Effects of Physical Activity, Fruit and Vegetables Intake, and Alcohol Consumption on Overweight or Obesity: Meta-Analysis
Bibliographic record
Abstract
Background: Overweight or obesity is a public health problem worldwide which is increasing both in the general population and in people with chronic diseases. Overall both children and adults are vulnerable to overweight or obesity. This study aims to examine the effect of physical activity, consumption of vegetables and alcohol on overweight or obesity using a meta-analysis.Subjects and Method: Meta-analysis was carried out using the PRISMA flowchart and the PICO model. Population: age 6 to 64 years. Intervention: low physical activity, low vegetable and fruit consumption, and high alcohol. Comparison: high physical activity, high vegetable and fruit consumption, and alcohol low. Outcome: overweight or obese. The databases used are Google Scholar, PubMed, and Proquest with keywords (Overweight OR Obesity OR "BMI Status") AND (“Physical Activity” OR Exercise OR Sport OR Inactive) AND (Vegetable AND Fruit) AND Alcohol AND "Cross Sectional" AND aOR. There were 22 cross-sectional studies published in 2012-2022 that met the inclusion criteria. Analysis was performed with RevMan 5.3.Results: A meta-analysis was conducted on 22 articles with a cross-sectional study design originating from Indonesia, Ghana, Arab Emirates, Texas, Ethiopia, Uganda, Botswana, Congo, Bahir Dar, North Western, Toronto, Zambia, Cameroon and Tanzania involving 91,031 ages 6-64 years. The results of the meta-analysis showed that someone with low physical activity had a risk of being overweight or obese 1.35 times compared to high physical activity (aOR= 1.35; 95% CI= 1.09 to 1.68; p<0.001), someone with high consumption of vegetables and fruit have a risk of experiencing overweight or obesity 1.40 times compared to high consumption of vegetables and fruits (aOR= 1.40; 95%CI= 0.94 to 2.08; p<0.001), and someone with high alcohol has a risk of experiencing overweight or obesity 1.47 times compared low alcohol (aOR= 1.47; 95% CI= 1.31 to 1.65; p<0.001).Conclusion: Low vegetable and fruit consumption, high alcohol consumption and low physical activity can increase the risk of being overweight or obese. Keywords: social support, self-efficacy, social cognitive theory, hypertension, medication adherence Correspondence: Dena Tri Solehaini. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: dena35tri@gmail.com. Mobile: +6282329210977.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.085 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".