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David R. Bassett Jr.

The built environment has profound effects on physical activity and health. Many communities in the US are built around the automobile, with little consideration given to pedestrians, cyclists, and transit users. These places tend to have higher rates of physical inactivity (defined as “no leisure time physical activity”) and higher rates of obesity, diabetes, heart disease, and stroke. However, in some European countries and selected US cities, communities have been constructed in ways that encourage active modes of transportation. In these places, a large segment of the population meets physical activity guidelines, due in part to the activity they acquire in performing daily tasks. In addition to promoting active transportation, these environments promote recreational walking, jogging, and cycling. Kinesiologists can and should work with urban planners, transportation officials, developers, public health practitioners, and the general public to design cities in ways that enhance physical activity and health.

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Scott A. Conger and David R. Bassett Jr.

The purpose of this study was to develop a compendium of wheelchair-related physical activities. To accomplish this, we conducted a systematic review of the published energy costs of activities performed by individuals who use wheelchairs. A total of 266 studies were identified by a literature search using relevant keywords. Inclusion criteria were studies utilizing individuals who routinely use a manual wheelchair, indirect calorimetry as the criterion measurement, energy expenditure expressed as METs or VO2, and physical activities typical of wheelchair users. Eleven studies met the inclusion criteria. A total of 63 different wheelchair activities were identified with energy expenditure values ranging from 0.8 to 12.5 kcal·kg-1·hr-1. The energy requirements for some activities differed between individuals who use wheelchairs and those who do not. The compendium of wheelchair-related activities can be used to enhance scoring of physical activity surveys and to promote the benefits of activity in this population.

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Randall J. Bergman, David R. Bassett Jr. and Diane A. Klein

Background:

This 2-part study examined validity of selected motion sensors for assessing physical activity in older adults residing in assisted-living communities.

Methods:

Twenty-one older adults (mean age = 78.6 ± 13.1 years) wore the StepWatch 3 Step Activity Monitor (SW3) and the Yamax Digi-Walker SW-200 pedometer (DW). Part I compared accuracy of these devices for measuring steps taken over 161 m. Part II compared devices over a 1-day (24-hour) period.

Results:

In part I, the DW recorded 51.9% (r 2 = –.08, P = .75) and the SW3 recorded 102.6% (r 2 = .99, P < .001) of steps. In part II, the DW measured significantly fewer steps (1587 ± 1057 steps) than did the SW3 (6420 ± 3180 steps).

Conclusions:

The SW3 pedometer was more accurate in counting steps and recorded higher 24-hour step counts than the DW pedometer. Thus, the SW3 is a valid research instrument for monitoring activity in the assisted-living population.

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David R. Bassett, John Pucher Jr., Ralph Buehler, Dixie L. Thompson and Scott E. Crouter

Purpose:

This study was designed to examine the relationship between active transportation (defined as the percentage of trips taken by walking, bicycling, and public transit) and obesity rates (BMI ≥ 30 kg · m−2) in different countries.

Methods:

National surveys of travel behavior and health indicators in Europe, North America, and Australia were used in this study; the surveys were conducted in 1994 to 2006. In some cases raw data were obtained from national or federal agencies and then analyzed, and in other cases summary data were obtained from published reports.

Results:

Countries with the highest levels of active transportation generally had the lowest obesity rates. Europeans walked more than United States residents (382 versus 140 km per person per year) and bicycled more (188 versus 40 km per person per year) in 2000.

Discussion:

Walking and bicycling are far more common in European countries than in the United States, Australia, and Canada. Active transportation is inversely related to obesity in these countries. Although the results do not prove causality, they suggest that active transportation could be one of the factors that explain international differences in obesity rates.

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Merrill D. Funk, Cindy L. Salazar, Miriam Martinez, Jesus Gonzalez, Perla Leyva, David Bassett Jr. and Murat Karabulut

Fifty-two participants walked on a treadmill at 4.8 km/h for 500 steps while wearing four Samsung Galaxy S4 smartphones on the arm, waist, pocket, and hand while each phone simultaneously ran five popular smartphone apps. Actual steps were measured using a hand tally device. Steps were recorded from each smartphone app and compared to the tally counter using repeated measures analysis of variance (ANOVA) tests, and equivalence testing. Of the 20 step measurements recorded (five apps at four locations), all but four (Accupedo at the arm, waist, and pocket; S-Health at the pocket) produced mean underestimations of step counts. ANOVAs showed significant differences between the phone at the hand location for all apps compared to the tally counter (p < .05); three apps had differences at the waist (p < .01), Runtastic had differences at the arm (p < .001), and no differences occurred between the pocket location and the hand tally counter for any of the apps (p > .05). The 90% confidence interval for all apps, except for G-Fit, fell within the equivalence zone for the phone in the pocket while the phone at the hand location included only S-Health within the equivalence zone. Using a Samsung Galaxy S4 smartphone to measure steps at a 4.8 km/h walking pace while carrying the phone in the hand may produce significant errors. However, using the S-Health app while carrying a phone in the pocket appears to provide the most accurate step count in a controlled environment.

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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

Background: Little is known about the daily physical activity (PA) levels of people employed in different occupational categories. Methods: Nine ActiGraph accelerometer-derived daily PA variables are presented and ranked for adults (N = 1465, 20–60 y) working in the 22 occupational categories assessed by NHANES 2005–2006. A composite score was generated for each occupational category by summing the rankings of 3 accelerometer-derived daily PA variables known to have strong associations with health outcomes (total activity counts [TAC], moderate to vigorous PA minutes per week in modified 10-minute bouts [MVPA 10], and percentage of time spent in sedentary activity [SB%]). Results: Classified as high-activity occupational categories, “farming, fishing, forestry,” and “building & grounds cleaning, maintenance” occupations had the greatest TAC (461 996 and 449 452), most MVPA 10 (149.6 and 97.8), most steps per day (10 464 and 11 602), and near the lowest SB% (45.2% and 45.4%). “Community, social services” occupations, classified as low-activity occupational categories, had the second lowest TAC (242 085), least MVPA 10 (12.1), fewest steps per day (5684), and near the highest SB% (64.2%). Conclusions: There is a strong association between occupational category and daily activity levels. Objectively measured daily PA permitted the classification of the 22 different occupational categories into 3 activity groupings.

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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.