Concept: Metabolic equivalent
It has recently been recommended that sedentary behavior be defined as sitting or reclining activities expending less than 1.5 metabolic equivalents (METs), which is distinct from the traditional viewpoint based on insufficient moderate-vigorous activity or formal exercise. This study was designed to determine the energy expenditure associated with common sedentary behaviors. Twenty-five African American adults (BMI 27.8±5.5) participated in the metabolic chamber study. Participants entered the metabolic chamber in the morning and their basal metabolic rate was estimated. They were fed breakfast and then engaged in four different sedentary behaviors sequentially, lasting 30 minutes each. The activities included reclining, watching TV, reading, and typing on a computer. In the afternoon, the participants were fed lunch and then the activities were repeated. The results show that the energy expenditure values between the morning and afternoon sessions were not significantly different (p = .232). The mean energy expenditure of postprandial reclining (0.97 METs) was slightly, but significantly, lower than postprandial watching TV (p = .021) and typing (p<.001). There were no differences in energy cost (1.03-1.06 METs) between the seated (i.e., reading, typing, watching TV) sedentary activities. The energy expenditure of several common sedentary behaviors was approximately 1.0 METs in the postprandial state. The results support the conclusion that the average energy cost of common sedentary behaviors is narrowly banded around 1.0 METs in the postprandial state.
Objectively measured light-intensity lifestyle activity and sedentary time are independently associated with metabolic syndrome: a cross-sectional study of Japanese adults
- The international journal of behavioral nutrition and physical activity
- Published over 7 years ago
BACKGROUND: Reducing sedentary time and increasing lifestyle activities, including light-intensity activity, may be an option to help prevent metabolic syndrome (MetS). The purpose of the present study was to examine whether objectively measured light-intensity lifestyle activity and sedentary time is associated with MetS, independent of moderate–vigorous intensity physical activity (MVPA). METHODS: The participants in this cross-sectional study were 483 middle-aged Japanese adults, aged 30–64 years. The participants were divided into those with or without MetS according to the Japanese criteria for MetS. A triaxial accelerometer was used to measure light-intensity lifestyle activity [1.6–2.9 metabolic equivalents (METs)] and sedentary time (<=1.5 METs). Logistic regression was used to predict MetS from the levels of light-intensity lifestyle activity and sedentary time with age, sex, smoking, calorie intake, accelerometer wear time, and MVPA as covariates. RESULTS: The odds ratios (OR) for MetS in the highest and middle tertiles of light-intensity lifestyle activity were 0.44 [95% confidence interval (CI): 0.24 to 0.81] and 0.51 (95% CI: 0.29 to 0.89) relative to the lowest tertile, after adjustment for age, sex, smoking, calorie intake, accelerometer wear time and MVPA (Ptrend = 0.012). Sedentary time was also associated with the risk of MetS (Ptrend = 0.018). Among participants in the highest tertile of sedentary time, the risk of MetS was 2.27-times greater than that in the lowest tertile (95% CI: 1.25 to 4.11). The risk of MetS was not significantly increased in subjects in the middle tertile of sedentary time. CONCLUSIONS: We found that light-intensity lifestyle activity and sedentary time were significantly associated with the risk of MetS, independent of MVPA. The results of our study suggest that public health messages and guidelines should be refined to include increases in light-intensity lifestyle activity and/or decreases in sedentary time, alongside promoting MVPA, to prevent MetS.
There has not been a recent comprehensive effort to examine existing studies on resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcalkgh) to determine the relationship of age, sex, and obesity status to RMR as compared to the commonly accepted value for the metabolic equivalent or the MET (e.g., 1.0 kcalkgh).
IMPORTANCE Muscle pain, fatigue, and weakness are common adverse effects of statin medications and may decrease physical activity in older men. OBJECTIVE To determine whether statin use is associated with physical activity, longitudinally and cross-sectionally. DESIGN, SETTING, AND PARTICIPANTS Men participating in the Osteoporotic Fractures in Men Study (N = 5994), a multicenter prospective cohort study of community-living men 65 years and older, enrolled between March 2000 and April 2002. Follow-up was conducted through 2009. EXPOSURES Statin use as determined by an inventory of medications (taken within the last 30 days). In cross-sectional analyses (n = 4137), statin use categories were users and nonusers. In longitudinal analyses (n = 3039), categories were prevalent users (baseline use and throughout the study), new users (initiated use during the study), and nonusers (never used). MAIN OUTCOMES AND MEASURES Self-reported physical activity at baseline and 2 follow-up visits using the Physical Activity Scale for the Elderly (PASE). At the third visit, an accelerometer measured metabolic equivalents (METs [kilocalories per kilogram per hour]) and minutes of moderate activity (METs ≥3.0), vigorous activity (METs ≥6.0), and sedentary behavior (METs ≤1.5). RESULTS At baseline, 989 men (24%) were users and 3148 (76%) were nonusers. The adjusted difference in baseline PASE between users and nonusers was -5.8 points (95% CI, -10.9 to -0.7 points). A total of 3039 men met the inclusion criteria for longitudinal analysis: 727 (24%) prevalent users, 845 (28%) new users, and 1467 (48%) nonusers. PASE score declined by a mean (95% CI) of 2.5 (2.0 to 3.0) points per year for nonusers and 2.8 (2.1 to 3.5) points per year for prevalent users, a nonstatistical difference (0.3 [-0.5 to 1.0] points). For new users, annual PASE score declined at a faster rate than nonusers (difference of 0.9 [95% CI, 0.1 to 1.7] points). A total of 3071 men had adequate accelerometry data, 1542 (50%) were statin users. Statin users expended less METs (0.03 [95% CI, 0.02-0.04] METs less) and engaged in less moderate physical activity (5.4 [95% CI, 1.9-8.8] fewer minutes per day), less vigorous activity (0.6 [95% CI, 0.1-1.1] fewer minutes per day), and more sedentary behavior (7.6 [95% CI, 2.6-12.4] greater minutes per day). CONCLUSIONS AND RELEVANCE Statin use was associated with modestly lower physical activity among community-living men, even after accounting for medical history and other potentially confounding factors. The clinical significance of these findings deserves further investigation.
