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. Author manuscript; available in PMC: 2026 Feb 19.
Published in final edited form as: Ment Health Phys Act. 2025 Oct 15;29:100722. doi: 10.1016/j.mhpa.2025.100722

Improving health behaviors and symptoms in youth with mental health disorders: The GamerFit RCT

April Bowling a,*, Alyssa Button b,d, Robbie Beyl b, James Slavet c, Tara Daly a, Peyton Murray b, Phillip Nauta b, Amanda E Staiano b
PMCID: PMC12915985  NIHMSID: NIHMS2117714  PMID: 41716700

Abstract

Objective:

We tested GamerFit, a theory-based health coaching and exergaming intervention delivered via mHealth app for feasibility and preliminary efficacy to improve physical activity (PA), sleep, perceptions of barriers and support, and mental health outcomes in youth with heterogeneous/comorbid mental health disorders (HCMHD).

Methods:

A convenience sample of youth ages 13–17 with HCMHD were recruited via clinical referral, listservs, and media platforms. The primary outcome of feasibility was assessed relative to the original intervention, which was not delivered via mHealth app. Groupings were randomized 1:1 to the 12-wk GamerFit intervention arm (GamerFit) or active comparator arm (AC) using single-blind design to assess preliminary efficacy. GamerFit participants used the mHealth app and a Fitbit™ to follow a progressive exergaming and gamified step program. AC participants were given PA/sleep tips and Fitbit™ to track PA/sleep. Intervention feasibility was assessed for the GamerFit group using process data and parental/participant report; PA duration/intensity (actigraphy, self-report), sleep duration/quality (actigraphy, self-report), sleep hygiene, perceptions of social support, self-regulation, positive/negative affect, and global quality of life were measured at wks 0, 12 and 16. Mixed effects linear models were used to account for the repeated measures correlation over time with an unstructured covariance matrix. The covariates in the model included the main effect for time and treatment as well as the interaction of these effects.

Results:

62 participants were randomized (15.0 ± 1.5 avg age, 24 % female-identifying, 21 % non-white, 74 % on medication). GamerFit averaged 88 % coaching attendance, 34.8 min/wk exergaming, and 8033 steps/day, exceeding the original intervention’s feasibility benchmarks for coaching attendance and steps per day, but not exergaming minutes per week. Parental/participant acceptability/accessibility was high. Days/wk of PA increased significantly in GamerFit vs. AC (diff 2.0 ± 0.9, 95 % CI, p = 0.04). GamerFit significantly improved sleep hygiene, sleep quality, barriers to exercise, emotional and informational support, affect, and reduced unhealthy days; AC did not. GamerFit showed clinically meaningful improvements on all self-regulation sub-scales; AC did not.

Conclusions:

GamerFit shows promise to improve health behaviors and outcomes among youth with HCMHD. An effectiveness RCT should take place in a more racially/ethnically diverse population.

1. Introduction

Diagnoses of heterogeneous/comorbid mental health disorders (HCMHD) among youth are on the rise (Bilu et al., 2023; Bommersbach et al., 2023; Kieling et al., 2024). These conditions include psychiatric disorders such as anxiety and depression and neurodevelopmental disorders such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In the United States, about 1 in 4 children receive at least one mental health diagnosis before age 17; about 26 % aged 12–17 report seeking mental health services in a given year (Bitsko et al., 2022; Polanczyk et al., 2015). HCMHD prevalence has worsened post-pandemic (Deng et al., 2023; Matsumoto et al., 2023), with high comorbidity rates (Cuffe et al., 2020). Individuals with HCMHD, particularly multiple and more severe HCMHDs, have higher risk of preventable chronic diseases including obesity and type 2 diabetes (Blaine, 2008; Chen et al., 2018; Cortese et al., 2016; Gariepy et al., 2010; McCoy et al., 2016), leading to higher rates of early mortality.

Lifestyle behaviors such as adequate physical activity (PA) and sleep can lead to improved physical and mental health (Ash et al., 2017; Biddle, 2016; Donnelly et al., 2016). The bidirectional relationship between healthy levels of PA and sleep has been shown to amplify improved self-regulation, affect, and sense of wellbeing in youth with a variety of HCMHD (Bailey et al., 2018; Blake & Allen, 2020; Firth et al., 2020; Hosker et al., 2019; Sampasa-Kanyinga et al., 2020). Despite this, lifestyle behaviors may be under-emphasized in pediatric mental health treatment due to competing treatment demands and lack of evidence-based, feasible, and engaging interventions. For example, youth with HCMHD face barriers to participation in PA programming and are more likely to have screen-based leisure time preferences that decrease PA and negatively impact sleep (Hickingbotham et al., 2021). These youth and their families often face increased demands on resources and lower levels of social support, which make lifestyle intervention engagement difficult (Bowling et al., 2022).

