Prospective Real World Study on Therapy Prediction Algorithm Training

Mobio Interactive

Status: Completed

Conditions: Emotional Wellbeing; Stress

Treatments: Device: AmDTx

Study type: Observational

Funder types: Industry

Identifiers

NCT06265909

PRW001

Details and patient eligibility

About

This study examines the impact of using an algorithm to select therapy content for patients engaged with the mobile mental health platform AmDTx (Mobio Interactive). The algorithm is to be trained with three separate sources of data. Two sources of data come from self-reports by the patients themselves, provided before and after engaging with therapy content. The third source of data comes from an objective measurement of psychological stress, made possible through artificial analysis of computer vision data captured from the mobile device camera as the patient completes a 30 second selfie video before and after engaging with therapy content.

Full description

From 2,786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and post-session self-report and facial biometric data via the AmDTx mental health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 "super users" that completed at least 28 sessions with all pre- and post-session measures. AmDTx is a clinically validated mental health platform that provides patients with audio recordings supporting mental wellbeing (asynchronous and on-demand psychotherapy). AmDTx also contains easy to use tools that rapidly assess mental wellbeing, including an objective measure of psychological stress derived from AI analysis of facial biomarkers (Objective Stress Level; ∆OSL), and ecological momentary assessments (EMAs). Two commonly used EMAs within AmDTx are self-reported stress (∆SRS) and self-reported mood (∆SRM). These three data sources were used to independently train an algorithm designed to predict what future therapy sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual's self-selected sessions.

Enrollment: 67 patients

Sex: All

Ages: 18 to 66 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Completion of at least 28 English-language psychotherapy sessions that contained the required session payloads for algorithm inclusion, and only when the objective and two subjective measures were all completed both before and after each session.

Exclusion criteria

• Under 18 years old

Trial design

67 participants in 1 patient group

AmDTx engaged

Description:

Data were collected between March 2015 and December 2022 on 36,160 unique users in a manner compliant with the Health Insurance Portability and Accountability Act (HIPAA), Personal Health Information Protection Act (PHIPA), and General Data Protection Regulation (GDPR). Of these, 2,786 unique individuals engaged in biometric and self-report data collection. To protect the real-world applicability of the results, users were not given any special instruction or information about the nature or possibility of the current analyses. As consequence, user data varied greatly in terms of engagement and app-use characteristics. To create a single, unified, and consistent dataset that could be leveraged across all intended analyses, data were filtered to only include English-language psychotherapy sessions that contained the required session payloads for algorithm inclusion (see below), and only when all the objective and two subjective measures were completed both before and after each session.

Treatment:

Device: AmDTx