Effect of a Mobile Phone–Based Glucose-Monitoring and Feedback System for Type 2 Diabetes Management in Multiple Primary Care Clinic Settings: Cluster Randomized Controlled Trial

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Recent evidence of the effectiveness of mobile phone–based diabetes management systems is generally based on studies conducted in tertiary hospitals or professional diabetes clinics.

Objective

This study aimed to evaluate the clinical efficacy and applicability of a mobile phone–based glucose-monitoring and feedback system for the management of type 2 diabetes mellitus (T2DM) in multiple primary care clinic settings.

Methods

In this multicenter, cluster-randomized controlled, open trial, 13 primary care clinics in Seoul and other large cities in South Korea were voluntarily recruited. Overall, 150 (9 clinics) and 97 (4 clinics) participants with T2DM were assigned to the intervention and control groups, respectively (2:1 allocation). Every month, participants in both groups attended face-to-face physicians’ consultation for the management of diabetes in the clinic. For the intervention group, participants were required to upload their daily self-monitoring of blood glucose (SMBG) results using the mobile phone app in addition to outpatient care for 3 months. The results were automatically transmitted to the main server. Physicians had to check their patients’ SMBG results through an administrator’s website and send a short feedback message at least once a week. At baseline and 3 months, both groups had anthropometry and blood tests, including hemoglobin A _1c (HbA _1c), and responded to questionnaires about treatment satisfaction and compliance.

Results

At 3 months, participants in the intervention group showed significantly more improvement in HbA _1c (adjusted mean difference to control −0.30%, 95% CI −0.50 to −0.11; P=.003) and fasting plasma glucose (−17.29 mg/dL, 95% CI −29.33 to −5.26; P=.005) than those in the control group. In addition, there was significantly more reduction in blood pressure, and the score regarding treatment satisfaction and motivation for medication adherence increased more in the intervention group than in the control group. In the subgroup analyses, the effect on glycemic control was more significant among younger patients and higher baseline HbA _1c levels.

Conclusions

The mobile phone–based glucose-monitoring and feedback system was effective in glycemic control when applied in primary care clinic settings. This system could be utilized effectively with diverse institutions and patients.

Trial Registration

Clinical Research Information Service (CRIS) https://tinyurl.com/tgqawbz

Related collections

Most cited references 25

Record: found
Abstract: found
Article: not found

Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes.

Peter Gæde, Pernille Vedel, Nicolai H. Larsen … (2003)

Cardiovascular morbidity is a major burden in patients with type 2 diabetes. In the Steno-2 Study, we compared the effect of a targeted, intensified, multifactorial intervention with that of conventional treatment on modifiable risk factors for cardiovascular disease in patients with type 2 diabetes and microalbuminuria. The primary end point of this open, parallel trial was a composite of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, revascularization, and amputation. Eighty patients were randomly assigned to receive conventional treatment in accordance with national guidelines and 80 to receive intensive treatment, with a stepwise implementation of behavior modification and pharmacologic therapy that targeted hyperglycemia, hypertension, dyslipidemia, and microalbuminuria, along with secondary prevention of cardiovascular disease with aspirin. The mean age of the patients was 55.1 years, and the mean follow-up was 7.8 years. The decline in glycosylated hemoglobin values, systolic and diastolic blood pressure, serum cholesterol and triglyceride levels measured after an overnight fast, and urinary albumin excretion rate were all significantly greater in the intensive-therapy group than in the conventional-therapy group. Patients receiving intensive therapy also had a significantly lower risk of cardiovascular disease (hazard ratio, 0.47; 95 percent confidence interval, 0.24 to 0.73), nephropathy (hazard ratio, 0.39; 95 percent confidence interval, 0.17 to 0.87), retinopathy (hazard ratio, 0.42; 95 percent confidence interval, 0.21 to 0.86), and autonomic neuropathy (hazard ratio, 0.37; 95 percent confidence interval, 0.18 to 0.79). A target-driven, long-term, intensified intervention aimed at multiple risk factors in patients with type 2 diabetes and microalbuminuria reduces the risk of cardiovascular and microvascular events by about 50 percent. Copyright 2003 Massachusetts Medical Society

