Currently submitted to: JMIR Formative Research
Date Submitted: Jul 23, 2026
Open Peer Review Period: Aug 4, 2026 - Sep 29, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Hidden Depression: Conversational AI Identifies Clinically Meaningful Depressive Symptoms in Older Adults with High Self-Rated Health
ABSTRACT
Background:
Depression in older adults is a major driver of morbidity, functional decline, and mortality, yet remains systematically under-detected due to symptom normalization, stigma, and atypical presentations. Self-rated health (SRH) measures, widely used in population health surveillance, demonstrate only moderate correlation with validated depression instruments and may miss clinically meaningful mental health burden. This study examined whether conversational AI can identify depressive symptoms among older adults who rate their health positively, a population that conventional SRH-based screening would not flag for evaluation.
Objective:
To determine the prevalence of hidden depressive symptoms, defined as PHQ-9 positivity among individuals reporting good, very good, or excellent health, detected through conversational AI-guided telephone assessment, and to validate AI-detected depression against expected SRH-depression associations.
Methods:
Cross-sectional analysis of 454 community-dwelling older adults who completed an AI-powered health risk assessment within OhioHealth, a large Midwestern integrated health system. Depressive symptoms were measured using the PHQ-9 (≥2 symptoms = positive screen). SRH was dichotomized into high (good/very good/excellent) and low (poor/fair). Chi-square tests, odds ratios, and adjusted logistic regression examined associations.
Results:
Among participants (mean age 75.6 years, 60% female), 83.9% reported high SRH. Of these, 11.8% screened positive for clinically meaningful depressive symptoms—a hidden-risk population missed by conventional screening. Among those with low SRH, 46.6% screened positive. The association was highly significant (χ² = 51.51, p < .001; OR = 6.49, 95% CI: 3.73–11.36) and remained robust after adjustment for age and gender (adjusted OR = 5.88). Findings persisted across alternative PHQ-9 thresholds.
Conclusions:
Conversational AI identifies depressive symptoms in approximately 1 in 8 older adults who perceive themselves as healthy, a hidden burden that conventional SRH screening would overlook. These findings support AI-enabled proactive mental health screening to identify at-risk older adults before functional decline and adverse outcomes accumulate. Clinical Trial: N/A
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