Traditional and Emerging Lifestyle Risk Behaviors and All-Cause Mortality in Middle-Aged and Older Adults: Evidence from a Large Population-Based Australian Cohort
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{"title"=>"Traditional and Emerging Lifestyle Risk Behaviors and All-Cause Mortality in Middle-Aged and Older Adults: Evidence from a Large Population-Based Australian Cohort", "type"=>"journal", "authors"=>[{"first_name"=>"Ding", "last_name"=>"Ding", "scopus_author_id"=>"55558568100"}, {"first_name"=>"Kris", "last_name"=>"Rogers", "scopus_author_id"=>"35086793300"}, {"first_name"=>"Hidde", "last_name"=>"van der Ploeg", "scopus_author_id"=>"15763818500"}, {"first_name"=>"Emmanuel", "last_name"=>"Stamatakis", "scopus_author_id"=>"55847987800"}, {"first_name"=>"Adrian E.", "last_name"=>"Bauman", "scopus_author_id"=>"25421265100"}], "year"=>2015, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491676", "pui"=>"607689973", "pmid"=>"26645683", "isbn"=>"1549-1277\\r1549-1676", "doi"=>"10.1371/journal.pmed.1001917", "sgr"=>"84953792041", "scopus"=>"2-s2.0-84953792041"}, "id"=>"d024f046-96d0-35e5-ad48-ce9f9f4ee4cd", "abstract"=>"BACKGROUND: Lifestyle risk behaviors are responsible for a large proportion of disease burden worldwide. Behavioral risk factors, such as smoking, poor diet, and physical inactivity, tend to cluster within populations and may have synergistic effects on health. As evidence continues to accumulate on emerging lifestyle risk factors, such as prolonged sitting and unhealthy sleep patterns, incorporating these new risk factors will provide clinically relevant information on combinations of lifestyle risk factors.\\n\\nMETHODS AND FINDINGS: Using data from a large Australian cohort of middle-aged and older adults, this is the first study to our knowledge to examine a lifestyle risk index incorporating sedentary behavior and sleep in relation to all-cause mortality. Baseline data (February 2006- April 2009) were linked to mortality registration data until June 15, 2014. Smoking, high alcohol intake, poor diet, physical inactivity, prolonged sitting, and unhealthy (short/long) sleep duration were measured by questionnaires and summed into an index score. Cox proportional hazards analysis was used with the index score and each unique risk combination as exposure variables, adjusted for socio-demographic characteristics. During 6 y of follow-up of 231,048 participants for 1,409,591 person-years, 15,635 deaths were registered. Of all participants, 31.2%, 36.9%, 21.4%, and 10.6% reported 0, 1, 2, and 3+ risk factors, respectively. There was a strong relationship between the lifestyle risk index score and all-cause mortality. The index score had good predictive validity (c index = 0.763), and the partial population attributable risk was 31.3%. Out of all 96 possible risk combinations, the 30 most commonly occurring combinations accounted for more than 90% of the participants. Among those, combinations involving physical inactivity, prolonged sitting, and/or long sleep duration and combinations involving smoking and high alcohol intake had the strongest associations with all-cause mortality. Limitations of the study include self-reported and under-specified measures, dichotomized risk scores, lack of long-term patterns of lifestyle behaviors, and lack of cause-specific mortality data.\\n\\nCONCLUSIONS: Adherence to healthy lifestyle behaviors could reduce the risk for death from all causes. Specific combinations of lifestyle risk behaviors may be more harmful than others, suggesting synergistic relationships among risk factors.", "link"=>"http://www.mendeley.com/research/traditional-emerging-lifestyle-risk-behaviors-allcause-mortality-middleaged-older-adults-evidence-la-1", "reader_count"=>77, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>7, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>9, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>7, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>9, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>8, "Computer Science"=>3, "Economics, Econometrics and Finance"=>2, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>7, "Materials Science"=>1, "Medicine and Dentistry"=>23, "Neuroscience"=>2, "Sports and Recreations"=>6, "Physics and Astronomy"=>1, "Psychology"=>5, "Social Sciences"=>6}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>23}, "Social Sciences"=>{"Social Sciences"=>6}, "Sports and Recreations"=>{"Sports and Recreations"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>5}, "Unspecified"=>{"Unspecified"=>9}, "Engineering"=>{"Engineering"=>2}, "Neuroscience"=>{"Neuroscience"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Japan"=>1, "Luxembourg"=>1, "Australia"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>3}

