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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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Brian Tyo, Rebecca Spataro-Kearns and David R. Bassett Jr.

Purpose: The purpose of this study was twofold: (1) to determine if the Digi-Walker SW-200 (SW-200), New Lifestyles NL-2000 (NL-2000), and Omron HJ-303 (HJ-303) yield similar daily step counts compared to the StepWatch-3; and (2) to determine if pedometer error is influenced by adiposity and/or stepping rate in African American women. Methods: 60 participants (28.0 ± 9.8 y) wore the devices for three weekdays. ANOVAs were performed to determine if body mass index (BMI) and device were related to steps per day, and to determine if BMI and device were related to error. Stepwise linear regressions were performed to determine which variables contributed to pedometer error. Results: StepWatch-3 counted significantly more steps than all other devices within each BMI category (p < .01). The NL-2000 had significantly less error in the normal (−13.4%) and overweight (−14.9%) groups compared to the SW-200 (−26.2% and −33.3%) and HJ-303 (−32.5% ad −31.5%) (p < .05). The SW-200 had significantly more error in the obese group (−50.7%) compared to the NL-2000 (−17.1%) and HJ-303 (−26.0%) (p < .05). NL-2000 error was not related to any variables while the SW-200 error was related to waist circumference (WC) and the HJ-303 error was related to percentage of slow steps. Conclusion: In African American women adiposity is more strongly related to more pedometer error in a device using a spring-levered mechanism (SW-200). Accumulating steps at a slow rate is related to more pedometer error when using a device with a step filter (HJ-303).

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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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Ryan A. Burchfield, Eugene C. Fitzhugh and David R. Bassett

Purpose:

To study the association between weather-related measures and objectively measured trail use across 3 seasons.

Background:

Weather has been reported as a barrier to outdoor physical activity (PA), but previous studies have explained only a small amount of the variance in PA using weather-related measures.

Methods:

The dependent variable of this study was trail use measured as mean hourly trail counts by an infrared trail counter located on a greenway. Each trail count represents 1 person breaking the infrared beam of the trail counter. Two sources of weather-related measures were obtained by a site-specific weather station and a public domain weather source.

Results:

Temperature, relative humidity, and precipitation were significantly correlated with trail counts recorded during daylight hours. More precise hourly weather-related measures explained 42% of the variance in trail counts, regardless of the weather data source with temperature alone explaining 18% of the variance in trail counts. After controlling for all seasonal and weekly factors, every 1°F increase in temperature was associated with an increase of 1.1 trail counts/hr up to 76°F, at which point trail use began to slightly decrease.

Conclusion:

Weather-related factors have a moderate association with trail use along an urban greenway.

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Dana L. Wolff-Hughes, Eugene C. Fitzhugh, David R. Bassett and Christopher R. Cherry

Background:

Greenways (GW) can be sited and designed in a variety of ways. However, the extent to which siting and design relate to GW user’s demographic characteristics and physical activity (PA) is unknown. The purpose of this study was to compare 2 GWs that differed in terms of their siting and design, with respect to the aforementioned variables.

Methods:

A trail intercept survey measuring PA, modes of GW access, and demographics was administered on 2 GWs (GWlinear vs. GWloop), which varied in siting and design characteristics.

Results:

GWlinear (n = 216), compared with GWloop users (n = 400), accumulated significantly greater volumes of PA from both accessing and using the GW (P = .012). GWlinear users were more likely to be younger, male, and never married; they were also more likely to engage in transportational PA (10.6 vs. 0.3%, P ≤ .001) and access the GW via active transit modes (37.0% vs. 4.2%, P ≤ .001).

Conclusions:

GW siting and design appears to be related to user characteristics, and the types and volumes of PA performed. These results should be considered by GW planners and designers to best serve the PA needs of the community.

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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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Yuri Feito, David R. Bassett, Dixie L. Thompson and Brian M. Tyo

Background:

Activity monitors are widely used in research, and are currently being used to study physical activity (PA) trends in the US and Canada. The purpose of this study was to determine if body mass index (BMI) affects the step count accuracy of commonly used accelerometer-based activity monitors during treadmill walking.

Methods:

Participants were classified into BMI categories and instructed to walk on a treadmill at 3 different speeds (40, 67, and 94 m·min−1) while wearing 4 accelerometer-based activity monitors (ActiGraph GT1M, ActiCal, NL-2000, and StepWatch).

Results:

There was no significant main effect of BMI on pedometer accuracy. At the slowest speed, all waist-mounted devices significantly underestimated actual steps (P < .001), with the NL-2000 recording the greatest percentage (72%). At the intermediate speed, the ActiGraph was the least accurate, recording only 80% of actual steps. At the fastest speed, all of the activity monitors demonstrated a high level of accuracy.

Conclusion:

Our data suggest that BMI does not greatly affect the step-counting accuracy of accelerometer-based activity monitors. However, the accuracy of the ActiGraph, ActiCal, and NL-2000 decreases at slower speeds. The ankle-mounted StepWatch was the most accurate device across a wide range of walking speeds.