur. This idiographic portrait of the relationship between an intervention and symptoms makes such designs particularly useful during Stages 1 and 3 of treatment evaluation ( Barlow & Nock, 2009 ). Indeed, these designs have played a prominent role in developing clinical guidelines and best practices, underscoring their importance in evidence-based practice ( American Psychological Association, 2002 ). Understanding treatment-related change at the individual level provides an opportunity for intervention refinement prior to initiating costly large-scale evaluations. After large-scale clinical evaluations have been conducted, single-case designs can again be useful to evaluate the intervention’s applicability to individuals in new settings or with different symptom profiles or when implemented by clinicians of different training backgrounds. Single-case and multiple-baseline trials are also relatively cost-efficient, making them valued designs in the context of limited funding.

Generally, single-case experimental designs employ a systematic, repeated-measures approach wherein data related to a specific dependent variable (i.e., clinical target) are collected across a baseline and treatment phase. Researchers must balance a data collection schedule that is frequent enough to provide clues about when treatment-related changes occur, while avoiding potential subject response fatigue ( Barlow, Nock, & Hersen, 2009 ; Gallo, Comer, & Barlow, 2013 ; Kazdin, 2001 ). Typically, the baseline phase is referred to as the “A” phase. Dependent variable data are collected across a baseline phase (rather than at a single time point) to document the stability of the target behavior as it occurs naturally. These data are then compared to observations collected during the treatment phase (or “B” phase) of the design. Capturing stability of the behavior during the baseline period is critical for attributing changes to the treatment, rather than attributing changes to a natural cycle of fluctuations of that behavior ( Gallo et al., 2013 ).

The most traditional representation of the single-case experimental design is the A–B design , in which a target behavior is measured repeatedly across both a baseline A phase and a treatment B phase. This design allows the researcher to capture data on a naturally occurring, preintervention behavior, which is then directly compared to observations of the behavior after the intervention has been introduced. A more rigorous permutation adds an additional A or baseline phase of data collection. In such an A–B–A design , the A and B phases are followed by an additional phase of data collection during which the intervention has been withdrawn (A). Such introduction and removal of treatment allow for stronger conclusions. Importantly, within clinical psychology, withdrawal designs are often hard to attain and perhaps even less desirable. For example, unlike psychopharmacology evaluations in which a patient can simply stop taking a medication, “unlearning” a specific coping skill or behavioral strategy can be difficult, if not impossible, and in some cases may even be unethical.

Some investigators will add a second B phase to an A–B–A design to address some of the shortcomings of the A–B–A design. Such an A–B–A–B design adds rigor by offering an inherent replication of findings and the ethical shortcomings of withdrawing an effective intervention. Another permutation, the B–A–B design allows the investigator to begin the evaluation with the intervention. This is useful when assessing a clinical behavior that requires immediate attention and for which waiting throughout a baseline period may be contraindicated (e.g., suicidal ideation, self-injury), but it does not provide observations of target behaviors as they occur naturally in the absence of intervention. Moreover, to control for possible placebo effects, the A–B–C–B design introduces a third “C” phase that corresponds to a placebo condition (e.g., education, support, and attention). Addition of a C phase allows investigators to more readily attribute improvements seen in the treatment B phase to the specific intervention, rather than broadly to any intervention that could have been applied.

For researchers evaluating psychosocial interventions that do not provide opportunity for withdrawal phases, a multiple-baseline design can serve as a valuable alternative. Multiple-baseline designs employ an A–B design that differentially extends the length of the baseline (A) phase across behaviors, subjects, or settings. To establish intervention efficacy, improvements in the target behavior must be seen only after the treatment phase (B) is initiated. The baseline phase length may be determined prior to beginning a study or a researcher may wait until the target behavior stabilizes in participants and then initiate treatment (B phase) only after stabilization is achieved.

Multiple-baseline designs may occur across behaviors , across subjects , and across settings . A multiple-baseline design across behaviors evaluates the effects of an intervention on different behaviors, but within the same individual. Improvements seen in the clinical targets are attributed to intervention only if they occur after initiation of the phase of the intervention in which they were specifically targeted. Multiple-baseline designs across subjects evaluate the effects of a single intervention on multiple individuals who share a similar clinical presentation (e.g., Comer et al., 2012 ; Jarrett & Ollendick, 2012 ; Ollendick, 1995 ; Suveg, Kendall, Comer, & Robin, 2006). Each subject is assigned to a baseline period of varying and randomly determined lengths, and efficacy is demonstrated when improvements in target behaviors occur after the treatment phase is initiated, regardless of the duration of the baseline period assigned. In multiple-baseline designs across settings, intervention is applied sequentially in different settings for the same individual (e.g., at home, at school). To demonstrate treatment efficacy, improvements in the target behavior should occur in a specific setting only after intervention has been implemented in that setting.

Strengths of the multiple-baseline design include its ability to circumvent challenges of withdrawal designs when applied to psychosocial interventions. Moreover, multiple-baseline designs allow researchers to examine an intervention across multiple behaviors, settings, or individuals, which yields more generalizable findings. Some have argued that the strength of the multiple-baseline design decreases when fewer than three or four behaviors, individuals, or settings are measured ( Barlow & Nock, 2009 ; Gallo et al., 2013 ), although there is some debate about this.

Randomized controlled trials.

Whereas single-case experimental designs offer idiographic data and inferences regarding treatment effects on individual children and adolescents, to examine causal impacts of therapeutic interventions in ways that can inform clinical and policy decision-making, a treatment must be tested with tightly controlled procedures derived from experimental science in a nomothetic manner. By maximizing internal validity and systematically manipulating the intervention as the independent variable in a randomized controlled trial, researchers can more confidently and robustly conclude whether observed changes in clinical targets resulted from the intervention itself or from other extraneous factors ( Kendall, Comer, & Chow, 2013 ).

The RCT can take the form of a small pilot RCT or a larger scale clinical trial. The small pilot RCT represents a randomized, controlled study design with a restricted sample size and is useful at the end of Stage 1 research following refinement of the intervention but before entering into a larger, more costly RCT (for an example, see Comer et al., 2017 ). Small pilot RCTs ensure the intervention is suitable for a randomized study design and identify issues related to feasibility to be addressed before reaching Stage 2 research. Large-scale RCTs represent Stage 2 evaluations of therapeutic intervention and use adequately powered sample sizes to examine nomothetic effects across groups of children and adolescents with similar clinical portraits.

Regardless of sample size, the defining characteristic of the RCT is random assignment between groups . Youth are randomly assigned to either an active treatment condition where the independent variable (e.g., a given therapeutic intervention) is applied or a control condition where the experimental intervention is absent. Assignment to treatment conditions must be determined randomly and independent of baseline symptom levels, family preferences, or therapist/investigator sense of which condition would be best for a given child. At trial outset, each child has an equal chance of being assigned to various conditions (although for variations, see Kendall et al., 2013 ). Including both a treatment and control condition allows researchers to directly compare observations of a target behavior across youth who have been similarly matched on key clinical characteristics. Because of the controlled study design, changes seen uniquely or more prominently in the treatment group can confidently be attributed to the therapeutic intervention.

Importantly, randomly assigning youth across treatment conditions does not guarantee ultimate comparability across conditions, although the likelihood of such is high. Simply due to chance, participants in one group may be older, more impaired, or different on any number of meaningful variables. After data collection is complete, researchers can evaluate the comparability of youth across groups, and if baseline group differences are found, such differences are attended to as covariates at the data analysis phase. Alternately, to ensure children and adolescents across groups are matched on key characteristics, researchers can use randomized block assignments . Participants are arranged into small, equal numbered subgroups based on comparability on key characteristics (e.g., subgroups of one boy and one girl to ensure gender comparability across groups). Randomization then occurs at the subgroup level, rather than at the individual child level, retaining the randomized element while also ensuring comparability across groups.

Sequenced treatment designs.

In clinical care settings, treatments result in a range of outcomes, including improvements on target symptoms (treatment response), worsening of target symptoms (deterioration), no change in target symptoms (nonresponse), or some, but not sufficient, improvement of target symptoms (partial response). Throughout the course of treatment, therapists make clinical decisions based on response to that point to determine what, if any, changes should be made to the child’s treatment plan (e.g., continuing with the treatment course vs. switching to another treatment). The rigor and structure of the traditional RCT does not allow for flexibility during treatment implementation and therefore cannot inform clinical decision-making in cases of nonresponse, partial response, or clinical deterioration during the course of treatment.

Sequenced treatment designs and adaptive treatment regimens retain randomization procedures while also systematically evaluating shifting treatment strategies across time for children and adolescents who are not sufficiently improving. The most common and increasingly popular adaptive treatment design is the sequential multiple-assignment randomized trial (i.e., the SMART design ) ( Dawson & Lavori, 2012 ; Murphy, 2005 ), which yields quality data with which to develop evidence-based adaptive treatment algorithms that differentially incorporate the benefits of intervention forms across critical points in treatment. A SMART includes multiple intervention stages, but as each child moves through intervention stages, randomization options at key decision points are determined by the child’s treatment response at that point (see Barlow & Comer, 2013 ). Indeed, the design of a SMART improves on traditional factorial RCT designs focused on broad main effects of treatment conditions across a single treatment phase and instead recognizes the true multiphase nature of the treatment process for the majority of children and adolescents in clinical practice. The sample SMART design illustrated in Figure 8.1 examines sequences of treatment in the context of behavioral parent training (BPT) and individual child therapy (ICT) and yields data to meaningfully inform eight distinct adaptive treatment regimens. This single design requires a very large sample size but can efficiently inform sequenced treatment decisions for children and adolescents showing a range of clinical responses to different forms of initial intervention. Despite the adaptive nature of children’s individual intervention courses, the randomization element of a SMART at critical decision points still affords causal conclusions ( Barlow & Comer, 2013 ; Lei, Nahum-Shani, Lynch, Oslin, & Murphy, 2012 ). Accordingly, the SMART offers a hybrid of the nomothetic groups-based (factorial) design strategy that typically informs policy decisions and the more idiographic single-case experimental designs that clarify individualized changes.

A sample sequential multiple-assignment randomized trial (SMART) design.

A recent SMART in clinical child and adolescent psychology ( Pelham et al., 2016 ), for example, found that central nervous system stimulant medication for attention deficit hyperactivity disorder (ADHD) is most effective when it is used as a supplemental second-line treatment following an adequate course of quality low-dose behavior therapy, rather than as a first-line treatment. Pelham and colleagues were also able to document that the behavioral-first treatment strategy was far less expensive for the healthcare system than starting treatment with medication. This SMART has the potential to meaningfully influence treatment sequencing for children with ADHD in primary care, where medication alone has traditionally been the most often used treatment, with poor long-term outcomes and high associated costs.

Control Condition Considerations in Clinical Child and Adolescent Psychology

Once the investigator has decided on an appropriate study design, the investigator must select an appropriate control condition. In a “controlled” evaluation, comparable children and adolescents are randomly assigned to either the treatment condition and receive the experimental intervention or a control condition and do not receive the intervention. By contrasting changes between youth across conditions, the efficacy of the intervention beyond outcomes produced by extraneous factors (e.g., passage of time, family expectations) can be assessed. Control conditions take many forms, each carrying a unique set of strengths and limitations that affect the inferences that can be made.

No-treatment control condition.

Youth assigned to groups in which they receive no treatment are considered to be in a no-treatment control condition . This straightforward design allows researchers to draw comparisons between treatment and no treatment and consider the effect of intervention above and beyond the passage of time. Comparing intervention outcomes to outcomes in a no-treatment control condition allows the investigator to rule out the possibility that intervention effects are simply due to the regression of extreme scores to the mean across the study time period. Importantly, however, a no-treatment control condition does not rule out other explanatory factors beyond the possibility that changes represent what might naturally unfold with the passage of time. Sometimes when participants simply know they are going to get treatment, it affects their expectancies, and they show symptom improvements. Accordingly, a no-treatment control condition cannot rule out the possibility that superior changes in the treatment condition are accounted for by differences in participant expectancies associated with being assigned to (any) treatment. Accordingly, no-treatment control conditions are best suited for early stages of treatment development and evaluation and are not appropriate to meaningfully address conceptual questions about treatment efficacy and active treatment components. That said, pragmatic considerations make no-treatment controlled designs hard to implement, given difficulties of recruiting and retaining participants in a no-treatment condition.

Wait-list control condition.

An improvement over the no-treatment control condition that accounts for patient expectancies is the wait-list control condition. In a wait-list controlled design, children are assigned to receive the treatment either immediately or after a predetermined waiting period. At outset, all participants know they will receive treatment at some point in the study and likely hold similar expectations that their symptoms will improve, regardless of condition. Target clinical behaviors are assessed at uniform intervals throughout both conditions. For example, if an experimental intervention is 12 weeks, then the wait-list interval would ideally be 12 weeks as well.

Although wait-list control conditions effectively account for the passage of time as well as patient expectancies of ultimate symptom improvement, wait-list control conditions do not account for inherent benefits associated with receiving care and attention from clinical staff that have nothing to do with the specific therapeutic components hypothesized to be responsible for treatment-related change. Further, participants in a wait-list control condition are prohibited from accessing other care services during the interim wait period. Accordingly, attrition from wait-list control conditions can be high. Moreover, it can be unethical to implement a wait-list control design when alternative treatments for the clinical target have been supported in previous work. In such cases, a multiple-treatment comparison design (discussed further in this chapter) is more appropriate.

With these limitations in mind, similar to the no-treatment control, wait-list control conditions are best suited for early stages of treatment development and evaluation. Importantly, wait-list and no-treatment control conditions can carry with them ethical dilemmas. Children and adolescents in these control conditions must be regularly monitored throughout study participation to ensure they do not show serious clinical deterioration that would suggest they should be withdrawn from their assigned condition. Indeed, these control condition designs are not suitable for clinical populations that cannot tolerate a wait-list or no-treatment phase (e.g., adolescents showing suicidal behaviors).

Attention-placebo control condition.

Attention-placebo control conditions are valuable for investigators looking to additionally rule out “common factors” associated with all therapeutic interventions (e.g., receiving care and attention from warm clinical staff, having an outlet through which problems can be discussed). These designs contrive a control condition that mimics elements of treatment by inviting participants to receive face-to-face interactions with attentive clinical staff. Importantly, the attention-placebo control condition is explicitly devoid of elements that are believed to be specifically effective in the experimental intervention. Attention-placebo control conditions typically consist of general psychoeducation, clinical monitoring, and broad patient support.

Despite the advantage that attention-placebo control conditions have for accounting for common, nonspecific therapeutic factors, it can be difficult to establish credibility (for patients and for therapists) when implementing these control conditions. It is useful for therapists to hold equally positive expectations of improvement for participants across conditions ( Kazdin, 2003 ), and establishing positive expectations for a condition oriented around nonspecific treatment factors can be difficult to achieve. Thus, researchers utilizing attention-placebo control conditions should measure participant expectations across conditions so that participant expectancy effects can be accounted for in analyses.

Standard-treatment comparison condition.

A standard-treatment comparison or treatment-as-usual control condition consists of an invention that is routinely given and allows the investigator to evaluate the incremental benefits of an experimental intervention over and above the existing standard of care. Ethical concerns that arise in no-treatment, wait-list, and attention-placebo conditions are minimized because children in this condition are receiving exactly what they would have received for their problems had the study never taken place. Moreover, attrition is minimized as all children receive active care, and patient and therapist expectations for change are likely to be more comparable. Despite these benefits, however, what exactly constitutes “treatment as usual” has been difficult to operationalize as it varies widely across settings, making it difficult to integrate findings across studies incorporating these control conditions. Further, differences between an experimental intervention and a treatment-as-usual condition might be attributed to differences in therapist quality, training, supervision, or organization, rather than to differences specific to the hypothesized active ingredients of the experimental intervention. Moreover, it can be difficult to match the intensity, dosing, or duration of treatments when comparing an experimental treatment condition to a treatment-as-usual condition. For example, suppose an experimental treatment protocol calls for weekly 60-minute sessions with a therapist for 12 weeks, whereas the standard care that is currently offered in a setting entails 20-minute sessions every other week for up to 8 weeks. If the investigator changes the treatment-as-usual condition to have control participants meet weekly and for longer periods of time with therapists, the control group is no longer a “standard care” condition; it is a new condition contrived by the investigator. Alternatively, if the investigator in this scenario compares the experimental condition to the true treatment as usual, it is possible that differences between the conditions could simply be due to differences in the frequency and intensity of care and not to the putative active ingredients of the experimental treatment.

Multiple-treatment comparisons.

Some more rigorous and revealing studies include multiple active treatment conditions and are thus able to address issues of relative or comparative efficacy. These studies offer direct comparisons of alternative active treatments. For multiple treatment comparisons, it is important that each treatment is comparable on a number of characteristics, including duration, session length, and frequency; setting; and level of credibility. For example, if children who received Treatment A were found to show superior outcomes to children in Treatment B, but Treatment A was 8 weeks and Treatment B was 4 weeks, the investigator would not be able to determine whether Treatment A had stronger effects than Treatment B or whether the study just found that 8 weeks of treatment was better than 4 weeks of treatment. Further, multiple-treatment comparison studies must ensure comparability of therapists across conditions. Therapists should be matched on their levels of training and experience, expertise in administration of study treatment protocols, and attitudes toward the treatments, including their allegiance to specific therapeutic approaches and their intervention expectancies. For example, it would be problematic if in a multiple-treatment comparison design a group of psychodynamic therapists conducted both a behavioral intervention (in which their expertise is low) and a psychodynamic therapy (in which their expertise is high). If outcomes differed across conditions, it would not be clear whether this was the result of true differences between behavioral and psychodynamic approaches or whether this was simply due to differences in therapist expectancies across the conditions.

Researchers using a multiple-treatment comparison design must also consider issues of sample size and outcome measurement. Whereas comparisons of active conditions against inactive control conditions typically yield large effect sizes, comparisons of multiple active conditions typically yield smaller effect sizes and accordingly require larger samples for adequate power. Moreover, to avoid potential biases, measures should cover a range of target clinical symptoms, and assessments should be equally sensitive to expected changes associated with each treatment type. For example, a measure that primarily evaluates children’s self-talk may be a well-suited measure for examining the impact of cognitive behavioral therapy but may not evaluate meaningful changes associated with antidepressant medication, for which the direct targeting of children’s self-talk is not a proposed mechanism of change ( Comer & Kendall, 2013a ).

Independent Variable Considerations in Clinical Child and Adolescent Psychology

In the context of a clinical trial, the independent variable that is manipulated is treatment assignment, that is, whether a child does or does not receive treatment or which treatment condition a child will receive. As in any experimental study, this independent variable must be carefully operationalized and implemented with integrity. Specifically, when evaluating an experimental intervention, the treatment must be adequately detailed and described in order to replicate the evaluation or to be able to communicate to others how to conduct the treatment ( Comer & Kendall, 2013a ). A treatment protocol that clearly defines the intervention and dictates how it is to be administered is critical for internal validity and ensuring the integrity of the independent variable. However, manualized intervention protocols can limit external validity, especially when attempting to generalize findings to settings and practitioners who do not typically use treatment manuals to guide their services. Some critics argue that manualized treatment protocols are overly rigid and do not afford clinicians needed flexibility to adapt to the complex and individualized patient needs encountered in routine practice settings ( Addis & Krasnow, 2000 ). Although most supported treatment manuals have always afforded a great deal of flexibility to individual patient needs, more modern treatment protocols are increasingly taking a modular approach, in which supported practices for specific identified problems are structured as free-standing modules, and decision flowcharts guide treatment component sequencing and module selection ( Chorpita, 2007 ; Comer, Elkins, Chan, & Jones, 2014 ). Modularized treatment protocols address complex comorbidities and shifting clinical needs by accommodating personalized tailoring of care for specific problems presenting in each child.

Dependent Variable Considerations in Clinical Child and Adolescent Psychology

The investigator must decide which dependent variables will be assessed and how they will be measured. Indeed, it is critical to measure outcomes using a variety of methods in order to minimize bias. Given research documenting poor cross-informant agreement in the assessment of child psychopathology (e.g., Comer & Kendall, 2004 ; De Los Reyes & Kazdin, 2005 ; Grills & Ollendick, 2003 ), investigators are wise to collect reports from multiple informants (e.g., parents, teachers, therapists, children). Such a multi-informant strategy allows researchers to evaluate symptoms that may differentially present across various contexts and life domains or that may be perceived differently across key people in children’s lives ( Silverman & Ollendick, 2005 ). Features of cognitive development can interfere with the accuracy of young children’s reports, and demand characteristics may cause children to offer what they believe to be desired responses. Accordingly, it is important to collect data simultaneously from important adults in children’s lives who observe their behavior across different settings. On the other hand, parents and teachers may not be privy to more internal and unobservable symptoms (e.g., anxiety).

Multimodal assessment strategies draw on multiple modes of assessment (e.g., observations, questionnaires) to evaluate the same dependent variable. For example, positive parenting practices might be measured via behavioral codings of structured parent–child interactions, as well as parent self-reports. For other dependent variables, objective records (e.g., medical or school records) might be collected. Data on peer relations might draw on sociometric data and peer nominations.

Finally, multiple targets should be assessed ( De Los Reyes & Kazdin, 2006 ). Improvement can take many forms, including decreased symptoms, loss of clinical diagnosis, improved quality of life, higher academic functioning, and improved interpersonal functioning. No single dependent variable independently and sufficiently captures treatment response. Inherent in a multiple-domain assessment strategy, however, is the fact that treatments rarely produce uniform effects across assessed domains. For example, one treatment might improve child anxiety but not peer relationships, whereas another treatment might improve children’s peer relationships but not anxiety. If a clinical trial were to compare these two treatments, it is not readily apparent which treatment should be deemed more efficacious ( Comer & Kendall, 2013a ). Typically, the investigator selects a primary outcome, as well as secondary and exploratory outcomes that provide more nuanced information about treatment responses. Importantly, selection of a primary outcome variable must occur prior to collection and review of the findings, so that decisions about which variables are most important are made a priori and are not biased by the significance of results. De Los Reyes and Kazdin (2006) have argued for a multidimensional conceptualization of intervention change, and similarly we caution consumers of the treatment literature against simplistic dichotomous appraisals of treatments as effective or not.

Assessment Point Considerations in Clinical Child and Adolescent Psychology

Evaluating a novel intervention through an experimental design requires a clinical researcher to take careful observations of the dependent variables across the duration of the study at key time points. Target clinical behaviors are selected for measurement and should be assessed at the outset of the study to provide baseline data . Baseline data serve as benchmarks against which subsequent observations of dependent variables are assessed. Post-treatment assessments are another critical time point for assessment, as those observations speak to acute treatment outcomes or the impact of an experimental intervention on clinical symptoms immediately after treatment is complete.

Although post-treatment data are critical, post-treatment data do not allow researchers to examine enduring treatment effects. To demonstrate lasting treatment gains or maintenance , researchers must also measure clinical outcomes at predetermined intervals after treatment has been completed (e.g., 3 months post-treatment, 6 months post-treatment). Such follow-up evaluations add methodological rigor to a study. For example, in a study comparing multiple active treatments, acute post-treatment outcomes may be comparable, but follow-up assessments may reveal that children in one experimental treatment condition showed higher maintenance of treatment gains with continued time. Importantly, for follow-up assessments to capture true lasting effects, participants should not have contact with other clinical services during the follow-up assessment period. Because follow-up intervals can be lengthy, it is not always feasible or ethical to prevent participants from receiving outside services during a follow-up interval. Many investigators, accordingly, include a naturalistic follow-up component that allows participants to seek outside services during the interval between post-treatment and follow-up evaluation. Additional service use after treatment completion may actually be a variable of interest, and when it is not, outside service use during the follow-up interval should be controlled for statistically.

Investigators are also increasingly incorporating assessments at different points during treatment, or midtreatment, to establish growth curves, consider the rate and shape of change during the treatment phase, and better understand potential mediators of treatment response. Midtreatment assessments provide revealing data on when symptom changes occur during treatment, at what pace, and how changes across different domains of response may unfold and interact with one another across time ( Chu, Skriner, & Zandberg, 2013 ; Gallo, Cooper-Vince, Hardway, Pincus, & Comer, 2014 ; Kendall et al., 2009 ; Marker, Comer, Abramova, & Kendall 2013 ).

Sample and Setting Considerations in Clinical Child and Adolescent Psychology

Careful consideration is needed when selecting a sample to best represent the clinical population of interest. Those youth chosen to participate in the trial will strongly influence the extent to which findings can be generalized to the larger population of youth who may benefit from the treatment. A genuine clinical sample made up of youth shown to have a disorder and who are seeking treatment will afford greatest external validity and generalizability. However, genuine clinical samples can be difficult for researchers to recruit into studies; moreover, they frequently carry more complex clinical portraits, which can threaten the internal validity of the study. Alternatively, analogue or selected samples can afford a higher degree of control and internal validity in study design, but youth in such samples are not necessarily comparable to the majority of patients typically seen in clinical practice.

Broadly speaking, it is important that the sample in a study evaluating an experimental intervention reflect the population for which that intervention is intended to ultimately benefit. Thus, in addition to considering a sample’s clinical characteristics, researchers in clinical child and adolescent psychology must consider sociodemographic diversity. Race, ethnicity, gender, socioeconomic status, education level, and other related demographic characteristics must all be considered when recruiting an appropriate study sample that can generalize to the general population.

The setting in which a study takes place will also have important implications for the generalizability of results. Early stage evaluations of therapeutic interventions are often conducted in clinical research laboratory settings and require investigators to recruit subjects to participate. Therapists in these trials are typically part of the investigator’s research team, and as such their outcomes may not generalize to the practices of front-line clinicians who differ from research staff clinicians with regard to experience, caseload size, supervision, and oversight. It is ultimately critical to demonstrate the transportability of an intervention to front-line service settings. Therefore, later stage evaluations of therapeutic interventions must evaluate outcomes beyond tightly controlled research settings.

Conducting a Clinical Trial

Once a clinical trial has been carefully designed, it does not simply run itself. The investigator must play a highly active role in organizing and implementing each aspect of the study in order to ensure a successful trial.

Training and organizing study staff merits special attention. Independent evaluators ( IEs ) refer to staff members who participate in assessment procedures and who are masked to each participant’s treatment assignment. IEs must be trained to a prespecified criterion (e.g., must match the diagnostic profile generated by the principal investigator on at least three consecutive diagnostic interviews) prior to their active participation on the trial, and throughout the course of the study periodic reliability checks are further necessary to ensure interrater reliability across study IEs. Systematic safeguards must be put in place to guarantee that IEs are kept unaware of each participant’s treatment assignment. IEs should try to avoid patient waiting rooms in which they might run into families assigned to treatment. IEs should not attend clinical supervision meetings that would reveal participant assignment information. For smaller teams in which the same staff members serve as both IEs and as therapists on different cases, multiple supervision teams are required, and staff members can only serve as IEs on cases carried on opposite supervision teams. Prior to post-treatment assessments, participating families should be reminded that their post-treatment assessor does not know which treatment they received (or even whether they received treatment if it is a wait-list controlled trial), and families should be cautioned against speaking about any treatment experiences during the interview.

