Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning
Ramos,Lucas A. Blankers,Matthijs van Wingen,Guido de Bruijn,Tamara Pauws,Steffen C. Goudriaan,Anneke E.
Ramos,Lucas A.
Blankers,Matthijs
van Wingen,Guido
de Bruijn,Tamara
Pauws,Steffen C.
Goudriaan,Anneke E.
Abstract
Background Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement. Methods We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. Results From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. Discussion Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.
Description
Funding Information: This project under the name Digging for goals using data science: Improving E-health alcohol and substance use interventions using machine learning and longitudinal clustering Digging for goals using data science was funded by ZonMw under (Grant No. 555003024). Publisher Copyright: © Copyright © 2021 Ramos, Blankers, van Wingen, de Bruijn, Pauws and Goudriaan.
Date
2021-09-03
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Keywords
machine learning, eHealth, ATOD, Substance Use Disorder, addiction, log data analysis, CBT, ILLICIT DRUG-USE, GLOBAL BURDEN, DEPENDENCE, PREVENTION, ADHERENCE, DRINKING, SMOKING, DISEASE, ABRUPT, GOAL, SDG 3 - Good Health and Well-being
Citation
Ramos, L A, Blankers, M, van Wingen, G, de Bruijn, T, Pauws, S C & Goudriaan, A E 2021, 'Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning', Frontiers in Psychology, vol. 12, 734633. https://doi.org/10.3389/fpsyg.2021.734633