JMIR Publications

ABSTRACT

Background:

Depressive symptoms in older adults are amplified by social isolation and limited access to clinic-based mental health care. Ecological momentary assessment (EMA) permits remote self-monitoring and unobtrusively captures response times (RTs) that may index psychomotor and cognitive functioning.

Objective:

This study investigated utility of EMA-based RT dynamics for predicting symptom change and profiling potential responders for repeated self-monitoring in late-life depression.

Methods:

Forty-nine community-dwelling adults aged ≥65 years (mean 70.7, SD 5.8; 72% female) with a history of major depressive disorder received case management incorporating daily EMA via the BIG4+ mobile application. Participants provided self-reports of mood, appetite, sleep quality, and general well-being. Pre- and post-assessments were measured with the 15-item Geriatric Depression Scale (GDS-15), Center for Epidemiologic Studies Depression Scale–Revised (CESD-R), Patient Health Questionnaire-9 (PHQ-9), and Beck Anxiety Inventory (BAI). RTs were cleaned with an asymmetric interquartile-range rule, z-standardized within person × response level, and modeled with exponential decay curves over successive EMA trials. Correlational analyses between various RT-based features and symptom change score were conducted. Visual inspection of grouped RT trajectories were presented to further examine the utility of RT dynamics.

Results:

Steeper RT decay, superior model fit, and higher asymptote, especially for the “feeling” item, were associated with larger reductions in GDS-15 and CESD-R scores (r = –0.40 to 0.34). Responders, who benefitted from EMA-adjunctive care, displayed early RT stabilization and narrower latency distributions, whereas non-responders showed initial slowing and greater variability. RT dynamics were less predictive of anxiety change. Additionally, all symptom scales improved significantly (eg, CESD-R mean change –11.5, SE 2.3; P<.001; GDS-15 Cohen d = 0.76) after 4 weeks of EMA-based self monitoring with case management.

Conclusions:

Dynamic characteristics of EMA-based response times emerged as a sensitive proxy for monitoring changes in depressive symptoms among older adults at risk. These findings highlight the potential utility of response time as a digital biomarker derived from brief, low-burden EMA self-monitoring, supporting the development of scalable and personalized mental health interventions for geriatric populations.