Study therapists must be adequately trained. This typically involves initial didactic training on the study protocol and knowledge quizzes, followed by role plays. Ideally, there is opportunity for trainee therapists to shadow and then cotreat several cases using the study protocol prior to their active participation as a therapist on the study. Investigators should set a criterion that must be met by study therapists prior to their carrying study cases independently (e.g., complete didactic training, achieve a score of 80% or greater on a knowledge quiz, shadow one case, and cotreat one case). Regular supervision is critical to avoid therapist drift and to ensure treatment integrity.

Just because study therapists have been trained to criterion does not guarantee that they will deliver the independent variable (treatment) as intended. In the course of a study, the treatment that was assigned may not in fact be the treatment that is provided (see also Perepletchikova & Kazdin, 2005 ). To ensure that study treatments are implemented as intended, treatment integrity checks should be conducted. Therapy sessions should be regularly recorded such that independent raters can view them and provide quantifiable judgments on the implementation of key treatment components. McLeod, Islam, and Wheat (2013) provided more detailed descriptions of procedural issues in the conduct of quality assurance and treatment integrity checks.

Throughout the course of a clinical trial, someone must be responsible for monitoring the sample and the data and for ensuring that all data are collected as designed and intended. This person must be omniscient: The person must know the condition of every participant, must know who is assigned to which cases and who is responsible for collecting each piece of data, must know who can know about each participant’s condition, and must be aware of where each family is in the flow of study phases. Even with such a person dedicated to this role, data will inadvertently be missed, blinds will unintentionally be broken, and families will mistakenly not be contacted at their appropriate follow-up points. For a large clinical trial, this is a full-time job, but for smaller studies, a principal investigator can often perform in this role. Most important, the individual in charge of tracking cannot serve as an IE or as an IE supervisor because their role inherently unmasks them to all study-related information that could bias responses.

Throughout treatment, study staff must regularly monitor adverse events, and an individual or panel of individuals must be responsible for deciding whether a particular child suffering adverse events should be withdrawn from the study. This is particularly important when treatment conditions include medications that can introduce unfavorable side effects, but psychological treatments can also be stressful and associated with adverse events.

Retaining the sample throughout the study can be challenging. It is recommended that study staff phone, email, or text families weekly to “welcome” them to their next session or appointment. During the treatment phase, there is an attrition risk when study treatment cannot address complex and shifting patient needs that may present. For example, a family crisis, an emergent academic issue, or a serious peer conflict may present, and such unforeseen events may become a clinical priority for the family that is not explicitly addressed by the treatment protocol. Adjunctive services and attrition prevention ( ASAP ) procedures (e.g., Abikoff et al., 2002 ) are often implemented to maintain the sample, in which each case in a trial is allowed a prespecified number of additional sessions during the treatment phase to address exigencies or clinical crises that fall outside of the scope of the treatment protocol. Even children who do not complete treatment should be invited to participate in as many post-treatment and follow-up evaluations as possible. Monthly calls, birthday wishes, and holiday cards are recommended during follow-up intervals in order to maintain contact with families between the end of treatment and follow-up evaluation, thus maximizing sample participation at follow-up.

Analyzing Trial Outcomes

After conducting a clinical trial, the data analysis phase entails the active process through which the investigative team extracts relevant information from the collected data in ways that permit statistical inferences about the larger population of youth the sample was recruited to represent. A comprehensive outline of clinical trial data analysis is beyond the scope of the present chapter (the interested reader is referred to Jaccard & Guilamo-Ramos, 2002a , 2002b ; Read, Kendall, Carper, & Rausch, 2013 ); here, we briefly address (a) missing data and attrition; (b) evaluation of clinical significance; and (c) evaluation of change mechanisms.

Missing data and attrition.

Even in the most diligently organized and carefully implemented clinical trial, not every child randomized will actually complete participation. Mason (1999) estimated that on average roughly 20% of participants withdraw prematurely from their participation in a clinical trial. Attrition can be problematic for data analysis, particularly when large numbers of youth do not complete treatment or when rates of attrition vary across study conditions ( Leon et al., 2006 ).

When there is a meaningful discrepancy between the number of children randomized to the various treatment conditions and the number of children who completed their participation, the investigator can conduct and report two sets of data analyses: (a) treatment completer analyses that evaluate only those youth who completed the full course of their treatment and (b) intent-to-treat analyses that include all those initially randomized. Treatment completer analyses evaluate intervention effects when someone receives a full “dose” of treatment. Those who drop out of treatment, those who refuse treatment, and those who do not adhere to treatment are not included in such analyses ( Kendall et al., 2013 ). Treatment completer outcomes may be somewhat inflated because they only capture the results of children who fully adhered to and completed treatment. At the same time, treatment completer analyses directly examine outcomes associated with true exposure to the experimental manipulation and therefore provide very valuable information.

On the other hand, intent-to-treat analyses are more conservative and evaluate outcomes for all children involved at the point of randomization. Such analyses speak more directly to issues of generalizability of findings as they incorporate information about treatment tolerability. A simplistic method for handling missing data for intent-to-treat analyses is the last observation carried forward ( LOCF ) method, which assumes that the scores for children who withdraw from treatment remain constant from their last assessment point throughout the conclusion of the study. For example, if a family withdraws participation at Week 8, then the data values from that child’s Week 7 assessment (or most recently completed assessment) would be substituted for all subsequent assessment points. However, LOCF introduces systematic bias and fails to take into account the uncertainty of postdropout functioning ( Leon et al., 2006 ). Accordingly, LOCF methods have fallen out of fashion, in favor of (a) multiple imputation methods , which impute a range of values for missing data by incorporating the uncertainty of the true values of missing data ( Little & Rubin, 2002 ); and (b) mixed-effects modeling , which relies on regression modeling to address missing data in the context of random (e.g., child) and fixed (e.g., treatment condition, gender) effects. Mixed-effects modeling is a particularly strong approach to handling missing data when numerous assessments are collected across a treatment trial (e.g., weekly data are collected).

Evaluation of statistical and clinical significance.

Statistical significance is identified when the mean difference between treatment conditions is beyond that which could have resulted by chance alone (most commonly defined as p < .05). Tests of statistical significance are critical as they indicate how likely it is that observed differences between conditions were not due solely to chance. However, tests of statistical significance alone do not provide compelling information on the clinical significance of group differences. Relying solely on statistical significance can lead an investigator to interpret treatment gains as meaningful when in fact they may be clinically insignificant ( Kendall et al., 2013 ). For example, suppose that a treatment for disruptive behavior problems results in significantly lower scores on the Externalizing Scale of the Child Behavior Checklist (CBCL). An examination of CBCL means however reveals only a small but reliable shift from a mean of 81 to a mean of 78. With a large enough sample size, this change can achieve statistical significance at the conventional p < .05 level, but a 3-point change from 81 to 78 on the CBCL Externalizing Scale is of limited practical significance. At both baseline and still at post-treatment, the scores are within the clinically elevated range, and such a small magnitude of change may have little effect on a child’s functioning.

Clinical significance refers to the meaningfulness or persuasiveness of the magnitude of change ( Jacobson & Truax, 1991 ; Kendall, 1999 ). Whereas tests of statistical significance ask the question, “Were there intervention-related changes?” tests of clinical significance ask the question, “Were intervention-related changes convincing and meaningful?” Clinical significance can be evaluated by (a) considering the extent to which treated youth are returned within normal limits (i.e., they are indistinguishable from a normative sample of youth; Kendall, Marrs-Garcia, Nath, & Sheldrick, 1999 ); (b) evaluating the magnitude of effect sizes of change, regardless of statistical significance; or (c) computing the Reliable Change Index (RCI; Jacobson & Truax, 1991 ) across participants. To calculate the RCI, the investigator assesses the extent to which each individual participant’s change pre- to post-treatment was reliable, versus the possible result of simple measurement error. For each participant, the investigator calculates a difference score (e.g., post score minus baseline score) and compares it to the standard error of measurement (i.e., ±1.96 SE ). As such, the RCI is determined by two factors: (a) the magnitude of change and (b) the reliability of measurement. Each of these approaches to assessing clinical significance (e.g., normative comparisons, effect size interpretations, RCI) provides an important, but unique, perspective on the meaningfulness of treatment outcomes; thus, they are often used in conjunction with one another. For example, an investigator might use published norms of a measure to evaluate which participants crossed over from the clinical range to the nonclinical range and also calculate an RCI for each participant. The investigator can use these data to group participants into the following categories: recovered (i.e., passed both normative cutoff and RCI criteria), unchanged (i.e., passed neither criteria), improved (passed RCI but not normative cutoff criteria), or deteriorated (i.e., passed RCI criteria in the negative direction) ( Comer & Kendall, 2013a ; Jacobson & Truax, 1991 ; McGlinchey, Atkins, & Jacobson, 2002 ).

Evaluation of change mechanisms.

Researchers and funding agencies are increasingly interested in identifying the conditions that determine when an intervention is more or less potent (moderation) and the processes through which an intervention produces change (mediation). A moderator is a variable that delineates the conditions under which a given intervention is related to an outcome. Conceptually, moderators identify on whom and under which circumstances treatments have different effects, and they are usually measured prior to treatment ( Kendall et al., 2013 ; Kraemer, Wilson, Fairburn, & Agras, 2002 ). Functionally, a moderator is a variable that influences either the direction or magnitude of an association between the independent variable (treatment condition) and a dependent variable (outcome). Treatment moderators help identify which youth might be most responsive to which interventions and for which youth alternative interventions might be appropriate. Of note, when a variable broadly predicts treatment response across all treatment conditions in a clinical trial, conceptually that variable is simply a predictor , not a moderator (see Kraemer et al., 2002 ).

A mediator, on the other hand, is a variable that is measured during treatment and clarifies the process by which an intervention influences an outcome. Conceptually, mediators identify how and why treatments have the effect they do ( Kraemer et al., 2002 ). The mediator effect reveals the mechanism through which treatment is associated with outcomes. Significant meditation affords causal conclusions. If a supported treatment for child anxiety was found to influence negative self-talk, which in turn was found to have a significant influence on child anxiety and avoidance, then negative self-talk might be considered to mediate the treat-to-outcome association. Specific statistical methods used to evaluate the presence of treatment moderation and mediation can be found elsewhere ( MacKinnon, Lockhart, Baraldi, & Gelfand, 2013 ).

Funding agencies are increasingly prioritizing interventions research that explicitly examines mechanisms that can explain treatment effects. The experimental therapeutics paradigm has the researcher first hypothesize a “target” or mechanism of action. Rather than focusing on clinical effects and treatment response, the experimental therapeutics researcher studies an intervention first as a manipulation to verify whether the intervention has a predicted effect on the target mechanism (i.e., target engagement). Once target engagement has been documented, the experimental therapeutics researcher then examines whether clinical outcomes are indeed related to successful target engagement.

Reporting Results

Presenting the written study findings to the scientific community in a peer-reviewed outlet is the final step of a clinical trial. A well-constructed data report must present all relevant methodological and study-related information with enough context to afford meaningful interpretation of results and to allow for replication. Study aims and results must be placed in the context of related research to illustrate how the current findings compare to previous results, and the investigator must discuss how the results build on, support, or diverge from other findings in the field. A candid and nondefensive articulation of study limitations and shortcomings is also critical in order to direct future research.

To avoid potential bias in the reporting of clinical trial results, a multidisciplinary panel of experts established a checklist of guidelines for maximizing transparency in reporting (i.e., CONSORT guidelines; see Begg et al., 1996 ). CONSORT (i.e., Consolodated Standards of Reporting Trials) guidelines offer a minimum set of recommendations for preparing reports of clinical trial findings that ensure transparent, comprehensive reporting to facilitate critical evaluation and interpretation. Chief among the CONSORT items is inclusion of a graphical representation of the flow of study participation from baseline to study completion. Such a “CONSORT chart” provides important information on recruitment, randomization, retainment, and participant attrition across treatment conditions and assessment time points.

We remain at a relatively nascent stage in the science of clinical child and adolescent psychology, with the majority of work ahead of us. Having reviewed key considerations for planning, conducting, analyzing, and reporting clinical evaluations of child and adolescent treatments, it is clear that no individual investigation, even with optimal design and procedures, is able to adequately answer all relevant questions. Rather, a collection and series of investigations, drawing on a broad range of methodological strategies, is needed to progress our understanding of best practices for the widely diverse range of mental health problems that present in childhood and adolescence.

Those looking for the “correct” research methodology with which to address all questions in clinical child and adolescent psychology are misguided. Throughout this chapter, we have outlined how for each testable question there are many research strategies that can be used to reveal meaningful information, each with strengths and limitations. Collectively, the methods and design considerations outlined in this chapter detail a portfolio of modern research strategies for the continually evolving science of clinical child and adolescent psychology: a set of alternative and complementary “directions” so to speak for advancing our field from where we are now to where we need to be.

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Search strategy, data extraction, risk of bias, data synthesis and analysis, medications, youth-directed psychosocial treatments, parent support, school interventions, cognitive training, neurofeedback, nutrition and supplements, complementary, alternative, or integrative medicine, combined medication and behavioral treatments, moderation of treatment response, long-term outcomes, clinical implications, strengths and limitations, future research needs, acknowledgments, treatments for adhd in children and adolescents: a systematic review.

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Bradley S. Peterson , Joey Trampush , Margaret Maglione , Maria Bolshakova , Mary Rozelle , Jeremy Miles , Sheila Pakdaman , Morah Brown , Sachi Yagyu , Aneesa Motala , Susanne Hempel; Treatments for ADHD in Children and Adolescents: A Systematic Review. Pediatrics April 2024; 153 (4): e2024065787. 10.1542/peds.2024-065787

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Effective treatment of attention-deficit/hyperactivity disorder (ADHD) is essential to improving youth outcomes.

This systematic review provides an overview of the available treatment options.

We identified controlled treatment evaluations in 12 databases published from 1980 to June 2023; treatments were not restricted by intervention content.

Studies in children and adolescents with clinically diagnosed ADHD, reporting patient health and psychosocial outcomes, were eligible. Publications were screened by trained reviewers, supported by machine learning.

Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Data were pooled using random-effects models. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.

In total, 312 studies reported in 540 publications were included. We grouped evidence for medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. Several treatments improved ADHD symptoms. Medications had the strongest evidence base for improving outcomes, including disruptive behaviors and broadband measures, but were associated with adverse events.

We found limited evidence of studies comparing alternative treatments directly and indirect analyses identified few systematic differences across stimulants and nonstimulants. Identified combination of medication with youth-directed psychosocial interventions did not systematically produce better results than monotherapy, though few combinations have been evaluated.

A growing number of treatments are available that improve ADHD symptoms and other outcomes, in particular for school-aged youth. Medication therapies remain important treatment options but are associated with adverse events.

Attention-deficit/hyperactivity disorder (ADHD) is a common mental health problem in youth, with a prevalence of ∼5.3%. 1 , 2   Youth with ADHD are prone to future risk-taking problems, including substance abuse, motor vehicle accidents, unprotected sex, criminal behavior, and suicide attempts. 3   Although stimulant medications are currently the mainstay of treatment of school-age youth with ADHD, other treatments have been developed for ADHD, including cognitive training, neurofeedback, neuromodulation, and dietary and nutritional interventions. 4   – 7  

This systematic review summarizes evidence for treatments of ADHD in children and adolescents. The evidence review extends back to 1980, when contemporary diagnostic criteria for ADHD and long-acting stimulants were first introduced. Furthermore, we did not restrict to a set of prespecified known interventions for ADHD, and instead explored the range of available treatment options for children and adolescents, including novel treatments. Medication evaluations had to adhere to a randomized controlled trial (RCT) design, all other treatments could be evaluated in RCTs or nonrandomized controlled studies that are more common in the psychological literature, as long as the study reported on a concurrent comparator. Outcomes were selected with input from experts and stakeholders and were not restricted to ADHD symptoms. To our knowledge, no previous review for ADHD treatments has been as comprehensive in the range of interventions, clinical and psychosocial outcomes, participant ages, and publication years.

The review aims were developed in consultation with the Agency for Healthcare Research and Quality (AHRQ), the Patient-Centered Outcomes Research Institute, the topic nominator American Academy of Pediatrics (AAP), key informants, a technical expert panel (TEP), and public input. The TEP reviewed the protocol and advised on key outcomes. Subgroup analyses and key outcomes were prespecified. The review is registered in PROSPERO (#CRD42022312656) and the protocol is available on the AHRQ Web site as part of a larger evidence report on ADHD. The systematic review followed Methods of the (AHRQ) Evidence-based Practice Center Program. 8  

Population: Children or adolescents with a clinical diagnosis of ADHD, age <18 years

Interventions: Any ADHD treatment, alone or in combination, and ≥4 weeks’ treatment

Comparators: No treatment, waitlist, placebo, passive comparators, or active comparators

Outcomes: Patient health and psychosocial outcomes

Setting: Any

Study designs: RCTs for medication; RCTs, controlled clinical trials without random assignment, or cohort studies comparing 1 or more treatment groups for nondrug treatments. Studies either had to be large or demonstrate that they could detect effects as a standalone study (operationalized as ≥100 participants or a power calculation)

Other limiters: English-language (to ensure transparency for a US guideline), published from 1980

We searched the databases PubMed, Embase, PsycINFO, ERIC, and ClinicalTrials.gov. We identified reviews for reference-mining through PubMed, Cochrane Database of Systematic Reviews, Campbell Collaboration, What Works in Education, PROSPERO, ECRI Guidelines Trust, G-I-N, and ClinicalKey. The search underwent peer review; the full strategy is in the Online Appendix. All citations were reviewed by trained literature reviewers supported by machine learning to ensure no studies were inadvertently missed. Two independent reviewers assessed full-text studies for eligibility. Publications reporting on the same participants were consolidated into 1 record so that no study entered the analyses more than once. The TEP reviewed studies to ensure all were captured.

The data abstraction form included extensive guidance to aid reproducibility and standardization in recording study details, outcomes, 9   – 12   study quality, and applicability. One reviewer abstracted data, and a methodologist checked its accuracy and completeness. Data are publicly available in the Systematic Review Data Repository.

We assessed 6 domains 13   : Selection, performance, attrition, detection, reporting, and study-specific biases ( Supplemental Figs 6 and 7 ).

We organized analyses by treatment and comparison type. We grouped treatments according to intervention content and target (eg, youth or parents). The intervention taxonomy differentiated medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. We differentiated effects versus passive control groups (eg, placebo) and comparative effects (ie, comparing to an alternative treatment). The following outcomes were selected as key outcomes: (1) ADHD symptoms (eg, ADHD Rating Scale 14 , 15   ), (2) disruptive behavior (eg, conduct problems), (3) broadband measures (eg, Clinical Global Impression 16   ), (4) functional impairment (eg, Weiss Functional Impairment Rating Scale 17 , 18   ), (5) academic performance (eg, grade point average), (6) appetite suppression, and (7) number of participants reporting adverse events.

Studies reported on a large range of outcome measures as documented in the evidence table in the Online Appendix. To facilitate comparisons across studies, we converted outcomes to scale-independent standardized mean differences (SMDs) for continuous symptom outcome variables and relative risks (RRs) for categorical reports, presenting summary estimates and 95% confidence intervals (CIs) for all analyses. We used random-effects models performed in R with Metafor_v4.2-0 for statistical pooling, correcting for small numbers of studies when necessary, to synthesize available evidence. 19   We conducted sensitivity analyses for all analyses that included studies without random assignment. We also compared treatment effectiveness indirectly across studies in meta-regressions that added potential, prespecified effect modifiers to the meta-analytic model. In particular, we assessed whether ADHD presentation or cooccurring disorders modified intervention effects. We tested for heterogeneity using graphical displays, documented I 2 statistics (values >50% are highlighted in the text), and explored sources of heterogeneity in subgroup and sensitivity analyses. 20  

We assessed publication bias with Begg and Egger tests 21 , 22   and used the trim-and-fill methods for alternative estimates where necessary. 23   Applicability of findings to real-world clinical practices in typical US settings was assessed qualitatively using AHRQ’s Methods Guide. An overall strength of evidence (SoE) assessment communicating our confidence in each finding was determined initially by 1 researcher with experience in use of specified standardized criteria 24   ( Supplemental Information ), then discussed with the study team. We downgraded SoE for study limitations, imprecision, inconsistency, and reporting bias, and we differentiated high, moderate, low, and insufficient SoE.

We screened 23 139 citations and retrieved 7534 publications as full text against the eligibility criteria. In total, 312 treatment studies, reported in 540 publications (see list of included studies in the Online Appendix), met eligibility criteria ( Fig 1 ).

Literature flow diagram.

Although studies from 1980 were eligible, the earliest study meeting all eligibility criteria was from 1995. All included studies are documented in the evidence table in the Supplemental Information . The following highlights key findings. Results for intervention groups and individual studies, subgroup and sensitivity analyses, characteristics of participants and interventions contributing to the analyses, and considerations that determined the SoE for results are documented in the Online Appendix.

As a class, traditional stimulants (methylphenidate, amphetamines) significantly improved ADHD symptom severity (SMD, −0.88; CI, −1.13 to −0.63; studies = 12; n = 1620) and broadband measures (RR, 0.38; CI, 0.30–0.48; studies = 12; n = 1582) (both high SoE), but not functional impairment (SMD, 1.00; CI, −0.25 to 2.26; studies = 4; n = 540) ( Fig 2 , Supplemental Fig 8 , Supplemental Table 1 ). Methylphenidate formulations significantly improved ADHD symptoms (SMD, −0.68; CI, −0.91 to −0.46; studies = 7; n = 863) ( Fig 2 , Supplemental Table 1 ) and broadband measures (SMD, 0.66; CI, 0.04–1.28; studies = 2; n = 302). Only 1 study assessed academic performance, reporting large improvements compared with a control group (SMD, −1.37; CI, −1.72 to −1.03; n = 156) ( Supplemental Fig 9 ). 25   Methylphenidate statistically significantly suppressed appetite (RR, 2.80; CI, 1.47–5.32; studies = 8; n = 1110) ( Fig 3 ), and more patients reported adverse events (RR, 1.32; CI, 1.25–1.40; studies = 6; n = 945). Amphetamine formulations significantly improved ADHD symptoms (SMD, −1.16; CI, −1.64 to −0.67; studies = 5; n = 757) ( Fig 2 , Supplemental Table 1 ) but not broadband measures (SMD, 0.68; CI, −0.72 to 2.08; studies = 3; n = 561) ( Supplemental Fig 9 ). Amphetamines significantly suppressed appetite (RR, 7.08; CI, 2.72–18.42; studies = 8; n = 1229) ( Fig 3 ), and more patients reported adverse events (RR, 1.41; CI, 1.25–1.58; studies = 8; n = 1151). Modafinil (US Food and Drug Administration [FDA]-approved to treat narcolepsy and sleep apnea but not ADHD) in each individual study significantly improved ADHD symptoms, but aggregated estimates were nonsignificant (SMD, −0.76; CI, −1.75 to 0.23; studies = 4; n = 667) ( Fig 2 , Supplemental Table 1 ) because of high heterogeneity (I 2 = 91%). It did not improve broadband measures (RR, 0.49; CI, −0.12 to 2.07; studies = 3; n = 539) ( Supplemental Fig 9 ), and it significantly suppressed appetite (RR, 4.44; CI, 2.27–8.69; studies = 5; n = 780) ( Fig 3 ).

Medication effects on ADHD symptom severity. S-AMPH-LDX, lisdexamfetamine; S-AMPH-MAS, mixed amphetamines salts; S-MPH-DEX, dexmethylphenidate; S-MPH-ER, extended-release methylphenidate; S-MPH-IR, immediate release methylphenidate; S-MPH-OROS, osmotic-release oral system methylphenidate; S-MPH-TP, dermal patch methylphenidate; NS-NRI-ATX, atomoxetine; NS-NRI-VLX, viloxazine; NS-ALA-CLON, clonidine; NS-ALA-GXR, guanfacine extended-release.

Medication effects on appetite suppression. Abbreviations as in legend for Fig 2 .

As a class, nonstimulants significantly improved ADHD symptoms (SMD, −0.52; CI, −0.59 to −0.46; studies = 37; n = 6065; high SoE) ( Fig 2 , Supplemental Table 1 ), broadband measures (RR, 0.66; CI, 0.58–0.76; studies = 12; n = 2312) ( Supplemental Fig 8 ), and disruptive behaviors (SMD, 0.66; CI, 0.22–1.10; studies = 4; n = 523), but not functional impairment (SMD, 0.20; CI, −0.05 to 0.44; studies = 6; n = 1163). Norepinephrine reuptake inhibitors (NRI) improved ADHD symptoms (SMD, −0.55; CI, −0.62 to −0.47; studies=28; n = 4493) ( Fig 2 , Supplemental Table 1 ) but suppressed appetite (RR, 3.23; CI, 2.40–4.34; studies = 27; n = 4176) ( Fig 3 ), and more patients reported adverse events (RR, 1.31; CI, 1.18–1.46; studies = 15; n = 2600). Alpha-agonists (guanfacine and clonidine) improved ADHD symptoms (SMD, −0.52; CI, −0.67 to −0.37; studies = 11; n = 1885) ( Fig 2 , Supplemental Table 1 ), without (guanfacine) significantly suppressing appetite (RR, 1.49; CI, 0.94–2.37; studies = 4; n = 919) ( Fig 3 ), but more patients reported adverse events (RR, 1.21; CI, 1.11–1.31; studies = 14, n = 2544).

One study compared amphetamine versus methylphenidate, head-to-head, finding more improvement in ADHD symptoms (SMD, −0.46; CI, −0.73 to −0.19; n = 222) and broadband measures (SMD, 0.29; CI, 0.02–0.56; n = 211), but not functional impairment (SMD, 0.16; CI, −0.11 to 0.43; n = 211), 26   with lisdexamfetamine (an amphetamine) than osmotic-release oral system methylphenidate. No difference was found in appetite suppression (RR, 1.01; CI, 0.72–1.42; studies = 2, n = 414) ( Fig 3 ) or adverse events (RR, 1.11; CI, 0.93–1.33; study = 1, n = 222). Indirect comparisons yielded significantly larger effects for amphetamine than methylphenidate in improving ADHD symptoms ( P = .02) but not broadband measures ( P = .97) or functional impairment ( P = .68). Stimulants did not differ in appetite suppression ( P = .08) or adverse events ( P = .35).

One study provided information on NRI versus alpha-agonists by directly comparing an alpha-agonist (guanfacine) with an NRI (atomoxetine), 27   finding significantly greater improvement in ADHD symptoms with guanfacine (SMD, −0.47; CI, −0.73 to −0.2; n = 226) but not a broadband measure (RR, 0.84; CI, 0.68–1.04; n = 226). It reported less appetite suppression for guanfacine (RR, 0.48; CI, 0.27–0.83; n = 226) but no difference in adverse events (RR, 1.14; CI, 0.97–1.34; n = 226). Indirect comparisons did not indicate significantly different effect sizes for ADHD symptoms ( P = .90), disruptive behaviors ( P = .31), broadband measures ( P = .41), functional impairment ( P = .46), or adverse events ( P = .06), but suggested NRIs more often suppressed appetite compared with guanfacine ( P = .01).

