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Dana L. Wolff-Hughes, Eugene C. Fitzhugh, David R. Bassett and James R. Churilla

Background:

Accelerometer-derived total activity count is a measure of total physical activity (PA) volume. The purpose of this study was to develop age- and gender-specific percentiles for daily total activity counts (TAC), minutes of moderate-to-vigorous physical activity (MVPA), and minutes of light physical activity (LPA) in U.S. adults.

Methods:

Waist-worn accelerometer data from the 2003-2006 National Health and Nutrition Examination Survey were used for this analysis. The sample included adults >20 years with >10 hours accelerometer wear time on >4 days (N = 6093). MVPA and LPA were defined as the number of 1-minute epochs with counts >2020 and 100 to 2019, respectively. TAC represented the activity counts acquired daily. TAC, MVPA, and LPA were averaged across valid days to produce a daily mean.

Results:

Males in the 50th percentile accumulated 288 140 TAC/day, with 357 and 22 minutes/day spent in LPA and MVPA, respectively. The median for females was 235 741 TAC/day, with 349 and 12 minutes/day spent in LPA and MVPA, respectively.

Conclusions:

Population-referenced TAC percentiles reflect the total volume of PA, expressed relative to other adults. This is a different approach to accelerometer data reduction that complements the current method of looking at time spent in intensity subcategories.

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Dana L. Wolff-Hughes, Eugene C. Fitzhugh, David R. Bassett and James R. Churilla

Purpose:

To contrast associations of accelerometer-measured moderate-to-vigorous physical activity (MVPA) accumulated in bouts and total activity counts (TAC) with cardiometabolic biomarkers in U.S. adults.

Methods:

Using 2003–2006 National Health and Nutrition Examination Survey (NHANES) data, the sample was comprised of adults ≥ 20 years, not pregnant or lactating, with self-reported PA and at least 4 days of ≥ 10 hours accelerometer wear time (N = 5668). Bouted MVPA represented the minutes/day with ≥ 2020 counts/minute in bouts of 10 minutes or longer and TAC represented the total activity counts per day. Biomarkers included: cholesterol, triglyceride, glycohemoglobin, plasma glucose, C-peptide, insulin, C-reactive protein, homocysteine, blood pressure, body mass index (BMI), waist circumference, and skinfolds. Nested regression models were conducted which regressed each biomarker on bouted MVPA and TAC simultaneously, while adjusting for relevant covariates.

Results:

Results indicated TAC was more strongly associated with 11 biomarkers: HDL-C, triglyceride, plasma glucose, C-peptide, insulin, C-reactive protein, homocysteine, systolic blood pressure, waist circumference, triceps skinfold, and subscapular skinfold. Bouted MVPA, however, only displayed stronger associations with BMI.

Conclusions:

The total volume of physical activity, represented by TAC, appears to have stronger associations with cardiometabolic biomarkers than MVPA accumulated in bouts.

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Sze Yen Tan, Marijka Batterham and Linda Tapsell

Background:

Knowing the total energy expenditure (TEE) of overweight adults is important for prescribing weight loss interventions. However, objective measurements of TEE may not always be readily available and can be expensive. This study aimed to investigate the validity of RT3 accelerometers in predicting the TEE of sedentary overweight adults, and to identify any sensitivity to anthropometric changes.

Methods:

The analysis used data from a 12-week weight loss study. At baseline and 12-week, TEE was predicted using RT3 accelerometers during whole room calorimeter stays. Bias between 2 methods was compared at and between the baseline and 12-week measurement points. Multiple regression analyses of TEE data were conducted.

Results:

Predicted and measured values for TEE were not different at baseline (P = .677) but were significantly different after weight loss (P = .007). However, the mean bias between methods was small (<100 kcal/d) and was not significantly different between 2 time-points. RT3 activity counts explained an additional 2% of the variation in TEE at 12-week but not at baseline.

Conclusion:

RT3 accelerometers are not sensitive to body composition changes and do not explain variation in TEE of overweight and obese individuals in a sedentary environment.

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Jungyun Hwang, I-Min Lee, Austin M. Fernandez, Charles H. Hillman and Amy Shirong Lu

nondominant wrist with a silicone wristband and at the anterior axillary line of the nondominant hip with a belt clip to measure upper- and lower-body movements, respectively, during the exergaming sessions ( 30 ). We downloaded steps and activity counts from 3 axes ( x axis: anteroposterior, y axis

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Scott E. Crouter, Paul R. Hibbing and Samuel R. LaMunion

The purpose of this study was to conduct a comprehensive evaluation of the ActiGraph GT3X+ (AG) and activPAL (AP) for assessing time spent in sedentary behaviors (SB) in youth using structured and free-living activities. Forty-four participants (Mage, 12.7±0.8 yrs) completed up to eight structured activities and approximately 2 hrs of free-living activity while wearing an AG (right hip) and AP (right thigh). A Cosmed K4b2 was used for measured energy expenditure (METy; activity VO2 ÷ resting VO2). Direct observation was used during the structured activities. SB time was estimated using the inclinometer function of the AP and AG, and count thresholds with AG (<75 vector magnitude [VM] counts/10-s; <25 vertical axis [VA] counts/10-s; and <50, 100, 150, and 200 VA counts/min). For the structured activities, the AG inclinometer and AP correctly classified supine rest about 45% of the time, seated activities 54.6% and 65.1% of the time, respectively, and walking and running >96% of the time. For the free-living measurement, the VA <25 counts/10-s had the lowest RMSE (20.6 min), while the VM <75 counts/10-s had the lowest MAPE (69.2%). The AG inclinometer was within 0.2 minutes of measured time, but had the highest MAPE (107.1%). The AP was within 1.6 minutes of measured time, but had the highest RMSE (28.5 minutes). Compared to measured SB time, the VA <25 counts/10-s and VM <75 counts/10-s provided the most precise estimates of SB during free-living activity. Further refinement is needed to improve the AP and AG posture estimates.

