Improving health-related quality of life through an evidence-based obesity reduction program: the Healthy Weights Initiative
Bibliographic record
Abstract
Mark E Lemstra,1 Marla R Rogers,21Alliance Health, Moose Jaw, 2Department of Physical Medicine and Rehabilitation, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada Abstract: When evaluating any health intervention, it is critical to include the impact of the intervention on health-related quality of life (HRQL). Among those who are obese, HRQL is often lower than the general population and even more when considering obesity-related comorbidities and bodily pain. The objectives of this paper were to determine the impact of a multidisciplinary, community-based obesity reduction program on HRQL and to determine the independent risk factors for lack of improvement from baseline to follow-up. HRQL was measured using the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) at baseline and follow-up (24 weeks). To date, 84.5% of those who completed the program had improvements in their overall SF-36 score. Significant increases in the mean scores on eight dimensions of health were also observed. Lack of improvement was independently affected by smoking status (odds ratio 3.75; 95% confidence interval 1.44–9.78; P=0.007) and not having a buddy to attend the program (odds ratio 3.70; 95% confidence interval 1.28–10.68; P=0.015). Obesity reduction programs that target increasing exercise, improving diet, and cognitive behavioral therapy can positively impact HRQL in obese adults. Social support has a strong role to play in improving outcomes. Keywords: obesity, health-related quality of life, social- support, SF-36, Canada
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".