Managing obesity with lifestyle modification, outcomes, and recommendations
Bibliographic record
Abstract
Introduction: Obesity is a chronic illness affecting people from all regions and socioeconomic classes. Body Mass Index (BMI) is the measure used to define overweight and obese individuals. A BMI between 25 to 29.9 is considered overweight and more than 30 is considered obese. The prevalence of obesity has increased drastically in the last few decades making it an important health problem worldwide. Objectives: The objective of this research paper is to identify various recent scholarly sources that focus on the importance of lifestyle modifications in the prevention of obesity and associated illnesses. Methods: The articles were searched using databases such as PubMed, NCBI, UpToDate, BMJ, and other sources. The keywords used were obesity, overweight, lifestyle modifications, dietary modifications, exercise, modifiable factors, and other related words. Articles published after 2000, those with a high number of citations, larger sample size, experimental evidence, and done in larger institutes were selected. Results: The studies provided evidence that lifestyle modifications as the most important factor in the reduction of overweight and obesity. Dietary modifications such as low-calorie, low-carbohydrate, Mediterranean diet, etc., done regularly or intermittently helped in weight loss. Exercise and physical activity were identified as the second-best interventions for weight management. Additionally, behavior and cognitive therapy is another intervention. The research paper also identifies outcomes of lifestyle modifications and the challenges and barriers faced by individuals. Conclusion: Obesity can be successfully reduced by modification of dietary habits, improving patient-physician relationships, educating the masses about management strategies, and most importantly, inculcating more physical activity in daily life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".