{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:38:28Z","timestamp":1787161108332,"version":"build-2736575974"},"reference-count":51,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72204071"],"award-info":[{"award-number":["72204071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY23G030005"],"award-info":[{"award-number":["LY23G030005"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["4265C50221204119"],"award-info":[{"award-number":["4265C50221204119"]}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Health Data Sci"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. This study aimed to investigate the associations between accelerometer-measured PA and brain age.\n                    <jats:bold>Methods:<\/jats:bold>\n                    A total of 16,972 eligible participants with both valid\n                    <jats:italic toggle=\"yes\">T<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    -weighted neuroimaging and accelerometer data from the UK Biobank was included. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM). Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders.\n                    <jats:bold>Results:<\/jats:bold>\n                    The brain age estimated by LightGBM achieved an appreciable performance (\n                    <jats:italic toggle=\"yes\">r<\/jats:italic>\n                    = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (\n                    <jats:italic toggle=\"yes\">r<\/jats:italic>\n                    = 0.90, MAE = 3.03). We found that LPA (\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    = 2.47,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    = 0.04), MPA (\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    = 6.49,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &lt; 1 \u00d7 10\n                    <jats:sup>\u2212300<\/jats:sup>\n                    ), VPA (\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    = 4.92,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    = 2.58 \u00d7 10\n                    <jats:sup>\u22125<\/jats:sup>\n                    ), and MVPA (\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    = 6.45,\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    &lt; 1 \u00d7 10\n                    <jats:sup>\u2212300<\/jats:sup>\n                    ) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Furthermore, mediation analysis suggested that BAG partially mediated the associations between PA and cognitive functions as well as brain-related disorders.\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Our study revealed a U-shaped association between accelerometer-measured PA and BAG, highlighting that advanced brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.\n                  <\/jats:p>","DOI":"10.34133\/hds.0257","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T11:43:32Z","timestamp":1740743012000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":14,"title":["Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age"],"prefix":"10.34133","volume":"5","author":[{"given":"Han","family":"Chen","sequence":"first","affiliation":[{"name":"School of Public Health, \rHangzhou Normal University, Hangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Public Health, \rHangzhou Normal University, Hangzhou, China."},{"name":"Department of Psychiatry, Sir Run Run Shaw Hospital, \rZhejiang University School of Medicine, Hangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Health, \rHangzhou Normal University, Hangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Integrative Medicine, Public Health Science and Engineering College, \rTianjin University of Traditional Chinese Medicine, Tianjin, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaogang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Integrative Medicine, Public Health Science and Engineering College, \rTianjin University of Traditional Chinese Medicine, Tianjin, China."},{"name":"School of Public Health, \rTianjin Medical University, Tianjin, China."},{"name":"National Institute of Health Data Science at Peking University, \rPeking University, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenjie","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Public Health, \rHangzhou Normal University, Hangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2021.3107372"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2006.07.001"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2017.10.001"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119210","article-title":"Deep neural networks learn general and clinically relevant representations of the ageing brain","volume":"256","author":"Leonardsen EH","year":"2022","unstructured":"Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, H\u00f8gest\u00f8l EA, Lange AM, et al. 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