{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:24:04Z","timestamp":1791235444356,"version":"4.1.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["15H01972"],"award-info":[{"award-number":["15H01972"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20K18931"],"award-info":[{"award-number":["20K18931"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003478","name":"Ministry of Health, Labour and Welfare","doi-asserted-by":"publisher","award":["H28-Choju-Ippan-002"],"award-info":[{"award-number":["H28-Choju-Ippan-002"]}],"id":[{"id":"10.13039\/501100003478","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP17dk0110017"],"award-info":[{"award-number":["JP17dk0110017"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP18dk0110027"],"award-info":[{"award-number":["JP18dk0110027"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP18ls0110002"],"award-info":[{"award-number":["JP18ls0110002"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP18le0110009"],"award-info":[{"award-number":["JP18le0110009"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007312","name":"National Center for Geriatrics and Gerontology","doi-asserted-by":"publisher","award":["29-42"],"award-info":[{"award-number":["29-42"]}],"id":[{"id":"10.13039\/501100007312","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J GEN INTERN MED"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>It is important to identify older adults at high risk of functional disability and to take preventive measures for them at an early stage. To our knowledge, there are no studies that predict functional disability among community-dwelling older adults using machine learning algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>To construct a model that can predict functional disability over 5\u00a0years using basic machine learning algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Design<\/jats:title>\n                <jats:p>A cohort study with a mean follow-up of 5.4\u00a0years.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Participants<\/jats:title>\n                <jats:p>We used data from the Japan Gerontological Evaluation Study, which involved 73,262 people aged \u2009\u2265\u200965\u00a0years who were not certified as requiring long-term care. The baseline survey was conducted in 2013 in 19 municipalities.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Main Measures<\/jats:title>\n                <jats:p>We defined the onset of functional disability as the new certification of needing long-term care that was ascertained by linking participants to public registries of long-term care insurance. All 183 candidate predictors were measured by self-report questionnaires.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Results<\/jats:title>\n                <jats:p>During the study period, 16,361 (22.3%) participants experienced the onset of functional disability. Among machine learning\u2013based models, ridge regression (<jats:italic>C<\/jats:italic> statistic\u2009=\u20090.818) and gradient boosting (0.817) effectively predicted functional disability. In both models, we identified age, self-rated health, variables related to falls and posture stabilization, and diagnoses of Parkinson\u2019s disease and dementia as important features. Additionally, the ridge regression model identified the household characteristics such as the number of members, income, and receiving public assistance as important predictors, while the gradient boosting model selected moderate physical activity and driving. Based on the ridge regression model, we developed a simplified risk score for functional disability, and it also indicated good performance at the cut-off of 6\/7 points.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Machine learning\u2013based models showed effective performance prediction over 5\u00a0years. Our findings suggest that measuring and adding the variables identified as important features can improve the prediction of functional disability.