Experiences of people with prediabetes in a self-compassion and physical activity intervention: a qualitative study using participatory Theme Elicitation
Bibliographic record
Abstract
Having a Type 2 Diabetes risk (i.e. prediabetes) is a call to action for health behaviour change. Physical activity can reduce one’s diabetes risk, but difficult emotions, stigma, and lack of support can prevent people with prediabetes from getting active. Self-compassion is a psychological resource that may help people cope with prediabetes and increase their physical activity. The MOVE IT program is an 8-week videoconferencing intervention that teaches people with prediabetes self-compassion and physical activity behaviour change strategies. This qualitative study aimed to explore how participants in the MOVE IT program used self-compassion to cope with their prediabetes and increase their physical activity. Participant discussions from fourteen group-based MOVE IT sessions were transcribed verbatim, representing N = 14 participants (Mage = 54 years, SDage = 7 years; 100% women). Participatory Theme Elicitation (a participatory qualitative analysis) was conducted in collaboration with five ‘Person With Lived Experience’ co-researchers who had previously completed the MOVE IT self-compassion program. Four themes were generated showing how participants mindfully reflected on their current behaviours and identified opportunities for growth (Theme 1), used self-kindness to cope with physical activity setbacks (Theme 2), took self-compassionate action to work through challenges (Theme 3), and eventually embraced self-compassion (Theme 4). By qualitatively examining participants’ experiences, this study advances the understanding of how self-compassion can be operationalised in real-world behaviour change interventions, providing insight into both the emotional and action-oriented pathways through which self-compassion supports health behaviour change among individuals with prediabetes.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".