Objective:(1) To examine the associations between 3 measures of grip strength: static grip strength, change in grip strength, and the combination of grip strength and its change, with all-cause and cardiovascular mortality, and (2) to determine which measure is the most powerful predictor of all-cause and cardiovascular mortality among the European older population.Method:Data come from the first 4 waves of the Survey of Health, Ageing and Retirement in Europe (SHARE). A Cox proportional hazard model and a competing risk regression model were used to assess the associations. To determine the best predictor, Akaike information criterion was applied.Results:Grip strength and the combination of grip strength and its change were associated with all-cause and cardiovascular mortality. Change in grip strength was correlated with only all-cause mortality. Among the 3 measures, the static measure of grip strength was the best predictor of cardiovascular mortality whereas the combined measure is that of all-cause mortality.Discussion:Grip strength is a significant indicator of all-cause and cardiovascular mortality. The combination of grip strength and its change can be used to increase the accuracy for prediction of all-cause mortality among older persons.

Background In this study, we analyzed elderly people in Thailand to identify the validity of suggested cutoff points of physical measures, handgrip strength, usual walking speed, and a composite score of both measures to predict functional limitations. Moreover, we examined whether these physical performance measures are accurate indicators of the investigated health outcomes. Methods Using Receiver Operating Characteristics (ROC) analysis, we investigated a sample of 8272 respondents aged 60 to 79 years. All data were based on the 2009 National Health Examination Survey (NHES IV) of Thailand. Results For males aged 60 to 69 years, handgrip strength was used as an indicator of functional limitations. The cutoff point for disabilities in the activities of daily living (ADLs) was 29.5 kg, while in other limitations it ranged from 28.7 to 31.3 kg. In contrast, usual walking speed was able to indicate ADL disabilities at 0.7 m per second (m/s). As one might expect, the cutoff points for males aged 70 to 79 years were lower than for males in the 60 to 69 age group. For females, handgrip strength was able to indicate ADL disabilities at 16.5 kg for both the 60 to 69, and 70 to 79 age groups. Likewise, walking speed was indicative of ADL disabilities at 0.6 m/s for both age groups. Interestingly, the composite measure increases the ability to detect ADL disabilities in the younger group but not in the older group. The area under the curve (AUC) of cutoffs measuring the detection power of a diagnostic test was varied, ranging from 0.535 to 0.7386. Conclusions The cutoff points of three measures varied according to sex and type of functional limitations. Our findings also showed that physical performance measures were useful for identifying people with an increased risk of functional limitations, particularly for ADL disabilities. However, although the AUC of the cutoffs of other functional limitations were relatively low, they should be considered with caution. Electronic supplementary material The online version of this article (10.1186/s12877-019-1093-3) contains supplementary material, which is available to authorized users.

Aim This study aimed to identify differences in physical performance across various socioeconomic groups within an older population and to convert those differences into a common metric to facilitate comparisons of aging speed across socioeconomic subgroups. Methods We employed data from the 2009 National Health Examination Survey of Thailand. Physical performance was assessed using three health characteristics: grip strength, as a measure of upper body strength; walking speed, as a measure of lower body strength; and a combined measure of grip strength and walking speed, to capture the strength of the whole body. Education level and income were used to distinguish socioeconomic subpopulations. We followed a characteristic-based age approach to transform these population characteristics, which were measured in different units, into a common and comparable aging metric, referred to as α − age. Results Physical aging trajectories varied by sex and socioeconomic status. Some education, particularly secondary or higher education levels, was significantly associated with greater physical strength in older age for both men and women, whereas higher income was significantly associated with physical strength only for men. Across the three health characteristics, having a primary education slowed age-related declines by up to 6.3 years among men and 2.8 years among women, whereas being in a higher income group slowed age-related declines by 8.2 years among men and up to 4.9 years among women. Conclusions This study adds new evidence from a developing Asian country regarding the difference in aging speeds across subpopulations associated with different levels of education and income.