A Systems-Theoretic Intervention Protocol for ME/CFS and Long COVID

This paper presents a phenotype-guided, systems-theoretic intervention protocol for ME/CFS and Long COVID, based on the Persistent Systemic Stress-Signalling State (PSSS) model, which provides the theoretical foundation for this protocol. The protocol conceptualizes post-acute infection syndromes (PAIS) as persistent pathological regulatory states within a nonlinear, multisystem network characterized by reduced effective recovery capacity. Rather than targeting symptoms in isolation, it focuses on the controlled modulation of system dynamics through state-dependent, phenotype-guided interventions. Participants are stratified based on dominant regulatory instability (autonomic, metabolic, or immune/neuroinflammatory) using continuous physiological monitoring, including heart rate variability and glycemic dynamics. Interventions are introduced sequentially in a cumulative and state-dependent manner, meaning that each intervention is added and maintained upon demonstrated stabilization, rather than replaced in a linear sequence. Progression, continuation, or regression is governed by predefined decision rules based on longitudinal physiological trends. Although the protocol is operationalized as a sequence of intervention phases, it should not be interpreted as a linear treatment pathway. Instead, it represents a closed-loop control strategy applied to a nonlinear physiological system, in which decisions are continuously informed by system behavior and may lead to stabilization, continuation, or step-back at any stage. The study is designed as a series of parallel n-of-1 trials, prioritizing within-subject causal interpretability over population-average treatment effects. This approach allows the capture of delayed, nonlinear, and state-dependent responses that are not adequately addressed by conventional trial designs. The protocol explicitly distinguishes between stabilization and resolution. While sequential intervention aims to reduce regulatory instability and increase recovery capacity, it does not assume that stabilization alone is sufficient to induce transition out of a pathological state. Instead, it defines the physiological conditions under which such transitions may become possible and observable. This work provides a structured and interpretable framework for intervention within complex post-infectious conditions and may serve as a methodological bridge between symptom-based clinical management and mechanistic, hypothesis-driven research focused on system dynamics, recovery processes, and state transitions.