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Jeremy A. Steeves, Catrine Tudor-Locke, Rachel A. Murphy, George A. King, Eugene C. Fitzhugh, David R. Bassett, Dane Van Domelen, John M. Schuna Jr and Tamara B. Harris

-derived daily PA variables closely linked to health outcomes (total activity counts [TAC] per day, MVPA minutes per week in modified 10-minute bouts (MVPA 10), and proportion of wear time spent in sedentary intensity activity). 1 – 3 , 5 , 18 , 19 Methods This analysis used data from the occupational

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Dinesh John, Qu Tang, Fahd Albinali and Stephen Intille

, & Churilla, 2015 ). However, ActiGraph’s popular activity counts has several flaws. This summary metric demonstrates variable relationships with increasing frequency of movement based on wear location (e.g., hip vs. wrist) ( John, Tyo, & Bassett, 2010 ; LaMunion, Bassett, Toth, & Crouter, 2017 ), yield

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Elizabeth Lawinger, Tim L. Uhl, Mark Abel and Srinath Kamineni

Objective:

The overarching goal of this study was to examine the use of triaxial accelerometers in measuring upper-extremity motions to monitor upper-extremity-exercise compliance. There were multiple questions investigated, but the primary objective was to investigate the correlation between visually observed arm motions and triaxial accelerometer activity counts to establish fundamental activity counts for the upper extremity.

Study Design:

Cross-sectional, basic research.

Setting:

Clinical laboratory.

Participants:

Thirty healthy individuals age 26 ± 6 y, body mass 24 ± 3 kg, and height 1.68 ± 0.09 m volunteered.

Intervention:

Participants performed 3 series of tasks: activities of daily living (ADLs), rehabilitation exercises, and passive shoulder range of motion at 5 specific velocities on an isokinetic dynamometer while wearing an accelerometer on each wrist. Participants performed exercises with their dominant arm to examine differences between sides. A researcher visually counted all arm motions to correlate counts with physical activity counts provided by the accelerometer.

Main Outcome Measure:

Physical activity counts derived from the accelerometer and visually observed activity counts recorded from a single investigator.

Results:

There was a strong positive correlation (r = .93, P < .01) between accelerometer physical activity counts and visual activity counts for all ADLs. Accelerometers activity counts demonstrated side-to-side difference for all ADLs (P < .001) and 5 of the 7 rehabilitation activities (P < .003). All velocities tested on the isokinetic dynamometer were shown to be significantly different from each other (P < .001).

Conclusion:

There is a linear relationship between arm motions counted visually and the physical activity counts generated by an accelerometer, indicating that arm motions could be potentially accounted for if monitoring arm usage. The accelerometers can detect differences in relatively slow arm-movement velocities, which is critical if attempting to evaluate exercise compliance during early phases of shoulder rehabilitation. These results provide fundamental information that indicates that triaxial accelerometers have the potential to objectively monitor and measure arm activities during rehabilitation and ADLs.

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David Xiaoqian Sun, Gordon Schmidt and Sock Miang Teo-Koh

This is a validation study of the RT3 accelerometer for measuring physical activities of children in simulated free-living conditions. Twenty-five children age 12–14 years completed indoor testing, and 18 of them completed outdoor testing. Activity counts from the RT3 accelerometer estimated activity energy expenditure (AEE) and the Cosmed K4b2 analyzer measured oxygen uptake. Correlations were found between activity counts and metabolic cost (r = .95, p < .001), metabolic cost and RT3 estimated AEE (r = .96, p < .001) in the indoor test, activity counts and RT3 estimated AEE (r = .97, p < .001) in the outdoor test, and activity counts and metabolic cost when all activities were combined (r = .77, p < .001). Results indicate that the RT3 accelerometer might be used to provide acceptable estimates of free-living physical activity in children.

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Stamatis Agiovlasitis, Robert W. Motl, John T. Foley and Bo Fernhall

This study examined the relationship between energy expenditure and wrist accelerometer output during walking in persons with and without Down syndrome (DS). Energy expenditure in metabolic equivalent units (METs) and activity-count rate were respectively measured with portable spirometry and a uniaxial wrist accelerometer in 17 persons with DS (age: 24.7 ± 6.9 years; 9 women) and 21 persons without DS (age: 26.3 ± 5.2 years; 12 women) during six over-ground walking trials. Combined groups regression showed that the relationship between METs and activity-count rate differed between groups (p < .001). Separate models for each group included activity-count rate and squared activity-count rate as significant predictors of METs (p ≤ .005). Prediction of METs appeared accurate based on Bland-Altman plots and the lack of between-group difference in mean absolute prediction error (DS: 17.07%; Non-DS: 18.74%). Although persons with DS show altered METs to activity-count rate relationship during walking, prediction of their energy expenditure from wrist accelerometry appears feasible.