Predictors of Frailty and Vitality in Older Adults Aged 75 years and Over: Results from the Longitudinal Aging Study Amsterdam

Introduction: Frailty can be seen as a continuum, from fit to frail. While many recent studies have focused on frailty, much less attention has been paid to the other end of the continuum: the group of older adults that remain (relatively) vital. Moreover, there is a lack of studies on frailty and vitality that investigate predictors from multiple domains of functioning simultaneously. The aim of this study was to identify predictors of frailty as well as vitality among older adults aged 75 years and over. Methods: We used longitudinal data from 569 adults aged >= 75 years who participated in the Longitudinal Aging Study Amsterdam. Predictors from the sociodemographic, social, psychological, lifestyle, and physical domains of functioning were measured at T1 (2008-2009). We used the frailty index (FI) to identify frail (FI >= 0.25) and vital (FI <= 0.15) respondents at follow-up, 3 years later (T2: 2011-2012). We conducted logistic regression analyses with backward stepwise selection to develop and internally validate our prediction models. Results: The prevalence of frailty in our sample at follow-up was 49.4% and the prevalence of vitality was 18.3%. Predictors of frailty and vitality partly overlapped and included age, depressive symptoms, number of chronic diseases, and self-rated health. We also found predictors that did not overlap. Male sex, moderate alcohol use, more emotional support received, and no hearing problems, were predictors of vitality. Lower cognitive functioning, polypharmacy, and pain were predictors of frailty. The final model for vitality explained 42% of the variance and the final model for frailty explained 48%. Both models had a good discriminative value (area under ROC-curve [AUC] vitality: 0.88; AUC frailty: 0.85). Conclusion: Among older adults aged 75 years and over, predictors of frailty only partially overlap with predictors of vitality. The readily accessible predictors in our models may help to identify older adults who are likely to be vital, or who are at risk of frailty.