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Many adolescents in special education are affected by anxiety in addition to their behavioral problems. Anxiety leads to substantial long-term problems and may underlie disruptive behaviors in the classroom as a result of the individual’s inability to tolerate anxiety-provoking situations. Thus, interventions in special needs schools that help adolescents cope with anxiety and, in turn, diminish disruptive classroom behaviors are needed.
This study aimed to evaluate the effect of a virtual reality biofeedback game, DEEP, on daily levels of state-anxiety and disruptive classroom behavior in a clinical sample. In addition, the study also aimed to examine the duration of the calm or relaxed state after playing DEEP.
A total of 8 adolescents attending a special secondary school for students with behavioral and psychiatric problems participated in a single-case experimental ABAB study. Over a 4-week period, participants completed 6 DEEP sessions. In addition, momentary assessments (ie, 3 times a day) of self-reported state-anxiety and teacher-reported classroom behavior were collected throughout all A and B phases.
From analyzing the individual profiles, it was found that 6 participants showed reductions in anxiety, and 5 participants showed reductions in disruptive classroom behaviors after the introduction of DEEP. On a group level, results showed a small but significant reduction of anxiety (
This study demonstrates the potential of the game, DEEP, as an intervention for anxiety and disruptive classroom behavior in a special school setting. Future research is needed to fully optimize and personalize DEEP as an intervention for the heterogeneous special school population.
Adolescents attending schools for special education in the Netherlands (called “cluster 4 schools”) are characterized by profound behavioral and psychiatric problems [
Conventional school-based interventions for anxiety are usually based on cognitive behavioral therapy (CBT) [
A promising alternative approach to enhance mental health among children and adolescents is the use of video games. A recent review showed that video games provide youths with immersive emotional experiences, teaching them new forms of emotional, cognitive, and behavioral strategies [
Recently, a virtual reality biofeedback game (DEEP) was developed as a potential intervention to reduce anxiety in youths [
The mechanism through which DEEP teaches players how to regulate physiological responses is diaphragmatic breathing [
The breathing exercises incorporated in DEEP are based on biofeedback, which is defined as the process of feeding information back to the individual about one’s physiological state to gain awareness and control over physiological processes [
Visual circle that is depicted in the players’ visual field corresponding to an inhalation (left) and exhalation peak (right).
Preliminary evidence for the efficacy of DEEP as an intervention for anxiety has been demonstrated in a recent pilot study [
Next to the potential of DEEP as an intervention for anxiety, an additional yet uncertain beneficial effect of DEEP could be an improvement in the ability to regulate disruptive classroom behaviors. Previous research has shown that traditionally delivered deep-breathing exercises reduced rates of disruptive classroom behaviors (assessed by blind independent observers) among youths attending a special school for students with behavioral problems [
Second, playing DEEP could potentially reduce disruptive behaviors through its effect on interoceptive awareness: the ability to recognize internal physiological states [
Our primary aim in this study was to investigate the effect of DEEP on daily levels of state-anxiety and disruptive classroom behavior in a clinical sample. It was expected that playing DEEP would reduce both participants’ state-anxiety and disruptive classroom behavior. Second, we explored the duration of the calm or relaxed state of participants after playing DEEP. No specific hypotheses were formed as this is the first study examining the duration of the effect of playing DEEP. To meet the research aims, we conducted a single-case experimental design (SCED) study in a special school setting. The SCED methodology was particularly appropriate because it allowed us to evaluate the efficacy of an intervention for individuals with heterogeneous characteristics (as is the case in special education [
Disruptive classroom behavior outcomes were personalized per adolescent as difficulties to regulate oneself may manifest differently in each individual. This idiographic assessment approach may provide insight into whether the intervention is impacting the problems that teachers frequently observe and consider most important [
Participants were 8 adolescents (mean age 14.67, SD 1.83 years) attending a secondary special school for students with behavioral and psychiatric problems in the northern part of the Netherlands. Adolescents were considered eligible for the study if teachers had the impression that the adolescent showed symptoms of anxiety, displayed disruptive behaviors in the classroom, and could handle the burden of completing momentary questionnaires (ie, 3 times a day). In total, teachers put forward 10 eligible adolescents. A school clinician contacted the parents of those 10 adolescents by phone to inform them about the study goals and to invite their child for participation. A total of 8 adolescents and their parents expressed their interest to participate in the study. The research team contacted these parents by phone to provide detailed information about the study’s procedure. All adolescents and their parents provided initial verbal consent and received an information and consent letter. Parental written informed consent was sent by mail. Individual demographic characteristics including participants’ age, gender, educational level, pretest trait-anxiety score, diagnoses, current medication, and treatment are provided in
Individualized description of participants’ demographics at pretest.
