24-h Movement and Nonmovement Behaviors in Older Adults. The IMPACT65+ Study.

Introduction: 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.

[Daily activities assessed by a high-tech pattern-recognition monitor in older adults: preliminary findings from the IMPACT65+ study].

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.

Interday Reliability of the IDEEA Activity Monitor for Measuring Movement and Nonmovement Behaviors in Older Adults.

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.

Automated algorithms for detecting sleep period time using a multi-sensor pattern-recognition activity monitor from 24 h free-living data in older adults.

Objectives: 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.