Online Circuit Training Increases Adherence to Physical Activity: A Randomized Controlled Trial of Men with Obesity
Bibliographic record
Abstract
PURPOSE: This study aimed to examine adherence to the weekly physical activity guidelines (≥150 min of aerobic activities at moderate-to-vigorous intensity and two or more sessions of strength training (yes or no)) and health outcomes during the COVID-19 pandemic for men living with obesity, 46 wk after being offered an online muscle-strengthening circuit program for 12 wk. METHODS: Sixty men (age ≥19 yr) living with obesity (body fat percentage ≥25%) were randomly assigned to the intervention group ( n = 30) or the control condition ( n = 30) for 12 wk. The intervention group was offered an online circuit training, three sessions per week, whereas the control group received a website helping them to reach the physical activity guidelines. Adherence to the weekly physical activity guidelines was evaluated 46 wk after enrolling in the program using a heart rate tracker (Fitbit Charge 3) and an exercise log. Health outcomes (e.g., anthropometrics, body composition) were measured at baseline and after 12, 24, and 46 wk. RESULTS: The intervention group had higher adherence to physical activity guidelines at 46 wk (36.8%) than the control group (5.3%; P = 0.02). However, no difference in health outcomes was observed between participants in the intervention group compared with the control group after 12, 24, and 46 wk. CONCLUSIONS: Increasing adherence to exercise in men living with obesity is challenging. The proposed program increased adherence to the physical activity guidelines after about a year for men living with obesity; however, more studies are needed to understand how to improve health outcomes when following an online delivery exercise program in this population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".