Sources of variance in daily physical activity levels as measured by an accelerometer.
OBJECTIVE
To examine sources of variance in objective measures of physical activity in a group of healthy adults (N = 92) participating in a physical activity measurement study.


METHODS
Physical activity was assessed for up to 21 consecutive days using the Computer Science Applications (CSA) accelerometer. Day-of-the-week effects were evaluated for activity counts (ct.min(-1).d(-1), ct.d(-1)) and time (min.d(-1)) spent in inactivity (0-499 ct), moderate-1 (500-1951 ct), and moderate-2-vigorous activity (> or =1952 ct). Random effects models were employed to estimate variance components for subject, day of the week, and residual error from which the number of days of assessment required to achieve 80% reliability were estimated.


RESULTS
Physical inactivity was lower on weekend days, and Saturday was the least inactive day of the week for both men and women. Inter-individual variation, or differences between subjects, was proportionally the largest source of variance (55-60% of total) in accelerometer counts and time spent in moderate to vigorous activity. Differences within subjects (intra-individual variation) accounted for 30-45% of the overall variance, and day-of-the-week effects accounted for 1-8%. For activity counts, and time spent in moderate to vigorous activity, at least 3-4 d of monitoring were required to achieve 80% reliability. Reliable measures of physical inactivity required at least 7 d of monitoring.


CONCLUSIONS
These findings provide insight for understanding the behavioral variability in the activity patterns of adults and suggest that reliable measures of activity behaviors require at least 7 d of monitoring.
Matthews
Charles E
CE
Department of Epidemiology and Biostatistics, Norman J. Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA. cematthe@sph.sc.edu
Ainsworth
Barbara E
BE
Thompson
Raymond W
RW
Bassett
David R
DR
D000054:Acceleration; D000328:Adult; D000367:Age Factors; D000368:Aged; D000704:Analysis of Variance; D015992:Body Mass Index; D015331:Cohort Studies; D002149:Energy Intake; D004734:Energy Metabolism / Q000502:physiology; D015444:Exercise; D005260:Female; D006801:Humans; D007899:Leisure Activities; D008019:Life Style; D008297:Male; D008875:Middle Aged; D010809:Physical Fitness / Q000502:physiology*; D011336:Probability; D012737:Sex Factors; D013178:Sports Medicine / Q000295:instrumentation*; D013997:Time Factors