Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 22, 2023
Date Accepted: May 16, 2024

The final, peer-reviewed published version of this preprint can be found here:

Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: Systematic Literature Review and Meta-Analysis

Kerr D, Ahn D, Waki K, Wang J, Breznen B, Klonoff DC

Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: Systematic Literature Review and Meta-Analysis

J Med Internet Res 2024;26:e55757

DOI: 10.2196/55757

PMID: 39037772

PMCID: 11301119

Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: A Systematic Literature Review and Meta Analysis

  • David Kerr; 
  • David Ahn; 
  • Kayo Waki; 
  • Jing Wang; 
  • Boris Breznen; 
  • David C. Klonoff

ABSTRACT

Background:

The proliferation of digital technology has the potential to transform diabetes management. One of the critical aspects of modern diabetes management remains the achievement of glycemic targets to avoid acute and long-term complications.

Objective:

We aimed to describe the landscape of evidence pertaining to the relative effectiveness/efficacy and safety of various digital interventions for the self-management of type 2 diabetes mellitus (T2DM), with a primary focus on reducing glycated hemoglobin A1c (HbA1c) levels.

Methods:

A systematic literature review (SLR) was conducted by searching Embase, MEDLINE®, and CENTRAL on April 5, 2022. Study selection, data extraction, and quality assessment were performed by two independent reviewers. The primary meta-analysis was restricted to studies that reported lab measured HbA1c. In secondary analyses, meta-regression was performed with intensity of coaching in the digital intervention as a categorical covariate.

Results:

In total 28 studies were included in this analysis. Most studies (82%) used the reduction of HbA1c levels as primary endpoint, either directly or as a part of a multi-component outcome. Twenty-one studies reported statistically significant results with this primary endpoint. When stratified into three intervention categories by the intensity of the intervention supporting the digital health technology, (analyzing all 28 studies) the success rate appeared to be proportional to the coaching intensity (i.e., higher-intensity studies reported higher success rates). When the analysis was restricted to randomized controlled trials (RCTs) using the comparative improvement of HbA1c levels, the effectiveness of the interventions was less clear. Only half of the included RCTs reported statistically significant results. The meta-analyses were broadly aligned with the results of the SLR. The primary analysis estimated greater reduction in HbA1c associated with digital interventions compared to usual care (-0.31%; 0.95% confidence interval [CI]: -0.45, -0.16; P<.0001). Meta-regression estimated reductions of -0.45% (95% CI: -0.81, -0.09; P<.02); -0.29% (95% CI: -0.48, -0.11; P<.003); and -0.28% (95% CI: -0.65, 0.09; P<.2) associated with high, medium, and low intensity interventions, respectively.

Conclusions:

These findings suggest that reducing HbA1c levels in individuals with T2DM with the help of digital interventions is feasible, effective, and acceptable. One common feature of effective digital health interventions was the availability of timely and responsive personalized coaching by a dedicated healthcare professional.


 Citation

Please cite as:

Kerr D, Ahn D, Waki K, Wang J, Breznen B, Klonoff DC

Digital Interventions for Self-Management of Type 2 Diabetes Mellitus: Systematic Literature Review and Meta-Analysis

J Med Internet Res 2024;26:e55757

DOI: 10.2196/55757

PMID: 39037772

PMCID: 11301119

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.