Feasibility and effect of in-home physical exercise training delivered via telehealth before bariatric surgery
Bibliographic record
Abstract
Optimal physical activity (PA) interventions are needed to increase PA in individuals with severe obesity, and optimize the results of bariatric surgery (BS). The aim of this study was to assess the feasibility and effect of Pre-Surgical Exercise Training (PreSET) delivered in-home via telehealth (TelePreSET) in subjects awaiting BS. Six women following the TelePreSET were compared to the women from a previous study (12 performing the PreSET in a gymnasium and 11 receiving usual care). In-home TelePreSET (12-weeks of endurance and strength training) was supervised twice weekly using videoconferencing. Physical fitness, quality of life, exercise beliefs, anthropometric measures and telehealth perception were assessed before and after 12-weeks. Satisfaction was evaluated with questionnaires at the end of the intervention. The TelePreSET participants attended 96% of the exercise sessions, and were very satisfied by the TelePreSET. The baseline telehealth perception score was high, and increased significantly after the intervention. The TelePreSET group significantly increased their physical fitness compared to the usual care group. No significant change was noted in other outcomes. The TelePreSET is feasible and seems effective to improve the physical fitness of women awaiting BS. Further studies are needed to confirm beneficial effects of this innovative mode of delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".