Introduction: The time dedicated to movement (e.g., physical activity) and non-movement behaviours (e.
View Article and Find Full Text PDFBackground: Sitting time (ST) constitutes a significant aspect of sedentary behavior, and its worldwide escalation raises concerns regarding public health. International guidelines recommend limiting sedentary time and replacing it with physical activity (PA) to reduce the risk of diseases and mortality. This study examines the impact of replacing ST with PA on all-cause, cardiovascular disease (CVD), and cancer mortality in a representative cohort of the population of Spain.
View Article and Find Full Text PDFThe aim of this study was to examine, theoretically, how reallocating time between the intensity of mutually exclusive categories of physical activity and sedentary behavior time is associated with metabolic syndrome. Four hundred and six older adults (61.6% women) from the second wave of the EpiFloripa Aging Cohort Study were included in the study (mean age 71.
View Article and Find Full Text PDFBackground: Although clinical gait speed may indicate health and well-being in older adults, there is a lack of studies comparing clinical tests with ambulatory gait speed with regard to several health outcomes.
Objective: The objective of this study was to examine the associations of clinical gait speed, measured by the 2.44-m walk test and the ambulatory gait speed with several physical, mental, and cognitive health outcomes in older adults.
Background: To assess the validity of the single question to determine sedentary behavior (SB) by using the Global Physical Activity Questionnaire (GPAQ) in older adults.
Methods: The sample included 163 participants (96 women) aged 65-92 years. Self-reported SB was obtained from the GPAQ.
Objectives: The aims of the present study were: (i) to analyze the associations of the time spent in daily activities (i.e., lie, recline, passive sit, active sit, stand, walk at slow pace, walk at average pace, walk at brisk pace, and other activities) with body mass index (BMI) and waist circumference (WC); and (ii) to examine how theoretically reallocating time between these daily activities is associated with BMI and WC.
View Article and Find Full Text PDFIntroduction: The aims of this study were: (i) to provide a detailed description of movement and nonmovement behaviors objectively assessed over the complete 24-h period in a sample of older adults, and (ii) to analyze differences in these behaviors by sex, age, educational level, body mass index, self-rated health, and chronic conditions.
Methods: The sample comprised 607 high-functioning community-dwelling older adults (383 women), 65 to 92 yr, who participated in the IMPACT65+ study. Movement and nonmovement behaviors were assessed by the Intelligent Device for Energy Expenditure and Activity, which provide estimates on both temporal and spatial gait parameters, and identify specific functional activities on the basis of acceleration and position information.
Introduction: Physical activity and physical inactivity patterns can affect health status. In the elderly people, their study is relevant given the importance that they have on the morbidity and mortality.
Objective: To present preliminary data on activity and inactivity patterns of a sub-sample of older adults from the IMPACT65+ Study.
The interday reliability of the Intelligent Device for Energy Expenditure and Activity (IDEEA) has not been studied to date. The study purpose was to examine the interday variability and reliability on two consecutive days collected with the IDEEA, as well as to predict the number of days needed to provide a reliable estimate of several movement (walking and climbing stairs) and nonmovement (lying, reclining, and sitting) behaviors and standing in older adults. The sample included 126 older adults (74 women) who wore the IDEEA for 48 hr.
View Article and Find Full Text PDFObjectives: The aims of the present study were (i) to develop automated algorithms to identify the sleep period time in 24 h data from the Intelligent Device for Energy Expenditure and Activity (IDEEA) in older adults, and (ii) to analyze the agreement between these algorithms to identify the sleep period time as compared to self-reported data and expert visual analysis of accelerometer raw data.
Approach: This study comprised 50 participants, aged 65-85 years. Fourteen automated algorithms were developed.