Identifying Symptom Dynamics and Profiles of Subgroups at Risk for Major Depression and Suicidal Ideation Among...

Traditional clinical assessments in psychiatric research or clinical practice rely on global retrospective self-report depression measures, which do not adequately capture intra- and interindividual variability in depressive symptoms over time and across contexts.

This study aimed to (1) assess the sensitivity of mobile ecological momentary assessment (EMA) for monitoring depressive symptoms compared with traditional depression scales, (2) investigate changes in depressive symptoms and recall consistency observed between the first week (FW) and the second week (SW), and (3) identify subgroups at higher risk for depression and suicidal ideation, and characterize their sociodemographic, psychological, and psychiatric profiles.

Participants' self-reports were collected once daily for 14 consecutive days via a mobile app-based EMA to monitor the presence of 20 depressive symptoms based on a 24-hour recall period, thereby capturing naturalistic severity and variability. Baseline questionnaires measured sociodemographic characteristics, digital sensitivity, and personality traits. On day 14, postquestionnaires were administered to assess their clinical symptoms. In addition, modified depression scales were administered on day 7 for 1-week recall, and day 14 for both 1-week and 2-week recalls. For cluster analysis, 3 active EMA-derived features were included: mean symptom severity, within-person symptom variability, and frequency of suicidal ideation.

Generalized Linear Mixed Model analysis (model 1) revealed that traditional 2-week recall explained only 35.5% of the variance in daily symptom presence (odds ratio 0.666, 95% CI 0.659-0.674; P<.001). Additional Generalized Linear Mixed Model analysis (model 2) identified a robust interaction, indicating that the consistency between weekly retrospective recall and daily EMA differed significantly between FW and SW (F1, 81876=124.550; P<.001). While overall symptom severity scores significantly decreased from FW to SW across both assessment methods (Cohen d=0.10-0.34; P<.001), the estimated mean of the probability of symptom reporting showed a contrasting upward trend from 0.853 to 0.908. In cluster analysis, 695 participants (244 males and 451 females; aged 19-73 years, mean 36.14, SD 10.71 years) who completed at least 7 EMA sessions over a 2-week period were classified into three distinct clusters: (1) no or low risk (n=445, 64.0%), (2) moderate risk (n=223, 32.1%), and (3) high risk (n=27, 3.9%). While depressive symptom severity, frequency of suicidal ideation, and psychiatric profiles progressively increased across clusters (cluster 1<2<3), the highest symptom variability was observed in cluster 2 (cluster 1<3<2).

This study demonstrated that mobile EMA effectively reduced recall bias in assessing depressive symptoms and revealed symptom dynamics. Using a minimal set of active EMA-derived features, we differentiated 3 distinct risk clusters; within-person symptom variability emerged as a clinically significant indicator not captured by traditional 2-week recall-based assessments. Despite the brief 2-week assessment period, these findings suggest that mobile EMA may serve as a robust real-world data collection tool.