Exploring Work-Time Affective States Through Ecological Momentary Assessment in an Office-Based Intervention to Reduce Occupational Sitting
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to examine whether a low-cost standing desk intervention that reduced occupational sitting was associated with changes in work-time cognitive-affective states in real time using ecological momentary assessments at the start and end of the trial. METHODS: Forty-one office employees (91.7% female, mean age = 39.8 [10.1] y) were randomized to receive a low-cost standing desk or a waitlist control. Participants received 5 surveys each day for 5 workdays via smartphone application prior to randomization and at trial's end. Ecological momentary assessment assessed current work-time psychological states (valence and arousal, stress, fatigue, and perceived productivity). Multilevel models assessed whether changes in work-time outcomes over the course of the intervention were significantly different between treatment groups. RESULTS: There were no significant differences in outcomes between the groups except for fatigue, with the control group reporting a significant decrease in daily fatigue following the intervention (P < .001). The intervention group reported no significant changes in any of the work-time outcomes across the study period (P > .05). CONCLUSIONS: A low-cost standing desk intervention to reduce occupational sedentary behavior did not negatively impact work-time outcomes such as productivity and fatigue in the short term.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".