Jaba Tkemaladze
Background. Allostatic load (AL) indices suffer from extreme methodological heterogeneity (I² = 94.24%) and non‑specificity across clinical populations . Recent empirical work on “digital phenotyping” of AL demonstrates associations between traditional AL indices and wearable‑assessed physiological responses (energy expenditure, heart rate, detrended fluctuation analysis) , motivating the development of principled, dynamic AL measures grounded in predictive processing theories . Methods. We introduce v4 of the Ze‑AL formalism: (i) substitution of predictive uncertainty σ_E(t) with calibration variance σ_calib² — the expected prediction variance from a stress‑free baseline; (ii) multivariate Mahalanobis distance across physiological channels. (Variational free energy analysis is provided in Supplementary Materials.) All variants were tested against simulated ground‑truth burden, a nonlinear outcome surrogate, and a truly independent behavioral target (theoretical ceiling r = 0.30). An antagonistic‑channel condition tested whether Mahalanobis distance requires coherent channel coupling. Key robustness tests: (a) multi‑seed analysis (10 seeds); (b) colored noise (1/f spectrum, β = 1.0); (c) unsupervised contamination (no information about which baseline samples are contaminated) with robust covariance estimation (DDCMV) . Results. Under clean calibration, all GP‑based methods achieved r_burden ≈ 0.95–0.96. Under 10% calibration contamination, only σ_calib retained practical utility (r_burden = 0.893 [95% CI 0.872–0.911]), while the moving‑average baseline reversed sign catastrophically (r = −0.842). Under 30% contamination, σ_calib still dominated (r_burden = 0.938). Under colored noise (1/f), σ_calib maintained r_burden > 0.87. Under unsupervised contamination, σ_calib with robust covariance estimation (DDCMV) achieved r_burden = 0.824 (95% CI 0.781–0.862). Antagonistic‑channel coupling did not degrade Mahalanobis performance (r_burden = 0.951). Multi‑seed analysis confirmed robustness (σ_calib: SD = 0.003). Conclusion. Under realistic calibration contamination — unavoidable in field studies — univariate σ_calib normalisation is the most robust estimator. For unsupervised settings, robust covariance estimation (DDCMV) restores performance. v4 σ_calib is recommended as the primary deployable measure for real‑world allostatic load research.