Attrition Within Digital Health Interventions for People With Multiple Sclerosis: Systematic Review and Meta-analysis.

Background: Digital health interventions have revolutionized multiple sclerosis (MS) care by supporting people with MS to better self-manage their disease. It is now understood that the technological elements that comprise this category of digital health interventions can influence participant engagement in self-management programs, and people with MS can experience significant barriers, influenced by these elements, to remaining engaged during a period of learning. It is essential to explore the influence of technological elements in mitigating attrition.
Objective: This study aimed to examine the study design and technological elements of documented digital health interventions targeted at people with MS-digital health interventions that were intended to support a program of engagement over a defined period-and to explore how these correlated with attrition among participants of randomized controlled trials (RCTs).
Methods: We conducted a systematic review and meta-analysis of RCTs (n=32) describing digital health self-management interventions for people with MS. We analyzed attrition in included studies, using a random-effects model and meta-regression to measure the association between potential moderators.
Results: There were no measured differences in attrition between the intervention and control arms; however, some of the heterogeneity observed was explained by the composite technological element score. The pooled attrition rates for the intervention and control arms were 14.7% and 15.6%, respectively.
Conclusions: This paper provides insight into the technological composition of digital health interventions designed for people with MS and describes the degree of attrition in both study arms. This paper will aid in the design of future studies in this area, particularly for digital health interventions of this type.
(©William Bevens, Tracey Weiland, Kathleen Gray, George Jelinek, Sandra Neate, Steve Simpson-Yap. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.02.2022.)