Effect of ActiGraph's low frequency extension for estimating steps and physical activity intensity (original) (raw)
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Estimates of Physical Activity in Older Adults Using the ActiGraph Low-Frequency Extension Filter
Journal for the Measurement of Physical Behaviour
As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = ...
Pedometer Measures of Free-Living Physical Activity: Comparison of 13 Models
Medicine & Science in Sports & Exercise, 2004
The purpose of this study was to compare the step values of multiple brands of pedometers over a 24-h period. The following 13 electronic pedometers were assessed in the study: Accusplit Alliance 1510 (AC), Freestyle Pacer Pro (FR), Colorado on the Move (CO), Kenz Lifecorder (KZ), New-Lifestyles NL-2000 (NL), Omron HJ-105 (OM), Oregon Scientific PE316CA (OR), Sportline 330 (SL330) and 345 (SL345), Walk4Life LS 2525 (WL), Yamax Skeletone EM-180 (SK), Yamax Digi-Walker SW-200 (YX200), and the Yamax Digi-Walker SW-701 (YX701). Methods: Ten males (39.5 Ϯ 16.6 yr, mean Ϯ SD) and 10 females (43.3 Ϯ 16.6 yr) ranging in BMI from 19.8 to 35.4 kg•m Ϫ2 wore two pedometers for a 24-h period. The criterion pedometer (YX200) was worn on the left side of the body, and a comparison pedometer was worn on the right. Steps counted by each device were recorded at the end of the day for each of the thirteen pedometers. Results: Subjects took an average of 9244 steps•d Ϫ1. The KZ, YX200, NL, YX701, and SL330 yielded mean values that were not significantly different from the criterion. The FR, AC, SK, CO, and SL345 significantly underestimated steps (P Ͻ 0.05) and the WL, OM, and OR significantly overestimated steps (P Ͻ 0.05) when compared with the criterion. In addition, some pedometers underestimated by 25% whereas others overestimated by 45%. Conclusion: The KZ, YX200, NL, and YX701 appear to be suitable for most research purposes. Given the potential for pedometers in physical activity research, it is necessary that there be consistency across studies in the measurement of "steps per day.
PLoS ONE, 2011
Background: The quantification of the relationships between walking and health requires that walking is measured accurately. We correlated different measures of step accumulation to body size, overall physical activity level, and glucose regulation. Methods: Participants were 25 men and 25 women American Indians without diabetes (Age: 20-34 years) in Phoenix, Arizona, USA. We assessed steps/day during 7 days of free living, simultaneously with three different monitors (Accusplit-AX120, MTI-ActiGraph, and Dynastream-AMP). We assessed total physical activity during free-living with doubly labeled water combined with resting metabolic rate measured by expired gas indirect calorimetry. Glucose tolerance was determined during an oral glucose tolerance test. Findings: Based on observed counts in the laboratory, the AMP was the most accurate device, followed by the MTI and the AX120, respectively. The estimated energy cost of 1000 steps per day was lower in the AX120 than the MTI or AMP. The correlation between AX120-assessed steps/day and waist circumference was significantly higher than the correlation between AMP steps and waist circumference. The difference in steps per day between the AX120 and both the AMP and the MTI were significantly related to waist circumference. Interpretation: Between-monitor differences in step counts influence the observed relationship between walking and obesity-related traits.
Canadian Journal of Applied Physiology, 2005
Purpose: (a) To establish pedometer steps/min intensity categories (i.e., light, moderate, hard, very hard) for adults under controlled conditions, and (b) use these cut-points to ascertain the number of steps expected in 30 minutes of moderate intensity activity. Methods: 25 men and 25 women, ages 18-39 years, performed 6-min exercise bouts at 3 treadmill speeds (4.8, 6.4, and 9.7 km/hr). Yamax SW-200 pedometers indicated steps, and steady-state [Formula: see text] was recorded. METs were calculated by dividing steady-state [Formula: see text] by 3.5 ml•kg−1 min−1. Linear regression was used to quantify the relationships between steps/min and METs across all speeds. Ten participants (5 M, 5 F) were randomly selected from the original 50 and constituted a holdout sample for cross-validation purposes (i.e, comparing actual and predicted METs; paired t-test). Results: The regression equation for males was: METs = −7.065 + (0.105*steps/min) r2 = 0.803. For females it was: METs = −8.805...