Given these realities, it is critical to design and test interventions that reduce such barriers and increase long-term adoption of healthy habits in youth with HCMHD. GamerFit is a theory- and evidence-based, PA and sleep intervention developed using inclusion team science and co-design principles, leveraging mobile health (mHealth), telehealth, and exergaming technology. GamerFit is developed from the efficacious GameSquad trial, in which youth with overweight/obesity reduced BMI z-score and improved blood pressure, cholesterol, and moderate-to-vigorous PA following a 12-week exergaming and telehealth intervention (Staiano et al., 2018). GameSquad was then adapted into Adaptive GameSquad (Bowling et al., 2021), which was piloted for feasibility and acceptability among HCMHD. That pilot indicated that the delivery of the intervention through an mHealth app could improve feasibility and acceptability; it also indicated that while exergames were effective at initial increasing PA levels, an explicit focus on coaching increases non-screen-based PA was important for sustaining PA changes (Bowling et al., 2021). New, HIPAA-compliant technologies now also allow for remote interaction between health coaches and participants, while emergent e/mHealth technology has reduced the burden of data collection and intervention activities on participants and their families. Recent studies have found that smartphone use is over 90 % in this population, and over 50 % of young people under the age of 25 y with psychiatric diagnoses have downloaded a health-related app (Seko et al., 2014; Torous et al., 2018). Recent studies have shown that rates of mobile device use are comparable among very low-income and even homeless youth, and that the reach of interventions to these populations is greatly increased by using e/mHealth approaches (Mohr et al., 2013; Woelfer & Hendry, 2010). Therefore, GamerFit leverages such an app, together with the leisure-time preferences of youth with HCMHD, to optimize early PA engagement through active video games, then transitions participants to non-screen-based PA, better sleep hygiene, and improved attitudes and self-efficacy related to healthy habit adoption and maintenance. This randomized control trial (RCT) tested the feasibility, acceptability and preliminary effectiveness of GamerFit to improve PA and sleep in youth with HCMHD.

2. Methods

2.1. Study design and participants

The current study was a two-arm, parallel-group, single-blind RCT to assess preliminary efficacy. Conditions included a 12-week intervention (GamerFit) and a 12-week active comparator (AC) in which participants received a Fitbit™ to track PA and sleep. Follow-up assessments occurred at week 12 and week 16. The study followed CONSORT reporting guidelines, and the protocol was published prior to recruitment and randomization of participants (Clinical Trials #NCT05505578). All study methods and materials were reviewed and approved by the Pennington Biomedical Research Center Institutional Review Board (IRB), and the study was overseen by a data safety and monitoring board. Written consent (caregivers) and assent (participants) were obtained prior to enrollment.

Inclusion criteria for participants included age (13–17 years old), at least one mental health diagnosis (excluding eating disorders) confirmed by parent report of physician diagnosis, no intellectual disability that precluded participation, ability to understand verbal English-language exergaming instructions, physically capable of exercise, and access to a smart phone or tablet. Parent inclusion criteria included ability to participate in telehealth coaching sessions with their child and ability to provide their own translator during coaching sessions if not fluent in English. Exclusion criteria included participants whom the principal investigators deemed clinically or medically unable to participate. Recruitment took place in 2022–2023 on a rolling basis from community mental health providers in Massachusetts and through targeted listservs and social media platforms. Due to COVID-19, all assessments and the intervention were conducted remotely. Study data were collected and managed using REDCap electronic data capture tools hosted at Pennington Biomedical Research Center (Harris et al., 2009). Interested caregivers completed a prescreen questionnaire via REDCap to determine initial eligibility, followed by a meeting with the study coordinator, potential participant, and parent via a secure video conferencing platform to review study information and provide consent/assent, if appropriate. Participants were allowed to keep the equipment provided to them (GamerFit: Fitbits™, exergames and gaming platform; AC: Fitbits™) after study end. Participants in the AC were additionally provided $15 gift cards for each follow-up assessment completed to improve retention and equity.

2.2. Randomization and blinding

Participants were randomized in a 1:1 ratio via REDCap after consent/assent and baseline data collection was complete. The statistician designed the randomization module stratified by sex. All study researchers were blinded to group assignment until statistical analyses were complete. Participants and caregivers could not be blinded given the nature of the intervention.

2.3. Intervention

GamerFit is a theory-based (Bowling et al., 2022), 12-week intervention delivered via an mHealth application (app) installed on participants’ smartphones (conceptual model in Fig. 1). The intervention and app were designed and beta-tested by the study team; the app was programmed by a third-party developer. Participants followed a gamified PA prescription on the app which asked them to perform at least 3 exergaming sessions/week, building from approximately ten (10) minutes per session in week 1, to approximately 60 min per session in week 12. Exergaming was performed using specific modes within two games (RingFit Adventure™, Just Dance 2021®) on the Nintendo Switch™ platform. Participants were encouraged to exergame with family members and friends, and games provided multi-player options. Participants wore a Fitbit™ synced to the GamerFit app to track steps, with tailored, increasing step goals building from at least 2000/day in week 1, to at least 6500/day in week 12. The app additionally featured: 1) on-demand exercise videos to help meet step goals; 2) motivational text messages and short health tip videos featuring near-peers offering encouragement and discussing simple ways to increase PA, improve sleep hygiene, and reduce screen-time; 3) a daily PA, sleep, and mood log; and 4) a trouble shooting page for the intervention equipment. Participants used a numeric code to access the app; no protected health information was collected via the app.

Fig. 1.

Fig. 1.

