{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:30:33Z","timestamp":1784694633695,"version":"3.55.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2019,9,9]]},"abstract":"<jats:p>Multiple Sclerosis requires long-term disease management, but tracking patients through the use of clinical surveys is hindered by high costs and patient burden. In this work, we investigate the feasibility of using data from ubiquitous sensing to predict MS patients' fatigue and health status, as measured by the Fatigue Severity Scale (FSS) and EQ-5D index. We collected data from 198 MS patients who are given connected wellness devices for over 6 months. We examine how accurately can the collected data predict reported FSS and EQ-5D scores per patient using an ensemble of regressors. In predicting for both FSS and EQ-5D, we are able to achieve errors aligning with the instrument' standard measurement error (SEM), as well as strong and significant correlations between predicted and ground truth values. We also show a simple adaptation method that greatly reduces prediction errors through the use of just 1 user-supplied ground truth datapoint. For FSS (SEM 0.7), the universal model predicts weekly scores with MAE 1.00, while an adapted model predicts with MAE 0.58. For EQ-5D (SEM 0.093), the universal model predicts weekly scores with MAE 0.097, while an adapted model predicts with MAE 0.065. Our study represents the first sets of results showing that fatigue and health state of MS patients can be measured using data from connected wellness devices and a small number of background features, with promising prediction performance with errors within the accepted range of error in the widely used clinically-validated questionnaires. Future extensions and potential applications of our results can positively impact MS patient disease management and support clinical research.<\/jats:p>","DOI":"10.1145\/3351264","type":"journal-article","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T15:58:26Z","timestamp":1568131106000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connnected Wellness Devices"],"prefix":"10.1145","volume":"3","author":[{"given":"Catherine","family":"Tong","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Craner","sequence":"additional","affiliation":[{"name":"Nuffield Department of Clinical Neurosciences, University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Vegreville","sequence":"additional","affiliation":[{"name":"Withings, Issy-les-Moulineaux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas D.","family":"Lane","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. 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