Effective behavioral change techniques in m-health app supported interventions for glycemic control among patients with type 2 diabetes: A meta-analysis and meta-regression analysis of randomized controlled trials
Objective This review examined the effectiveness of mobile-health (m-health) app-supported interventions in improving patient health outcomes. It also sought to describe the behavior change techniques (BCTs) used in these interventions and identify effective BCTs or combinations of BCTs to facilitat...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251326126 |
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| Summary: | Objective This review examined the effectiveness of mobile-health (m-health) app-supported interventions in improving patient health outcomes. It also sought to describe the behavior change techniques (BCTs) used in these interventions and identify effective BCTs or combinations of BCTs to facilitate glycemic control. Method MEDLINE, EMBASE, and Web of Science databases were searched for relevant studies published until November 2024. Forty-three randomized controlled trials (RCTs) examining the effects of m-health app on glycemic control were identified and included in the analysis. The BCTs utilized in each intervention were coded based on a widely used BCT taxonomy. Health outcomes (e.g. change in hemoglobin A1c [HbA1c]) were meta-analyzed using random effect models. Meta-regression models were used to examine associations between the use of BCTs and HbA1c improvements. Cochrane Collaboration's tool was used for the evaluation of the risk of bias. Results Using m-health app-supported interventions significantly reduced HbA1c, fasting blood glucose, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, weight, and waist circumference. The examined interventions utilized 5.77 BCTs on average. The most frequently used BCTs included “credible source,” followed by “social support (general),” “self-monitoring of outcome(s) of behavior,” “biofeedback,” “self-monitoring of behavior,” and “instruction on how to perform a behavior (skills training).” Interventions that utilized “problem solving” and “reward and threat” were associated with greater HbA1c improvement than those did not. Using BCTs in groupings of “feedback and monitoring,” “social support,” and “comparison of outcomes” formed the simplest and most effective combination. Conclusion Our study provides evidence about effective BCTs and combinations for better glycemic control. However, optimal BCT combinations warrant further examination. Future RCTs that clearly report the BCTs used are recommended. Experimental designs such as a multiphase optimization strategy should be used to examine the effects of single BCTs and their interactions. |
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| ISSN: | 2055-2076 |