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GamerFit conceptual model.

Participants met once/week with a professionally trained health coach via a secure video conference portal linked within the GamerFit app. Telehealth coaching sessions were approximately 15 min in length and scripted to improve implementation fidelity, featuring motivational interviewing techniques and focusing on goal setting, action planning, and troubleshooting. The first week of coaching consisted of introductions, preview of the intervention, and assistance with any equipment set-up questions. Weeks two to six focused on use of the app and meeting PA prescription goals. Weeks seven through twelve focused on reviewing sleep hygiene tips and participant PA, sleep, and mood logs to encourage recognition of impacts of health behaviors on mood and self-regulation.

2.4. Outcome measures

Assessments were conducted at baseline (week 0), end of intervention (week 12), and 4 weeks post-intervention (week 16), while process data was collected weekly. Primary outcome measures included feasibility (percentage of planned exergaming sessions completed, minutes of exergaming completed, average steps/day, percentage of planned coaching sessions attended, caregiver rating of feasibility using the validated 4-item Feasibility of Intervention Measure (Weiner et al., 2017)) and acceptability (caregiver ratings of acceptability using the validated 4-item Acceptability of Intervention Measure and appropriateness using the validated 4-item Intervention Appropriateness Measure (Weiner et al., 2017), participant rating of overall satisfaction). Caregiver ratings of feasibility, acceptability and appropriateness and participant ratings of overall satisfaction were collected at Week 12. Feasibility and acceptability outcomes were assessed relative to the original Adaptive GameSquad intervention (Bowling et al., 2021).

Secondary outcome measures were collected at Weeks 0, 12 and 16, and included average daily minutes of PA and sleep. All participants were mailed Actigraph GT3X-BT® accelerometers with instructions to be worn for 7-days, 24 h/day, around the participant’s waist. Following the 7-day period, participants returned the accelerometers to the research team via pre-labeled packaging. Treuth cut-points were used to determine level of PA (light, moderate, vigorous) (Trost et al., 2011), and a previously published algorithm was used to differentiate sleep (Tudor-Locke et al., 2014). Self-report minutes of PA and sleep were collected using items from the Godin-Shepherd Leisure-Time questionnaire (Godin, 2011) and items adapted from NHANES (Belcher et al., 2015). Participants self-reported sleep hygiene and quality using the validated Adolescent Sleep Hygiene Scale (Storfer-Isser et al., 2013), which includes 4 qualitative items to quantify sleep and wake times, and 28 quantitative items to calculate scores for physiological, cognitive, emotional, sleep environment, daytime sleep, substances, sleep stability, bedtime routine, and bed sharing subscales.

Exploratory outcomes included affect, assessed using the Positive and Negative Affect Scale Short Form (Thompson, 2007), validated for use with this population. Parent-report and child-report self-regulation was assessed at weeks 0 and 12 using the behavior regulation and emotion regulation indexes of the Behavior Rating Inventory of Executive Function, 2nd Edition (Roth et al., 2014). PA knowledge and beliefs were measured using scales from the Adolescent Physical Activity Perceived Benefits and Barriers Scale (Robbins et al., 2008) (youth) and the Exercise Benefits and Barriers Scale (Sechrist et al., 1987) (parents). Perceived social isolation and social support was measured via the NIH Patient-Reported Outcomes Measurement Information System scales (Dietz et al., 2022) (instrumental and informational support). Health-related quality of life was measured using the CDC Healthy Days (Measuring Healthy Days) measure.

2.5. Sample size calculation

Power analysis used a two-sample t-test with set alpha at 0.05 and power at 80 %. Based on retention in prior trials, we assumed an attrition rate of up to 20 % of the randomized participants. While the primary aims of this study were to show feasibility and acceptability, this study was powered on PA, a secondary aim. Estimates obtained from the Adaptive Game Squad study (Bowling et al., 2021), which used a similar intervention, showed a between group difference of 60 ± 72 min/day of PA between the intervention and control groups. Based on these estimates, 48 total participants would need to complete the study to provide 81 % power to test the PA hypothesis. Therefore, we aimed to enroll 60–65 participants to account for possible attrition.

2.6. Statistical analysis

Statistical analyses were intent-to-treat. T-tests and chi-squared tests were used to determine if any baseline differences occurred between treatment groups. Primary aims were assessed as descriptive statistics. Secondary/exploratory aims were evaluated using linear mixed models with random effects, testing for both within and between group differences. Mixed effects linear models were used to account for the repeated measures correlation over time, with an unstructured covariance matrix. The covariates in the models included the main effect for time and treatment, as well as the interaction of these effects. Results are presented as least square means; as findings are based on changes across time, the P values reported are based on changes in these least square means. The primary aim of this study was to test feasibility while exploring preliminary efficacy. Therefore, based on the CONSORT extension for feasibility RCTs (Eldridge et al., 2016) and several other reviews of statistical best practices for exploratory versus confirmatory trials (Feise, 2002; Parker & Weir, 2020; Streiner & Norman, 2011), we have elected not to utilize Bonferroni-type corrections for multiple comparisons. This is because the risk of Type II error (missing true effects) outweighs the risk of Type I error (finding a false positive) in such feasibility and exploratory designs (Feise, 2002; Parker & Weir, 2020); in the former case the confirmatory trial will not occur, while in the latter case a fully powered effectiveness trial will emphasize the pre vention of Type I error. All analyses were conducted in SAS 9.4®.

