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Dinesh John, David Bassett, Dixie Thompson, Jeffrey Fairbrother and Debora Baldwin

Although using a treadmill workstation may change the sedentary nature of desk jobs, it is unknown if walking while working affects performance on office-work related tasks.

Purpose:

To assess differences between seated and walking conditions on motor skills and cognitive function tests.

Methods:

Eleven males (24.6 ± 3.5 y) and 9 females (27.0 ± 3.9 y) completed a test battery to assess selective attention and processing speed, typing speed, mouse clicking/drag-and-drop speed, and GRE math and reading comprehension. Testing was performed under seated and walking conditions on 2 separate days using a counterbalanced, within subjects design. Participants did not have an acclimation period before the walking condition.

Results:

Paired t tests (P < .05) revealed that in the seated condition, completion times were shorter for mouse clicking (26.6 ± 3.0 vs. 28.2 ± 2.5s) and drag-and-drop (40.3 ± 4.2 vs. 43.9 ± 2.5s) tests, typing speed was greater (40.2 ± 9.1 vs. 36.9 ± 10.2 adjusted words · min−1), and math scores were better (71.4 ± 15.2 vs. 64.3 ± 13.4%). There were no significant differences between conditions in selective attention and processing speed or in reading comprehension.

Conclusion:

Compared with the seated condition, treadmill walking caused a 6% to 11% decrease in measures of fine motor skills and math problem solving, but did not affect selective attention and processing speed or reading comprehension.

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Dinesh John, Qu Tang, Fahd Albinali and Stephen Intille

Background: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2–5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20–100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

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David R. Bassett, Patty S. Freedson and Dinesh John

In recent years, there has been tremendous growth in the use of wearable activity trackers in biomedical research. Activity trackers are also becoming more popular with consumers, who are able to share their data with researchers and practitioners. Steps per day is a useful variable that is estimated from most wearable activity trackers. It has intuitive meaning, is strongly associated with health variables, and has the potential to be standardized across devices. Activity trackers and other wearable medical devices could provide new information on health-related behaviors and their interaction with genetic and environmental variables. If integrated into medical practice, wearable technologies could help motivate patients to change their health behaviors and might eventually be used to diagnose medical conditions. The convergence of wearable medical devices, computer applications, smart phones, and electronic medical records could influence the practice of lifestyle medicine.

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Amanda Hickey, Dinesh John, Jeffer E. Sasaki, Marianna Mavilia and Patty Freedson

Background:

There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting.

Methods:

Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi-Walker SW-200, 2 ActiGraph GT3Xs (1 in “low-frequency extension” [AGLFE] and 1 in “normal-frequency” mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings.

Results:

Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW.

Conclusion:

This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.

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David R. Bassett, Dinesh John, Scott A. Conger, Eugene C. Fitzhugh and Dawn P. Coe

Background:

Increases in childhood and adolescent obesity are a growing concern in the United States (U.S.), and in most countries throughout the world. Declines in physical activity are often postulated to have contributed to the rise in obesity rates during the past 40 years.

Methods:

We searched for studies of trends in physical activity and sedentary behaviors of U.S. youth, using nontraditional data sources. Literature searches were conducted for active commuting, physical education, high-school sports, and outdoor play. In addition, trends in sedentary behaviors were examined.

Results:

Data from the Youth Risk Behavior Surveillance System (YRBSS) and other national surveys, as well as longitudinal studies in the transportation, education, electronic media, and recreation sectors showed evidence of changes in several indicators. Active commuting, high school physical education, and outdoor play (in 3- to 12-year-olds) declined over time, while sports participation in high school girls increased from 1971 to 2012. In addition, electronic entertainment and computer use increased during the first decade of the 21st century.

Conclusions:

Technological and societal changes have impacted the types of physical activities performed by U.S. youth. These data are helpful in understanding the factors associated with the rise in obesity, and in proposing potential solutions.

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Dinesh John, Dixie L. Thompson, Hollie Raynor, Kenneth Bielak, Bob Rider and David R. Bassett

Purpose:

To determine if a treadmill-workstation (TMWS) increases physical activity (PA) and influences anthropometric, body composition, cardiovascular, and metabolic variables in overweight and obese office-workers.

Methods:

Twelve (mean age= 46.2 ± 9.2 years) overweight/obese sedentary office-workers (mean BMI= 33.9 ± 5.0 kg·m-2) volunteered to participate in this 9-month study. After baseline measurements of postural allocation, steps per day, anthropometric variables, body composition, cardiovascular, and metabolic variables, TMWS were installed in the participants’ offices for their use. Baseline measurements were repeated after 3 and 9 months. Comparisons of the outcome variables were made using repeated-measures ANOVAs or nonparametric Friedman’s Rank Tests.

Results:

Between baseline and 9 months, significant increases were seen in the median standing (146−203 min·day-1) and stepping time (52−90 min·day-1) and total steps/day (4351−7080 steps/day; P < .05). Correspondingly, the median time spent sitting/lying decreased (1238−1150 min·day-1; P < .05). Using the TMWS significantly reduced waist (by 5.5 cm) and hip circumference (by 4.8 cm), low-density lipoproteins (LDL) (by 16 mg·dL-1), and total cholesterol (by 15 mg·dL-1) during the study (P < .05).

Conclusion:

The additional PA energy expenditure from using the TMWS favorably influenced waist and hip circumferences and lipid and metabolic profiles in overweight and obese office-workers.

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Jeffer Eidi Sasaki, Amanda Hickey, Marianna Mavilia, Jacquelynne Tedesco, Dinesh John, Sarah Kozey Keadle and Patty S. Freedson

Objective:

The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities.

Methods:

Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities.

Results:

Mean bias for estimated energy expenditure for all activities was −4.5 ± 1.0 kcals/6 min (95% limits of agreement: −25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities.

Conclusion:

The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

Open access

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.