Physical inactivity and self-reported depression among middle- and older-aged population in South Asia: World health survey
Bibliographic record
Abstract
BACKGROUND: With the increase in the understanding of the influence of various lifestyle factors such as sedentary behaviour and level of physical activity (PA) on physical and mental health, there has been a growing research interest on how physical inactivity correlates with depressive outcomes across countries. The present study aimed to examine 1) the pattern of engaging in PA among middle- and older-aged population in four South Asian countries, and 2) whether PA is associated with higher prevalence of depression. METHODS: This cross-sectional study is based on country-representative data obtained from WHO's World Health Survey (WHS). Subjects were 7204 men and women aged above 50 years from Bangladesh, India, Nepal and Sri Lanka, all of which are classified as Low-and-middle-income countries (LMICs) in World Bank reports. Outcome variables were self-ported depression (SRD) and ever being diagnosed with depression. Association between frequency of moderate (MPA) and vigorous physical activity (VPA) and depression was analysed by multivariable regression methods. RESULT: Prevalence of self-reported depression was respectively 47.7%, 40.3%, 40.4% and 11.4% in Bangladesh, India, Nepal and Sri Lanka. Prevalence of being ever diagnosed with depression was highest in Nepal (38.7%), followed by India (17.7%), Bangladesh (2.5%) and Sri Lanka (2%). Multivariable analysis shown statistically significant association between PA and diagnosed depression in Bangladesh and India, but not with SRD. In Bangladesh, compared to those who reported engaging in MPA on daily basis, the odds of reporting diagnosed depression were more than five times higher [AOR = 5.512; 95% CI = 1.159-26.21] for those who never took MPA. In India, those never took VPA had 44% higher [AOR = 1.442; 95% CI = 1.046-1.987] odds of being diagnosed with depression compared those who never engaged in VPA. CONCLUSION: Lower frequency of vigorous physical activity were significantly associated with higher rates of depression diagnosed. Based on the findings, it is recommendable that health programs targeting mental health among middle- and older-aged population take steps to promote the level of PA within a multi-dimensional depression prevention framework. Longitudinal studies are needed to understand the role of vigorous and moderate physical activity on the onset and intervention of depression among elderly population in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".