3. Results

A total of 62 participants were randomized to the GamerFit and AC groups; the study CONSORT diagram is shown in Fig. 2. Participants averaged 15.0 years, were 24 % female-identifying, 21 % non-white, and 13 % of Hispanic, Latino or Spanish ethnicity; 82 % had a neuro developmental psychiatric diagnosis, 74 % had a non-neurodevelopmental psychiatric diagnosis, and 74 % reported taking medication. Most participants reported multiple diagnoses (74 %). Additional participant demographics are provided in Table 1. No significant differences in demographics were found between groups.

Fig. 2.

Fig. 2.

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Study CONSORT diagram.

Table 1.

Participant characteristics.

Overall n = 62 GamerFit Intervention n = 31 Fitbit Comparator n = 31
M ± SD M ± SD M ± SD
Child age (years) 15.04 ± 1.45 15.02 ± 1.44 15.06 ± 1.44
Child height (inches) 65.08 ± 3.94 65.2 ± 3.59 64.96 ± 4.32
Child weight (lbs) 149.77 ± 49.08 155.52 ± 54.23 144.03 ± 43.47
Child BMI 24.63 ± 6.73 25.31 ± 6.75 23.96 ± 6.76
Child BMI Percentile 70.75 ± 29.30 74.61 ± 29.05 66.88 ± 29.51
Number of people in child’s household 4.16 ± 1.33 4.26 ± 1.5 4.06 ± 1.15
N (%) N (%) N (%)
Child gender identity
 Male 47/62 (75.81 %) 23/31 (74.19 %) 24/31 (77.42 %)
 Female 15/62 (24.19 %) 8/31 (25.81 %) 7/31 (22.58 %)
Child BMI Category
Healthy Weight 41/62 (66.13 %) 20/31 (64.52 %) 21/31 (67.74 %)
Overweight 12/62 (19.35 %) 5/31 (16.13 %) 7/31 (22.58 %)
Obese 9/62 (14.52 %) 6/31 (19.35 %) 3/31 (9.68 %)
Child Ethnicity
 Not of Hispanic, Latino/a, or Spanish origin 54/62 (87.1 %) 26/31 (83.87 %) 28/31 (90.32 %)
 Hispanic Puerto Rican 3/62 (4.84 %) 2/31 (6.45 %) 1/31 (3.23 %)
 Other Hispanic, Latino or Spanish origin 5/62 (8.06 %) 3/31 (9.68 %) 2/31 (6.45 %)
Child Race
 Black or African American		 5/62 (8.06 %) 2/31 (6.45 %) 3/31 (9.68 %)
 Combined race 6/62 (9.68 %) 3/31 (9.68 %) 3/31 (9.68 %)
 Other 2/62 (3.23 %) 1/31 (3.23 %) 1/31 (3.23 %)
 White 49/62 (79.03 %) 25/31 (80.65 %) 24/31 (77.42 %)
Diagnoses
 Developmental diagnosis		 51/62 (82.26 %) 25/31 (80.65 %) 26/31 (83.87 %)
 Psychiatric/behavioral health diagnosis 46/62 (74.19 %) 25/31 (80.65 %) 21/31 (67.74 %)
Child medications
 No		 16/62 (25.81 %) 7/31 (22.58 %) 9/31 (29.03 %)
 Yes 46/62 (74.19 %) 24/31 (77.42 %) 22/31 (70.97 %)
Combined household annual income
 $10,000 - $29,999 6/62 (9.68 %) 5/31 (16.13 %) 1/31 (3.23 %)
 $30,000 - $49,999 7/62 (11.29 %) 4/31 (12.9 %) 3/31 (9.68 %)
 $50,000 - $69,999 9/62 (14.52 %) 3/31 (9.68 %) 6/31 (19.35 %)
 $70,000 - $89,999 8/62 (12.9 %) 5/31 (16.13 %) 3/31 (9.68 %)
 $90,000 - $109,999 7/62 (11.29 %) 2/31 (6.45 %) 5/31 (16.13 %)
 $110,000 - $139,999 3/62 (4.84 %) 2/31 (6.45 %) 1/31 (3.23 %)
 $140,000 and above 13/62 (20.97 %) 6/31 (19.35 %) 7/31 (22.58 %)
 Prefer not to say 9/62 (14.52 %) 4/31 (12.9 %) 5/31 (16.13 %)
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3.1. Feasibility and acceptability outcomes

Participant enrollment or retention was not significantly different by group. GamerFit participants attended an average of 88 % of planned coaching sessions which averaged 13.2 min each; attendance was 83 % in the original Adaptive GameSquad (Bowling et al., 2021). Participants averaged 50 % (34.5 min) of planned exergame sessions/week; 57 % of planned exergame sessions/week were completed in the original Adaptive GameSquad (Bowling et al., 2021). On-demand exercise videos were watched an average of 1.3 times/week. Average steps were 8033 (±5052)/day, with a high of 8487 (±5030) in week 5 and a low of 7757 (±5047) in week 12; this was higher than the original intervention, in which participants averaged 3559 steps/day (Bowling et al., 2021). Caregivers rated the feasibility of the intervention as 4.6 out of 5 (±0.1).

