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To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This longitudinal cohort study will recruit 200 community-dwelling adults aged \u226565 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants\u2019 homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features\u2014such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns\u2014will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      -score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/79490","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:48:36Z","timestamp":1775072916000},"page":"e79490-e79490","source":"Crossref","is-referenced-by-count":3,"title":["Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor\u2013Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study"],"prefix":"10.2196","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9459-7340","authenticated-orcid":false,"given":"James Patrick","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9620-2875","authenticated-orcid":false,"given":"Khalid","family":"Abdul Jabbar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9012-2272","authenticated-orcid":false,"given":"Tony Chin Ian","family":"Tay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9330-8172","authenticated-orcid":false,"given":"Laura","family":"Tay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4213-7163","authenticated-orcid":false,"given":"Rathi","family":"Mahendran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9585-855X","authenticated-orcid":false,"given":"Tze Pin","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3303-4058","authenticated-orcid":false,"given":"Shian Ming","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4590-548X","authenticated-orcid":false,"given":"Wilbur Zhi Hao","family":"Koh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6546-8008","authenticated-orcid":false,"given":"Weng Yan","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0378-4069","authenticated-orcid":false,"given":"Ah-Hwee","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0788-422X","authenticated-orcid":false,"given":"Tih-Shih","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8214-6454","authenticated-orcid":false,"given":"Iris","family":"Rawtaer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"R1","unstructured":"World population prospects 2024. 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