Exploring the effect of an eHealth intervention on women's physical activity: Design and rationale for a randomized controlled trial
Bibliographic record
Abstract
Objective: This manuscript reports on the protocol for a three-arm randomized controlled trial aiming to assess the effect a self-determination theory-based eHealth intervention on physical activity among insufficiently active women who are overweight or obese. Methods: The intervention-of-interest provided (A) six weekly behavioural support emails, (B) a wearable activity tracker, and (C) a copy and verbal explanation of the Canadian physical activity guidelines, and was compared to an intervention that provided (B + C) and another that provided (C). Women from a local community were invited to participate in this study. Participants were recruited between September 2018 and March 2019. Data were collected using self-report and direct measures three times: at baseline (week 0), post-intervention (week 7), and at follow-up (week 21). The primary outcome was self-reported total metabolic equivalent minutes of physical activity per week (MET-m/week); exploratory outcomes included number of days of strength training per week, self-determination theory constructs (i.e. motivational regulations, basic psychological needs satisfaction and thwarting), and well-being indicators (i.e. affect, vitality, depression). Conclusion: Findings will provide insight into which combination of intervention components may be more effective at promoting physical activity among insufficiently active women who are overweight or obese, and thus inform the design of future interventions aiming to promote physical activity.
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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 | 0.129 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".