COMPUTATIONAL PSYCHOMETRICS AND DIGITAL BIOMARKER MODELING FOR PRECISION MENTAL HEALTH DIAGNOSTICS

Abstract

This study addressed the problem that mental health screening based mainly on self-report may miss ecologically valid behavioral signals, limiting precision diagnostics in real-world cloud and enterprise deployments; therefore, the purpose was to test whether integrating computational psychometrics with engineered digital biomarkers improves prediction of a continuous Precision Diagnostic Index (PDI) in a quantitative, cross-sectional, case-based design. The sample comprised 220 cloud/enterprise cases (N = 220; 56.4% female; age M = 26.8, SD = 6.1) who completed a Likert 5-point instrument and provided sufficient passive data for biomarker computation. Key psychometric variables were Affective Distress (M = 3.62, α = .88), Cognitive Dyscontrol (M = 3.31, α = .84), Functional Impairment (M = 3.45, α = .86), and Self-Regulation Capacity (reverse-coded; M = 2.74, α = .81). Digital biomarkers were z-scored Routine Irregularity, Sleep Disruption Proxy, Mobility Constriction, and Interaction Volatility, and the outcome PDI was standardized (M = 0.12, SD = 0.94). The analysis plan used descriptives and reliability checks, Pearson correlations, and hierarchical multiple regression comparing psychometrics-only, biomarkers-only, and integrated models. Headline findings showed strong psychometric alignment with PDI (Affective Distress r = .62; Functional Impairment r = .58; Cognitive Dyscontrol r = .49; Self-Regulation r = −.46; all p < .001) and moderate biomarker associations, strongest for Routine Irregularity (r = .38, p < .001) and Sleep Disruption (r = .32, p < .001). In regression, psychometrics explained R² = .49 of PDI variance (β = .41 distress, β = .29 impairment, β = .18 dyscontrol, β = −.14 self-regulation), biomarkers explained R² = .22 (β = .24 routine irregularity, β = .19 sleep disruption), and the integrated model increased prediction to R² = .56 with ΔR² = .07 (ΔF p < .001); routine irregularity (β = .16, p = .001) and sleep disruption (β = .12, p = .018) remained significant after controls, with acceptable multicollinearity (VIF 1.18–2.36). These results imply that enterprise screening systems can retain interpretable psychometric anchors while adding a small, theory-guided subset of passive biomarkers that delivers incremental validity and supports more precise triage and better resource allocation decisions across services.