Effectiveness of Interventions for Changing More Than One Behavior at a Time to Manage Chronic Conditions: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Health behaviors play a significant role in chronic disease management. Rather than being independent of one another, health behaviors often co-occur, suggesting that targeting more than one health behavior in an intervention has the potential to be more effective in promoting better health outcomes. PURPOSE: We aimed to conduct a systematic review and meta-analysis of randomized trials of interventions that target more than one behavior to examine the effectiveness of multiple health behavior change interventions in patients with chronic conditions. METHODS: Five electronic databases (Web of Science, PubMed, CINAHL, EMBASE, and Cochrane) were systematically searched in November 2023, and studies included in previous reviews were also consulted. We included randomized trials of interventions aiming to change more than one health behavior in individuals with chronic conditions. Two independent reviewers screened and extracted data, and used Cochrane's Risk of Bias 2 tool. Meta-analyses were conducted to estimate the effects of interventions on change in health behaviors. Results were presented as Cohen's d for continuous data, and risk ratio for dichotomous data. RESULTS: Sixty-one studies were included spanning a range of chronic diseases: cardiovascular (k = 25), type 2 diabetes (k = 15), hypertension (k = 10), cancer (k = 7), one or more chronic conditions (k = 3), and multiple conditions (k = 1). Most interventions aimed to change more than one behavior simultaneously (rather than in sequence) and most targeted three particular behaviors at once: "physical activity, diet and smoking" (k = 20). Meta-analysis of 43 eligible studies showed for continuous data (k = 29) a small to substantial positive effect on behavior change for all health behaviors (d = 0.081-2.003) except for smoking (d = -0.019). For dichotomous data (k = 23) all analyses showed positive effects of targeting more than one behavior on all behaviors (RR = 1.026-2.247). CONCLUSIONS: Targeting more than one behavior at a time is effective in chronic disease management and more research should be directed into developing the science of multiple behavior 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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.051 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".