Richard P Troiano

National Institutes of Health, Maryland, United States

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Publications (77)385.73 Total impact

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    ABSTRACT: The use of accelerometers in physical activity (PA) research has increased exponentially over the past 20 years. The first commercially available accelerometer for assessing PA, the Caltrac, was worn on the waist and estimated PA energy expenditure (PAEE) in kilocalories. Around 1995, the emphasis shifted to measuring minutes of moderate-to-vigorous PA (MVPA), especially for bouts of 10 min or longer. Recent studies, however, show that light-intensity PA and intermittent (non-bout) MVPA also have important health benefits. The total volume of PA performed is an important variable, since it takes the frequency, intensity, and duration of activity bouts, and condenses them down to a single metric. The total volume of PA is appropriate for many research applications, and can enhance comparisons between studies. In the future, machine learning algorithms will provide improved accuracy for activity type recognition and estimation of PAEE. However, in the current landscape of objectively measured PA, total activity counts per day (TAC/d) is a proxy for the total volume of PA. TAC/d percentiles for age and gender-specific groups have been developed from NHANES ActiGraph data (2003-06), providing a novel way to assess PA. The use of TAC/d, or standardized units of acceleration, could harmonize PA across studies. TAC/d should be viewed as an additional metric, not intended to replace other metrics (e.g., sedentary time, light-intensity PA, moderate PA, and vigorous PA) that may also be related to health. As future refinements to wearable monitors occur, researchers should continue to consider metrics that reflect the total volume of PA, in addition to existing PA metrics.
    Medicine and science in sports and exercise. 08/2014;
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    ABSTRACT: The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.
    British journal of sports medicine 04/2014; · 3.67 Impact Factor
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    ABSTRACT: Systematic investigations into the structure of measurement error of physical activity questionnaires are lacking. We propose a measurement error model for a physical activity questionnaire that uses physical activity level (the ratio of total energy expenditure to basal energy expenditure) to relate questionnaire-based reports of physical activity level to true physical activity levels. The 1999-2006 National Health and Nutrition Examination Survey physical activity questionnaire was administered to 433 participants aged 40-69 years in the Observing Protein and Energy Nutrition (OPEN) Study (Maryland, 1999-2000). Valid estimates of participants' total energy expenditure were also available from doubly labeled water, and basal energy expenditure was estimated from an equation; the ratio of those measures estimated true physical activity level ("truth"). We present a measurement error model that accommodates the mixture of errors that arise from assuming a classical measurement error model for doubly labeled water and a Berkson error model for the equation used to estimate basal energy expenditure. The method was then applied to the OPEN Study. Correlations between the questionnaire-based physical activity level and truth were modest (r = 0.32-0.41); attenuation factors (0.43-0.73) indicate that the use of questionnaire-based physical activity level would lead to attenuated estimates of effect size. Results suggest that sample sizes for estimating relationships between physical activity level and disease should be inflated, and that regression calibration can be used to provide measurement error-adjusted estimates of relationships between physical activity and disease.
    American journal of epidemiology 04/2013; · 5.59 Impact Factor
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    ABSTRACT: BACKGROUND: To examine the benefits of physical activity (PA) on diseases with a long developmental period, it is important to determine reliability of long-term PA recall. METHODS: We investigated 15-year reproducibility of PA recall. Participants were 3605 White and African-American adults in the Coronary Artery Risk Development in Young Adults study, aged 33--45 at the time of recall assessment. Categorical questions assessed PA before and during high school (HS) and overall PA level at Baseline, with the same timeframes recalled 15 years later. Moderate- and vigorous-intensity scores were calculated from reported months of participation in specific activities. RESULTS: HS PA recall had higher reproducibility than overall PA recall (weighted kappa = 0.43 vs. 0.21). Correlations between 15-year recall and Baseline reports of PA were r = 0.29 for moderate-intensity scores, and r = 0.50 for vigorous-intensity. Recall of vigorous activities had higher reproducibility than moderate-intensity activities. Regardless of number of months originally reported for specific activities, most participants recalled either no activity or activity during all 12 months. CONCLUSION: PA recall from the distant past is moderately reproducible, but poor at the individual level, among young and middle aged adults.