Studies directly comparing nonstimulants versus stimulants (all were the NRI atomoxetine and stimulants methylphenidate in all but 1) tended to favor stimulants but did not yield significance for ADHD symptom severity (SMD, 0.23; CI, −0.03 to 0.49; studies = 7; n = 1611) ( Fig 2 ). Atomoxetine slightly but statistically significantly produced greater improvements in disruptive behaviors (SMD, −0.08; CI, −0.14 to −0.03; studies = 4; n = 608) ( Supplemental Fig 10 ) but not broadband measures (SMD, −0.16; CI, −0.36 to 0.04; studies = 4; n = 1080) ( Supplemental Fig 9 ). They did not differ significantly in appetite suppression (RR, 0.82; CI, 0.53–1.26; studies = 8; n = 1463) ( Fig 3 ) or number with adverse events (RR, 1.11; CI, 0.90–1.37; studies = 4; n = 756). Indirect comparisons indicated significant differences favoring stimulants over nonstimulants in improving ADHD symptom severity ( P < .0001), broadband measures ( P = .0002), and functional impairment ( P = .04), but not appetite suppression ( P = .31) or number with adverse events ( P = .12).

Several studies assessed whether adding nonstimulant to stimulant medication (all were alpha-agonists added to different stimulants) improved outcomes compared with stimulant medication alone, yielding a small but significant additional improvement in ADHD symptoms (SMD, −0.36; CI, −0.52 to −0.19; studies = 5; n = 724) ( Fig 4 ).

Combination treatment. CLON, clonidine, GXR guanfacine.

We identified 32 studies evaluating psychosocial, psychological, or behavioral interventions targeting ADHD youth, either alone or combined with components for parents and teachers. Interventions were highly diverse, and most were complex with multiple components (see supplemental results in the Online Appendix). They significantly improved ADHD symptoms (SMD, −0.35; CI, −0.51 to −0.19; studies = 14; n = 1686; moderate SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.36; CI, −0.53 to −0.19; removing high-risk-of-bias studies left 7 with similar effects SMD, −0.38; CI, −0.69 to −0.07), with minimal heterogeneity (I 2 = 52%); but not disruptive behaviors (SMD, −0.18; CI, −0.48 to 0.12; studies = 8; n = 947) or academic performance (SMD, −0.07; CI, −0.49 to 0.62; studies = 3; n = 459) ( Supplemental Fig 11 ).

We identified 19 studies primarily targeting parents of youth aged 3 to 18 years, though only 3 included teenagers. Interventions were highly diverse (see Online Appendix), but significantly improved ADHD symptoms (SMD, −0.31; CI, −0.57 to −0.05; studies = 11; n = 1078; low SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.35; CI, −0.61 to −0.09; removing high-risk-of-bias studies yielded the same point estimate, but CIs were wider, and the effect was nonsignificant SMD, −0.31; CI, −0.76 to 0.14). There was some evidence of publication bias (Begg P = .16; Egger P = .02), but the trim and fill method to correct it found a similar effect (SMD, −0.43; CI, −0.63 to −0.22). Interventions improved broadband scores (SMD, 0.41; CI, 0.23–0.58; studies = 7; n = 613) and disruptive behaviors (SMD, −0.52; CI, −0.85 to −0.18; studies = 4; n = 357) but not functional impairment (SMD, 0.35; CI, −0.69 to 1.39; studies = 3; n = 252) (all low SoE) ( Supplemental Fig 12 ).

We identified 10 studies, mostly for elementary or middle schools (see Online Appendix). Interventions did not significantly improve ADHD symptoms (SMD, −0.50; CI, −1.05 to 0.06; studies = 5; n = 822; moderate SoE) ( Fig 4 ), but there was evidence of heterogeneity (I 2 = 87%). Although most studies reported improved academic performance, this was not statistically significant across studies (SMD, −0.19; CI, −0.48 to 0.09; studies = 5; n = 854) ( Supplemental Fig 13 ).

We identified 22 studies, for youth aged 6 to 17 years without intellectual disability (see Online Appendix). Cognitive training did improve ADHD symptoms (SMD, −0.37; CI, −0.65 to −0.06; studies = 12; n = 655; low SoE) ( Fig 4 ), with some heterogeneity (I 2 = 65%), but not functional impairment (SMD, 0.41; CI, −0.24 to 1.06; studies = 5; n = 387) ( Supplemental Fig 14 ) or disruptive behaviors (SMD, −0.29; CI, −0.84 to 0.27; studies [all RCTs] = 5; n = 337). It improved broadband measures (SMD, 0.50; CI, 0.12–0.88; studies = 6; n = 344; RCTs only: SMD, 0.43; CI, −0.06 to 0.93) (both low SoE). It did not increase adverse events (RR, 3.30; CI, 0.03–431.32; studies = 2; n = 402).

We identified 21 studies: Two-thirds involved θ/β EEG marker modulation, and one-third modulation of slow cortical potentials (see Online Appendix). Neurofeedback significantly improved ADHD symptoms (SMD, −0.44; CI, −0.65 to −0.22; studies = 12; n = 945; low SoE) ( Fig 4 ), with little heterogeneity (I 2 = 33%); restricting to the 10 RCTs yielded the same point estimate, also statistically significant (SMD, −0.44; CI, −0.71 to −0.16). Neurofeedback did not systematically improve disruptive behaviors (SMD, −0.33; CI, −1.33 to 0.66; studies = 4; n = 372), or functional impairment (SMD, 0.21; CI, −0.14 to 0.55; studies = 3; n = 332) ( Supplemental Fig 15 ).

We identified 39 studies with highly diverse nutrition interventions (see Online Appendix), including omega-3 (studies = 13), vitamins (studies = 3), or diets (studies = 3), and several evaluated supplements as augmentation to stimulants. Most were placebo-controlled. Across studies, interventions improved ADHD symptoms (SMD, −0.39; CI, −0.67 to −0.12; studies = 23; n = 2357) ( Fig 4 ), even when restricting to RCTs (SMD, −0.32; CI, −0.55 to −0.08), with high heterogeneity (I 2 = 89%) but no publication bias. The group of nutritional approaches also improved disruptive behaviors (SMD, −0.28; CI, −0.37 to −0.18; studies [all RCTs] = 5; n = 360) ( Supplemental Fig 16 , low SoE), without increasing the number reporting adverse events (RR, 0.77; CI, 0.47–1.27; studies = 8; n = 735). However, we did not identify any specific supplements that consistently improved outcomes, including omega-3 (eg, ADHD symptoms: SMD, −0.11; CI, −0.45, 0.24; studies = 7; n = 719; broadband measures: SMD, 0.04; CI, −0.24 to 0.32; studies = 7; n = 755, low SoE).

We identified 6 studies assessing acupuncture, homeopathy, and hippotherapy. They did not individually or as a group significantly improve ADHD symptoms (SMD, −0.15; CI, −1.84 to 1.53; studies = 3; n = 313) ( Fig 4 ) or improve other outcomes across studies (eg, broadband measures: SMD, 0.03; CI, −3.66 to 3.73; studies = 2; n = 218) ( Supplemental Fig 17 ).

Eleven identified studies evaluated a combination of medication- and youth-directed psychosocial treatments. Most allowed children to have common cooccurring conditions, but intellectual disability and severe neurodevelopmental conditions were exclusionary. Medication treatments were stimulant or atomoxetine. Psychosocial treatments included multimodal psychosocial treatment, cognitive behavioral therapy, solution-focused therapy, behavioral therapy, and a humanistic intervention. Studies mostly compared combinations of medication and psychosocial treatment to medication alone, rather than no treatment or placebo. Combined therapy did not statistically significantly improve ADHD symptoms across studies (SMD, −0.36; CI, −0.73 to 0.01; studies = 7; n = 841; low SoE; only 2 individual studies reported statistically significant effects) ( Fig 5 ) or broadband measures (SMD, 0.42; CI, −0.72 to 1.56; studies = 3; n = 171), but there was indication of heterogeneity (I 2 = 71% and 62%, respectively).

Nonmedication intervention effects on ADHD symptom severity.

We found little evidence that either ADHD presentation (inattentive, hyperactive, combined-type) or cooccurring psychiatric disorders modified treatment effects on any ADHD outcome, but few studies addressed this question systematically (see Online Appendix).

Only a very small number of studies (33 of 312) reported on outcomes at or beyond 12 months of follow-up (see Online Appendix). Many did not report on key outcomes of this review. Studies evaluating combined psychosocial and medication interventions, such as the multimodal treatment of ADHD study, 28   did not find sustained effects beyond 12 months. Analyses for medication, psychosocial, neurofeedback, parent support, school intervention, and provider-focused interventions did not find sustained effects for more than a single study reporting on the same outcome. No complementary medicine, neurostimulation, physical exercise, or cognitive training studies reported long-term outcomes.

We identified a large body of evidence contributing to knowledge of ADHD treatments. A substantial number of treatments have been evaluated in strong study designs that provide evidence statements regarding the effects of the treatments on children and adolescents with ADHD. The body of evidence shows that numerous intervention classes significantly improve ADHD symptom severity. This includes large but variable effects for amphetamines, moderate-sized effects for methylphenidate, NRIs, and alpha-agonists, and small effects for youth-directed psychosocial treatment, parent support, neurofeedback, and cognitive training. The SoE for effects on ADHD symptoms was high across FDA-approved medications (methylphenidate, amphetamines, NRIs, alpha-agonists); moderate for psychosocial interventions; and low for parent support, neurofeedback, and nutritional interventions. Augmentation of stimulant medication with non-stimulants produced small but significant additional improvement in ADHD symptoms over stimulant medication alone (low SoE).

We also summarized evidence for other outcomes beyond specific ADHD symptoms and found that broadband measures (ie, global clinical measures not restricted to assessing specific symptoms and documenting overall psychosocial adjustment), methylphenidate (low SoE), nonstimulant medications (moderate SoE), and cognitive training (low SoE) yielded significant, medium-sized effects, and parent support small effects (moderate SoE). For disruptive behaviors, nonstimulant medications (high SoE) and parent support (low SoE) produced significant improvement with medium effect. No treatment modality significantly improved functional impairment or academic performance, though the latter was rarely assessed as a treatment outcome.

The enormous variability in treatment components and delivery of youth-directed psychotherapies, parent support, neurofeedback, and nutrition and supplement therapies, and in ADHD outcomes they have targeted, complicates the synthesis and meta-analysis of their effects compared with the much more uniform interventions, delivery, and outcome assessments for medication therapies. Moreover, most psychosocial and parent support studies compared an active treatment against wait list controls or treatment as usual, which did not control well for the effects of parent or therapist attention or other nonspecific effects of therapy, and they have rarely been able to blind adequately either participants or study assessors to treatment assignment. 29 , 30   These design limitations weaken the SoE for these interventions.

The large number of studies, combined with their medium-to-large effect sizes, indicate collectively and with high SoE that FDA-approved medications improve ADHD symptom severity, broadband measures, functional impairment, and disruptive behaviors. Indirect comparison showed larger effect sizes for stimulants than for nonstimulants in improving ADHD symptoms and functional impairment. Results for amphetamines and methylphenidate varied, and we did not identify head-to-head comparisons of NRIs versus alpha-agonists that met eligibility criteria. Despite compelling evidence for their effectiveness, stimulants and nonstimulants produced more adverse events than did other interventions, with a high SoE. Stimulants and nonstimulant NRIs produced significantly more appetite suppression than placebo, with similar effect sizes for methylphenidate, amphetamine, and NRI, and much larger effects for modafinil. Nonstimulant alpha-agonists (specifically, guanfacine) did not suppress appetite. Rates of other adverse events were similar between NRIs and alpha-agonists.

Perhaps contrary to common belief, we found no evidence that youth-directed psychosocial and medication interventions are systematically better in improving ADHD outcomes when delivered as combination treatments 31   – 33   ; both were effective as monotherapies, but the combination did not signal additional statistically significant benefits (low SoE). However, it should be noted that few psychosocial and medication intervention combinations have been studied to date. We also found that treatment outcomes did not vary with ADHD presentation or the presence of cooccurring psychiatric disorders, but indirect analyses are limited in detecting these effect modifiers, and more research is needed. Furthermore, although children of all ages were eligible for inclusion in the review, we note that very few studies assessed treatments (especially medications) in children <6 years of age; evidence is primarily available for school-age children and adolescents. Finally, despite the research volume, we still know little about long-term effects of ADHD treatments. The limited available body of evidence suggests that most interventions, including combined medication and psychological treatment, yield few significant long-term improvements for most ADHD outcomes.

This review provides compelling evidence that numerous, diverse treatments are available and helpful for the treatment of ADHD. These include stimulant and nonstimulant medications, youth-targeted psychosocial treatments, parent support, neurofeedback, and cognitive training, though nonmedication interventions appear to have considerably weaker effects than medications on ADHD symptoms. Nonetheless, the body of evidence provides youth with ADHD, their parents, and health care providers with options.

The paucity of head-to-head studies comparing treatments precludes research-based recommendations regarding which is likely to be most helpful and which should be tried first, and decisions need to be based on clinical considerations and patient preferences. Stimulant and nonstimulant NRI medications, separately and in head-to-head comparisons, have shown similar effectiveness and rates of side effects, including appetite suppression, across identified studies. The moderate effect sizes for nonstimulant alpha-agonists, their low rate of appetite suppression, and their evidence for effectiveness in augmenting the effects of stimulant medications in reducing ADHD symptom severity provides additional treatment options. Furthermore, we found low SoE that neurofeedback and cognitive training improve ADHD symptoms. We also found that nutritional supplements and dietary interventions improve ADHD symptoms and disruptive behaviors. The SoE for nutritional interventions, however, is still low, and despite the research volume, we did not identify systematic benefits for specific supplements.

Clinical guidelines currently advise starting treatment of youth >6 years of age with FDA-approved medications, 33   which the findings of this review support. Furthermore, FDA-approved medications have been shown to significantly improve broadband measures, and nonstimulant medications have been shown to improve disruptive behaviors, suggesting their clinical benefits extend beyond improving only ADHD symptoms. Clinical guidelines for preschool children advise parent training and/or classroom behavioral interventions as the first line of treatment, if available. These recommendations remain supported by the present review, given the paucity of studies in preschool children in general, and because many existing studies, in particular medication and youth-directed psychosocial interventions, do not include young children. 31   – 33  

This review incorporated publications dating from 1980, assessing diverse intervention targets (youth, parent, school) and ADHD outcomes across numerous functional domains. Limitations in its scope derive from eligibility criteria. Requiring treatment of 4 weeks ensured that interventions were intended as patient treatment rather than proof of concept experiments, but it also excluded some early studies contributing to the field and other brief but intense psychosocial interventions. Requiring studies to be sufficiently large to detect effects excluded smaller studies that contribute to the evidence base. We explicitly did not restrict to RCTs (ie, a traditional medical study design), but instead identified all studies with concurrent comparators so as not to bias against psychosocial research; nonetheless, the large majority of identified studies were RCTs. Our review aimed to provide an overview of the diverse treatment options and we abstracted findings regardless of the suitability of the study results for meta-analysis. Although many ADHD treatments are very different in nature and the clinical decision for 1 treatment approach over another is likely not made primarily on effect size estimates, future research could use the identified study pool and systematically analyze comparative effectiveness of functionally interchangeable treatments in a network meta-analysis, building on previous work on medication options. 34  

Future studies of psychosocial, parent, school-based, neurofeedback, and nutritional treatments should employ more uniform interventions and study designs that provide a higher SoE for effectiveness, including active attention comparators and effective blinding of outcome assessments. Higher-quality studies are needed for exercise and neuromodulation interventions. More trials are needed that compare alternative interventions head-to-head or compare combination treatments with monotherapy. Clinical trials should assess patient-centered outcomes other than ADHD symptoms, including functional impairment and academic performance. Much more research is needed to assess long-term treatment effectiveness, compliance, and safety, including in preschool youth. Studies should assess patient characteristics as modifiers of treatment effects, to identify which treatments are most effective for which patients. To aid discovery and confirmation of these modifiers, studies should make publicly available all individual-level demographic, clinical, treatment, and outcome data.

We thank the following individuals providing expertise and helpful comments that contributed to the systematic review: Esther Lee, Becky Nguyen, Cynthia Ramirez, Erin Tokutomi, Ben Coughli, Jennifer Rivera, Coleman Schaefer, Cindy Pham, Jerusalem Belay, Anne Onyekwuluje, Mario Gastelum, Karin Celosse, Samantha Fleck, Janice Kang, and Sreya Molakalaplli for help with data acquisition. We thank Kymika Okechukwu, Lauren Pilcher, Joanna King, and Robyn Wheatley from the American Academy of Pediatrics; Jennie Dalton and Paula Eguino Medina from the Patient-Centered Outcomes Research Institute; Christine Chang and Kim Wittenberg from AHRQ; and Mary Butler from the Minnesota Evidence-based Practice Center. We thank Glendy Burnett, Eugenia Chan, MD, MPH; Matthew J. Gormley, PhD; Laurence Greenhill, MD; Joseph Hagan, Jr, MD; Cecil Reynolds, PhD; Le’Ann Solmonson, PhD, LPC-S, CSC; and Peter Ziemkowski, MD, FAAFP; who served as key informants. We thank Angelika Claussen, PhD; Alysa Doyle, PhD; Tiffany Farchione, MD; Matthew J. Gormley, PhD; Laurence Greenhill, MD; Jeffrey M. Halperin, PhD; Marisa Perez-Martin, MS, LMFT; Russell Schachar, MD; Le’Ann Solmonson, PhD, LPC-S, CSC; and James Swanson, PhD; who served as a technical expert panel. Finally, we thank Joel Nigg, PhD; and Peter S. Jensen, MD; for their peer review of the data.

Drs Peterson and Hempel conceptualized and designed the study, collected data, conducted the analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Trampush conducted the critical appraisal; Drs Bolshakova and Pakdaman, and Ms Rozelle, Ms Maglione, and Ms Brown screened citations and abstracted the data; Dr Miles conducted the analyses; Ms Yagyu designed and executed the search strategy; Ms Motala served as data manager; and all authors provided critical input for the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

This study is registered at PROSPERO, #CRD42022312656. Data are available in SRDRPlus.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2024-065854 .

FUNDING: The work is based on research conducted by the Southern California Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 75Q80120D00009). The Patient-Centered Outcomes Research Institute funded the research (Publication No. 2023-SR-03). The findings and conclusions in this manuscript are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of the AHRQ or the Patient-Centered Outcomes Research Institute, its board of governors or methodology committee. Therefore, no statement in this report should be construed as an official position of the Patient-Centered Outcomes Research Institute, the AHRQ, or the US Department of Health and Human Services.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

attention-deficit/hyperactivity disorder

Agency for Healthcare Research and Quality

US Food and Drug Administration

confidence interval

norepinephrine reuptake inhibitors

randomized controlled trial

relative risk

standardized mean difference

strength of evidence

technical expert panel

Supplementary data

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Child and Adolescent Development for Educators

Research output : Book/Report › Book › Other › peer-review

T1 - Child and Adolescent Development for Educators

AU - Bergin, Christi Crosby

AU - Bergin, David Allen

AU - Walker, Sue

AU - Daniel, Graham

AU - Fenton, Angela

AU - Subban, Pearl

N2 - Child and Adolescent Development for Educators covers development from early childhood through high school. This text provides authentic, research-based strategies and guidelines for the classroom, helping future teachers to create an environment that promotes optimal development in children.The authors apply child development concepts to topics of high interest and relevance to teachers, including classroom behaviour management, constructivism, social-emotional development, and many others. The text combines core theory with practical implications for educational contexts, and shows how child development links to the Australian Professional Standards for Graduate Teachers. Case studies and real-world vignettes illustrate concepts, while research, including the Longitudinal Study of Australian Children, and Longitudinal Study of Indigenous children, bridges the distance between research and the classroom.

AB - Child and Adolescent Development for Educators covers development from early childhood through high school. This text provides authentic, research-based strategies and guidelines for the classroom, helping future teachers to create an environment that promotes optimal development in children.The authors apply child development concepts to topics of high interest and relevance to teachers, including classroom behaviour management, constructivism, social-emotional development, and many others. The text combines core theory with practical implications for educational contexts, and shows how child development links to the Australian Professional Standards for Graduate Teachers. Case studies and real-world vignettes illustrate concepts, while research, including the Longitudinal Study of Australian Children, and Longitudinal Study of Indigenous children, bridges the distance between research and the classroom.

SN - 9780170388665

BT - Child and Adolescent Development for Educators

PB - Cengage Learning

CY - Southbank Vic Australia

• Open access
• Published: 24 October 2013

Research priorities for child and adolescent physical activity and sedentary behaviours: an international perspective using a twin-panel Delphi procedure

• Lauren Gillis 1 ,
• Grant Tomkinson 1 ,
• Timothy Olds 1 ,
• Carla Moreira 2 ,
• Candice Christie 3 ,
• Claudio Nigg 4 ,
• Ester Cerin 5 ,
• Esther Van Sluijs 6 ,
• Gareth Stratton 7 ,
• Ian Janssen 8 ,
• Jeremy Dorovolomo 9 ,
• John J Reilly 10 ,
• Jorge Mota 2 ,
• Kashef Zayed 11 ,
• Kent Kawalski 12 ,
• Lars Bo Andersen 13 ,
• Manuel Carrizosa 14 ,
• Mark Tremblay 15 ,
• Michael Chia 16 ,
• Mike Hamlin 17 ,
• Non Eleri Thomas 18 ,
• Ralph Maddison 19 ,
• Stuart Biddle 20 ,
• Trish Gorely 21 ,
• Vincent Onywera 22 &
• Willem Van Mechelen 23  

International Journal of Behavioral Nutrition and Physical Activity volume  10 , Article number:  112 ( 2013 ) Cite this article

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The quantity and quality of studies in child and adolescent physical activity and sedentary behaviour have rapidly increased, but research directions are often pursued in a reactive and uncoordinated manner.

To arrive at an international consensus on research priorities in the area of child and adolescent physical activity and sedentary behaviour.

Two independent panels, each consisting of 12 experts, undertook three rounds of a Delphi methodology. The Delphi methodology required experts to anonymously answer questions put forward by the researchers with feedback provided between each round.

The primary outcome of the study was a ranked set of 29 research priorities that aimed to be applicable for the next 10 years. The top three ranked priorities were: developing effective and sustainable interventions to increase children’s physical activity long-term; policy and/or environmental change and their influence on children’s physical activity and sedentary behaviour; and prospective, longitudinal studies of the independent effects of physical activity and sedentary behaviour on health.

Conclusions

These research priorities can help to guide decisions on future research directions.

Recent research has shown that both physical activity and sedentary behaviour are associated with a wide range of current and future health outcomes [ 1 – 3 ]. In fact, physical activity and sedentary behaviour are two independent and not mutually exclusive behaviours with different effects on health outcomes [ 4 ]. In the short term, physical activity has been shown to be moderately and positively associated with bone health, aerobic fitness, blood lipid levels, self-esteem, mental activity and fundamental movement skills in children and adolescents [ 1 – 3 , 5 ]. In the long term, both physical activity and sedentary behaviour have been identified as major, independent, modifiable risk factors for mortality and morbidity from many chronic, non-communicable and potentially preventable diseases [ 6 – 9 ]. New evidence also suggests that the relation between sedentary behaviour and all-cause end cardiovascular disease mortality is independent of physical activity levels [ 7 ].

Chronic diseases place a large economic burden on health services and impose significant costs on society (e.g. premature death, underappreciated economic effects and greater reliance on treatment) [ 8 ]. Although the ill effects of chronic disease largely manifest in adulthood, it is increasingly understood that the development typically begins in childhood or adolescence [ 9 ]. Therefore, physical activity levels and sedentary behaviour performed in the early years could potentially influence the development of disease later on in life.

At present, a large quantity of research is being conducted into the physical activity and sedentary behaviour of children, yet the research community remains challenged to provide a solid evidence base [ 10 ]. This is in part due to a lack of international research collaboration and a high degree of study repetition. The aim of this study therefore was to arrive at a set of international research priorities for physical activity and sedentary behaviour to guide more meaningful and focussed research. Specifically, this study aimed to answer the following research question: “What are the most important international research issues for the next 10 years in child and adolescent physical activity and sedentary behaviour?” Agreement on research priorities may help to inform evidence-based policy, guide funding allocation, and direct research options for postgraduate students [ 11 , 12 ].

Existing literature

To identify existing evidence in this area, a systematic review of the English and non-English literature was performed using the following search terms: physical activit* OR motor activity (MeSH) OR sedentary behavio* AND child* OR adolescen* OR youth* AND research priorit* OR research agenda* OR research issue*. The databases PsychINFO (1887–), SPORTDiscus (1949–), Cochrane (1992–), CINAHL (1937–), ERIC (1966–) and PubMed (1950–) were searched in May 2012. Additional studies were also identified by contacting experts, Google searching and identifying potential studies in the reference lists of identified studies. Only four previously published papers that arrived at research priorities in child physical activity and/or sedentary behaviour were identified [ 11 , 13 – 15 ]. A working paper by Bull et al. [ 11 ] identified research priorities in physical activity with a focus on low to middle income countries. Evenson and Mota [ 13 ] highlighted research on the determinants and outcomes of physical activity and made recommendations for future study designs. Mountjoy et al. [ 15 ] identified existing gaps in physical activity research for children, with a focus on the need for greater collaboration between sport and existing programmes. The final study by Fulton et al. [ 14 ] had two aims. Firstly, the study aimed to review the current knowledge of existing methods for assessing physical activity and sedentary behaviour. Secondly, on the basis of this, the study aimed to set research priorities on the use of reliable and valid measurement tools to assess physical activity and sedentary behaviour in children aged 2–5 years.

While these studies were valuable contributions, they also had many limitations, including unsystematic participant selection, unstructured data collection procedures, and limited reporting on the process followed to arrive at the research priorities. Furthermore, the participants involved in the decision-making processes did not always represent the broader community of researchers, either from a geographical or institutional point of view. In addition, the anonymity of participants was not maintained during the consensus process. These limitations warranted a further study with an aim to arrive at a set of research priorities by employing a structured and rigorous methodology and improving reporting quality.

Methodology

Ethical approval for all aspects of the methodology was granted by the University of South Australia Human Research Ethics Committee in September 2011.

This study employed a Delphi procedure. This procedure is appropriate for research questions which cannot be answered with complete certainty, but rather by the subjective opinion of a collective group of informed experts [ 16 ]. It allowed systematic refinement of the experts’ opinions over the course of several rounds while minimising confounding factors present in other group response methods [ 17 – 20 ].

The experts who participated in the Delphi procedure were identified by a 3–step procedure. Firstly, the lead study investigators independently recommended known researchers for the study. Secondly, a lengthy and extensive search was carried out to identify potential researchers from every world region and sub-region. Identifying potential experts from these regions involved searching for staff of relevant international bodies, government departments, non-government organisations, professional organisations and educational institutions. Thirdly, following email communication with the experts who have previously been identified, new experts were referred to the study investigators.

