Objective measurement of physical activity and sedentary behavior among South Asian adults: A systematic review
Bibliographic record
Abstract
BACKGROUND: South Asians are one of the fastest growing ethnic groups in western countries with a high incidence of chronic diseases like metabolic syndrome and cardiovascular disease occurring at younger ages and lower body weight compared with white Europeans. Physically active lifestyle and reduced sedentary time are modifiable risk factors that can decrease burden of chronic diseases. Population-level surveys based on self-report show South Asians engage in low levels of physical activity. Because of known limitations with self-report data, we aimed to synthesize available evidence to generate a physical activity /sedentary time profile of South Asians from studies using accelerometry. METHODS: We systematically searched Medline, EMBASE, CINAHL, PsycINFO, and SportDiscus. We included studies applying accelerometry to measure physical activity /sedentary time under free-living. Studies with an exclusive focus on drugs or including participants with health conditions/physical disability, and special populations (athletes/pregnant women) were excluded. Two authors independently adjudicated inclusion of citations at title/abstract and full text. We applied a standardized data abstraction form to extract relevant data. We evaluated methodological quality using Newcastle Ottawa Quality Assessment Scale. Due to variability and inconsistencies in measurement and reporting of physical activity /sedentary time, we only provide a narrative synthesis. FINDINGS: We identified only 14 studies(n = 1,338). Despite using similar accelerometry assumptions, we noted variability in reported outcomes for physical activity and sedentary time. Sedentary time ranged from 482(98) to 587 min/day. Mean light physical activity ranged from 211.69(67. 38) to 574(227) min/day. Moderate to vigorous physical activity among South Asian women ranged from 17-41 min/day and among men, 32-43 min/day. CONCLUSION: South Asians exhibited higher levels of physical activity when compared to the Canadian population level survey but not when compared to the American population level survey. Overall, fewer studies, and small sample sizes led to considerable variability limiting any effective comparisons. Results highlight the importance of conducting methodologically robust studies based on random sampling to advance the field, and to capture true levels of sedentary time and physical activity in the South Asian population.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".