Assessment of physical activity with the Computer Science and Applications, Inc., accelerometer: laboratory versus field validation.
ABSTRACT Our purpose was to compare the validity of the Computer Science and Applications, (CSA) Inc., accelerometer in laboratory and field settings and establish CSA count ranges for light, moderate, and vigorous physical activity. Validity was determined in 60 adults during treadmill exercise, using oxygen consumption (VO2) as the criterion measure, while 30 adults walked and jogged outdoors on a 400-m track. The relationship between CSA counts and VO2 was linear (R2 = .89 SEE = 3.72 ml.kg-1.min-1), as was the relationship between velocity and counts in the field (R2 = .89, SEE = 0.89 mi.hr-1). However, significant differences were found (p < .05) between laboratory and field measures of CSA counts for light and vigorous intensity. We conclude that the CSA can be used to quantify walking and jogging outdoors on level ground; however, laboratory equations may not be appropriate for use in field settings, particularly for light and vigorous activity.
Article: Physical activity levels in patients with early knee osteoarthritis measured by accelerometry.[show abstract] [hide abstract]
ABSTRACT: Physical activity (PA) is recommended for osteoarthritis (OA) management to reduce pain and improve function. The purpose of this study was to objectively assess the level and pattern of PA in male and female knee OA patients to determine adherence to Centers for Disease Control and Prevention/American College of Sports Medicine and Exercise and Physical Activity Conference recommendations for PA. Early OA patients (n = 255, 76% women, mean +/- SD age 54.6 +/- 7.1 years, mean +/- SD body mass index 27.8 +/- 4.3 kg/m(2)) with Kellgren/Lawrence-defined grade II (no higher) radiographic OA in at least 1 knee wore an accelerometer for 6-7 contiguous days. Light (LPA), moderate (MPA), and vigorous (VPA) PA intensities were defined as accelerometer recordings of 100-2,224, 2,225-5,950, and >5,950 counts per minute, respectively. Patients wore accelerometers for a mean +/- SD of 6.8 +/- 0.3 days and 13.8 +/- 2.2 hours/day, and spent much more time (P < 0.001) in MPA (23.6 +/- 17.2 minutes/day) than VPA (0.95 +/- 3.5 minutes/day). Men spent significantly (P < 0.05) more time in all PA intensities than women. Only 30% of patients achieved recommended PA levels. The proportion of men (47%) achieving the recommendation was significantly (P = 0.04) higher than women (24%). Knee OA patients accumulate little VPA and most (70%) do not achieve recommended levels for MPA or greater. New strategies to increase levels of PA in this population are needed.Arthritis & Rheumatism 10/2008; 59(9):1229-36. · 7.87 Impact Factor
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ABSTRACT: The purpose of this study was to validate a two-regression model for predicting energy expenditure (EE) from ActiGraph GT1M accelerometer-generated activity counts using a whole-room indirect calorimeter and the doubly labeled water (DLW) technique. We also investigated if a low-pass filter (LPF) approach would improve the model's accuracy in the minute-to-minute EE prediction. Thirty-four healthy volunteers (age = 20-67 yr, body mass index = 19.3-52.1 kg.m) spent approximately 24 h in a room calorimeter while wearing a GT1M monitor and performed structured and self-selected activities followed by overnight sleep. The EE predicted by the models and expressed in metabolic equivalents (MET-minutes) during waking times was compared with the room calorimeter-measured EE. A subset of volunteers (n = 22) completed a 14-d DLW protocol in free living while wearing an ActiGraph. The average daily EE predicted by the models (MET-minutes) was compared with the DLW. Compared with the room calorimeter, the two-regression model overpredicted EE by 10.2% +/- 11.4% (1282 +/- 125 and 1174 +/- 152 MET.min, P < 0.001) and time spent in moderate physical activity (PA) by 36.9 +/- 46.0 min while underestimating the time spent in light PA by -48.3 +/- 55.0 min (P < 0.05). The LPF reduced the squared and mean absolute error in the EE prediction (P < 0.05) but not the prediction error in time spent in moderate or light PA (both P > 0.05). The EE measured by DLW (2108 +/- 358 MET.min.d) and predicted by both filtered and unfiltered models (2104 +/- 218 and 2192 +/- 228 MET.min.d, respectively) were similar (P > 0.05). The two-regression model with LPF showed good agreement with total EE measured using room calorimeter and DLW. However, the individual variability in assessing time spent in sedentary, low, and moderate PA intensities and related EE remains significant.Medicine and science in sports and exercise 09/2010; 42(9):1785-92. · 3.71 Impact Factor
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ABSTRACT: Studies of the built environment and physical activity have implicitly assumed that a substantial amount of activity occurs near home, but in fact the location is unknown. This study aims to examine associations between built environment variables within home and work buffers and moderate-to-vigorous physical activity (MVPA) occurring within these locations. Adults (n=148) from Massachusetts wore an accelerometer and GPS unit for up to 4 days. Levels of MVPA were quantified within 50-m and 1-km home and work buffers. Multiple regression models were used to examine associations between five objective built environment variables within 1-km home and work buffers (intersection density, land use mix, population and housing unit density, vegetation index) and MVPA within those areas. The mean daily minutes of MVPA accumulated in all locations=61.1+/-32.8, whereas duration within the 1-km home buffers=14.0+/-16.4 minutes. Intersection density, land use mix, and population and housing unit density within 1-km home buffers were positively associated with MVPA in the buffer, whereas a vegetation index showed an inverse relationship (all p<0.05). None of these variables showed associations with total MVPA. Within 1 km of work, only population and housing unit density were significantly associated with MVPA within the buffer. Findings are consistent with studies showing that certain attributes of the built environment around homes are positively related to physical activity, but in this case only when the outcome was location-based. Simultaneous accelerometer-GPS monitoring shows promise as a method to improve understanding of how the built environment influences physical activity behaviors by allowing activity to be quantified in a range of physical contexts and thereby provide a more explicit link between physical activity outcomes and built environment exposures.American journal of preventive medicine 04/2010; 38(4):429-38. · 4.24 Impact Factor