Abstract
BACKGROUND: Adolescent depression is a significant public health concern. The presentation of depressive symptoms varies widely among individuals, fluctuating in intensity over time. Ecological momentary assessment (EMA) offers a unique advantage by enhancing ecological validity and reducing recall bias, allowing for a more accurate and nuanced understanding of major depressive disorder (MDD) symptoms. This methodology provides valuable insights into the fluctuating nature of depression, which could inform more personalized and timely interventions. OBJECTIVE: This study aims to (1) evaluate the feasibility of collecting smartphone-based EMA data alongside activity and sleep tracking in adolescents with depression; (2) investigate the severity and variability of mood symptoms reported over time; and (3) explore the relationship between mood, activity, and sleep. METHODS: Thirty-six participants (23 with MDD, 13 unaffected controls; 75% [n=27] female, mean age 19.50 y) completed twice-daily EMA check-ins over 2 weeks, complemented by continuous activity and sleep monitoring using FitBit Charge 3 devices. The study examined feasibility, usability of the EMA app, symptom severity and variability, and relationships between mood, activity, and sleep. We applied linear mixed-effects regression to the data to examine relationships between variables. RESULTS: Participants completed a total of 923 unique check-ins (mean check-ins per participant=25.60). Overall compliance rates were high (91.57%), indicating the approach is highly feasible. MDD participants demonstrated greater symptom severity and variability over time compared with controls (β=34.48, P<.001). Individuals with MDD exhibited greater diurnal variation (β=-2.54, P<.001) with worse mood in the morning and worse mood than anxiety scores over time (β=-6.93, P<.001). Life stress was a significant predictor of more severe EMA scores (β=24.50, P<.001). MDD cases exhibited more inconsistent sleep patterns (β=32.14, P<.001), shorter total sleep times (β=-94.38, P<.001), and a higher frequency of naps (β=14.05, P<.001). MDD cases took fewer steps per day (mean 5828.64, SD 6188.85) than controls (mean 7088.47, SD 5378.18) over the course of the study, but this difference was not significant (P=.33), and activity levels were not significantly predictive of EMA score (P=.75). CONCLUSIONS: This study demonstrates the feasibility of integrating smartphone-based EMA with wearable activity tracking in adolescents with depression. High compliance rates support the practicality of this approach, while EMA data provide valuable insights into the dynamic nature of depressive symptoms, particularly in relation to sleep and life stress. Future studies should validate these findings in larger, more diverse samples. Clinically, EMA and wearable tracking may enhance routine assessments and inform personalized interventions by capturing symptom variability and external influences in real time.