A Gamified mHealth App to Promote Physical Activity and Reduce Sedentary Behavior in Autistic Adults: Protocol for a Remotely Delivered Pilot Intervention Study
Bibliographic record
Abstract
BACKGROUND: Research indicates that many autistic adults are insufficiently active and overly sedentary. There is limited evidence on effective strategies to increase physical activity (PA) and reduce sedentary behavior (SB) in this population. Gamified mobile health (mHealth) interventions show promise for addressing these challenges by leveraging autistic individuals' strengths in visuospatial learning and their affinity for digital gaming. Despite this potential, it remains unclear how well these interventions translate to real-world settings. This gap is compounded by the lack of community-based participatory approaches in the development of mHealth intervention for autistic adults. OBJECTIVE: This study aims to (1) formulate gamification and behavior change strategies for the PuzzleWalk v2 app using a community-based participatory approach and (2) evaluate its feasibility and acceptability for increasing PA and reducing SB among autistic adults, including those with mild intellectual disability, in real-world settings. METHODS: This study, consisting of 2 sequential phases, will be conducted entirely remotely: (1) online community-based design workshops to refine the PuzzleWalk gamified mHealth system with input from key autism stakeholders, including autistic adults and caregivers, and (2) an 8-week field deployment to assess real-world usability and engagement. In Phase I (completed), weekly workshops and usability testing focused on understanding autistic adults' technology preferences, evaluating PuzzleWalk v1 and v2 prototypes, and incorporating stakeholder feedback into iterative app development (n=9). Phase II (in progress) will involve a single-arm clinical trial where approximately 70 participants will use the app alongside research-grade activity-tracking accelerometers to measure PA and SB. Outcome measures, including sedentary time, step counts, PA intensities, and app engagement (eg, time spent using the app), will be collected across 4 specific time points (ie, baseline and weeks 3, 5, and 8). Repeated measures ANCOVA will be performed to examine changes in participants' objective levels of PA and sedentary time before, during, and after the intervention. RESULTS: Phase I of the study, involving community-based participatory design workshops and usability testing, was completed in November 2024. Key autism stakeholders recognized the gamified PuzzleWalk app as a viable tool for enhancing motivation toward PA and SB changes among autistic adults. Data collection for Phase II, the field deployment, is currently underway and is expected to end in August 2025. As of July 2025, we had enrolled 69 participants in the study. The findings of these studies will be shared in a subsequent peer-reviewed publication. CONCLUSIONS: Results of the ongoing field deployment study (Phase II) will further clarify the app's effectiveness and real-world applicability. TRIAL REGISTRATION: ClinicalTrials.gov NCT06566131; https://clinicaltrials.gov/study/NCT06566131. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71631.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".