Once participants had been identified, it was important to determine their eligibility for inclusion in the study. Thus they were assessed using pre-determined inclusion and exclusion criteria. To be eligible, a researcher had to be an author of at least one peer-reviewed scientific publication on the physical activity or sedentary behaviour of children or adolescents, and must hold (at the time of selection) a senior position in their organisation. In addition, the experts were deliberately chosen to give geographical coverage of every world region and sub-region. Relevant information was gathered from staff homepages, Scopus author searches, the Journal and Author Name Estimator ( http://www.biosemantics.org/jane/ ) and other relevant Internet searches to ascertain whether a researcher met these criteria.

Forty-six eligible experts were invited to participate, with each sent information and consent forms via email. As a whole, these participants were representative of every region and sub-region. Of those invited, 20 did not respond to the invitation, two declined to participate, and 24 returned signed consent forms. An outline of this process is illustrated in Figure  1 .

Purposive sampling process undertaken.

The 24 participating experts (17 male and 7 female) were randomly allocated to either Panel A or Panel B and assigned identification code names accordingly. Furthermore the following major institution types were represented by the selected experts; educational institutions, government organisations, non-government organisations, professional organisations and community organisations.

The Delphi procedure used three rounds [ 21 ], each consisting of data collection, data analysis and controlled feedback. The survey was administered entirely online using a Survey Gizmo questionnaire. A novel feature of this study was the use of two parallel panels of experts. The existence of an alternate panel was only made known to the participants in Round 3, when each panel was asked to rank the priorities of the other panel. This allowed quantitative comparisons to be made between each panel’s rankings of each research issue and cross-validated the rankings of research priorities developed by each panel.

To commence each round, experts were sent an email containing a direct link to the online questionnaire. Briefly, Round 1 required each expert to answer the question “What are the five most important research issues for the next 10 years in the area of child and adolescent physical activity and sedentary behaviour?” Each expert put forward five research issues which they believed were priorities in the area. They also provided a brief description of each issue and reasons why they believed the issue to be a priority. The three study investigators reviewed all issues that were provided by each panel, with common issues combined into a single issue. The experts were then fed back their panel’s list of research issues and asked to ensure that the five research issues they provided were accurately represented.

Round 2 then asked experts to “review the research issues put forward in Round 1 and rate how important they believe each issue is for global research in child and adolescent physical activity and sedentary behaviour”. Experts rated each research issue independently using a 5-point Likert scale (5 = very important, 4 = important, 3 = moderately important, 2 = of little importance and 1 = unimportant). The three study investigators then short-listed each panel’s research issues to 20 according to those with highest mean Likert scale ratings. Following this, the top 20 research issues from each panel were fed back to the experts of the relevant panels.

In Round 3, experts were first asked to “rank their panel’s top 20 research issues in order of perceived international importance in child and adolescent physical activity and sedentary behaviour over the next 10 years”. The experts were then similarly asked to rank the alternate panel’s top 20 research priorities. The data analysis procedure was as follows. Firstly, the overall sum of each panel’s rankings was calculated for Panel A and Panel B’s top 20 research issues. Secondly, the two lists of research issues were combined with common issues provided by both panels merged. This resulted in 29 unique issues. Thirdly, the experts’ individual rankings for each research issue were summed. This allowed the issues to be ranked according to the sum of Panel A and Panel B’s overall rankings for each issue. Intra-panel agreement was quantified using Spearman’s rho by creating a matrix to compare individuals’ rankings to one another within the same panel. Inter-panel agreement was also quantified using Spearman’s rho to compare the overall sum and rank for each issue between panels.

Expert demographics

All 24 experts completed the three Delphi rounds. Data was collected on the 24 experts’ geographical distributions, institutional affiliations and years worked in the study area.

As a group, the 24 experts represented every geographical region and 12 sub-regions. This geographical distribution is illustrated in Figure  2 .

Geographical distributions of participating experts. The numbers indicate the number of participating experts from that region.

In terms of institutional affiliation, twenty-three experts acknowledged they were affiliated with an educational institution, eleven were affiliated with a professional organisation, six with an international organisation, six with a non-government organisation and four with a government organisation. It was noted that due to the nature of their work, experts were often affiliated with more than one institution type.

In regards to years worked in the study area, twelve experts had worked in for greater than 16 years, five had worked for 11 to 15 years, four had worked for 6 to10 years and three had worked for less than five years.

Results from Delphi rounds

In Round 1, each expert put forward five research issues. Collectively this provided a total of 120 issues across all 24 experts, with 60 for each panel. Following qualitative reduction of overlapping issues, 26 issues from Panel A and 34 issues from Panel B, were carried forward to Round 2. On reviewing the amended list, all exerts agreed that the issues they had raised were adequately represented.

From Round 2, the mean Likert-scale ratings were used to determine the top 20 issues for each panel. For Panel A, the mean Likert-scale ratings of the top 20 issues ranged from 3.5 to 5.0, with 18 of 20 issues having a median rating of >4.0 (“important”). For Panel B, the mean Likert-scale ratings of the top 20 issues ranged from 4.0 to 4.8, with all 20 research issues having a median rating of >4.0.

In Round 3, the 20 issues from Panel A and 20 issues from Panel B were qualitatively analysed to form one list. Eleven of each panel’s top 20 research issues were common to both panels and were therefore combined, with the remaining 18 issues (nine from each panel) unique. The resultant was a set of 29 unique research issues that were then ranked in order of importance by summing Panel A and Panel B’s rankings for each issue Table  1 .

There was only weak intra-panel agreement. The mean inter-individual rho ( ± 95% CI) was 0.20 ±0.05 for Panel A and 0.13 ±0.04 for Panel B. The average standard deviation of the rankings for individual issues was 5.1 (Panel A) and 5.3 (Panel B). When Panel B ranked Panel A’s issues, the correlation was very strong ( rho ± 95% CI: 0.79 ±0.17), and when Panel A ranked Panel B’s issues, the correlation was strong ( rho ± 95% CI: 0.52 ±0.31). Figures  3 and 4 clearly illustrate the correlations for each research issue.

Agreement between Panel A’s rankings and Panel B’s rankings of Panel A’s identified issues. The line shown is the identity line.

Agreement between Panel B’s rankings and Panel A’s rankings of Panel B’s identified issues. The line shown is the identity line.

Study outcomes

The primary outcome of this study was the development of 29 international research priorities in child and adolescent physical activity and sedentary behaviour. In order for the research priorities to be useful, it is important that they be neither too general nor too specific. The research priorities in this study appear broad enough to enable them to be transferable to researchers’ specific regions and contexts.

The final set of research priorities address a broad range of areas from epidemiology, determinants and correlates, through to intervention effectiveness and translational research. Of the 29 identified research priorities, ten related directly to translational research centred on intervention design and effectiveness. These focussed on specific behaviours (active transport, screen time, sport, physical education), settings (schools, communities, whole of population), or vehicles (mass advertising, policy). Translational research, centred on intervention design and effectiveness, can potentially guide governments and stakeholders to fund interventions that are the most effective, sustainable and transferable for changing behaviours [ 7 ]. This is important because to date, the research community has not been very successful at developing interventions for children and adolescents that bring about long-term and sustained change in health behaviours [ 10 ]. In addition, little attention has been given to the importance of the intervention setting and establishing what works in what situation and with whom [ 22 ].

Nine of the research priorities had a focus on capturing and quantifying the health benefits of engaging in physical activity and limiting sedentary behaviour, These research priorities were concerned with the impact of physical activity and sedentary behaviour on obesity, cognition, and general health and well being, and on describing behavioural patterns (across the day or the life-course or in specific populations such as pre-school children). Epidemiological research was considered important to address the cause, distribution and patterns of childhood physical activity and sedentary behaviour on current and future health [ 2 , 6 , 9 , 23 ].

Six research issues related to determinants and correlates research such as psychosocial and cultural/parental factors, the impact of technology, and the importance of enjoyment and lifestyle in general. Research that focuses on the determinants and correlates of behaviours is important. This is because while many correlates appear to be intuitively obvious, at present they have mixed support from high quality research [ 3 ].

Four issues did not fit into the aforementioned categories. They were related to the theory of behaviour change, injury prevention, measurement of behaviours and the physical education in culture of movement. Objective measurement of behaviours was ranked highly and is thought to be a “necessary first step for conducting meaningful epidemiological surveillance, public health research and intervention research” [ 14 ] p.124.

Strengths and limitations

Unlike previously identified priority reports [ 11 , 13 – 15 ] this study employed a Delphi method to arrive at a more valid set of research priorities. Strengths related to the Delphi method include participant blinding, iterative data collection and controlled feedback between rounds. For example, the identities and responses of the experts were anonymised so that the identified research priorities could not be dominated by certain individuals [ 24 ]. Furthermore, the provision of controlled feedback allowed experts to individually consider their views in light of their panel’s collective opinion.

Other strengths related to the methodology were the use of criterion and purposive sampling methods. This procedure meant that all participants held a senior position in their respective organisations and had published in the study area. In addition, experts collectively represented every major world region and a wide range of discipline areas, affiliations and interests. This approach meant that the identified research issues were more likely to reflect the most important physical activity and sedentary behaviour issues facing the children and adolescents worldwide.

A novel component of this study was split-panel approach, which allowed comparisons to be made between the rankings given by the two expert panels. The experts from each panel were taken from the same population, given the same study information, answered identical online questionnaires and participated simultaneously and independently. One can therefore be confident that comparing the Round 3 rankings of Panel A and Panel B experts would provide valid measures of inter-panel agreement.

The weak intra-panel agreement was weak, which is likely a reflection of the natural variation of individual’s opinions and areas of interest within the broad study area. This weak agreement could also highlight the advantages of the methodology which retained anonymity and used an online mode of data collection. There were fewer pressures to conform to others opinions due to decreased likelihood of peer dominance and status. Evidence to reinforce confidence in the results is the strong to very strong (rho = 0.52–0.79) inter-panel agreement. While experts were invited from every United Nations sub-region (United Nations 2011), no experts from the following sub-regions took part: Southern Africa, Middle Africa, Caribbean, Eastern Europe, Australia, Central Asia and Western Asia. This was significant because many of these sub-regions are heavily involved in physical activity and sedentary behaviour research. Consequently, caution should be applied when recommending that the identified research priorities truly provide a global perspective. Nonetheless, these research priorities provide an international context from which priorities at the regional, national and local levels can be developed.

In addition the priorities were set for the broad area of child and adolescent physical activity and sedentary behaviour. Due to the generality of this topic, it may be that the research priorities are not relevant when conducting research into minority populations. For example, children and adolescents with disabilities may warrant different research issues not identified in this study.

Implications for research

We hope that the identification of a set of ranked research priorities may contribute to more co-ordinated international research. For example, research priorities can help inform post-graduate students regarding where the current evidence gaps exist. This may be especially helpful for researchers who reside in less developed or marginalised research regions. In addition, encouraging more guided research can help to conceptualise how findings can be used as a basis for policy decisions. Lastly, research priorities can help to direct valuable funding into priority areas and away from studies on over-researched or lower priority topics.

This study engaged two panels of study experts in a three-round Delphi communication procedure. The outcome of this procedure was the identification of a ranked set of 29 research priorities in child and adolescent physical activity and sedentary behaviour. For example, the top three ranked priorities were: developing effective and sustainable interventions to increase children’s physical activity long-term; policy and/or environmental change and their influence on children’s physical activity and sedentary behaviour; and prospective, longitudinal studies of the independent effects of physical activity and sedentary behaviour on health. We hope these research priorities will help inform the spectrum of future studies undertaken, guide post-graduate study choices, guide allocation of funding to priority areas and assist with policy decisions.

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Acknowledgements

The authors would like to acknowledge the Health and Use of Time Group at the University of South Australia.

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The study was conceived by GT and TO. LG was primarily responsible for conducting the participant selection process and the three rounds of data collection. LG, GT and TO were each involved in data analysis. LG produced the first draft of the paper with all other authors providing sections and critically reviewing the paper. All authors approved submission.

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Gillis, L., Tomkinson, G., Olds, T. et al. Research priorities for child and adolescent physical activity and sedentary behaviours: an international perspective using a twin-panel Delphi procedure. Int J Behav Nutr Phys Act 10 , 112 (2013). https://doi.org/10.1186/1479-5868-10-112

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Child and adolescent obesity

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• Paediatric research

The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. Obesity arises when a mix of genetic and epigenetic factors, behavioural risk patterns and broader environmental and sociocultural influences affect the two body weight regulation systems: energy homeostasis, including leptin and gastrointestinal tract signals, operating predominantly at an unconscious level, and cognitive–emotional control that is regulated by higher brain centres, operating at a conscious level. Health-related quality of life is reduced in those with obesity. Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. Treatment incorporates a respectful, stigma-free and family-based approach involving multiple components, and addresses dietary, physical activity, sedentary and sleep behaviours. In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. Prevention of obesity requires a whole-system approach and joined-up policy initiatives across government departments. Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities.

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Introduction

The prevalence of child and adolescent obesity remains high and continues to rise in low-income and middle-income countries (LMICs) at a time when these regions are also contending with under-nutrition in its various forms 1 , 2 . In addition, during the COVID-19 pandemic, children and adolescents with obesity have been more likely to have severe COVID-19 requiring hospitalization and mechanical ventilation 3 . At the same time, the pandemic was associated with rising levels of childhood obesity in many countries. These developments are concerning, considering that recognition is also growing that paediatric obesity is associated with a range of immediate and long-term negative health outcomes, a decreased quality of life 4 , 5 , an increased presentation to health services 6 and increased economic costs to individuals and society 7 .

Body weight is regulated by a range of energy homeostatic and cognitive–emotional processes and a multifactorial interplay of complex regulatory circuits 8 . Paediatric obesity arises when multiple environmental factors — covering preconception and prenatal exposures, as well as broader changes in the food and physical activity environments — disturb these regulatory processes; these influences are now widespread in most countries 9 .

The treatment of obesity includes management of obesity-associated complications, a developmentally sensitive approach, family engagement, and support for long-term behaviour changes in diet, physical activity, sedentary behaviours and sleep 10 . New evidence highlights the role, in adolescents with more severe obesity, of bariatric surgery 11 and pharmacotherapy, particularly the potential for glucagon-like peptide 1 (GLP1) receptor agonists 12 .

Obesity prevention requires a whole-system approach, with policies across all government and community sectors systematically taking health into account, avoiding harmful health impacts and decreasing inequity. Programmatic prevention interventions operating ‘downstream’ at the level of the child and family, as well as ‘upstream’ interventions at the level of the community and broader society, are required if a step change in tackling childhood obesity is to be realized 13 , 14 .

In this Primer, we provide an overview of the epidemiology, causes, pathophysiology and consequences of child and adolescent obesity. We discuss diagnostic considerations, as well as approaches to its prevention and management. Furthermore, we summarize effects of paediatric obesity on quality of life, and open research questions.

Epidemiology

Definition and prevalence.

The World Health Organization (WHO) defines obesity as “abnormal or excessive fat accumulation that presents a risk to health” 15 . Paediatric obesity is defined epidemiologically using BMI, which is adjusted for age and sex because of the physiological changes in BMI during growth 16 . Global prevalence of paediatric obesity has risen markedly over the past four decades, initially in high-income countries (HICs), but now also in many LMICs 1 .

Despite attempts to standardize the epidemiological classification, several definitions of paediatric obesity are in use; hence, care is needed when comparing prevalence rates. The 2006 WHO Child Growth Standard, for children aged 0 to 5 years, is based on longitudinal observations of multiethnic populations of children with optimal infant feeding and child-rearing conditions 17 . The 2007 WHO Growth Reference is used for the age group 5–19 years 18 , and the 2000 US Centers for Disease Control and Prevention (CDC) Growth Charts for the age group 2–20 years 19 . The WHO and CDC definitions based on BMI-for-age charts are widely used, including in clinical practice. By contrast, the International Obesity Task Force (IOTF) definition, developed from nationally representative BMI data for the age group 2–18 years from six countries, is used exclusively for epidemiological studies 20 .

For the age group 5–19 years, between 1975 and 2016, the global prevalence of obesity (BMI >2 standard deviations (SD) above the median of the WHO growth reference) increased around eightfold to 5.6% in girls and 7.8% in boys 1 . Rates have plateaued at high levels in many HICs but have accelerated in other regions, particularly in parts of Asia. For the age group 2–4 years, between 1980 and 2015, obesity prevalence (IOTF definition, equivalent to an adult BMI of ≥30 kg/m 2 ) increased from 3.9% to 7.2% in boys and from 3.7% to 6.4% in girls 21 . Obesity prevalence is highest in Polynesia and Micronesia, the Middle East and North Africa, the Caribbean and the USA (Fig.  1 ). Variations in prevalence probably reflect different background levels of obesogenic environments, or the sum total of the physical, economic, policy, social and cultural factors that promote obesity 22 . Obesogenic environments include those with decreased active transport options, a ubiquity of food marketing directed towards children, and reduced costs and increased availability of nutrient-poor, energy-dense foods. Particularly in LMICs, the growth of urbanization, new forms of technology and global trade have led to reduced physical activity at work and leisure, a shift towards Western diets, and the expansion of transnational food and beverage companies to shape local food systems 23 .

Maps showing the proportions of children and adolescents living with overweight or obesity (part  a , boys; part b , girls) according to latest available data from the Global Obesity Observatory . Data might not be comparable between countries owing to differences in survey methodology.

The reasons for varying sex differences in prevalence in different countries are unclear but may relate to cultural variations in parental feeding practices for boys and girls and societal ideals of body size 24 . In 2016, obesity in the age group 5–19 years was more prevalent in girls than in boys in sub-Saharan Africa, Oceania and some middle-income countries in other regions, whereas it was more prevalent in boys than in girls in all HICs, and in East and South-East Asia 21 . Ethnic and racial differences in obesity prevalence within countries are often assumed to mirror variations in social deprivation and other social determinants of obesity. However, an independent effect of ethnicity even after adjustment for socioeconomic status has been documented in the UK, with Black and Asian boys in primary school having higher prevalence of obesity than white boys 25 .

Among individuals with obesity, very high BMI values have become more common in the past 15 years. The prevalence of severe obesity (BMI ≥120% of the 95th percentile (CDC definition), or ≥35 kg/m 2 at any age 26 , 27 ) has increased in many HICs, accounting for one-quarter to one-third of those with obesity 28 , 29 . Future health risks of paediatric obesity in adulthood are well documented. For example, in a data linkage prospective study in Israel with 2.3 million participants who had BMI measured at age 17 years, those with obesity (≥95th percentile BMI for age) had a much higher risk of death from coronary heart disease (HR 4.9, 95% CI 3.9–6.1), stroke (HR 2.6, 95% CI 1.7–4.1) and sudden death (HR 2.1, 95% CI 1.5–2.9) compared with those whose BMI fell between the 5th and 24th percentiles 30 .

Causes and risk factors

Early life is a critical period for childhood obesity development 9 , 31 , 32 , 33 . According to the Developmental Origins of Health and Disease framework, the early life environment may affect organ structure and function and influence health in later life 34 , 35 . Meta-analyses have shown that preconception and prenatal environmental exposures, including high maternal pre-pregnancy BMI and, to a lesser extent, gestational weight gain, as well as gestational diabetes and maternal smoking, are associated with childhood obesity, potentially through effects on the in utero environment 33 , 36 , 37 , 38 . Paternal obesity is also associated with childhood obesity 33 . Birthweight, reflecting fetal growth, is a proxy for in utero exposures. Both low and high birthweights are associated with later adiposity, with high birthweight linked to increased BMI and low birthweight to central obesity 33 , 39 .

Growth trajectories in early life are important determinants of later adiposity. Rapid weight gain in early childhood is associated with obesity in adolescence 32 . Also, later age and higher BMI at adiposity peak (the usual peak in BMI around 9 months of age), as well as earlier age at adiposity rebound (the lowest BMI reached between 4 and 7 years of age), are associated with increased adolescent and adult BMI 40 , 41 . Specific early life nutritional factors, including a lower protein content in formula food, are consistently associated with a lower risk of childhood obesity 42 , 43 . These also include longer breastfeeding duration, which is generally associated with a lower risk of childhood obesity 42 . However, some controversy exists, as these effects are affected by multiple sociodemographic confounding factors and their underlying mechanisms remain uncertain 44 . Some studies comparing higher and lower infant formula protein content have reported that the higher protein group have a greater risk of subsequent obesity, especially in early childhood 41 , 42 ; however, one study with a follow-up period until age 11 years found no significant difference in the risk of obesity, but an increased risk of overweight in the high protein group was still observed 42 , 43 , 45 . A high intake of sugar-sweetened beverages is associated with childhood obesity 33 , 46 .

Many other behavioural factors are associated with an increased risk of childhood obesity, including increased screen time, short sleep duration and poor sleep quality 33 , 47 , reductions in physical activity 48 and increased intake of energy-dense micronutrient-poor foods 49 . These have been influenced by multiple changes in the past few decades in the broader social, economic, political and physical environments, including the widespread marketing of food and beverages to children, the loss of walkable green spaces in many urban environments, the rise in motorized transport, rapid changes in the use of technology, and the move away from traditional foods to ultraprocessed foods.

Obesity prevalence is inextricably linked to relative social inequality, with data suggesting a shift in prevalence over time towards those living with socioeconomic disadvantage, and thus contributes to social inequalities. In HICs, being in lower social strata is associated with a higher risk of obesity, even in infants and young children 50 , whereas the opposite relationship occurs in middle-income countries 51 . In low-income countries, the relationship is variable, and the obesity burden seems to be across socioeconomic groups 52 , 53 .

Overall, many environmental, lifestyle, behavioural and social factors in early life are associated with childhood obesity. These factors cannot be seen in isolation but are part of a complex interplay of exposures that jointly contribute to increased obesity risk. In addition to multiple prenatal and postnatal environmental factors, genetic variants also have a role in the development of childhood obesity (see section Mechanisms/pathophysiology).

Comorbidities and complications

Childhood obesity is associated with a wide range of short-term comorbidities (Fig.  2 ). In addition, childhood obesity tracks into adolescence and adulthood and is associated with complications across the life course 32 , 41 , 54 , 55 .

Obesity in children and adolescents can be accompanied by various other pathologies. In addition, childhood obesity is associated with complications and disorders that manifest in adulthood (red box).

Increased BMI, especially in adolescence, is linked to a higher risk of many health outcomes, including metabolic disorders, such as raised fasting glucose, impaired glucose tolerance, type 2 diabetes mellitus (T2DM), metabolic syndrome and fatty liver disease 56 , 57 , 58 , 59 . Other well-recognized obesity-associated complications include coronary heart disease, asthma, obstructive sleep apnoea syndrome (itself associated with metabolic dysfunction and inflammation) 60 , orthopaedic complications and a range of mental health outcomes including depression and low self-esteem 27 , 55 , 57 , 61 , 62 , 63 .

A 2019 systematic review showed that children and adolescents with obesity are 1.4 times more likely to have prediabetes, 1.7 times more likely to have asthma, 4.4 times more likely to have high blood pressure and 26.1 times more likely to have fatty liver disease than those with a healthy weight 64 . In 2016, it was estimated that, at a global level by 2025, childhood obesity would lead to 12 million children aged 5–17 years with glucose intolerance, 4 million with T2DM, 27 million with hypertension and 38 million with fatty liver disease 65 . These high prevalence rates have implications for both paediatric and adult health services.

Mechanisms/pathophysiology

Body weight regulation.

Body weight is regulated within narrow limits by homeostatic and cognitive–emotional processes and a multifactorial interplay of hormones and messenger substances in complex regulatory circuits (Fig.  3 ). When these regulatory circuits are disturbed, an imbalance between energy intake and expenditure leads to obesity or to poor weight gain. As weight loss is much harder to achieve than weight gain in the long term due to the regulation circuits discussed below, the development of obesity is encouraged by modern living conditions, which enable underlying predispositions for obesity to become manifest 8 , 66 .

Body weight is predominantly regulated by two systems: energy homeostasis and cognitive–emotional control. Both homeostatic and non-homeostatic signals are processed in the brain, involving multiple hormone and receptor cascades 217 , 218 , 219 . This overview depicts the best-known regulatory pathways. The homeostatic system, which is mainly regulated by brain centres in the hypothalamus and brainstem, operates on an unconscious level. Both long-term signals from the energy store in adipose tissue (for example, leptin) and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status. During gastric distension or after the release of gastrointestinal hormones (multiple receptors are involved) and insulin, a temporary feeling of fullness is induced. The non-homeostatic or hedonic system is regulated by higher-level brain centres and operates at the conscious level. After integration in the thalamus, homeostatic signals are combined with stimuli from the environment, experiences and emotions; emotional and cognitive impulses are then induced to control food intake. Regulation of energy homeostasis in the hypothalamus involves two neuron types of the arcuate nucleus: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). Leptin stimulates these neurons via specific leptin receptors (LEPR) inducing anabolic effects in case of decreasing leptin levels and catabolic effects in case of increasing leptin levels. Leptin inhibits the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production resulting in the feeling of hunger. Leptin directly stimulates POMC production in POMC neurons. POMC is cleaved into different hormone polypeptides including α-melanocyte-stimulating hormone which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. CART, cocaine and amphetamine responsive transcript; IR, insulin receptor.

In principle, there are two main systems in the brain which regulate body weight 8 , 66 (Fig.  3 ): energy homeostasis and cognitive–emotional control. Energy homeostasis is predominantly regulated by brain centres in the hypothalamus and brainstem and operates at an unconscious level. Both long-term signals from the adipose tissue energy stores and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status 8 , 66 . For example, negative energy balance leading to reduced fat mass results in reduced leptin levels, a permanently reduced urge to exercise and an increased feeling of hunger. During gastric distension or after the release of gastrointestinal hormones and insulin, a temporary feeling of fullness is induced 8 , 66 . Cognitive–emotional control is regulated by higher brain centres and operates at a conscious level. Here, the homeostatic signals are combined with stimuli from the environment (sight, smell and taste of food), experiences and emotions 8 , 66 . Disorders at the level of cognitive–emotional control mechanisms include emotional eating as well as eating disorders. For example, the reward areas in the brain of people with overweight are more strongly activated by high-calorie foods than those in the brain of people with normal weight 67 . Both systems interact with each other, and the cognitive–emotional system is strongly influenced by the homeostatic control circuits.

Disturbances in the regulatory circuits of energy homeostasis can be genetically determined, can result from disease or injury to the regulatory centres involved, or can be caused by prenatal programming 8 , 66 . If the target value of body weight has been shifted, the organism tries by all means (hunger, drive) to reach the desired higher weight. These disturbed signals of the homeostatic system can have an imperative, irresistible character, so that a conscious influence on food intake is no longer effectively possible 8 , 66 . The most important disturbances of energy homeostasis are listed in Table  1 .

The leptin pathway

The peptide hormone leptin is primarily produced by fat cells. Its production depends on the amount of adipose tissue and the energy balance. A negative energy balance during fasting results in a reduction of circulating leptin levels by 50% after 24 h (ref. 68 ). In a state of weight loss, leptin production is reduced 69 . In the brain, leptin stimulates two neuron types of the arcuate nucleus in the hypothalamus via specific leptin receptors: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). High leptin levels inhibit the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production. By contrast, leptin directly stimulates POMC production in POMC neurons (Fig.  3 ). POMC is a hormone precursor that is cleaved into different hormone polypeptides by specific enzymes, such as prohormone convertase 1 (PCSK1). This releases α-melanocyte-stimulating hormone (α-MSH) which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. Rare, functionally relevant mutations in the genes for leptin and leptin receptor, POMC , PCSK1/3 or MC4R lead to extreme obesity in early childhood. These forms of obesity are potential indications for specific pharmacological treatments, for example setmelanotide 70 , 71 . MC4R mutations are the most common cause of monogenic obesity, as heterozygous mutations can be symptomatic depending on the functional impairment and with variable penetrance and expression. Other genes have been identified, in which rare heterozygous pathological variants are also associated with early onset obesity (Table  1 ).

