Design Features Associated With Engagement in Mobile Health Physical Activity Interventions Among Youth: Systematic Review of Qualitative and Quantitative Studies
Bibliographic record
Abstract
BACKGROUND: Globally, 81% of youth do not meet the physical activity (PA) guidelines. Youth of families with a low socioeconomic position are less likely to meet the recommended PA guidelines. Mobile health (mHealth) interventions are preferred by youth over traditional in-person approaches and are in line with their media preferences. Despite the promise of mHealth interventions in promoting PA, a common challenge is to engage users in the long term or effectively. Earlier reviews highlighted the association of different design features (eg, notifications and rewards) with engagement among adults. However, little is known about which design features are important for increasing engagement among youth. OBJECTIVE: To inform the design process of future mHealth tools, it is important to investigate the design features that can yield effective user engagement. This systematic review aimed to identify which design features are associated with engagement in mHealth PA interventions among youth who were aged between 4 and 18 years. METHODS: A systematic search was conducted in EBSCOhost (MEDLINE, APA PsycINFO, and Psychology & Behavioral Sciences Collection) and Scopus. Qualitative and quantitative studies were included if they documented design features associated with engagement. Design features and related behavior change techniques and engagement measures were extracted. Study quality was assessed according to the Mixed Method Assessment Tool, and one-third of all screening and data extraction were double coded by a second reviewer. RESULTS: Studies (n=21) showed that various features were associated with engagement, such as a clear interface, rewards, multiplayer game mode, social interaction, variety of challenges with personalized difficulty level, self-monitoring, and variety of customization options among others, including self-set goals, personalized feedback, progress, and a narrative. In contrast, various features need to be carefully considered while designing mHealth PA interventions, such as sounds, competition, instructions, notifications, virtual maps, or self-monitoring, facilitated by manual input. In addition, technical functionality can be considered as a prerequisite for engagement. Research addressing youth from low socioeconomic position families is very limited with regard to engagement in mHealth apps. CONCLUSIONS: Mismatches between different design features in terms of target group, study design, and content translation from behavior change techniques to design features are highlighted and set up in a design guideline and future research agenda. TRIAL REGISTRATION: PROSPERO CRD42021254989; https://tinyurl.com/5n6ppz24.
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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 | 0.068 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".