{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T21:38:36Z","timestamp":1772660316366,"version":"3.50.1"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.<\/jats:p>","DOI":"10.3389\/fninf.2023.1135300","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T05:03:33Z","timestamp":1681275813000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["The role of wrist-worn technology in the management of Parkinson\u2019s disease in daily life: A narrative review"],"prefix":"10.3389","volume":"17","author":[{"given":"Peng","family":"Li","sequence":"first","affiliation":[]},{"given":"Richard","family":"van Wezel","sequence":"additional","affiliation":[]},{"given":"Fei","family":"He","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-64181-3","article-title":"Using an unbiased symbolic movement representation to characterize Parkinson\u2019s disease states.","volume":"10","author":"Abrami","year":"2020","journal-title":"Sci. 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