When it comes to sedentary behaviour modification, should people be told what to do? A randomized comparison trial among home-based office workers living in Ontario, Canada
Bibliographic record
Abstract
The effects of adding choice architecture to a theory-based (Health Action Process Approach; HAPA) sedentary intervention remain unknown. To investigate whether choice architecture enhances a theory-based sedentary behaviour reduction intervention in home-based office workers. A 4-week HAPA-based intervention was conducted in London, Canada. Choice architecture was tested as an enhancement via a two (group: 'Choice of Intervention' vs. 'No Choice Intervention') by two (time: Baseline vs. Week 4) factorial repeated measure randomized comparison design. Sedentary behaviour reduction strategies focussed on obtaining a sedentary break frequency (BF) of every 30-45 min with break durations (BD) of 2-3 min. BF, BD, sitting, standing, and moving time were objectively measured (activPAL4™) at both time points. Participants (n = 148) were 44.9 ± 11.4 years old and 72.3% female. BF and total sitting time showed a time effect (P < .001), where both groups improved over the 4 weeks; there were no significant differences between groups across time. BD, standing, and moving time had a significant group by time effect where the 'No Choice' group showed significant increases in BD (P < .001), standing (P = .006), and moving time (P < .001) over the 4 weeks. Augmenting a theory-based intervention with choice architecture resulted in change in some sedentary behaviours in at home office workers. Specifically, while BF increased for all participants, the 'No Choice' group exhibited greater changes for BD, standing, and moving time compared with the 'Choice' group. Overall, these changes exceeded the intervention BF and BD goals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".