January 28, 2023

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Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach | BMC Public Health

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  • Ferrer A, Formiga F, Sanz H, de Vries OJ, Badia T, Pujol R, et al. Multifactorial assessment and targeted intervention to reduce falls among the oldest-old: a randomized controlled trial. Clin Interv Aging. 2014;9:383–93.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • WHO. Falls fact sheet. 2018.

  • Esain I, Rodriguez-Larrad A, Bidaurrazaga-Letona I, Gil SM. Health-related quality of life, handgrip strength and falls during detraining in elderly habitual exercisers. Health Qual Life Outcomes. 2017;15(1):226.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haagsma JA, Olij BF, Majdan M, van Beeck EF, Vos T, Castle CD, et al. Falls in older aged adults in 22 European countries: incidence, mortality and burden of disease from 1990 to 2017. Inj Prev. 2020;26(Supp 1):i67.

    Article 
    PubMed 

    Google Scholar
     

  • Statbel. Kerncijfers – Statistisch overzicht van België. 2020.

  • James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017. Inj Prev. 2020;26(Suppl 2):i3.

    Article 
    PubMed 

    Google Scholar
     

  • WHO. Global report on falls prevention in older age. Geneva: World Health Organization; 2008.

  • Lu Z, Lam F, Leung J, Kwok T. The U-Shaped relationship between levels of bouted activity and fall incidence in community-dwelling older adults: a prospective cohort study. J Gerontol A Biol Sci Med Sci. 2020;75(10):e145–51.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Aranyavalai T, Jalayondeja C, Jalayondeja W, Pichaiyongwongdee S, Kaewkungwal J, Laskin J. Association between walking 5000 step/day and fall incidence over six months in urban community-dwelling older people. BMC Geriatr. 2020;20(1):194.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Gazibara T, Kurtagic I, Kisic-Tepavcevic D, Nurkovic S, Kovacevic N, Gazibara T, et al. Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatr. 2017;17(4):215–23.

    Article 

    Google Scholar
     

  • Pérez-Ros P, Martínez-Arnau F, Orti-Lucas R, Tarazona-Santabalbina F. A predictive model of isolated and recurrent falls in functionally independent community-dwelling older adults. Braz J Phys Ther. 2019;23(1):19–26.

    Article 
    PubMed 

    Google Scholar
     

  • Kim T, Choi SD, Xiong S. Epidemiology of fall and its socioeconomic risk factors in community-dwelling Korean elderly. PLoS ONE. 2020;15(6):e0234787.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Carrasco C, Tomas-Carus P, Bravo J, Pereira C, Mendes F. Understanding fall risk factors in community-dwelling older adults: A cross-sectional study. Int J Older People Nurs. 2020;15(1):e12294.

    Article 
    PubMed 

    Google Scholar
     

  • Lahiri A, Jha S, Chakraborty A. Elders suffering recurrent injurious falls: causal analysis from a rural tribal community in the eastern part of India. Rural Remote Health. 2020;20(4):6042.

    PubMed 

    Google Scholar
     

  • Criter R, Honaker J. Audiology patient fall statistics and risk factors compared to non-audiology patients. Int J Audiol. 2016;55(10):564–70.

    Article 
    PubMed 

    Google Scholar
     

  • Woo N, Kim S. Sarcopenia influences fall-related injuries in community-dwelling older adults. Geriatric Nursing (New York, NY). 2014;35(4):279–82.

    Article 

    Google Scholar
     

  • Zhou H, Peng K, Tiedemann A, Peng J, Sherrington C. Risk factors for falls among older community dwellers in Shenzhen. China Injury Prevent. 2019;25(1):31–5.

    Article 

    Google Scholar
     

  • Kamińska M, Brodowski J, Karakiewicz B. Fall risk factors in community-dwelling elderly depending on their physical function, cognitive status and symptoms of depression. Int J Environ Res Public Health. 2015;12(4):3406–16.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Janakiraman B, Temesgen MH, Jember G, Gelaw AY, Gebremeskel BF, Ravichandran H, et al. Falls among community-dwelling older adults in Ethiopia; A preliminary cross-sectional study. PLoS ONE. 2019;14(9):e0221875.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Wang L, Wang X, Song P, Han P, Fu L, Chen X, et al. Combined depression and malnutrition as an effective predictor of first fall onset in a chinese community-dwelling population: a 2-year prospective cohort study. Rejuvenation Res. 2020;23(6):498–507.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Tanaka T, Matsumoto H, Son B, Imaeda S, Uchiyama E, Taniguchi S, et al. Environmental and physical factors predisposing middle-aged and older Japanese adults to falls and fall-related fractures in the home. Geriatr Gerontol Int. 2018;18(9):1372–7.

    Article 
    PubMed 

    Google Scholar
     

  • Stewart Williams J, Kowal P, Hestekin H, O’Driscoll T, Peltzer K, Yawson A, et al. Prevalence, risk factors and disability associated with fall-related injury in older adults in low- and middle-incomecountries: results from the WHO Study on global AGEing and adult health (SAGE). BMC Med. 2015;13(1):147.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • WHO. 10 facts on ageing and health. 2017.

