Smartphone data could help clinicians identify and treat mental health disorders (www.pittwire.pitt.edu)

Researchers from the University of Pittsburgh and collaborators reported in JAMA Network Open (July 3) that passive smartphone sensor data can be linked not just to specific diagnoses but to broader, transdiagnostic mental health symptom dimensions. Using the ILIADD dataset (N=557), the team—led by Whitney Ringwald with co-PI Colin Vize and colleagues—analyzed GPS (time at home, max distance), activity (walking/running/stationary), screen-on time, call counts, battery status and sleep gathered via a University of Oregon app. They used Mplus for statistical modeling to test correlations between these features and six evidence-based symptom domains (internalizing, detachment, disinhibition, antagonism, thought disorder, somatoform) plus the overarching p‑factor (a general psychopathology signal). The study’s significance lies in demonstrating that unobtrusive, continuous behavioral data can capture symptom patterns that cut across traditional diagnostic boundaries, potentially giving clinicians richer, more objective information between visits. Important caveats remain: findings are at the group level (averages, not individual diagnostics), behavior is heterogeneous, and more validation is needed before clinical deployment. The authors frame this technology as a supplementary tool to enhance assessment and treatment monitoring rather than a replacement for clinicians; the work was supported by NIAAA, NIMH, and Pitt’s CTSA.