Digital interventions to promote physical activity among inactive adults: A study protocol for a hybrid type I effectiveness-implementation randomized controlled trial
Bibliographic record
Abstract
Introduction Physical inactivity is the fourth leading risk factor for global mortality, and inactive adults have a higher risk to develop lifestyle diseases. To date, there is preliminary evidence of the efficacy of fitness technologies and other digital interventions for physical activity (PA) promotion. Intervention studies are needed to test the effectiveness and implementation of innovative PA promotion strategies. Methods and analysis The ONWARDS study is a hybrid type I effectiveness-implementation randomized control trial aiming at an inactive and presumably high-risk population living in Northern Norway. One hundred and eighty participants will be assigned to 3 groups in a 1:1:1 ratio and participate for 18 months. Participants in group A will be provided an activity tracker with the personalized metric Personal Activity Intelligence (PAI). Participants in group B will be provided with both an activity tracker with the personalized metric PAI and access to online training videos (Les Mills+) to perform home-based training. Participants in group C will be provided an activity tracker with the personalized metric PAI, home-based online training and additional peer support via social media. The primary objective is to test which combination of interventions is more effective in increasing PA levels and sustaining long-term exercise adherence. Secondary objectives include: proportion of participants reaching PA recommendations; exercise adherence; physical fitness; cardiovascular risk; quality of life; perceived competence for exercise; self-efficacy; social support; usability; users' perspectives on implementation outcomes (adoption, acceptability, adherence, sustainability). The study design will allow testing the effectiveness of the interventions while gathering information on implementation in a real-world situation. Discussion This study can contribute to reduce disparities in PA levels among inactive adults by promoting PA and long-term adherence. Increased PA might, in turn, result in better prevention of lifestyle diseases. Digital interventions delivered at home can become an alternative to training facilities, making PA accessible and feasible for inactive populations and overcoming known barriers to PA. If effective, such interventions could potentially be offered through national health portals to citizens who do not meet the minimum recommendations on PA or prescribed by general practitioners or specialists. Trial registration https://clinicaltrials.gov/ct2/show/NCT04526444 , Registered 23 April 2021, identifier: 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 | 0.052 | 0.037 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".