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Janet E. Fulton, Xuewen Wang, Michelle M. Yore, Susan A. Carlson, Deborah A. Galuska, and Carl J. Caspersen

Background:

To examine the prevalence of television (TV) viewing, computer use, and their combination and associations with demographic characteristics and body mass index (BMI) among U.S. youth.

Methods:

The 1999 to 2006 National Health and Nutrition Examination Survey (NHANES) was used. Time spent yesterday sitting and watching television or videos (TV viewing) and using the computer or playing computer games (computer use) were assessed by questionnaire.

Results:

Prevalence (%) of meeting the U.S. objective for TV viewing (≤2 hours/day) ranged from 65% to 71%. Prevalence of no computer use (0 hours/day) ranged from 23% to 45%. Non-Hispanic Black youth aged 2 to 15 years were less likely than their non-Hispanic White counterparts to meet the objective for TV viewing. Overweight or obese school-age youth were less likely than their normal weight counterparts to meet the objective for TV viewing

Conclusions:

Computer use is prevalent among U.S. youth; more than half of youth used a computer on the previous day. The proportion of youth meeting the U.S. objective for TV viewing is less than the target of 75%. Time spent in sedentary behaviors such as viewing TV may contribute to overweight and obesity among U.S. youth.

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Shujun Gao, Lisa Harnack, Kathryn Schmitz, Janet Fulton, Leslie Lytle, Pamela Van Coevering, and David R. Jacobs Jr.

Background:

We assessed the validity and reliability of a modified Godin-Leisure-Time Exercise Questionnaire in youth in grades 6 through 8.

Methods:

The questionnaire was completed by 250 children twice at a 1 wk interval to assess reliability. After the second questionnaire administration the children wore an accelerometer for 7 d (criterion measure).

Results:

Pearson correlations between the first and second reports of frequency of participation in strenuous and moderate physical activity were 0.68 and 0.51, respectively. Self-reported participation in strenuous activity was weakly correlated with strenuous activity as measured by accelerometer (r = 0.23, P = 0.01). A weak non-significant correlation was found between reported versus measured engagement in moderate activity (r = 0.13, P = 0.07).

Conclusion:

Findings suggest the questionnaire evaluated in this study may be of very limited use for assessing children’s physical activity.

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Mindy Millard-Stafford, Jeffrey S. Becasen, Michael W. Beets, Allison J. Nihiser, Sarah M. Lee, and Janet E. Fulton

A systematic review of literature was conducted to examine the association between changes in health-related fitness (e.g., aerobic capacity and muscular strength/endurance) and chronic disease risk factors in overweight and/or obese youth. Studies published from 2000–2010 were included if the physical activity intervention was a randomized controlled trial and reported changes in fitness and health outcomes by direction and significance (p < .05) of the effect. Aerobic capacity improved in 91% and muscular fitness improved in 82% of measures reported. Nearly all studies (32 of 33) reported improvement in at least one fitness test. Changes in outcomes related to adiposity, cardiovascular, musculoskeletal, metabolic, and mental/emotional health improved in 60%, 32%, 53%, 41%, and 33% of comparisons studied, respectively. In conclusion, overweight and obese youth can improve physical fitness across a variety of test measures. When fitness improves, beneficial health effects are observed in some, but not all chronic disease risk factors.

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Louise C. Mâsse, Janet E. Fulton, Kathleen B. Watson, Susan Tortolero, Harold W. Kohl III, Michael C. Meyers, Steven N. Blair, and William W. Wong

Background:

The purpose of this study was to compare the validity of 2 physical activity questionnaire formats—one that lists activities (Checklist questionnaire) and one that assesses overall activities (Global questionnaire) by domain.

Methods:

Two questionnaire formats were validated among 260 African-American and Hispanic women (age 40–70) using 3 validation standards: 1) accelerometers to validate activities of ambulation; 2) diaries to validate physical activity domains (occupation, household, exercise, yard, family, volunteer/church work, and transportation); and 3) doubly-labeled water to validate physical activity energy expenditure (DLW-PAEE).

Results:

The proportion of total variance explained by the Checklist questionnaire was 38.4% with diaries, 9.0% with accelerometers, and 6.4% with DLW-PAEE. The Global questionnaire explained 17.6% of the total variance with diaries and about 5% with both accelerometers and with DLW-PAEE. Overall, associations with the 3 validation standards were slightly better with the Checklist questionnaire. However, agreement with DLW-PAEE was poor with both formats and the Checklist format resulted in greater overestimation. Validity results also indicated the Checklist format was better suited to recall household, family, and transportation activities.

Conclusions:

Overall, the Checklist format had slightly better measurement properties than the Global format. Both questionnaire formats are better suited to rank individuals.

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Janet E. Fulton, Charlene R. Burgeson, Geraldine R. Perry, Bettylou Sherry, Deborah A. Galuska, Maria P. Alexander, Howell Wechsler, and Carl J. Caspersen

An expert panel workshop had two specific aims: (a) to review the current state of knowledge of existing methods for assessing physical activity and sedentary behavior in order to determine their reliability, validity, feasibility, strengths, and limitations and (b) to set research priorities and recommendations to enable the use of reliable and valid instruments for assessing physical activity and sedentary behavior within the context of three public health functions for children ages 2–5 years. Experts presented four major recommendations for research priorities at the conclusion of the 2-day workshop. The need to develop valid methods for measuring physical activity and sedentary behavior was considered the necessary first step to accomplish meaningful physical activity surveillance, public health research, and intervention research for children ages 2–5 years.

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Janet E. Fulton, David M. Buchner, Susan A. Carlson, Deborah Borbely, Kenneth M. Rose, Ann E. O’Connor, Janelle P. Gunn, and Ruth Petersen

Physical activity can reduce the risk of at least 20 chronic diseases and conditions and provide effective treatment for many of these conditions. Yet, physical activity levels of Americans remain low, with only small improvements over 20 years. The Centers for Disease Control and Prevention (CDC) considered what would accelerate progress and, as a result, developed Active People, Healthy NationSM, an aspirational initiative to improve physical activity in 2.5 million high school youth and 25 million adults, doubling the 10-year improvement targets of Healthy People 2020. Active People, Healthy NationSM will implement evidence-based guidance to improve physical activity through 5 action steps centered on core public health functions: (1) program delivery, (2) partnership mobilization, (3) effective communication, (4) cross-sectoral training, and (5) continuous monitoring and evaluation. To achieve wide-scale impact, Active People, Healthy NationSM will need broad engagement from a variety of sectors working together to coordinate activities and initiatives.

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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.