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11606-023-08215-2","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T17:01:50Z","timestamp":1682960510000},"page":"2486-2493","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Learning\u2013Based Prediction of Functional Disability: a Cohort Study of Japanese Older Adults in 2013\u20132019"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5975-817X","authenticated-orcid":false,"given":"Yongjian","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8418-8535","authenticated-orcid":false,"given":"Koryu","family":"Sato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masato","family":"Nagai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirokazu","family":"Miyatake","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katsunori","family":"Kondo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoki","family":"Kondo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"8215_CR1","unstructured":"World Health Organization. Ageing and health. World Health Organization. Published October 4, 2021. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health.Accessed 12 Aug 2022."},{"key":"8215_CR2","doi-asserted-by":"publisher","unstructured":"Okoro CA. Prevalence of Disabilities and Health Care Access by Disability Status and Type Among Adults \u2014 United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67. https:\/\/doi.org\/10.15585\/mmwr.mm6732a3.","DOI":"10.15585\/mmwr.mm6732a3."},{"key":"8215_CR3","doi-asserted-by":"crossref","unstructured":"Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004;59(3):255-263.","DOI":"10.1093\/gerona\/59.3.M255"},{"key":"8215_CR4","doi-asserted-by":"publisher","unstructured":"Kojima G, Taniguchi Y, Iliffe S, Jivraj S, Walters K. Transitions Between Frailty States Among Community-Dwelling Older People: a Systematic Review and Meta-analysis. Ageing Res Rev. 2019;50:81-88. https:\/\/doi.org\/10.1016\/j.arr.2019.01.010.","DOI":"10.1016\/j.arr.2019.01.010."},{"key":"8215_CR5","doi-asserted-by":"publisher","unstructured":"Van Grootven B, van Achterberg T. Prediction Models for Functional Status in Community Dwelling Older Adults: a Systematic Review. BMC Geriatr. 2022;22(1):465. https:\/\/doi.org\/10.1186\/s12877-022-03156-7.","DOI":"10.1186\/s12877-022-03156-7."},{"key":"8215_CR6","doi-asserted-by":"publisher","unstructured":"Tsuji T, Kondo K, Kondo N, Aida J, Takagi D. Development of a Risk Assessment Scale Predicting Incident Functional Disability Among Older People: Japan Gerontological Evaluation Study. Geriatr Gerontol Int. 2018;18(10):1433-1438. https:\/\/doi.org\/10.1111\/ggi.13503.","DOI":"10.1111\/ggi.13503."},{"key":"8215_CR7","doi-asserted-by":"publisher","unstructured":"Aida J, Kondo K, Kawachi I, et al. Does Social Capital Affect the Incidence of Functional Disability in Older Japanese? A Prospective Population-Based Cohort Study. J Epidemiol Community Health. 2013;67(1):42-47. https:\/\/doi.org\/10.1136\/jech-2011-200307.","DOI":"10.1136\/jech-2011-200307."},{"key":"8215_CR8","doi-asserted-by":"publisher","unstructured":"Ashida T, Kondo N, Kondo K. Social Participation and the Onset of Functional Disability by Socioeconomic Status and Activity Type: the JAGES Cohort Study. Prev Med. 2016;89:121-128. https:\/\/doi.org\/10.1016\/j.ypmed.2016.05.006.","DOI":"10.1016\/j.ypmed.2016.05.006."},{"key":"8215_CR9","doi-asserted-by":"publisher","unstructured":"Fujihara S, Miyaguni Y, Tsuji T, Kondo K. Community-Level Social Participation and Functional Disability Among Older Adults: a JAGES Multilevel Longitudinal Study. Arch Gerontol Geriatr. 2022;100:104632. https:\/\/doi.org\/10.1016\/j.archger.2022.104632.","DOI":"10.1016\/j.archger.2022.104632."},{"key":"8215_CR10","doi-asserted-by":"publisher","unstructured":"Watanabe R, Tsuji T, Ide K, et al. Predictive Validity of the Modified Kihon Checklist for the Incidence of Functional Disability Among Older People: a 3-Year Cohort Study from the JAGES. Geriatr Gerontol Int. 2022;22(8):667-674. https:\/\/doi.org\/10.1111\/ggi.14439.","DOI":"10.1111\/ggi.14439."},{"key":"8215_CR11","doi-asserted-by":"crossref","unstructured":"Houde SC, Gautam R, Kai I. Long-term care insurance in Japan: implications for U.S. long-term care policy. J Gerontol Nurs. 2007;33(1):7\u201313.","DOI":"10.3928\/00989134-20070101-04"},{"key":"8215_CR12","unstructured":"Ministry of Health, Labour and Welfare. Long-term care insurance system of Japan. Ministry of Health, Labour and Welfare. Published November 2016. https:\/\/www.mhlw.go.jp\/english\/policy\/care-welfare\/care-welfare-elderly\/dl\/ltcisj_e.pdf.Accessed 15 Sept .2022"},{"key":"8215_CR13","unstructured":"Ministry of Health, Labour and Welfare. Older Adult Care in 2015: Toward the Establishment of Care That Supports the Dignity of Older Adults. Ministry of Health, Labour and Welfare; 2003. https:\/\/www.mhlw.go.jp\/topics\/kaigo\/kentou\/15kourei\/sankou3.html.Accessed 9 Mar 2023."