The Weight of the Past: History-Dependent Structure in Transitions to Sustained Inactivity

Objective: This study aims to evaluate whether short-term behavioral history improves the prediction of transitions into sustained inactivity in continuous rest–activity data beyond the instantaneous activity level alone.Methods: Minute-level wrist actigraphy data from individuals with major depressive disorder, schizophrenia, attention-deficit/hyperactivity disorder, and healthy controls were analyzed (2.53 million observations). Activity was transformed into trajectory-based features capturing instantaneous level, short-timescale variability, and directional drift memory. The primary outcome, D20_onset, was defined as the onset of ≥20 consecutive minutes of zero recorded activity and treated as an operational marker of transition into sustained inactivity. Logistic regression models with interaction terms evaluated whether recent trajectory features improved prediction beyond memoryless formulations.Results: Transition probability exhibited a stable and reproducible structure within the behavioral state space. Sustained activity level showed a strong suppressive association with D20_onset probability across diagnostic groups. Variability and drift memory exerted conditional effects, amplifying transition probability primarily within low-activity regimes. A significant three-way interaction among activity level, variability, and drift indicated a nonlinear, history-sensitive structure. Predicted probability surfaces showed concentration of transition probability within a low-activity, high-instability region, consistent with a structured region of the state space.Conclusion: Transitions into sustained inactivity in naturalistic settings are temporally embedded and depend on short-term behavioral history. Because actigraphy reflects behavior within circadian rest–activity organization, these transitions likely represent a mixture of behavioral disengagement, rest, and phase-related quiescence rather than a single process. The findings support history-aware, state-space modeling of rest–activity dynamics without presupposing a specific underlying mechanism.