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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

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...

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Autores principales: Wu, Yuan, Yao, Xun, Vespasiani, Giacomo, Nicolucci, Antonio, Dong, Yajie, Kwong, Joey, Li, Ling, Sun, Xin, Tian, Haoming, Li, Sheyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373677/
https://www.ncbi.nlm.nih.gov/pubmed/28292740
http://dx.doi.org/10.2196/mhealth.6522
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author Wu, Yuan
Yao, Xun
Vespasiani, Giacomo
Nicolucci, Antonio
Dong, Yajie
Kwong, Joey
Li, Ling
Sun, Xin
Tian, Haoming
Li, Sheyu
author_facet Wu, Yuan
Yao, Xun
Vespasiani, Giacomo
Nicolucci, Antonio
Dong, Yajie
Kwong, Joey
Li, Ling
Sun, Xin
Tian, Haoming
Li, Sheyu
author_sort Wu, Yuan
collection PubMed
description 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%-0.78%) 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 0.37%, 95% CI –0.12%-0.86%). 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 0.69%, 95% CI –0.18%-0.53%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA(1c) reduction (with clinical decision making: MD 0.19%, 95% CI –0.24%-0.63% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.14). 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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spelling pubmed-53736772017-04-10 Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy Wu, Yuan Yao, Xun Vespasiani, Giacomo Nicolucci, Antonio Dong, Yajie Kwong, Joey Li, Ling Sun, Xin Tian, Haoming Li, Sheyu JMIR Mhealth Uhealth Original Paper 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%-0.78%) 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 0.37%, 95% CI –0.12%-0.86%). 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 0.69%, 95% CI –0.18%-0.53%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA(1c) reduction (with clinical decision making: MD 0.19%, 95% CI –0.24%-0.63% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.14). 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. JMIR Publications 2017-03-14 /pmc/articles/PMC5373677/ /pubmed/28292740 http://dx.doi.org/10.2196/mhealth.6522 Text en ©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. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://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.
spellingShingle Original Paper
Wu, Yuan
Yao, Xun
Vespasiani, Giacomo
Nicolucci, Antonio
Dong, Yajie
Kwong, Joey
Li, Ling
Sun, Xin
Tian, Haoming
Li, Sheyu
Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title_full Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title_fullStr Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title_full_unstemmed Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title_short Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
title_sort mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373677/
https://www.ncbi.nlm.nih.gov/pubmed/28292740
http://dx.doi.org/10.2196/mhealth.6522
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