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J Gerontol A Biol Sci Med Sci. 2016 Apr;71(4):521-8. doi: 10.1093/gerona/glv204. Epub 2015 Oct 29.

Analysis and Interpretation of Accelerometry Data in Older Adults: The LIFE Study.

Author information

1
Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina. rejeski@wfu.edu.
2
Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina.
3
SNHP Exercise Science & Health, Arizona State University, Tempe.
4
Nutrition, Exercise Physiology and Sarcopenia Laboratory, Tufts University, Boston, Massachusetts.
5
Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
6
Department of Aging and Geriatric Research. University of Florida, Gainesville.
7
General Internal Medicine and Geriatrics and Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Abstract

BACKGROUND:

Accelerometry has become the gold standard for evaluating physical activity in the health sciences. An important feature of using this technology is the cutpoint for determining moderate to vigorous physical activity (MVPA) because this is a key component of exercise prescription. This article focused on evaluating what cutpoint is appropriate for use with older adults 70-89 years who are physically compromised.

METHODS:

The analyses are based on data collected from the Lifestyle Interventions and Independence for Elders (LIFE) study. Accelerometry data were collected during a 40-minute, overground, walking exercise session in a subset of participants at four sites; we also used 1-week baseline and 6-month accelerometry data collected in the main trial.

RESULTS:

There was extreme variability in median counts per minute (CPM) achieved during a controlled bout of exercise (n = 140; median = 1,220 CPM (25th, 75th percentile = 715, 1,930 CPM). An equation combining age, age(2), and 400 m gait speed explained 61% of the variance in CPM achieved during this session. When applied to the LIFE accelerometry data (n = 1,448), the use of an individually tailored cutpoint based on this equation resulted in markedly different patterns of MVPA as compared with using standard fixed cutpoints.

CONCLUSIONS:

The findings of this study have important implications for the use and interpretations of accelerometry data and in the design/delivery of physical activity interventions with older adults.

KEYWORDS:

Accelerometry; Cutpoints; LIFE-study; Mobility disability; Older adults

PMID:
26515258
PMCID:
PMC5175451
DOI:
10.1093/gerona/glv204
[Indexed for MEDLINE]
Free PMC Article

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