Participant | Age (years) | Gender | Educational level | Trait-anxiety scoresa | Diagnosesb | Current medication | Current treatment |
1 | 12.94 | Male | Lower secondary vocational education | 44 | ADHDc and ASDd | Methylphenidate | None |
2 | 12.91 | Male | Lower secondary vocational education | 31 | ADHD and ASD | Atomoxetine | None |
3 | 13.90 | Male | Lower and higher secondary vocation | 37 | ADHD and ASD | Methylphenidate | Psychotherapy |
4 | 13.03 | Male | Lower and higher secondary vocation | 22 | RADe, ODDf, and ADHD | None | Psychomotor therapy |
5 | 16.48 | Male | Middle level vocational education | 47 | ASD | None | None |
6 | 17.34 | Male | Lower secondary education | 36 | ASD | None | Ambulatory care offered by the school clinicians |
7 | 16.52 | Male | Lower secondary education | 36 | ADHD and ASD | Methylphenidateg | Psychotherapy |
8 | 14.22 | Female | Lower secondary education | 27 | ASD, PDh, SADi, and EDj | None | None |
aTo gain a general impression of adolescents’ predisposition toward anxiety, the trait-scale of the State-Trait Anxiety Inventory for Children [
bDiagnoses were derived from the electronic school database by the school clinician.
cADHD: attention-deficit/hyperactivity disorder.
dASD: autism spectrum disorder.
eRAD: reactive attachment disorder.
fODD: oppositional defiant disorder.
gParticipant 7 stopped taking medication on day 18, 19, and 20 of the study.
hPD: personality disorder.
iSAD: social anxiety disorder.
jED: eating disorder.
This study followed an ABAB withdrawal or reversal design, with baseline (A0), intervention (B), and withdrawal or no intervention (A1) phases. A and B phases were alternated several times over a period of 4 weeks (ie, 20 schooldays in total). All participants started with the A0 baseline phase, which lasted for 5 or 6 days. After the baseline phase, the first intervention period (ie, B phase) began. The B phases usually lasted for 1 day, in which participants completed 1 DEEP session in the morning. However, some participants played DEEP on 2 subsequent days because the planned DEEP sessions did not synchronize with the participants’ schedule. The intervention days (ie, B phases) were alternated with withdrawal days, in which participants did not play DEEP (ie, A1 phases). All participants completed 6 DEEP sessions throughout the study period, except for participant 8 who completed 5 DEEP sessions because of illness. Another exception to this design was participant 5, who was doing an internship outside of the school for 2 days a week. Therefore, he participated in the study 3 days a week for 8 subsequent weeks (ie, 24 schooldays in total).
Throughout all phases, state-anxiety and disruptive classroom behavior were assessed with paper-and-pencil questionnaires 3 times per day. Adolescents reported their state-anxiety around 10:00 AM, 12:00 PM, and 2:00 PM. However, during B phases, participants filled in the first state-anxiety questionnaire directly after the DEEP session (between 8:45 AM and 10:15 AM). Teachers reported what they had observed about participants’ disruptive classroom behavior in the past 2 hours around 10:20 AM, 12:25 PM, and 2:30 PM. During B phases, teachers reported what they had observed about participants’ behavior
Participant 4 was observed by 3 teachers: 1 teacher observed for 4 days a week, 1 teacher observed for 1 day a week, and 1 teacher observed for 2 days in total when one of the other 2 teachers was ill. Participants 6 and 7 were classmates and were also observed by 2 teachers: 1 teacher observed for 4 days a week, and the other observed for 1 day a week. The remaining participants were observed by a single teacher. Participants 1 and 2 were classmates, so their behavior was observed by the same teacher.
