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Meta-Analysis
. 2019 Jan 15;7(1):e12297.
doi: 10.2196/12297.

The Efficacy of Mobile Phone Apps for Lifestyle Modification in Diabetes: Systematic Review and Meta-Analysis

Xinghan Wu  1 , Xitong Guo  1 , Zhiwei Zhang  2
Affiliations
Meta-Analysis

The Efficacy of Mobile Phone Apps for Lifestyle Modification in Diabetes: Systematic Review and Meta-Analysis

Xinghan Wu et al. JMIR Mhealth Uhealth. .

Abstract

Background: Diabetes and related complications are estimated to cost US $727 billion worldwide annually. Type 1 diabetes, type 2 diabetes, and gestational diabetes are three subtypes of diabetes that share the same behavioral risk factors. Efforts in lifestyle modification, such as daily physical activity and healthy diets, can reduce the risk of prediabetes, improve the health levels of people with diabetes, and prevent complications. Lifestyle modification is commonly performed in a face-to-face interaction, which can prove costly. Mobile phone apps provide a more accessible platform for lifestyle modification in diabetes.

Objective: This review aimed to summarize and synthesize the clinical evidence of the efficacy of mobile phone apps for lifestyle modification in different subtypes of diabetes.

Methods: In June 2018, we conducted a literature search in 5 databases (Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, and PsycINFO). We evaluated the studies that passed screening using The Cochrane Collaboration's risk of bias tool. We conducted a meta-analysis for each subtype on the mean difference (between intervention and control groups) at the posttreatment glycated hemoglobin (HbA1c) level. Where possible, we analyzed subgroups for short-term (3-6 months) and long-term (9-12 months) studies. Heterogeneity was assessed using the I2 statistic.

Results: We identified total of 2669 articles through database searching. After the screening, we included 26 articles (23 studies) in the systematic review, of which 18 studies (5 type 1 diabetes, 11 type 2 diabetes, and 2 prediabetes studies) were eligible for meta-analysis. For type 1 diabetes, the overall effect on HbA1c was statistically insignificant (P=.46) with acceptable heterogeneity (I2=39%) in the short-term subgroup (4 studies) and significant heterogeneity between the short-term and long-term subgroups (I2=64%). Regarding type 2 diabetes, the overall effect on HbA1c was statistically significant (P<.01) in both subgroups, and when the 2 subgroups were combined, there was virtually no heterogeneity within and between the subgroups (I2 range 0%-2%). The effect remained statistically significant (P<.01) after adjusting for publication bias using the trim and fill method. For the prediabetes condition, the overall effect on HbA1c was statistically insignificant (P=.67) with a large heterogeneity (I2=65%) between the 2 studies.

Conclusions: There is strong evidence for the efficacy of mobile phone apps for lifestyle modification in type 2 diabetes. The evidence is inconclusive for the other diabetes subtypes.

Keywords: behavior therapy; diabetes mellitus; diet; lifestyle; mobile applications; physical activity; smartphone.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of included studies. ICT: information and communication technology; RCT: randomized controlled trial.
Figure 2
Figure 2
Risk of bias in each study. Green: low risk of bias; yellow: unclear risk of bias; red: high risk of bias.
Figure 3
Figure 3
Overall risk of each type of bias.
Figure 4
Figure 4
Forest plot of short- and long-term effects of apps for type 1 diabetes mellitus. IV: inverse variance.
Figure 5
Figure 5
Forest plot of short- and long-term effects of apps for type 2 diabetes mellitus. IV: inverse variance.
Figure 6
Figure 6
Funnel plot of publication bias. HbA1c: glycated hemoglobin.
Figure 7
Figure 7
Trim and fill plot of publication bias. HbA1c: glycated hemoglobin; open circles: estimated unpublished studies with negative findings.
Figure 8
Figure 8
Forest plot the effect of prediabetes apps. IV: inverse variance.
See this image and copyright information in PMC

References

    1. American Diabetes Association 3. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018 Dec;41(Suppl 1):S28–S37. doi: 10.2337/dc18-S003. - DOI - PubMed
    1. IDF Diabetes Atlas, 8th edition. Brussels, Belgium: International Diabetes Federation; 2017.
    1. Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Cavan D, Shaw JE, Makaroff LE. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017 Jun;128:40–50. doi: 10.1016/j.diabres.2017.03.024. - DOI - PubMed
    1. Schellenberg ES, Dryden DM, Vandermeer B, Ha C, Korownyk C. Lifestyle interventions for patients with and at risk for type 2 diabetes: a systematic review and meta-analysis. Ann Intern Med. 2013 Oct 15;159(8):543–51. doi: 10.7326/0003-4819-159-8-201310150-00007. - DOI - PubMed
    1. Kebede MM, Liedtke TP, Möllers T, Pischke CR. Characterizing active ingredients of eHealth interventions targeting persons with poorly controlled type 2 diabetes mellitus using the behavior change techniques taxonomy: scoping review. J Med Internet Res. 2017 Oct 12;19(10):e348. doi: 10.2196/jmir.7135. http://www.jmir.org/2017/10/e348/ - DOI - PMC - PubMed

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