Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping

Digital phenotyping studies using smartphone-sensed data have identified several behavioral markers associated with depression. However, the generalizability of these markers is constrained by multiple factors, including variability in depressive symptoms and associated behaviors, both between and within individuals over time. This study examines heterogeneity in depression and aims to identify behavioral markers indicative of depression in smartphone-sensed data collected from participants diagnosed with depression. We analyzed smartphone-sensed behavioral data from 62 patients with major depressive episodes across three subgroups: major depressive disorder (MDD, n=41), borderline personality disorder (BPD, n=12), and bipolar disorder (BD, n=9). Depression symptoms were assessed with the 9-item Patient Health Questionnaire (PHQ-9). Symptoms varied between subgroups and across severity levels. Association analysis revealed variability in correlations between depression severity and behavioral markers, both between participants and over time. Multilevel modeling revealed demographic predictors: employment status (β = −4.79, 95% CI = [−7.65, −1.80], P = .004) and age (β = −0.12, 95% CI = [−0.25, −0.00], P = .050) and lower nighttime movement (β = −0.79, 95% CI = [−1.29, −0.29], P = .024), as behavioral markers of depression.