At the start of the study, interviews with the teachers were held to discuss specific disruptive classroom behaviors of each participant. On the basis of their input, a personalized questionnaire about each participant’s disruptive behavior was developed. Afterward, participants filled in questionnaires regarding their demographics and trait-anxiety at pretest. Then, participants completed the ABAB study that lasted for 4 weeks. The study procedure was followed twice; in the first block, participants 1 to 4 participated, and in the second block, participants 6 to 8 participated. Participant 5 participated in both blocks. After participation, participants and their teachers both received a monetary compensation of €25.00. A total of 2 teachers received a monetary compensation of €37.50 because they had observed 2 adolescents for 4 or 5 days a week. Ethical approval for this study was obtained from the Radboud University Ethics Committee Social Sciences (ECSW-2017-038R1).
Adolescents’ state-anxiety was assessed with the 6-item short form of the state scale of the Dutch State-Trait Anxiety Inventory (STAI) [
On the basis of the interviews with the teachers, 2 or 3 disruptive behaviors were defined per participant (
Disruptive classroom behaviors of each participant, indicated by their teacher.
Participant | Behavior 1 | Behavior 2 | Behavior 3 |
1 | Clears throat or sniffs nose | Asks for confirmationa | Gets off the chair and walks out of the classroom |
2 | Talks (loudly) out of turn | Asks for confirmationa | Gets off the chair and walks around the classroom |
3 | Taps fingers on the table or chair leg | Plays with or pulls hair | Gets off the chair and walks around or out of the classroom |
4 | Talks out of turn | Gets off the chair and walks around the classroom | Asks for confirmationa |
5 | Looks around during an independent work hour | Talks or laughs with classmates during an independent work hour | Asks for confirmationa |
6 | Shouts or talks loudly | Talks out of turn | N/Ab |
7 | Looks around during an independent work hour | Asks for confirmationa | N/A |
8 | Asks a questionc | Makes contact with classmatesc | N/A |
aFor example: “Am I doing this right?” and “What are we going to do now?”.
bN/A: not applicable, because the teachers mentioned only 2 disruptive classroom behaviors for these participants.
cThe desired effect for participant 8 was an increase in behavior rather than a decrease.
Participants completed DEEP sessions in a separate room at school. Upon arrival, participants first sat in a turnaround desk chair after which the DEEP breathing belt was placed around the abdomen. The DEEP belt contains an Arduino-compatible FLORA wearable electronic platform [
Means and standard deviations of A0, B, and A1 phase assessments of state-anxiety were calculated for each participant. In addition, the percentage of data points exceeding the median (PEM [
To gain a general impression of the data, a visual analysis was performed involving the examination of trend (for the A0 baseline phase only), variability, and level [
To assess changes in level, a visual analysis usually focuses on mean score differences between phases [
After the visual analysis, nonoverlap of all pairs (NAP [
Finally, the between-case standardized mean difference (BC-SMD [
The same procedure, as described earlier, was repeated for classroom behavior. As NAP scores revealed the same pattern of results for all observed behaviors within the majority of participants, it was decided to only report the results of behavior 1 of each participant. However, for participants 2 and 3, behavior 2 was reported, and for participant 5, behavior 3 was reported because these were the only behaviors that showed medium effects for these participants; all other behaviors of these participants showed no effect.
In terms of missingness, 133 (27.0%) out of the 492 state-anxiety assessments were missing for reasons such as practical lessons, national holidays, or illness. Furthermore, 161 (32.7%) out of the 492 disruptive classroom behavior assessments were missing because of the absence of the adolescent (eg, practical lessons in front of another teacher) or lack of time. We decided not to use imputation strategies, as these percentages of missing data may not affect the quality of statistical inferences [
Means and standard deviations of anxiety by phase and percentage of data points exceeding the median scores for each participant.
Participant | A0 baseline phase, mean (SD) | B phases, mean (SD) | A1 phases, mean (SD) | Data points exceeding the median, n (%) |
1 | 5.49 (2.24) | 3.32 (1.79) | 3.55 (1.47) | 11 (92) |
2 | 1.95 (1.59) | 1.19 (1.60) | 0.91 (1.07) | 12 (80) |
3 | 3.17 (1.68) | 1.52 (0.90) | 1.79 (1.02) | 17 (100) |
4 | 1.98 (0.56) | 1.68 (0.33) | 2.21 (0.71) | 14 (82) |
5 | 4.11 (1.45) | 2.75 (1.78) | 1.95 (0.51) | 12 (86) |
6 | 2.91 (1.49) | 2.52 (1.16) | 3.45 (1.13) | 11 (61) |
7 | 3.30 (1.29) | 2.62 (1.27) | 2.40 (0.99) | 14 (78) |
8 | 3.73 (0.99) | 3.58 (1.21) | 4.00 (1.19) | 10 (67) |
Each participant’s mean anxiety score is represented graphically in
Finally, the changes in levels of anxiety that were identified by recursive partitioning are represented in
The effect of DEEP on anxiety for 8 adolescents. Every 3 data points represent 1 day (measured around 10:00 AM, 12:00 PM, and 2:00 PM). The dashed and stepped lines represent the relatively stable anxiety levels that were identified using recursive partitioning.
