Dual processing approach to sedentary behavior and physical activity in the workplace
Bibliographic record
Abstract
Regular physical activity is an important health promoting behavior. Yet, many adults live sedentary lifestyles, especially during their workday. The current study applies an extended theory of planned behavior model, incorporating affective attitudes and instrumental attitudes, along with habit, to predict limiting sedentary behavior and physical activity within an office environment. Theory of planned behavior constructs and habit were assessed with an online survey on a sample of 180 full-time office workers, with self-reported behavior assessed 1 week later (Mage = 25.97, SDage = 10.24; 44 males, 134 females, and 2 nonbinary). Model fit was indicated by BRMSEA (M = 0.057, SD = 0.023), B γ^ (M = 0.984, SD = 0.010) and BCFI (M = 0.959, SD = 0.026), accounting for 46.1% of variance in intention, 21.6% of variance in sedentary behavior, and 17.4% of variance in physical activity behavior. A Bayesian structural equation model revealed direct effects of instrumental attitudes and perceived behavioral control on intention to limit sedentary behavior, direct effects of intention and perceived behavioral control on limiting sedentary behavior, and direct effects of perceived behavioral control and habit on engaging in physical activity. The current study indicates intentions to be active in the office are primarily driven by beliefs about the benefits of activity and individuals' perceived level of control, rather than normative or affective beliefs. As behavior was predicted by both intention and habit, findings also indicate office-based activity is likely not always a consciously driven decision. These findings may have implications for improving activity levels in this highly sedentary population.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".