A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions
Open accessAbstract
Objectives: Our aim was to conduct a systematic review to determine which technology-driven diabetes prevention interventions were effective in producing clinically significant weight loss, and to identify the behaviour change techniques and digital features frequently used in effective interventions. Methods: We searched five databases (CINAHL, EMBASE, MEDLINE, PsychINFO, and Pubmed) from inception to September 2018 and reviewed 19 experimental and non-experimental studies of 21 technology-driven diet plus physical activity interventions for adults (≥18 years) at risk of developing type 2 diabetes. Behaviour change techniques were coded using the BCT taxonomy v1, and digital features were identified via thematic analysis of intervention descriptions. Results: Sixty-three per cent of interventions were effective in the short term (achieving ≥3% weight loss at ≤6 months), using an average of 5.6 more behaviour change techniques than non-effective interventions, and 33% were effective in the long term (achieving ≥5% weight loss at ≥12 months), using 3.7 more behaviour change techniques than non-effective interventions. The techniques of social support (unspecified), goal setting (outcome/behaviour), feedback on behaviour, and self-monitoring of outcome(s) of behaviour were identified in over 90% of effective interventions. Interventions containing digital features that facilitated health and lifestyle education, behaviour/outcome tracking, and/or online health coaching were most effective. Conclusion: The integration of specific behaviour change techniques and digital features may optimise digital diabetes prevention interventions to achieve clinically significant weight loss. Additional research is needed to identify the mechanisms in which behaviour change techniques and digital features directly influence physical activity, dietary behaviours, and intervention engagement.
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Relevant systematic reviews and meta-analyses have confirmed the effectiveness of DHIs on behavior change, blood glucose control, and weight loss in patients with diabetes.
“Relevant systematic reviews and meta-analyses have confirmed the effectiveness of DHIs on behavior change, blood glucose control, and weight loss in patients with diabetes [21-24].”
From Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis (2025) · cites this paper as [24]
The Discussion reports short-term weight loss and improvements in A1c and fasting glucose in some technology-driven interventions. But the review concerns adults at risk of developing type 2 diabetes, not patients with diabetes; its Future directions section also notes that relatively few studies reported changes in physical activity or diet, so the broad claim of confirmed behavior-change effectiveness is overstated. Page numbers were not available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMany 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 [83]
The Discussion (Behaviour change techniques; page not provided) identifies goal setting, feedback and self-monitoring among techniques frequently used in effective interventions. But the review concerns type 2 diabetes prevention, not diabetes management, and its Future directions section says it cannot infer causal effects of individual techniques. It therefore does not establish that these techniques had significant treatment effects in diabetes management. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBCT 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 [83]
The Methods (Data extraction) say the review coded interventions using BCT Taxonomy v1, and the Results (Intervention characteristics) describe website- and smartphone-delivered diabetes-prevention programs. The paper does not compare their techniques with videoconferencing interventions or establish that the latter emulate them; its focus is prevention rather than interventions for people with diabetes. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe 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”
Multicomponent intervention packages have the highest efficacy for behavioral and glycemic outcomes.
“Multicomponent intervention packages have the highest efficacy for behavioral and glycemic outcomes (300,309,311).”
From 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes:<i>Standards of Care in Diabetes—2023</i> (2023) · cites this paper as [309]
Health behavior change approaches may be delivered via digital health tools.
“These approaches may be delivered via digital health tools (309,313,315).”
From 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes:<i>Standards of Care in Diabetes—2023</i> (2023) · cites this paper as [309]
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Type 2 diabetes prevention goes digital
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- Authors
- Van Rhoon, L., Byrne, M., Morrissey, E., Murphy, J., & McSharry, J.
- Affiliations
- Health Behaviour Change Research Group, School of Psychology, National University of Ireland Galway, Ireland
- Ollscoil na Gaillimhe – University of Galway (IE)
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2020-01
- Volume
- 6
- Pages
- 2055207620914427-2055207620914427
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC7093696
- OpenAlex
- W3014088184
- Cited by
- 182
- References
- 76
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 38 · 2023: 28 · 2022: 30 · 2021: 27 · 2020: 3
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Digital Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National University of Ireland, Galway (Hardiman Scholarship)
- Irish Research Council (GOIPG/2019/1355)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 2 diabetes prevention, behaviour change techniques, digital health interventions, weight loss, BCT taxonomy, social support, goal setting, self-monitoring, lifestyle education, intervention engagement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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