Temporal Dynamics of Recovery Latency Reveal Early Warning Signals of Loss of Physiological Resilience: A Multi-Dataset Analysis

Background Recovery Latency (RL) is a longitudinal metric capturing the time required for physiological variables to return to baseline following training or competitive loads. Prior work in this series demonstrated that RL reflects adaptive states across large longitudinal athletic datasets. However, whether the temporal dynamics of RL carry information about the stability of the underlying physiological system — beyond mean recovery levels — remains unknown. Theory from complex adaptive systems predicts that systems approaching a critical transition exhibit statistical precursors, notably increasing variance and lag-1 autocorrelation, collectively referred to as Early Warning Signals (EWS). Objective To determine whether temporal dynamics of Recovery Latency — specifically rolling variance and rolling autocorrelation — exhibit patterns consistent with Early Warning Signals of declining physiological resilience in longitudinal athlete datasets. Methods We analysed 28,472 recovery events from 649 athletes across three independent datasets within the RL series dataset (recuperação_VFC_100k, athlete_physiological, lesões_multimodal). For each athlete, rolling variance (EWS₁) and rolling lag-1 autocorrelation (EWS₂) of RL were computed using a 14-event window. Trend analysis was performed using Kendall τ statistics, complemented by sliding-window Kendall τ (window=20), flickering dynamics (state-transition rate), surrogate testing (n=100 permutations), and a Composite Early Warning Index (CEWI). Athletes were stratified into Declining (RL slope > 0.015 d/event, n=155) and Stable (RL slope < −0.010 d/event, n=131) adaptive states. An independent screen-time dataset (N=400) was analysed to examine environmental circadian perturbation as a potential driver of resilience loss. Results Rolling variance of RL increased significantly in athletes with declining adaptive capacity compared with stable athletes across all datasets (VFC_100k: Δτ=+0.516, p<0.001; Athlete Physiol.: Δτ=+0.556, p=0.001; Lesões Multimodal: Δτ=+0.281, p<0.001). Sliding-window Kendall τ confirmed temporal divergence between groups in the final observation window (τ_last combined: Declining=−0.009 vs Stable=−0.284, p=0.005). Rolling autocorrelation (p=0.052–0.922) and flickering dynamics (p=0.261–0.985) did not significantly differentiate groups. Screen exposure was strongly associated with a RL proxy index (ρ=+0.620, p<0.001), mediated through sleep quality degradation and stress elevation, supporting a circadian disruption pathway to resilience loss. Conclusions Temporal dynamics of Recovery Latency contain detectable Early Warning Signals of physiological resilience loss, primarily expressed as increasing rolling variance in athletes with deteriorating adaptive capacity. Replication across three independent datasets supports RL variability as a scalable marker of adaptive instability. Associations with screen exposure further suggest that environmental circadian perturbations may contribute to resilience decline, linking chronobiology, behavioral factors, and recovery physiology.