JMIR Publications

ABSTRACT

Background:

Alexithymia, defined by difficulty identifying and describing one’s emotions, has been identified as a transdiagnostic emotion process that impacts the course, severity and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, it is an important process to accurately measure and identify in clinical contexts. However, research identifying the relationship between the experience of alexithymia and psychopathology has been limited by an over-reliance on self-report scales, which have restricted utility 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 is needed.

Objective:

In this cross-sectional study (N = 96), we aimed to determine if facial, vocal and language phenotypes extracted from one-minute recordings of war veterans with PTSD describing a traumatic event could be utilized to identify those experiencing alexithymia.

Methods:

Specialized software was used to extract facial, vocal and language features from the recordings. These features were then integrated into machine learning (Extreme Gradient Boost (XGBoost)) classification models that were trained and tested within a five-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, average AUC score = 0.87). 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.