The longitudinal impact of health behaviours on mental health, diabetes distress, and quality of life in people with type 1, type 2, and gestational diabetes: A scoping review
Bibliographic record
Abstract
Introduction There can be a considerable mental health burden to living with diabetes. Health behaviours are modifiable factors that influence mental health in the general population. However, despite the centrality of health behaviours to diabetes management, there are significant gaps in our understanding of their longitudinal impact on mental health in people with diabetes. Objectives This scoping review aimed to synthesise the longitudinal evidence from observational and intervention research on the impact of health behaviours on mental health and related psychological factors in people with type 1, type 2, and gestational diabetes. Methods PubMed, PsychINFO, Embase, CINAHL, and PsycArticles were searched for intervention and observational studies examining the effect of health-promoting, health-risk, or diabetes-specific health behaviours on aspects of mental health, diabetes distress, and quality of life, in people with all types of diabetes. Abstracts, titles, and full texts were screened by two independent reviewers. Results In total, 100 relevant studies were identified, including 29 observational studies and 71 intervention studies. Studies had a mean follow-up time of 12.9 ± 17.8 months. The health behaviours investigated in the included studies were adherence, alcohol, carbohydrate-counting, diet, diet and exercise combined, exercise, fasting, medical visits, sleep, self-monitoring blood glucose, smoking, and weight. The review identified knowledge gaps surrounding diabetes-specific behaviours, behaviour interactions/clusters, sleep, sedentary behaviour, screen-time, gestational diabetes, mood and ecologically valid and objective measurements. Exercise was the most freqently investigated health behaviour and also the most likely to be found to have a mental health promoting impact. Conclusions Findings suggest that health-promoting behaviours influence mental health in people with diabetes. There is less conclusive evidence regarding the impact of health-risk or diabetes-specific health behaviours. In particular, a broad range of physical activities may improve mental health and wellbeing in people with diabetes. Disclosure of Interest None Declared
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".