Using accelerometry to evaluate physical activity and sedentary behaviour patterns in bariatric surgery patients
Bibliographic record
Abstract
Background Sedentarism or prolonged sitting time is an emerging risk factor for obesity, independent of physical activity (PA). Bariatric surgery seems to be most effective form of treatment that works for severely obese individuals (Body Mass Index (BMI) of >40 kg/m2). Objectives The purpose of this investigation was to characterize with the use of accelerometers, daily steps/day and sedentary behavior (SB) by measuring time-spent sitting/lying in severely obese patients before and after bariatric surgery. Setting McGill University Health Center (MUHC). Methods Seventeen patients (58.5% of the patients were female) with a mean age of 46.5 ± 10.1 years, and a Body Mass Index (BMI) of 48.8 ± 6.2 kg/m2 scheduled for bariatric surgery took part in this study. ActivPal™ accelerometers were attached to the patient's upper leg and worn 24 hours a day for seven consecutive days. Mean steps, transitions from sitting to standing and hours of sitting/lying per day were measured before, and 3 and 6 months following post bariatric surgery. Results Prior to bariatric surgery, participants spent 18.6 ± 1.5 hours/day sitting/lying, representing over 75 % of their day. Patterns in total sitting/lying time did not significantly change at three or six months after surgery. Baseline accumulated steps/day averaged 6139 ± 2720 and did not increase across time periods. PA patterns did not increase on either weekdays or weekend days. Conclusions Given that PA and SB are believed to independently contribute to energy balance and health outcomes, clinician's and health professionals should intervene more rigorously to simultaneously reduce sedentarism and increase levels of PA to help bariatric surgery patients successfully manage their weight.
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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 | 0.001 | 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) | 0.001 | 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".