Identifying Barriers and Facilitators to Diet and Physical Activity Behaviour Change in Type 2 Diabetes Using a Design Probe Methodology
Bibliographic record
Abstract
Treatment of Type 2 Diabetes (T2D) typically involves pharmacological methods and adjunct behavioural modifications, focused on changing diet and physical activity (PA) behaviours. Changing diet and physical activity behaviours is complex and any behavioural intervention in T2D, to be successful, must use an appropriate suite of behaviour change techniques (BCTs). In this study, we sought to understand the perceived barriers and facilitators to diet and PA behaviour change in persons with T2D, with a view to creating artefacts to facilitate the required behaviour changes. The Design Probe was chosen as the most appropriate design research instrument to capture the required data, as it enabled participants to reflect and self-document, over an extended period of time, on their daily lived experiences and, following this reflection, to identify their barriers and facilitators to diet and PA behaviour change. Design Probes were sent to 21 participants and 13 were fully completed. A reflective thematic analysis was carried out on the data, which identified themes of food environment, mental health, work schedule, planning, social support, cravings, economic circumstances and energy associated with diet behaviour. Similar themes were identified for PA as well as themes of physical health, weather, motivation and the physical environment.
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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 | 0.062 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".