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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only \u2018distance moved walking or running\u2019 was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.<\/jats:p>","DOI":"10.1038\/s41746-023-00974-w","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T07:03:08Z","timestamp":1703228588000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices"],"prefix":"10.1038","volume":"6","author":[{"given":"Varsha","family":"Gupta","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-6017","authenticated-orcid":false,"given":"Sokratis","family":"Kariotis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7591-6203","authenticated-orcid":false,"given":"Mohammed D.","family":"Rajab","sequence":"additional","affiliation":[]},{"given":"Niamh","family":"Errington","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2147-9715","authenticated-orcid":false,"given":"Elham","family":"Alhathli","sequence":"additional","affiliation":[]},{"given":"Emmanuel","family":"Jammeh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2739-587X","authenticated-orcid":false,"given":"Martin","family":"Brook","sequence":"additional","affiliation":[]},{"given":"Naomi","family":"Meardon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-6826","authenticated-orcid":false,"given":"Paul","family":"Collini","sequence":"additional","affiliation":[]},{"given":"Joby","family":"Cole","sequence":"additional","affiliation":[]},{"given":"Jim M.","family":"Wild","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9167-7380","authenticated-orcid":false,"given":"Steven","family":"Hershman","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Javed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-4551","authenticated-orcid":false,"given":"A. 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