Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study

Before meaningful progress toward precision psychiatry is possible, objective (unbiased) assessment of patient mental well-being must be validated and adopted broadly.

This study aims to compare the fidelity of a precision psychiatry therapy recommendation algorithm when trained with an objective quantification of psychological stress versus subjective ecological momentary assessments (EMAs) of stress and mood.

From 2786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and postsession self-report and facial biometric data via a mobile health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 "super users" that completed a minimum of 28 sessions with all pre- and postsession measures. The platform used has previously demonstrated reduced psychiatric symptom severity and improved overall mental well-being. Psychotherapy recordings ("sessions") within the platform, available asynchronously and on demand, span mindfulness, meditation, cognitive behavioral therapy, client-centered therapy, music therapy, and self-hypnosis. The platform also has the unusual ability to rapidly assess mental well-being without bias via an easy-to-use objective measure of psychological stress derived from artificial intelligence-based analysis of facial biomarkers (objective stress level [OSL]). In tandem with the objective measure, EMAs obtain self-reported values of stress (SRS) and mood (SRM). ∆OSL, ∆SRS, and ∆SRM (with delta referring to the presession subtracted from the postsession measurement) were used to independently train a therapy recommendation algorithm designed to predict what future sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual's self-selected sessions.

The objective measure of psychological stress provided a differentiated delta for the measurement of therapeutic efficacy compared to the 2 EMA deltas, as shown by clear divergence in ∆OSL vs ∆SRS or ∆SRM (r<0.03), while the EMA deltas showed significant convergence (r=0.53, P<.01). The recommendation algorithm selected increasingly efficacious therapy sessions as a function of training data when trained with either ∆OSL (F_1,16=5.37, P=.03) or ∆SRM data (F_1,16=3.69, P<.05). However, the sequential improvement in prediction efficacy only surpassed the efficacy of self-selected therapy when the algorithm was trained using objective data (P<.01). Training the algorithm with EMA data showed potential trends that did not reach significance (∆SRS: P=.09; ∆SRM: P=.12). As a final insight, self-selected therapy sessions were overrepresented among the algorithmically recommended sessions, an effect most pronounced when the algorithm was trained with ∆OSL data (F_1,14=30.94, P<.001).

These prospective data demonstrate that a rapid, scalable, and objective measure of psychological stress, in combination with a robust recommendation algorithm, can autonomously identify clinically meaningful therapy for individuals. More broadly, this work illustrates the potential for objective data on mental well-being to improve precision psychiatry and the capacity for mental health care professionals to match global demand.

ClinicalTrials.gov NCT06265909; https://clinicaltrials.gov/ct2/show/NCT06265909.