Psychosocial factors of physical activity among people with disabilities: Prospective cohort study.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: This study aimed to (a) explore the associations between psychosocial factors and physical activity behavior in people with physical disabilities and/or chronic diseases, both between and within persons over time; and (b) examine whether these associations differ for people initiating and people maintaining physical activity behavior. RESEARCH METHOD/DESIGN: Data of 1,256 adults with physical disabilities and/or chronic diseases enrolled in the prospective cohort study Rehabilitation, Sports, and Active lifestyle (ReSpAct) were analyzed. Self-reported physical activity and four main psychosocial factors (i.e., self-efficacy, attitude, motivation, social support) were measured with questionnaires 3-6 weeks before discharge (T0) and 14 (T1), 33 (T2), and 52 (T3) weeks after discharge from rehabilitation. Hybrid multilevel regression models (corrected for age, sex, education level, diagnosis, counseling support) were used. RESULTS: = .035). No significant associations were found for social support, amotivation, external regulation, and introjected regulation. CONCLUSION/IMPLICATIONS: This study is the first that explored the between- and within-subject associations between psychosocial factors and physical activity over time in a large cohort of adults with physical disabilities and/or chronic diseases. The findings indicate the importance of intrinsic motivation, identified regulation, and self-efficacy in initiating and maintaining physical activity behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".