24
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Adverse associations of car time with markers of cardio-metabolic risk

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective

          To examine associations of time spent sitting in cars with markers of cardio-metabolic risk in Australian adults.

          Method

          Data were from 2800 participants (age range: 34–65) in the 2011–12 Australian Diabetes, Obesity and Lifestyle Study. Self-reported time spent in cars was categorized into four groups: ≤ 15 min/day; > 15 to ≤ 30 min/day; > 30 to ≤ 60 min/day; and > 60 min/day. Markers of cardio-metabolic risk were body mass index (BMI), waist circumference, systolic and diastolic blood pressure, triglycerides, HDL (high-density lipoprotein)-cholesterol, fasting plasma glucose, 2-h plasma glucose, a clustered cardio-metabolic risk score, and having the metabolic syndrome or not. Multilevel linear and logistic regression analyses examined associations of car time with each cardio-metabolic risk outcome, adjusting for socio-demographic and behavioral variables and medication use for blood pressure and cholesterol/triglycerides.

          Results

          Compared to spending 15 min/day or less in cars, spending more than 1 h/day in cars was significantly associated with higher BMI, waist circumference, fasting plasma glucose, and clustered cardio-metabolic risk, after adjusting for socio-demographic attributes and potentially relevant behaviors including leisure-time physical activity and dietary intake. Gender interactions showed car time to be associated with higher BMI in men only.

          Conclusions

          Prolonged time spent sitting in cars, in particular over 1 h/day, was associated with higher total and central adiposity and a more-adverse cardio-metabolic risk profile. Further studies, ideally using objective measures of sitting time in cars and prospective designs, are needed to confirm the impact of car use on cardio-metabolic disease risk.

          Highlights

          • Time spent in cars was adversely associated with markers of cardio-metabolic risk.

          • Spending more than 1 h per day in cars may be particularly detrimental.

          • Future research with objective measures of car time and prospective design is needed.

          Related collections

          Most cited references35

          • Record: found
          • Abstract: found
          • Article: not found

          Compendium of physical activities: an update of activity codes and MET intensities.

          We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).

            We examined the associations of objectively measured sedentary time and physical activity with continuous indexes of metabolic risk in Australian adults without known diabetes. An accelerometer was used to derive the percentage of monitoring time spent sedentary and in light-intensity and moderate-to-vigorous-intensity activity, as well as mean activity intensity, in 169 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) participants (mean age 53.4 years). Associations with waist circumference, triglycerides, HDL cholesterol, resting blood pressure, fasting plasma glucose, and a clustered metabolic risk score were examined. Independent of time spent in moderate-to-vigorous-intensity activity, there were significant associations of sedentary time, light-intensity time, and mean activity intensity with waist circumference and clustered metabolic risk. Independent of waist circumference, moderate-to-vigorous-intensity activity time was significantly beneficially associated with triglycerides. These findings highlight the importance of decreasing sedentary time, as well as increasing time spent in physical activity, for metabolic health.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Obesity relationships with community design, physical activity, and time spent in cars.

              Obesity is a major health problem in the United States and around the world. To date, relationships between obesity and aspects of the built environment have not been evaluated empirically at the individual level. To evaluate the relationship between the built environment around each participant's place of residence and self-reported travel patterns (walking and time in a car), body mass index (BMI), and obesity for specific gender and ethnicity classifications. Body Mass Index, minutes spent in a car, kilometers walked, age, income, educational attainment, and gender were derived through a travel survey of 10,878 participants in the Atlanta, Georgia region. Objective measures of land use mix, net residential density, and street connectivity were developed within a 1-kilometer network distance of each participant's place of residence. A cross-sectional design was used to associate urban form measures with obesity, BMI, and transportation-related activity when adjusting for sociodemographic covariates. Discrete analyses were conducted across gender and ethnicity. The data were collected between 2000 and 2002 and analysis was conducted in 2004. Land-use mix had the strongest association with obesity (BMI >/= 30 kg/m(2)), with each quartile increase being associated with a 12.2% reduction in the likelihood of obesity across gender and ethnicity. Each additional hour spent in a car per day was associated with a 6% increase in the likelihood of obesity. Conversely, each additional kilometer walked per day was associated with a 4.8% reduction in the likelihood of obesity. As a continuous measure, BMI was significantly associated with urban form for white cohorts. Relationships among urban form, walk distance, and time in a car were stronger among white than black cohorts. Measures of the built environment and travel patterns are important predictors of obesity across gender and ethnicity, yet relationships among the built environment, travel patterns, and weight may vary across gender and ethnicity. Strategies to increase land-use mix and distance walked while reducing time in a car can be effective as health interventions.
                Bookmark

                Author and article information

                Contributors
                Journal
                Prev Med
                Prev Med
                Preventive Medicine
                Academic Press
                0091-7435
                1096-0260
                1 February 2016
                February 2016
                : 83
                : 26-30
                Affiliations
                [a ]Centre for Design Innovation, Faculty of Health Arts & Design, Swinburne University of Technology, Melbourne, VIC, Australia
                [b ]School of Population Health, University of South Australia, Adelaide, SA, Australia
                [c ]Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia
                [d ]Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
                [e ]MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
                [f ]School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC, Australia
                [g ]School of Population Health, The University of QLD, Brisbane, QLD, Australia
                [h ]Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
                [i ]School of Sport Science, Exercise and Health The University of Western Australia, Perth, WA, Australia
                [j ]Central Clinical School, Monash University, Melbourne, VIC, Australia
                [k ]Mary MacKillop Institute of Health Research, Australian Catholic University, Melbourne, VIC, Australia
                Author notes
                [* ]Corresponding author at: Centre for Design Innovation, Faculty of Health Arts & Design, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.Centre for Design InnovationFaculty of Health Arts & DesignSwinburne University of TechnologyHawthornVIC3122Australia tsugiyama@ 123456swin.edu.au
                Article
                S0091-7435(15)00365-5
                10.1016/j.ypmed.2015.11.029
                5405044
                26656405
                8f0ca940-73ec-4ca5-8b68-1c30d0dc407a
                © 2015 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Article

                Medicine
                sedentary behavior,motorized transport,automobile,adiposity
                Medicine
                sedentary behavior, motorized transport, automobile, adiposity

                Comments

                Comment on this article