Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression

Farhud Shams; Andy M Y Tai; Jane Kim; Marisha Boyd; Maximilian Meyer; Alireza Kazemi; Reinhard Michael Krausz

doi:10.1177/20552076231203876

Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression

Digit Health. 2023 Sep 28:9:20552076231203876. doi: 10.1177/20552076231203876. eCollection 2023 Jan-Dec.

Authors

Farhud Shams¹, Andy M Y Tai¹, Jane Kim¹, Marisha Boyd¹, Maximilian Meyer^{1

2}, Alireza Kazemi¹, Reinhard Michael Krausz¹

Affiliations

¹ Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
² Department of Psychiatry, University of Basel Psychiatric Clinics, University of Basel, Basel, Switzerland.

Abstract

Background: Substance use disorders affect 36 million people globally, but only a small proportion of them receive the necessary treatment. E-health interventions have been developed to address this issue by improving access to substance use treatment. However, concerns about participant engagement and adherence to these interventions remain. This review aimed to evaluate adherence to e-health interventions targeting substance use and identify hypothesized predictors of adherence.

Methods: A systematic review of literature published between 2009 and 2020 was conducted, and data on adherence measures and hypothesized predictors were extracted. Meta-analysis and meta-regression were used to analyze the data. The two adherence measures were (a) the mean proportion of modules completed across the intervention groups and (b) the proportion of participants that completed all modules. Four meta-regression models assessed each covariate including guidance, blended treatment, intervention duration and recruitment strategy.

Results: The overall pooled adherence rate was 0.60 (95%-CI: 0.52-0.67) for the mean proportion of modules completed across 30 intervention arms and 0.47 (95%-CI: 0.35-0.59) for the proportion of participants that completed all modules across 9 intervention arms. Guidance, blended treatment, and recruitment were significant predictors of adherence, while treatment duration was not.

Conclusion: The study suggests that more research is needed to identify predictors of adherence, in order to determine specific aspects that contribute to better exposure to intervention content. Reporting adherence and predictors in future studies can lead to improved meta-analyses and the development of more engaging interventions. Identifying predictors can aid in designing effective interventions for substance use disorders, with important implications for e-health interventions targeting substance use.

Keywords: adherence; e-Health interventions; meta-Analysis; meta-Regression; substance use disorder; systematic review.

Publication types

Review