Examining the long-term effects of a commercial mHealth app: A 24-month quasi-experimental study of 516,818 app users
Bibliographic record
Abstract
BACKGROUND: The long-term (i.e., ≥ 12 months) effects of commercial mHealth apps on physical activity (PA) have been scarcely studied. PURPOSE: To conduct a longitudinal examination of a ‘top tier’ commercial mHealth app on population-level PA. METHODS: A 24-month pre-post quasi-experimental study was conducted between December 2016 and June 2019 with Carrot Rewards app users in Ontario, Canada. Users with valid smartphone-assessed baseline step count data were included. Simple linear regression models analyzed weekly mean daily step counts. Post-hoc estimates examined differences in weekly mean daily step count from baseline. RESULTS: The total sample included 516,818 users (% female: 62.83; age [SD]: 33.46 [12.65] years; baseline daily step count [SD]: 6,035 [3,706]). Compared to baseline, weekly mean daily step counts were 464 (95% CIs: 453, 475) and 242 (95% CIs: 221, 264) steps/day higher at 12 and 24 months, respectively. CONCLUSION: This commercial mHealth app increased population-level PA over 24 months.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".