Clinician Perspectives on Incorporating Physical Activity and Sleep Prescriptions Using eHealth for Youth With Comorbid Psychiatric Disorders: Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Physical activity and sleep prescriptions are indicated for the treatment of psychiatric disorders among youth. However, there is limited clinical adoption of these practices. Exergaming (ie, games that require physical activity) is a feasible intervention to promote physical activity and sleep hygiene and is appealing to youth given their interest in video gaming. Integrating exergaming prescriptions into clinical mental health practices may offer an opportunity to expand access to these interventions, yet pragmatic considerations for adopting these programs are poorly understood. OBJECTIVE: This study aimed to gain feedback from practicing clinicians on adopting GamerFit, an app-based intervention that incorporates exergames, step and sleep tracking, and online coaching to promote physical activity and sleep, as a tool in treatment plans for youth aged 13 to 17 years with psychiatric disorders. METHODS: Mental health clinicians participated in 2 online focus groups. A semistructured interview collected information on perceptions of the importance of physical activity and sleep, considerations for using GamerFit with clients, and approaches for incorporating GamerFit into standard care. Qualitative analysis included a hierarchical thematic coding system of isolated quotes, with the structure, frequency, and interrelationships of the coded quotes used for analysis. RESULTS: All clinicians (8/8, 100%) endorsed physical activity and sleep prescriptions as important interventions, although they were not typically a focus of treatment. Clinicians reported varying levels of self-efficacy in encouraging physical activity goals (6/8, 75%) and, to a lesser extent, sleep hygiene (4/8, 50%). Most perceived eHealth approaches positively (7/8, 88%) and noted their appeal given the accessibility of this physical activity option via gaming (2/4, 50%). Clinicians were optimistic about the feasibility of using GamerFit; the exergame and health coaching aspects of GamerFit were perceived favorably (5/8, 62%). Clinicians desired to access app data in electronic health systems to incorporate in therapeutic sessions (4/8, 50%) and recommended using the app in residential settings with continued use at home (2/8, 25%). Clinicians expressed concern regarding the implementation of GamerFit with families with low technology literacy, noting that some patients would likely require parental assistance to help with reminders and technology use (1/8, 12%). Suggestions for improvement included a greater variety of exergames and features to increase adolescents' engagement (6/8, 75%). There was a considerable willingness to incorporate this technology into clinicians' clinical practices and a strong desire for insurance provisions to cover coaching and technological components (7/8, 88%). CONCLUSIONS: Clinicians perceived GamerFit as a feasible and acceptable clinical approach to physical activity and sleep prescriptions for youth with psychiatric disorders. The remote delivery of this intervention was perceived to be of interest to patients and provided helpful guidance for clinicians who were short on time to address many important topics within limited session time frames.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".