Exploring environmental cues to instigate physical movement in the workplace
Bibliographic record
Abstract
Background: With the increase of sedentary jobs and the health risks associated with a sedentary lifestyle, finding novel methods to increase physical activity should be a priority. Environmental cues within the workplace can serve as cues to action for initiating light physical activity. Aim: To qualitatively explore the environmental cues that can instigate light physical activity within an office workplace context. Identification of these cues can inform behaviour change programmes designed to promote habitual physical movement within the workplace. Method: Purposive sampling was used to recruit full-time sedentary office workers who self-report as having a highly sedentary job. Interviews followed a semi-structured design and thematic analysis was used to explore environmental cues within commercial, home, and mixed office settings. Results: Forty-three office workers were interviewed, 16 from a commercial office, 12 from a home office, and 15 with a flexible work arrangement whereby they worked from both a commercial and home office. The findings of this study indicate that across all three groups the main instigator of movement was influenced by office layout (e.g. getting up for beverages and taking bathroom breaks), social environment (e.g. informal and formal meetings), and taking active breaks, both job-related (e.g. printing and filing) and non-job-related (e.g. household chores). Conclusions: These findings provide valuable insight for behaviour change programmes utilising environmental cues to inform habit-based interventions designed to instigate movement within the workplace.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".