Physical activity, diet, and weight loss in patients recruited from primary care settings: An update on obesity management interventions
Bibliographic record
Abstract
BACKGROUND: Obesity and related comorbidities are the most common chronic conditions in North America where behavior modification including the adoption of physical activity (PA) and a healthful diet are primary treatment strategies. Patients are more likely to engage in behavior modification if encouraged by their physician; however, behavioral counseling in primary care rarely occurs due to lack of training and resources. A more effective method may be to refer patients from clinical settings to other health professionals. OBJECTIVE: This systematic review examines the effectiveness of behavior-based counseling for obesity management among participants referred from clinical settings. METHODS: PubMed, CINAHL, and EMBASE were used to identify randomized clinical trials (2014-2020) for weight loss with the following inclusion criteria: trial duration ≥12 months, included a control or usual care group, recruited adults with overweight or obesity from primary care and/or treated in the primary care setting, and the intervention included counseling on PA and diet. RESULTS: . In 11 (52%) of the intervention groups, significant weight loss in the intervention group was observed compared to usual care (mean weight loss: 4.9[2.1] kg vs. 1.0[0.9] kg). In 13 out of 18 interventions (72%) reporting weight loss at two time points, weight regain was observed by 12 months. Statistically significant weight loss was observed in one intervention (of two total) that was longer than 12 months. CONCLUSIONS: Sustained weight loss regardless of the behavior-based, intervention strategy remains a challenge for most adults. Given the established benefits of routine PA and a healthful diet, prioritizing the adoption of healthy behaviors regardless of weight loss may be a more effective strategy for ensuring long-term health benefit.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".