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      Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy

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          Abstract

          Background

          Mobile health apps for diabetes self-management have different functions. However, the efficacy and safety of each function are not well studied, and no classification is available for these functions.

          Objective

          The aims of this study were to (1) develop and validate a taxonomy of apps for diabetes self-management, (2) investigate the glycemic efficacy of mobile app-based interventions among adults with diabetes in a systematic review of randomized controlled trials (RCTs), and (3) explore the contribution of different function to the effectiveness of entire app-based interventions using the taxonomy.

          Methods

          We developed a 3-axis taxonomy with columns of clinical modules, rows of functional modules and cells of functions with risk assessments. This taxonomy was validated by reviewing and classifying commercially available diabetes apps. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Chinese Biomedical Literature Database, and ClinicalTrials.gov from January 2007 to May 2016. We included RCTs of adult outpatients with diabetes that compared using mobile app-based interventions with usual care alone. The mean differences (MDs) in hemoglobin A 1c (HbA 1c) concentrations and risk ratios of adverse events were pooled using a random-effects meta-analysis. After taxonomic classification, we performed exploratory subgroup analyses of the presence or absence of each module across the included app-based interventions.

          Results

          Across 12 included trials involving 974 participants, using app-based interventions was associated with a clinically significant reduction of HbA 1c (MD 0.48%, 95% CI 0.19%-0.77%) without excess adverse events. Larger HbA 1c reductions were noted among patients with type 2 diabetes than those with type 1 diabetes (MD 0.67%, 95% CI 0.30%-1.03% vs MD 0.36%, 95% CI 0.08%-0.81%). Having a complication prevention module in app-based interventions was associated with a greater HbA 1c reduction (with complication prevention: MD 1.31%, 95% CI 0.66%-1.96% vs without: MD 0.38%, 95% CI 0.09%-0.67%; intersubgroup P=.01), as was having a structured display (with structured display: MD 0.69%, 95% CI 0.32%-1.06% vs without: MD 0.16%, 95% CI 0.16%-0.48%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA 1c reduction (with clinical decision making: MD 0.18%, 95% CI 0.21%-0.56% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.10).

          Conclusions

          The use of mobile app-based interventions yields a clinically significant HbA 1c reduction among adult outpatients with diabetes, especially among those with type 2 diabetes. Our study suggests that the clinical decision-making function needs further improvement and evaluation before being added to apps.

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          Most cited references31

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          Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China.

          In China, diabetes prevalence has increased substantially in recent decades, but there are no reliable estimates of the excess mortality currently associated with diabetes.
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            Introduction.

            (2017)
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              Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis.

                To assess the effect of mobile phone intervention on glycaemic control in diabetes self-management. We searched three electronic databases (PubMed, EMBASE and Cochrane Library) using the following terms: diabetes or diabetes mellitus and mobile phone or cellular phone, or text message. We also manually searched reference lists of relevant papers to identify additional studies. Clinical studies that used mobile phone intervention and reported changes in glycosylated haemoglobin (HbA(1c) ) values in patients with diabetes were reviewed. The study design, intervention methods, sample size and clinical outcomes were extracted from each trial. The results of the HbA(1c) change in the trials were pooled using meta-analysis methods.   A total of 22 trials were selected for the review. Meta-analysis among 1657 participants showed that mobile phone interventions for diabetes self-management reduced HbA(1c) values by a mean of 0.5% [6 mmol/mol; 95% confidence interval, 0.3-0.7% (4-8 mmol/mol)] over a median of 6 months follow-up duration. In subgroup analysis, 11 studies among Type 2 diabetes patients reported significantly greater reduction in HbA(1c) than studies among Type 1 diabetes patients [0.8 (9 mmol/mol) vs. 0.3% (3 mmol/mol); P=0.02]. The effect of mobile phone intervention did not significantly differ by other participant characteristics or intervention strategies.   Results pooled from the included trials provided strong evidence that mobile phone intervention led to statistically significant improvement in glycaemic control and self-management in diabetes care, especially for Type 2 diabetes patients. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                March 2017
                14 March 2017
                : 5
                : 3
                : e35
                Affiliations
                [1] 1Department of Endocrinology and Metabolism West China Hospital Sichuan University ChengduChina
                [2] 2Department of Academic Affairs West China School of Medicine Sichuan University ChengduChina
                [3] 3Diabetes Unit Madonna del Soccorso Hospital San Benedetto del Tronto (AP)Italy
                [4] 4Center for Outcomes Research and Clinical Epidemiology PescaraItaly
                [5] 5Chinese Evidence-Based Medicine Center West China Hospital Sichuan University ChengduChina
                Author notes
                Corresponding Author: Sheyu Li lisheyu@ 123456gmail.com
                Author information
                http://orcid.org/0000-0003-0952-6132
                http://orcid.org/0000-0002-2882-5675
                http://orcid.org/0000-0002-6930-485X
                http://orcid.org/0000-0002-5939-6850
                http://orcid.org/0000-0003-1038-9075
                http://orcid.org/0000-0002-7473-3827
                http://orcid.org/0000-0001-7689-1856
                http://orcid.org/0000-0002-6554-7088
                http://orcid.org/0000-0002-1921-4733
                http://orcid.org/0000-0003-0060-0287
                Article
                v5i3e35
                10.2196/mhealth.6522
                5373677
                28292740
                c067f3ec-1a60-4912-9d03-e930090790ea
                ©Yuan Wu, Xun Yao, Giacomo Vespasiani, Antonio Nicolucci, Yajie Dong, Joey Kwong, Ling Li, Xin Sun, Haoming Tian, Sheyu Li. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 14.03.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 22 August 2016
                : 11 September 2016
                : 20 October 2016
                : 25 February 2017
                Categories
                Original Paper
                Original Paper

                mobile health,mhealth,mobile applications,mobile apps,diabetes mellitus,classification

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