{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:32:11Z","timestamp":1760239931640,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,22]],"date-time":"2019-01-22T00:00:00Z","timestamp":1548115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["689238"],"award-info":[{"award-number":["689238"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessment of physical performance by standard clinical tests such as the 30-s Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 \u00b1 2.4 years, 87 females, median LLFDI 72.31, range: 44.33\u2013100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60\u20130.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60\u20130.77) and 0.65 (95%CI 0.56\u20130.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61\u201370 years.<\/jats:p>","DOI":"10.3390\/s19030449","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T03:52:32Z","timestamp":1548301952000},"page":"449","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61\u201370 Years Old"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3317-6200","authenticated-orcid":false,"given":"Alice","family":"Coni","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d (DEI), University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6939-0487","authenticated-orcid":false,"given":"Jeanine M.","family":"Van Ancum","sequence":"additional","affiliation":[{"name":"Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, 1081 BT Amsterdam, The Netherlands"}]},{"given":"Ronny","family":"Bergquist","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}]},{"given":"A. Stefanie","family":"Mikolaizak","sequence":"additional","affiliation":[{"name":"Department of Clinical Gerontology, Robert Bosch Medical Foundation, 70376 Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7688-0188","authenticated-orcid":false,"given":"Sabato","family":"Mellone","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d (DEI), University of Bologna, 40136 Bologna, Italy"},{"name":"Health Sciences and Technologies\u2014Interdepartmental Center for Industrial Research (HST-ICIR), University of Bologna, 40126 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-4370","authenticated-orcid":false,"given":"Lorenzo","family":"Chiari","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering \u201cGuglielmo Marconi\u201d (DEI), University of Bologna, 40136 Bologna, Italy"},{"name":"Health Sciences and Technologies\u2014Interdepartmental Center for Industrial Research (HST-ICIR), University of Bologna, 40126 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7206-1724","authenticated-orcid":false,"given":"Andrea B.","family":"Maier","sequence":"additional","affiliation":[{"name":"Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, 1081 BT Amsterdam, The Netherlands"},{"name":"Department of Medicine and Aged Care, @AgeMelbourne, University of Melbourne, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8416-2602","authenticated-orcid":false,"given":"Mirjam","family":"Pijnappels","sequence":"additional","affiliation":[{"name":"Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, 1081 BT Amsterdam, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/S0749-3797(03)00174-0","article-title":"Assessing the building blocks of function: Utilizing measures of functional limitation","volume":"25","author":"Guralnik","year":"2003","journal-title":"Am. 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