{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:05:05Z","timestamp":1792785905268,"version":"4.5.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Association for Clinical and Translational Sciences","award":["KL2TR001421"],"award-info":[{"award-number":["KL2TR001421"]}]},{"name":"National Association for Clinical and Translational Sciences","award":["UL1TR001420"],"award-info":[{"award-number":["UL1TR001420"]}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["N01-AG-6-2101"],"award-info":[{"award-number":["N01-AG-6-2101"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["N01-AG-6-2103"],"award-info":[{"award-number":["N01-AG-6-2103"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["N01-AG-6-2106"],"award-info":[{"award-number":["N01-AG-6-2106"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01-AG028050"],"award-info":[{"award-number":["R01-AG028050"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000056","name":"National Institute of Nursing Research","doi-asserted-by":"publisher","award":["R01-NR012459"],"award-info":[{"award-number":["R01-NR012459"]}],"id":[{"id":"10.13039\/100000056","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wake Forest School of Medicine Clinical and Translational Science Institute","award":["KL2TR001421"],"award-info":[{"award-number":["KL2TR001421"]}]},{"name":"Wake Forest School of Medicine Clinical and Translational Science Institute","award":["UL1TR001420"],"award-info":[{"award-number":["UL1TR001420"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Area under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gerona\/glab269","type":"journal-article","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T14:06:15Z","timestamp":1631801175000},"page":"1072-1078","source":"Crossref","is-referenced-by-count":22,"title":["Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data"],"prefix":"10.1093","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-8730","authenticated-orcid":false,"given":"Jaime L","family":"Speiser","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn E","family":"Callahan","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4811-4205","authenticated-orcid":false,"given":"Edward H","family":"Ip","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael E","family":"Miller","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janet A","family":"Tooze","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3336-6781","authenticated-orcid":false,"given":"Stephen B","family":"Kritchevsky","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denise K","family":"Houston","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"2022050521192852800_CIT0001","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev.pu.17.050196.000325","article-title":"Disability as a public health outcome in the aging population","volume":"17","author":"Guralnik","year":"1996","journal-title":"Annu Rev Public Health"},{"issue":"8","key":"2022050521192852800_CIT0002","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.2105\/AJPH.2011.300631","article-title":"Mobility and aging: new directions for public health action","volume":"102","author":"Satariano","year":"2012","journal-title":"Am J Public Health"},{"issue":"9614","key":"2022050521192852800_CIT0003","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/S0140-6736(08)60342-6","article-title":"Complex interventions to improve physical function and maintain independent living in elderly people: a systematic review and meta-analysis","volume":"371","author":"Beswick","year":"2008","journal-title":"Lancet"},{"issue":"11","key":"2022050521192852800_CIT0004","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1001\/jama.2013.276566","article-title":"Mobility limitation in the older patient: a clinical review","volume":"310","author":"Brown","year":"2013","journal-title":"JAMA"},{"issue":"502","key":"2022050521192852800_CIT0005","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1080\/01621459.2013.770307","article-title":"Partially ordered mixed hidden Markov model for the disablement process of older adults","volume":"108","author":"Ip","year":"2013","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"2022050521192852800_CIT0006","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.gerinurse.2007.07.002","article-title":"Risk factors for mobility limitation in community-dwelling older adults: a social ecological perspective","volume":"29","author":"Yeom","year":"2008","journal-title":"Geriatr Nurs"},{"issue":"11","key":"2022050521192852800_CIT0007","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1016\/j.apmr.2008.05.013","article-title":"Obesity influences transitional states of disability in older adults with knee pain","volume":"89","author":"Rejeski","year":"2008","journal-title":"Arch Phys Med Rehabil"},{"issue":"2","key":"2022050521192852800_CIT0008","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1111\/j.1532-5415.2005.00586.x","article-title":"The dynamic nature of mobility disability in older persons","volume":"54","author":"Gill","year":"2006","journal-title":"J Am Geriatr Soc"},{"issue":"4","key":"2022050521192852800_CIT0009","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.exger.2013.02.002","article-title":"Identification of high-risk individuals for the development of disability in activities of daily living. A ten-year follow-up study","volume":"48","author":"den Ouden","year":"2013","journal-title":"Exp Gerontol"},{"issue":"2","key":"2022050521192852800_CIT0010","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1093\/ageing\/afu159","article-title":"Incident disability in older adults: prediction models based on two British prospective cohort studies","volume":"44","author":"N\u00fcesch","year":"2015","journal-title":"Age