Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use.

Abstract

Introduction: This study aimed to evaluate effects of three machine learning based adjustments made to an eHealth intervention for mild alcohol use disorder, regarding (a) early dropout, (b) participation duration, and (c) success in reaching personal alcohol use goals. Additionally, we aimed to replicate earlier machine learning analyses.

Methods: We used three cohorts of observational log data from the Jellinek Digital Self-help intervention. First, a cohort before implementation of adjustments (T0; n = 320); second, a cohort after implementing two adjustments (i.e., sending daily emails in the first week and nudging participants towards a "no alcohol use" goal; T1; n = 146); third, a cohort comprising the prior adjustments complemented with eliminated time constraints to reaching further in the intervention (T2; n = 236).

Results: We found an increase in participants reaching further in the intervention, yet an increase in early dropout after implementing all adjustments. Moreover, we found that more participants aimed for a quit goal, whilst participation duration declined at T2. Intervention success increased, yet not significantly. Lastly, machine learning demonstrated reliability for outcome prediction in smaller datasets of an eHealth intervention.

Conclusion: Strong correlates as indicated by machine learning analyses were found to affect goal setting and use of an eHealth program for alcohol use problems.