Frontiers in Physiology, 2017
We examined the agreement in time spent on different physical activity (PA) levels using (1) mean amplitude deviation (MAD) of raw acceleration from the hip, (2) wrist-worn Polar Active, and (3) hip-worn Actigraph counts using Freedson's cut-points among adults under free-living conditions. PA was measured in 41 volunteers (mean age 47.6 years) for 14 days. Two MET-based threshold sets were used for MAD and Polar Active for sedentary time (ST) and time spent in light (LPA), moderate (MPA), and vigorous (VPA) PA. Actigraph counts were divided into PA classes, ≤100 counts/min for ST and Freedson's cut-points for LPA, MPA, and VPA. Analysis criteria were simultaneous use of devices for at least 4 days of >500 min/d. The between-method differences were analyzed using a repeated measures analysis of variance test. Bland-Altman plots and ROC graphs were also employed. Valid data were available from 27 participants. Polar Active produced the highest amount of VPA with both thresholds (≥5 and ≥6 MET; mean difference 17.9-30.9 min/d, P < 0.001). With the threshold 3-6 MET for MPA, Polar Active indicated 19.2 min/d more than MAD (95% CI 5.8-32.6) and 51.0 min/d more than Actigraph (95% CI 36.7-65.2). The results did not differ with 3.5-5 MET for MPA [F = (1.44, 37.43) 1.92, P = 0.170]. MAD and Actigraph were closest to each other for ST with the threshold <1.5 MET (mean difference 22.2 min/d, 95% CI 7.1-37.3). With the threshold <2 MET, Polar Active and Actigraph provided similar results (mean difference 7.0 min/d, 95% CI −17.8-31.7). Moderate to high agreement (area under the ROC curve 0.806-0.963) was found between the methods for the fulfillment of the recommendation for daily moderate-to-vigorous PA of 60 min. In free-living conditions the agreement between MAD, Polar Active, and Actigraph for measuring time spent on different activity levels in adults was dependent on the activity thresholds used and PA intensity. ROC Leinonen et al. Comparing Physical Activity Measurement Methods analyses showed moderate to high agreement for the fulfillment of the recommendation for daily MVPA. Without additional statistical adjustment, these methods cannot be used interchangeably when measuring daily PA, but any of the methods can be used to identify persons with insufficient daily amount of MVPA.
PLOS ONE, 2017
Introduction Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn Acti-Graph (wGT3X-BT) in free-living conditions. Methods 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (!10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman's rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. Results The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76-0.91; ICC: 0.73-0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8-28.1%) for overall steps, 16.9% (6.8-30.3%) vs. 15.1% (4.5-27.3%) in males and females, and 20.4% (8.7-35.9%) vs. 9.6% (1.0-18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants.
The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults
Medicine & Science in Sports & Exercise, 2017
The activPAL ™ (AP) monitor is well established for distinguishing sitting, standing and stepping time. However, its validity in predicting time in physical activity intensity categories in a freeliving environment has not been determined. Purpose-To determine the validity of the AP in estimating time spent in sedentary, light and moderate-to-vigorous physical activity (MVPA) in a free-living environment. Methods-Thirteen participants (mean (SD) age 24.8 y (5.2), BMI 23.8 kg.m 2 (1.9)) were directly observed (DO) for three 10-hr periods wearing an AP. A custom R program was developed and used to summarize detailed active and sedentary behavior variables from the AP. AP estimates were compared to DO. Results-The AP accurately and precisely estimated time in activity intensity categories (bias (95% CI) sedentary = 0.8 min (−2.9, 4.5), light = 1.7 min (2.2, 5.7) and −2.6 min (−5.8, 0.7)). The overall accuracy rate for time in intensity categories was 96.2%. The AP also accurately estimated guideline minutes, guideline bouts, prolonged sitting minutes and prolonged sitting bouts. Conclusion-The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.
Accelerometer-determined moderate intensity lifestyle activity and cardiometabolic health
Preventive Medicine, 2011
Objective. To assess the relationship between moderate intensity lifestyle activity (LA) and cardiometabolic health using accelerometer data from the National Health and Nutrition Examination Survey (NHANES) 2005-2006. Methods. One thousand three hundred and seventy-one adults (50% men; 71% non-Hispanic white) provided valid data to quantify time in LA [760-2019 counts per minute (CPM)] and moderate-to-vigorous physical activity (MVPA; ≥ 2020 CPM). Associations between LA [minutes per day (min/day); steps per day (steps/day)], and cardiometabolic risk factors [triglycerides, HDL-cholesterol (HDL-C), blood pressure, glucose, waist circumference], metabolic syndrome, self-reported hypertension and diabetes were investigated using logistic regression. Analyses were adjusted for age, gender, race/ethnicity, and MVPA categories.