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  • {"files"=>["https://ndownloader.figshare.com/files/4295092"], "description"=>"<p>Scoring of risk factors in the lifestyle risk index based on the 45 and Up Study.</p>", "links"=>[], "tags"=>["Behavioral risk factors", "risk factors", "combination", "lifestyle risk index", "Cohort BackgroundLifestyle risk behaviors", "lifestyle risk factors.Methods", "mortality registration data", "lifestyle risk index score", "index score", "alcohol intake", "lifestyle behaviors", "lifestyle risk behaviors", "dichotomized risk scores", "Lifestyle Risk Behaviors", "lifestyle risk factors"], "article_id"=>1621747, "categories"=>["Medicine", "Biotechnology", "Developmental Biology", "Cancer", "Mental Health", "Infectious Diseases"], "users"=>["Ding Ding", "Kris Rogers", "Hidde van der Ploeg", "Emmanuel Stamatakis", "Adrian E. Bauman"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001917.t001"], "stats"=>{"downloads"=>0, "page_views"=>70, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scoring_of_risk_factors_in_the_lifestyle_risk_index_based_on_the_45_and_Up_Study_/1621747", "title"=>"Scoring of risk factors in the lifestyle risk index based on the 45 and Up Study.", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-12-08 06:53:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/4295104"], "description"=>"<p>Prevalence of all 96 combinations of lifestyle risk behaviors and adjusted hazard ratios for their associations with all-cause mortality among middle-aged and older adults based on the 45 and Up Study, Australia (2006–2014, <i>n</i> = 231,048).</p>", "links"=>[], "tags"=>["Behavioral risk factors", "risk factors", "combination", "lifestyle risk index", "Cohort BackgroundLifestyle risk behaviors", "lifestyle risk factors.Methods", "mortality registration data", "lifestyle risk index score", "index score", "alcohol intake", "lifestyle behaviors", "lifestyle risk behaviors", "dichotomized risk scores", "Lifestyle Risk Behaviors", "lifestyle risk factors"], "article_id"=>1621748, "categories"=>["Medicine", "Biotechnology", "Developmental Biology", "Cancer", "Mental Health", "Infectious Diseases"], "users"=>["Ding Ding", "Kris Rogers", "Hidde van der Ploeg", "Emmanuel Stamatakis", "Adrian E. Bauman"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001917.t004"], "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prevalence_of_all_96_combinations_of_lifestyle_risk_behaviors_and_adjusted_hazard_ratios_for_their_associations_with_all_cause_mortality_among_middle_aged_and_older_adults_based_on_the_45_and_Up_Study_Australia_2006_2014_n_231_048_/1621748", "title"=>"Prevalence of all 96 combinations of lifestyle risk behaviors and adjusted hazard ratios for their associations with all-cause mortality among middle-aged and older adults based on the 45 and Up Study, Australia (2006–2014, <i>n</i> = 231,048).", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-12-08 06:53:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/4295077", "https://ndownloader.figshare.com/files/4295080", "https://ndownloader.figshare.com/files/4295083", "https://ndownloader.figshare.com/files/4295086"], "description"=>"<div><p>Background</p><p>Lifestyle risk behaviors are responsible for a large proportion of disease burden worldwide. Behavioral risk factors, such as smoking, poor diet, and physical inactivity, tend to cluster within populations and may have synergistic effects on health. As evidence continues to accumulate on emerging lifestyle risk factors, such as prolonged sitting and unhealthy sleep patterns, incorporating these new risk factors will provide clinically relevant information on combinations of lifestyle risk factors.</p><p>Methods and Findings</p><p>Using data from a large Australian cohort of middle-aged and older adults, this is the first study to our knowledge to examine a lifestyle risk index incorporating sedentary behavior and sleep in relation to all-cause mortality. Baseline data (February 2006– April 2009) were linked to mortality registration data until June 15, 2014. Smoking, high alcohol intake, poor diet, physical inactivity, prolonged sitting, and unhealthy (short/long) sleep duration were measured by questionnaires and summed into an index score. Cox proportional hazards analysis was used with the index score and each unique risk combination as exposure variables, adjusted for socio-demographic characteristics.