Just how special are the physical activity cognitions in diseased populations? Preliminary evidence for integrated content in chronic disease prevention and rehabilitation
Bibliographic record
Abstract
BACKGROUND: The extant literature is building on subdividing physical activity (PA) correlates and interventions by health condition (e.g., diabetes, cancer, etc.). PURPOSE: The purpose of this study was to compare the mean values and correlations of a population sample divided by mutually exclusive health condition status ("nondiseased," cardiovascular disease and risk factors, cancer, diabetes, and arthritis) on theory of planned behavior beliefs and physical activity after adjusting for sociodemographic factors. The relationship between compounding health conditions/comorbidities and these beliefs with PA was also evaluated. METHODS: Participants were a U.S. sample of 6,739 adults (M age = 49.65, SD = 16.04) who completed relevant social and medical demographics, measures of behavioral, normative, and control beliefs, and self-reported PA. RESULTS: Mean analyses identified greater health barriers to PA for the arthritis population compared to the other groups (- .025), whereas physician norms and health barriers were higher for compounding health condition populations compared to the nondiseased group (- .025). Belief-behavior correlations, however, were not different across health conditions (- .19), and nondiseased and single disease populations had larger control belief-behavior correlations than those populations with compounding health conditions (- .19). CONCLUSIONS: These data generally provide preliminary evidence for an integrated approach to PA promotion content in primary prevention and health rehabilitation with some possible tailoring in the areas of health barriers. This area of research will benefit from future studies that build off of these results.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".