Explaining Body Composition After Bariatric Surgery using Accelerometry
Bibliographic record
Abstract
Obesity, a complex condition involving genetic, behavioral, socioeconomic, and environmental factors, poses significant health risks and contributes to increased morbidity and mortality. Bariatric surgery is an effective treatment for individuals with severe obesity, resulting in substantial weight loss. However, weight regain remains a significant challenge in the long-term after surgery. This study focuses on analyzing movement patterns of patients who have undergone bariatric surgery using wearable accelerometry to investigate the relationships between movement behaviors, body composition, and weight regain. An intelligent system employing machine learning techniques was utilized to predict total fat percentage and visceral fat. Results indicate that Multivariate Adaptive Regression Splines and Gradient Boosting models show promising performance in predicting fat percentage and visceral fat. Furthermore, the study reveals associations between age, sedentary behavior, post-surgery BMI, day-and night-time movement, and body composition following bariatric surgery. These findings contribute to a better understanding of factors influencing weight regain and may inform future interventions to promote long-term weight loss maintenance in bariatric surgery patients.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".