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Jeffer Eidi Sasaki, Cheryl A. Howe, Dinesh John, Amanda Hickey, Jeremy Steeves, Scott Conger, Kate Lyden, Sarah Kozey-Keadle, Sarah Burkart, Sofiya Alhassan, David Bassett Jr, and Patty S. Freedson

Background:

Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.

Methods:

Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.

Results:

Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.

Conclusion:

This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Open access

Antje Ullrich, Sophie Baumann, Lisa Voigt, Ulrich John, and Sabina Ulbricht

Background: The purposes of this study were to examine accelerometer measurement reactivity (AMR) in sedentary behavior (SB), physical activity (PA), and accelerometer wear time in 2 measurement periods and to quantify AMR as a human-related source of bias for the reproducibility of SB and PA estimates. Methods: In total, 136 participants (65% women, mean age = 54.6 y) received 7-day accelerometry at the baseline and after 12 months. Latent growth models were used to identify AMR. Intraclass correlations were calculated to examine the reproducibility using 2-level mixed-effects linear regression analyses. Results: Within each 7-day accelerometry assessment, the participants increased their time spent in SB (b = 2.4 min/d; b = 3.8 min/d) and reduced their time spent in light PA (b = −2.0 min/d; b = −3.2 min/d), but did not change moderate to vigorous PA. The participants reduced their wear time (b = −5.2 min/d) only at the baseline. The intraclass correlations ranged from .42 for accelerometer wear time to .74 for SB. The AMR was not identified as a source of bias in any regression model. Conclusions: AMR may influence SB and PA estimates differentially. Although 7-day accelerometry seems to be a reproducible measure, our findings highlight accelerometer wear time as a crucial confounder in analyzing SB and PA data.

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Louise C. Mâsse and Judith E. de Niet

Background:

Over the years, self-report measures of physical activity (PA) have been employed in applications for which their use was not supported by the validity evidence.

Methods:

To address this concern this paper 1) provided an overview of the sources of validity evidence that can be assessed with self-report measures of PA, 2) discussed the validity evidence needed to support the use of self-report in certain applications, and 3) conducted a case review of the 7-day PA Recall (7-d PAR).

Results:

This paper discussed 5 sources of validity evidence, those based on: test content; response processes; behavioral stability; relations with other variables; and sensitivity to change. The evidence needed to use self-report measures of PA in epidemiological, surveillance, and intervention studies was presented. These concepts were applied to a case review of the 7-d PAR. The review highlighted the utility of the 7-d PAR to produce valid rankings. Initial support, albeit weaker, for using the 7-d PAR to detect relative change in PA behavior was found.

Conclusion:

Overall, self-report measures can validly rank PA behavior but they cannot adequately quantify PA. There is a need to improve the accuracy of self-report measures of PA to provide unbiased estimates of PA.

Open access

Christopher C. Moore, Aston K. McCullough, Elroy J. Aguiar, Scott W. Ducharme, and Catrine Tudor-Locke

Background: The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies. Purpose: To catalog studies validating step-counting wearable technologies during treadmill ambulation. Methods: The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. Where reported, speed- and wear location–specific mean absolute percentage error (MAPE) values were tabulated. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment. Results: Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8 m/s; their weighted median MAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and ≤1% for thigh-worn devices. Conclusions: Despite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies.

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Richard P. Troiano, Kelley K. Pettee Gabriel, Gregory J. Welk, Neville Owen, and Barbara Sternfeld

Context:

Advances in device-based measures have led researchers to question the value of reported measures of physical activity or sedentary behavior. The premise of the Workshop on Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods, held in July 2010, was that assessment of behavior by self-report is a valuable approach.

Objective:

To provide suggestions to optimize the value of reported physical activity and sedentary behavior, we 1) discuss the constructs that devices and reports of behavior can measure, 2) develop a framework to help guide decision-making about the best approach to physical activity and sedentary behavior assessment in a given situation, and 3) address the potential for combining reported behavior methods with device-based monitoring to enhance both approaches.

Process:

After participation in a workshop breakout session, coauthors summarized the ideas presented and reached consensus on the material presented here.

Conclusions:

To select appropriate physical activity assessment methods and correctly interpret the measures obtained, researchers should carefully consider the purpose for assessment, physical activity constructs of interest, characteristics of the population and measurement tool, and the theoretical link between the exposure and outcome of interest.

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Paul J. Collings, Diane Farrar, Joanna Gibson, Jane West, Sally E. Barber, and John Wright

pregnancy body mass index, socioeconomic status, parity, season of physical activity assessment, maternal smoking in pregnancy, neonate sex, delivery mode, gestational age, and birth weight. Beside estimates are P values. Bold font denotes significantly different values compared with the referent inactive

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Amanda L. Penko, Jacob E. Barkley, Anson B. Rosenfeldt, and Jay L. Alberts

Physical Activity Assessment Parkinson’s disease motor symptoms, fall frequency, and physical activity assessments were completed at 3 time points: baseline (ie, 1 wk preceding the intervention); postintervention (ie, within 1 wk following intervention); and 4-week postintervention. For PD motor symptoms

Open access

Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson, and John R. Sirard

( 1 ), 141 – 151 . doi:10.1901/jaba.1991.24-141 10.1901/jaba.1991.24-141 Mckenzie , T.L. ( 2002 ). The use of direct observation to assess physical activity . In: Welk , G , ed. Physical activity assessments for health-related research (pp.  179 – 195 ). Champaign, IL : Human Kinetics

Open access

Ignacio Perez-Pozuelo, Thomas White, Kate Westgate, Katrien Wijndaele, Nicholas J. Wareham, and Soren Brage

. , & Ekelund , U. ( 2005 ). Integration of physiological and accelerometer data to improve physical activity assessment . Medicine & Science in Sports & Exercise, 37 ( Suppl. 11 ), S563 – 71 . 10.1249/01.mss.0000185650.68232.3f Thompson , D. , Batterham , A.M. , Bock , S. , Robson , C

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Jessica Gorzelitz, Chloe Farber, Ronald Gangnon, and Lisa Cadmus-Bertram

physical activity ( Coughlin & Stewart, 2016 ; O’Driscoll et al., 2018 ). Publications on the validity of wearable trackers for physical activity assessment have used many different strategies, devices, and criteria for validation. To date, no systematic review has addressed minutes of activity (e