Background: 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. Objective: 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. Methods: 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, 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. Results: 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 =0.78; GDS-15: mean Δ 2.14, Cohen =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: ||≈0.29; Δ9-item Patient Health Questionnaire: ||≈0.32; ΔBeck Anxiety Inventory: ||≈0.35) but were not significantly related to change in geriatric depression (ΔGDS-15: ||≈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 : =−0.398, asymptote : =−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. Conclusions: 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.