Step Up to Your Health 2.0: A Qualitative Evaluation of a Behavioural Change-Based Walking Programme Co-Designed with a Senior-Citizen Group
Bibliographic record
Abstract
Abstract Background People worldwide recognize the value of physical activity for health and are taking a more active role in promoting their own wellbeing. This study explores community-dwelling older people’s qualitative evaluation of a community-based behavioural change-based walking programme, titled ‘Step Up to Your Health’. Methods This programme has been co-designed and co-produced between a seniors’ organisation and university partners, and this article evaluates a re-designed version of it. The programme, based on behavioural change techniques chosen by the group, incorporates the use of activity trackers, a co-designed workbook, and a weekly walking group. In the present study, 11 participants participated in semi-structured interviews on their experience. Results Three themes were generated through reflexive thematic analysis: ‘Physical activity promotion integrated into the community’, ‘A flexible and inclusive programme’ and ‘Psychological factors in keeping active’. Our findings suggest that programmes that are inclusive and flexible, integrated in the community, and provide opportunities for social engagement can facilitate multidimensional health and wellbeing benefits to older adults. Conclusion This study presents an example of co-design and co-delivery of a walking programme with community-dwelling older adults. The discussion offers a comparative analysis with a previous iteration of the programme, alongside actionable applications of our findings, in order to inform more sustainable, effective and inclusive community-based physical activity interventions.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".