On the efficacy of behavior change techniques in mHealth for self-management of diabetes: A meta-analysis
Open accessAbstract
BACKGROUND: Diabetes prevalence has become a global crisis. Due to the substantial rise in smartphone use, a variety of mobile interventions have been developed to help improve the clinical outcomes of diabetes patients. OBJECTIVES: This study seeks to examine specific behavior change theories and techniques used in the design of self-management mobile app-based interventions aimed at achieving glycemic control in type 1 and type 2 diabetes. METHODS: A meta-analysis of randomized control trials published in PubMed/Medline and Web of Science between January 2010 and October 2020 was conducted using studies that included diabetes patients, reported on well-described mobile app-based interventions, compared mHealth to usual care, and evaluated glycated hemoglobin (HbA1c) at baseline and follow-up. RESULTS: We reported on 21 studies with a total of 1,920 diabetes patients. Our findings show that mHealth apps led to statistically significant clinical outcomes as compared to standard care for glycemic control (-0.38, 95% CI = -0.50 to -0.25, p < 0.0001) indicating that such interventions result in a reduction in HbA1c. Interventions that used behavior theory for developing mHealth apps were not statistically different from those that did not (p = 0.18). However, increased use of behavior change techniques (BCTs) may result in slightly higher HbA1c reduction. Among all BCTs, the most effective ones appear to be "Action planning" and "Self-monitoring of outcome(s) of behavior. CONCLUSIONS: The current meta-analysis provides evidence that mHealth is likely to be beneficial for diabetes patients when the right behavior change techniques are applied to realize the full advantage of the intervention. Further investigation of the role of theory in the design of mHealth app-based interventions is warranted.
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The average number of BCTs used (5.77) was similar to those reported in similar existing reviews (average number of BCTs: 4.5 to 9.07).
“This number was similar to those reported in similar existing reviews (average number of BCTs: 4.5 to 9.07).17,18,24,65”
BCT Taxonomy v1 techniques used in videoconferencing interventions appear to emulate asynchronous e-interventions for individuals with diabetes and programs for health promotion delivered using the internet or mobile phones.
“BCT Taxonomy v1 techniques used in videoconferencing interventions appear to emulate asynchronous e-interventions for individuals with diabetes and programs for health promotion delivered using the internet or mobile phones [83-85].”
From Features of Structured, One-to-One Videoconference Interventions That Actively Engage People in the Management of Their Chronic Conditions: Scoping Review (2025) · cites this paper as [85]
Many techniques in the goals and planning and feedback and monitoring clusters, also found in this review, have had significant treatment effects in diabetes management.
“Many techniques in the goals and planning and feedback and monitoring clusters, also found in this review, have had significant treatment effects in diabetes management [83-85].”
From Features of Structured, One-to-One Videoconference Interventions That Actively Engage People in the Management of Their Chronic Conditions: Scoping Review (2025) · cites this paper as [85]
mHealth apps led to statistically significant reduction in HbA1c compared to standard care (MD -0.38, 95% CI -0.50 to -0.25, p < 0.0001).
“mHealth apps led to statistically significant clinical outcomes compared to standard care for glycemic control (-0.38, 95% CI = -0.50 to -0.25, p < 0.0001), indicating a reduction in HbA1c.”
From Digital diabetes management technologies for type 2 diabetes: a systematic review of home-based care interventions (2025) · cites this paper as [9]
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- Authors
- El-Gayar, O., Ofori, M., & Nawar, N.
- Affiliations
- Dakota State University (US)
- Alexandria University (EG)
- Corresponding author
- El-Gayar, Omar
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 119
- Pages
- 103839
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3169485423
- Cited by
- 45
- References
- 71
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 12 · 2023: 14 · 2022: 2
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health, behaviour change techniques, diabetes self-management, glycemic control, HbA1c, mobile health apps, type 1 diabetes, type 2 diabetes, action planning, self-monitoring, behaviour change theory, smartphone-based intervention
- MeSH terms
- Smartphone, Self-Management, Diabetes Mellitus, Type 2, Humans, Telemedicine, Mobile Applications
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