Using #Digital Phenotypes to Identify Individuals With Alexithymia in Posttraumatic Stress Disorder: Cross-Sectional Study
Background: Alexithymia, defined as difficulty identifying and describing one’s emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, alexithymia is an important process to accurately measure and identify in clinical contexts. However, research identifying the association between the experience of alexithymia and psychopathology has been limited by an overreliance on self-report scales, which have restricted use for measuring constructs that involve deficits in self-awareness, such as alexithymia. Hence, more suitable and effective methods of measuring and identifying those experiencing alexithymia in clinical samples are needed. Objective: In this cross-sectional study, we aimed to determine if facial, vocal, and language phenotypes extracted from 1-minute recordings of war veterans with PTSD describing a traumatic event could be used to identify those experiencing alexithymia. Methods: A total of 96 participants were included in this cross-sectional study. Specialized software was used to extract facial, vocal, and language features from the recordings. These features were then integrated into machine learning (extreme gradient boosting [XGBoost]) classification models that were trained and tested within a 5-fold nested cross-validation pipeline for their capacity to classify veterans scoring above the cutoff for alexithymia on the Toronto Alexithymia Scale-20. Results: The best performing XGBoost classification model trained in the nested cross-validation pipeline was able to classify those experiencing alexithymia with a good level of accuracy (average F1-score=0.78, SD 0.07; average area under the curve score=0.87, SD 0.12). Consistent with theoretical models and past research into phenotypes of alexithymia, language, vocal, and facial features all contributed to the accuracy of the XGBoost classification model. Conclusions: These findings indicate that facial, vocal, and language phenotypes incorporated in machine learning models could represent a promising alternative to identifying individuals with PTSD who are experiencing alexithymia. The further validation and use of this #Approach could facilitate more tailored and effective allocation of treatment resources to individuals experiencing alexithymia in clinical settings.