Participants rated their overall satisfaction with the intervention as 3.8 out of 4 (±0.9). Seventy-six percent reported finding the coaching sessions helpful, while 82 % reported coaching sessions as enjoyable. Seventy-one percent reported using the Fitbit™ to track steps was helpful, 59 % felt they would continue to play the exergames after the intervention end, and 51 % found the motivational text messages helpful. Acceptability metrics were similar for the original intervention (Bowling et al., 2021).

3.2. PA and sleep outcomes

PA and sleep changes are shown in Table 2. There were no significant differences in PA or sleep duration between groups at baseline. Participants in the GamerFit group reported decreased barriers to PA engagement at week 16 relative to the AC (− 7.4 ± 3.27, p = 0.027). A larger than predicted number of participants (n = 14) were unable to complete minimum wear times for week 12 and week 16 actigraphy, decreasing power to detect between group differences in objectively measured PA and sleep duration. Although not statistically significant, actigraphy showed a clinically meaningful increase in minutes of light PA/day in the GamerFit group versus the AC in week 12 (diff 15.5 ± 23.7), which was partially sustained through week 16 (diff 8.8 ± 24.1). Self-reported days/week of PA were also significantly higher in the GamerFit group at week 16 (diff 2.0 ± 0.9, p = 0.04).

Table 2.

Changes in PA and sleep outcomes.

Outcome GamerFit Intervention
 Fitbit Comparator
 Between Group Δ
Week 0 Week 12 Δ p Week 16 Δ p Week 0 Week 12 Δ p Week 16 Δ p Week 12 p Week 16 p
Objective Measures: Actigraph Treuth Cut Point (minutes)
Sleep 593.6 ± 17.4 587.1 ± 17.3 −6.5 ± 19.1 0.74 582.1 ± 18.9 −11.5 ± 22.6 0.61 584.5 ± 17.7 604.1 ± 19.6 19.6 ± 21.4 0.36 586.1 ± 17.3 1.6 ± 21.8 0.94 −26.0 ± 28.7 0.37 −13.1 ± 31.4 0.68
Sedentary 550.3 ± 14.7 539.8 ± 19.7 −10.5 ± 20.9 0.62 547.6 ± 21.8 −2.7 ± 21.5 0.90 581.5 ± 14.9 554.2 ± 21.7 −27.3 ± 23.1 0.24 580.1 ± 21.6 −1.4 ± 21.7 0.95 16.9 ± 31.1 0.59 −1.3 ± 30.6 0.97
Light 286.2 ± 14.3 304.6 ± 17.6 18.4 ± 15.7 0.25 294.6 ± 20.7 8.5 ± 17.4 0.63 265.0 ± 14.3 267.9 ± 19.2 2.9 ± 17.8 0.87 264.7 ± 19.7 −0.3 ± 16.7 0.99 15.5 ± 23.7 0.52 8.8 ± 24.1 0.72
Moderate 10.2 ± 1.6 11.7 ± 2.0 1.5 ± 1.7 0.38 12.7 ± 2.7 2.5 ± 2.4 0.30 7.7 ± 1.6 8.0 ± 2.1 0.3 ± 2.0 0.88 8.9 ± 2.6 1.2 ± 2.3 0.62 1.2 ± 2.6 0.64 1.4 ± 3.3 0.69