Results from visual analysis (baseline trend, variability, and change in level) and nonoverlap of all pairs on anxiety.
Participant | A0 baseline trend | Variability | Change in level | Comparison: A vs B | Comparison: A0 baseline vs B | |||
|
Slope (% of data points within envelope) | MADa A0 phase | MAD B phases |
|
NAPb | 95% CIc | NAP | 95% CIc |
1 | −0.11 (67) | 3.41 | 3.85 | Seems to decrease | 0.72d | 0.55-0.89 | 0.82d | 0.66-0.98 |
2 | 0.04 (20) | 0.59 | 0.30 | Potential floor effect | 0.63 | 0.45-0.82 | 0.74d | 0.55-0.93 |
3 | −0.16 (47) | 2.82 | 1.04 | Seems to decrease | 0.66d | 0.51-0.81 | 0.80d | 0.64-0.96 |
4 | 0.03 (63) | 2.97 | 1.63 | No change in level | 0.75d | 0.61-0.88 | 0.68d | 0.49-0.87 |
5 | −0.21 (38) | 1.48 | 1.48 | Seems to decrease | 0.58 | 0.39-0.77 | 0.81d | 0.63-0.98 |
6 | −0.11 (33) | 1.85 | 2.00 | No change in level | 0.65 | 0.48-0.83 | 0.59 | 0.37-0.80 |
7 | −0.35 (43) | 2.00 | 1.63 | Partly decreases | 0.55 | 0.38-0.72 | 0.67d | 0.48-0.86 |
8 | −0.02 (60) | 2.52 | 3.56 | No change in level | 0.58 | 0.39-0.77 | 0.56 | 0.35-0.77 |
aMAD: median absolute deviation.
bNAP: nonoverlap of all pairs.
cConfidence intervals are asymptotic.
dMedium effect.
NAP scores including all A and B phases are presented in
The assumptions of normality and absence of clear baseline trends were tested and met. Therefore, the BC-SMD analysis was deemed appropriate. The overall A vs B comparison yielded an effect size of
Means and standard deviations of disruptive classroom behavior by phase and percentage of data points exceeding the median scores for each participant.
Participant | A0 baseline phase, mean (SD) | B phases, mean (SD) | A1 phases, mean (SD) | Data points exceeding the median, n (%) |
1 - behavior 1 | 0.46 (0.66) | 0.91 (1.14) | 1.17 (1.64) | 0 (0) |
2 - behavior 2 | 2.23 (1.30) | 1.38 (0.65) | 1.06 (0.68) | 7 (54) |
3 - behavior 2 | 2.93 (0.83) | 2.27 (0.96) | 2.23 (0.75) | 8 (53) |
4 - behavior 1 | 3.00 (1.10) | 1.31 (1.25) | 2.50 (1.58) | 10 (77) |
5 - behavior 3 | 1.93 (0.62) | 1.21 (0.80) | 1.63 (0.50) | 8 (57) |
6 - behavior 1 | 2.00 (1.26) | 2.38 (0.89) | 2.14 (0.86) | 2 (13) |
7 - behavior 1 | 2.22 (1.20) | 2.40 (1.30) | 2.64 (0.50) | 3 (20) |
8 - behavior 1 | 6.00 (0.00) | 5.27 (0.47) | 5.78 (0.44) | 8 (73) |
Each participants’ classroom behavior score is represented graphically in
Finally, the changes in relatively stable levels of disruptive classroom behavior that were identified by recursive partitioning are represented in
The effect of DEEP on disruptive classroom behavior for 8 adolescents. Every 3 data points represent 1 day (measured around 10:20 AM, 12:25 PM, and 2:30 PM). The dashed and stepped lines represent the relatively stable disruptive classroom behavior levels that were identified using recursive partitioning.
Results from visual analysis (baseline trend, variability, and change in level) and nonoverlap of all pairs on disruptive classroom behavior.
