The utility of a protection motivation theory framework for understanding sedentary behavior
Bibliographic record
Abstract
Multilevel determinants of sedentary behavior (SB), including constructs couched within evidence-based psychological frameworks, can contribute to more efficacious interventions designed to decrease sitting time. This study aimed to: (1) examine the factor structure and composition of sedentary-derived protection motivation theory (PMT) constructs and (2) determine the utility of these constructs in predicting general and leisure sedentary goal intention (GI), implementation intention (II), and self-reported SB. Sedentary-derived PMT (perceived severity, PS; perceived vulnerability, PV; response efficacy, RE; self-efficacy, SE), GI, and II constructs, and a modified SB questionnaire were completed by undergraduate students (n = 596). SE was broken into three psychological (productive, focused, tired), and two situational (studying, leisure) constructs to capture the main barriers to reducing sitting time. After completing socio-demographics and the PMT items, participants were randomized to complete general or leisure GI and II. Based on model assignment, they completed either the general or leisure SB questionnaire one week later. Irrespective of model, exploratory followed by confirmatory factor analysis revealed that the PMT items grouped into eight coherent and interpretable factors consistent with the theory's threat and coping appraisal tenets: PV, PS, RE, and five scheduling SE constructs (tired, productive/focused, TV/video games/computer, studying at home, studying in library/Wi-Fi area). Using linear regression, general and leisure models predicted 5% and 1% of the variance in GI, 10% and 16% of the variance in II, and 3% and 1% of the variance in SB, respectively. Variables that made unique and significant contributions were: RE (general) and SE (leisure) for goal intention; PV and RE (general), PV, RE, and SE (leisure) for implementation intention; and only goal intention (leisure) for SB. Support now exists for the tenability of an eight-factor PMT sedentary model and its utility in predicting II and to a lesser extent GI and behavior.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".