Pathological changes in adipose tissue

Adipose tissue can be classified into two types, white and brown adipose tissue. White adipose tissue comprises unilocular fat cells and brown adipose tissue contains multilocular fat cells, which are rich in mitochondria 72 . A third type of adipocyte, beige adipocytes, within the white adipose tissue are induced by prolonged exposure to cold or adrenergic signalling, and show a brown adipocyte-like morphology 72 . White adipose tissue has a large potential to change its volume to store energy and meet the metabolic demands of the body. The storage capacity and metabolic function of adipose tissue depend on the anatomical location of the adipose tissue depot. Predominant enlargement of white adipose tissue in the visceral, intra-abdominal area (central obesity) is associated with insulin resistance and an increased risk of metabolic disease development before puberty. Accumulation of adipose tissue in the hips and flanks has no adverse effect and may be protective against metabolic syndrome. In those with obesity, adipose tissue is characterized by an increased number of adipocytes (hyperplasia), which originate from tissue-resident mesenchymal stem cells, and by enlarged adipocytes (hypertrophy) 73 . Adipocytes with a very large diameter reach the limit of the maximal oxygen diffusion distance, resulting in hypoxia, the development of an inflammatory expression profile (characterized by, for example, leptin, TNF and IL-6) and adipocyte necrosis, triggering the recruitment of leukocytes. Resident macrophages switch from the anti-inflammatory M2 phenotype to a pro-inflammatory M1 phenotype, which is associated with insulin resistance, further promoting local sterile inflammation and the development of fibrotic adipose tissue. This process limits the expandability of the adipose tissue for further storage of triglycerides. In the patient, the increase in fat mass in obesity is associated with insulin resistance and systemic low-grade inflammation characterized by elevated serum levels of C-reactive protein and pro-inflammatory cytokines. The limitation of adipose tissue expandability results in storage of triglycerides in other organs, such as the liver, muscle and pancreas 74 .

Genetics and epigenetics in the general population

Twin studies have found heritability estimates for BMI of up to 70% 75 , 76 . In contrast to rare monogenic forms of obesity, which are often caused by a single genetic defect with a large effect, the genetic background of childhood obesity in the general population is shaped by the joint effects of many common genetic variants, each of which individually makes a small contribution to the phenotype. For adult BMI, genome-wide association studies, which examine associations of millions of such variants across the genome at the same time, have identified around 1,000 genetic loci 77 . The largest genome-wide association studies in children, which include much smaller sample sizes of up to 60,000 children, have identified 25 genetic loci for childhood BMI and 18 for childhood obesity, the majority of which overlap 78 , 79 . There is also a clear overlap with genetic loci identified in adults, for example for FTO , MC4R and TMEM18 , but this overlap is not complete, some loci are specific to early life BMI, or have a relatively larger contribution in childhood 78 , 79 , 80 . These findings suggest that biological mechanisms underlying obesity in childhood are mostly similar to those in adulthood, but the relative influence of these mechanisms may differ at different phases of life.

The role of epigenetic processes in childhood and adolescent obesity has gained increasing attention. In children, several studies found associations between DNA methylation and BMI 81 , 82 , 83 , 84 , but a meta-analysis including data from >4,000 children identified only minimal associations 85 . Most studies support the hypothesis that DNA methylation changes are predominantly a consequence rather than a cause of obesity, which may explain the lower number of identified (up to 12) associations in children, in whom duration of exposure to a higher BMI is shorter than in adults, in whom associations with DNA methylation at hundreds of sites have been identified 85 , 86 , 87 . In addition to DNA methylation, some specific circulating microRNAs have been found to be associated with obesity in childhood 84 .

The field of epigenetic studies in childhood obesity is relatively young and evolving quickly. Future studies will need to focus on defining robust associations in blood as well as other tissues and on identifying cause-and-effect relationships. In addition, other omics, such as metabolomics and proteomics, are promising areas that may contribute to an improved aetiological understanding or may provide biological signatures that can be used as predictive or prognostic markers of childhood obesity and its comorbidities.

Parental obesity and childhood obesity

There is an established link between increased parental BMI and increased childhood BMI 88 , 89 . This link may be due to shared genetics, shared environment, a direct intrauterine effect of maternal BMI or a combination of these factors. In the case of shared genetics, the child inherits BMI-increasing genetic variants from one or both parents. Shared environmental factors, such as diet or lifestyle, may also contribute to an increased BMI in both parents and child. In addition, maternal obesity might create an intrauterine environment that programmes metabolic processes in the fetus, which increases the risk of childhood obesity. Some studies show larger effects of maternal than paternal BMI, indicating a potential causal intrauterine mechanism of maternal obesity, but evidence showing similar maternal and paternal effects is increasing. The data may indicate that there is only a limited direct intrauterine effect of maternal obesity on childhood obesity; rather, genetic effects inherited from the mother or father, or both, and/or shared environmental factors may contribute to childhood obesity risk 90 , 91 , 92 , 93 , 94 , 95 .

Diagnosis, screening and prevention

Diagnostic work-up.

The extent of overweight in clinical practice is estimated using BMI based on national charts 96 , 97 , 98 , 99 , 100 . Of note, the clinical classification of overweight or obesity differ depending on the BMI charts used and national recommendations; hence, local guidelines should be referred to. For example, the US CDC Growth Charts and several others use the 85th and 95th centile cut-points to denote overweight and obesity, respectively 19 . The WHO Growth Reference for children aged 5–19 years defines cut-points for overweight and obesity as a BMI-for-age greater than +1 and +2 SDs for BMI for age, respectively 18 . For children <5 years of age, overweight and obesity are defined as weight-for-height greater than +2 and +3 SDs, respectively, above the WHO Child Growth Standards median 17 . The IOTF and many countries in Europe use cut-points of 85th, 90th and 97th to define overweight, obesity and extreme obesity 26 .

BMI as an indirect measurement of body fat has some limitations; for example, pronounced muscle tissue leads to an increase in BMI, and BMI is not independent of height. In addition, people of different ethnicities may have different cut-points for obesity risk; for example, cardiometabolic risk occurs at lower BMI values in individuals with south Asian than in those with European ancestry 101 . Thus, BMI is best seen as a convenient screening tool that is supplemented by clinical assessment and investigations.

Other measures of body fat may help differentiate between fat mass and other tissues. Some of these tools are prone to low reliability, such as body impedance analyses (high day-to-day variation and dependent on level of fluid consumption) or skinfold thickness (high inter-observer variation), or are more expensive or invasive, such as MRI, CT or dual-energy X-ray absorptiometry, than simpler measures of body composition or BMI assessment.

Primary diseases rarely cause obesity in children and adolescents (<2%) 102 . However, treatable diseases should be excluded in those with obesity. A suggested diagnostic work-up is summarized in Fig.  4 . Routine measurement of thyroid-stimulating hormone (TSH) is not recommended 96 . Moderately elevated TSH levels (usually <10 IU/l) are frequently observed in obesity and are a consequence, and not a cause, of obesity 103 . In a growing child with normal height velocity, a normal BMI at the age of 2 years and normal cognitive development, no further diagnostic steps are necessary to exclude primary diseases 96 , 104 .

Concerning findings from a detailed medical history and physical examination will lead to further examinations. In individuals with early onset, extreme obesity (before age 3 years) and signs of hyperphagia, serum leptin level should be measured to rule out the extremely rare condition of congenital leptin deficiency. In individuals with normal or high leptin levels, genetic testing is indicated to search for monogenetic obesity. In individuals with intellectual disability, a syndromic disease may be present. Signs of impaired growth velocity or the history of central nervous system trauma or surgery will result in deeper endocrine evaluation and/or brain MRI. BDNF , brain-derived neurotropic factor; FT4, free thyroxin; KSR2 , kinase suppressor of ras 2; MC4R , melanocortin 4 receptor; POMC , pro-opiomelanocortin; SH2B1 , Src-homology 2 (SH2) B adapter protein 1; SIM1 , single-minded homologue 1; TSH, thyroid-stimulating hormone.

Clinical findings which need no further examination include pseudogynaecomastia (adipose tissue mimicking breast development; differentiated from breast tissue by ultrasonography), striae (caused by rapid weight increase) and a hidden penis in suprapubic adipose tissue (differentiated from micropenis by measurement of stretched penis length while pressing down on the suprapubic adipose tissue) 96 , 105 . Girls with obesity tend to have an earlier puberty onset (usually at around 8–9 years of age) and boys with severe obesity may have a delayed puberty onset (usually at around 13–14 years of age) 106 . Thus, if pubertal onset is slightly premature in girls or slightly delayed in boys, no further endocrine assessment is necessary.

Assessment of obesity-associated comorbidities

A waist to height ratio of >0.5 is a simple tool to identify central obesity 107 , 108 . Screening for cardiometabolic risk factors and fatty liver disease is recommended, especially in adolescents, and in those with more severe obesity or central adiposity, a strong family history of T2DM or premature heart disease, or relevant clinical symptoms, such as high blood pressure or acanthosis nigricans 96 , 97 , 98 , 99 , 109 . Investigations generally include fasting glucose levels, lipid profile, liver function and glycated haemoglobin, and might include an oral glucose tolerance test, polysomnography, and additional endocrine tests for polycystic ovary syndrome 96 , 97 , 98 , 99 .

T2DM in children and adolescents often occurs in the presence of a strong family history and may not be related to obesity severity 110 . T2DM onset usually occurs during puberty, a physiological state associated with increased insulin resistance 111 and, therefore, screening for T2DM should be considered in children and adolescents with obesity and at least one risk factor (family history of T2DM or features of metabolic syndrome) starting at pubertal onset 112 . As maturity-onset diabetes of the young (MODY) type II and type III are more frequent than T2DM in children and adolescents in many ethnicities, genetic screening for MODY may be appropriate 112 . Furthermore, type 1 diabetes mellitus (T1DM) should be excluded by measurement of autoantibodies in any individual with suspected diabetes with obesity. The differentiation of T2DM from MODY and T1DM is important as the diabetes treatment approaches differ 112 .

Several comorbidities of obesity should be considered if specific symptoms occur 96 , 109 . For polycystic ovary syndrome in hirsute adolescent girls with oligomenorrhoea or amenorrhoea, moderately increased testosterone levels and decreased sex hormone binding globulin levels are typical laboratory findings 113 . Obstructive sleep apnoea can occur in those with more severe obesity and who snore, have daytime somnolence or witnessed apnoeas. Diagnosis is made by polysomnography 114 . Minor orthopaedic disorders, such as flat feet and genu valgum, are frequent in children and adolescents with obesity and may cause pain. Major orthopaedic complications include slipped capital femoral epiphyses (acute and chronic), which manifest with hip and knee pain in young adolescents and are characterized by reduced range of hip rotation and waddling gait; and Blount disease (tibia vara), typically occurring in children aged 2–5 years 105 , 115 . In addition, children and adolescents with extreme obesity frequently have increased dyspnoea and decreased exercise capacity. A heightened demand for ventilation, elevated work of breathing, respiratory muscle inefficiency and diminished respiratory compliance are caused by increased truncal fat mass. This may result in a decreased functional residual capacity and expiratory reserve volume, ventilation to perfusion ratio abnormalities and hypoxaemia, especially when supine. However, conventional respiratory function tests are only mildly affected by obesity except in extreme cases 116 . Furthermore, gallstones should be suspected in the context of abdominal pain after rapid weight loss, which can be readily diagnosed via abdominal ultrasonography 105 . Finally, pseudotumor cerebri may present with chronic headache, and depression may present with flat affect, chronic fatigue and sleep problems 105 .

Obesity in adolescents can also be associated with disordered eating, eating disorders and other psychological disorders 117 , 118 . If suspected, assessment by a mental health professional is recommended.

A comprehensive approach

The 2016 report of the WHO Commission on Ending Childhood Obesity stated that progress in tackling childhood obesity has been slow and inconsistent, with obesity prevention requiring a whole-of-government approach in which policies across all sectors systematically take health into account, avoiding harmful health impacts and, therefore, improving population health and health equity 13 , 119 . The focus in developing and implementing interventions to prevent obesity in children should be on interventions that are feasible, effective and likely to reduce health inequalities 14 . Importantly, the voices of children and adolescents living with social disadvantage and those from minority groups must be heard if such interventions are to be effective and reduce inequalities 120 .

Figure  5 presents a system for the prevention of childhood obesity within different domains of the socioecological model 121 and highlights opportunities for interventions. These domains can be described on a continuum, from (most downstream) individual and interpersonal (including parents, peers and wider family) through to organizational (including health care and schools), community (including food, activity and environment), society (including media and finally cultural norms) and (most upstream) public policy (from local to national level). Interventions to prevent childhood obesity can be classified on the Nuffield intervention ladder 122 . This framework was proposed by the Nuffield Council on Bioethics in 2007 (ref. 122 ) and distributes interventions on the ladder steps depending on the degree of agency required by the individual to make the behavioural changes that are the aim of the intervention. The bottom step of the ladder includes interventions that provide information, which requires the highest agency and relies on a child, adolescent and/or family choosing (and their ability to choose) to act on that information and change behaviour. The next steps of the ladder are interventions that enable choice, guide choice through changing the default policy, guide choice through incentives, guide choice through disincentives, or restrict choice. On the top-most step of the ladder (lowest agency required) are interventions that eliminate choice.

This schematic integrates interventions that were included in a Cochrane review 127 of 153 randomized controlled trials of interventions to prevent obesity in children and are high on the Nuffield intervention ladder 122 . The Nuffield intervention ladder distributes interventions depending on the degree of agency required for the behavioural changes that are the aim of the intervention. The socioecological model 121 comprises different domains (or levels) from the individual up to public policy. Interventions targeting the individual and interpersonal domains can be described as downstream interventions, and interventions within public policy can be described as the highest level of upstream interventions. Within each of these domains, arrow symbols with colours corresponding to the Nuffield intervention ladder category are used to show interventions that were both included in the Cochrane review 127 and that guide, restrict or eliminate choice as defined by the Nuffield intervention ladder 122 . Upstream interventions, and interventions on the top steps of the Nuffield ladder, are more likely to reduce inequalities. NGO, non-governmental organization.

Downstream and high-agency interventions (on the bottom steps of the Nuffield ladder) are more likely to result in intervention-generated inequalities 123 . This has been elegantly described and evidenced, with examples from the obesity prevention literature 124 , 125 . A particularly strong example is a systematic review of 38 interventions to promote healthy eating that showed that food price (an upstream and low-agency intervention) seemed to decrease inequalities, all interventions that combined taxes and subsidies consistently decreased inequalities, and downstream high-agency interventions, especially dietary counselling, seemed to increase inequalities 126 .

Effectiveness of prevention interventions

A 2019 Cochrane review of interventions to prevent obesity in children 127 included 153 randomized controlled trials (RCTs), mainly in HICs (12% were from middle-income countries). Of these RCTs, 56% tested interventions in children aged 6–12 years, 24% in children aged 0–5 years, and 20% in adolescents aged 13–18 years. The review showed that diet-only interventions to prevent obesity in children were generally ineffective across all ages. Interventions combining diet and physical activity resulted in modest benefits in children aged 0–12 years but not in adolescents. However, physical activity-only interventions to prevent obesity were effective in school-age children (aged 5–18 years). Whether the interventions were likely to work equitably in all children was investigated in 13 RCTs. These RCTs did not indicate that the strategies increased inequalities, although most of the 13 RCTs included relatively homogeneous groups of children from disadvantaged backgrounds.

The potential for negative unintended consequences of obesity prevention interventions has received much attention 128 . The Cochrane review 127 investigated whether children were harmed by any of the strategies; for example, by having injuries, losing too much weight or developing damaging views about themselves and their weight. Of the few RCTs that did monitor these outcomes, none found any harms in participants.

Intervention levels

Most interventions (58%) of RCTs in the Cochrane review aimed to change individual lifestyle factors via education-based approaches (that is, simply provide information) 129 . In relation to the socioecological model, only 11 RCTs were set in the food and physical activity environment domain, and child care, preschools and schools were the most common targets for interventions. Of note, no RCTs were conducted in a faith-based setting 130 . Table  2 highlights examples of upstream interventions that involve more than simply providing information and their classification on the Nuffield intervention ladder.

Different settings for interventions to prevent childhood obesity, including preschools and schools, primary health care, community settings and national policy, offer different opportunities for reach and effectiveness, and a reduction in inequalities.

Preschools and schools are key settings for public policy interventions for childhood obesity prevention, and mandatory and voluntary food standards and guidance on physical education are in place in many countries. Individual schools are tasked with translating and implementing these standards and guidance for their local context. Successful implementation of a whole-school approach, such as that used in the WHO Nutrition-Friendly Schools Initiative 131 , is a key factor in the effectiveness of interventions. Careful consideration should be given to how school culture can, and needs to, be shifted by working with schools to tailor the approach and manage possible staff capacity issues, and by building relationships within and outside the school gates to enhance sustainability 132 , 133 .

Primary health care offers opportunities for guidance for obesity prevention, especially from early childhood to puberty. Parent-targeted interventions conducted by clinicians in health-care or community settings have the strongest level of evidence for their effectiveness in reducing BMI z -score at age 2 years 134 . These interventions include group programmes, clinic nurse consultations, mobile phone text support or nurse home visiting, and focusing on healthy infant feeding, healthy childhood feeding behaviours and screen time.

A prospective individual participant data meta-analysis of four RCTs involving 2,196 mother–baby dyads, and involving nurse home visiting or group programmes, resulted in a small but significant reduction in BMI in infants in the intervention groups compared with control infants at age 18–24 months 134 . Improvements were also seen in television viewing time, breastfeeding duration and feeding practices. Interventions were more effective in settings with limited provision of maternal and child health services in the community. However, effectiveness diminished by age 5 years without further intervention, highlighting the need for ongoing interventions at each life stage 135 . Evidence exists that short-duration interventions targeting sleep in very early childhood may be more effective than nutrition-targeted interventions in influencing child BMI at age 5 years 136 .

Primary care clinicians can provide anticipatory guidance, as a form of primary prevention, to older children, adolescents and their families, aiming to support healthy weight and weight-related behaviours. Clinical guidelines recommend that clinicians monitor growth regularly, and provide guidance on healthy eating patterns, physical activity, sedentary behaviours and sleep patterns 97 , 100 . Very few paediatric trials have investigated whether this opportunistic screening and advice is effective in obesity prevention 100 . A 2021 review of registered RCTs for the prevention of obesity in infancy found 29 trials 137 , of which most were delivered, or were planned to be delivered, in community health-care settings, such as nurse-led clinics. At the time of publication, 11 trials had reported child weight-related outcomes, two of which showed a small but significant beneficial effect on BMI at age 2 years, and one found significant improvements in the prevalence of obesity but not BMI. Many of the trials showed improvements in practices, such as breastfeeding and screen time.

At the community level, local public policy should be mindful of the geography of the area (such as urban or rural) and population demographics. Adolescents usually have more freedom in food and beverage choices made outside the home than younger children. In addition, physical activity levels usually decline and sedentary behaviours rise during adolescence, particularly in girls 138 , 139 . These behavioural changes offer both opportunities and barriers for those developing community interventions. On a national societal level, public policies for interventions to prevent obesity in children include the control of advertising of foods and beverages high in fat, sugar and/or salt in some countries. Industry and the media, including social media, can have a considerable influence on the food and physical activity behaviours of children 13 , 119 .

Public policy may target interventions at all domains from the individual to the societal level. The main focus of interventions in most national public policies relies on the ability of individuals to make the behavioural changes that are the aim of the intervention (high-agency interventions) at the individual level (downstream interventions). An equal focus on low-agency and upstream interventions is required if a step change in tackling childhood obesity is to be realized 140 , 141 .

COVID-19 and obesity

Early indications in several countries show rising levels of childhood obesity, and an increase in inequalities in childhood obesity during the COVID-19 pandemic 142 . The substantial disruptions in nutrition and lifestyle habits of children during and since the pandemic include social isolation and addiction to screens 143 . Under-nutrition is expected to worsen in poor countries, but obesity rates could increase in middle-income countries and HICs, especially among vulnerable groups, widening the gap in health and social inequalities 143 . Public health approaches at national, regional and local levels should include strategies that not only prevent obesity and under-nutrition, but also reduce health inequalities.

In summary, although most trials of obesity prevention have occurred at the level of the individual, the immediate family, school or community, effective prevention of obesity will require greater investment in upstream, low-agency interventions.

Treatment goals

Treatment should be centred on the individual and stigma-free (Box  1 ) and may aim for a reduction in overweight and improvement in associated comorbidities and health behaviours. Clinical considerations when determining a treatment approach should include age, severity of overweight and the presence of associated complications 144 , 145 .

Box 1 Strategies for minimizing weight stigma in health care 220 , 221 , 222

Minimizing weight bias in the education of health-care professionals

Improved education of health professionals:

pay attention to the implicit and explicit communication of social norms

include coverage of the broader determinants of obesity

include discussion of harms caused by social and cultural norms and messages concerning body weight

provide opportunities to practise non-stigmatizing care throughout education

Provide causal information focusing on the genetic and/or socioenvironmental determinants of weight.

Provide empathy-invoking interventions, emphasizing size acceptance, respect and human dignity.

Provide a weight-inclusive approach, by emphasizing that all individuals, regardless of size, have the right to equal health care.

Addressing health facility infrastructure and processes

Provide appropriately sized chairs, blood pressure cuffs, weight scales, beds, toilets, showers and gowns.

Use non-stigmatizing language in signage, descriptions of clinical services and other documentation.

Providing clinical leadership and using appropriate language within health-care settings

Senior clinicians and managers should role-model supportive and non-biased behaviours towards people with obesity and indicate that they do not tolerate weight-based discrimination in any form.

Staff should identify the language that individuals prefer in referring to obesity.

Use person-first language, for example a ‘person with obesity’ rather than ‘an obese person’.

Treatment guidelines

Clinical guidelines advise that first-line management incorporates a family-based multicomponent approach that addresses dietary, physical activity, sedentary and sleep behaviours 97 , 99 , 109 , 146 . This approach is foundational, with adjunctive therapies, especially pharmacotherapy and bariatric surgery, indicated under specific circumstances, usually in adolescents with more severe obesity 144 , 145 . Guideline recommendations vary greatly among countries and are influenced by current evidence, and functionality and resourcing of local health systems. Hence, availability and feasibility of therapies differs internationally. In usual clinical practice, interventions may have poorer outcomes than is observed in original studies or anticipated in evidence-based guidelines 147 because implementation of guidelines is more challenging in resource-constrained environments 148 . In addition, clinical trials are less likely to include patients with specialized needs, such as children from culturally diverse populations, those living with social disadvantage, children with complex health problems, and those with severe obesity 149 , 150 .

Behavioural interventions

There are marked differences in individual responses to behavioural interventions, and overall weight change outcomes are often modest. In children aged 6–11 years, a 2017 Cochrane review 150 found that mean BMI z -scores were reduced in those involved in behaviour-changing interventions compared with those receiving usual care or no treatment by only 0.06 units (37 trials; 4,019 participants; low-quality evidence) at the latest follow-up (median 10 months after the end of active intervention). In adolescents aged 12–17 years, another 2017 Cochrane review 149 found that multicomponent behavioural interventions resulted in a mean reduction in weight of 3.67 kg (20 trials; 1,993 participants) and reduction in BMI of 1.18 kg/m 2 (28 trials; 2,774 participants). These effects were maintained at the 24-month follow-up. A 2012 systematic review found significant improvements in LDL cholesterol triglycerides and blood pressure up to 1 year from baseline following lifestyle interventions in children and adolescents 151 .

Family-based behavioural interventions are recommended in national level clinical practice guidelines 97 , 100 , 146 , 152 . They are an important element of intensive health behaviour and lifestyle treatments (IHBLTs) 109 . Family-based approaches use behavioural techniques, such as goal setting, parental monitoring or modelling, taught in family sessions or in individual sessions separately to children and care givers, depending on the child’s developmental level. The priority is to encourage the whole family to engage in healthier behaviours that result in dietary improvement, greater physical activity, and less sedentariness. This includes making changes to the family food environment and requires parental monitoring.

Family-based interventions differ in philosophy and implementation from those based on family systems theory and therapy 153 . All are intensive interventions that require multiple contact hours (26 or more) with trained specialists delivered over an extended period of time (6–12 months) 10 . Changing family lifestyle habits is challenging and expensive, and the therapeutic expertise is not widely available. Moving interventions to primary care settings, delivered by trained health coaches, and supplemented by remote contact (for example by phone), will improve access and equity 154 .

Very few interventions use single psychological approaches. Most effective IHBLTs are multicomponent and intensive (many sessions), and include face-to-face contact. There has been interest in motivational interviewing as an approach to delivery 155 . As client-centred counselling, this places the young person at the centre of their behaviour change. Fundamental to motivational interviewing is the practitioner partnership that helps the young person and/or parents to explore ambivalence to change, consolidate commitment to change, and develop a plan based on their own insights and expertise. Evidence reviews generally support the view that motivational interviewing reduces BMI. Longer interventions (>4 months), those that assess and report on intervention fidelity, and those that target both diet and physical activity are most effective 155 , 156 .

More intensive dietary interventions

Some individuals benefit from more intensive interventions 98 , 144 , 157 , 158 , which include very low-energy diets, very low-carbohydrate diets and intermittent energy restriction 159 . These interventions usually aim for weight loss and are only recommended for adolescents who have reached their final height. These diets are not recommended for long periods of time due to challenges in achieving nutritional adequacy 158 , 160 , and lack of long-term safety data 158 , 161 . However, intensive dietary interventions may be considered when conventional treatment is unsuccessful, or when adolescents with comorbidities or severe obesity require rapid or substantial weight loss 98 . A 2019 systematic review of very low-energy diets in children and adolescents found a mean reduction in body weight of −5.3 kg (seven studies) at the latest follow‐up, ranging from 5 to 14.5 months from baseline 161 .

Pharmacological treatment

Until the early 2020s the only drug approved in many jurisdictions for the treatment of obesity in adolescents was orlistat, a gastrointestinal lipase inhibitor resulting in reduced uptake of lipids and, thereby, a reduced total energy intake 162 . However, the modest effect on weight in combination with gastrointestinal adverse effects limit its usefulness overall 163 .

