Evaluation of the NHS active 10 walking app intervention through time-series analysis in 201,688 individuals
Bibliographic record
Abstract
Despite widespread interest in integrating mobile health apps into primary care to prevent and manage physical inactivity-related health conditions, the effectiveness of these apps remains unclear. We quantified the effects of Active 10 (a goal setting and self-monitoring app developed by Public Health England) on brisk and non-brisk walking using a single-group interrupted time-series analysis of individual-level data collected between July 2021 and January 2024. Among Active 10 users (n = 201,668 l; 51.4 ± 14.4 years; 75.4% women) brisk and non-brisk walking increased by 9.0 (95% confidence interval (CI) 8.9, 9.1; 73% above baseline) and 2.6 min/day (95% CI 2.4, 2.8; 9% above baseline), respectively, on the day of app download. Post-download, brisk and non-brisk walking decreased by 0.15 (95% CI -0.17, -0.13) and 0.06 (95% CI -0.08, -0.03) min/day/month, respectively, but remained above baseline. Our findings suggest that Active 10 may be effective in facilitating increases in brisk and non-brisk walking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".