Online interventions to increase physical activity levels in self-reported inactive adults: the ONWARDS randomised controlled trial
Bibliographic record
Abstract
Background: Digital interventions have the potential to increase physical activity in adults with the use of few resources, but evidence of long-term effectiveness is limited. This study aimed to evaluate the effects of three digital interventions on physical activity. Methods: 183 self-reported inactive adults (<150 min per week of moderate to vigorous physical activity (MVPA)) aged 22-55 years were included in a hybrid type 1 effectiveness-implementation trial over 18 months and were randomised to three fully web-based interventions: (A) activity tracker with the personalised metric Personal Activity Intelligence on a mobile app, (B) group A+home-based online training and (C) group B+online peer support through social media. Physical activity was measured with hip-worn accelerometers (ActiGraph GT3X-BT) at baseline, 6, 12 and 18 months. Outcome measures included MVPA, light and total physical activity, steps, adherence to physical activity recommendations, waist circumference (WC), quality of life, perceived competence for exercise, self-efficacy for exercise, social support and reasons for performing physical activity. Longitudinal changes in outcomes were evaluated using linear mixed models adjusted for baseline values. Results: Mean MVPA in all groups at baseline was over two times higher than the criteria for inactive and decreased from 69.7 min per day (95% CI: 67.3 to 72.1) to 60.2 min (95% CI: 56.8 to 63.7) through 18 months (p<0.001). No time by group interaction was observed (p=0.97). Similar patterns were observed for light and total physical activity (main effect of time: both p<0.02, time by group interaction: both p>0.59). WC increased from baseline through follow-up (all p<0.001), but with no time by group interaction (all p>0.15). Conclusion: Self-reported physically inactive adults receiving an activity tracker with a mobile app accumulated high physical activity levels at baseline but decreased their activity levels over 18 months. Adding home-based online training and peer support did not provide additional effects. Trial registration number: Prospectively registered, 23 of April 2021, identifier: NCT04526, https://clinicaltrials.gov/ct2/show/NCT04526444.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".