Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jul 7, 2019
Date Accepted: Feb 9, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Effectiveness of Mobile Health Model on Glycemic Control in Type 2 Diabetes: A Retrospective Propensity Score-matched Cohort Study
ABSTRACT
Background:
Mobile health (mHealth) tools or models appear to be promising in diabetes; however, it is unclear how effective these mHealth tools or models are in changing glycemic control based on real-world evidence.
Objective:
This study aimed to evaluate the effectiveness of mHealth model on glycemic control in type 2 diabetes based on real-world population data.
Methods:
This retrospective propensity score-matched cohort study analyzed longitudinal data from the Diabetes Medical Association Registration System, an electronic health database. The study population included 37913 patients with type 2 diabetes at cohort entry between October 1, 2016, and July 31, 2018. A total of 2400 patients were propensity score-matched 1:1 into the usual care and mHealth groups by sex, age group, comorbidity (hyperlipidemia, hypertension), glycated hemoglobin A1c (HbA1c) level, and low-density lipoprotein (LDL) cholesterol level. Statistical analysis was conducted from September 2018 to March 2019. The primary outcomes included control rates of HbA1c, fasting blood glucose (FBG), and postprandial 2-hour blood glucose (P2BG). General linear model was used to calculate repeated measures ANOVAs to examine the differences of two groups. Subgroup and sensitivity analyses were performed.
Results:
Of the 2400 patients included in the analysis, 1440 (60%) were male; mean (SD) age was 52.24 (11.56) years. At the 3-, 6-, 9-, and 12-month follow-ups, the mHealth group reported higher control rates of HbA1c than usual care (P＜.01), which were 69.97%, 71.89%, 75.38%, and 72.31%, respectively. The control rates of FBG in mHealth group were higher than usual care (P＜.001), which were 59.24%, 56.61%, 59.54%, and 59.77%, respectively. The control rates of P2BG in two groups were obvious different (P＜.01), which of mHealth group were higher (79.72%, 80.20%, 81.97%, and 76.19%, respectively). There were obvious reductions of HbA1c in mHealth group compared with usual care, with absolute reductions of 8.66% (95% CI 6.69-10.63), 10.60% (95% CI 8.66-12.54), 10.64% (95% CI 8.70-12.58), and 8.11% (95% CI 6.08-10.14), respectively. Large reductions were also observed in P2BG in mHealth group compared with usual care, with absolute reductions of 8.44% (95% CI 7.41-10.73), 17.77% (95% CI 14.98-20.23), 16.23% (95% CI 13.05-19.35), and 16.91% (95% CI 13.17-19.84), respectively. Starting from the sixth month, the mean HbA1c, FBG, and P2BG values of two groups increased slightly, and the differences of HbA1c, FBG, and P2BG among 9-month follow-up, 12-month follow-up, and baseline decreased.
Conclusions:
The mHealth model improved glycemic control rates of patients with type 2 diabetes, enabled glycemic control targets to be achieved early, and maintained glycemic control for a long time. Starting from the sixth month, intensive management should be conducted to maintain long-term effectiveness of mHealth model.
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