BACKGROUND: frailty is an important geriatric syndrome linked to increased mortality, morbidity and falls risk. METHODS: a total of 399 community-dwelling older adults were assessed using Fried's frailty phenotype and the timed up and go (TUG) test. Tests were quantified using shank-mounted inertial sensors. We report a regression-based method for assessment of frailty using inertial sensor data obtained during TUG. For comparison, frailty was also assessed using the same method based on grip strength and manual TUG time. RESULTS: using inertial sensor data, participants were classified as frail or non-frail with mean accuracy of 75.20% (stratified by gender). Using TUG time alone, frailty status was classified correctly with mean classification accuracy of 71.82%. Similarly, using grip strength alone, the frailty status was classified correctly with mean classification accuracy of 77.65%. Stratifying sensor data by gender yielded significantly (p<0.05) increased accuracy in classifying frailty when compared with equivalent manual TUG time-based models. CONCLUSION: results suggest that a simple protocol involving assessment using a well-known mobility test (Timed Up and Go (TUG)) and inertial sensors can be a fast and effective means of automatic, non-expert assessment of frailty.
BACKGROUND: frailty is an important geriatric syndrome linked to increased mortality, morbidity and falls risk. METHODS: a total of 399 community-dwelling older adults were assessed using Fried's frailty phenotype and the timed up and go (TUG) test. Tests were quantified using shank-mounted inertial sensors. We report a regression-based method for assessment of frailty using inertial sensor data obtained during TUG. For comparison, frailty was also assessed using the same method based on grip strength and manual TUG time. RESULTS: using inertial sensor data, participants were classified as frail or non-frail with mean accuracy of 75.20% (stratified by gender). Using TUG time alone, frailty status was classified correctly with mean classification accuracy of 71.82%. Similarly, using grip strength alone, the frailty status was classified correctly with mean classification accuracy of 77.65%. Stratifying sensor data by gender yielded significantly (p<0.05) increased accuracy in classifying frailty when compared with equivalent manual TUG time-based models. CONCLUSION: results suggest that a simple protocol involving assessment using a well-known mobility test (Timed Up and Go (TUG)) and inertial sensors can be a fast and effective means of automatic, non-expert assessment of frailty.
Entities:
Keywords:
TUG; community dwelling older adults; frailty; inertial sensor; mobility; older people
Authors: Jeong Bae Ko; Kwang Bok Kim; Young Sub Shin; Hun Han; Sang Kuy Han; Duk Young Jung; Jae Soo Hong Journal: Clin Interv Aging Date: 2021-09-27 Impact factor: 4.458
Authors: Janet M Pritchard; Sarah Karampatos; Karen A Beattie; Lora M Giangregorio; George Ioannidis; Stephanie A Atkinson; Lehana Thabane; Hertzel Gerstein; Zubin Punthakee; Jonathan D Adachi; Alexandra Papaioannou Journal: J Aging Res Date: 2015-01-27
Authors: Alicia Martínez-Ramírez; Ion Martinikorena; Marisol Gómez; Pablo Lecumberri; Nora Millor; Leocadio Rodríguez-Mañas; Francisco José García García; Mikel Izquierdo Journal: J Neuroeng Rehabil Date: 2015-05-24 Impact factor: 4.262
Authors: Ana Maria Teixeira; José Pedro Ferreira; Eef Hogervorst; Margarida Ferreira Braga; Stephan Bandelow; Luís Rama; António Figueiredo; Maria João Campos; Guilherme Eustáquio Furtado; Matheus Uba Chupel; Filipa Martins Pedrosa Journal: Front Public Health Date: 2016-06-27
Authors: Antoneta Granic; Carol Jagger; Karen Davies; Ashley Adamson; Thomas Kirkwood; Tom R Hill; Mario Siervo; John C Mathers; Avan Aihie Sayer Journal: PLoS One Date: 2016-03-02 Impact factor: 3.240