Vigorous 1.4 ± 0.8 1.4 ± 0.8 0.0 ± 1.0 0.97 1.6 ± 1.0 0.2 ± 1.1 0.88 2.2 ± 0.9 3.1 ± 0.9 0.8 ± 1.1 0.43 1.7 ± 0.9 −0.5 ± 1.0 0.64 −0.8 ± 1.4 0.58 0.6 ± 1.5 0.66
Moderate-Vigorous 11.6 ± 2.2 13.0 ± 2.6 1.5 ± 2.5 0.56 14.3 ± 3.4 2.7 ± 3.0 0.38 9.9 ± 2.2 11.0 ± 2.8 1.0 ± 2.8 0.72 10.5 ± 3.3 0.6 ± 3.0 0.85 0.4 ± 3.8 0.91 2.1 ± 4.2 0.62
Self-report Measures
Sleep duration (minutes) 562.6 ± 17.6 612.68 ± 41.88 50.07 ± 41.43 0.23 556.97 ± 23.57 −5.64 ± 27.27 0.84 566.9 ± 17.0 585.9 ± 40.6 19.0 ± 39.9 0.64 544.4 ± 22.0 −22.5 ± 25.5 0.38 31.1 ± 57.5 0.59 16.9 ± 37.3 0.65
Sleep quality 2.9 ± 0.1 3.0 ± 0.2 0.1 ± 0.2 0.68 3.2 ± 0.1 0.3 ± 0.1 0.02 3.1 ± 0.1 2.9 ± 0.2 −0.2 ± 0.2 0.26 3.2 ± 0.1 0.1 ± 0.1 0.29 0.3 ± 0.2 0.27 0.2 ± 0.2 0.30
Sleep quantity 2.9 ± 0.1 3.0 ± 0.2 0.1 ± 0.2 0.58 3.2 ± 0.1 0.3 ± 0.2 0.10 3.0 ± 0.1 3.0 ± 0.2 −0.0 ± 0.2 0.94 3.2 ± 0.1 0.2 ± 0.2 0.27 0.1 ± 0.3 0.67 0.1 ± 0.2 0.68
Physical activity days (N) 2.7 ± 0.4 4.4 ± 0.4 1.7 ± 0.4 <0.01 4.4 ± 0.6 1.7 ± 0.7 0.01 3.5 ± 0.4 4.2 ± 0.5 0.7 ± 0.4 0.12 3.3 ± 0.6 −0.2 ± 0.7 0.73 1.0 ± 0.6 0.11 2.0 ± 0.9 0.04
Strenuous exercise (N) 1.5 ± 0.4 3.4 ± 1.3 1.9 ± 1.4 0.16 2.7 ± 0.8 1.3 ± 0.8 0.10 2.7 ± 0.4 6.2 ± 1.4 3.5 ± 1.5 0.02 3.3 ± 0.8 0.6 ± 0.7 0.42 −1.5 ± 2.0 0.44 0.7 ± 1.1 0.53
Moderate exercise (N) 2.2 ± 0.7 5.8 ± 2.3 3.6 ± 2.6 0.17 7.1 ± 8.9 4.9 ± 8.9 0.58 4.8 ± 0.7 10.2 ± 2.5 5.4 ± 2.7 0.05 20.7 ± 8.3 15.8 ± 8.3 0.06 −1.8 ± 3.8 0.64 −10.9 ± 12.2 0.38
Mild exercise (N) 5.1 ± 1.2 6.7 ± 2.1 1.6 ± 2.5 0.52 4.0 ± 7.5 −1.2 ± 7.5 0.89 5.2 ± 1.2 7.1 ± 2.2 1.9 ± 2.6 0.46 19.7 ± 7.0 14.5 ± 7.1 0.04 −0.3 ± 3.6 0.93 −15.6 ± 10.3 0.14
Working up a sweat 2.3 ± 0.1 2.0 ± 0.3 −0.3 ± 0.2 0.05 2.1 ± 0.2 −0.3 ± 0.2 0.15 2.2 ± 0.1 1.8 ± 0.2 −0.2 ± 0.2 0.03 1.9 ± 0.2 −0.2 ± 0.2 0.14 0.1 ± 0.2 0.79 −0.0 ± 0.2 0.96
Overall sleep hygiene 4.4 ± 0.1 4.4 ± 0.1 −0.0 ± 0.1 0.90 4.6 ± 0.1 0.2 ± 0.1 0.03 4.5 ± 0.1 4.5 ± 0.1 −0.0 ± 0.1 0.90 4.6 ± 0.1 0.1 ± 0.1 0.47 0.0 ± 0.2 0.99 0.2 ± 0.1 0.28
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Adolescent Sleep Hygiene Scale (ASHS) score improved from baseline to week 16 in the GamerFit group (0.23 ± 0.1, p = 0.03) but not the AC (0.07 ± 0.1, p = 0.47). Baseline sleep duration was adequate; actigraphy showed both groups averaged >9 h of sleep per night. Self-reported sleep quality improved significantly within the GamerFit group by week 16 (0.29 ± 0.12, p = 0.016) but not within the AC (0.12 ± 0.11, p = 0.29).