The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB, < 1.50 METs) in successful weight loss maintainers (WLMs), normal weight controls (NC), and controls with overweight/obesity (OC).
Background: Whether lifestyle affects Alzheimer’s disease (AD) risk remains controversial. Objective: Test whether exercise, diet, or statins affect AD mortality in 153,536 participants of the National Runners' and Walkers' Health Studies. Methods: Hazard ratios (HR) and 95% confidence intervals (95% CI) were obtained from Cox proportional hazard analyses for AD mortality versus baseline metabolic equivalent (MET) hours/d of exercise energy expenditure (1 MET equals approximately 1 km run), statin use, and fruit intake when adjusted for age, race, gender, education, and exercise mode. Results: The National Death Index identified 175 subjects who died with AD listed as an underlying (n = 116) or contributing (n = 59) cause of death during 11.6-year average mortality surveillance. Relative to exercising <1.07 MET-hours/d, AD mortality was 6.0% lower for 1.07 to 1.8 MET-hours/d (HR: 0.94, 95% CI: 0.59 to 1.46, p = 0.79), 24.8% lower for 1.8 to 3.6 MET-hours/d (HR: 0.75, 95% CI: 0.50 to 1.13, p = 0.17), and 40.1% lower for ≥3.6 MET-hours/d (HR: 0.60, 95% CI: 0.37 to 0.97, p = 0.04). Relative to non-use, statin use was associated with 61% lower AD mortality (HR: 0.39, 95% CI: 0.15 to 0.82, p = 0.01), whereas use of other cholesterol-lowering medications was not (HR: 0.78, 95% CI: 0.40 to 1.38, p = 0.42). Relative to <1 piece of fruit/day, consuming 2 to 3 pieces daily was associated with 39.7% lower AD mortality (HR: 0.60, 95% CI: 0.39 to 0.91, p = 0.02) and ≥3 pieces/day with 60.7% lower AD mortality (HR: 0.39, 95% CI: 0.22 to 0.67, p = 0.0004). Conclusions: Exercise, statin, and fruit intake were associated with lower risk for AD mortality.
PURPOSE: Habitual running has been associated with reduced risk of cataract development in one prospective study. The purpose of the current analyses is to provide further evidence of this potentially important benefit of vigorous exercise, and to test whether moderate exercise (e.g., walking) provides a significant and equivalent reduction in cataract risk as vigorous exercise (e.g. running). METHODS: Cox proportional hazard analyses of self-reported, physician-diagnosed incident cataracts vs. baseline energy expenditure (metabolic equivalents or METs) in 32,610 runners and 14,917 walkers during 6.2-year follow-up. Results are reported as hazard ratios (HR), percent risk reductions (100*(HR-1)), and 95% confidence intervals (95%CI). RESULTS: Runners and walkers reported 733 and 1,074 incident cataracts during follow-up, respectively. When adjusted for sex, race, age, education, smoking, and intakes of meat, fruit and alcohol, lower cataract risk was significantly associated with both running (HR=0.960 per METh/d, 95%CI 0.935 to 0.986) and walking (HR=0.918 per METh/d, 95%CI: 0.881 to 0.956,), with no significant difference in the per METh/d risk reduction between running and walking, or between men and women. Compared to running or walking at or below guideline levels (≤1.8 METh/d), incident cataract risk was significantly lower for running or walking 1.8 to 3.6 (16.4% lower, 95%CI: 6.4% to 25.3%), 3.6 to 5.4 (19.0% lower, 95%CI: 5.6% to 30.4%), 5.4 to 7.2 (26.2% lower, 95%CI: 11.2% to 38.7%), 7.2 to 9.0 (34.1% lower, 95%CI: 10.0% to 51.2%), and ≥9 METh/d (41.6% lower, 95%CI: 19.8% to 57.4%). CONCLUSIONS: Moderate (walking) and vigorous (running) exercise were both significantly associated with lower cataract risk, and their effects similar. Cataract risk appears to decrease linearly with increasing exercise energy expenditure through 9 METh/d.