Participant | A0 baseline trend | Variability | Change in level | Comparison: A vs B | Comparison: A0 baseline vs B | |||
|
Slope (% of data points within envelope) | MADa A0 phase | MAD B phases |
|
NAPb | 95% CIc | NAP | 95% CIc |
1 - behavior 1 | −0.07 (0) | 0.00 | 1.48 | Partly increases | 0.45 | 0.24-0.65 | 0.40 | 0.16-0.63 |
2 - behavior 2 | 0.07 (38) | 1.48 | 1.48 | Seems to decrease | 0.50 | 0.31-0.68 | 0.68d | 0.47-0.89 |
3 - behavior 2 | 0.00 (57) | 0.00 | 1.48 | No change in level | 0.57 | 0.40-0.75 | 0.69d | 0.50-0.89 |
4 - behavior 1 | 0.12 (44) | 1.48 | 1.48 | Partly decreases | 0.77d | 0.62-0.91 | 0.83d | 0.68-0.98 |
5 - behavior 3 | 0.00 (64) | 0.00 | 1.48 | Partly decreases | 0.68d | 0.50-0.86 | 0.73d | 0.55-0.92 |
6 - behavior 1 | 0.00 (36) | 1.48 | 0.00 | Seems to increase | 0.41 | 0.23-0.59 | 0.40 | 0.17-0.63 |
7 - behavior 1 | 0.30 (67) | 1.48 | 1.48 | No change in level | 0.52 | 0.31-0.73 | 0.45 | 0.21-0.69 |
8 - behavior 1 | 0.00 (100) | 0.00 | 0.00 | Seems to decrease | 0.82d | 0.64-0.99 | 0.86d | 0.70-1.00 |
aMAD: median absolute deviation.
bNAP: nonoverlap of all pairs.
cConfidence intervals are asymptotic.
dMedium effect.
NAP scores including all A and B phases are presented in
The assumptions of normality and absence of clear baseline trends were tested and met. The overall A vs B comparison of the BC-SMD analysis yielded a value of
This SCED study evaluated the efficacy of the virtual reality biofeedback game, DEEP, as an intervention to reduce anxiety and disruptive classroom behaviors in adolescents in a special school setting. The primary aim of the study was to test the effect of playing DEEP on daily levels of state-anxiety and disruptive classroom behavior. On a group level, results indicated a small-sized reduction of state-anxiety after the introduction of DEEP. On the individual level, strong evidence was found for 5 out of 8 participants as their NAP scores indicated a medium-sized reduction and their level of anxiety seemed to decrease over the course of the intervention. Moderate evidence was found for 1 participant, as NAP scores indicated a medium-sized reduction, but no change was found in the level of anxiety. In terms of disruptive classroom behavior, a small, nonsignificant reduction was found on the group level. On the individual level, strong evidence was found for 4 out of 8 participants as their NAP scores indicated a medium-sized reduction and their level of disruptive behavior seemed to decrease. Moderate evidence was found for 1 participant as the NAP score indicated a medium change, but no change was found in the level of disruptive behavior. The secondary aim of our study was to investigate the duration of the calm or relaxed state of participants after playing DEEP. Results indicated that, on average, the effect of playing DEEP lasted for 2 hours.
