Effectiveness of Cognitive Health Focused Training on body mass index and quality of life among obese adults during pandemic
Bibliographic record
Abstract
Abstract Introduction: The overall prevalence of obesity among adolescents was estimated to be 9.9% in India. Patients with morbid obesity are at higher risk for health complications, such as diabetes, heart disease, stroke, hypertension, gallbladder disease, osteoarthritis, sleep apnea and other breathing problems and some forms of cancer. They frequently suffer from low self-esteem, impaired body image, and depression. The aim of this study was to determine the effectiveness of Cognitive Health Focused Training (CHF-T) on body mass index and quality of life among obese adults. Methodology: A quasi experimental study was conducted to assess the effectiveness of CHF-T among obese adults in selected community area, Kelambakkam, Chennai. Samples were selected by non-probability convenient sampling as per the inclusion criteria; 30participants were assigned as experimental group and 30 in control group. CHF-T was given and the level of obesity was assessed by calculating the body mass index (BMI), and the quality of life (QOL) was assessed using the adopted Moorhead quality of life questionnaire.Results: In the experimental group, there significant reduction in the mean BMI score and improve in QOL score in post-test compared to pre-test. Sex and hours spend on television had statistically significant association with posttest level of BMI at p<.01 and p<.05 level. No statistically significant association related to QOL. Conclusion: Cognitive Health Focused Training (CHF-T) among obese people was an effective method in reducing the BMI and increasing QOL. It needs commitment of the healthcare professional to create awareness among the public.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".