Moderators of a mHealth Intervention for Adolescent Physical Activity: Intervention Refinement Study
Bibliographic record
Abstract
Background: An adaptive text messaging intervention to promote adolescent physical activity has demonstrated feasibility, acceptability, and preliminary efficacy in a recent proof-of-concept study. To inform future intervention development, a secondary analysis of the data examined how physical activity is influenced by mood, environment, and physical feelings of energy and fatigue. Objective: This study aims to understand how both macro- and microtemporal variables (eg, psychological and environmental variables at both levels) influence the efficacy of a brief mobile health intervention (ie, NUDGE) for physical activity. Methods: Using a matched control design, we evaluated the effect of daily positive and negative affect, perceptions of the weather, energy, and fatigue as moderators of the effect of the intervention on 21 intervention participants and 21 matched controls. Results: Consistent with study hypotheses, macrotemporal (levels of the variable on a 3-week timescale) moderators of intervention effectiveness were observed for positive affect (P<.001), negative affect (P=.03), energy (P<.001), fatigue (P<.001), and perceived weather barriers (P<.001) for moderate-to-vigorous physical activity. These effects were observed more consistently for moderate-to-vigorous physical activity than for sedentary behavior, which was only significant for energy (P<.001). No effects for microtemporal variables (at the day level) were observed. Conclusions: There appears to be an optimization opportunity for mobile health physical activity interventions that can be achieved by personalizing intervention features and content based on approximately monthly assessments of affect, physical feeling states, and perceived weather barriers.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".