Response Time Dynamics From Noncognitive Ordinal Ecological Momentary Assessment as a Proxy for Symptom Change in...

Depressive symptoms in older adults are amplified by social isolation and limited access to clinic-based mental health care. Ecological momentary assessment (EMA) enables remote self-monitoring and unobtrusively captures response times (RTs), which may serve as indicators of psychomotor and cognitive functioning.

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

Forty-nine community-dwelling adults aged 65 years or older (mean age 70.7, SD 5.8 years; female: 35; male: 14) with a history of major depressive disorder received case management incorporating daily EMA. Participants provided self-reports of mood, appetite, sleep quality, and general well-being. Preassessment and postassessment included the 15-item Short Geriatric Depression Scale (GDS-15), the Center for Epidemiologic Studies Depression Scale-Revised (CESD-R), the 9-item Patient Health Questionnaire, and the Beck Anxiety Inventory. RTs were cleaned with an asymmetric IQR rule, z standardized within-person × response level, and modeled with exponential decay curves over successive EMA trials. The efficacy of EMA-adjunctive care was evaluated using pre-post comparisons of symptom scales. We then examined associations between RT-derived features and symptom change using correlational analyses. Finally, Bayesian multilevel modeling was applied to assess the clinical relevance of RT dynamics, including group differences in adaptation patterns.

Older adults at risk for depression showed significant symptom reductions over the 4-week EMA-adjunctive care period across all 4 psychological scales (CESD-R: mean Δ 11.5; rank-biserial r=0.78; GDS-15: mean Δ 2.14, Cohen d=0.76), alongside high EMA adherence (>90%). In correlational analyses, descriptive EMA score metrics and raw RTs showed modest, symptom-specific associations with symptom change (ΔCESD-R: |r|≈0.29; Δ9-item Patient Health Questionnaire: |r|≈0.32; ΔBeck Anxiety Inventory: |r|≈0.35) but were not significantly related to change in geriatric depression (ΔGDS-15: |r|≈0.24). In contrast, exponential-decay model parameters derived from standardized RT were significantly associated with geriatric depressive symptom change (Δ GDS-15), with the strongest effects observed for the feeling item (eg, decay rate θb: r=-0.398, asymptote θc: r=-0.321). Bayesian multilevel modeling further indicated that EMA-adjunctive care responders showed faster RT adaptation than nonresponders (median decay-rate ratio≈4.9, 95% credible interval 1.44-14.31), whereas differences in postadaptation RT levels were smaller and uncertain (median postadaptation RT ratio≈1.25, 95% credible interval 0.95-1.58). Sensitivity analyses showed consistent decay-rate effects across alternative specifications.

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