A new generation of drugs has been developed for the treatment of both T2DM and obesity. These drugs are based on gastrointestinal peptides with effects both locally and in the central nervous system. GLP1 is an incretin that reduces appetite and slows gastric motility. The GLP1 receptor agonist liraglutide is approved for the treatment of obesity in those aged 12 years and older both in the USA and Europe 164 , 165 . Liraglutide, delivered subcutaneously daily at a higher dose than used for T2DM resulted in a 5% better BMI reduction than placebo after 12 months 166 . A 2022 trial of semaglutide, another GLP1 receptor agonist, delivered subcutaneously weekly in adolescents demonstrated 16% weight loss after 68 weeks of treatment, with modest adverse events and a low drop-out rate 12 . Tirzepatide, an agonist of both GLP1 and glucose-dependent insulinotropic polypeptide (GIP), is approved by the FDA for the treatment of T2DM in adults 167 . Subcutaneous tirzepatide weekly in adults with obesity resulted in ~20% weight loss over 72 weeks 168 . Of note, GIP alone increases appetite, but the complex receptor–agonist interaction results in downregulation of the GIP receptors 169 , illustrating why slightly modified agonists exert different effects. A study of the use of tirzepatide in adolescents with T2DM has been initiated but results are not expected before 2027 (ref. 170 ). No trials of tirzepatide are currently underway in adolescents with obesity but without T2DM.

Hypothalamic obesity is difficult to treat. Setmelanotide is a MC4R agonist that reduces weight and improves quality of life in most people with LEPR and POMC mutations 71 . In trials of setmelanotide, 8 of 10 participants with POMC deficiency and 5 of 11 with LEPR deficiency had weight loss of at least 10% at ~1 year. The mean percentage change in most hunger score from baseline was −27.1% and −43.7% in those with POMC deficiency and leptin receptor deficiency, respectively 71 .

In the near future, effective new drugs with, hopefully, an acceptable safety profile will be available that will change the way we treat and set goals for paediatric obesity treatment 171 .

Bariatric surgery

Bariatric surgery is the most potent treatment for obesity in adolescents with severe obesity. The types of surgery most frequently used are sleeve gastrectomy and gastric bypass, both of which reduce appetite 172 . Mechanisms of action are complex, involving changes in gastrointestinal hormones, neural signalling, bile acid metabolism and gut microbiota 173 . Sleeve gastrectomy is a more straightforward procedure and the need for vitamin supplementation is lower than with gastric bypass. However, long-term weight loss may be greater after gastric bypass surgery 174 .

Prospective long-term studies demonstrate beneficial effects of both sleeve gastrectomy and gastric bypass on weight loss and comorbidities in adolescents with severe obesity 175 , 176 . In a 5-year follow-up period, in 161 participants in the US TEEN-LABS study who underwent gastric bypass, mean BMI declined from 50 to 37 kg/m 2 (ref. 11 ). In a Swedish prospective study in 81 adolescents who underwent gastric bypass, the mean decrease in BMI at 5 years was 13.1 kg/m 2 (baseline BMI 45.5 kg/m 2 ) compared with a BMI increase of 3.1 kg/m 2 in the control group 176 . Both studies showed marked inter-individual variations. Negative adverse effects, including gastrointestinal problems, vitamin deficits and reduction in lean body mass, are similar in adults and adolescents. Most surgical complications following bariatric surgery in the paediatric population are minor, occurring in the early postoperative time frame, but 8% of patients may have major perioperative complications 177 . Up to one-quarter of patients may require subsequent related procedures within 5 years 109 . However, many adolescents with severe obesity also have social and psychological problems, highlighting the need for routine and long-term monitoring 109 , 178 .

Recommendations for bariatric surgery in adolescents differ considerably among countries, with information on long-term outcomes emerging rapidly. In many countries, bariatric surgery is recommended only from Tanner pubertal stage 3–4 and beyond, and only in children with severe obesity and cardiometabolic comorbidities 177 . The 2023 American Academy of Pediatrics clinical practice guidelines recommend that bariatric surgery be considered in adolescents ≥13 years of age with a BMI of ≥35 kg/m 2 or 120% of the 95th percentile for age and sex, whichever is lower, as well as clinically significant disease, such as T2DM, non-alcoholic fatty liver disease, major orthopaedic complications, obstructive sleep apnoea, the presence of cardiometabolic risk, or depressed quality of life 109 . For those with a BMI of ≥40 kg/m 2 or 140% of the 95th percentile for age and sex, bariatric surgery is indicated regardless of the presence of comorbidities. Potential contraindications to surgery include correctable causes of obesity, pregnancy and ongoing substance use disorder. The guidelines comment that further evaluation, undertaken by multidisciplinary centres that offer bariatric surgery for adolescents, should determine the capacity of the patient and family to understand the risks and benefits of surgery and to adhere to the required lifestyle changes before and after surgery.

Long-term weight outcomes

Few paediatric studies have investigated long-term weight maintenance after the initial, more intensive, weight loss phase. A 2018 systematic review of 11 studies in children and adolescents showed that a diverse range of maintenance interventions, including support via face-to-face psychobehavioural therapies, individual physician consultations, or adjunctive therapeutic contact via newsletters, mobile phone text or e-mail, led to stabilization of BMI z -score compared with control participants, who had increases in BMI z -score 179 . Interventions that are web-based or use mobile devices may be particularly useful in young people 180 .

One concern is weight regain which occurs after bariatric surgery in general 181 but may be more prevalent in adolescents 176 . For example, in a Swedish prospective study, after 5 years, 25–30% of participants fulfilled the definitions of low surgical treatment effectiveness, which was associated with poorer metabolic outcomes 176 . As with adults, prevention of weight regain for most at-risk individuals might be possible with the combination of lifestyle support and pharmacological treatment 182 . Further weight maintenance strategies and long-term outcomes are discussed in the 2023 American Academy of Pediatrics clinical practice guidelines 109 . The appropriate role and timing of other therapies for long-term weight loss maintenance, such as anti-obesity medications, more intensive dietary interventions and bariatric surgery, are areas for future research.

In summary, management of obesity in childhood and adolescence requires intensive interventions. Emerging pharmacological therapies demonstrate greater short-term effectiveness than behavioural interventions; however, long-term outcomes at ≥2 years remain an important area for future research.

Quality of life

Weight bias describes the negative attitudes to, beliefs about and behaviour towards people with obesity 183 . It can lead to stigma causing exclusion, and discrimination in work, school and health care, and contributes to the inequities common in people with obesity 184 . Weight bias also affects social engagement and psychological well-being of children.

Children and adolescents with obesity score lower overall on health-related quality of life (HRQoL) 4 , 5 . In measures that assess domains of functioning, most score lower in physical functioning, physical/general health and psychosocial areas, such as appearance, and social acceptance and functioning. HRQoL is lowest in treatment-seeking children and in those with more extreme obesity 185 . Weight loss interventions generally increase HRQoL independent of the extent of weight loss 186 , especially in the domains most affected. However, changes in weight and HRQoL are often not strongly correlated. This may reflect a lag in the physical and/or psychosocial benefit from weight change, or the extent of change that is needed to drive change in a child’s self-perception.

Similar observations apply to the literature on self-esteem. Global self-worth is reduced in children and adolescents with obesity, as is satisfaction with physical appearance, athletic competence and social acceptance 187 . Data from intensive interventions suggest the psychological benefit of weight loss may be as dependent on some feature of the treatment environment or supportive social network as the weight loss itself 188 . This may include the daily company of others with obesity, making new friendships, and experienced improvements in newly prioritized competences.

There is a bidirectional relationship between HRQoL and obesity 189 , something also accepted in the relationship with mood disorder. Obesity increases the risk of depression and vice versa, albeit over a longer period of time and which may only become apparent in adulthood 190 . Obesity also presents an increased risk of anxiety 191 .

Structured and professionally delivered weight management interventions ameliorate mood disorder symptoms 192 and improve self-esteem 193 . Regular and extended support are important components beyond losing weight. Such interventions do not increase the risk of eating disorders 194 . This is despite a recognition that binge eating disorder is present in up to 5% of adolescents with overweight or obesity 195 . They are five times more likely to have binge eating symptoms than those with average weight. Importantly, adolescents who do not have access to professionally delivered weight management may be more likely to engage in self-directed dieting, which is implicated in eating disorder development 196 .

The literature linking childhood obesity with either attention deficit hyperactivity disorder or autism spectrum disorder is complex and the relationship is uncertain. The association seems to be clearer in adults but the mechanisms and their causal directions remain unclear 109 , 197 . Young children with obesity, especially boys, are more likely to be parent-rated as having behavioural problems 198 . This may be a response to the behaviour of others rather than reflect clinical diagnoses such as attention deficit hyperactivity disorder or autism spectrum disorder. Conduct and peer relationship problems co-occur in children, regardless of their weight.

Children with obesity experience more social rejection. They receive fewer friendship nominations and more peer rejections, most pronounced in those with severe obesity 199 . This continues through adolescence and beyond. Children with obesity are more likely to report being victimized 200 . Younger children may respond by being perpetrators themselves. While it is assumed that children are victimized because of their weight, very few studies have looked at the nature or reason behind victimization. A substantial proportion of children with obesity fail to identify themselves as being fat-teased 187 . Although the stigma associated with obesity should be anticipated in children, especially in those most overweight, it would be inappropriate to see all as victims. A better understanding of children’s resilience is needed.

Many gaps remain in basic, translational and clinical research in child and adolescent obesity. The mechanisms (genetic, epigenetic, environmental and social) behind the overwhelming association between parental obesity and child and adolescent obesity are still unclear given the paradoxically weak association in BMI between adopted children and their parents in combination with the modest effect size of known genetic loci associated with obesity 201 .

Early manifestation of extreme obesity in childhood suggests a strong biological basis for disturbances of homeostatic weight regulation. Deep genotyping (including next-generation sequencing) and epigenetic analyses in these patients will reveal new genetic causes and causal pathways as a basis for the development of mechanism-based treatments. Future work aiming to understand the mechanisms underlying the development of childhood obesity should consider the complex biopsychosocial interactions and take a systems approach to understanding causal pathways leading to childhood obesity to contribute to evidence-based prevention and treatment strategies.

Long-term outcome data to better determine the risks of eating disorders are required. Although symptoms improve during obesity treatment in most adolescents, screening and monitoring for disordered eating is recommended in those presenting for treatment 202 and effective tools for use in clinical practice are required. A limited number of tools are validated to identify binge eating disorder in youth with obesity 203 but further research is needed to screen appropriately for the full spectrum of eating disorder diagnoses in obesity treatment seeking youth 203 . Recent reviews provide additional detail regarding eating disorder risk in child and adolescent obesity 117 , 202 , 204 .

Most studies of paediatric obesity treatment have been undertaken in HICs and predominantly middle-class populations. However, research is needed to determine which strategies are best suited for those in LMICs and low-resource settings, for priority population groups including indigenous peoples, migrant populations and those living with social disadvantage, and for children with neurobehavioural and psychiatric disorders. We currently have a limited understanding of how best to target treatment pathways for different levels of genetic risk, age, developmental level, obesity severity, and cardiometabolic and psychological risk. Current outcomes for behavioural interventions are relatively modest and improved treatment outcomes are needed to address the potentially severe long-term health outcomes of paediatric obesity. Studies also need to include longer follow-up periods after an intervention, record all adverse events, incorporate cost-effectiveness analyses and have improved process evaluation.

Other areas in need of research include the role of new anti-obesity medications especially in adolescents, long-term outcomes following bariatric surgery and implementation of digital support systems to optimize outcomes and reduce costs of behavioural change interventions 205 . We must also better understand and tackle the barriers to implementation of treatment in real-life clinical settings, including the role of training of health professionals. Importantly, treatment studies of all kinds must engage people with lived experience — adolescents, parents and families — to understand what outcomes and elements of treatment are most valued.

Obesity prevention is challenging because it requires a multilevel, multisectoral approach that addresses inequity, involves many stakeholders and addresses both the upstream and the downstream factors influencing obesity risk. Some evidence exists of effectiveness of prevention interventions operating at the level of the child, family and school, but the very poor progress overall in modifying obesity prevalence globally highlights many areas in need of research and evidence implementation. Studies are needed especially in LMICs, particularly in the context of the nutrition transition and the double burden of malnutrition. A focus on intergenerational research, rather than the age-based focus of current work, is also needed. Systems research approaches should be used, addressing the broader food and physical activity environments, and links to climate change 206 . In all studies, strategies are needed that enable co-production with relevant communities, long-term follow-up, process evaluation and cost-effectiveness analyses. In the next few years, research and practice priorities must include a focus on intervention strategies in the earliest phases of life, including during pregnancy. The effects of COVID-19 and cost of living crises in many countries are leading to widening health inequalities 207 and this will further challenge obesity prevention interventions. Available resourcing for prevention interventions may become further constrained, requiring innovative solutions across agendas, with clear identification of co-benefits. For example, public health interventions for other diseases, such as dental caries or depression, or other societal concerns, such as urban congestion or climate change, may also act as obesity prevention strategies. Ultimately, to implement obesity prevention, societal changes are needed in terms of urban planning, social structures and health-care access.

Future high-quality paediatric obesity research can be enabled through strategies that support data sharing, which avoids research waste and bias, and enables new research questions to be addressed. Such approaches require leadership, careful engagement of multiple research teams, and resourcing. Four national or regional level paediatric weight registries exist 208 , 209 , 210 , 211 , which are all based in North America or Europe. Such registries should be established in other countries, especially in low-resource settings, even if challenging 208 . Another data-sharing approach is through individual participant data meta-analyses of intervention trials, which can include prospectively collected data 212 and are quite distinct from systematic reviews of aggregate data. Two recent examples are the Transforming Obesity Prevention in Childhood (TOPCHILD) Collaboration, which includes early interventions to prevent obesity in the first 2 years of life 213 , and the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration, which aims to identify characteristics of individuals or trials that increase or protect against eating disorder risk following obesity treatment 214 . Formal data linkage studies, especially those joining up routine administrative datasets, enable longer-term and broader outcome measures to be assessed than is possible with standard clinical or public health intervention studies.

Collaborative research will also be enhanced through the use of agreed core outcome sets, supporting data harmonization. The Edmonton Obesity Staging System – Paediatric 215 is one option for paediatric obesity treatment. A core outcome set for early intervention trials to prevent obesity in childhood (COS-EPOCH) has been recently established 216 . These efforts incorporate a balance between wanting and needing to share data and adhering to privacy protection regulations. Objective end points are ideal, including directly measured physical activity and body composition.

Collaborative efforts and a systems approach are paramount to understand, prevent and manage child and adolescent obesity. Research funding and health policies should focus on feasible, effective and equitable interventions.

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Natalie B. Lister & Louise A. Baur

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Natalie B. Lister

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Louise A. Baur

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Janine F. Felix

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Introduction (L.A.B., J.F.F. and N.B.L.); Epidemiology (L.A.B. and J.F.F.); Mechanisms/pathophysiology (L.A.B., J.F.F., T.R. and M.W.); Diagnosis, screening and prevention (L.A.B., N.B.L., T.R., C.S. and M.W.); Management (L.A.B., N.B.L., A.J.H., C.M. and T.R.); Quality of life (L.A.B., N.B.L. and A.J.H.); Outlook (L.A.B., N.B.L., J.F.F., A.J.H., C.M., T.R., C.S. and M.W.); Overview of the Primer (L.A.B. and N.B.L.).

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A.J.H. reports receiving payment for consultancy advice for Slimming World (UK). L.A.B. reports receiving honoraria for speaking in forums organized by Novo Nordisk in relation to management of adolescent obesity and the ACTION-Teens study, which is sponsored by Novo Nordisk. L.A.B. is the Australian lead of the study. T.R. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). T.R. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk and Lilly, as well as honoraria for lectures in symposia organized by Novo Nordisk, Novartis and Merck. C.M. receives payments for consultancy advice and advisory board participation from Novo Nordisk, Oriflame Wellness, DeFaire AB and Itrim AB. C.M. also receives honoraria for speaking at meetings organized by Novo Nordisk and Astra Zeneca. C.M. is a shareholder and founder of Evira AB, a company that develops and sells systems for digital support for weight loss, and receives grants from Novo Nordisk for epidemiological studies of the effects of weight loss on future heath. M.W. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). M.W. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk, Regeneron, Boehringer Ingelheim and LG Chem, as well as honoraria for speaking in symposia organized by Novo Nordisk, Rhythm Pharmaceuticals and Infectopharm. M.W. is principal investigator in phase II and phase III studies of setmelanotide sponsored by Rhythm Pharmaceuticals. N.B.L., J.F.F. and C.S. declare no competing interests.

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Lister, N.B., Baur, L.A., Felix, J.F. et al. Child and adolescent obesity. Nat Rev Dis Primers 9 , 24 (2023). https://doi.org/10.1038/s41572-023-00435-4

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Associations between mental and physical conditions in children and adolescents: An umbrella review

Affiliations.

• 1 Mind-Brain Group, Institute for Culture and Society, University of Navarra, Pamplona, Spain; Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK. Electronic address: [email protected].
• 2 Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada; Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada; Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
• 3 Pain and Rehabilitation Centre and Department of Health, Medicine and Caring Sciences, Linköping University, Linkoping, Sweden; Research Laboratory Psychology of Patients, Families & Health Professionals, Department of Nursing, School of Health Sciences, University of Ioannina, Ioannina, Greece.
• 4 Faculty of Education and Psychology, University of Navarra, Pamplona, Spain.
• 5 Servicio Navarro de Salud-Osasunbidea, Navarra, Spain.
• 6 Innovation in Mental and Physical Health and Clinical Treatment (IMPACT) Strategic Research Centre, Deakin University, Geelong, VIC, Australia.
• 7 Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK.
• 8 Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK; OASIS Service, South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
• 9 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; School of Medical Sciences, Örebro University, Örebro, Sweden.
• 10 The Zucker Hillside Hospital, Division of Psychiatry Research, Northwell Health, Glen Oaks, New York, NY, USA; Department of Psychiatry and Molecular Medicine, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitäts medizin, Berlin, Germany.
• 11 Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK; Solent NHS Trust, Southampton, UK; Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK; Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, NY, US.
• PMID: 35427644
• DOI: 10.1016/j.neubiorev.2022.104662

We mapped the evidence on the type and strength of associations between a broad range of mental and physical conditions in children and adolescents, by carrying out an umbrella review, i.e., a quantitative synthesis of previous systematic reviews and meta-analyses. We also assessed to which extent the links between mental and physical conditions vary across disorders or, by contrast, are transdiagnostic. Based on a pre-established protocol, we retained 45 systematic reviews/meta-analyses, encompassing around 12.5 million of participants. In analyses limited to the most rigorous estimates, we found evidence for the following associations: ADHD-asthma, ADHD-obesity, and depression-asthma. A transdiagnostic association was confirmed between asthma and anxiety/ASD/depression/bipolar disorder, between obesity and ADHD/ASD/depression, and between dermatitis and ASD/ADHD. We conclude that obesity and allergic conditions are likely to be associated with mental disorders in children and adolescents. Our results can help clinicians explore potential links between mental and physical conditions in children/adolescent and provide a road map for future studies aimed at shading light on the underlying factors.

Keywords: Mental; Physical; Transdiagnostic; Umbrella review; meta-analysis.

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

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• Research Support, Non-U.S. Gov't
• Anxiety Disorders
• Asthma* / complications
• Asthma* / epidemiology
• Obesity / complications
• Obesity / epidemiology

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• RP-2017-08-ST2-006/DH_/Department of Health/United Kingdom
• BRC-1215-20005/DH_/Department of Health/United Kingdom

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• PMC9377379.1 ; 2021 Dec 1
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Research in child and adolescent anxiety and depression: treatment uncertainties prioritised by youth and professionals

Brynhildur axelsdóttir.

1 Regional Centre for Child and Adolescent Mental Health, Eastern and Southern Norway, Oslo, 0484, Norway

Lise Mette Eidet

Ragnhild thoner, sølvi biedilæ, ingrid borren, mari elvsåshagen, kristine horseng ludvigsen, astrid dahlgren, associated data, underlying data.

Harvard Dataverse: Priorities for research in child and adolescent anxiety and depression: a priority setting partnership with youth and professionals https://doi.org/10.7910/DVN/UQPYVT . 28

This project contains the following underlying data:

• • Coding_priorities from participants_Clinicians_final_25.09.2019.tab
• • Coding_Priorities_Adolescents_Final_07.11.2019.tab

Extended data

This project contains the following extended data:

• • Tables 3-6 (in Norwegian, pdf.)
• • Appendix I (Copy of survey no.1, no.2. and no.3.)
• • IN SUM Search strategies_2021.pdf

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver ( CC0 1.0 Public domain dedication ).

Version Changes

Revised. amendments from version 1.

We wish to thank the reviewers for valuable comments on the manuscript. The main differences compared with the previous version are: We have changed the title of the paper due to discrepancies in our approaches and of the James Lind Alliances. We have also tried to highlight the differences between the two approaches in methods and discussion and have revised the manuscript to make this clearer. We have rephrased the objectives to make it clearer. We have given a definition on key concepts such as treatment, outcomes, research uncertainties and research priorities, in the introduction, for clarification.

Peer Review Summary

Background: A starting point for evaluating the effectiveness of treatments should be to identify evidence gaps. Furthermore, such evaluations should consider the perspectives of patients, clinicians and carers to ensure relevance and potentially influence future research initiatives.

Methods: Our approach, inspired by the James Lind Alliance methods, involved three steps. First, we performed a document analysis by identifying interventions and outcomes in two recently published overviews of systematic reviews, which summarised the effects of interventions for anxiety and depression in children and adolescents. Second, we surveyed children and adolescents with personal experiences of depression or anxiety as well as clinicians, and asked them to suggest treatments and outcomes associated with uncertainty. Finally, we facilitated a consensus process where clinicians and youth mental health patient representatives were invited to prioritise research uncertainties in separate consensus processes.

Results: The survey included 674 respondents who reported a total of 1267 uncertainties. Independent coding by four investigators revealed 134 suggestions for treatments of anxiety, 90 suggestions for treatments of depression, 84 for outcomes of interventions for anxiety and 71 suggestions for outcomes of interventions for depression. Two separate priority setting workshops with eight clinicians and ten youth resulted in four independent top ten priority lists.

Conclusion: Top ten lists of treatments and outcome domains of anxiety and depression in children and adolescents was identified by youth and clinicians. The results may influence the research agenda, and ultimately benefit patients.

Introduction

Anxiety and depression are common mental disorders in adolescence. Anxiety is characterised by restlessness or nervousness, poor concentration, and irritability. Depression is characterised by persistent low mood, loss of interest and enjoyment. 1

The prevalence of anxiety and depression increases during adolescence, and the comorbidity between these diagnoses is high among young people. 2 Almost 10% of adolescents will meet the criteria of an anxiety disorder. 3 The one-year prevalence rate of adolescent depression is estimated to be 5.6%. 4 In Norway, the prevalence of diagnosed depression in girls 15-17 years has increased from 1.5% to 2.3% from 2010-2013. 5

Both anxiety and depression in adolescence are associated with functional impairment and can affect academic achievement, which may have a lifelong effect on employment. 6 , 7 According to the WHO’s Global Burden of Disease, the leading cause of years lost due to disability (YLDs) for both genders 10-24 years is unipolar depressive disorders. 8 The serious consequences of anxiety and depression in adolescence highlights the need for efficient interventions, and the importance of including perspectives of their own experiences.

Currently, recommended treatments for anxiety and depression are psychological therapy, pharmacotherapy, or a combination of both. 9 – 11 By “treatment” we refer to any action or intervention used to change an aspect of a young person’s mental health, that being medicines or school-based interventions. Such treatments may also have an impact on other aspects of the young person’s life that may be important to consider in research. There are also many other treatments used for both anxiety and depression. Some based on well-founded scientific research while others can be regarded as treatment uncertainties, as there is uncertainty about the effectiveness of the treatment. Such uncertainties are either consequences of a lack of research, or that the research is not adequately performed and therefore the evidence is weak. 12 A starting point for new research on treatments should be to identify treatment uncertainties (evidence gaps), in order to shape future research priorities. 13 , 14

Evidence gaps can be prioritised through user involvement. 20 The purpose of user involvement in research is to ensure that research becomes as relevant to the population in question as possible. When initiating research on treatment effects, it has not always been common practice to obtain the perspectives of patients, clinicians or carers. 15 , 16 Thus, important research questions remain unanswered, and research funding may not be used where most needed. 17 A recent systematic review, based on 83 studies involving 15,722 participants, demonstrated how uncommon it is to involve children and their caregivers in setting research priorities in the field of childhood chronic disease. 16

A recent publication by Chevance et al ., 18 published in 2020, described a similar process with adult participants in an international survey, identifying outcomes for depression that matter to patients, informal caregivers, and health-care professionals. Another process of developing an Overall Paediatric Health Standard Set [OPH-SS] of outcome measures which matters to young people and their families, internationally, was also published in 2020. 19 The current study complements both papers, as this paper looks at both children and adolescents, as well as desired research priorities in terms of treatments, as well as outcomes.

We recently produced two overviews of systematic reviews on the effects of interventions for anxiety and depression in children and young people, respectively. 10 , 11 This left us with a momentum for inviting young representatives from these populations (youth) and those providing mental health services to identify and prioritise research uncertainties associated with these conditions.

The objective of this study was to a) to obtain suggestions from youth and clinicians of treatments and treatment outcomes not identified in our overviews of systematic reviews on depression and anxiety. b) to have the two groups prioritise the ten most favoured suggestions and subsequently vote on their ranking in preferred order of importance.

REK, Regional Committees for Medical and Health Research Ethics, Norway was contacted for approval of the project. They concluded that the project did not require their approval as there was no registered personal data. All information was collected through Nettskjema (a web-based survey system), ascertaining a high level of data security and safety.

All respondents were given information about the purpose of the study and how the results would be managed and presented and were informed that by responding to the survey, they consented to participation in the study. The questionnaire was anonymous and once submitted, the information could not be traced back to the respondent.

In the current study, we included both qualitative and quantitative methods in three stages:

• 1. Document analysis : identification of interventions and outcome measures used for treating children and adolescents with anxiety and depression in two previously published overviews of systematic reviews. 10 , 11
• 2. Mapping study (surveys) : we encouraged identification by clinicians and patient representatives (children and adolescents who have, or have had, anxiety or depression) of additional priorities outside of those previously identified.
• 3. Consensus process : prioritisation of research uncertainties by clinicians and patient representatives.

Our approach was partly inspired by a method developed by the JLA. 20 The method involves patients and clinicians in suggesting research priorities. The method is designed to raise awareness of important evidence gaps, with the potential of influencing new research initiatives. 15

The stages of the prioritisation process are outlined in Figure 1 .

Document analysis: identification of interventions and outcomes in existing research

In two recently published overviews of systematic reviews, we have summarised the effects of interventions for anxiety and depression in children and adolescents. 10 , 11 Although these publications are in Norwegian, the methodology of the review process have been published in registered protocols and is available in English through the PROSPERO database; CRD42020159883 (depression) and CRD42020159884 (anxiety). To provide context to this paper, we briefly describe the inclusion criteria and search strategy of the reviews here. Both overviews adhered to the PRISMA guidelines 21 and to the following inclusion criteria:

Publications: Systematic reviews published 2012 and later, fulfilling the DARE-criteria .

Language: English, Norwegian, Danish, or Swedish.

Participants: Children and adolescents under the age of 18, with or without an identified risk of developing mental health problems or those who have already developed these problems.

Intervention: Any intervention aimed at preventing or reducing mental health problems or welfare interventions, including psychological therapy, pharmaceutical interventions, psychosocial interventions etc.