    BMC Public Health 02/2013; 13(1):180. · 2.08 Impact Factor
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    Catrine Tudor-Locke, Sarah M Camhi, Richard P Troiano
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    ABSTRACT: The National Health and Nutrition Examination Survey (NHANES) included accelerometry in the 2003-2006 data collection cycles. Researchers have used these data since their release in 2007, but the data have not been consistently treated, examined, or reported. The objective of this study was to aggregate data from studies using NHANES accelerometry data and to catalogue study decision rules, derived variables, and cut point definitions to facilitate a more uniform approach to these data. We conducted a PubMed search of English-language articles published (or indicated as forthcoming) from January 2007 through December 2011. Our initial search yielded 74 articles, plus 1 article that was not indexed in PubMed. After excluding 21 articles, we extracted and tabulated details on 54 studies to permit comparison among studies. The 54 articles represented various descriptive, methodological, and inferential analyses. Although some decision rules for treating data (eg, criteria for minimal wear-time) were consistently applied, cut point definitions used for accelerometer-derived variables (eg, time spent in various intensities of physical activity) were especially diverse. Unique research questions may require equally unique analytical approaches; some inconsistency in approaches must be tolerated if scientific discovery is to be encouraged. This catalog provides a starting point for researchers to consider relevant and/or comparable accelerometer decision rules, derived variables, and cut point definitions for their own research questions.
    Preventing chronic disease 06/2012; 9:E113. · 1.82 Impact Factor
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    ABSTRACT: To assess primary care physicians' (PCPs) knowledge of energy balance related guidelines and the association with sociodemographic characteristics and clinical care practices. As part of the 2008 U.S. nationally representative National Survey of Energy Balance Related Care among Primary Care Physicians (EB-PCP), 1776 PCPs from four specialties who treated adults (n=1060) or children and adolescents (n=716) completed surveys on sociodemographic information, knowledge of energy balance guidelines, and clinical care practices. EB-PCP response rate was 64.5%. For PCPs treating children, knowledge of guidelines for healthy BMI percentile, physical activity, and fruit and vegetables intake was 36.5%, 27.0%, and 62.9%, respectively. For PCPs treating adults, knowledge of guidelines for overweight, obesity, physical activity, and fruit and vegetables intake was 81.4%, 81.3%, 70.9%, and 63.5%, respectively. Generally, younger, female physicians were more likely to exhibit correct knowledge. Knowledge of weight-related guidelines was associated with assessment of body mass index (BMI) and use of BMI-for-age growth charts. Knowledge of energy balance guidelines among PCPs treating children is low, among PCPs treating adults it appeared high for overweight and obesity-related clinical guidelines and moderate for physical activity and diet, and was mostly unrelated to clinical practices among all PCPs.
    Preventive Medicine 05/2012; 55(1):28-33. · 3.50 Impact Factor
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    ABSTRACT: The focus of the PhenX (Phenotypes and eXposures) Toolkit is to provide researchers whose expertise lies outside a particular area with key measures identified by experts for uniform use in large-scale genetic studies and other extensive epidemiologic efforts going forward. The current paper specifically addresses the PhenX Toolkit research domain of physical activity and physical fitness (PA/PF), which are often associated with health outcomes. A Working Group (WG) of content experts completed a 6-month consensus process in which they identified a set of 14 high-priority, low-burden, and scientifically supported measures. During this process, the WG considered self-reported and objective measures that included the latest technology (e.g., accelerometers, pedometers, and heart-rate monitors). They also sought the input of measurement experts and other members of the research community during their deliberations. A majority of the measures include protocols for children (or adolescents), adults, and older adults or are applicable to all ages. Measures from the PA/PF domain and 20 other domains are publicly available and found at the PhenX Toolkit website, www.phenxtoolkit.org. The use of common measures and protocols across large studies enhances the capacity to combine or compare data across studies, benefiting both PA/PF experts and non-experts. Use of these common measures by the research community should increase statistical power and enhance the ability to answer scientific questions that previously might have gone unanswered.
