Epidemiology of physical inactivity in Nigeria: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Physical activity is crucial to preventing noncommunicable diseases. This study aimed to provide up-to-date evidence on the epidemiology of insufficient physical activity across Nigeria to increase awareness and prompt relevant policy and public health response. METHODS: A systematic literature search of community-based studies on physical inactivity was conducted. We constructed a meta-regression epidemiologic model to determine the age-adjusted prevalence and number of physically inactive persons in Nigeria for 1995 and 2020. RESULTS: Fifteen studies covering a population of 13 814 adults met our selection criteria. The pooled crude prevalence of physically inactive persons in Nigeria was 52.0% (95% CI: 33.7-70.4), with prevalence in women higher at 55.8% (95% CI: 29.4-82.3) compared to men at 49.3% (95% CI: 24.7-73.9). Across settings, prevalence of physically inactive persons was significantly higher among urban dwellers (56.8%, 35.3-78.4) compared to rural dwellers (18.9%, 11.9-49.8). Among persons aged 20-79 years, the total number of physically inactive persons increased from 14.4 million to 48.6 million between 1995 and 2020, equivalent to a 240% increase over the 25-year period. CONCLUSIONS: A comprehensive and robust strategy that addresses occupational policies, town planning, awareness and information, and sociocultural and contextual issues is crucial to improving physical activity levels in Nigeria.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".