In line with our hypothesis, the current findings indicate that, on a group level, DEEP reduces daily levels of state-anxiety. These results corroborate the previous pilot study conducted by Van Rooij et al [
This study is the first study examining the duration of the effect of DEEP on state-anxiety and found that, on average, the calm or relaxed state of participants after playing DEEP lasted for 2 hours. This duration indicates that the effect of playing DEEP does not persist through the whole school day but may be particularly valuable to use in specific anxiety-provoking situations in class, such as during exams or when giving a speech. Although this study provides insight into the duration of the effect of DEEP on a group level, insight into individual variability in the duration of the effect is lacking. The NAP and visual analysis strategies used in this study are not suitable to analyze the duration of an effect on an individual level. It is possible that some individuals mainly reported reduced levels of state-anxiety directly after gameplay, whereas others still felt calmer or more relaxed at the end of a day playing DEEP. In addition, it is unknown if there are differences in the duration of the effect of DEEP between the 6 sessions. Participant 6, for example, showed a steep increase in anxiety during the day after the first and second DEEP session but seemed to feel calm or relaxed for a longer period after the last couple of DEEP sessions (
Analyses yielded mixed results for the efficacy of DEEP to reduce levels of disruptive classroom behavior. The results partly confirmed our hypothesis as 5 out of 8 individuals showed a reduction of disruptive classroom behavior after the introduction of DEEP. DEEP may have affected participants’ behavior in various ways. First, as high levels of anxiety may underlie escape-driven disruptive behaviors [
Second, DEEP may have reduced participants’ disruptive behavior through its effect on interoceptive awareness. Although empirical evidence is yet lacking, it is likely that participants’ interoceptive awareness improved over the course of the intervention because participants were continuously informed about their stage of breathing while playing DEEP. It has been theorized that increased interoceptive awareness may enable individuals to mobilize self-regulation resources [
Contrary to our hypothesis, we found a small, nonsignificant effect of DEEP on disruptive behavior on a group level. The 3 participants that did not seem to benefit from DEEP in terms of their behavior may have cancelled out the effect of the participants that did, leading to an unobservable effect on a group level. There are several possible explanations why DEEP did not affect the behaviors of those participants. First, anxiety may not have been the cause of disruptive behaviors in the classroom for these individuals. Rather, neurological deficits in individuals with ADHD or ASD that are associated with attention-related problems [
A clear strength of this investigation is that the study was conducted in a special school setting, thereby addressing ecological validity issues relevant to the school setting. Another strength is that we used an SCED, which is a suitable design to test interventions in the heterogeneous special school population [
Regarding the RoBiNt recommendations for internal validity, not all recommendations were carried out. First of all, we did not randomize the beginning of the study phases. We explicitly chose not to randomize the conditions because of constraints of the school setting (eg, scheduling conflicts) and characteristics of the target group (eg, in need of structure). In terms of sampling of the target behaviors, the required minimum of 3 data points in each study phase [
Although this study provided initial insight into the efficacy of DEEP in a special school setting, future research is needed to fully optimize DEEP as an intervention for this heterogeneous population. The intervention effects of DEEP could be optimized using a parametric analysis of the optimal amount of play time for a given individual [
The aforementioned limitations notwithstanding, this study has important implications for clinical practice. Although it should be noted that findings need to be interpreted cautiously in a study of this type, this study demonstrated the potential of DEEP as an intervention to reduce daily levels of state-anxiety and disruptive classroom behavior in a special school setting. The results implicate the clinical techniques of diaphragmatic breathing and biofeedback in treating individuals with anxiety. Moreover, the results demonstrate that an applied game incorporating those techniques can be used either in isolation or as an add-on to existing interventions in a clinical sample. On overage, our results showed that the calm or relaxed state of participants after playing DEEP lasted for 2 hours. Therefore, school clinicians are recommended to tailor implementation strategies of DEEP to the individual with different needs early or later in the day, considering individual variation in the duration of the effect of DEEP. The implementation of game-based interventions might be a promising avenue for the special school setting, as video games can be tailored to the diverse needs and learning paces of the heterogeneous special school population.
This study also has strong implications for future research on behavioral health interventions. This study demonstrated that there are individual differences in the extent to which an applied game to enhance mental health is effective. Therefore, we need to tailor behavioral interventions to the personal needs of different individuals. Although the randomized controlled trial may be the golden standard in behavioral health intervention research, this costly and time-invasive method is limited because of a lack of attention paid to individual differences [
DEEP trailer.
Participants’ mean anxiety scores measured around 10:00 AM, 12:00 PM, or 2:00 PM, split out by phases.
attention-deficit/hyperactivity disorder
autism spectrum disorder
between-case standardized mean difference
cognitive behavioral therapy
median absolute deviation
nonoverlap of all pairs
percentage of data points exceeding the median
Risk of Bias in N-of-1 Trials
single-case experimental design
State-Trait Anxiety Inventory
The authors would like to gratefully acknowledge the developers of DEEP, Owen Harris and Niki Smit. They would also like to thank the participating school, school clinicians, teachers, and adolescents. This work was supported by funding from the Netherlands Organisation for Scientific Research (NWO, grant number 406-16-524), the Netherlands Organisation for Health Research and Development (ZonMw, grant number 912-15-207), and by funding from the Radboud University, Behavioural Science Institute. The funding sources had no role in the design of the study, data collection, analyses, interpretation of data, writing the manuscript, and in the decision to submit the study for publication.
None declared.