Dataset Information

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults.

ABSTRACT:

Purpose

The aim of the present study was to examine the convergent validity of two commonly-used accelerometers for estimating time spent in various physical activity intensities in adults.

Methods

The sample comprised 37 adults (26 males) with a mean (SD) age of 37.6 (12.2) years from San Diego, USA. Participants wore ActiGraph GT3X+ and Actical accelerometers for three consecutive days. Percent agreement was used to compare time spent within four physical activity intensity categories under three counts per minute (CPM) threshold protocols: (1) using thresholds developed specifically for each accelerometer, (2) applying ActiGraph thresholds to regression-rectified Actical CPM data, and (3) developing new 'optimal' Actical thresholds.

Results

Using Protocol 1, the Actical estimated significantly less time spent in light (-16.3%), moderate (-2.8%), and vigorous (-0.4%) activity than the ActiGraph, but greater time spent sedentary (+20.5%). Differences were slightly more pronounced when the low frequency extension filter on the ActiGraph was enabled. The two adjustment methods (Protocols 2 and 3) improved agreement in this sample.

Conclusions

Our findings show that ActiGraph and Actical accelerometers provide significantly different estimates of time spent in various physical activity intensities. Regression and threshold adjustment were able to reduce these differences, although some level of non-agreement persisted. Researchers should be aware of the inherent limitations of count-based physical activity assessment when reporting and interpreting study findings.

SUBMITTER: Duncan S

PROVIDER: S-EPMC5997323 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults.

Duncan Scott S Stewart Tom T Bo Schneller Mikkel M Godbole Suneeta S Cain Kelli K Kerr Jacqueline J

PloS one 20180612 6

<h4>Purpose</h4>The aim of the present study was to examine the convergent validity of two commonly-used accelerometers for estimating time spent in various physical activity intensities in adults.<h4>Methods</h4>The sample comprised 37 adults (26 males) with a mean (SD) age of 37.6 (12.2) years from San Diego, USA. Participants wore ActiGraph GT3X+ and Actical accelerometers for three consecutive days. Percent agreement was used to compare time spent within four physical activity intensity cate ...[more]

PMID: 29894485

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Project description:PURPOSE:This study aimed to examine the comparability of the ActiGraph 7164 and wGT3X-BT wear time, count-based estimates, and average time per day in physical activity of different intensities. METHODS:We studied 87 Coronary Artery Risk Development in Young Adults (CARDIA) participants 48-60 yr of age who simultaneously wore the 7164 and wGT3X-BT accelerometers at the waist in 2015-2016, with wear time of ?4 of 7 d, ?10 h·d for both monitors. Freedson cutpoints (counts per minute) were used to define sedentary (<100), light (100-1951), moderate (1952-5724), and vigorous activity (?5725). Agreement was evaluated using paired-difference tests, intraclass correlation coefficients, and Bland-Altman plots. Given systematic differences in count-based estimates between monitors, a calibration formula applied to the wGT3X-BT values was obtained by linear regression. RESULTS:Total detected wear time minutes per day was nearly identical between the 7164 and the wGT3X-BT (881.5 ± 70.9 vs 880.3 ± 78.1, P = 0.72). The wGT3X-BT values were calibrated to the 7164 values by dividing counts by 1.088. After calibration, no differences were observed between the 7164 and the wGT3X-BT in total counts per day (310,184 ± 129,189 vs 307,085 ± 135,362, P = 0.48), average counts per min per day (349.5 ± 139.5 vs 346.5 ± 147.2, P = 0.54), sedentary (513.2 ± 93.6 vs 509.6 ± 98.6, P = 0.23), light (335.3 ± 81.5 vs 338.7 ± 81.1, P = 0.22), moderate (31.0 ± 21.9 vs 30.3 ± 23.4, P = 0.31), or moderate-to-vigorous minutes per day (33.1 ± 24.6 vs 32.0 ± 26.0, P = 0.13). A significant difference was observed for vigorous minutes per day (0.2 ± 1.0 vs 0.0 ± 0.3, P < 0.01); however, the absolute difference was marginal. Intraclass correlation coefficients showed excellent agreement for all measures (0.95-0.99). CONCLUSIONS:After applying a calibration formula, the 7164 and wGT3X-BT were comparable for total wear time, count-based estimates, and average minutes per day in sedentary, light, moderate, and moderate-to-vigorous activity. Findings illustrate a novel methodological approach to facilitate accelerometer data harmonization.

Project description:BackgroundThe rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA.ObjectiveThe study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions.MethodsNineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps).ResultsPearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps.ConclusionsOverall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

Project description:BackgroundA primary aim of the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study is to examine the association between total physical activity levels (comprising occupational, household and recreational activity) and the incidence of cancer. We examined the validity and long-term repeatability of total physical activity measurements estimated from the past-year recall EPIC questionnaire, using accelerometers as an objective reference measure.MethodsParticipants included 100 men and 82 women aged 50-65 years. Criterion validity was assessed by comparing the physical activity estimates from the EPIC questionnaire with total activity estimated from the average of three separate 7-day accelerometer periods during the same (past-year) period. Long-term repeatability of the EPIC questionnaire was assessed by comparing the responses from the baseline and 10-month administrations. Past-year EPIC estimates were also compared with the Friedenreich Lifetime Total Physical Activity Questionnaire to examine whether recent activity reflected lifetime activity.ResultsAccelerometer total metabolic equivalent (MET)-hours/week were positively associated with a total physical activity index (Spearman rank correlation rho = 0.29, 95% confidence interval (CI) 0.15, 0.42) and with non-occupational activity estimated in MET-hours/week (rho = 0.21, 95% CI 0.07, 0.35). Stratified analyses suggested stronger correlations for non-occupational activity for participants who were male, had a lower BMI, were younger, or were not full-time workers, although the differences in correlations between groups were not statistically significant. The weighted kappa coefficient for repeatability of the total physical activity index was 0.62 (95% CI 0.53, 0.71). Spearman correlations for repeatability of components of activity were 0.65 (95% CI 0.55, 0.72) for total non-occupational, 0.58 (95% CI 0.48, 0.67) for recreational and 0.73 (95% CI 0.66, 0.79) for household activity. When past-year activity was compared to lifetime estimates of activity, there was fair agreement for non-occupational (rho = 0.26) activity, which was greater for household activity (rho = 0.46) than for recreational activity (rho = 0.21).ConclusionOur findings suggest that the EPIC questionnaire has acceptable measurement characteristics for ranking participants according to their level of total physical activity. The questionnaire should be able to identify the presence or absence of reasonably strong aetiological associations when either recent or long-term activity is the responsible factor.

Dataset Information

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults.

Purpose

Methods

Results

Conclusions

Publications

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults.

Similar Datasets

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