Objective: Digital interventions may assist patients with type 2 diabetes with improving glycaemic control. We aimed to synthesize effect sizes of digital interventions on glycated hemoglobin (HbA1c) levels and to identify effective features of digital interventions targeting patients with poorly controlled type 2 diabetes.
Research Design and Methods: MEDLINE, ISI Web of Science and PsycInfo were searched for randomized controlled trials (RCTs) comparing the effects of digital interventions with usual care. Two reviewers independently assessed studies for eligibility and determined study quality, using the Cochrane risk of bias assessment tool. The Behavioural Change Technique Taxonomy v1 was employed to identify behavior change techniques (BCTs) employed in interventions. Mean HbA1c differences were pooled using Analysis of Covariance to adjust for baseline differences and pre-post correlations. To examine effective intervention features and to evaluate differences in effects sizes across groups, meta-regression and subgroup analyses were performed.
Results: Twenty three arms of 21 RCTs were included in the meta-analysis (n= 3787 patients, 52.6% in intervention arms). The mean HbA1c baseline differences ranged from -0.2% to 0.64%. The pooled mean HbA1c change was statistically significant (-0.39 (95% CI: [-0.51, -0.26]) with substantial heterogeneity (I-squared statistic, 80.8%)) and a significant HbA1c reduction was noted for web-based interventions. A baseline HbA1c level above 7.5%, β=-0.44 (95% CI: [-0.81, -0.06]) and the BCTs ’problem solving’, β=-1.30(95% CI: [-2.05, -0.54]) and ’self-monitoring outcomes of behavior’, β=-1.21 (95% CI: [-1.95, -0.46]) were significantly associated with reduced HbA1c levels.
Conclusions: Digital interventions appear effective for reducing HbA1c levels in patients with poorly controlled type 2 diabetes.
M.M. Kebede: None. H. Zeeb: None. M. Peters: None. T.L. Heise: None. C.R. Pischke: None.