This paper is in the following e-collection/theme issue:

Research Instruments, Questionnaires, and Tools (576) Decision Support for Health Professionals (1185) Home Care (170) Epidemiology (159)

Published on in Vol 17, No 2 (2015): February

FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community

FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community

FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community

Authors of this article:

Luca Cattelani1 Author Orcid Image ;   Pierpaolo Palumbo1 Author Orcid Image ;   Luca Palmerini1 Author Orcid Image ;   Stefania Bandinelli2 Author Orcid Image ;   Clemens Becker3 Author Orcid Image ;   Federico Chesani4 Author Orcid Image ;   Lorenzo Chiari1 Author Orcid Image
Article Authors Cited by (43) Tweetations (16) Metrics

Journals

  1. Leahy‐Warren P, Day M, Philpott L, Glavin K, Gjevjon E, Steffenak A, Nordhagen L, Egge H, Healy E, Mulcahy H. A falls case summary: Application of the public health nursing intervention wheel. Public Health Nursing 2018;35(4):307 View
  2. Greene B, Redmond S, Caulfield B. Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data. IEEE Journal of Biomedical and Health Informatics 2017;21(3):725 View
  3. Shigematsu H, Wada M, Miyata S, Kisanuki O, Tatsumi H, Nishimori K, Hara R, Tanaka M, Kawasaki S, Suga Y, Yamamoto Y, Okuda A, Tanaka Y. Can the loco-check be used as a self-check tool for evaluating fall risk among older subjects? A prospective study. Journal of Orthopaedic Science 2021;26(5):891 View
  4. Iqbal U, Celi L, Li Y. How Can Artificial Intelligence Make Medicine More Preemptive?. Journal of Medical Internet Research 2020;22(8):e17211 View
  5. 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. Brazilian Journal of Physical Therapy 2019;23(1):19 View
  6. de Clercq H, Naudé A, Bornman J. Factors included in adult fall risk assessment tools (FRATs): a systematic review. Ageing and Society 2021;41(11):2558 View
  7. Palumbo P, Klenk J, Cattelani L, Bandinelli S, Ferrucci L, Rapp K, Chiari L, Rothenbacher D. Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts. Journal of the American Medical Directors Association 2016;17(12):1106 View
  8. Castellini G, Demarchi A, Lanzoni M, Castaldi S. Fall prevention: is the STRATIFY tool the right instrument in Italian Hospital inpatient? A retrospective observational study. BMC Health Services Research 2017;17(1) View
  9. Cattelani L, Murri M, Chesani F, Chiari L, Bandinelli S, Palumbo P. Risk Prediction Model for Late Life Depression: Development and Validation on Three Large European Datasets. IEEE Journal of Biomedical and Health Informatics 2019;23(5):2196 View
  10. Powell-Cope G, Campbell R, Hahm B, Bulat T, Westphal J. Sociotechnical probabilistic risk modeling to predict injurious falls in community living centers. Journal of Rehabilitation Research and Development 2016;53(6):881 View
  11. Hamm J, Money A, Atwal A. Fall Prevention Self-Assessments Via Mobile 3D Visualization Technologies: Community Dwelling Older Adults’ Perceptions of Opportunities and Challenges. JMIR Human Factors 2017;4(2):e15 View
  12. Menezes M, Meziat-Filho N, Lemos T, Ferreira A. ‘Believe the positive’ aggregation of fall risk assessment methods reduces the detection of risk of falling in older adults. Archives of Gerontology and Geriatrics 2020;91:104228 View
  13. Palumbo P, Palmerini L, Bandinelli S, Chiari L, Wang Y. Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?. PLOS ONE 2015;10(12):e0146247 View
  14. Obrist S, Rogan S, Hilfiker R. Development and Evaluation of an Online Fall-Risk Questionnaire for Nonfrail Community-Dwelling Elderly Persons: A Pilot Study. Current Gerontology and Geriatrics Research 2016;2016:1 View
  15. Cattelani L, Chesani F, Palmerini L, Palumbo P, Chiari L, Bandinelli S. A rule-based framework for risk assessment in the health domain. International Journal of Approximate Reasoning 2020;119:242 View
  16. Menezes M, de Mello Meziat-Filho N, Araújo C, Lemos T, Ferreira A. Agreement and predictive power of six fall risk assessment methods in community-dwelling older adults. Archives of Gerontology and Geriatrics 2020;87:103975 View
  17. Maiga A, Farjah F, Blume J, Deppen S, Welty V, D’Agostino R, Colditz G, Kozower B, Grogan E. Risk Prediction in Clinical Practice: A Practical Guide for Cardiothoracic Surgeons. The Annals of Thoracic Surgery 2019;108(5):1573 View
  18. Ruggieri M, Palmisano B, Fratocchi G, Santilli V, Mollica R, Berardi A, Galeoto G. Validated Fall Risk Assessment Tools for Use with Older Adults: A Systematic Review. Physical & Occupational Therapy In Geriatrics 2018;36(4):331 View
