title: Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy.
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 A1c(HbA1c) 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 HbA1c(MD 0.48%, 95% CI 0.19%-0.77%) without excess adverse events. Larger HbA1creductions 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 HbA1creduction (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 HbA1creduction (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 HbA1creduction 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.
authors.affiliation: Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Yuan
authors.identifier: http://orcid.org/0000-0003-0952-6132
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Academic Affairs, West China School of Medicine, Sichuan University, Chengdu, China.
authors.forename: Xun
authors.identifier: http://orcid.org/0000-0002-2882-5675
authors.initials: X
authors.lastname: Yao
authors.affiliation: Diabetes Unit, Madonna del Soccorso Hospital, San Benedetto del Tronto (AP), Italy.
authors.forename: Giacomo
authors.identifier: http://orcid.org/0000-0002-6930-485X
authors.initials: G
authors.lastname: Vespasiani
authors.affiliation: Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy.
authors.forename: Antonio
authors.identifier: http://orcid.org/0000-0002-5939-6850
authors.initials: A
authors.lastname: Nicolucci
authors.affiliation: Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Yajie
authors.identifier: http://orcid.org/0000-0003-1038-9075
authors.initials: Y
authors.lastname: Dong
authors.affiliation: Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Joey
authors.identifier: http://orcid.org/0000-0002-7473-3827
authors.initials: J
authors.lastname: Kwong
authors.affiliation: Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Ling
authors.identifier: http://orcid.org/0000-0001-7689-1856
authors.initials: L
authors.lastname: Li
authors.affiliation: Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Xin
authors.identifier: http://orcid.org/0000-0002-6554-7088
authors.initials: X
authors.lastname: Sun
authors.affiliation: Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Haoming
authors.identifier: http://orcid.org/0000-0002-1921-4733
authors.initials: H
authors.lastname: Tian
authors.affiliation: Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Sheyu
authors.identifier: http://orcid.org/0000-0003-0060-0287
authors.initials: S
authors.lastname: Li
keywords: classification; diabetes mellitus; mHealth; mobile applications; mobile apps; mobile health