Although there is some evidence that total dietary antioxidant capacity (TDAC) is inversely associated with the presence of obesity, no longitudinal studies have been performed investigating the effect of TDAC on comprehensive measures of body composition over time. In this study, we included 4595 middle-aged and elderly participants from the Rotterdam Study, a population-based cohort. We estimated TDAC among these individuals by calculating a ferric reducing ability of plasma (FRAP) score based on data from food-frequency questionnaires. Body composition was assessed by means of dual X-ray absorptiometry at baseline and every subsequent 3-5 years. From these data, we calculated fat mass index (FMI), fat-free mass index (FFMI), android-to-gynoid fat ratio (AGR), body fat percentage (BF%) and body mass index (BMI). We also assessed hand grip strength at two time points and prevalence of sarcopenia at one time point in a subset of participants. Data were analyzed using linear mixed models or multinomial logistic regression models with multivariable adjustment. We found that higher FRAP score was associated with higher FFMI (0.091 kg/m2 per standard deviation (SD) higher FRAP score, 95% CI 0.031; 0.150), lower AGR (-0.028, 95% CI -0.053; -0.003), higher BMI (0.115, 95% CI 0.020; 0.209) and lower BF% (-0.223, 95% CI -0.383; -0.064) across follow-up after multivariable adjustment. FRAP score was not associated with hand grip strength or sarcopenia. Additional adjustment for adherence to dietary guidelines and exclusion of individuals with comorbid disease at baseline did not change our results. In conclusion, dietary intake of antioxidants may positively affect the amount of lean mass and overall body composition among the middle-aged and elderly.
Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
Background: Falls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest. Methods: This study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model. Results: We found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults. Conclusion: The interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.