  19. Pozaic T, Lindemann U, Grebe A, Stork W. Sit-to-Stand Transition Reveals Acute Fall Risk in Activities of Daily Living. IEEE Journal of Translational Engineering in Health and Medicine 2016;4:1 View
  20. Akahane M, Maeyashiki A, Yoshihara S, Tanaka Y, Imamura T. Relationship Between Difficulties in Daily Activities and Falling: Loco-Check as a Self-Assessment of Fall Risk. interactive Journal of Medical Research 2016;5(2):e20 View
  21. Boulton E, Hawley-Hague H, Vereijken B, Clifford A, Guldemond N, Pfeiffer K, Hall A, Chesani F, Mellone S, Bourke A, Todd C. Developing the FARSEEING Taxonomy of Technologies: Classification and description of technology use (including ICT) in falls prevention studies. Journal of Biomedical Informatics 2016;61:132 View
  22. Hu X, Zhao J, Peng D, Sun Z, Qu X. Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model. Sensors 2018;18(2):421 View
  23. Palmerini L, Klenk J, Becker C, Chiari L. Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls. Sensors 2020;20(22):6479 View
  24. Greene B, McManus K, Ader L, Caulfield B. Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning. Sensors 2021;21(14):4770 View
  25. Inacio M, Moldovan M, Whitehead C, Sluggett J, Crotty M, Corlis M, Visvanathan R, Wesselingh S, Caughey G. The risk of fall-related hospitalisations at entry into permanent residential aged care. BMC Geriatrics 2021;21(1) View
  26. Souza L, Batista R, Camapanharo C, Costa P, Lopes M, Okuno M. Factors associated with risk, perception and knowledge of falls in elderly people. Revista Gaúcha de Enfermagem 2022;43 View
  27. Kerber K, Bi R, Skolarus L, Burke J. Trajectories in physical performance and fall prediction in older adults: A longitudinal population‐based study. Journal of the American Geriatrics Society 2022;70(12):3413 View
  28. Goldwater D, Wenger N. Patient-centered care in geriatric cardiology. Trends in Cardiovascular Medicine 2023;33(1):13 View
  29. Dormosh N, Heymans M, van der Velde N, Hugtenburg J, Maarsingh O, Slottje P, Abu-Hanna A, Schut M. External Validation of a Prediction Model for Falls in Older People Based on Electronic Health Records in Primary Care. Journal of the American Medical Directors Association 2022;23(10):1691 View
  30. Böttinger M, Bauer J, Gordt-Oesterwind K, Litz E, Jansen C, Becker C. Digitales geriatrisches Self-Assessment – ein narratives Review. Zeitschrift für Gerontologie und Geriatrie 2022;55(5):368 View
  31. Hartley P, Forsyth F, Rowbotham S, Briggs R, Kenny R, Romero-Ortuno R. The use of the World Guidelines for Falls Prevention and Management’s risk stratification algorithm in predicting falls in The Irish Longitudinal Study on Ageing (TILDA). Age and Ageing 2023;52(7) View
  32. Goggins S. The importance of a comprehensive geriatric assessment for older people admitted onto a virtual ward. British Journal of Nursing 2023;32(18):882 View
  33. ŞEN E, ÇETİNKAYA DUMAN Z. Psikiyatri Kliniğinde Çalışan Hemşirelerin Hasta Düşmeleri ve Önlenmesine İlişkin Bilgi ve Görüşleri. Etkili Hemşirelik Dergisi 2023;16(4):487 View
  34. Xiao X, Li L, Yang H, Peng L, Guo C, Cui W, Liu S, Yu R, Zhang X, Zhang M. Analysis of the incidence of falls and related factors in elderly patients based on comprehensive geriatric assessment. AGING MEDICINE 2023;6(3):245 View
  35. M. K, Josyula S, S. J, J. H, M. N, J. V. Revolutionizing Sports Rehabilitation: Unleashing the Power of Tele-Rehabilitation for Optimal Physiotherapy Results. Telemedicine and e-Health 2024;30(4):e1180 View
  36. Dormosh N, Schut M, Heymans M, Maarsingh O, Bouman J, van der Velde N, Abu-Hanna A. Predicting future falls in older people using natural language processing of general practitioners’ clinical notes. Age and Ageing 2023;52(4) View
  37. Lameky V. Risk Assessment of Falls Among Older Adults Based on Probe Reaction Time During Water-Carrying Walking [Letter]. Clinical Interventions in Aging 2024;Volume 19:119 View
  38. Takeshima N, Fujita E, Kohama T, Osuka Y, Kojima N, Kusunoki M, Brechue W, Sasai H. Potential of Kinect-assessed stepping test for assessing fall risk in community-dwelling older women. Archives of Gerontology and Geriatrics Plus 2024;1(4):100077 View
  39. Gökseven Arda Y, Zeren Ozturk G, Aksu S. Evaluation of Nutrition, Pressure Ulcer and Fall Risk Status and Related Factors in Individuals Receiving Home Health Care Services. Clinical and Experimental Health Sciences 2024;14(3):843 View

Books/Policy Documents

  1. Leach J, Mellone S, Palumbo P, Chiari L. Converging Clinical and Engineering Research on Neurorehabilitation III. View
  2. El Miedany Y. New Horizons in Osteoporosis Management. View
  3. El Miedany Y. New Horizons in Osteoporosis Management. View