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James J. McClain, David Grant, Gordon Willis and David Berrigan

Background:

Question design can influence the validity and reliability of physical activity (PA) self-report instruments. This study assesses the effect of temporal domain (“days” walked versus “times” walked) on survey questions about walking behavior.

Methods:

A 2005 California Health Interview Survey (CHIS) sub-sample (n = 6332) reported the number of days or times they walked for leisure or transportation in the past 7 days and the usual time spent per day or per time. Question order was randomized by temporal domain. Minutes walked per week (mean ± SE) and adherence to PA guidelines (≥150 min/wk) were assessed.

Results:

Estimates of leisure walking remained stable across temporal domain (days = 71.4 ± 2.5 min; times = 73.4 ± 2.4 min), but transportation walking differed depending on domain (days = 70.4 ± 3.2 min; times = 52.5 ± 2.6 min). Adherence to PA guidelines based on leisure walking was stable across temporal domain (days = 14.9 ± 0.6%; times = 14.9 ± 0.6%), but again differed by domain for transportation walking (days = 10.4 ± 0.6%; times = 7.8 ± 0.5%). A large order effect (number-of-days versus number-of-times asked first) was observed for reports of days walking for transportation (days first = 87.8 ± 2.9 min; times first = 52.3 ± 2.5 min).

Conclusion:

Temporal domain influences estimates of self-reported transportation walking behavior. Current efforts to capture PA from both transportation and leisure activities in health research appear to present distinct methodological challenges.

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K. Robin Yabroff, Richard P. Troiano and David Berrigan

Background:

Several studies have reported positive associations between pet ownership and a variety of health outcomes. In this study, we explored associations between pet ownership and physical activity in a large, ethnically diverse population-based sample in California.

Method:

Data from the California Health Interview Survey (CHIS) were used to assess the associations between pet ownership (ie, dog, dog and cat, cat, and non–pet owners) and transportation and leisure walking in a sample of 41,514 adults. Logistic regression was used to assess associations between pet ownership and type of walking, and linear regression was used to assess associations between pet ownership and total minutes walking per week.

Results:

Dog owners were slightly less likely to walk for transportation than were non–pet owners (OR = 0.91; 95% CI: 0.85 to 0.99) but more likely to walk for leisure than non–pet owners (OR = 1.6; 95% CI: 1.5 to 1.8) in multivariate analyses. Overall, dog owners walked 18.9 (95% CI: 11.4 to 26.4) minutes more per week than non–pet owners. Walking behaviors of cat owners were similar to non–pet owners.

Conclusion:

Our findings support the moderate association between dog ownership and higher levels of physical activity.

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Prabasaj Paul, Susan A. Carlson, Dianna D. Carroll, David Berrigan and Janet E. Fulton

Background:

Walking, the most commonly reported physical activity among U.S. adults, is undertaken in various domains, including transportation and leisure.

Methods:

This study examined prevalence, bout length, and mean amount of walking in the last week for transportation and leisure, by selected characteristics. Self-reported data from the 2010 National Health Interview Survey (N = 24,017) were analyzed.

Results:

Prevalence of transportation walking was 29.4% (95% CI: 28.6%–30.3%) and of leisure walking was 50.0% (95% CI: 49.1%–51.0%). Prevalence of transportation walking was higher among men; prevalence of leisure walking was higher among women. Most (52.4%) transportation walking bouts were 10 to 15 minutes; leisure walking bouts were distributed more evenly (28.0%, 10–15 minutes; 17.1%, 41–60 minutes). Mean time spent in transportation walking was higher among men, decreased with increasing BMI, and varied by race/ethnicity and region of residence. Mean time spent leisure walking increased with increasing age and with decreasing BMI.

Conclusion:

Demographic correlates and patterns of walking differ by domain. Interventions focusing on either leisure or transportation walking should consider correlates for the specific walking domain. Assessing prevalence, bout length, and mean time of walking for transportation and leisure separately allows for more comprehensive surveillance of walking.