</p><p>During 6 y of follow-up of 231,048 participants for 1,409,591 person-years, 15,635 deaths were registered. Of all participants, 31.2%, 36.9%, 21.4%, and 10.6% reported 0, 1, 2, and 3+ risk factors, respectively. There was a strong relationship between the lifestyle risk index score and all-cause mortality. The index score had good predictive validity (<i>c</i> index = 0.763), and the partial population attributable risk was 31.3%. Out of all 96 possible risk combinations, the 30 most commonly occurring combinations accounted for more than 90% of the participants. Among those, combinations involving physical inactivity, prolonged sitting, and/or long sleep duration and combinations involving smoking and high alcohol intake had the strongest associations with all-cause mortality. Limitations of the study include self-reported and under-specified measures, dichotomized risk scores, lack of long-term patterns of lifestyle behaviors, and lack of cause-specific mortality data.</p><p>Conclusions</p><p>Adherence to healthy lifestyle behaviors could reduce the risk for death from all causes. Specific combinations of lifestyle risk behaviors may be more harmful than others, suggesting synergistic relationships among risk factors.</p></div>", "links"=>[], "tags"=>["Behavioral risk factors", "risk factors", "combination", "lifestyle risk index", "Cohort BackgroundLifestyle risk behaviors", "lifestyle risk factors.Methods", "mortality registration data", "lifestyle risk index score", "index score", "alcohol intake", "lifestyle behaviors", "lifestyle risk behaviors", "dichotomized risk scores", "Lifestyle Risk Behaviors", "lifestyle risk factors"], "article_id"=>1621749, "categories"=>["Medicine", "Biotechnology", "Developmental Biology", "Cancer", "Mental Health", "Infectious Diseases"], "users"=>["Ding Ding", "Kris Rogers", "Hidde van der Ploeg", "Emmanuel Stamatakis", "Adrian E. Bauman"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001917.s001", "https://dx.doi.org/10.1371/journal.pmed.1001917.s002", "https://dx.doi.org/10.1371/journal.pmed.1001917.s003", "https://dx.doi.org/10.1371/journal.pmed.1001917.s004"], "stats"=>{"downloads"=>22, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Traditional_and_Emerging_Lifestyle_Risk_Behaviors_and_All_Cause_Mortality_in_Middle_Aged_and_Older_Adults_Evidence_from_a_Large_Population_Based_Australian_Cohort_/1621749", "title"=>"Traditional and Emerging Lifestyle Risk Behaviors and All-Cause Mortality in Middle-Aged and Older Adults: Evidence from a Large Population-Based Australian Cohort", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-12-08 06:53:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/4295098"], "description"=>"<p>Socio-demographic and health characteristics of adults by lifestyle risk index score in New South Wales, Australia (2006–2009, <i>n</i> = 231,048).</p>", "links"=>[], "tags"=>["Behavioral risk factors", "risk factors", "combination", "lifestyle risk index", "Cohort BackgroundLifestyle risk behaviors", "lifestyle risk factors.Methods", "mortality registration data", "lifestyle risk index score", "index score", "alcohol intake", "lifestyle behaviors", "lifestyle risk behaviors", "dichotomized risk scores", "Lifestyle Risk Behaviors", "lifestyle risk factors"], "article_id"=>2642542, "categories"=>["Medicine", "Biotechnology", "Developmental Biology", "Cancer", "Mental Health", "Infectious Diseases"], "users"=>["Ding Ding", "Kris Rogers", "Hidde van der Ploeg", "Emmanuel Stamatakis", "Adrian E. Bauman"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001917.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Socio_demographic_and_health_characteristics_of_adults_by_lifestyle_risk_index_score_in_New_South_Wales_Australia_2006_2009_i_n_i_231_048_/2642542", "title"=>"Socio-demographic and health characteristics of adults by lifestyle risk index score in New South Wales, Australia (2006–2009, <i>n</i> = 231,048).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-12-08 06:53:05"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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