From Daily Movement to Prediagnostic Inference: Temporal Validation of Wearable and Sleep Signals in Parkinson's Disease

Abstract Wearable and ambient sensors can observe daily function between clinic visits, creating opportunities to investigate Parkinson's disease before a recorded diagnosis. The resulting data are dense in time but remain limited by the number of independent participants and clinical events. This selected narrative review examines wrist accelerometry, instrumented gait, smartwatch measures, and nocturnal breathing, with emphasis on the distinction between recognizing established disease, measuring progression, and forecasting a future diagnosis. Landmark studies motivate the field, yet their results do not establish an interchangeable screening capability across devices, populations, or time horizons. This article proposes a temporal validation framework organized around four clocks: biological change, sensor observation, healthcare contact, and endpoint ascertainment. It also presents a staged study design that separates measurement validation, retrospective prediction, prospective silent evaluation, and assessment of a clinical pathway. The framework requires explicit handling of repeat measurements, missing wear time, device changes, treatment, competing events, and incomplete outcome ascertainment. Performance is expressed through calibration, precision, referral burden, lead time, and uncertainty at the participant level. Original design recommendations address the comparison between continuous monitoring and intermittent sampling without assuming that more measurements necessarily provide more independent evidence. For NeuralCipher.ai, the immediate application is a transparent research specification rather than an asserted diagnostic service. No participant-level analysis, new clinical results, or independent validation of the project is reported. 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