},{"key":"8215_CR14","doi-asserted-by":"crossref","unstructured":"James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning: With Applications in R. 2nd Edition. Springer; 2021.","DOI":"10.1007\/978-1-0716-1418-1"},{"key":"8215_CR15","unstructured":"Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer; 2016."},{"key":"8215_CR16","doi-asserted-by":"publisher","unstructured":"Hoerl AE, Kennard RW. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics. 1970;12(1):55-67. https:\/\/doi.org\/10.1080\/00401706.1970.10488634.","DOI":"10.1080\/00401706.1970.10488634."},{"key":"8215_CR17","doi-asserted-by":"publisher","unstructured":"Hoerl AE, Kennard RW. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics. 1970;12(1):69-82. https:\/\/doi.org\/10.2307\/1267352.","DOI":"10.2307\/1267352."},{"key":"8215_CR18","doi-asserted-by":"publisher","unstructured":"Natekin A, Knoll A. Gradient Boosting Machines, a Tutorial. Front Neurorobotics. 2013;7:21. https:\/\/doi.org\/10.3389\/fnbot.2013.00021.","DOI":"10.3389\/fnbot.2013.00021."},{"key":"8215_CR19","doi-asserted-by":"publisher","unstructured":"Breiman L. Random Forests. Mach Learn. 2001;45(1):5-32. https:\/\/doi.org\/10.1023\/A:1010933404324.","DOI":"10.1023\/A:1010933404324."},{"key":"8215_CR20","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916. Association for Computing Machinery; 2016:785\u2013794. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785."},{"key":"8215_CR21","doi-asserted-by":"publisher","unstructured":"Stekhoven DJ, B\u00fchlmann P. MissForest\u2014Non-parametric Missing Value Imputation for Mixed-Type Data. Bioinformatics. 2012;28(1):112-118. https:\/\/doi.org\/10.1093\/bioinformatics\/btr597.","DOI":"10.1093\/bioinformatics\/btr597."},{"key":"8215_CR22","doi-asserted-by":"publisher","unstructured":"Noguchi T, Kondo K, Saito M, Nakagawa-Senda H, Suzuki S. Community Social Capital and the Onset of Functional Disability Among Older Adults in Japan: a Multilevel Longitudinal Study Using Japan Gerontological Evaluation Study (JAGES) Data. BMJ Open. 2019;9(10):e029279. https:\/\/doi.org\/10.1136\/bmjopen-2019-029279.","DOI":"10.1136\/bmjopen-2019-029279."},{"key":"8215_CR23","unstructured":"Tomata Y, Hozawa A, Ohmori-Matsuda K, et al. Validation of the Kihon Checklist for predicting the risk of 1-year incident long-term care insurance certification: the Ohsaki Cohort 2006 Study. Nihon Koshu Eisei Zasshi Jpn J Public Health. 2011;58(1):3-13."},{"key":"8215_CR24","doi-asserted-by":"publisher","unstructured":"Tinetti ME, Williams CS. The Effect of Falls and Fall Injuries on Functioning in Community-Dwelling Older Persons. J Gerontol Ser A. 1998;53A(2):M112-M119. https:\/\/doi.org\/10.1093\/gerona\/53A.2.M112.","DOI":"10.1093\/gerona\/53A.2.M112."},{"key":"8215_CR25","doi-asserted-by":"publisher","unstructured":"Murray AM, Bennett DA, Mendes de Leon CF, Beckett LA, Evans DA. A Longitudinal Study of Parkinsonism and Disability in a Community Population of Older People. J Gerontol Ser A. 2004;59(8):M864-M870. https:\/\/doi.org\/10.1093\/gerona\/59.8.M864.","DOI":"10.1093\/gerona\/59.8.M864."},{"key":"8215_CR26","doi-asserted-by":"publisher","unstructured":"Greiner PA, Snowdon DA, Schmitt FA. The Loss of Independence in Activities of Daily Living: the Role of Low Normal Cognitive Function in Elderly Nuns. Am J Public Health. 1996;86(1):62-66. https:\/\/doi.org\/10.2105\/ajph.86.1.62.","DOI":"10.2105\/ajph.86.1.62."},{"key":"8215_CR27","doi-asserted-by":"publisher","unstructured":"Armstrong MJ, Okun MS. Diagnosis and Treatment of Parkinson Disease: a Review. JAMA. 2020;323(6):548-560. https:\/\/doi.org\/10.1001\/jama.2019.22360.","DOI":"10.1001\/jama.2019.22360."},{"key":"8215_CR28","doi-asserted-by":"publisher","unstructured":"Sauvaget C, Yamada M, Fujiwara S, Sasaki H, Mimori Y. Dementia as a Predictor of Functional Disability: a Four-Year Follow-up Study. Gerontology. 2002;48(4):226-233. https:\/\/doi.org\/10.1159\/000058355.","DOI":"10.1159\/000058355."},{"key":"8215_CR29","doi-asserted-by":"publisher","unstructured":"Idler EL, Kasl SV. Self-ratings of Health: Do They Also Predict Change in Functional Ability? J Gerontol B Psychol Sci Soc Sci. 1995;50(6):S344-353. https:\/\/doi.org\/10.1093\/geronb\/50b.6.s344.","DOI":"10.1093\/geronb\/50b.6.s344."