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Mary O. Hearst, John R. Sirard, Leslie Lytle, Donald R. Dengel and David Berrigan

Background:

The association of physical activity (PA), measured 3 ways, and biomarkers were compared in a sample of adolescents.

Methods:

PA data were collected on 2 cohorts of adolescents (N = 700) in the Twin Cities, Minnesota, 2007–2008. PA was measured using 2 survey questions [Modified Activity Questionnaire (MAQ)], the 3-Day Physical Activity Recall (3DPAR), and accelerometers. Biomarkers included systolic (SBP) and diastolic blood pressure (DBP), lipids, percent body fat (%BF), and body mass index (BMI) percentile. Bivariate relationships among PA measures and biomarkers were examined followed by generalized estimating equations for multivariate analysis.

Results:

The 3 measures were significantly correlated with each other (r = .22–.36, P < .001). Controlling for study, puberty, age, and gender, all 3 PA measures were associated with %BF (MAQ = −1.93, P < .001; 3DPAR = −1.64, P < .001; accelerometer = −1.06, P = .001). The MAQ and accelerometers were negatively associated with BMI percentile. None of the 3 PA measures were significantly associated with SBP or lipids. The percentage of adolescents meeting the national PA recommendations varied by instrument.

Conclusions:

All 3 instruments demonstrated consistent findings when estimating associations with %BF, but were different for prevalence estimates. Researchers must carefully consider the intended use of PA data when choosing a measurement instrument.

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Jordan A. Carlson, J. Aaron Hipp, Jacqueline Kerr, Todd S. Horowitz and David Berrigan

Objectives: To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity– or nutrition-related behaviors and/or environments. Methods: Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. Results: Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). Conclusions: Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.

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Britni R. Belcher, Richard P. Moser, Kevin W. Dodd, Audie Atienza, Rachel Ballard-Barbash and David Berrigan

Background:

Discrepancies in self-report and accelerometer-measured moderate-to-vigorous physical activity (MVPA) may influence relationships with obesity-related biomarkers in youth.

Methods:

Data came from 2003–2006 National Health and Nutrition Examination Surveys (NHANES) for 2174 youth ages 12 to 19. Biomarkers were: body mass index (BMI, kg/m2), BMI percentile, height and waist circumference (WC, cm), triceps and subscapular skinfolds (mm), systolic & diastolic blood pressure (BP, mmHg), high-density lipoprotein (HDL, mg/dL), total cholesterol (mg/dL), triglycerides (mg/dL), insulin (μU/ml), C-reactive protein (mg/dL), and glycohemoglobin (%). In separate sex-stratified models, each biomarker was regressed on accelerometer variables [mean MVPA (min/day), nonsedentary counts, and MVPA bouts (mean min/day)] and self-reported MVPA. Covariates were age, race/ethnicity, SES, physical limitations, and asthma.

Results:

In boys, correlations between self-report and accelerometer MVPA were stronger (boys: r = 0.14−0.21; girls: r = 0.07−0.11; P < .010) and there were significant associations with BMI, WC, triceps skinfold, and SBP and accelerometer MVPA (P < .01). In girls, there were no significant associations between biomarkers and any measures of physical activity.

Conclusions:

Physical activity measures should be selected based on the outcome of interest and study population; however, associations between PA and these biomarkers appear to be weak regardless of the measure used.

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Karin A. Pfeiffer, Kathleen B. Watson, Robert G. McMurray, David R. Bassett, Nancy F. Butte, Scott E. Crouter, Stephen D. Herrmann, Stewart G. Trost, Barbara E. Ainsworth, Janet E. Fulton, David Berrigan and For the CDC/NCI/NCCOR Research Group

Purpose: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. Methods: PAEE estimates were derived by pooling data from 5 studies. Participants, 6–18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6–9, 10–12, 13–15, and 16–18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0–12.99 y)] versus the standard (a single value across all ages). Results: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from −0.2% to 21.7% for age groups and −0.23% to 18.2% for age-in-years compared with the standard. Conclusions: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.