    American journal of preventive medicine 05/2012; 42(5):486-92. · 4.24 Impact Factor
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    ABSTRACT: A significant proportion of the average annual body weight (BW) gain in US adults (~0.5-1 kg/y) may result from modest episodes of positive energy balance during the winter holiday season. We tested whether holiday BW gain was reduced in participants with high baseline total energy expenditure (TEE) or whether it varied by BMI (in kg/m(2)). In a secondary analysis of previously published data, ΔBW normalized over 90 d from mid-September/mid-October 1999 to mid-January/early March 2000 was analyzed by sex, age, and BMI in 443 men and women (40-69 y of age). TEE was measured by doubly labeled water. High or low energy expenditure was assessed as residual TEE after linear adjustment for age, height, and BW. No correlations between ΔBW and TEE or TEE residuals were found. Sixty-five percent of men and 58% of women gained ≥0.5 kg BW, with ~50% of both groups gaining ≥1% of preholiday BW. Obese men (BMI ≥30) gained more BW than did obese women. A high preholiday absolute TEE or residual TEE did not protect against BW gain during the winter holiday quarter. It is not known whether higher than these typical TEE levels would protect against weight gain or if the observed gain may be attributed to increased food consumption and/or reduced physical activity during the holiday quarter.
    American Journal of Clinical Nutrition 03/2012; 95(3):726-31. · 6.50 Impact Factor
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    ABSTRACT: Sedentary behaviors predominate modern life, yet we do not fully understand the adverse effects of these behaviors on mortality after considering the benefits of moderate-vigorous physical activity (MVPA). We tested the hypotheses that higher amounts of overall sitting time and television viewing are positively associated with mortality and described the independent and combined effects of these sedentary behaviors and MVPA on mortality. In the NIH-AARP Diet and Health Study, we examined 240,819 adults (aged 50-71 y) who did not report any cancer, cardiovascular disease, or respiratory disease at baseline. Mortality was ascertained over 8.5 y. Sedentary behaviors were positively associated with mortality after adjustment for age, sex, education, smoking, diet, race, and MVPA. Participants who reported the most television viewing (≥7 h compared with <1 h/d) were at greater risk of all-cause (HR: 1.61; 95% CI: 1.47, 1.76), cardiovascular (HR: 1.85; 95% CI: 1.56, 2.20), and cancer (HR: 1.22; 95% CI: 1.06, 1.40) mortality after adjustment for MVPA. Overall sitting was associated with all-cause mortality. Even among adults reporting high levels of MVPA (>7 h/wk), high amounts of television viewing (≥7 h/d) remained associated with increased risk of all-cause (HR: 1.47; 95% CI: 1.20, 1.79) and cardiovascular (HR: 2.00; 95% CI: 1.33, 3.00) mortality compared with those reporting the least television viewing (<1 h/d). Time spent in sedentary behaviors was positively associated with mortality, and participation in high levels of MVPA did not fully mitigate health risks associated with prolonged time watching television. Adults should be encouraged to reduce time spent in sedentary behaviors, when possible, and to participate in MVPA at recommended levels. The NIH-AARP Diet and Health Study was registered at clinicaltrials.gov as NCT00340015.
    American Journal of Clinical Nutrition 01/2012; 95(2):437-45. · 6.50 Impact Factor
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    ABSTRACT: This article provides recommendations for the use of wearable monitors for assessing physical activity. We have provided recommendations for measurement researchers, end users, and developers of activity monitors. We discuss new horizons and future directions in the field of objective measurement of physical activity and present challenges that remain for the future. These recommendations are based on the proceedings from the workshop "Objective Measurement of Physical Activity: Best Practices and Future Direction," held on July 20-21, 2009, and also on data and information presented since the workshop.
    Medicine and science in sports and exercise 01/2012; 44(1 Suppl 1):S1-4. · 4.48 Impact Factor
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    ABSTRACT: 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. 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. After participation in a workshop breakout session, coauthors summarized the ideas presented and reached consensus on the material presented here. 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.
