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      Patterns of Diet, Physical Activity, Sitting and Sleep Are Associated with Socio-Demographic, Behavioural, and Health-Risk Indicators in Adults

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          Abstract

          Our understanding of how multiple health-behaviours co-occur is in its infancy. This study aimed to: (1) identify patterns of physical activity, diet, sitting, and sleep; and (2) examine the association between sociodemographic and health-risk indicators. Pooled data from annual cross-sectional telephone surveys of Australian adults (2015–2017, n = 3374, 51.4% women) were used. Participants self-reported physical activity, diet, sitting-time, sleep/rest insufficiency, sociodemographic characteristics, smoking, alcohol use, height and weight to calculate body mass index (BMI), and mental distress frequency. Latent class analysis identified health-behaviour classes. Latent class regression determined the associations between health-behaviour patterns, sociodemographic, and health-risk indicators. Three latent classes were identified. Relative to a ‘moderate lifestyle’ pattern (men: 43.2%, women: 38.1%), a ‘poor lifestyle’ pattern (men: 19.9%, women: 30.5%) was associated with increased odds of a younger age, smoking, BMI ≥ 30.0 kg/m2, frequent mental distress (men and women), non-partnered status (men only), a lower Socioeconomic Index for Areas centile, primary/secondary education only, and BMI = 25.0–29.9 kg/m 2 (women only). An ‘active poor sleeper’ pattern (men: 37.0%, women: 31.4%) was associated with increased odds of a younger age (men and women), working and frequent mental distress (women only), relative to a ‘moderate lifestyle’ pattern. Better understanding of how health-behaviour patterns influence future health status is needed. Targeted interventions jointly addressing these behaviours are a public health priority.

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          Sitting time and all-cause mortality risk in 222 497 Australian adults.

          Prolonged sitting is considered detrimental to health, but evidence regarding the independent relationship of total sitting time with all-cause mortality is limited. This study aimed to determine the independent relationship of sitting time with all-cause mortality. We linked prospective questionnaire data from 222 497 individuals 45 years or older from the 45 and Up Study to mortality data from the New South Wales Registry of Births, Deaths, and Marriages (Australia) from February 1, 2006, through December 31, 2010. Cox proportional hazards models examined all-cause mortality in relation to sitting time, adjusting for potential confounders that included sex, age, education, urban/rural residence, physical activity, body mass index, smoking status, self-rated health, and disability. During 621 695 person-years of follow-up (mean follow-up, 2.8 years), 5405 deaths were registered. All-cause mortality hazard ratios were 1.02 (95% CI, 0.95-1.09), 1.15 (1.06-1.25), and 1.40 (1.27-1.55) for 4 to less than 8, 8 to less than 11, and 11 or more h/d of sitting, respectively, compared with less than 4 h/d, adjusting for physical activity and other confounders. The population-attributable fraction for sitting was 6.9%. The association between sitting and all-cause mortality appeared consistent across the sexes, age groups, body mass index categories, and physical activity levels and across healthy participants compared with participants with preexisting cardiovascular disease or diabetes mellitus. Prolonged sitting is a risk factor for all-cause mortality, independent of physical activity. Public health programs should focus on reducing sitting time in addition to increasing physical activity levels.
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            Gauging the Impact of Growing Nonresponse on Estimates from a National RDD Telephone Survey

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              The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life

              Background Health behaviours do not occur in isolation. Rather they cluster together. It is important to examine patterns of health behaviours to inform a more holistic approach to health in both health promotion and illness prevention strategies. Examination of patterns is also important because of the increased risk of mortality, morbidity and synergistic effects of health behaviours. This study examines the clustering of health behaviours in a nationally representative sample of Irish adults and explores the association of these clusters with mental health, self-rated health and quality of life. Methods TwoStep Cluster analysis using SPSS was carried out on the SLÁN 2007 data (national Survey of Lifestyle, Attitudes and Nutrition, n = 10,364; response rate =62%; food frequency n = 9,223; cluster analysis n = 7,350). Patterns of smoking, drinking alcohol, physical activity and diet were considered. Associations with positive and negative mental health, quality of life and self-rated health were assessed. Results Six health behaviour clusters were identified: Former Smokers, 21.3% (n = 1,564), Temperate, 14.6% (n = 1,075), Physically Inactive, 17.8% (n = 1,310), Healthy Lifestyle, 9.3% (n = 681), Multiple Risk Factor, 17% (n = 1248), and Mixed Lifestyle, 20% (n = 1,472). Cluster profiles varied with men aged 18-29 years, in the lower social classes most likely to adopt unhealthy behaviour patterns. In contrast, women from the higher social classes and aged 65 years and over were most likely to be in the Healthy Lifestyle cluster. Having healthier patterns of behaviour was associated with positive lower levels of psychological distress and higher levels of energy vitality. Conclusion The current study identifies discernible patterns of lifestyle behaviours in the Irish population which are similar to those of our European counterparts. Healthier clusters (Former Smokers, Temperate and Healthy Lifestyle) reported higher levels of energy vitality, lower levels of psychological distress, better self-rated health and better quality of life. In contrast, those in the Multiple Risk Factor cluster had the lowest levels of energy and vitality and the highest levels of psychological distress. Identification of these discernible patterns because of their relationship with mortality, morbidity and longevity is important for identifying national and international health behaviour patterns.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                04 July 2019
                July 2019
                : 16
                : 13
                : 2375
                Affiliations
                [1 ]School of Medicine & Public Health; Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia
                [2 ]Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia
                [3 ]Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton Queensland 4702, Australia
                Author notes
                Author information
                https://orcid.org/0000-0002-6769-1454
                https://orcid.org/0000-0002-4445-8094
                https://orcid.org/0000-0002-9166-6195
                Article
                ijerph-16-02375
                10.3390/ijerph16132375
                6651368
                31277386
                c72e0214-6de0-44d0-83c4-9fc31ead5b99
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 May 2019
                : 01 July 2019
                Categories
                Article

                Public health
                health behaviour,lifestyle,body mass index,mental health
                Public health
                health behaviour, lifestyle, body mass index, mental health

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