  • Cheng M, Chang S. Frailty as a risk factor for falls among community dwelling people: evidence from a meta-analysis. J Nursing Scholarship :Off Publ Sigma Theta Tau Int Honor Soc Nursing. 2017;49(5):529–36.

    Article 

    Google Scholar
     

  • Sezgin D, O’Donovan M, Cornally N, Liew A, O’Caoimh R. Defining frailty for healthcare practice and research: A qualitative systematic review with thematic analysis. Int J Nurs Stud. 2019;92:16–26.

    Article 
    PubMed 

    Google Scholar
     

  • Huang C-Y, Lee W-J, Lin H-P, Chen R-C, Lin C-H, Peng L-N, et al. Epidemiology of frailty and associated factors among older adults living in rural communities in Taiwan. Arch Gerontol Geriatr. 2020;87:103986.

    Article 
    PubMed 

    Google Scholar
     

  • Bishop CM, Nasrabadi NM. Pattern recognition and machine learning. Springer; 2006.


    Google Scholar
     

  • Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2013;16(1):441.

    Article 

    Google Scholar
     

  • He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Brunton SL, Kutz JN. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press; 2022.

    Book 

    Google Scholar
     

  • Greene BR, Redmond SJ, Caulfield B. Fall risk assessment through automatic combination of clinical fall risk factors and body-worn sensor data. IEEE J Biomed Health Inform. 2017;21(3):725–31.

    Article 
    PubMed 

    Google Scholar
     

  • Cella A, De Luca A, Squeri V, Parodi S, Vallone F, Giorgeschi A, et al. Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults. PLoS ONE. 2020;15(6):e0234904.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Menezes M, de Mello Meziat-Filho NA, Araújo CS, Lemos T, Ferreira AS. Agreement and predictive power of six fall risk assessment methods in community-dwelling older adults. Arch Gerontol Geriatr. 2020;87:103975.

    Article 
    PubMed 

    Google Scholar
     

  • Park S-H. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2018;30(1):1–16.

    Article 
    PubMed 

    Google Scholar
     

  • Zhang L, Ding Z, Qiu L, Li A. Falls and risk factors of falls for urban and rural community-dwelling older adults in China. BMC Geriatr. 2019;19(1):379.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nicklett EJ, Taylor RJ. Racial/Ethnic predictors of falls among older adults: the health and retirement study. J Aging Health. 2014;26(6):1060–75.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • De Donder L, De Witte N, Verté D, Dury S. Developing evidence-based age-friendly policies. Particip Res Proj. 2014.

  • Ward JH. Hierarchical Grouping to Optimize an Objective Function. J Am Stat Assoc. 1963;58(301):236–44.

    Article 

    Google Scholar
     

  • Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32.

    Article 

    Google Scholar
     

  • Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63(1):3–42.

    Article 

    Google Scholar
     

  • James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning : with applications in R: New York. Springer; 2013.

    Book 

    Google Scholar
     

  • Rokach L, Maimon O. data mining with decision trees. World Sci. 2013;328.

  • Van Rossum G, Drake FL, Jr. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.

  • Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0:fundamental algorithms for scientific computing in python. Nat Methods. 2020;17:261–72.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357–62.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Hunter JD. Matplotlib: a 2d graphics environment. Comput Sci Eng. 2007;9(3):90–5.

    Article 

    Google Scholar
     

  • McKinney W. Data structures for statistical computing in python. in: van der walt s, millman j, editors. Proc 9th Python Sci Conf. 2010;56–61.

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(85):2825–30.


    Google Scholar
     

  • De Witte N, Gobbens R, De Donder L, Dury S, Buffel T, Schols J, et al. The comprehensive frailty assessment instrument: development, validity and reliability. Geriatr Nurs. 2013;34(4):274–81.

    Article 
    PubMed 

    Google Scholar
     

  • Gobbens RJ, Luijkx KG, Wijnen-Sponselee MT, Schols JM. Toward a conceptual definition of frail community dwelling older people. Nurs Outlook. 2010;58(2):76–86.

    Article 
    PubMed 

    Google Scholar
     

  • Lambotte D. Care and support in later life: A study on the dynamics of care networks of frail, community-dwelling older adults. Brussels: ASP / VUBPRESS; 2018.


    Google Scholar
     

  • Kojima G, Kendrick D, Skelton D, Morris R, Gawler S, Iliffe S. Frailty predicts short-term incidence of future falls among British community-dwelling older people: a prospective cohort study nested within a randomised controlled trial. BMC Geriatr. 2015;15:155.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xue Q-L. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Jehu DA, Davis JC, Falck RS, Bennett KJ, Tai D, Souza MF, et al. Risk factors for recurrent falls in older adults: A systematic review with meta-analysis. Maturitas. 2021;144:23–8.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Almada M, Brochado P, Portela D, Midão L, Costa E. Prevalence of falls and associated factors among community-dwelling older adults: a cross-sectional study. J Filty Ageing. 2021;10(1):10–6.

    CAS 

    Google Scholar
     

  • Byun M, Kim J, Kim JE. Physical and psychological factors contributing to incidental falls in older adults who perceive themselves as unhealthy: a cross-sectional study. Int J Environ Res Public Health. 2021;18(7):3738.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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