3.3. Exploratory outcomes

Support and Quality of Life:

Exploratory outcomes are shown in Table 3. Participants in the GamerFit group reported sustained, significantly increased emotional (4.6 ± 2.1, p = 0.034) and information support at week 16 compared to the AC (5.4 ± 2.0, p = 0.007). They also reported reduced physically unhealthy days (week 12: − 3.9 ± 2.4, p = 0.106; week 16: − 3.2 ± 1.7, p = 0.063), mentally unhealthy days (week 12: − 2.8 ± 3.0, p = 0.353; week 16: 3.7 ± 2.6, p = 0.17), and total unhealthy days (week 12: − 7.0 ± 4.3, p = 0.112; week 16: − 7.0 ± 3.4, p = 0.044) relative to the AC.

Table 3.

Changes in exploratory outcomes.

Outcome GamerFit Intervention
 Fitbit Comparator
 Between Group Δ
Week 0 Week 12 Δ p Week 16 Δ p Week 0 Week 12 Δ p Week 16 Δ p Week 12 p Week 16 p
Affect
Positive affect 39.0 ± 1.7 42.5 ± 2.1 3.5 ± 1.7 0.04 45.2 ± 1.9 6.1 ± 2.0 <0.01 43.1 ± 1.7 42.2 ± 2.2 −0.9 ± 1.8 0.62 46.6 ± 1.7 3.5 ± 1.8 0.057 4.4 ± 2.5 0.08 2.7 ± 2.7 0.33
Negative affect 33.5 ± 2.1 28.6 ± 2.4 −4.9 ± 2.1 0.02 30.6 ± 2.9 −2.9 ± 2.6 0.27 34.2 ± 2.2 33.2 ± 2.5 −1.0 ± 2.3 0.66 34.7 ± 2.7 0.5 ± 2.3 0.824 −3.9 ± 3.1 0.21 −3.4 ± 3.4 0.33
Self-Regulation
Inhibit 80.4 ± 3.2 75.6 ± 4.5 −4.8 ± 4.0 0.23 – – – 78.8 ± 3.1 79.7 ± 4.8 0.9 ± 4.4 0.83 – – – −5.7 ± 5.9 0.33 – –
Self-monitor 80.3 ± 4.0 77.7 ± 4.1 −2.6 ± 4.7 0.58 – – – 75.2 ± 4.0 75.6 ± 4.5 0.4 ± 5 0.94 – – – −3.0 ± 6.8 0.67 – –
Shift 90.6 ± 2.4 87.1 ± 2.6 −3.5 ± 3.0 0.24 – – – 89.2 ± 2.4 90.4 ± 2.8 1.2 ± 3.2 0.71 – – – −4.7 ± 4.4 0.29 – –
Emotional control 80.7 ± 3.1 74.5 ± 4.1 −6.2 ± 4.2 0.15 – – – 78.2 ± 3.1 80.6 ± 4.5 2.4 ± 4.6 0.60 – – – −8.6 ± 6.3 0.17 – –
Behavior regulation 79.7 ± 3.6 76.4 ± 3.9 −3.3 ± 3.7 0.38 – – – 77.6 ± 3.5 79.5 ± 4.2 1.9 ± 4.0 0.64 – – – −5.1 ± 5.5 0.35 – –
Emotion regulation 87.5 ± 2.6 81.2 ± 3.4 −6.2 ± 3.7 0.09 – – – 86.2 ± 2.6 86.8 ± 3.7 0.7 ± 4.0 0.87 – – – −6.9 ± 5.4 0.21 – –
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Affect and Self-regulation:

Positive and negative affect significantly improved within the GamerFit group from baseline to week 12; improvement was sustained in positive affect at week 16. No significant changes were observed in the AC; however, only positive affect was borderline significantly different between groups at week 12 (4.4 ± 2.5, p = 0.08). Both groups had elevated BRIEF-2 subscale t-scores for behavioral and emotional regulation at baseline (Table 3). Despite not being powered for this outcome, the GamerFit group showed improvements across all four sub-scales, while the AC worsened or stayed the same.

4. Discussion

PA and sleep quality have well-documented and synergistic effects on mental and physical health (Firth et al., 2020; Hosker et al., 2019), but youth with HCMHD get worse sleep and less PA than typically developing peers on average (Godin, 2011; Halstead et al., 2021; Hickingbotham et al., 2021). Evidence-based, tailored interventions targeting both PA and sleep improvements in youth with HCMHD are lacking, despite the critical need. Such tailoring should address the specific facilitators (e.g. leisure time preferences) and barriers (e.g. caregiver reserve capacity) of health behaviors in this population. Therefore, we designed GamerFit as a low-burden, highly engaging, scalable PA and sleep mHealth intervention with maximum reach among youth with HCMHD. The primary aim tested the feasibility and acceptability of the GamerFit intervention to improve both PA and sleep for youth with HCMHD. Despite the challenges faced by this population, retention (94 %) and engagement with the intervention was very high; 88 % of coaching sessions attended and average step counts exceeding goal all weeks. Participants consistently met with health coaches over the 12-week intervention; average daily step counts exceeded minimum goals throughout the intervention. Engagement metrics were high in context with other exercise interventions in this population (Anthony et al., 2023); participants completed over half of planned exergaming sessions, which were the most time intensive aspect of the intervention. Importantly, average step counts exceeded weekly goals and remained consistent despite the decrease in exergaming sessions over the course of the intervention, indicating a transition to non-screen-based PA. The remote, home-based delivery and use of a mobile app to coordinate intervention components were rated highly feasible by caregivers. Acceptability metrics were high among both participants and their caregivers. Participants reported enjoying and finding the coaching sessions helpful. Likewise, more than three-quarters found the Fitbit™ PA tracking helpful, and a majority planned to continue using the exergames. These feasibility and acceptability metrics align with or exceed those found in a recent systematic review of exercise interventions (Anthony et al., 2023) to improve psychiatric symptoms in adolescents and, as hypothesized, were as good or better than the original non-app based Adaptive GameSquad intervention (Bowling et al., 2021), upon which this intervention was based. Importantly, unlike most of the interventions included in the Anthony et al. review, this was a home-based intervention without supervised exercise sessions, potentially helping to improve feasibility.

Relative to the AC group, the GamerFit group reported several improvements to predictors of health behaviors and those behaviors themselves. In terms of PA, unsurprisingly, GamerFit participants reported decreased perceptions of barriers to exercise relative to the AC. The GamerFit group experienced an increase of nearly 20 min/day of light PA. This was a clinically important increase given previous studies of dose-response relationships between light PA and positive health outcomes (Bowling et al., 2017/03; Dohrn et al., 2018). Due to reduced actigraph wear at weeks 12 and 16, statistical power was hindered. However, self-reported PA metrics supported an increase relative to the AC; GamerFit participants reported significantly higher number of days/week of PA at week 16 compared to the AC, and significantly improved sleep hygiene scores within group while the AC did not. This may be why, while both groups had adequate sleep duration, only the GamerFit group improved self-reported sleep quality.

