Digital interventions self-management education for type 1 and 2 diabetes: A systematic review and meta-analysis.

OBJECTIVE: To describe and assess digital health-led diabetes self-management education and support (DSMES) effectiveness in improving glycosylated hemoglobin, diabetes knowledge, and health-related quality of life (HrQoL) of Type 1 and 2 Diabetes in the past 10 years. DESIGN: Systematic Review and Meta-Analysis. The protocol was registered on PROSPERO registration number CRD42019139884. DATA SOURCES: PubMed, EMBASE, Cochrane library, Web of Science, and Scopus between January 2010 and August 2019. Study Selection and Appraisal: Randomized control trials of digital health-led DSMES for Type 1 (T1DM) or 2 (T2DM) diabetes compared to usual care were included. Outcomes were change in HbA1c, diabetes knowledge, and HrQoL. Cochrane Risk of Bias 2.0 tool was used to assess bias and GRADEpro for overall quality. The analysis involved narrative synthesis, subgroup and pooled meta-analyses. RESULTS: From 4286 articles, 39 studies (6861 participants) were included. Mean age was 51.62 years, range (13-70). Meta-analysis revealed intervention effects on HbA1c for T2DM with difference in means (MD) from baseline -0.480% (-0.661, -0.299), I275% (6 months), -0.457% (-0.761, -0.151), I2 81% (12 months), and for T1DM -0.41% (-1.022, 0.208) I2 83% (6 months), -0.03% (-0.210, 0.142) I2 0% (12 months). Few reported HrQoL with Hedges' g 0.183 (-0.039, 0.405), I2 0% (6 months), 0.153 (-0.060, 0.366), I2 0% (12 months) and diabetes knowledge with Hedges' g 1.003 (0.068, 1.938), I2 87% (3 months). CONCLUSION: Digital health-led DSMES are effective in improving HbA1c and diabetes knowledge, notably for T2DM. Research shows non-significant changes in HrQoL. Intervention effect on HbA1c was more impressive if delivered through mobile apps or patient portals. Further research is needed on the impact of DSMES on these outcomes, especially for newly diagnosed diabetes patients.

Digital Health Interventions for Diabetes Self-Management Education/Support in Type 1 & 2 Diabetes Mellitus.

INTRODUCTION: Diabetes Mellitus is a major global concern. DSME (Diabetes Self-Management Education and Support) helps improve outcomes. Over the years, newer technologies have delivered DSME warranting an update of evidence. OBJECTIVES: Describe current digital health interventions employed in DSMES and their effectiveness in improving glycemic control and quality of life of Diabetes Mellitus (DM) patients compared to usual care. METHODS: Systematic review design and meta-analysis. We will search data from 2010 to 2019. The review will only include randomized control trials (RCTs) with digital health interventions (mobile health, social media, e-health) as the main intervention to deliver DSMES. EXPECTED RESULTS: We expect digital health intervention-led DSMES to improve target outcomes compared to usual care. This study will aggregate information on usage and challenges for these tools. It will also suggest the direction of effect of such tools in different populations.

Strengthening Behavior and Social Functioning Among Persons with Autism Spectrum Conditions Using Artificial Intelligence and Behavioral Activation: Protocol for the Well-Being and Health for Loved onEs with ASD (WHOLE) Psychosocial Pilot Randomized Controlled Trial.

Although a proportion of families and communities in low resource settings continue to provide care to loved ones with autism spectrum conditions, many of the affected persons remain undiagnosed and without access to proven therapies due to high treatment cost and cultural pressures, in particular. Use of conversational agents on mobile phones in combination with behavior activation home care may provide an innovative, culturally appropriate and affordable platform for strengthening behavior and social functioning outcomes, in addition to an opportunity for participation of the persons with autism spectrum conditions in the intervention development process. We aim to assess the effectiveness of an intervention that incorporates artificial intelligence conversational agent technologies and behavioral activation therapy techniques.