Does the use of consumer health information technology improve outcomes in the patient self-management of diabetes? A meta-analysis and narrative review of randomized controlled trials
Abstract
Purpose To assess whether the use of consumer health information technologies (CHITs) improves outcomes in the patient self-management of diabetes.Method The evidence from randomized controlled trials (RCTs) on the effects of CHITs on patient outcomes was analyzed using either meta-analysis or a narrative synthesis approach. A systematic search of seven electronic databases was conducted to identify relevant reports of RCTs for the analysis. In the meta-analyses, standardized mean differences in patient outcomes were calculated and random-effects models were applied in cases where the heterogeneity of the results was moderate or high, otherwise fixed-effects models were used.Results Sixty-two studies, representing 67 RCTs, met the inclusion criteria. The results of the meta-analyses showed that the use of CHITs was associated with significant reductions in HbA1c, blood pressure, total cholesterol, and triglycerides levels when compared with the usual care. The findings from the narrative synthesis indicated that only a small proportion of the trials reported positive effects of CHITs on patient outcomes.Conclusions The use of CHITs in supporting diabetes self-management appears to have potential benefits for patients' self-management of diabetes. However, the effectiveness of the technologies in improving patient outcomes still awaits confirmation in future studies.
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The effect size for HbA1c was similar to those reported in other systematic reviews (SMD range −0.31 to −0.44, MD range −0.40% to −0.49%).
“This effect size was similar to those reported in other systematic reviews (SMD range −0.31 to −0.44, MD range −0.40% to −0.49%).10,15,16,18”
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Self-citationLiu, K., Xie, Z., & Or, C. K. (2020) · JMIR mHealth and uHealth · doi:10.2196/15779 · Cited by 181 · see citing works
Self-citationSheon, A. R., Bolen, S. D., Callahan, B., Shick, S., & Perzynski, A. T. (2017) · JMIR Diabetes · doi:10.2196/diabetes.6751 · Cited by 62 · see citing works
Nguyen, L., Keshavjee, K., Archer, N., Patterson, C., Gwadry-Sridhar, F., & Demers, C. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.06.001 · Cited by 22 · see citing works
Liu, K., & Xia, Y. (2025) · Digital Health · doi:10.1177/20552076251326126 · Cited by 7 · see citing works
“10,15,16 However, very few studies have examined the underlying theoretical basis of the intervention.”
Influential
- Authors
- Or, C. K. L., & Tao, D.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Or, Calvin K.L.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 83
- Issue
- 5
- Pages
- 320-329
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W1992810702
- Cited by
- 76
- References
- 91
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 10 · 2019: 8 · 2018: 7 · 2017: 17 · 2016: 13 · 2015: 7 · 2014: 1
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- University of Hong Kong
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- diabetes self-management, patient self-management, HbA1c, blood pressure, cholesterol, triglycerides, patient outcomes, usual care comparison
- MeSH terms
- Diabetes Mellitus, Humans, Medical Informatics, Quality of Life, Self Care, Randomized Controlled Trials as Topic, Information Dissemination, Consumer Health Information
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.
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