Joseph Shiu Kwong Kwan1, Bobo Hi Po Lau2, Karen Siu Lan Cheung3. 1. Department of Medicine, The University of Hong Kong, Hong Kong, China. Electronic address: jskkwan@hku.hk. 2. Department of Psychology, The University of Hong Kong, Hong Kong, China. 3. Sau Po Center on Aging and Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China.
Abstract
OBJECTIVES: A better understanding of the essential components of frailty is important for future developments of management strategies. We aimed to assess the incremental validity of a Comprehensive Model of Frailty (CMF) over Frailty Index (FI) in predicting self-rated health and functional dependency amongst near-centenarians and centenarians. DESIGN: Cross-sectional, community-based study. SETTING: Two community-based social and clinical networks. PARTICIPANTS: One hundred twenty-four community-dwelling Chinese near-centenarians and centenarians. MEASUREMENTS: Frailty was first assessed using a 32-item FI (FI-32). Then, a new CMF was constructed by adding 12 items in the psychological, social/family, environmental, and economic domains to the FI-32. Hierarchical multiple regressions explored whether the new CMF provided significant additional predictive power for self-rated health and instrumental activities of daily living (IADL) dependency. RESULTS: Mean age was 97.7 (standard deviation 2.3) years, with a range from 95 to 108, and 74.2% were female. Overall, 16% of our participants were nonfrail, 59% were prefrail, and 25% were frail. Frailty according to FI-32 significantly predicted self-rated health and IADL dependency beyond the effect of age and gender. Inclusion of the new CMF into the regression models provided significant additional predictive power beyond FI-32 on self-rated health, but not IADL dependency. CONCLUSIONS: A CMF should ideally be a multidimensional and multidisciplinary construct including physical, cognitive, functional, psychosocial/family, environmental, and economic factors.
OBJECTIVES: A better understanding of the essential components of frailty is important for future developments of management strategies. We aimed to assess the incremental validity of a Comprehensive Model of Frailty (CMF) over Frailty Index (FI) in predicting self-rated health and functional dependency amongst near-centenarians and centenarians. DESIGN: Cross-sectional, community-based study. SETTING: Two community-based social and clinical networks. PARTICIPANTS: One hundred twenty-four community-dwelling Chinese near-centenarians and centenarians. MEASUREMENTS: Frailty was first assessed using a 32-item FI (FI-32). Then, a new CMF was constructed by adding 12 items in the psychological, social/family, environmental, and economic domains to the FI-32. Hierarchical multiple regressions explored whether the new CMF provided significant additional predictive power for self-rated health and instrumental activities of daily living (IADL) dependency. RESULTS: Mean age was 97.7 (standard deviation 2.3) years, with a range from 95 to 108, and 74.2% were female. Overall, 16% of our participants were nonfrail, 59% were prefrail, and 25% were frail. Frailty according to FI-32 significantly predicted self-rated health and IADL dependency beyond the effect of age and gender. Inclusion of the new CMF into the regression models provided significant additional predictive power beyond FI-32 on self-rated health, but not IADL dependency. CONCLUSIONS: A CMF should ideally be a multidimensional and multidisciplinary construct including physical, cognitive, functional, psychosocial/family, environmental, and economic factors.
Authors: Henry Brodaty; Claudia Woolf; Stacy Andersen; Nir Barzilai; Carol Brayne; Karen Siu-Lan Cheung; Maria M Corrada; John D Crawford; Catriona Daly; Yasuyuki Gondo; Bo Hagberg; Nobuyoshi Hirose; Henne Holstege; Claudia Kawas; Jeffrey Kaye; Nicole A Kochan; Bobo Hi-Po Lau; Ugo Lucca; Gabriella Marcon; Peter Martin; Leonard W Poon; Robyn Richmond; Jean-Marie Robine; Ingmar Skoog; Melissa J Slavin; Jan Szewieczek; Mauro Tettamanti; José Viña; Thomas Perls; Perminder S Sachdev Journal: BMC Neurol Date: 2016-04-21 Impact factor: 2.474