Level, Motivation and Barriers to Participate in Physical Activity among Geriatric Population at Ahmedabad City, India: An Epidemiological Factsheet
Bibliographic record
Abstract
Objectives: To estimate the level of physical activity among geriatric population, to determine the motivating factors for being active and identifying barriers that prevent participants from engaging in physical activity. Methods: A community-based cross-sectional study was carried out at one of the wards within Ahmedabad city following multi-stage random sampling. The calculated sample size was 230. A pre-designed, validated, short version International Physical Activity Questionnaire (IPAQ) and Behaviour Regulation in Exercise Questionnaire (BREQ-3) were used for data collection by personal interview. From selected sampling-frame, geriatric people residing in every 5th household were interviewed after obtaining oral informed consent following simple-random sampling. Results: Of total 230 study participants, 67 (29.13%) were physically active (cumulative for Category 2 and Category 3), while the remaining 163 (70.87%) were found physically inactive (i.e., minimally active [Category 1]). Motivational scores, particularly in identified regulation, showed higher median scores across subdomains of the BREQ-3. Amotivation exhibited a strong negative correlation with physical activity, while intrinsic regulation displayed a strong positive correlation. Conclusion: More than two-third of study participants were physically inactive. Level of educational status, type of previous occupation involved, presence of addiction, BMI, electronic device usage duration per day and presence of chronic illness were statistically significant determinants to decide involvement of elderly people in category of physical activity. Amotivation, external and introjected regulation had negative correlation with physical activity, while intrinsic regulation and RAI (Relative Autonomy Index) showed positive correlation with physical activity. None of the behavioural regulators had statistically significant association with category of physical activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".