{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:05:45Z","timestamp":1792695945548,"version":"4.5.0"},"reference-count":109,"publisher":"The Korean Movement Disorder Society","issue":"2","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["e-jmd.org"],"crossmark-restriction":true},"short-container-title":["JMD"],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>Sleep disturbances are highly prevalent and clinically significant nonmotor features of Parkinson\u2019s disease (PD). Although in-laboratory polysomnography remains the gold standard method for investigating these disturbances, its limited scalability and ecological validity constrain longitudinal and real-world assessments. Recent advances in digital health technologies have introduced a broad spectrum of portable, wearable, and contactless tools for sleep monitoring. In this scoping review, we systematically map the landscape of digital sleep technologies in PD by using a tiered framework based on technical maturity and clinical validation (Tiers 1\u20134); moreover, we further classify them by signal modality and sleep symptom domain. Through a systematic review of the literature, we identified 19 studies (Tiers 2\u20134) that applied digital biomarkers to assess sleep disturbances in PD, including REM sleep behavior disorder, nocturnal immobility, insomnia, circadian rhythm disturbances, excessive daytime sleepiness, and sleep-related respiratory and movement disorders. We additionally contextualize these findings against the rapid expansion of multimodal and AI-driven Tier 3\u20134 platforms in the general population. Despite this technological progress, a major translational gap persists in PD, which is characterized by limited disease-specific validation, small cohort sizes, and insufficient multimodal benchmarking. Multimodal systems leveraging machine learning offer a promising direction by enabling the more precise characterization of complex and overlapping sleep phenotypes. Emerging contactless systems further expand the potential for continuous, low-burden monitoring, although their clinical validity remains to be established. Future development of digital sleep biomarkers in PD will require prospective validation against established standards and the integration of multimodal data to enable scalable, longitudinal phenotyping and clinical trial applications.<\/jats:p>","DOI":"10.14802\/jmd.26098","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:00:15Z","timestamp":1776142815000},"page":"119-134","update-policy":"https:\/\/doi.org\/10.14802\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Digital Technology for Sleep Symptoms in Parkinson\u2019s Disease: A Scoping Review"],"prefix":"10.14802","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8071-9968","authenticated-orcid":true,"given":"Kye Won","family":"Park","sequence":"first","affiliation":[{"name":"Department of Neurology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, 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