Stepping is a convenient form of scalable high-intensity interval training (HIIT) that may lead to health benefits. However, the accurate personalised prescription of stepping is hampered by a lack of evidence on optimal stepping cadences and step heights for various populations. This study examined the acute physiological responses to stepping exercise at various heights and cadences in young (n = 14) and middle-aged (n = 14) females in order to develop an equation that facilitates prescription of stepping at targeted intensities. Participants completed a step test protocol consisting of randomised three-minute bouts at different step cadences (80, 90, 100, 110 steps·min-1) and step heights (17, 25, 30, 34 cm). Aerobic demand and heart rate values were measured throughout. Resting metabolic rate was measured in order to develop female specific metabolic equivalents (METs) for stepping. Results revealed significant differences between age groups for METs and heart rate reserve, and within-group differences for METs, heart rate, and metabolic cost, at different step heights and cadences. At a given step height and cadence, middle-aged females were required to work at an intensity on average 1.9 ± 0.26 METs greater than the younger females. A prescriptive equation was developed to assess energy cost in METs using multilevel regression analysis with factors of step height, step cadence and age. Considering recent evidence supporting accumulated bouts of HIIT exercise for health benefits, this equation, which allows HIIT to be personally prescribed to inactive and sedentary women, has potential impact as a public health exercise prescription tool.
Abstract Objective . This systematic review synthesizes the evidence on the cost-effectiveness of population-level interventions to promote physical activity. Data Source . A systematic literature search was conducted between May and August 2013 in four databases: PubMed, Scopus, Web of Science, and SPORTDiscus. Study Inclusion and Exclusion Criteria . Only primary and preventive interventions aimed at promoting and maintaining physical activity in wide population groups were included. An economic evaluation of both effectiveness and cost was required. Secondary interventions and interventions targeting selected population groups or focusing on single individuals were excluded. Data Extraction . Interventions were searched for in six different categories: (1) environment, (2) built environment, (3) sports clubs and enhanced access, (4) schools, (5) mass media and community-based, and (6) workplace. Data Synthesis . The systematic search yielded 2058 articles, of which 10 articles met the selection criteria. The costs of interventions were converted to costs per person per day in 2012 U.S. dollars. The physical activity results were calculated as metabolic equivalent of task hours (MET-hours, or MET-h) gained per person per day. Cost-effectiveness ratios were presented as dollars per MET-hours gained. The intervention scale and the budget impact of interventions were taken into account. Results . The most efficient interventions to increase physical activity were community rail-trails ($.006/MET-h), pedometers ($.014/MET-h), and school health education programs ($.056/MET-h). Conclusion . Improving opportunities for walking and biking seems to increase physical activity cost-effectively. However, it is necessary to be careful in generalizing the results because of the small number of studies. This review provides important information for decision makers.
There is a high prevalence of metabolic syndrome in liver transplant recipients, a population that tends to be physically inactive. The aim of this study was to characterize physical activity and evaluate the relationship between physical activity and metabolic syndrome after liver transplantation. A cross-sectional analysis was performed in patients more than 3 months after transplantation. Metabolic syndrome was classified according to National Cholesterol Education Panel Adult Treatment Panel III guidelines. Physical activity, including duration, frequency, and metabolic equivalents of task (METs), was assessed. The study population consisted of 204 subjects, with 156 more than 1 year after transplantation. The median time after transplantation was 53.5 months (range = 3-299 months). The mean duration of exercise was 90 ± 142 minutes, and the mean MET score was 3.6 ± 1.5. Metabolic syndrome was observed in 58.8% of all subjects and in 63.5% of the subjects more than 1 year after transplantation. In a multivariate analysis involving all subjects, metabolic syndrome was associated with a time after transplantation greater than 1 year [odds ratio (OR) = 2.909, 95% confidence interval (CI) = 1.389-6.092] and older age (OR = 1.036, 95% CI = 1.001-1.072). A second analysis was performed for only patients more than 1 year after transplantation. In a multivariate analysis, metabolic syndrome was associated with lower exercise intensity (OR = 0.690, 95% CI = 0.536-0.887), older age (OR = 1.056, 95% CI = 1.014-1.101), and pretransplant diabetes (OR = 4.246, 95% CI = 1.300-13.864). In conclusion, metabolic syndrome is common after liver transplantation, and the rate is significantly higher in patients more than 1 year after transplantation. The observation that exercise intensity is inversely related to metabolic syndrome after transplantation is novel and suggests that physical activity might provide a means for reducing metabolic syndrome complications in liver transplant recipients. Liver Transpl, 2013. © 2013 AASLD.