I Sit but I Don’t Know Why: Investigating the Multiple Precursors of Leisure-Time Sedentary Behaviors
Bibliographic record
Abstract
Background: Precursors driving leisure-time sedentary behaviors remain poorly investigated, despite their detrimental consequences. This study aimed to investigate the predictive validity of controlled and automatic motivational precursors toward reducing sedentary behaviors and being physically active on leisure-time sedentary behaviors. The influence of demographic, physical, socio-professional, interpersonal, and environmental variables was also examined and compared with the associations of motivational precursors. Methods: 125 adults completed questionnaires measuring controlled motivational precursors (i.e., intentions, perceived competence), demographical (i.e., sex and age), physical (i.e., body mass index), and interpersonal (i.e., number of children) variables. Regarding automatic motivational precursors, habit strength and approach-avoidance tendencies were captured using the Self-Report Behavioral Automaticity Index and a manikin task. Time at work was computed as a socio-professional variable, days of the week and weather conditions were recorded as environmental precursors. Participants wore an accelerometer for 7 days and leisure time was identified using notebooks. Associations between the different precursors and the leisure-time sedentary behaviors were examined in linear mixed effect models. Results: Intention to be physically active and habit strength toward physical activity were negatively associated with leisure-time sedentary behaviors. Sex, body mass index, time at work, number of children, day of the week, and weather conditions were more strongly associated with leisure-time sedentary behaviors. Conclusion: Our findings show that, in comparison with other variables, the influence of motivational precursors on leisure-time sedentary behaviors is limited. This study supports the adoption of a broad-spectrum of precursors when predicting sedentary behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".