Ageing"},{"key":"2022050521192852800_CIT0011","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/1471-2318-11-22","article-title":"Age, gender and disability predict future disability in older people: the Rotterdam Study","volume":"11","author":"Ta\u015f","year":"2011","journal-title":"BMC Geriatr"},{"issue":"4","key":"2022050521192852800_CIT0012","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1093\/gerona\/glaa138","article-title":"Machine learning in aging: an example of developing prediction models for serious fall injury in older adults","volume":"76","author":"Speiser","year":"2021","journal-title":"J Gerontol A Biol Sci Med Sci"},{"issue":"3","key":"2022050521192852800_CIT0013","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1177\/014662167700100306","article-title":"The CES-D scale: a self-report depression scale for research in the general population","volume":"1","author":"Radloff","year":"1977","journal-title":"Appl Psychol Meas"},{"issue":"8","key":"2022050521192852800_CIT0014","first-page":"314","article-title":"The Modified Mini-Mental State (3MS) examination","volume":"48","author":"Teng","year":"1987","journal-title":"J Clin Psychiatry"},{"key":"2022050521192852800_CIT0015","volume-title":"R: A Language and Environment for Statistical Computing","author":"R Development Core Team.","year":"2008"},{"key":"2022050521192852800_CIT0016","author":"Yoshida","year":"2015"},{"issue":"49","key":"2022050521192852800_CIT0017","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.21105\/joss.02017","article-title":"Ggalluvial: layered grammar for alluvial plots","volume":"5","author":"Brunson","year":"2020","journal-title":"J Open Source Softw"},{"issue":"1","key":"2022050521192852800_CIT0018","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest\u2014non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"2022050521192852800_CIT0019","doi-asserted-by":"crossref","first-page":"103763","DOI":"10.1016\/j.jbi.2021.103763","article-title":"A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data","volume":"117","author":"Speiser","year":"2021","journal-title":"J Biomed Inform"},{"key":"2022050521192852800_CIT0020","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.chemolab.2019.01.002","article-title":"BiMM forest: a random forest method for modeling clustered and longitudinal binary outcomes","volume":"185","author":"Speiser","year":"2019","journal-title":"Chemometr Intell Lab Syst"},{"key":"2022050521192852800_CIT0021","author":"Paluszynska","year":"2017"},{"key":"2022050521192852800_CIT0022","author":"Bates","year":"2015"},{"key":"2022050521192852800_CIT0023","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"2022050521192852800_CIT0024","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1471-2105-7-3","article-title":"Gene selection and classification of microarray data using random forest","volume":"7","author":"D\u00edaz-Uriarte","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2022050521192852800_CIT0025","author":"LeDell","year":"2014"},{"issue":"20","key":"2022050521192852800_CIT0026","doi-asserted-by":"crossref","first-page":"3940","DOI":"10.1093\/bioinformatics\/bti623","article-title":"ROCR: visualizing classifier performance in R","volume":"21","author":"Sing","year":"2005","journal-title":"Bioinformatics"},{"issue":"1","key":"2022050521192852800_CIT0027","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1186\/s12877-019-1192-1","article-title":"Development of a clinical prediction model for the onset of functional decline in people aged 65\u201375 years: pooled analysis of four European cohort studies","volume":"19","author":"Jonkman","year":"2019","journal-title":"BMC Geriatr"},{"issue":"3","key":"2022050521192852800_CIT0028","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1159\/000485135","article-title":"Predicting trajectories of functional decline in 60- to 70-year-old people","volume":"64","author":"Jonkman","year":"2018","journal-title":"Gerontology"},{"issue":"10","key":"2022050521192852800_CIT0029","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1093\/gerona\/glaa045","article-title":"Distinct trajectories of individual physical performance measures across 9 years in 60- to 70-year-old adults","volume":"75","author":"Hoekstra","year":"2020","journal-title":"J Gerontol A Biol Sci Med Sci"},{"issue":"9","key":"2022050521192852800_CIT0030","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/S0895-4356(01)00357-2","article-title":"Collaborative Research Group. Self-reported preclinical disability identifies older women with early declines in performance and early disease","volume":"54","author":"Fried","year":"2001","journal-title":"J Clin Epidemiol"}],"container-title":["The Journals of Gerontology: Series A"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/biomedgerontology\/advance-article-pdf\/doi\/10.1093\/gerona\/glab269\/40854903\/glab269.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomedgerontology\/article-pdf\/77\/5\/1072\/43552043\/glab269.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomedgerontology\/article-pdf\/77\/5\/1072\/43552043\/glab269.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T17:29:10Z","timestamp":1651771750000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomedgerontology\/article\/77\/5\/1072\/6371263"}},"subtitle":[],"editor":[{"given":"Lewis A","family":"Lipsitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,16]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,9,16]]},"published-print":{"date-parts":[[2022,5,5]]}},"URL":"https:\/\/doi.org\/10.1093\/gerona\/glab269","relation":{},"ISSN":["1079-5006","1758-535X"],"issn-type":[{"value":"1079-5006","type":"print"},{"value":"1758-535X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,5,1]]},"published":{"date-parts":[[2021,9,16]]}}}