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. Results showed low variability between the 2 days, and the reliability was from moderate (intraclass coefficient correlation = .34) to high (.80) in most of movement and nonmovement behaviors analyzed. The Bland-Altman plots showed high-moderate agreement between days, and the Spearman-Brown formula estimated that 1.2 and 9.1 days of monitoring with the IDEEA are needed to achieve intraclass coefficient correlations ≥ .70 in older adults for sitting and climbing stairs, respectively.

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. RESULTS: The final sample with valid data was 432 older adults (284 women). Around 30.7% of daily time was engaged in sedentary behavior (SB), whereas 33.5% and 35.8% was represented by physical activity (PA) and sleep, respectively. Sitting passive was the most prevalent SB (vs lying and reclining), whereas most light PA was by standing (vs active sitting and walking at <2.5 mph). Time spent walking at ≥2.5 mph was the major contributor to moderate-to-vigorous PA. No differences were found in sleep time by sociodemographic or health-related characteristics, but there were relevant differences in sedentary and PA behaviors. CONCLUSIONS: This study offers a detailed description of the distribution of SB, PA, and sleep in elderly across the 24-h spectrum. The results could be used to focus the strategies aimed to improve health in the old age.

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. Participants reported their bedtime and waking time on the days on which they wore the device. A well-trained expert reviewed each IDEEA file in order to visually identify bedtime and waking time on each day. To explore the agreement between methods, Pearson correlations, mean differences, mean percentage errors, accuracy, sensitivity and specificity, and the Bland-Altman method were calculated. MAIN RESULTS: With 87 d of valid data, algorithms 6, 7, 11 and 12 achieved higher levels of agreement in determining sleep period time when compared to self-reported data (mean difference = -0.34 to 0.01 h d-1; mean absolute error = 10.66%-11.44%; r = 0.515-0.686; accuracy = 95.0%-95.6%; sensitivity = 93.0%-95.8%; specificity = 95.7%-96.4%) and expert visual analysis (mean difference = -0.04 to 0.31 h d-1; mean absolute error = 5.0%-6.97%; r = 0.620-0.766; accuracy = 97.2%-98.0%; sensitivity = 94.5%-97.6%; specificity = 98.4%-98.8%). Bland-Altman plots showed no systematic biases in these comparisons (all p > 0.05). Differences between methods did not vary significantly by gender, age, obesity, self-rated health, or the presence of chronic conditions. SIGNIFICANCE: These four algorithms can be used to identify easily and with adequate accuracy the sleep period time using the IDEEA activity monitor from 24 h free-living data in older adults.

[Daily activities assessed by a high-tech pattern-recognition monitor in older adults: preliminary findings from the IMPACT65+ study]. / Actividades diarias evaluadas mediante monitor de actividad de alta precisión en adultos mayores: resultados preliminares del Estudio IMPACT65.

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. MATERIAL AND METHODS: The sample included the first 84 participants (57% women) over 65 years (age 70.7±4.7). Time spent in activity and inactivity patterns was obtained from an Intelligent Device for Energy Expenditure and Activity monitor over a continuous period of 24hours. The patterns analysed were: standing, lying down, sitting or reclining, and the transition between them. The physical activity patterns analysed were; walking, step up or step down, running, and jumping. RESULTS: Time spent in inactivity patterns like reclining, lying down, and sitting was 16.1±1.9hours (67% day), while the amount of time spent in activity patterns was 2.4±1.9hours (10% day). Differences were observed between men and women in the amount of hours sitting (9.7±3 men vs. 7.5±2.7 women) and standing (4.5±1.4 men vs. 5.6±2.7 women). These differences were greater in the older participants. CONCLUSIONS: Preliminary results show that older adults spend a great part of day in inactivity patterns like sitting, and that gender is the only factor analysed that affects the time spent in the activity patterns analysed.