Do Individual Characteristics and Social Support Increase Children's Use of an MHealth Intervention? Findings from the Evaluation of a Behavior Change MHealth App, Aim2Be
Bibliographic record
Abstract
Purpose: Mobile health (mHealth) apps may support improved health behavior practice among youth living in larger bodies. However, long-term use is low, limiting effectiveness. This study evaluated whether youths' motivation, satisfaction, engagement with social features, or parent co-participation supported long-term use of an app named Aim2Be. Methods: A secondary analysis of two versions of Aim2Be (preteen and teen versions) using covariate-adjusted multivariable regression was conducted. We evaluated associations between social support features (a virtual coach, a social poll, or a social wall), parent co-participation (time spent in the parent app), and app satisfaction on use (time spent in Aim2Be). Models were stratified by age and satisfaction was explored as a moderator. Results: Preteens ( n = 83) engagement with the social poll ( β = 0.26, p < 0.001), virtual health coach ( β = 0.24, p = 0.01), app satisfaction ( β = 0.31, p = 0.01), and parent co-participation ( β = 0.24, p = 0.01) predicted use. In teens ( n = 90), engagement with the virtual coach ( β = 0.31, p < 0.001) and full utilization of social wall features ( β = 0.41, p < 0.001) predicted use. Furthermore, satisfaction moderated the effects of partial utilization of the social wall among teens ( β = 0.32 p = 0.02). Conclusion: Social support in mHealth apps may impact users differently depending on age. Features that include health professionals or peers may be more advantageous across ages. App developers should consider age when designing interventions. Clinical Trial Registration NCT03651284
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".