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Completed

NCT Number: NCT06265909

Prospective Real World Study on Therapy Prediction Algorithm Training

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.

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Key information

Conditions

Stress Behavior Behavioral Symptoms Emotional Wellbeing Stress, Psychological

Age range

18 year–66 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Mobio Interactive Pte Ltd

Singapore, 389637

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

AmDTx

Device

AmDTx (Mobio Interactive Pte Ltd, Singapore), is an advanced mobile health platform equipped with computer vision and AI to objectively quantify psychological stress and benchmarked ecological momentary assessments to subjectively measure stress, valence, and arousal. Asynchronous and on-demand psychotherapy available as audio files within AmDTx has been clinically validated across the mental illness severity spectrum. Psychotherapy within AmDTx primarily leverages meditation/mindfulness techniques to enhance relaxation, build stress resilience, improve focus and decision making, and influence behaviour and affect bias.

Primary outcomes

  1. Objective Stress Level (OSL)

    Time frame: Continuous

    Objective stress level (∆OSL). Objective stress within AmDTx was obtained via a 30-second "selfie" video captured with the front-facing camera of a mobile device (smartphone or table). The computer vision data extracted from the videos in real time through study completion, an average of 27.1 measures per person per year, were then passed through a deep neural network (DNN) to compute the objective stress level (∆OSL) at that moment in time. ∆OSL is represented with a value between 0 to 1, with greater values representing more stress.

  2. Self-Reported Stress (SRS)

    Time frame: Continuous

    Subjective, self-reported stress (∆SRS). Subjective stress within AmDTx was quantified via an animated digital "slider". Users reported their current level of stress either by dragging a marker on the slider to a position of their choosing between "none" (0) and "extreme" (10), or by tapping on one of four faces positioned above the slider, with each face visually depicting stress levels at the mid-points of four quadrants (i.e., values of 1.25, 3.75, 6.25, 8.75). Users were instructed input the stress that represents how they feel "right here, right now". Data collected in real time through study completion, an average of 27.1 measures per person per year.

  3. Self-Reported Mood (SRM)

    Time frame: Continuous

    Subjective, self-reported mood (∆SRM). Subjective mood within AmDTx was quantified via a "mood board", which asks users to select from 32 different words representing various emotions (e.g., "delighted", "content", "gloomy", "tense"). The mood board consists of two axes, one spanning from "unpleasant" to "pleasant" and the other from "mild" to "intense". Each quadrant contains 8 mood words. Users were instructed to tap on the words that represent how they feel "right here, right now". Data collected in real time through study completion, an average of 27.1 measures per person per year.

Sponsors and collaborators

Lead sponsor

Mobio Interactive PTE LTD

Industry

Registry information

Official study title

Prospective Real World Study on Therapy Prediction Algorithm Training Using Data From the Mobio Mental Health Platform AmDTx

Important dates

Study start
2015
Primary completion
2022
Study completion
2022
First posted
Feb 20, 2024
Registry last updated
Feb 20, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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