A Mobile-Based Intervention for Glycemic Control in Patients With Type 2 Diabetes: Retrospective, Propensity Score-Matched Cohort Study

Abstract

Background Mobile-based interventions appear to be promising in ameliorating huge burdens experienced by patients with type 2 diabetes. However, it is unclear how effective mobile-based interventions are in glycemic management of patients with type 2 diabetes based on real-world evidence. Objective This study aimed to evaluate the effectiveness of a mobile-based intervention on glycemic control in patients with type 2 diabetes based on real-world population data. Methods This retrospective, propensity score-matched cohort study analyzed longitudinal data from a clinical electronic health database. The study population included 37,913 patients with type 2 diabetes at cohort entry between October 1, 2016, and July 31, 2018. A total of 2400 patients were matched 1:1, using propensity score matching, into the usual care and mobile health (mHealth) groups. The primary outcomes of glycemic control included control rates of glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and postprandial 2-hour blood glucose (P2BG). Mean values and variation trends of difference with 95% CI were the secondary outcomes. The general linear model was used to calculate repeated-measures analyses of variance to examine the differences between the two groups. Subgroup and sensitivity analyses were performed. Results Of the 2400 patients included in the analysis, 1440 (60.00%) were male and the mean age was 52.24 years (SD 11.56). At baseline, the control rates of HbA1c, FBG, and P2BG in the mHealth and usual care groups were 45.75% versus 47.00% (P=.57), 38.03% versus 32.76% (P=.07), and 47.32% versus 47.89% (P=.83), respectively. At the 3-, 6-, 9-, and 12-month follow-ups, the mHealth group reported higher control rates of HbA1c than did the usual care group: 69.97% versus 46.06% (P<.001), 71.89% versus 61.24% (P=.004), 75.38% versus 53.44% (P<.001), and 72.31% versus 46.70% (P<.001), respectively. At the four follow-up sessions, the control rates of FBG in the mHealth and usual care groups were statistically different: 59.24% versus 34.21% (P<.001), 56.61% versus 35.14% (P<.001), 59.54% versus 34.99% (P<.001), and 59.77% versus 32.83% (P<.001), respectively. At the four follow-up sessions, the control rates of P2BG in the mHealth group were statistically higher than in the usual care group: 79.72% versus 48.75% (P<.001), 80.20% versus 57.45% (P<.001), 81.97% versus 54.07% (P<.001), and 76.19% versus 54.21% (P=.001), respectively. At the four follow-up sessions, the percentages of HbA1c reduction in the mHealth group were 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. At the four follow-up sessions, the percentages of P2BG reduction in the mHealth group were 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 and P2BG values in the two groups increased slightly. Conclusions This mobile-based intervention delivered by a multidisciplinary team can better improve glycemic control rates of patients with type 2 diabetes than usual care. These effects were best sustained within the first 6 months. Starting from the sixth month, intensive management needs to be conducted to maintain long-term effectiveness of the mobile-based intervention.

Digital Healthcare Intervention to Improve Self-Management for Patients with Type 2 Diabetes: A Scoping Review

“…Li et al [19] demonstrate the control rates of HbA1c, FBG, and P2BG were higher in the mHealth than usual care groups: 45.75% vs 47.00% (P = .57), 38.03% vs 32.76% (P = .07), and 47.32% vs 47.89% (P = .83), respectively.…”

“…Using an mHealth system with advice regarding behavioral and motivational messages, a tracking health app, a Bluetooth device (eg. glucometer) and a web-platform can improve the management and education in their own home [19,26,33,35,43]. The benefits of digital intervention can reduce the frequency of failed contact with patients, and they also have the potential to enhance work practice patterns and health service delivery for clinicians and patients in diabetes management [4].…”

“…app, web-portal) and telephone consultation rather than the UC (e.g. written patient's records) because they can extract the patient's real-time data from the mHealth system, providing a better option for health professionals in order for them to appreciate if the treatment is working or not, also to personalize the topics the patient needs to improve or learn and making quick decisions based on the data [4,8,19,33,35].…”

“…To improve self-care data log of the patient (e.g. blood glucose, blood pressure, BMI), the interventions need to be a long-term study, of more than 6 months [10,19,23]. Sometimes, there are difficulties to perform a long-term study because the patient may quit during the intervention, therefore it is important to analyze the barriers and limitations of the study itself to prevent this.…”

Analysis of Effectiveness and Psychological Techniques Implemented in mHealth Solutions for Middle-Aged and Elderly Adults with Type 2 Diabetes: A Narrative Review of the Literature

“…The different settings chosen to recruit the sample were general tertiary hospitals (6 studies) [ 39 , 46 , 51 , 52 , 53 , 54 ], primary care settings (18 studies) [ 22 , 40 , 41 , 42 , 49 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ], outpatient endocrinology clinics from different healthcare centers (7 studies) [ 43 , 44 , 48 , 68 , 69 , 70 , 71 ], samples from both primary care and other health centers (4 studies) [ 45 , 50 , 72 , 73 ], other healthcare settings (not specified) [ 47 ], specific health facilities such as a medical center of a military area [ 74 ], and a clinic electronic health database from a research center [ 75 ].…”