Mobile phone applications and self‐management of diabetes: A systematic review with meta‐analysis, meta‐regression of 21 randomized trials and GRADE

Abstract

We conducted a systematic review with meta-analysis of randomized controlled trials that evaluated the effect of diabetes mobile phone applications. A total of 1550 participants from 21 studies were included. For type 1 diabetes, a significant 0.49% reduction in HbA1c was seen (95% CI, 0.04-0.94; I = 84%), with unexplained heterogeneity and a low GRADE of evidence. For type 2 diabetes, using diabetes apps was associated with a mean reduction of 0.57% (95% CI, 0.32-0.82; I = 77%). The results had severe heterogeneity that was explained by the frequency of HCP feedback. In studies with no HCP feedback, low frequency and high frequency HCP feedback, the mean reduction is 0.24% (95% CI, 0.02-0.49; I = 0%), 0.33% (95% CI, 0.07-0.59; I = 47%) and 1.12% (95% CI, 0.91-1.32; I = 0%), respectively, with a high GRADE of evidence. There is evidence that diabetes apps improve glycaemic control in type 1 diabetes patients. A reduction of 0.57% in HbA1c was found in type 2 diabetes patients. However, HCP functionality is important to achieve clinical effectiveness. Future studies are needed to explore the cost-effectiveness of diabetes apps and the optimal intensity of HCP feedback.

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

“…Research indicates that the use of smartphone technologies and mobile phone applications, such as mobile health (m-health), may facilitate diabetes self-management [12]. This is due to m-health being an interactive, inexpensive and dynamic means of supporting diabetes patients with self-care behaviours [13]. Thereby reducing mortality rates by delivering effective interventions to patients [14].…”

Updates and advances in technology for diabetes self-management

“…In the type 2 diabetes population, low-frequency feedback (i.e., as needed or once monthly) was associated with an A1C reduction of 0.33% (−0.59 to −0.07%, I 2 = 47%), whereas high-frequency feedback (i.e., more than once per month) was associated with an A1C reduction of 1.12% (−1.32 to −0.91%, I 2 = 0). 12 The authors did not identify a potential cause for this phenomenon, although a separate study found similar results. 13 Two possibilities for this difference in impact between diabetes types are 1) the small sample size in the studies investigating health professional feedback in type 1 diabetes, and 2) the different types of challenges faced daily by individuals living with type 1 and type 2 diabetes.…”

Effectiveness of diabetes self-management education via a smartphone application in insulin treated type 2 diabetes patients – design of a randomised controlled trial (‘TRIGGER study’)

“…A common feature of a diabetes app is self-monitoring of blood glucose levels, blood pressure, physical activity, weight, medication intake and food intake [ 3 , 5 – 7 ]. For blood glucose, blood pressure and physical activity (step counter), automatic data entry is often possible, but apps facilitating these features are likely to be associated with higher costs.…”

“…For blood glucose, blood pressure and physical activity (step counter), automatic data entry is often possible, but apps facilitating these features are likely to be associated with higher costs. Another common feature is interactive communication with health care providers [ 3 , 7 ]. Many of the apps that have been investigated showed to be effective [ 6 – 8 ]: HbA1c decreased [ 7 , 9 , 10 ] and self-management improved [ 11 ].…”

“…Another common feature is interactive communication with health care providers [ 3 , 7 ]. Many of the apps that have been investigated showed to be effective [ 6 – 8 ]: HbA1c decreased [ 7 , 9 , 10 ] and self-management improved [ 11 ]. However, for most patients, daily entry of personal data is a tedious task [ 6 ], often resulting in infrequent or non-use of the app [ 11 ].…”