Purpose The 2010 physical activity (PA) guidelines for older adults in the UK include a target of 150 minutes of moderate or vigorous PA (MVPA)/week and recommend minimizing time spent being sedentary in extended periods. There are few large studies of objectively measured PA in the elderly which can estimate the ...
Introduction Physical activity (PA), sedentary behavior (SB) and sleep form the behavioral tripartite of energy expenditure and appear inter- and independently related to cardiometabolic disease and cancer. The ability to collect valid data on all three behaviors contemporaneously using one type of accelerometer is important because it may improve our ...
Introduction Time spent in sedentary behavior (SB) has deleterious effects on health. As a result, there is a strong scientific need to evaluate methods to assess SB. Purpose To determine responsiveness of two motion sensors to detect change in free-living, occupational SB during an intervention to decrease sitting activity. Methods ...
Purpose Wrist accelerometers are being used in population level surveillance (i.e. NHANES) of physical activity (PA) but more research is needed to evaluate the validity of a wrist-worn device for predicting PA. In this study we compare accelerometers worn on the wrist and each hip for predicting PA type ...
Introduction Accelerometers provide objective measurements of human activity and have been used extensively in health studies. In many of these studies, analysis was done not based on the raw data, but on summarized metrics like “activity counts”, which are the result of proprietary pre-processing software. Such metrics do not have ...
Purpose Increased all-cause mortality has been consistently associated with longer (8-10 hours+) self-reported sleep duration. The possibility that longer sleep may impact survival through inactive lifestyles was proposed by Morgan (2007), and subsequently tested by Hartescu et al (2012) who concluded that, independent of health status, longer sleep duration, and the inevitably ...
Introduction Assessing time spent in different activity types may be important for early detection of mobility limitations in older adults. To date, accelerometer-based activity type prediction using machine learning algorithms have not been validated for this segment of the population. Therefore, the aim of this study was to use Random ...
Introduction In order to obtain accurate estimates of sedentary time and physical activity in young children objective methods are needed. Wrist worn accelerometers have shown good feasibility among participants in previous studies. The acceleration signal collected by many commercially available activity monitors is usually summarized in an arbitrary unit (counts). ...
Introduction Technology advances and manufacturing efficiency improvements have drastically increased the options and flexibility available to users of accelerometer-based products. These advancements have led to confusion among activity monitor users, and many have been led to believe that device output normalization is an obvious and easy step. In truth, there ...
Introduction Pattern recognition classification algorithms have successfully identified energy cost and activity type from hip-worn accelerometers (ACC) [1,2]. These algorithms evaluate attributes of the acceleration signal during high frequency sampling. ACC output similarities and differences across activity types are well characterized [3]. An often overlooked area of potential difference lies in movement ...