Interindividual differences in digital phenotypes of major depressive disorder: A passive sensing study using...

Major depressive disorder (MDD) is characterized by high levels of heterogeneity in symptom presentation across individuals. While previous research has identified distinct MDD subtypes using self-reported symptoms, few studies have leveraged objective data from smartphones and wearable devices to phenotype MDD symptoms. Passive sensing data from these devices can capture objective behavioral and physiological patterns, potentially revealing distinct digital phenotypes of MDD. We identified latent profiles based on digital biomarkers of depression collected from smartphones and Garmin smartwatches among 297 individuals with MDD. Digital biomarkers included sleep patterns, physical activity, screen time, social engagement, and heart rate variability. An exploratory aim examined whether identified profiles were associated with MDD severity and social and occupational functioning. A two-profile solution demonstrated best fit with the data: Profile 1 (85.7% of the sample) 'average in every way,' and Profile 2 (14.3%) 'deficient sleep, chronically low heart rate variability, and low social engagement.' While profiles did not significantly differ on MDD symptom severity (est = 0.322, S.E. = 0.767, p = 0.444), Profile 2 had lower social and occupational functioning compared to Profile 1 (est = -5.309, S.E. = 2.321, p = 0.023), though this was no longer statistically significant after correcting for type I error. Sleep dysregulation, low heart rate variability, and low social engagement seem to be important indicators of potential social and occupational impairments. Future research should incorporate additional digital biomarkers to refine the identification of digital phenotypes of MDD and validate these profiles against other clinical severity metrics in larger, more diverse samples.