Changing Sedentary Behavior in the Office: A Randomised Controlled Trial Comparing the Effect of Affective, Instrumental, and Self‐Regulatory Messaging on Sitting
Bibliographic record
Abstract
BACKGROUND: Although avoiding sedentary behavior has many health benefits, adults often sit for long periods at work. The purpose of this study was to compare affective attitude, instrumental attitude, and self-regulation messaging interventions on sitting in the workplace. METHODS: Using a cluster randomised controlled trial design, participants (N = 116) were assigned (by workplace) to: (a) instrumental, (b) affective, (c) self-regulation, or (d) control (nutrition information) groups. Measurements were taken online at baseline, 4 weeks, 8 weeks, and 12 weeks post-baseline. The interventions comprised three presentations delivered following baseline, week 4, and week 8 assessments. The primary outcome was self-reported average hours of sitting per day at work (registered trial number: NCT04082624). RESULTS: Controlling for baseline sitting, overall, the affective group sat for less time than the instrumental and self-regulation groups. Also, at week 4, the affective group sat for less time than the instrumental and self-regulation groups and, at week 8, the affective group sat for less time than the self-regulation and control groups. There were no differences between the groups at week 12. CONCLUSIONS: This investigation showed that workplace interventions targeting affective attitude can lead to less sitting time in the short term. Future research should explore additional strategies to minimise sedentary behavior in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".