},{"key":"8215_CR30","doi-asserted-by":"publisher","unstructured":"Lee Y. The Predictive Value of Self Assessed General, Physical, and Mental Health on Functional Decline and Mortality in Older Adults. J Epidemiol Community Health. 2000;54(2):123-129. https:\/\/doi.org\/10.1136\/jech.54.2.123.","DOI":"10.1136\/jech.54.2.123."},{"key":"8215_CR31","doi-asserted-by":"publisher","unstructured":"Takahashi S, Tanno K, Yonekura Y, et al. Poor Self-rated Health Predicts the Incidence of Functional Disability in Elderly Community Dwellers in Japan: a Prospective Cohort Study. BMC Geriatr. 2020;20(1):328. https:\/\/doi.org\/10.1186\/s12877-020-01743-0.","DOI":"10.1186\/s12877-020-01743-0."},{"key":"8215_CR32","doi-asserted-by":"crossref","unstructured":"Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav. 1997;38(1):21-37.","DOI":"10.2307\/2955359"},{"key":"8215_CR33","doi-asserted-by":"publisher","unstructured":"McLaughlin D, Leung J, Pachana N, Flicker L, Hankey G, Dobson A. Social Support and Subsequent Disability: It Is Not the Size of Your Network That Counts. Age Ageing. 2012;41(5):674-677. https:\/\/doi.org\/10.1093\/ageing\/afs036.","DOI":"10.1093\/ageing\/afs036."},{"key":"8215_CR34","doi-asserted-by":"publisher","unstructured":"Minkler M, Fuller-Thomson E, Guralnik JM. Gradient of Disability across the Socioeconomic Spectrum in the United States. N Engl J Med. 2006;355(7):695-703. https:\/\/doi.org\/10.1056\/NEJMsa044316.","DOI":"10.1056\/NEJMsa044316."},{"key":"8215_CR35","doi-asserted-by":"publisher","unstructured":"Zhong Y, Wang J, Nicholas S. Gender, Childhood and Adult Socioeconomic Inequalities in Functional Disability Among Chinese Older Adults. Int J Equity Health. 2017;16(1):165. https:\/\/doi.org\/10.1186\/s12939-017-0662-3.","DOI":"10.1186\/s12939-017-0662-3."},{"key":"8215_CR36","doi-asserted-by":"publisher","unstructured":"Gjon\u00e7a E, Tabassum F, Breeze E. Socioeconomic Differences in Physical Disability at Older Age. J Epidemiol Community Health. 2009;63(11):928-935. https:\/\/doi.org\/10.1136\/jech.2008.082776.","DOI":"10.1136\/jech.2008.082776."},{"key":"8215_CR37","doi-asserted-by":"publisher","unstructured":"Chen T, Honda T, Chen S, Kishimoto H, Kumagai S, Narazaki K. Potential Utility of Physical Function Measures to Improve the Risk Prediction of Functional Disability in Community-Dwelling Older Japanese Adults: a Prospective Study. BMC Geriatr. 2021;21(1):476. https:\/\/doi.org\/10.1186\/s12877-021-02415-3.","DOI":"10.1186\/s12877-021-02415-3."},{"key":"8215_CR38","doi-asserted-by":"publisher","unstructured":"Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability. N Engl J Med. 1995;332(9):556-561. https:\/\/doi.org\/10.1056\/NEJM199503023320902.","DOI":"10.1056\/NEJM199503023320902."},{"key":"8215_CR39","doi-asserted-by":"publisher","unstructured":"Ramspek CL, Steyerberg EW, Riley RD, et al. Prediction or Causality? A Scoping Review of Their Conflation Within Current Observational Research. Eur J Epidemiol. 2021;36(9):889-898. https:\/\/doi.org\/10.1007\/s10654-021-00794-w.","DOI":"10.1007\/s10654-021-00794-w."},{"key":"8215_CR40","doi-asserted-by":"publisher","unstructured":"Santos-Eggimann B, Cu\u00e9noud P, Spagnoli J, Junod J. Prevalence of Frailty in Middle-Aged and Older Community-Dwelling Europeans Living in 10 Countries. J Gerontol Ser A. 2009;64A(6):675-681. https:\/\/doi.org\/10.1093\/gerona\/glp012.","DOI":"10.1093\/gerona\/glp012."}],"container-title":["Journal of General Internal Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11606-023-08215-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11606-023-08215-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11606-023-08215-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T16:27:26Z","timestamp":1693326446000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11606-023-08215-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":40,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["8215"],"URL":"https:\/\/doi.org\/10.1007\/s11606-023-08215-2","relation":{},"ISSN":["0884-8734","1525-1497"],"issn-type":[{"value":"0884-8734","type":"print"},{"value":"1525-1497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"assertion":[{"value":"29 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations:"}},{"value":"The authors declare that they do not have a conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest:"}}]}}