Understanding physical activity facilitators and barriers during and following a supervised exercise programme in Type 2 diabetes: a qualitative study
Bibliographic record
Abstract
AIMS: To assess barriers and facilitators of participation in a supervised exercise programme, and adherence to exercise after programme completion. METHODS: Focus group discussions addressed factors which could facilitate attendance, current engagement in exercise, reasons for continuing or discontinuing regular exercise and ways to integrate exercise into daily life. Three focus groups, with a total of 16 participants, were led by a trained moderator; audiotapes were transcribed verbatim; transcripts were coded and themes were identified. Themes that recurred across all three focus groups were considered to have achieved saturation. RESULTS: Motivation was the most critical factor in exercising both during and following the programme. Participants appreciated the monitoring, encouragement and accountability provided by programme staff. They voiced a need for better transition to post-programme realities of less support and supervision. Co-morbid conditions were apt to derail them from a regular exercise routine. They viewed the optimal programme as having even greater scheduling flexibility and being closer to them geographically. Post-programme, walking emerged as the most frequent form of physical activity. CONCLUSIONS: Adults with Type 2 diabetes require long-term monitoring and support for physical activity and exercise.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".