Critically, exploratory outcomes indicated the preliminary effectiveness of GamerFit to improve health quality, affect, and self-regulation. If these findings are supported by a full effectiveness trial, they indicate that GamerFit could be a key adjunctive therapy for youth with HCMHD, with multiple cognitive, mental and physical health benefits. In addition to improved levels of physical activity and quality of sleep, a key mechanism of action may be the increased support provided by health coaches. In addition to high rates of coaching enjoyment, the GamerFit group increased perceptions of both emotional and information support after the intervention compared to the AC. Two recent reviews (Hickingbotham et al., 2021; Vancampfort et al., 2025) have found that support and trained staff to promote engagement are key facilitators for PA improvement in this population. A telehealth approach such as the one employed in GamerFit helps make such support and trained coaches available to populations regardless of geography or access to transportation.

4.1. Limitations and strengths

This study was designed as a feasibility, acceptability, and preliminary effectiveness trial focused on assessing GamerFit’s potential for a larger effectiveness and implementation trial. As such, we aimed to enroll a diverse sample of youth with HCMHD shortly after the COVID-19 pandemic. Therefore, some deliberate study design choices were not without limitations. While we worked with community mental health providers for recruitment, we did not require documentation of HCMHD diagnoses in order to reduce barriers to participation. Due to the pandemic, all planned assessments were conducted remotely, reducing rates of valid actigraphy and increasing attrition given increased burden on caregivers and reduced adherence among participants compared to prior studies. As a result, despite reminder texts and emails, as well as promotion during health coaching sessions, statistical power to detect between group differences in PA levels was reduced. Also, while coaching sessions were audited for fidelity, sessions were not rated. The emphasis on gaming resulted in better representation of male-identifying participants; however, a key limitation of this study is that despite working with clinical partners serving diverse populations and targeting social media recruiting toward communities with racial/ethnic diversity, participants were less diverse than in our previous studies (Staiano et al., 2018; Bowling et al., 2021; Bowling et al., 2017/03). This is potentially due to COVID-related inequalities, as recruitment rates of minority populations declined for other exercise intervention trials after the pandemic (Daniel et al., 2022), likely a reflection of the disproportionate health and economic impacts experienced (Tai et al., 2022). One solution would be deployment of GamerFit for testing in residential facilities serving more diverse youth with HCMHD. Finally, as the primary aim of the study was to evaluate feasibility and acceptability while exploring preliminary efficacy, the secondary and exploratory aims would be underpowered if we adjusted for all possible comparisons. However, the use of unadjusted models does not control for potential family-wide error rate, which should be accounted for in a fully-powered effectiveness RCT.

These limitations were balanced by considerable strengths in study design. In addition to a robust RCT design, we allowed for heterogeneous and comorbid disorders, greatly improving external generalizability and reach. We emphasized a pragmatic approach that minimized burdens to caregivers and participants, increasing engagement across youth with a diverse range of symptom constellations and barriers to participation. We used near-peer modeling in Tik-Tok style videos, and selected exergames that had been tested to maximize enjoyment and movement. As a result, engagement and acceptability metrics were very high among a population often resistant to PA engagement interventions without significant staff oversight.

5. Conclusion

The GamerFit intervention was feasible and acceptable to youth aged 13–17 with HCMHD. Preliminary assessments of effectiveness show promise to improve PA, sleep, and mental health outcomes, and those improvements may be sustained after intervention end. A larger study should evaluate effectiveness and implementation approaches in more diverse populations and treatment settings.

Data availability

The data that has been used is confidential.

Acknowledgements

We would like to acknowledge the participants and their families, without whom this research would not have been possible. Likewise, we are grateful to the youth and parents who gave input during the intervention design and beta-testing process and the student volunteers who participated in creating the on-demand videos featured in the GamerFit app.

Funding

Research reported in this article was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number R21HD106465. This work was partially supported by a NORC Center Grant #P30DK072476 entitled ‘Nutritional Programming: Environmental and Molecular Interactions’ and 1 U54 GM104940 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds the Louisiana Clinical and Translational Science Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

CRediT authorship contribution statement

April Bowling: Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Alyssa Button: Writing – review & editing, Methodology, Investigation. Robbie Beyl: Writing – review & editing, Methodology, Formal analysis, Data curation. James Slavet: Writing – review & editing, Methodology, Conceptualization. Tara Daly: Writing – review & editing, Project administration, Methodology, Data curation. Peyton Murray: Writing – review & editing, Methodology, Formal analysis, Data curation. Phillip Nauta: Writing – review & editing, Project administration, Methodology, Data curation. Amanda E. Staiano: Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Trial registration

ClinicalTrials.gov ID: NCT05505578.

Declaration of competing interest

The authors declare that they have no conflicts of interest to report for this manuscript.

Data sharing statement

Deidentified individual participant data will not be made available.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that has been used is confidential.

Deidentified individual participant data will not be made available.

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