Physical Activity Levels and Smoking Status in Relation to Weight Control after Bariatric Surgery
Bibliographic record
Abstract
Smokers typically exhibit lower body weights than non-smokers despite poorer metabolic and physiologic profiles. Nicotine, an appetite suppressant found in cigarettes and cigars, may play a role in weight control. Physical activity also contributes to lower body weights; however, this simultaneously reduces all-cause mortality, risk of coronary artery disease, and other chronic conditions. PURPOSE: To investigate if smoking status has an impact on weight loss and physical activity levels in patients 1-17 years after Roux-en-Y gastric bypass (RYGB). METHODS: A total of 509 individuals who had previously undergone RYGB (1-17 years post) were recruited for this study. To assess physical activity habits, participants were asked, “How many times per week do you exercise for 30 min or more at an intensity that makes you sweat or breathe hard?” Participants were also asked if they were a current smoker, ex-smoker or never smoked. RESULTS: The sample consisted of 22% smokers (114 total, 81 females), 47% never- smokers (239 total, 190 females) and 31% ex-smokers (156 total, 120 females). There were no significant differences in smoking status (p=.45) or physical activity (p=.57) between sexes. Current smokers had the highest BMI change (-21.2±.8kg/m2) compared to both never-smokers (-18.8±.6kg/m2; p=.01) and ex-smokers (-18.7±.7kg/m2; p=.02) while there was no significant difference between never-smokers and ex-smokers (p=.97). Ex-smokers reported being significantly more active (1.7±1.9bouts) compared to current smokers (1.1±1.7bouts; p=.01) while there were no differences in activity between never-smokers (1.5±1.7bouts) and current smokers (p=.07). CONCLUSIONS: Although smokers lost the greatest amount of weight post-surgery, they also reported being inadequately active. Post-surgical follow-ups should evaluate numerous health measures as indicators of surgical success, as long term weight change may also be equally affected by both healthy and unhealthy habits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".