Physical Activity Guidelines Advisory Committee Report, 2008. To the Secretary of Health and Human Services. Part A: executive summaryNutr Rev20096721142019178654

Nutrition Reviews 01/2008; 67:114-120. DOI: 10.1111/j.1753-4887.2008.00136.x
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    • "Low income and minority populations carry an excess burden due to early onset, later diagnosis, and poorer disease management outcomes [5-7]. Health-enhancing behaviors, including physical activity, healthy eating, and avoiding tobacco, can delay or prevent chronic disease [8-12]. In addition, management of existing conditions through health-enhancing behaviors has been found to improve quality of life and reduce healthcare costs [13]. "
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    ABSTRACT: BackgroundCancer and other chronic diseases reduce quality and length of life and productivity, and represent a significant financial burden to society. Evidence-based public health approaches to prevent cancer and other chronic diseases have been identified in recent decades and have the potential for high impact. Yet, barriers to implement prevention approaches persist as a result of multiple factors including lack of organizational support, limited resources, competing emerging priorities and crises, and limited skill among the public health workforce. The purpose of this study is to learn how best to promote the adoption of evidence based public health practice related to chronic disease prevention.Methods/designThis paper describes the methods for a multi-phase dissemination study with a cluster randomized trial component that will evaluate the dissemination of public health knowledge about evidence-based prevention of cancer and other chronic diseases. Phase one involves development of measures of practitioner views on and organizational supports for evidence-based public health and data collection using a national online survey involving state health department chronic disease practitioners. In phase two, a cluster randomized trial design will be conducted to test receptivity and usefulness of dissemination strategies directed toward state health department chronic disease practitioners to enhance capacity and organizational support for evidence-based chronic disease prevention. Twelve state health department chronic disease units will be randomly selected and assigned to intervention or control. State health department staff and the university-based study team will jointly identify, refine, and select dissemination strategies within intervention units. Intervention (dissemination) strategies may include multi-day in-person training workshops, electronic information exchange modalities, and remote technical assistance. Evaluation methods include pre-post surveys, structured qualitative phone interviews, and abstraction of state-level chronic disease prevention program plans and progress reports.Trial NCT01978054.
    Implementation Science 12/2013; 8(1):141. DOI:10.1186/1748-5908-8-141 · 4.12 Impact Factor
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    ABSTRACT: This study compared the ActiGraph accelerometer model 7164 (AM1) with the ActiGraph GT1M (AM2) during self-paced locomotion. Participants (n = 116, aged 18-73 yr, mean body mass index = 26.1 kg x m(-2)) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47 km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60-s epochs. To compare differences between monitors, the average difference (bias) in count output and steps output was computed at each speed. Time spent in different activity intensities (light, moderate, and vigorous) based on the cut points of Freedson et al. was compared for each minute. The mean +/- SD walking speed was 0.7 +/- 0.22 m x s(-1) for the slow speed, 1.3 +/- 0.17 m x s(-1) for medium, and 2.1 +/- 0.61 m x s(-1) for fast speeds. Ninety-five percent confidence intervals (95% CI) were used to determine significance. Across all speeds, step output was significantly higher for the AM1 (bias = 19.8%, 95% CI = -23.2% to -16.4%) because of the large differences in step output at slow speed. The count output from AM2 was a significantly higher (2.7%, 95% CI = 0.8%-4.7%) than that from AM1. Overall, 96.1% of the minutes were classified into the same MET intensity category by both monitors. The step output between models was not comparable at slow speeds, and comparisons of step data collected with both models should be interpreted with caution. The count output from AM2 was slightly but significantly higher than that from AM1 during the self-paced locomotion, but this difference did not result in meaningful differences in activity intensity classifications. Thus, data collected with AM1 should be comparable to AM2 across studies for estimating habitual activity levels.
    Medicine and science in sports and exercise 12/2009; 42(5):971-6. DOI:10.1249/MSS.0b013e3181c29e90 · 3.98 Impact Factor
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    ABSTRACT: This article 1) provides the calibration procedures and methods for metabolic and activity monitor data collection, 2) compares measured MET values to the MET values from the compendium of physical activities, and 3) examines the relationship between accelerometer output and METs for a range of physical activities. Participants (N = 277) completed 11 activities for 7 min each from a menu of 23 physical activities. Oxygen consumption (V O2) was measured using a portable metabolic system, and an accelerometer was worn. MET values were defined as measured METs (V O2/measured resting metabolic rate) and standard METs (V O2/3.5 For the total sample and by subgroup (age [young < 40 yr], sex, and body mass index [normal weight < 25 kg.m]), measured METs and standard METs were compared with the compendium, using 95% confidence intervals to determine statistical significance (alpha = 0.05). Average counts per minute for each activity and the linear association between counts per minute and METs are presented. Compendium METs were different than measured METs for 17/21 activities (81%). The number of activities different than the compendium was similar between subgroups or when standard METs were used. The average counts for the activities ranged from 11 counts per minute (dishes) to 7490 counts per minute (treadmill: 2.23 m.s, 3%). The r between counts and METs was 0.65. This study provides valuable information about data collection, metabolic responses, and accelerometer output for common physical activities in a diverse participant sample. The compendium should be updated with additional empirical data, and linear regression models are inappropriate for accurately predicting METs from accelerometer output.
    Medicine and science in sports and exercise 02/2010; 42(9):1776-84. DOI:10.1249/MSS.0b013e3181d479f2 · 3.98 Impact Factor
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