Comparison: Other relevant interventions, treatment as usual (TAU), no treatment or wait list.

Outcomes: All outcomes of mental health problems and child welfare evaluated in children and adolescents, including other health outcomes, quality of life, function, use of health care, attitudes and adverse effects of interventions.

The search for reviews that were included in these two overviews was largely based on the IN SUM database and was performed in April 2018, with an updated search in December 2018. IN SUM is a recently developed database of systematic reviews of the effects of interventions relevant to children and young people’s mental health and welfare. The database indexes systematic reviews from the following databases: Cochrane Database of Systematic Reviews, Campbell Library, PsycINFO, Medline, Embase, Web of Science, Database of Abstracts of Reviews of Effects (DARE) and Evidence-Based Mental Health. IN SUM is continuously updated monthly with the latest systematic reviews. In addition to IN SUM, we hand searched the websites of the Norwegian Institute of Public Health, the Swedish Agency for Health Technology Assessment and Assessment of Social Services, the Danish Health Authority for Systematic Reviews and the National Institute for Health and Care Excellence for evidence-based guidelines, UK. For complete search strategies see Extended data. 28

The first author (BA) extracted all interventions and outcomes reported in these two overviews in a simple document analysis and second author (AD) double-checked the extraction.

Mapping study (survey): identification of uncertainties in research

We created three surveys, each including four questions asking the respondents to report what treatments and outcomes ought to be topics for research, in their opinion. For each question, the recipients were presented with a list of the treatments and the outcomes already addressed in existing research (see Table 1 , Table 2 ), based on the two overviews of reviews. 10 , 11 The three surveys were distributed to clinicians and users as an electronic questionnaire via Nettskjema.

The survey questions had an open answer option (see Extended data 28 ). Respondents can be unfamiliar with research, and we therefore considered it more appropriate to let respondents formulate their need for research in their own words. 20 The purpose of the surveys was to collect suggestions for research uncertainties, consequently, the sample did not need to be representative. 20 Instead, we used convenience sampling to recruit the participants. Anyone living in Norway with experience and understanding of living with anxiety or depression was eligible to participate in the identification of uncertainties. This included children and adolescents with anxiety and/or depression, carers, family members and friends. Also, healthcare, and social care professionals who had worked with children and adolescents living with the conditions were eligible. We strived to ensure that professionals working in different levels in health and welfare services were represented, as well as users. No demographic data were collected as it is not a part of later analysis in priority setting partnerships. In contrast to the principles of JLA the priority lists in the current paper were not rewritten or rephrased as questions. Instead, the lists consist of keywords of outcomes and treatments. The background for this decision was related to the scope of the project; to have the participants choose among suggestions of treatments and treatment outcomes identified as evidence gaps. Our narrow scope did not require full phrased questions.

The first survey was sent on 22 nd February 2019, to our institution's contacts working with children and young people's mental health in the municipalities (Eastern and Southern Norway), including employees in child welfare institutions/orphanages, special education teachers working in schools, child welfare services, child welfare guards, family protection offices, refugee and immigration departments.

The second survey was distributed on 19 th March 2019, to professionals working in the specialist mental health service for children and adolescents. These were also contacted through our networks. In addition, we recruited respondents in collaboration with the Norwegian Association for Children and Young People’s Mental Health (NBUP) and from our institution’s newsletter.

The third survey was distributed on 25 th April 2019, to children and adolescents having personal experiences with depression and/or anxiety, as well as to their carers, in collaboration with the Norwegian organisation for youth mental health, Mental Helse Ungdom (MHU). We also sought to recruit respondents through social media platforms of our institution, e.g., Facebook and Instagram. We posted a link of the survey on the platforms 2 nd August 2019, with an invite to eligible participants to complete the survey.

Content analysis

The interventions and outcomes suggested by the respondents were coded independently by at least two investigators (IB, SB, LME and BA). This part of the process is both interpretative and subjective. Duplicates and similar submissions were combined to a common suggestion. Combining submissions can greatly reduce the volume of data in the process of finalising a top ten list. 20 Based on this analysis we created four “master-lists” including all suggestions for:

• 1) interventions for anxiety
• 2) interventions for depression
• 3) outcomes of interventions for anxiety
• 4) outcomes of interventions for depression

Consensus process: prioritisation of research uncertainties

Preparations for the consensus process

The next step was to prepare for the consensus process, where selected professionals and users were asked to prioritise the suggested research uncertainties. There is no gold standard for conducting a consensus process. However, group composition can have an impact and may lead to different judgements. 22

A multi-disciplinary team of professionals were recruited through our networks through convenience sampling. We received help recruiting clinicians from a local child and adolescent psychiatric outpatient clinic. Our contact person there, reached out via e-mail on 21 st August 2019, to clinicians with a request to participate in the consensus process. The criteria were clinicians who work, or have worked, with children and adolescents with anxiety or depression. A variety of professionals from different backgrounds and working at different levels of health and welfare services (such as psychologists, psychiatrists, physiotherapists, nurses, educators, and health nurses) came forward. Seven clinicians from the specialist mental health services and four from the municipal health services accepted the invitation to participate. For recruitment of user representatives, we contacted the Assistant General Secretary of MHU. She reached out via e-mail on 15 th September 2019, to their members of staff and youth with experience of the conditions, and twelve participants accepted the invitation.

Once recruited, we received contact information of 10 participants proposed by the assistant general secretary of the organisation on October 10 th ,2019. We emailed the four lists with the suggested interventions and outcomes for anxiety and depression, respectively to the participants. They were individually asked to put the suggestions in ranked order, by selecting only 10 options that were assigned 1 point each. For the three most important options we asked them to assign these 2 points. This resulted in the first drafts of prioritised lists of interventions, and outcomes of interventions, for anxiety and depression.

The results from this pre-prioritisation were summarised by two members of the research team (AD and BA), and four lists were created with the highest-ranking suggestions. The two overviews of systematic reviews documented which treatments and outcomes that lacked or had weak scientific evidence. 10 , 11 The participants of the workshops were made aware of this before conducting the interim prioritisation, also enabling them to prioritise among those.

The workshops

For practical reasons, it was not possible to host a shared workshop for professionals and users. Instead, separate workshops were held.

When conducting consensus processes, the criteria for establishing priorities should be applied using a systematic and transparent process. 22 Furthermore, group discussions should follow some basic rules that the participants have chosen jointly. Participants should listen to each other and show respect for each other’s ideas. 20

We applied the Nominal Group Technique for both workshops. This approach is characterised as a structured method for group brainstorming, encouraging discussion and facilitating agreement on the relative importance of issues in question. The process should be led by someone who is not part of the project group, who has no research background. The person will, therefore, have a more neutral role in the process. It is essential that the entire process has openness and justice as guiding principles. 20 For this study, we invited an experienced expert in consensus processes to facilitate and host the workshops (RT), the rest of the team played the part of silent observers and handled all practical needs (LME, SB, AD, and BA).

The first workshop was held at our organisation’s location in Oslo, Norway on 26 th September 2019, from 9:00 am to 3:00 pm. Three members of the project group attended the workshop in addition to the consensus host (LME, RT, AD, and BA). Eight out of 11 clinicians were able to participate in the workshop: psychologists, special educators, clinical social workers, and a physician. Three clinicians were unable to attend for various reasons such as sickness etc.

For the second workshop, we recruited youth from MHU. The workshop took place in their location on 11 th November 2019, from 9:00 am to 3:00 pm and was administrated in the same way as the workshop with the clinicians. Ten out of 12 invited youth were able to participate in the priority setting, and three members of the project group facilitated the workshop (RT, SB and LME). Two participants were unable to attend.

After formal introductions and light refreshments, the participants received an introduction for one hour, to the principles of research, systematic reviews, and evidence-based practice. They were also informed about the purpose and agenda of the day. Thereafter, the participants were divided into small groups based on their professional background, age and in the workshop with the youth, earlier experience with anxiety and/or depression. For each topic, the participants were then mixed in different groups with at least three participants in each group. This part of the workshops lasted for four hours with a half an hour lunch break.

The groups were assigned the task of selecting 10 options and prioritising these for each topic. The groups worked independently but were facilitated by the host when necessary. Other members of the project group were silent observers, taking notes. At the workshop with the professionals, the host used images of children and adolescents with depression and anxiety during this process, as a reminder of the perspectives of the target group involved.

The final hour of the workshops included individual prioritising. All four lists were entered into a voting app by one of the members of the project group and each participant was asked to anonymously rank the final top ten priorities per list. This resulted in four top ten lists of priorities ranked in order by their perceived importance [see Underlying data 28 ].

Summary of existing research

The results of the document analysis were collated and made into 4 lists. In the surveys, the respondents were presented with these lists (see Table 1 and Table 2 ). Note that for several of these treatments and outcomes, the quality of the evidence is graded as low or very low (marked with * in the tables). Therefore, these could still be suggested as research uncertainties.

Results of the surveys: identified research uncertainties by clinicians and patient representatives

Overall, 674 respondents submitted a total of 1267 research suggestions in the three surveys. After content analysis, 379 unique suggestions (134 treatments for anxiety, 90 treatments for depression, 84 outcomes for anxiety and 71 outcomes for depression), were sent for ranking via e-mail to the clinicians and youth participating in the workshops.

In response, the clinicians ranked and shortened the list to 70 suggestions. The youth ranked and shortened it to 51 suggestions. For full detail of the results of the process see Figure 2 .

Prioritisation of research uncertainties

Eight clinicians participated in the first workshop: psychologists, special educators, clinical social workers, and a physician. Two of the clinicians worked in the mental health services in the municipalities, and the six others worked in the specialist mental health service for children and adolescents.

The 10 youth participants from MHU participated in the second workshop. See detailed results of the process in Figure 2 and the final results of the workshops priority setting in Tables 3 , ​ ,4 4 , ​ ,5 5 and ​ and6 6 .

This study has demonstrated essential research priorities in terms of treatments that should be evaluated and outcomes that should be measured according to youth and clinicians. The top ten lists reflect both similarities and differences in what is considered important by the clinicians and the youth.

Clinicians ranked family and parent-based interventions as their top priority for both lists of treatments (anxiety and depression). Youth also ranked family and parent-based interventions as their top priority for treatments of anxiety. Functioning in daily life, and in the family are amongst the top ten treatment priorities by both groups. Other common priorities important to both clinicians and youth are increased cooperation between mental health services and schools, and multi-disciplinary cooperation.

Top priority for depression treatment among the adolescents, were easy access to treatment. The clinicians also emphasize increased cooperation between mental health services and schools, as well as group treatment and school-based interventions. Thus, the clinicians seem to focus on strengthening the environment around the youth to a greater extent than the adolescents do. School-based therapies, school functioning and access to a school psychologist are also similar priorities. The youth seem, however, to display a greater need for interventions for forming relationships, resilience groups, and life coping strategies, which is not mentioned at all in the clinicians’ list.

A unique priority suggested by the youth is therapy for transgender people, specifically regarding anxiety. This may demonstrate a difference between generations regarding the focus on gender identity and the need to cope with such issues.

On the lists of outcomes of interventions for both conditions, functioning in daily life, in the family, and at work were ranked very high by both the clinicians and the youth, as well as friends and social activities. Other important common suggestions are long-term follow-up of interventions, treatment satisfaction and user involvement. However, it is worth noting that the outcomes most important for the adolescents, for both anxiety and depression, were highly subjective/internal outcomes like resilience, faith in oneself, life skills, identity, daily life functioning and trust in other people. In contrast, the clinicians ranked friends and social activities on top of both lists, while this suggestion was not found on the adolescent’s lists. Thus, the clinicians seem to view the context the youth is in as more important than the youths do themselves, who to a greater extent emphasize personal coping skills, like faith in oneself and resilience. This difference may possibly tell us that contextual factors (friends, school or dropping out of school) are regarded less important for individuals struggling with mental health challenges, and that inner personal growth and mastery are key factors for these young people. The clinicians may, on the other hand, have been thinking more in terms of outcomes known to be preventive factors (like friendship and social structures). 23

Clinicians rated adverse events as important for both conditions. The lack of research of unwanted effects of treatments for depression in children and adolescents has recently been demonstrated in a mapping of systematic reviews. 24 Both the clinician’s views and Eidet’s article 24 point to the need for more research, and thus address adverse events in these treatment groups as an important evidence gap.

Strength and limitations

This study builds on rigorous qualitative and quantitative methods, including two extensive systematic reviews on the effects of treatments for anxiety and depression. To our knowledge this is also the first mapping study in Norway exploring research uncertainties related to treatments and associated outcomes for anxiety and depression.

The current study is in line with evidence-based practice as it is defined as ‘The conscientious, explicit and judicious use of current best evidence in making decisions about the care of the individual patient’. 25 Evidence-based practice highlights the consideration of the patient’s opinions in choice of treatment (alongside clinical opinions and research-based methods), and the current project contributes along these lines also, by letting patients voice their concerns regarding research gaps. We have integrated the best research evidence and involved clinical expertise both in the surveys and the workshop with clinicians. Furthermore, we have included the personal and unique values of the patients. All of these should be a part in any decision-making process concerning research and treatments for children and adolescents.

There has been increasing attention to patient-reported outcomes during recent years. Outcomes should be relevant and important to both patients, caregivers, health care professionals and other stakeholders making decisions about health care. 26 , 27 For discovering what outcomes are important to patients and health care professionals, consensus processes, as demonstrated in this study, are vital. This study is especially important because we succeeded in including the views of young people, considering how rare patient and family engagement are in research priority setting. 16

The importance of user involvement is demonstrated in feedback from participants in both workshops:

“ It feels very meaningful to be able to contribute to this project on behalf of all the patients I have been in contact with ”. “ Children and adolescents should always be involved in decision-making, not just clinicians ”.

Although the current study was partly inspired by the JLA framework there are some major discrepancies that need to be addressed. Firstly, we were unable to arrange a joint priority setting partnership between the two groups. Secondly, our study resulted in four different lists of priorities as it covers both treatments and treatment outcomes for anxiety and depression. Third, the lists in the current study consist of keywords and not fully phrased questions, due to the narrower scope aiming at extracting specific treatments and outcomes.

The limitation of consensus processes should be acknowledged. The current priorities are based on individual’s or groups’ point of views and their subjective opinions. We might, in our consensus process with a different pool of people in a different situation, reach a different result. 20 However, involving people together in a quality discussion to reach genuine consensus is of great value, as it represents an important contribution to the debate on research priorities. Bringing people together in a workshop enables them to exchange knowledge and information and make decisions in their meetings with the health services, based on a wider set of experiences.

Initially we intended to host only one priority setting workshop with both clinicians and the youth, however we were unable to find an appropriate date suitable for both groups. Although hosting a shared workshop would have had several benefits, we also found it useful to keep the groups separated. We were able to avoid challenges, such as ensuring the choice of participants being balanced, avoiding domination by one person, and reaching consensus when there may have been disagreement. The two separate processes allowed us to compare the results of professionals and the youth. It also provided a safe zone for professionals and the youth, where especially the latter could speak more freely and perhaps avoid feeling ‘led’ to conclusions by clinicians whom they perhaps could see as authority figures with more experience than themselves. However, keeping the groups separate meant that we also missed the opportunity of cross-fertilization of ideas and nuancing of perspectives, that mixing professionals and users may have contributed to.

We have demonstrated the possibility to develop an agreed four top ten lists of research priorities for anxiety and depression in children and adolescents, with contribution from youth experiencing anxiety or depression as well as clinicians. The perspectives from their individual lists, have the possibility to influence the research agenda according to the needs and opinions of both clinicians and the patients themselves.

Data availability

Acknowledgements.

We would like to thank the following for helping recruiting participants to the workshop with clinicians: Signe Revold, Akershus University Hospital, Morten Grøvli, Akershus University Hospital and a member of the RBUPs board and Kaja Kierulf, centre manager of RBUP. We would like to thank Thisbe Verner-Carlsson and Aida Tesfai at the Norwegian Mental Health Youth (Mental Helse Ungdom) for distributing the survey no. 3 and recruiting participants to the second workshop with the youth. We are grateful to NBUP, the Norwegian Associations of Mental Health Services for Children and Adolescents for help of distributing the survey no. 2. We also would like to thank our colleagues at RBUP, Siri Saugestad Helland, Kristian Rognstad and John Kjøbli for assistance with the first survey, distributed to persons working with children and young people's mental health in the municipalities. Finally, and most importantly, we would like to thank all the participants of both workshops.

[version 2; peer review: 2 approved]

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Reviewer response for version 2

Judith borghouts.

1 Department of Medicine, University of California Irvine, Irvine, CA, USA

I have looked at the authors’ responses and am satisfied with changes made.

Is the work clearly and accurately presented and does it cite the current literature?

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

Is the study design appropriate and is the work technically sound?

Are the conclusions drawn adequately supported by the results?

Are sufficient details of methods and analysis provided to allow replication by others?

Reviewer Expertise:

Academic researcher in Digital Mental Health and Human-Computer Interaction with 10 years of experience in quantitative and qualitative research.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Kristina Staley

1 TwoCan Associates, Ross-on-Wye, UK

I have looked at the authors’ responses and changes and am satisfied that the issues I raised have been addressed. I am happy to recommend the revised version for indexing.

I cannot comment. A qualified statistician is required.

Reviewer response for version 1

This paper describes an approach, inspired by James Lind Alliance (JLA) methods, to identify research priorities in child and adolescent anxiety and depression treatments. Strengths of the paper are the detailed descriptions of the methods used. I also appreciate the authors making the data available.

It was however quite unclear what the paper is trying to contribute, as the problem, objective and results do not seem aligned. The paper starts by highlighting the problem of treatment uncertainties, and that some treatments lack scientific evidence. The introduction then states that the objective of the study was to identify research priorities, which seems different from treatment uncertainties. Finally, it presents results of what types of treatments clinicians and youth would like to see. If this all relates to the same thing, the paper should do a better job explaining how these are all connected.

Related to my point above, the key terms are not well-defined. The abstract mentions treatment uncertainties but it is unclear what this is. It becomes a little bit clearer through examples given in the introduction (“uncertainties are either consequences of a lack of research, or the research is not adequately performed”), but it is then not clear how you ‘prioritize’ uncertainties? Do the authors mean which type of treatments should be given priority in future research? Furthermore, Table 4 and 6 mention the term ‘outcomes’, which in the context of treatment usually means treatment outcomes, such as measurable health symptoms. A number of the outcomes in these tables do not seem to be outcomes in the traditional sense; for example, how is ‘friends and social activities’ an outcome? Is this somehow related to social connectedness? The paper is currently lacking a clear explanation of all of these terms, concepts and how they relate to one another.

Lastly, if the objective was to identify research priorities, it was not clear to me why non-researchers were asked to identify uncertainties. As the paper states, respondents can be unfamiliar with research and may not be equipped to prioritize research. It seems that the paper instead collected a stakeholder perspective of important considerations in adolescent treatment for anxiety and depression, which is still important, but is not reflected in the paper’s objective at all.

I recommend the authors to clearly define the key concepts, clarify the problem, aim of the study, how the results address this problem and aim, and make this consistent throughout the paper.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Brynhildur Axelsdottir

Regional Centre for Child and Adolescent Mental Health, Eastern and Southern Norway (RBUP), Norway

1.This paper describes an approach, inspired by James Lind Alliance (JLA) methods, to identify research priorities in child and adolescent anxiety and depression treatments. Strengths of the paper are the detailed descriptions of the methods used. I also appreciate the authors making the data available.

Response: We see the reviewers point; however, we see this as the one influencing the other. What we hope with our process is that identified treatment uncertainties (treatments that lack scientific evidence) should become priorities in future research. Research priorities should be based on research uncertainties established by systematic reviews of the existing evidence. We have added a sentence to make this more explicit.

By “treatment” we refer  to any action or intervention used to change an aspect of a young person’s mental health, that being medicines or school-based interventions. Such treatments may also have an impact on other aspects of the young person’s life that may be important to consider in research. As we state in the paper, the outcomes found to be important to evaluate in research by researchers often differs from those of providers and patients. Thus, in many cases effects on outcomes important to patients and providers are unknown. This study tries to address this issue. Such outcomes may include a person’s ability to participate in social activities and so on.

Our aim was to enable the participants to suggest and prioritise preferred treatments and outcomes and thus highlight the needs of users and clinicians in hope that these needs could be met in future studies. The lists of priorities are the outcome of this whole process, presenting the interventions and outcomes that the involved groups would like to see in future studies. We acknowledge that this link may have not been sufficiently elaborated on and have therefore inserted some sentences that may help clarify the link between these stages of the process in the introduction and the discussion. The objectives have also been rephrased and hopefully appear clearer.

2. Lastly, if the objective was to identify research priorities, it was not clear to me why non-researchers were asked to identify uncertainties. As the paper states, respondents can be unfamiliar with research and may not be equipped to prioritize research. It seems that the paper instead collected a stakeholder perspective of important considerations in adolescent treatment for anxiety and depression, which is still important, but is not reflected in the paper’s objective at all.

Response: The idea here is to involve the perspectives of the patients involved and the professionals that treat them. They have unique insights in their needs, which may deviate from the priorities of a researcher. Further, user involvement is one of the main principles of the JLA guidebook, which have partly inspired us in conducting this study. The JLA initiative was established to bring both patients, carers and clinicians together in priority setting partnerships. This ensures shared decision-making processes, which is a cornerstone of evidence-based practice.

3.I recommend the authors to clearly define the key concepts, clarify the problem, aim of the study, how the results address this problem and aim, and make this consistent throughout the paper.

Response: We have, based on the reviewers’ responses, rephrased the aim, and sought to make the objective clearer. We believe that our responses to other remarks from the reviewers may also make the paper more accessible. In the introduction, we have described some of the key concepts for clarification.

We would like to thank the reviewers for their valuable comments.

This paper reports on a priority setting exercise which has drawn on the JLA approach but has changed so far from it that I question whether to make the links is appropriate. For example I'd challenge the use of the term priority setting partnership in the title.

The approach in this paper differs from the JLA process in two main ways:

Furthermore, I'd like the authors to comment on how the prioritised lists of interventions and outcomes might be used to shape future research.

• The final workshop - it is essential that all parties come together and reach a shared agreement of the Top Ten. It would seem important to find a date for such a meeting that all could attend rather than have separate meetings. And for the group discussion to inform the prioritised list rather than individuals voting on an app. 

So in general there seems to have been limited shared decision-making at each of the stages of this process which makes me question whether this was genuinely a partnership or actually different groups prioritising topics separately. This is what makes it very different to the JLA process.

The outputs are quite distinct from those of a JLA process - so I suggest the authors refer to the JLA perhaps once, and instead describe their own process and the rationale for how they have approached it, what they expect the impact to be, and their perceived value of their outputs. 

Different does not mean better or worse - this is a different process to the JLA and may have strengths or weaknesses as a result. Perhaps these could be explored in the article. The JLA is not a set of methods, but the principles and values that underpin partnership working are absolutely key to it and these are not described in the approach in this paper and I therefore recommend that the suggestions that this process is linked to the JLA approach are reduced.

I have worked on over a dozen JLA PSPs as an Information Specialist and have worked in the field of patient and carer involvement in research for over 20 years

1.This paper reports on a priority setting exercise which has drawn on the JLA approach but has changed so far from it that I question whether to make the links is appropriate. For example I'd challenge the use of the term priority setting partnership in the title.

Categorising the uncertainties collected via survey of young people and professionals. In a JLA process the Steering Group, a mix of professionals and affected patients/carers, are heavily involved in interpreting the responses to generate a list of uncertainties using phrasing and language that summarise the responses. The aim is always to stay faithful to the original responses. In this paper the researchers have drawn out interventions and outcomes as separate lists - not whole questions. I do not understand the rationale for this and would like a clearer explanation in the article. As they have identified, the language used and the priority given to different ways of understanding the issues makes it difficult to combine the youth and professionals' priority lists of interventions/outcomes. In the JLA process, this is done in the partnership of the Steering Group to reach a shared agreement of the list of topics to be prioritised, a shared understanding of what these mean so that people from all perspectives can understand and prioritise the shared list.

Response: We thank the reviewer for pointing this out and we acknowledge the differences of our study and the James Lind Alliance framework. We have therefore changed the title of the article. In addition, we have elaborated on these differences in methods and discussion.

As to the comment on how the prioritised lists of interventions and outcomes might be used to shape future research, we strongly believe that researchers can be inspired to see what interventions lack evidence (based on evidence gaps identified by the overviews of systematic reviews) as well as what outcomes should be measured when designing new studies on these subjects, based on the participants’ priorities. To highlight the desired interventions and outcomes of users and clinicians may hopefully bring awareness to researchers regarding the needs of these groups – potentially enhancing shared decision-making in future studies.

2. The final workshop - it is essential that all parties come together and reach a shared agreement of the Top Ten. It would seem important to find a date for such a meeting that all could attend rather than have separate meetings. And for the group discussion to inform the prioritised list rather than individuals voting on an app.

Response: There is no gold standard to priority setting of research uncertainties. JLA has however developed an extensive experience and evidence base in this area which has inspired our efforts.

We acknowledge that our approach differs from that of the JLA. We have revised our manuscript to make this clearer and have made it explicit which of the methodological choices recommended by JLA we have applied. We have also added a paragraph to the discussion about the potential limitations and strengths of the choices we made.

As the reviewer points out, the JLA is not a set of methods but suggests some principles and values that should be considered. The experts and patients taking part in our study were not able to meet in the same day for the consensus workshop, and thus our process resulted in two separate sets of priority lists. Although the resulting lists were not created in a partnership of patients and providers, the results of these two consensus processes provides the opportunity to compare the differences in priorities by patients and providers. This may have brought additional – and potentially valuable – information and possibly cover more evidence gaps.

Even though our process differs from that of JLA, we have used methods of high quality, including basing our process on high-quality systematic reviews, including both qualitative and quantitative feedback from experts and patients, and applying a recognized consensus-process methodology. We believe that the priorities-lists resulting from our study is an important contribution to this research and should be used to shape future research efforts.

3. The outputs are quite distinct from those of a JLA process - so I suggest the authors refer to the JLA perhaps once, and instead describe their own process and the rationale for how they have approached it, what they expect the impact to be, and their perceived value of their outputs.

Response: We accept and agree that our process varies from the one of JLA and we have erased the sentence of JLA in the abstract and reframed sentences where we mention JLA  in the method section, as well as reduced the numbers of references to the JLA guidance. We have elaborated on strengths and weaknesses of the current study in the discussion and added some clarifications in the introduction about the differences between our approach and the JLA method.