    Journal of Physical Activity and Health 01/2012; 9 Suppl 1:S68-75. · 1.95 Impact Factor
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    ABSTRACT: Adults often misreport dietary intake; the magnitude varies by the methods used to assess diet and classify participants. The objective was to quantify the accuracy of the Goldberg method for categorizing misreporters on a food frequency questionnaire (FFQ) and two 24-h recalls (24HRs). We compared the Goldberg method, which uses an equation to predict total energy expenditure (TEE), with a criterion method that uses doubly labeled water (DLW), in a study of 451 men and women. Underreporting was classified using recommended cut points and calculated values. Sensitivity and specificity, positive predictive value (PPV) and negative predictive value and the area under the receiver operating characteristic curve (AUC) were calculated. Predictive models of underreporting were contrasted for the Goldberg and DLW methods. AUCs were 0.974 and 0.972 on the FFQ, and 0.961 and 0.938 on the 24HR for men and women, respectively. The sensitivity of the Goldberg method was higher for the FFQ (92%) than the 24HR (50%); specificity was higher for the 24HR (99%) than the FFQ (88%); PPV was high for the 24HR (92%) and FFQ (88%). Simulation studies indicate attenuation in odds ratio estimates and reduction of power in predictive models. Although use of the Goldberg method may lead to bias and reduction in power in predictive models of underreporting, the method has high predictive value for both the FFQ and the 24HR. Thus, in the absence of objective measures of TEE or physical activity, the Goldberg method is a reasonable approach to characterize underreporting.
    European journal of clinical nutrition 11/2011; 66(5):569-76. · 3.07 Impact Factor
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    ABSTRACT: Physical inactivity is a risk factor for obesity, cardiovascular disease, hypertension, and other chronic diseases that are increasingly prevalent in the U.S. and worldwide. Time at work represents a major portion of the day for employed people. To determine how employment status (full-time, part-time, or not employed) and job type (active or sedentary) are related to daily physical activity levels in American adults. Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) were collected in 2003-2004 and analyzed in 2010. Physical activity was measured using Actigraph uniaxial accelerometers, and participants aged 20-60 years with ≥4 days of monitoring were included (N=1826). Accelerometer variables included mean counts/minute during wear time and proportion of wear time spent in various intensity levels. In men, full-time workers were more active than healthy nonworkers (p=0.004), and in weekday-only analyses, even workers with sedentary jobs were more active (p=0.03) and spent less time sedentary (p<0.001) than nonworkers. In contrast with men, women with full-time sedentary jobs spent more time sedentary (p=0.008) and had less light and lifestyle intensity activity than healthy nonworkers on weekdays. Within full-time workers, those with active jobs had greater weekday activity than those with sedentary jobs (22% greater in men, 30% greater in women). In men, full-time employment, even in sedentary occupations, is positively associated with physical activity compared to not working, and in both genders job type has a major bearing on daily activity levels.
    American journal of preventive medicine 08/2011; 41(2):136-45. · 4.24 Impact Factor
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    ABSTRACT: The Current Population Survey (CPS) and the American Time Use Survey (ATUS) use the 2002 census occupation system to classify workers into 509 separate occupations arranged into 22 major occupational categories. We describe the methods and rationale for assigning detailed Metabolic Equivalent (MET) estimates to occupations and present population estimates (comparing outputs generated by analysis of previously published summary MET estimates to the detailed MET estimates) of intensities of occupational activity using the 2003 ATUS data comprised of 20,720 respondents, 5323 (2917 males and 2406 females) of whom reported working 6+ hours at their primary occupation on their assigned reporting day. Analysis using the summary MET estimates resulted in 4% more workers in sedentary occupations, 6% more in light, 7% less in moderate, and 3% less in vigorous compared with using the detailed MET estimates. The detailed estimates are more sensitive to identifying individuals who do any occupational activity that is moderate or vigorous in intensity resulting in fewer workers in sedentary and light intensity occupations. Since CPS/ATUS regularly captures occupation data it will be possible to track prevalence of the different intensity levels of occupations. Updates will be required with inevitable adjustments to future occupational classification systems.
