Components of multiple health behaviour change interventions for patients with chronic conditions: a systematic review and meta-regression of randomized trials
Bibliographic record
Abstract
Interventions addressing more than one health behaviour at a time could be an efficient way of intervening to manage chronic conditions. Within a systematic review of multiple health behaviour change (MBHC) interventions, we identified key components of interventions in patients with chronic conditions, assessed how they are linked to theory, behaviour change techniques implemented, and evaluated their impact on intervention effectiveness. Studies were identified by systematically searching five electronic databases. Subgroup analyses and meta-regressions were conducted to analyse the association between intervention components and behavioural changes. In total, 61 studies were included spanning different chronic conditions (e.g., cardiovascular conditions, type 2 diabetes). Most interventions sought to change behaviours simultaneously (72%), often targeting the ‘physical activity, diet and smoking’ cluster of behaviours (33%), and were not theory informed (55%). A total of 36 behaviour change techniques were identified, most commonly goal setting behaviour and self-monitoring of behaviour. Subgroup analyses indicated that MHBC interventions delivered entirely face-to-face might not be as effective for physical activity outcomes, and not using goal setting (behaviour) might be more effective for smoking cessation outcomes. Meta-regressions indicated that a longer intervention duration may work best to achieve better physical activity outcomes. This review provides a comprehensive understanding of interventions and contributes to the field of MHBC by facilitating data-driven insights for future optimisation and dissemination.
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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 | 0.037 | 0.088 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".