Mixed structured physical exercise program for obese workers: combating sedentary and metabolic risk factors related to metabolism workplace syndrome
Bibliographic record
Abstract
To develop a program of high volume physical exercises, of moderate to high intensity, adapted to the working context of Kinshasa. In a trial study of structured, spontaneous and mixed physical exercise programs associated with nutrition education, lasting 3 months, from November 7 to January 7, 2014. 30 obese sedentary workers 44 ± 844 ± 8.6 on average, year-olds were randomly selected from 157 obese individuals out of a general population of 400 employees in the personal list of the selected company. These workers were divided into three groups of physical exercises, of which the first group consisted of 10 workers in a three-day, moderate to high intensity structured exercise program, including: jogging, aerobics, basketball, volleyball, swimming, abdominals and walking of 10,000 steps a day. The second group was also composed of 10 workers undergoing a spontaneous exercise program of the same duration, intensity and frequency including: walk, up and down the stairs and finally the third group composed and then the third group of 10 workers submitted to a mixed exercise program (the mixture of the two programs). Quantitative variables were expressed as mean ± standard deviation. The ANOVA test was used to compare the variables of two groups. A value of p-value ≤ 0.05 was considered a threshold of statistical significance. Decreased weight, waist circumference and body mass index were observed intra group before and after exercise programs. After the intervention, the group subjected to the mixed exercises improved more significantly (p = 0.0001) the morphological parameters of the obese than the group subjected to the structured and spontaneous exercises with diet. A moderate to high intensity, high volume mixed exercise program significantly improves the morphology of obese sedentary workers compared to the structured and spontaneous exercise program. It can be considered as an effective non-pharmacological strategy to combat the nutritional and epidemiological transition in the workplace, where obesity is the main cornerstone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".