Original articleValidation of a Body-Worn Accelerometer to Measure Activity Patterns in Octogenarians
Section snippets
Participants
Twenty-two people (mean age ± SD, 88.1±5y) from 2 retirement villages volunteered to participate (table 1). Eligible participants were aged ≥80 years, were living in either independent or long-term care (nursing home) facilities at the retirement village, and were able to transfer and walk independently with or without a walking aid. Ethical approval for the study was granted by the University of Auckland Ethics Committee. All participants gave written informed consent before commencement.
Procedure
The
Results
A total of 251.30 minutes of activity were analyzed from 22 participants (table 2). One participant did not complete the scripted activities, and 4 participants did not perform the lying component of the scripted activities.
Bland-Altman plots with mean differences and limits of agreement for each activity are shown in figure 1. When expressed as an absolute percentage error, the median absolute percentage error for each activity was as follows: sitting, −22.3% (interquartile range [IQR], −62.8%
Discussion
This study assessed the validity of the DynaPort MoveMonitor system for detection of gait and postures in older people in both a controlled setting and a home setting. There was close agreement between video observation and the accelerometer for both locomotion and lying, with an absolute percentage error of <1% for these activities. Since the slow gait speed of older people can result in misclassification of walking as standing, or the underestimation of step counts when using pedometers or
Conclusions
The DynaPort MoveMonitor provides a precise measure of lying and locomotion in people older than 80 years including the frail elderly, although there is an error of approximately 25% when discriminating sitting from standing postures. This error in discrimination needs to be considered when monitoring long-term habitual activity in this age group.
Suppliers
- a.
McRoberts BV, Raamweg 43, 2596 HN, The Hague, The Netherlands.
- b.
Sony Handycam (HDR-SR5); Sony Corp, 1-7-1 Konan, Minato-ku, Tokyo 108-0075, Japan.
- c.
Microsoft Corp, One Microsoft Way, Redmond, WA 98052.
- d.
The MathWorks, Inc, 3 Apple Hill Dr, Natick, MA 01760-2098.
- e.
SPSS Inc, 233 S Wacker Dr, 11th Fl, Chicago, IL 60606.
References (26)
- et al.
Measures of physical ability are unrelated to objectively measured physical activity behavior in older adults residing in continuing care retirement communities
Arch Phys Med Rehabil
(2009) - et al.
A description of an accelerometer-based mobility monitoring technique
Med Eng Phys
(2005) - et al.
Detection of gait and postures using a miniaturized triaxial accelerometer-based system: accuracy in patients with mild to moderate Parkinson's disease
Arch Phys Med Rehabil
(2010) - et al.
Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications
Maturitas
(2012) - et al.
Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population
J Am Geriatr Soc
(2000) Exercise comes of age: rationale and recommendations for a geriatric exercise prescription
J Gerontol A Biol Sci Med Sci
(2002)- et al.
Rehabilitation for older people in long-term care
Cochrane Database Syst Rev
(2009) - et al.
Interventions for preventing falls in older people in care facilities and hospitals
Cochrane Database Syst Rev
(2012) - et al.
Exercise for improving balance in older people
Cochrane Database Syst Rev
(2007) - et al.
Physical performance and daily walking duration: associations in 1271 women and men aged 65-90 years
Aging Clin Exp Res
(2012)
Day-to-day variability of physical activity of older adults living in the community
J Aging Phys Act
Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults
Age Ageing
Analyzing free-living physical activity of older adults in different environments using body-worn activity monitors
J Aging Phys Act
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Supported jointly by the research and development program of the Korea Ministry of Knowledge and Economy and Korea Evaluation Institute of Industrial Technology (grant no. KI001836: Development of Mediated Interface Technology for Human Robot Interaction) and by the New Zealand Ministry for Science and Innovation (grant no. 13635).
No commercial party having a direct financial interest in the results of the research supporting this article has conferred or will confer a benefit on the authors or on any organization with which the authors are associated.