    Journal of physical activity & health 05/2011; 8(4):581-6. · 1.95 Impact Factor
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    ABSTRACT: This study examined the association between objectively measured sedentary activity and metabolic syndrome among older adults. Data were from 1,367 men and women, aged ≥ 60 years who participated in the 2003-2006 National Health and Nutrition Examination Survey (NHANES). Sedentary time during waking hours was measured by an accelerometer (<100 counts per minute). A sedentary bout was defined as a period of time >5 min. A sedentary break was defined as an interruption in sedentary time (≥ 100 counts per minute). Metabolic syndrome was defined according to the Adult Treatment Panel (ATP) III criteria. On average, people spent 9.5 h (65% of wear time) as sedentary. Compared with people without metabolic syndrome, people with metabolic syndrome spent a greater percentage of time as sedentary (67.3 vs. 62.2%), had longer average sedentary bouts (17.7 vs. 16.7 min), had lower intensity during sedentary time (14.8 vs. 15.8 average counts per minute), and had fewer sedentary breaks (82.3 vs. 86.7), adjusted for age and sex (all P < 0.01). A higher percentage of time sedentary and fewer sedentary breaks were associated with a significantly greater likelihood of metabolic syndrome after adjustment for age, sex, ethnicity, education, alcohol consumption, smoking, BMI, diabetes, heart disease, and physical activity. The association between intensity during sedentary time and metabolic syndrome was borderline significant. The proportion of sedentary time was strongly related to metabolic risk, independent of physical activity. Current results suggest older people may benefit from reducing total sedentary time and avoiding prolonged periods of sedentary time by increasing the number of breaks during sedentary time.
    Diabetes care 02/2011; 34(2):497-503. · 7.74 Impact Factor
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    ABSTRACT: Individuals who smoke generally have a lower body mass index (BMI) than nonsmokers. The relative roles of energy expenditure and energy intake in maintaining the lower BMI, however, remain controversial. We tested the hypothesis that current smokers have higher total energy expenditure than never smokers in 308 adults aged 40-69 years old of which 47 were current smokers. Energy expenditure was measured by doubly labeled water during a two week period in which the subjects lived at home and performed their normal activities. Smoking status was determined by questionnaire. There were no significant differences in mean BMI (mean ± SD) between smokers and never smokers for either males (27.8+5.1 kg/m2 vs. 27.5+4.0 kg/m2) or females (26.5+5.3 kg/m2 vs. 28.1+6.6 kg/m2), although the difference in females was of similar magnitude to previous reports. Similarly, total energy expenditure of male smokers (3069+764 kcal/d) was not significantly different from that of never smokers (2854+468 kcal/d), and that of female smokers (2266+387 kcal/d) was not different from that of never smokers (2330+415 kcal/d). These findings did not change after adjustment for age, fat-free mass and self-reported physical activity. Using doubly labeled water, we found no evidence of increased energy expenditure among smokers, however, it should be noted that BMI differences in this cohort also did not differ by smoking status.
    Nutrition & Metabolism 11/2010; 7:81. · 3.16 Impact Factor
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    ABSTRACT: The purpose of this study was to examine the independent associations of self-reported and objectively measured (using accelerometers) moderate to vigorous physical activity (MVPA) with physiological and anthropometric biomarkers in a nationally representative sample of U.S. adults. Data from the cross-sectional National Health and Nutrition Examination Survey 2003-2006 data were analyzed. Adults 20 yr and older (N=5797) with self-reported PA and 4 d or more of accelerometer data were included in the analyses. Pregnant or lactating women were excluded. Outcomes were blood pressure, body mass index, waist circumference, triceps and subscapular skinfolds, cholesterol, triglyceride, C-reactive protein, homocysteine, and insulin resistance and hyperinsulinemia indices. Objectively measured MVPA displayed stronger independent associations with the biomarkers than did self-reported MVPA, even after adjusting for sociodemographic and health factors (adjusted Wald F values=3.9-85.6, P<0.05-0.0001). Self-reported and objectively measured MVPA were independently associated with skinfold measures, HDL, and C-reactive protein when both were included in the model. Objectively measured MVPA displayed stronger associations with physiological and anthropometric biomarkers than self-reported MVPA. However, self-reported and objectively measured MVPA appear to capture distinct aspects of PA that are independently associated with certain biomarkers. Further understanding of the distinct contributions of self-reported and objectively measured PA to health outcomes could help to better identify optimal activity level and pattern.
