Pilot Study of Smartphone Ecological Momentary Assessment and Wearable Activity Tracking in Pediatric Depression
Authors: Saavedra Jimena Unzueta , Deaso Emma A. , Austin Margot , Cadavid Laura , Kraff Rachel , Knowles Emma. E. M. 
Published: February 2025

Background

Adolescent depression is a significant public health concern. The presentation of depressive symptoms varies widely among individuals, fluctuating in intensity over time. EMA offers a unique advantage by enhancing ecological validity and reducing recall bias, allowing for a more accurate and nuanced understanding of MDD symptoms. This methodology provides valuable insights into the fluctuating nature of depression, which could inform more personalized and timely interventions.

Objectives

This study aims to: (1) evaluate the feasibility of collecting smartphone-based Ecological Momentary Assessment (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 Major Depressive Disorder (MDD), 13 unaffected controls; 75% female, mean age 19.50 years) completed twice-daily EMA check-ins over two weeks, complemented by continuous activity and sleep monitoring using FitBit Charge 3 devices. The study examined feasibility, usability of 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 checkins (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 to controls (β= 34.48,p= 2.17×10−06). Individuals with MDD exhibited greater diurnal variation (β= −2.54, p = 5.14×10−03) with worse mood in the morning and worse mood than anxiety scores over time (β= −6.93, p = 5.95×10−06). Life stress was a significant predictor of more severe EMA scores (β= 24.50, p = 9.99×10−03). MDD cases exhibited more inconsistent sleep patterns (β= 32.14, p = 5.44×10−04), shorter total sleep times (β= − 94.38, p = 2.82×10−03), and a higher frequency of naps (β= 14.05, p = 4.02×10−03). 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= 0.33), activity levels were not significantly predictive of EMA score (p= 0.75).

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.