Exercise Motivation Among Special Populations
Bibliographic record
Abstract
Motivational factors and barriers to exercise are key factors in exercise programming, particularly in programs intended for special populations. PURPOSE: 1) To investigate physical activity and exercise motivation among persons with various chronic health conditions, apparently healthy older adults, and among persons participating in community exercise programs associated with special populations, specifically older adults and those with chronic disease, and 2) Identify barriers to exercise among special populations. METHODS: Surveys comprising of demographics, physical activity (PA) (Physical Activity Scale for the Elderly (PASE)), and exercise motivation (Behavioral Regulation in Exercise Questionnaire (BREQ2)), and barriers to exercise, were distributed to participants of local community exercise programs targeting special populations, and apparently healthy older adults (n = 208). RESULTS: Significant effects were found for participation in a community exercise program regarding PASE score (F = 3.48, p = .01), amotivation (F = 8.54, p = .00), external regulation (F = 2.71, p = .03), introjected regulation (F = 24.07, p = .00), identified regulation (F = 1.18, p = .34), and for health condition on intrinsic regulation (F = 2.32, p = .03). Post hoc pairwise comparisons revealed significant differences in amotivation scores between those participating in a diabetes prevention program (DPP) (1.5 ± .88) and participants in Stay Active and Independent for Life (SAIL) (0.23 ± .01, p = .00), cardiac rehabilitation (0.16 ± .01, p = .00), Parkinson’s programs (.31 ± .04, p = .00), and Functionally Fit (.23 ± .02, p = .00). Most common barriers cited were COVID-19, time constraints, fatigue, pain, and stress. CONCLUSION: Overall health condition did not appear to have an impact on physical activity or exercise motivation. However, those with osteoporosis and cancer had higher levels of intrinsic motivation toward exercise. Participation in community programs appeared to have a positive impact on physical activity and exercise motivation. Specifically, those participating in cardiac rehab and Functionally Fit had higher levels of PA, and DPP participants had less motivation to exercise. Further analysis is expected comparing participation in community programs vs. no program.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".