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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

There were no significant associations between symptoms and dropout (all t.05) or diary adherence and engagement (all r.05) (see Tables S5 and S6 in Multimedia Appendix 1 for statistics). The Mobigene pilot study demonstrated that it was feasible to collect and link new data from an existing cohort that had already participated in extensive data collection procedures.

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

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis

Statistical modeling and analyses were performed in R (version 4.3.3; R foundation). Causal inference was performed in the structural causal modeling (SCM) framework. An SCM is described by a set of variables, a set of functions relating the variables, and a causal structure represented by a directed acyclic graph (DAG) that indicates the directionality of causal influence using arrows between random variables.

Mathew Varidel, Victor An, Ian B Hickie, Sally Cripps, Roman Marchant, Jan Scott, Jacob J Crouse, Adam Poulsen, Bridianne O'Dea, Sarah McKenna, Frank Iorfino

J Med Internet Res 2025;27:e71305