Developing habit-based health behaviour change interventions: twenty-one questions to guide future research
Bibliographic record
Abstract
OBJECTIVE: Habitual behaviours are triggered automatically, with little conscious forethought. Theory suggests that making healthy behaviours habitual, and breaking the habits that underpin many ingrained unhealthy behaviours, promotes long-term behaviour change. This has prompted interest in incorporating habit formation and disruption strategies into behaviour change interventions. Yet, notable research gaps limit understanding of how to harness habit to change real-world behaviours. METHODS: Discussions among health psychology researchers and practitioners, at the 2019 European Health Psychology Society 'Synergy Expert Meeting', generated pertinent questions to guide further research into habit and health behaviour. RESULTS: In line with the four topics discussed at the meeting, 21 questions were identified, concerning: how habit manifests in health behaviour (3 questions); how to form healthy habits (5 questions); how to break unhealthy habits (4 questions); and how to develop and evaluate habit-based behaviour change interventions (9 questions). CONCLUSIONS: While our questions transcend research contexts, accumulating knowledge across studies of specific health behaviours, settings, and populations will build a broader understanding of habit change principles and how they may be embedded into interventions. We encourage researchers and practitioners to prioritise these questions, to further theory and evidence around how to create long-lasting health behaviour change.
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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 | 0.076 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".