0 comments Cited 776 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Structured Self-Monitoring of Blood Glucose Significantly Reduces A1C Levels in Poorly Controlled, Noninsulin-Treated Type 2 Diabetes

William H. Polonsky, Lawrence Fisher, Charles Schikman … (2011)

OBJECTIVE To assess the effectiveness of structured blood glucose testing in poorly controlled, noninsulin-treated type 2 diabetes. RESEARCH DESIGN AND METHODS This 12-month, prospective, cluster-randomized, multicenter study recruited 483 poorly controlled (A1C ≥7.5%), insulin-naïve type 2 diabetic subjects from 34 primary care practices in the U.S. Practices were randomized to an active control group (ACG) with enhanced usual care or a structured testing group (STG) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose (SMBG). STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days. The primary end point was A1C level measured at 12 months. RESULTS The 12-month intent-to-treat analysis (ACG, n = 227; STG, n = 256) showed significantly greater reductions in mean (SE) A1C in the STG compared with the ACG: −1.2% (0.09) vs. −0.9% (0.10); Δ = −0.3%; P = 0.04. Per protocol analysis (ACG, n = 161; STG, n = 130) showed even greater mean (SE) A1C reductions in the STG compared with the ACG: −1.3% (0.11) vs. −0.8% (0.11); Δ = −0.5%; P < 0.003. Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients, regardless of the patient’s initial baseline A1C level: 179 (75.5%) vs. 61 (28.0%); <0.0001. Both STG and ACG patients displayed significant (P < 0.0001) improvements in general well-being (GWB). CONCLUSIONS Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB.

0 comments Cited 112 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

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

Yuan Wu, Xun Yao, Giacomo Vespasiani … (2017)

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 HbA1c 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 HbA1c 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 HbA1c 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 HbA1c 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.

0 comments Cited 112 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Jae-Hyoung Cho:

ORCID: https://orcid.org/0000-0003-2235-8874

Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St Mary’s HospitalCollege of MedicineThe Catholic University of Korea222 Banpo-daero, Seocho-guSeoul, 06591Republic of Korea82 2 2258 6008drhopper@ikoob.com

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: February 2020

Publication date (Electronic): 26 February 2020

Volume: 8

Issue: 2

Electronic Location Identifier: e16266

Affiliations

[1 ] Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St Mary’s Hospital College of Medicine The Catholic University of Korea Seoul Republic of Korea

[2 ] Catholic Smart Health Care Center The Catholic University of Korea Seoul Republic of Korea

[3 ] Department of Medical Informatics College of Medicine The Catholic University of Korea Seoul Republic of Korea

Author notes

Corresponding Author: Jae-Hyoung Cho drhopper@ 123456ikoob.com

Author information

Yeoree Yang https://orcid.org/0000-0003-0990-1502

Eun Young Lee https://orcid.org/0000-0002-4968-5026

Hun-Sung Kim https://orcid.org/0000-0002-7002-7300

Seung-Hwan Lee https://orcid.org/0000-0002-3964-3877

Kun-Ho Yoon https://orcid.org/0000-0002-9109-2208

Jae-Hyoung Cho https://orcid.org/0000-0003-2235-8874

Article

Publisher ID: v8i2e16266

DOI: 10.2196/16266

PMC ID: 7066511

PubMed ID: 32130172

SO-VID: 22f6d53e-d031-4d5a-8f28-3f82cdb8c2ab

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), 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

Date received : 14 September 2019

Date revision requested : 5 October 2019

Date revision received : 14 December 2019

Date accepted : 24 January 2020

Comments

Comment on this article

scite_

Cited by 28

See all cited by

Most referenced authors 807

See all reference authors

Submit your digital health research with an established publisher
- celebrating 25 years of open access