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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".