Activities Contributing to Total Energy Expenditure in the United States: Results from the NHAPS Study

Dong, Linda M.; Block, Gladys; Mandel, S. P. H.

doi:10.1186/1479-5868-1-4

Cited by 102 publications

(39 citation statements)

References 17 publications

(21 reference statements)

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“…However, the contribution of non-occupational non-leisure PA to total reported PA appeared modest, especially in men. It has been previously reported that household PA contributes more to total energy expenditure in women than in men [11]. Non-occupational non-leisure PA did not correlate significantly with the objective measure of total PA.…”

Section: Discussionmentioning

confidence: 84%

“…As indicated in a recent update [3], activity can be accumulated ''from bouts lasting 10 or more min'', which should facilitate incorporation of PA in daily life. There is currently increasing interest in PA performed outside leisure and work [10][11][12][13][14][15][16][17]. Previous reports have shown that domestic PA can significantly contribute to the daily PA amount required to confer health benefits [6,10,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Relationships of self-reported physical activity domains with accelerometry recordings in French adults

et al. 2009

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The objective was to examine the relationships of self-reported physical activity (PA) by domain (leisure, occupational, other) with PA and sedentary time as measured objectively by accelerometry. Subjects were adults with low habitual PA levels from a community in northern France. Among subjects in the lowest tertile of a PA score from a screening questionnaire, 160 (37% males, age: 41.0 +/- 10.8 years, BMI: 25.1 +/- 4.1 kg/m(2), mean +/- SD) completed a detailed instrument (Modifiable Activity Questionnaire), and wore an accelerometer (Actigraph) for seven consecutive days. Relationships between questionnaire domains (occupational, leisure, and "non-occupational non-leisure") and accelerometry measures (total activity and sedentary time) were assessed using Spearman correlation coefficients. In this population, the highest contributor to total reported PA (h/week) was occupational PA. Time spent in non-occupational non-leisure PA ranked second in women and third in men. The most frequent non-occupational non-leisure PA were shopping and household chores. In women, non-occupational non-leisure PA contributed more than occupational or leisure-time PA to total PA energy expenditure (median: 18.0, 9.1, and 4.9 MET-h/week, respectively). Total PA by accelerometry (count/day) was correlated to leisure-time PA in women (r = 0.22, P < 0.05) and to occupational (r = 0.43, P < 0.01) and total reported PA (r = 0.39, P < 0.01) in men (all in MET-h/week). There was an inverse relationship between accelerometry sedentary time (h/day) and non-occupational non-leisure PA (MET-h/week, r = -0.30, P < 0.001). These findings indicate the importance of assessing non-occupational non-leisure PA for a better understanding of how individuals partition their time between active or sedentary occupations.

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Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Relationships of self-reported physical activity domains with accelerometry recordings in French adults

et al. 2009

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“…Included physical activities are derived from the most relevant daily-life and leisure time activities determined by the National Human Activities Patterns Survey. 42 The Block EES for physical activities uses six categories (rarely or never, a few times a month, 1-2 times/week, 3-4 times/week, 5-6 times/week and everyday) to assess frequency and seven categories (> 30 min, 30-59 min, 1 h, 1.5 h, 2 h, 3-4 h and > 4 h) to assess duration. The validation study showed that the percent of body fat predicted by levels of physical activity estimated from this questionnaire correlated well with the percent body fat observed.…”

Section: Methodsmentioning

confidence: 99%

White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population

et al. 2012

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Keywords: DNA methylation, diet, lifestyle factors, cancer As aberrant DNA methylation is associated with cancer and other human diseases, there are growing interests in determining how environmental exposures may affect patterns of DNA methylation. Previous studies found older monozygotic twins were more diverse in DNA methylation patterns than were younger monozygotic twins.17 There is also evidence that DNA methylation patterns in individuals change over time, 18 supporting aging and/or external environment factors affecting DNA methylation. However, which environmental exposures, among many that individuals are exposed to over the life course, affect patterns of DNA methylation remains elusive.Chronic inflammation constitutes an important mechanism for cancer. 19 The specific pathways by which inflammation causes cancer are not fully understood. Interleukin (IL-6) is a proinflammatory cytokine released by inflammatory cells and can activate intracellular transcription factors leading to tumorigenesis. There are some suggestions that IL-6 may stimulate tumor proliferation by altering patterns of DNA methylation, 20-22 and

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“…William Langston, 2006; Siderowf & Lang, 2012; Stessman, Hammerman-Rozenberg, Cohen, Ein-Mor, & Jacobs, 2009) Furthermore, few studies have accounted for both leisure and non-leisure physical activity which is essential since the latter may constitute the majority of daily physical activity for many very old adults. (Dong, Block, & Mandel, 2004; Lin et al, 2011; Robertson, 2013) Thus, in the absence of studies employing validated objective measures of total daily physical activity, the natural history of physical activity in older adults and its role as a modifiable risk factor will remain unclear. (Baker, Francis, Soares, Weightman, & Foster, 2011; Daviglus Ml & et al, 2011)…”

Section: Introductionmentioning

confidence: 99%

Total daily activity declines more rapidly with increasing age in older adults

Buchman

Wilson

et al. 2014

Archives of Gerontology and Geriatrics

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Longitudinal studies of objectively measured physical activity are lacking in older adults. We tested whether objective measures of total daily activity decline more rapidly in older adults. This prospective, observational cohort study included 519 community-dwelling older persons from across metropolitan Chicago participating in the Rush Memory and Aging Project. Repeated total daily activity measures (leisure and non-leisure physical activity) were derived from actigraphic recordings for up to 10 days. Generalized estimating equation models which controlled for demographics measures were employed. At baseline, age was inversely related with the level of total daily activity (Estimate, −0.014, S.E., 0.002, p<0.001). During up to 6 years of follow-up, total daily activity declined by about 0.070 × 105 activity counts/day/yr (Estimate −0.065, S.E. 0.005, p<0.001). Total daily activity declined 3% more rapidly for each additional year of age at baseline (Estimate −0.002, S.E. 0.001, p=0.027). Thus, total daily activity declined almost twice as fast in an individual 91 years old at baseline versus an individual 71 years old. A higher level of education was associated with a slower rate of decline (Estimate 0.004, S.E. 0.002, p<0.018). The associations of age and education with the rate of declining total daily activity were unchanged when controlling for baseline level of motor and cognitive function, other late-life activities and chronic health conditions. These data suggest that total daily activity in very old adults declines more rapidly with increasing age. Thus, physical inactivity is likely to become a larger problem in our aging population.

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Activities Contributing to Total Energy Expenditure in the United States: Results from the NHAPS Study

Cited by 102 publications

References 17 publications

Relationships of self-reported physical activity domains with accelerometry recordings in French adults

Relationships of self-reported physical activity domains with accelerometry recordings in French adults

White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population

Total daily activity declines more rapidly with increasing age in older adults

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