A combined health action process approach and mHealth intervention to reduce workplace sitting time in office-working adults: a secondary analysis examining health-related quality of life and work performance outcomes
Bibliographic record
Abstract
OBJECTIVE: This secondary analysis study examined the effects of a 6-week theory-based planning and mHealth text message intervention targeting workplace sitting time on health-related quality of life and work performance in office workers. DESIGN: = 31) condition. OUTCOME MEASURES: Workplace sitting time, time spent in specific non-sedentary behaviours (e.g. standing), health-related outcomes (i.e. emotional well-being, energy/fatigue, perceived role limitations), and work performance were assessed at baseline and week 6. RESULTS: Significant group by time interaction effects, that favoured the intervention group, were found for perceived role limitations due to emotional health problems and emotional well-being. No significant interaction effects emerged for energy/fatigue, role limitations due to physical health problems or work performance. Significant correlations in the expected direction were found between sedentary/non-sedentary behaviours and health-related outcomes. No significant mediation effects were found to suggest the intervention affected health-related outcomes through reductions in sedentary behaviour. CONCLUSION: Reducing workplace sitting improves emotional well-being and contributes to fewer perceived role limitations due to emotional health problems among office workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".