Online exercise program for men living with obesity: Experiences, barriers, and enablers
Bibliographic record
Abstract
The prevalence of obesity is increasing among men, and this population remains under-represented in lifestyle and weight management interventions. The current study aims to explore the experiences of men living with obesity (body fat ≥25 %) toward a 12-week supervised online exercise platform. Ten men were interviewed for this qualitative study. Semi-structured, open-ended phone interviews were conducted, and the transcripts were thematically coded using the qualitative data analysis Nvivo QSR software package. The research findings are illustrated using quotes from participants. The results were organized into two main themes: those that removed barriers to exercise and those that improved the enablers of exercise. Eliminating barriers included not purchasing specialized equipment or travelling to a gym facility. The enablers to their success with the program included the structured format of the circuit program and having supervised sessions. By removing barriers and enhancing enablers, the 12-week online exercise circuit program increased compliance to and success of the exercise program for men living with obesity. Future research should explore the long-term effects of an online program for men living with obesity and its appeal beyond COVID-19.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".