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Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study

Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study

(E) Training in a machine learning predictive model in neural networks. (F) creation of a graphic user interface for clinical use. The minimum sample size was determined using Epidat 4.2 software (Xunta de Galicia), following the equation for estimating the comparison between independent means. The calculation was based on data reported by Rykov et al [30], in a study where patients were screened using wearable devices, and (MLMs were applied (Textbox 1).

Andrea P Garzón-Partida, Kimberly Magaña-Plascencia, Diana Emilia Martínez-Fernández, Joaquín García-Estrada, Sonia Luquin, David Fernández-Quezada

JMIR Res Protoc 2025;14:e71374

Feasibility of Collecting and Linking Digital Phenotyping, Clinical, and Genetics Data for Mental Health Research: Pilot Observational Study

Feasibility of Collecting and Linking Digital Phenotyping, Clinical, and Genetics Data for Mental Health Research: Pilot Observational Study

For compensation, participants were either entered into a draw for 10 Aus $100 (US $65) e-gift cards (cohort 1) or received an Aus $50 (US $33) e-gift card (cohort 2). Three unique identifiers were randomly assigned to each participant: one upon receipt of the study invitation; one during screening; and one upon confirmation of eligibility. These identifiers enabled linkage between baseline and digital phenotyping data for participants from AGDS.

Joanne R Beames, Omar Dabash, Michael J Spoelma, Artur Shvetcov, Wu Yi Zheng, Aimy Slade, Jin Han, Leonard Hoon, Joost Funke Kupper, Richard Parker, Brittany Mitchell, Nicholas G Martin, Jill M Newby, Alexis E Whitton, Helen Christensen

JMIR Form Res 2025;9:e71377