The efficacy of psychological interventions for child and adolescent PTSD: a network meta-analysis

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Pediatric post-traumatic stress disorder (PTSD) is a common and debilitating mental disorder and its effective treatment constitutes a health priority. Numerous randomized controlled trials (RCTs) have examined the efficacy of psychological interventions for pediatric PTSD. Yet, a comprehensive network meta-analysis (NMA) is lacking. The present work addresses this gap. A total of 67 RCTs met the inclusion criteria comprising 5,297 children and adolescents with full or sub-threshold PTSD. Five families of intervention were evaluated: trauma-focused cognitive behavior therapy (TF-CBT), eye movement desensitization and reprocessing (EMDR), other trauma-focused interventions, non-trauma-focused (non-TF) interventions, and multi-disciplinary treatments (MDTs). Most RCTs (73%) examined TF-CBT followed by EMDR. Other trauma-focused interventions had too few trials for analysis. At treatment endpoint, TF-CBT, EMDR, MDTs, and non-TF interventions were all effective in treating pediatric PTSD when compared to passive control conditions in random-effect NMA with large pooled effects (all Hedges’ gs ≥ 0.84, all ps < .001). TF-CBT, EMDR, and MDTs also yielded significant short-term treatment effects compared to active control conditions. In a sensitivity analysis including only high-quality trials, only TF-CBT and EMDR outperformed active control conditions. And in a sensitivity analysis including only trials with ≥ 50% of participants reporting multiple-event-related PTSD, only TF-CBT yielded significant short-term effects. Results for mid-term (up to 5 months posttreatment) and long-term efficacy (beyond 5 months posttreatment) were very similar. TF-CBT consistently yielded the highest treatment effects except being second to EMDR at mid-term. The present NMA is the most comprehensive NMA of psychological interventions for pediatric PTSD to date. Results confirm that TF-CBT can effectively treat PTSD in children and adolescents both in the short and long-term and also for multiple-event-related PTSD. More long-term data and multiple-event-related PTSD data are needed for EMDR, MDTs, and non-TF interventions to draw firmer conclusions regarding their efficacy. Results for TF-CBT are encouraging for clinical practice and may help to reduce common treatment barriers.

Competing Interest Statement

THH, MJ, LW, AK, PC and NM declare no competing interests. JM is funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. RM-S occasionally receives payment (from universities and private training providers) for training therapists in the delivery of cognitive therapy for PTSD for children and adolescents; is a co-investigator or chief investigator on four National Institute for Health and Care Research (NIHR)-funded or Medical Research Council-funded clinical trials of psychological therapies, particularly cognitive therapy for PTSD in children and young people; and was the chair of a steering committee for a trial addressing the online treatment of PTSD in adults. RM-S' institution (University of East Anglia) has received payment through the following research grants: "Addressing the trauma-related distress of young people in care: a randomised feasibility trial across social-care and mental health services" (NIHR RfPB NIHR200586); "Internet-delivered Cognitive Therapy (iCT) for young people with Post Traumatic Stress Disorder (PTSD)" (MRC DPFS MR/P017355/1); "Supporting services to deliver trauma-focused cognitive behavioural therapy for care-experienced young people: a pilot implementation study", NIHR Applied Research Collaboration West; "Cognitive Behavioural Therapy for the treatment of post-traumatic stress disorder (PTSD) in youth exposed to multiple traumatic stressors: a phase II randomised controlled trial" (NIHR CDF-2015-08-073). RM-S' institution part owns the intellectual property for an online guided self-help version of cognitive therapy for PTSD for children and young people as a result of RM-S' involvement in one of these trials.

Funding Statement

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

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All data analyzed were extracted from published journal articles. No new data were created. Ethical approval is not applicable for the present work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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The data set and the R script to reproduce results are available on request via email to Dr. Thole H. Hoppen ( thoppen{at}uni-muenster.de ).

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• Impact of social transition in relation to gender for children and adolescents: a systematic review
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• Ruth Hall ,
• http://orcid.org/0000-0001-5898-0900 Jo Taylor ,
• http://orcid.org/0000-0002-0415-3536 Catherine Elizabeth Hewitt ,
• Claire Heathcote ,
• Stuart William Jarvis ,
• Trilby Langton ,
• Lorna Fraser
• Department of Health Sciences , University of York , York , UK
• Correspondence to Professor Catherine Elizabeth Hewitt, Department of Health Sciences, University of York, York, UK; dohs-gender-research{at}york.ac.uk

Background Increasing numbers of children and adolescents experiencing gender dysphoria or incongruence are being referred to specialist gender services. Historically, social transitioning prior to assessment was rare but it is becoming more common.

Aim To identify and synthesise studies assessing the outcomes of social transition for children and adolescents (under 18) experiencing gender dysphoria/incongruence.

Methods A systematic review and narrative sythesis. Database searches (Medline, Embase, CINAHL, PsycINFO, Web of Science) were perfomed in April 2022. Studies reporting any outcome of social transition (full or partial) for children and adolescents experiencing gender dysphoria/incongruence were included. An adapted version of the Newcastle-Ottawa Scale for cohort studies was used to appraise study quality.

Results Eleven studies were included (children (n=8) and adolescents (n=3)) and most were of low quality. The majority were from the US, featured community samples and cross-sectional analyses. Different comparator groups were used, and outcomes related to mental health and gender identity reported. Overall studies consistently reported no difference in mental health outcomes for children who socially transitioned across all comparators. Studies found mixed evidence for adolescents who socially transitioned.

Conclusions It is difficult to assess the impact of social transition on children/adolescents due to the small volume and low quality of research in this area. Importantly, there are no prospective longitudinal studies with appropriate comparator groups assessing the impact of social transition on mental health or gender-related outcomes for children/adolescents. Professionals working in the area of gender identity and those seeking support should be aware of the absence of robust evidence of the benefits or harms of social transition for children and adolescents.

PROSPERO registration number CRD42021289659.

Data availability statement

Data sharing is not applicable as no datasets were generated and/or analysed for this study.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/archdischild-2023-326112

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Increasing numbers of children and adolescents experiencing gender dysphoria/incongruence are being referred for care at specialist paediatric gender services.

Historically, social transitioning prior to assessment in gender services was rare. Social role transition is increasingly common in children and adolescents.

The rates of mental health conditions in children/adolescents experiencing gender dysphoria/incongruence are higher than those of the general population.

WHAT THIS STUDY ADDS

The evidence base for all outcomes of social transitioning in childhood or adolescence is both limited and of low quality.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Guidelines should reflect the limited evidence regarding the outcomes of social transition for children and adolescents experiencing gender dysphoria/incongruence. Robust high-quality research is needed.

Introduction

The number of children and adolescents identifying as a gender different from the sex they were registered as at birth has increased markedly across the world over the last 10-15 years. 1 While there is no single definition of the term social transition, it is broadly understood to refer to social changes such as name change, using different pronouns or altering hair or clothing in order to live socially as a different gender, 2 3 but the degree and context of a social transition can vary widely. For some, using a preferred name or clothing may be limited to home while others may change their name officially and seek to make changes across all social settings. Additionally, some may publicly acknowledge that they have made a social transition while others may wish to keep their birth-registered sex private and only known by a few significant others.

Social transition is becoming more common with children and adolescents changing key social characteristics to fit more closely with a different gender role. Children and adolescents presenting at gender services are increasingly likely to have undergone a full social transition. In the UK, 54.6% of children and adolescents referred to the Gender Identity Development Service in 2012–2013 had socially transitioned, 4 with increasing numbers internationally. 5–7

Social transition among children is contentious with diverging views between clinicians as to its role and potential benefits or harms. 3 8 Social transition can be regarded as important for a child’s mental health and well-being with a child leading the way in their gender expression, in line with a model of gender affirming care. 3 8 Social transition is also seen as a significant intervention which may alter the course of gender development with medical and surgical interventions being sought by children whose gender dysphoria/incongruence might not have otherwise persisted beyond puberty. 9 Guidelines for children and adolescents experiencing gender dysphoria/incongruence published by the World Professional Association for Transgender Health (WPATH), 10 with version 8 published in 2022, 11 have shifted from recommending an approach to social transition of ‘watchful waiting’ for children, to a position of advocating for social transition as a way to improve a child’s mental health. Social transitioning among adolescents has not received the same level of interest in academic debate, nor do WPATH version 7 or 8 contain any specific discussion about the risks or benefits for adolescents.

Understanding what the evidence shows about possible benefits or harms is important for children and adolescents experiencing gender dysphoria/incongruence, parents who may be contemplating their child socially transitioning and for healthcare professionals and others whose advice and support may be sought on this question. Therefore, this systematic review aimed to synthesise primary research on outcomes related to social transition for children and adolescents experiencing gender dysphoria/incongruence.

The review forms part of a linked series of systematic reviews examining the epidemiology, care pathways, outcomes and experiences for children and adolescents experiencing gender dysphoria/incongruence and is reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 12 The protocol was registered on PROSPERO (CRD42021289659). 13

Search strategy

A single search strategy was developed to identify studies examining gender dysphoria/incongruence in children/adolescents (see online supplemental file 1 ). The following bibliographic databases were searched with no date restrictions: MEDLINE (OVID), EMBASE (OVID), CINAHL (EBSCO), PsycINFO (OVID) and Web of Science (Social Science Citation Index). The first search was conducted between 13 and 23 May 2021 and updated on 27 April 2022. The reference lists of eligible studies and any relevant systematic reviews or clinical guidelines that were identified were also checked.

Supplemental material

Inclusion criteria.

Studies were included in relation to the following criteria:

Population: children and adolescents up to age 18, or adults who experienced as a child/adolescent, gender dysphoria/incongruence or gender-related distress, or referral to a paediatric/adolescent gender identity service.

Intervention/exposure: a broad definition of social transition was adopted including any element of what is commonly understood to comprise a social transition, 3 for example, name change, use of pronouns, change in appearance.

Outcomes: any outcome of social transition in childhood or adolescence (eg, mental health).

Study design: primary studies published in English in a peer-reviewed journal of any design apart from case series and case reports.

Study selection

All search results were entered into Covidence and deduplicated. 14 Two reviewers independently assessed all titles and abstracts and full texts of those identified as potentially eligible. Conflicts were resolved through discussion or consensus with a third reviewer.

Data extraction

Data were extracted by one reviewer and second-checked by another. Replication of participants across studies was noted.

Quality assessment

Quality was assessed using a modified version of the Newcastle-Ottawa Scale, 15 a validated scale of eight items assessed across three domains: selection, comparability and outcome. Modification included, not scoring question(s) related to cross-sectional or longitudinal studies where relevant. The maximum possible score was 8. A score of 0–3.5 was deemed low quality, 4–5.5 moderate and 6–8 high. Two reviewers rated the papers independently with discussion to reach consensus.

Synthesis methods

Due to extensive differences in definition of social transition and measurement and reporting of outcomes, a narrative approach to synthesis and, where feasible an analysis of p values, effect direction and vote counting were used. The synthesis of data was led by the main comparisons in the included studies: child/adolescent, outcome and comparison group. Strength and direction of effects of social transition on outcomes was analysed from reported p values using albatross plots. 16 Vote counting 17 was also conducted and combined with quality assessment scores using harvest plots 18 and bar charts showing the number of studies reporting effects by direction and quality scores.

Overall, the searches identified 28 147 records, of which 3181 were considered as potentially relevant for the linked series of systematic reviews. From these, 13 studies were identified as relevant to this review of social transition. 19–31 On closer inspection, four studies were excluded: social transition not treated as an exposure (n=3) 22 24 26 or replication of analyses already published (n=1). 25 Two studies were identified as meeting the inclusion criteria from reference lists of guidelines 32 33 ( figure 1 ). Therefore, 11 studies were included in this review.

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Study flow diagram.

Study characteristics

Of the included studies, eight were cross-sectional, 19–21 23 27 28 30 33 one was a reanalysis of previously published cross-sectional data, 31 one a prospective longitudinal study, 32 and one a retrospective cohort study. 29 The majority (n=7) were conducted in the US and/or Canada 19 21 23 27 30–32 ; two in the Netherlands 29 33 ; one in Brazil 20 and one in Germany. 28 Community samples were recruited in eight studies 19–21 23 27 30–32 and gender service patients were recruited in three studies 28 29 33 ( online supplemental table 1 ).

Five US studies 19 21 23 30 32 included participants from the TransYouth Project, which is a longitudinal study of gender development among socially transitioned prepubertal children experiencing gender dysphoria/incongruence (age 3–12 years at start of study in 2013). 24 Four studies reported results from cross-sectional analyses of the cohort, 19 21 23 30 and one study from longitudinal analyses. 32 Children and their families were recruited to this cohort using convenience sampling from support groups (online and face to face) and the sample includes more children from affluent families than expected. Most had parents supportive of early social transition. There is some crossover of the samples between these studies but, as they are reporting different outcomes or child versus parent reports, all five studies were included. Two studies from the Amsterdam clinical population may also include overlapping samples, but this cannot be quantified so both were included. 29 33

Children and/or adolescents were recruited in eight studies, 19 21 23 28 29 31–33 and two studies recruited a mixed group of adolescents and young adults. 20 27 The final study recruited adults with a history of childhood/adolescent gender dysphoria/incongruence and created subgroups based on age of social transition (3–9 years childhood, 10–17 years adolescence, 18+ years adulthood). 30 How gender identity and/or gender dysphoria/incongruence were determined and definitions of social transition and how this was established varied between studies ( online supplemental table 1 ).

A range of mental health outcomes were reported across nine studies 19–21 23 27 28 30 31 33 ; internalising symptoms, externalising symptoms, self-worth, self-esteem, self-perception, suicidality, severe psychological distress, and drug and alcohol misuse. One study also included measures of gender positivity and gender distress. 20 The remaining two studies focused on the constancy of gender identity across time as the outcome. 29 32

Overall, the quality of the papers was low to moderate with scores ranging from 1.5 to 5 ( figure 2 ). Across all studies, the key methodological limitation was the approach to recruitment, relying on self-selecting groups or referral to gender services leading to samples which were unrepresentative of the broader population. A follow-up period between social transition and outcomes being measured was reported in three studies, 29 30 32 one of which relied on recall from adulthood. 30

Quality scores for included studies assessed using a modified Newcastle-Ottawa Scale. The grid indicates individual scores for each study on each of the criteria. Bars at the top (and numbers at top of bars) indicate overall score. SES, socioeconomic status.

Three studies used a standardised method of ascertaining social transition. 28 29 33 The remaining studies used parent or self-report measures. All studies controlled or matched to some extent for age, birth-registered sex or gender identity, and socioeconomic status, however, three additionally controlled for baseline parental/family support. 27 28 30

Seven studies used a comparison group drawn from the same population. 20 27–31 33 None of the studies using community samples of children included a suitable comparison group. Three studies compared children experiencing gender dysphoria/incongruence who had socially transitioned with a comparator group presumed not to experience gender dysphoria/incongruence, which included average population scores, and/or sibling and matched controls. 19 21 23 One study used previously published data for children with the same level of gender variance who had not socially transitioned, 31 however, they were reported by parents as having a gender identity that matched their birth-registered sex and so were not from the same population.

The findings are presented in online supplemental table 2 and a visual summary of key outcomes is provided in figure 3 .

Harvest plots showing direction of effect and quality scores (left) and albatross plots showing direction of effect, statistical significance and sample size (right) for included studies. Panels a, b and c separate studies into those comparing social transition against either those not expriencing gender dysphoria/incongruence (a) or those experiencing gender dysphoria/incongruence who have not socially transitioned (b, c), and also separates studies for children (a, b) and adolescents (c). SMD, standardised mean difference.

Socially transitioned children

Six studies reported outcomes related to mental health 19 21 23 28 31 33 and two studies reported gender stability/persistence. 29 32

Comparison group A: children not experiencing gender dysphoria/incongruence

Four studies reported mental health outcomes 19 21 23 31 ( figure 3A ). Three studies using the TransYouth Project data 19 21 23 found no significant difference in depressive symptoms compared with population averages, 19 23 siblings or matched controls across parent 19 21 23 and self-reported measures. 19 21

Variation was seen in results for levels of anxiety across groups and between parent and self-report measures. 19 21 23 Parent-reported levels of anxiety were significantly higher than population averages 19 23 or matched controls, 19 21 23 but this was not seen for self-report comparisons to population averages, 19 and there were inconsistent results for the comparisons with matched controls. 19 23 No significant, although some marginal, differences were seen in anxiety levels when compared to siblings across parent and self-reported measures. 19 21 23 Self-worth was explored in a single study and not found to be significantly different from matched controls or siblings. 19

One study used data from three published studies 22 23 34 to make comparisons between children who socially transitioned and children who were gender variant but who identified with their birth-registered sex. 31 They found no significant differences in parent-reported internalising scores, externalising symptoms or poor peer relations.

Comparison group B: children experiencing gender dysphoria/incongruence who have not socially transitioned

Three studies used this comparator 28 30 33 ( figure 3B ).

One clinic-based study found that the degree to which a child had socially transitioned was not associated with psychological functioning, 28 rather, socioeconomic status and poor peer relations were associated with internalising problems, and general family functioning and poor peer relations were associated with externalising problems. Another clinic-based study found no association between social transition status and any element of self-perception. 33 However, it found some differences when the sample was stratified by sex; birth-registered males who had socially transitioned reported poorer self-perception in scholastic competence and behavioural conduct compared with non-socially transitioned birth-registered males. 33 Conversely, birth-registered females who had socially transitioned scored higher on athletic competence than non-socially transitioned birth-registered females.

The third study’s comparison group were transgender adults who experienced gender dysphoria/incongruence as a child but did not socially transition until adulthood. 30 They looked at past-month severe psychological distress, lifetime illicit drug use, lifetime marijuana use, past-month binge drinking, and various measures of suicidality. The only significant result in either direction was lower odds of lifetime use of marijuana for those socially transitioning in childhood. Harassment based on gender identity during kindergarten to year 12 was not considered within the initial analysis, but post hoc analyses found that those who socially transitioned in childhood were significantly more likely to have been subject to harassment due to being thought of as transgender than those socially transitioning in adulthood. The study made no adjustment for other confounding variables when considering likelihood of harassment between groups that socially transitioned at different ages.

Socially transitioned adolescents

Comparison group c: adolescents experiencing gender dysphoria/incongruence who have not socially transitioned.

Three studies used this comparator group 20 27 30 ( figure 3C ).

Internalising symptoms were assessed by two studies. 20 27 Adolescents who preferred to be called by another name compared with no preferred name use reported fewer symptoms of depression but there was no signficant difference in anxiety. 20 In another study it was found that among those with a preferred name, chosen name use in more social contexts was associated with fewer depressive symptoms. 27

One study assessed severe psychological distress and found no significant association between social transition in adolescence compared with adulthood. 30 Outcomes related to suicide and suicidal ideation were assessed in two studies. 27 30 It was found that chosen name use in more contexts was associated with lower suicidal ideation and behaviour, 27 and social transition during adolescence was associated with greater odds of past-year suicidal ideation and lifetime suicide attempts compared with transition during adulthood. 30 In the latter paper, six different measures of suicidality were explored and these were the only significant findings ( online supplemental table 2 ).

A single study reported gender-related outcomes. 20 Adolescents who preferred to be called by another name compared with no preferred name use reported higher levels of gender distress but there was no signficant difference in gender positivity.

Comparison group: socially transitioned children

Only one study compared outcomes between children who socially transitioned and those transitioning in adolescence, and found no difference on any measure of mental health, suicidality or drug and alcohol use between the two groups. 30

Gender identity outcomes

Two studies assessed gender identity outcomes. 29 32 One study found a higher odds of persistence of gender dysphoria/incongruence in adolescence for children who had socially transitioned compared with those who had not socially transitioned. Analysis by birth-registered sex showed significant findings for birth-registered males but not for birth-registered females. 29 Another study found that 92.7% of those who socially transitioned between ages 3 and 12 continued to experience gender dysphoria/incongruence at the end of the study (on average, 5.4 years after socially transitioning). 32 The other 7.3% ‘re-transitioned’ at least once; 2.5% identified with their birth-registered sex, 3.5% identified as non-binary and 1.3% had retransitioned twice. They found those socially transitioning before age 6 were more likely to retransition than those socially transitioning after age 6. There was no association between birth-registered sex and retransitioning. 32

There is limited, low-quality evidence on the impact of social transition for children and adolescents experiencing gender dysphoria/incongruence. Most published studies are cross-sectional with non-representative samples and lack an appropriate comparator group, and most studies were undertaken in the US. Of note, there are no prospective longitudinal studies with appropriate comparator groups which have assessed the impact of social transition on the mental health or gender-related outcomes for children or adolescents.

Given the poor quality of studies and multiple comparisons across studies, all findings from this review should be interpreted with caution. There were also inconsistent results between studies. For example, two studies suggest there may be some benefit associated with use of chosen name in adolescence. 20–27 However, in another study lifetime suicide attempt and past-year suicidal ideation was higher among those socially transitioning as adolescents compared with those socially transitioning in adulthood. 30

Social transition has become the subject of clinical and academic debate, mainly centred on whether social transition is an active intervention with potential for benefits as well as risks or longer term consequences. Questions focus on the ways in which a social transition might alter the trajectory and development of gender identity and dysphoria/incongruence over time. Those concerned about altering the course of gender development in children cite previous studies demonstrating that only small numbers of prepubertal children who experienced gender dysphoria/incongruence continued to experience this after puberty. Published estimates on those ‘persisting’ range from 2% to 39% with an average of 15%. 35 The concern then is that if children undergo an early social transition they may find it difficult to socially revert to their former gender. 2 By extension, some children may also then unnecessarily pursue medical and surgical interventions, so raising concerns about iatrogenic harm. 9

In this review, two studies suggest that children who socially transition are more likely to continue to experience gender dysphoria/incongruence in adolescence, though one study found differences by birth-registered sex. 29 , 32 One of these studies also reported that the majority of those who socially transitioned progressed to medical interventions. 32

There has been a shift over time in recommendations around social transitioning for children. In WPATH version 7 10 the evidence base was insufficient to understand long-term outcomes of an early social transition and therefore it advised, in line with a watchful waiting approach, that parents treat social transition as ongoing exploration rather than an ‘irreversible situation’. Furthermore, it suggested that healthcare professionals could provide support in finding ‘in-between’ solutions rather than recommending full social transition. However, WPATH version 8 11 advocates more strongly in favour of childhood social transition, although continues to recommend psychosocial care to support gender exploration for prepubertal children. Three main arguments are put forward for supporting social transition; first, that there is now evidence of improved mental health outcomes; second, that fluidity of identity is an insufficient justification not to socially transition; and third, that not allowing a child to socially transition may in itself be harmful. These statements are not supported from the findings of this systematic review.

Social transitioning among adolescents has not been subject to the same level of debate as for children and there are no specific recommendations in either version of the WPATH guidelines. Version 7 states that adolescents are more likely to persist in their gender identity than children, citing a study in which adolescents were prescribed puberty suppression 36 and acknowledge the lack of prospective studies. Version 8 includes a separate chapter for adolescents containing recommendations that healthcare professionals should ‘ work with parents, schools and other organisations to promote acceptance and affirmation for instance through using preferred pronouns, preferred name, and supporting choices of clothing and hairstyle ’. There is not, however, discussion about potential benefits or harms of social transition and indeed no mention of this term.

This review has shown that we have little evidence of the benefits or harms of social transition for children and adolescents.

Strengths and limitations

Strengths include a published protocol with robust search strategies and comprehensive synthesis. The review only included studies published in English which is a limitation. The primary research included in this review was of low quality which limited the conclusions that could be drawn. As searches were conducted in April 2022 this review does not include more recently published studies; as this is a rapidly evolving area this is a limitation.

There is an urgent need to undertake high-quality and robust research to address the key unanswered questions:

Does social transition alter the trajectory of gender development?

Does social transition improve (or worsen) gender dysphoria?

Does social transition improve mental health outcomes?

What is the relationship between socially transitioning and outcomes not examined (eg, impact on peer relations/social difficulties, quality of life, body satisfaction)?

What are the long-term outcomes of social transition?

Conclusions

The studies included in this review are of low quality, therefore, it is difficult to assess the impact of social transition in this population. Importantly, there are no prospective longitudinal studies with appropriate comparator groups which have assessed the impact of social transition on the mental health or gender-related outcomes for children or adolescents experiencing gender dysphoria/incongruence. Healthcare professionals, clinical guidelines and advocacy organisations should acknowledge the lack of robust evidence of the benefits or harms of social transition when working with children, adolescents and their families.

Ethics statements

Patient consent for publication.

Not applicable.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3

Contributors LF, CEH and TL contributed to the conception and design of this review. Data collection was led by CH, JT and RH. Analyses were undertaken by RH, SWJ and LF. RH drafted the first version of the manuscript. All authors reviewed the manuscript prior to submission. CEH accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Funding This work was funded by NHS England to inform the Cass Review (Independent review of gender identity services for children and young people). The funder and Cass Review team had a role in commissioning the research programme but no role in the study conduct, interpretation or conclusion.

Competing interests None declared.

Provenance and peer review Commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Developmental Trajectories of Mental Health in Chinese Early Adolescents: School Climate and Future Orientation as Predictors

• Published: 16 April 2024

Cite this article

• Qianqian Gao 1 , 2 ,
• Wei Wang 1 , 2 ,
• Shan Zhao 3 ,
• Jiale Xiao 1 , 2 &
• Danhua Lin   ORCID: orcid.org/0000-0001-9858-4187 1 , 2  

There is growing support for the dual-continua model of mental health, which emphasizes psychopathology and well-being as related but distinct dimensions. Yet, little is known about how these dimensions co-develop from childhood to early adolescence and what factors predict their different trajectories. The current study aimed to identify distinct patterns of mental health in Chinese early adolescents, focusing on both psychopathological symptoms (i.e., depressive symptoms and self-harm behaviors) and subjective well-being (i.e., life satisfaction and affect balance). This study also examined the contributions of school climate and future orientation to these trajectories. A total of 1,057 students ( M age = 11.88, SD age = 1.67; 62.1% boys) completed four assessments over two years, with six-month intervals. Using parallel-process latent class growth modeling, we identified four groups: Flourishing (32.5%), Languishing (43.8%), Troubled with Stable Depressive Symptoms (16.1%), and Troubled with Increasing Self-Harm Risk (7.6%). Furthermore, school climate and future orientation contributed to adolescents’ membership in these trajectories, either independently or jointly. Specifically, higher levels of future orientation combined with higher school climate were associated with a lower likelihood of belonging to the Troubled with Increasing Self-Harm Risk trajectory, compared to the Flourishing group. Our findings identified four distinct mental health trajectories consistent with the dual-continua model, and demonstrated that the development of psychopathology and well-being were not always inversely related (e.g., the Languishing group). Adolescents with unique developmental profiles may benefit from tailored intervention strategies that build on the personal and environmental assets of the adolescent.

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Data Availability

The datasets analyzed in the current study are not publicly available but are available from the corresponding author on reasonable request.

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Qianqian Gao, Wei Wang, Jiale Xiao & Danhua Lin

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Q.G. participated in conceptualizing the study, conducting statistical analyses, and drafting the manuscript; L.N. participated in the interpretation of the data and provided critical reviews of the manuscript; W.W. participated in data collection and contributed to the production of the draft manuscript; S.Z. provided critical reviews of the manuscript; J.X. participated in data collection and interpreted the results; D.L. participated in the design and coordination of the study, provided critical reviews of the manuscript, and contributed to funding acquisition. All authors read and approved the final manuscript.

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Gao, Q., Niu, L., Wang, W. et al. Developmental Trajectories of Mental Health in Chinese Early Adolescents: School Climate and Future Orientation as Predictors. Res Child Adolesc Psychopathol (2024). https://doi.org/10.1007/s10802-024-01195-9

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