Open AccessEstimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort
Author(s) -
Pellegrini Amelia M.,
Huang Emily J.,
Staples Patrick C.,
Hart Kamber L.,
Lorme Jeanette M.,
Brown Hannah E.,
Perlis Roy H.,
Onnela JukkaPekka J.
Publication year - 2022
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.2077
Subject(s) - bipolar disorder , major depressive disorder , depression (economics) , cohort , schizoaffective disorder , rating scale , psychology , clinical psychology , medicine , psychiatry , mood , psychosis , developmental psychology , economics , macroeconomics
Background Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied. Methods We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8‐week study period, participants were evaluated with a rater‐administered Montgomery–Åsberg Depression Rating Scale (MADRS) biweekly, completed self‐report PHQ‐8 measures weekly on their smartphone, and consented to collection of smartphone‐based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone‐based PHQ‐8 and passive measures. Results Among the 45 individuals, 38 (84%) completed the 8‐week study. The average root‐mean‐squared error (RMSE) in predicting the MADRS score (scale 0–60) was 4.72 using passive data alone, 4.27 using self‐report measures alone, and 4.30 using both. Conclusions While passive measures did not improve MADRS score prediction in our cross‐disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long‐term via self‐report.