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While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We used the dataset collected from a 10-week single-arm clinical trial in older adults (50\u201370\u00a0years old) diagnosed with amnestic MCI (<jats:italic>N<\/jats:italic>\u2009=\u200930) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors\u2019 data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using \u201cleave-one-subject-out\u201d and \u201cleave-one-interval-out\u201d cross-validation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation <jats:italic>r<\/jats:italic>\u2009=\u20090.69 and intra-individual changes in executive function scores with <jats:italic>r<\/jats:italic>\u2009=\u20090.61.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12916-024-03252-y","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T06:01:50Z","timestamp":1706162510000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9966-7117","authenticated-orcid":false,"given":"Yuri G.","family":"Rykov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bikram A.","family":"Gangwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syaheed B.","family":"Jabar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacklyn","family":"Leonardo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kok Pin","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagaendran","family":"Kandiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"3252_CR1","first-page":"afac173","volume":"51","author":"W Bai","year":"2022","unstructured":"Bai W, Chen P, Cai H, Zhang Q, Su Z, Cheung T, et al. 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Written informed consent was obtained from all participants before enrolling in this study, in accordance with the guidelines outlined by the SingHealth CIRB. The informed consent process was designed to provide participants with a clear understanding of the study\u2019s purpose, procedures, risks, and benefits, as well as their right to withdraw at any time.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Y.G.R, B.A.G., M.D.P, S.B.J, and J.L. were or are employed by Neuroglee Therapeutics. K.P.N received no direct payment from the study sponsor for his contributions. N.K received support from Neuroglee Therapeutics as a paid consultant. Neuroglee Therapeutics provided financial support and was involved in the protocol design, data analysis and the decision to submit this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}