ArticleFrontiers in psychiatry2021
Analysis of the Emails From the Dutch Web-Based Intervention "Alcohol de Baas": Assessment of Early Indications of Drop-Out in an Online Alcohol Abuse Intervention.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.BMC psychology · 2025Article
- The promise and challenges of computer mouse trajectories in DMHIs - A feasibility study on pre-treatment dropout predictions.Internet interventions · 2025Article
- Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions.NPJ digital medicine · 2024Article
- Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions.Journal of healthcare informatics research · 2023Article
- Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach.Frontiers in psychiatry · 2023Article
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, traditional forms of psychotherapy are increasingly complemented by online interactions between client and counselor. In (some) web-based psychotherapeutic interventions, meetings are exclusively online through asynchronous messages. As the active ingredients of therapy are included in the exchange of several emails, this verbal exchange contains a wealth of information about the psychotherapeutic change process. Unfortunately, drop-out-related issues are exacerbated online. We employed several machine learning models to find (early) signs of drop-out in the email data from the "Alcohol de Baas" intervention by Tactus. Our analyses indicate that the email texts contain information about drop-out, but as drop-out is a multidimensional construct, it remains a complex task to accurately predict who will drop out. Nevertheless, by taking this approach, we present insight into the possibilities of working with email data and present some preliminary findings (which stress the importance of a good working alliance between client and counselor, distinguish between formal and informal language, and highlight the importance of Tactus' internet forum).
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