Daily exercises uptake and associated factors among Social Security and National Insurance Trust pensioners in the Greater Accra Region of Ghana
Bibliographic record
Abstract
BACKGROUND: With a growing body of evidence highlighting the positive impact of regular physical activity or exercise on achieving healthy aging, it is important to gain insight into the factors influencing daily exercises uptake. However, to the best of our knowledge, no study has been focused on factors predicting daily exercises uptake among pensioners, who form a substantial portion of Ghana's aging population. The goal of this preliminary study was to estimate the factors associated with daily exercises uptake among Social Security and National Insurance Trust (SSNIT) pensioners in Ghana. METHODS: Data for this study came from a cross-sectional study on survival strategies and quality of life among SSNIT pensioners in the Greater Accra Region of Ghana. Cluster and stratified sampling techniques were used to recruit the study participants. The analytic sample was 410 participants. Multivariable binary logistic regressions were used to estimate factors associated with daily exercises uptake among the participants. The significance of the test was pegged at a p-value of 0.05 or less. RESULTS: The results showed that 62% of the participants self-identified as male, 47.6% were aged between 60 and 64 years, 52.7% were employed in the public sector and 44.4% performed daily exercises. The results showed that those who were aged 60-64 years (AOR: 1.197, 95% CI: 1.019-1.405), aged 65-69 years (AOR:1.254, 95% CI: 1.071-1.468), who do not incur expenditure on their household in a month (AOR: 1.519, 95% CI: 1.127-2.046), earned less than GH¢260 (AOR: 1.221, 95% CI: 1.018-1.465), accessed/utilized herbalist medical services (AOR: 1.252, 95% CI: 1.129-1.388), very dissatisfied (AOR: 1.637, 95% CI: 1.242-2.157) and dissatisfied (AOR: 1.516, 95% CI: 1.212-1.896) with their sex life were more likely to undertake daily exercises and this was statistically significant. The results again demonstrated that participants who joined fitness club (AOR: 0.685, 95% CI: 0.614-0.764) and those who were very dissatisfied with their health services access/use (AOR: 0.598, 95% CI: 0.363-0.984) were less likely to undertake daily exercises and this was statistically significant. CONCLUSION: Findings of this study have provided important insights for policy makers and thus constitute a useful framework to help plan and shape future policies and programs on daily exercises uptake among pensioners in Ghana and other geographical contexts with similar cultural, demographic, and socio-economic characteristics.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".