{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:11:16Z","timestamp":1783923076807,"version":"3.55.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"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. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Parkinson's disease (PD) is typically assessed during short clinical visits using rating scales such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). These assessments provide only a snapshot of symptom severity and may not capture fluctuations in daily life. In this study, we examined whether wrist-worn actigraphy can be used to estimate MDS-UPDRS scores in people with Parkinson's disease (PwP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Continuous accelerometer recordings at 25 Hz were collected over up to 28 days using GeneActiv devices. From these recordings, three feature representations were derived: non-embedding actigraphy features, self-supervised accelerometer embeddings, and a combined feature set. A small set of regression models was evaluated using strict leave-one-participant-out cross-validation (LOPO-CV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Estimation performance varied across targets and feature sets. The strongest result was observed for MDS-UPDRS Part IV, where non-embedding features with Elastic Net achieved a mean absolute error (MAE) of 1.6 and a correlation of 0.83 between estimated and actual values. The combined feature set performed best for Part I (MAE\u2009=\u20093.0,\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.60), Part III (MAE\u2009=\u20098.2,\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.47), and the total MDS-UPDRS score (MAE\u2009=\u200913.3,\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.49), whereas non-embedding features performed best for Part II (MAE\u2009=\u20092.7,\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.61). Embedding-only models were competitive for some outcomes, but were not the best overall.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Overall, the results show that month-long wrist-worn actigraphy contains information related to PD severity in daily life, although estimation accuracy remains limited and depends on the MDS-UPDRS target. Wearable-derived measures may therefore provide complementary information to clinical assessments, particularly for motor complications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1876052","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:35:27Z","timestamp":1783920927000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Association between wrist-worn actigraphy and the MDS-UPDRS Parkinson\u2019s disease rating scale through machine learning: an exploratory study"],"prefix":"10.3389","volume":"8","author":[{"given":"Gent","family":"Ymeri","sequence":"first","affiliation":[{"name":"Sustainable Digitalisation Research Centre, Computer Science and Media Technology, Malm\u00f6 University","place":["Malm\u00f6, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Caramaschi","sequence":"additional","affiliation":[{"name":"Sustainable Digitalisation Research Centre, Computer Science and Media Technology, Malm\u00f6 University","place":["Malm\u00f6, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alban","family":"Haton","sequence":"additional","affiliation":[{"name":"Sustainable Digitalisation Research Centre, Computer Science and Media Technology, Malm\u00f6 University","place":["Malm\u00f6, Sweden"]},{"name":"Institut National Polytechnique Clermont-Auvergne","place":["Clermont, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl Magnus","family":"Olsson","sequence":"additional","affiliation":[{"name":"Sustainable Digitalisation Research Centre, Computer Science and Media Technology, Malm\u00f6 University","place":["Malm\u00f6, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myrthe","family":"Wassenburg","sequence":"additional","affiliation":[{"name":"Clinical Neuroscience, Karolinska Institutet","place":["Stockholm, Sweden"]},{"name":"Center for Neurology, Academic Specialist Center Torsplan","place":["Stockholm, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Per","family":"Svenningsson","sequence":"additional","affiliation":[{"name":"Clinical Neuroscience, Karolinska Institutet","place":["Stockholm, Sweden"]},{"name":"Center for Neurology, Academic Specialist Center Torsplan","place":["Stockholm, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Salvi","sequence":"additional","affiliation":[{"name":"Sustainable Digitalisation Research Centre, Computer Science and Media Technology, Malm\u00f6 University","place":["Malm\u00f6, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"B1","volume-title":"Neurological Disorders: Public Health Challenges","year":"2006"},{"key":"B2","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/S0140-6736(23)01419-8","article-title":"The epidemiology of Parkinson\u2019s disease","volume":"403","author":"Ben-Shlomo","year":"2024","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3389\/fcomp.2020.00020","article-title":"Innovative Parkinson\u2019s disease patients\u2019 motor skills assessment: the i-PROGNOSIS paradigm","volume":"2","author":"Dias","year":"2020","journal-title":"Front Comput Sci"},{"key":"B4","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S1474-4422(21)00330-6","article-title":"The neuropsychiatry of Parkinson\u2019s disease: advances and 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