    Medicine and science in sports and exercise 10/2010; 43(5):815-21. · 4.48 Impact Factor
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    Richard P Troiano, Patty S Freedson
    American journal of preventive medicine 06/2010; 38(6):682-3. · 4.24 Impact Factor
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    ABSTRACT: This study compares levels and patterns of objectively assessed physical activity in Sweden and the United States by using identical accelerometer metrics. Data of adult respondents with > or =4 days with > or =10 hours per day of accelerometer wear from Sweden (2001-2002, n = 1,172) and the United States (2003-2004, n = 2,925) were compared. Outcomes reported by age and body mass index within sex include accelerometer counts per minute and amounts and bouts of activity at different intensities, that is, sedentary, low, lifestyle, and moderate or higher intensity physical activity. The mean counts per minute were 375 (95% confidence interval (CI): 360, 390) and 377 (95% CI: 363, 391) for Swedish and US males, respectively, and 363 (95% CI: 347, 379) and 298 (95% CI: 289, 307) for Swedish and US females. Older respondents and those with higher body mass index had lower activity levels. Swedish and US males spent 36 (95% CI: 34, 38) and 33 (95% CI: 31, 36) minutes per day, and Swedish and US females spent 32 (95% CI: 29, 34) and 19 (95% CI: 17, 21) minutes per day in moderate or higher intensity physical activity. Older Swedes were more active in moderate or higher intensity activities than were older US respondents. However, younger Swedish males had more sedentary behavior time than did younger US males. These results provide a framework for international comparisons of physical activity levels and patterns, and they represent strong evidence for the importance of investment in objective measurement of physical activity.
    American journal of epidemiology 05/2010; 171(10):1055-64. · 5.59 Impact Factor
  • Daniel P. Beavers, Richard P. Troiano, Janet A. Tooze
    Medicine and Science in Sports and Exercise - MED SCI SPORT EXERCISE. 01/2010; 42.

Publication Stats

7k Citations
385.73 Total Impact Points

Institutions

  • 1999–2014
    • National Institutes of Health
      • • Division of Cancer Control and Population Sciences
      • • Branch of Risk Factor Monitoring and Methods
      Maryland, United States
  • 2001–2013
    • National Cancer Institute (USA)
      • • Applied Research Program (ARP)
      • • Nutritional Epidemiology
      • • Division of Cancer Control and Population Sciences
      Maryland, United States
  • 2009–2012
    • Pennington Biomedical Research Center
      • Walking Behavior Laboratory
      Baton Rouge, Louisiana, United States
  • 2010
    • Karolinska Institutet
      • Institutionen för neurobiologi, vårdvetenskap och samhälle - NVS
      Solna, Stockholm, Sweden
  • 2008
    • Vanderbilt University
      • Department of Medicine
      Nashville, MI, United States
    • U.S. Department of Health and Human Services
      Washington, Washington, D.C., United States
  • 2007
    • Wake Forest School of Medicine
      • Division of Public Health Sciences
      Winston-Salem, NC, United States
  • 2005
    • University of North Carolina at Chapel Hill
      • Department of Nutrition
      Chapel Hill, NC, United States
  • 1996–2001
    • Centers for Disease Control and Prevention
      • National Center for Health Statistics
      Druid Hills, GA, United States
  • 1990–1992
    • Cornell University
      • Department of Nutritional Sciences
      Ithaca, NY, United States