{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T03:35:00Z","timestamp":1793072100823,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung, Technologie und Raumfahrt","doi-asserted-by":"publisher","award":["01GY2021"],"award-info":[{"award-number":["01GY2021"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Geriatrics Gerontology Int"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Falls are a health concern among older adults, and near\u2010falls may serve as an early indicator of future falls, functional decline, and the need for targeted preventive interventions. While previous research has successfully demonstrated near\u2010fall detection using machine learning in controlled laboratory environments, it remains crucial to evaluate the application of such approaches in daily\u2010life settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study examines the application of a pre\u2010trained machine learning algorithm to detect gait perturbations in daily\u2010life and to explore how these classified gait perturbations are associated with participant characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from the SeFallED study was used, which followed up on individuals aged \u2265\u200960\u2009years who presented to the emergency department following a fall. Daily stepping time was recorded using a thigh\u2010worn accelerometer over 7\u2009days, segmented into windows, and analyzed using a laboratory\u2010based pre\u2010trained one\u2010dimensional convolutional neural network to detect near\u2010falls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 202 participants (median age 75\u2009years) were included in the analysis. On average, 58% of total stepping time windows were classified as gait perturbation windows. Higher age, lower cognitive and functional performance, greater concerns about falling, and higher levels of frailty were associated with a greater proportion of classified gait perturbations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The laboratory\u2010trained algorithm classified a high number of windows as gait perturbations, likely reflecting gait irregularities including misclassifications rather than exclusively near\u2010falls. Although these classifications still meaningfully reflect participant characteristics, further training and validation are required to enhance the algorithm's ability to accurately distinguish and objectively assess gait perturbations and near\u2010falls in daily\u2010life.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ggi.70862","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T11:31:38Z","timestamp":1792063898000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the Application of Machine Learning for Daily\u2010Life Near\u2010Fall Detection in Older Adults"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1330-6066","authenticated-orcid":false,"given":"Nina Marie","family":"Schmidt","sequence":"first","affiliation":[{"name":"Geriatric Medicine, Department for Health Services Research, School of Medicine and Health Sciences Carl von Ossietzky University  Oldenburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Zieschang","sequence":"additional","affiliation":[{"name":"Geriatric Medicine, Department for Health Services Research, School of Medicine and Health Sciences Carl von Ossietzky University  Oldenburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lea","family":"Feld","sequence":"additional","affiliation":[{"name":"Geriatric Medicine, Department for Health Services Research, School of Medicine and Health Sciences Carl von Ossietzky University  Oldenburg Germany"},{"name":"Department for Health Services Research, Assistance Systems and Medical Device Technology Carl von Ossietzky University  Oldenburg Germany"},{"name":"Department of Medical Physics and Acoustics, Medical Physics and Cluster of Excellence Hearing4all Carl von Ossietzky University  Oldenburg 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