Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach

Abstract

Background: Early dropout hinders the effective adoption of brief psychological interventions and is associated with poor treatment outcomes. This study examined if attendance and depression treatment outcomes could be improved by matching patients to either face-to-face or computerized low-intensity psychological interventions.

Methods: Archival clinical records were analysed for 85,664 patients who accessed face-to-face or computerized guided self-help (GSH). The primary outcome was early dropout (attending ≤3 sessions). Supervised machine learning analyses were applied in a training sample (n = 55,529). The trained algorithm was cross-validated in an independent test sample (n = 30,135). The clinical utility of the model was evaluated using logistic regression, chi-square tests, and sensitivity analyses in a balanced subsample.

Results: Patients who received their model-indicated treatment modality were 12% more likely to receive an adequate dose of treatment OR = 1.12 (95% CI = 1.02 to 1.24), p = .02, and the strength of this effect was larger in the balanced subsample (OR = 2.10, 95% CI = 1.65 to 2.68, p < .001). Patients had better treatment outcomes when matched to their model-indicated treatment modality.

Conclusions: Machine learning approaches may enable services to optimally match patients to the treatment modality that maximizes attendance.

Keywords: Computerized CBT; Dropout prediction; Guided self-help; Machine learning; Precision mental healthcare.