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Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials

EBioMedicine. 2024 Apr 4;103:105091. doi: 10.1016/j.ebiom.2024.105091. Online ahead of print.

ABSTRACT

BACKGROUND: Self-management is pivotal in addressing noncommunicable diseases, such as diabetes. The increased availability of digital behaviour change interventions (DBCIs) delivered through mobile health apps offers unprecedented opportunities to enhance self-management and improve health outcomes. However, little is known about the characteristics of DBCIs for diabetes that significantly impact glycaemic control. Therefore, our systematic review with meta-analysis aimed to summarize characteristics and behaviour change components in DBCIs for diabetes self-management and explore potential associations with metabolic outcomes.

METHODS: A systematic search was conducted in PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scopus to identify randomized controlled trials published until November 2023. The main outcome variable was the change in the mean difference of HbA1c levels between baseline and follow-up across intervention and control groups. Random-effects meta-regression was used to explore variation in glycaemic control as a function of prespecified characteristics of study designs and app interventions.

FINDINGS: A total of 57 studies was included in the analysis, showing a statistically significant percentage point reduction in HbA1c for the intervention group compared to the control arm (-0.36, 95% CI = -0.46 to -0.26, p < 0.001). The inclusion of “self-monitoring of behaviour” as a behaviour change technique (β = -0.22, p = 0.04) and “taking medication” as a target behaviour (β = -0.20, p = 0.05) was associated with improved metabolic outcomes.

INTERPRETATION: Our analyses endorse the use of diabetes self-management apps, highlighting characteristics statistically associated with intervention effectiveness and guiding the design of more effective DBCIs.

FUNDING: This project received funding from the European Union’s Horizon 2020 programme.

PMID:38579364 | DOI:10.1016/j.ebiom.2024.105091

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