Dataset Information

The association between driving time and unhealthy lifestyles: a cross-sectional, general population study of 386 493 UK Biobank participants.

ABSTRACT:

Background

Driving is a common type of sedentary behaviour; an independent risk factor for poor health. The study explores whether driving is also associated with other unhealthy lifestyle factors.

Methods

In a cross-sectional study of UK Biobank participants, driving time was treated as an ordinal variable and other lifestyle factors dichotomized into low/high risk based on guidelines. The associations were explored using chi-square tests for trend and binary logistic regression.

Results

Of the 386 493 participants who drove, 153 717 (39.8%) drove <1 h/day; 140 140 (36.3%) 1 h/day; 60 973 (15.8%) 2 h/day; and 31 663 (8.2%) ≥3 h/day. Following adjustment for potential confounders, driving ≥3 h/day was associated with being overweight/obese (OR = 1.74, 95% CI: 1.64-1.85), smoking (OR = 1.48, 95% CI: 1.37-1.63), insufficient sleep (1.70, 95% CI: 1.61-1.80), low fruit/vegetable intake (OR = 1.26, 95% CI: 1.18-1.35) and low physical activity (OR = 1.05, 95% CI: 1.00-1.11), with dose relationships for the first three, but was not associated with higher alcohol consumption (OR = 0.94, 95% CI: 0.87-1.02).

Conclusions

Sedentary behaviour, such as driving, is known to have an independent association with adverse health outcomes. It may have additional impact mediated through its effect on other aspects of lifestyle. People with long driving times are at higher risk and might benefit from targeted interventions.

SUBMITTER: Mackay A

PROVIDER: S-EPMC6889853 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

The association between driving time and unhealthy lifestyles: a cross-sectional, general population study of 386 493 UK Biobank participants.

Mackay A A Mackay D F DF Celis-Morales C A CA Lyall D M DM Gray S R SR Sattar N N Gill J M R JMR Pell J P JP Anderson J J JJ

Journal of public health (Oxford, England) 20190901 3

<h4>Background</h4>Driving is a common type of sedentary behaviour; an independent risk factor for poor health. The study explores whether driving is also associated with other unhealthy lifestyle factors.<h4>Methods</h4>In a cross-sectional study of UK Biobank participants, driving time was treated as an ordinal variable and other lifestyle factors dichotomized into low/high risk based on guidelines. The associations were explored using chi-square tests for trend and binary logistic regression. ...[more]

PMID: 30239914

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Project description:ImportanceAccelerated aging makes adults more vulnerable to chronic diseases and death. Whether childhood adversity is associated with accelerated aging processes, and to what extent lifestyle mediates the association, remain unknown.ObjectiveTo examine the associations of childhood adversity with a phenotypic aging measure and the role of unhealthy lifestyle in mediating these associations.Design, setting, and participantsA retrospective cohort analysis was conducted using data from adult participants in the UK Biobank baseline survey (2006-2010) and online mental health survey (2016). Data analysis was performed from September 1, 2021, to February 28, 2022.ExposuresChildhood adversity, including physical neglect, emotional neglect, sexual abuse, physical abuse, and emotional abuse, was assessed retrospectively through the online mental health survey (2016).Main outcomes and measuresA phenotypic aging measure, phenotypic age acceleration, was calculated, with higher values indicating accelerated aging. Body mass index, smoking status, alcohol consumption, physical activity, and diet were combined to construct an unhealthy lifestyle score (range, 0-5, with higher scores denoting a more unhealthy lifestyle).ResultsA total of 127 495 participants aged 40 to 69 years (mean [SD] chronological age at baseline, 56.4 [7.7] years; 70 979 women [55.7%]; 123 987 White participants [97.2%]) were included. Each individual type of childhood adversity and cumulative childhood adversity score were associated with phenotypic age acceleration. For instance, compared with participants who did not experience childhood adversity, those who experienced 4 (β = 0.296, 95% CI, 0.130-0.462) or 5 (β = 0.833; 95% CI, 0.537-1.129) childhood adversities had higher phenotypic age acceleration in fully adjusted models. The formal mediation analysis revealed that unhealthy lifestyle partially mediated the associations of childhood adversity with phenotypic age acceleration by 11.8% to 42.1%.Conclusions and relevanceIn this retrospective cohort study, childhood adversity was significantly associated with acceleration of aging and, more importantly, unhealthy lifestyle partially mediated these associations. These findings reveal a pathway from childhood adversity to health in middle and early older adulthood through lifestyle and underscore the potential of more psychological strategies beyond lifestyle interventions to promote healthy aging.

Project description:BackgroundFrailty is associated with older age and multimorbidity (two or more long-term conditions); however, little is known about its prevalence or effects on mortality in younger populations. This paper aims to examine the association between frailty, multimorbidity, specific long-term conditions, and mortality in a middle-aged and older aged population.MethodsData were sourced from the UK Biobank. Frailty phenotype was based on five criteria (weight loss, exhaustion, grip strength, low physical activity, slow walking pace). Participants were deemed frail if they met at least three criteria, pre-frail if they fulfilled one or two criteria, and not frail if no criteria were met. Sociodemographic characteristics and long-term conditions were examined. The outcome was all-cause mortality, which was measured at a median of 7 years follow-up. Multinomial logistic regression compared sociodemographic characteristics and long-term conditions of frail or pre-frail participants with non-frail participants. Cox proportional hazards models examined associations between frailty or pre-frailty and mortality. Results were stratified by age group (37-45, 45-55, 55-65, 65-73 years) and sex, and were adjusted for multimorbidity count, socioeconomic status, body-mass index, smoking status, and alcohol use.Findings493 737 participants aged 37-73 years were included in the study, of whom 16 538 (3%) were considered frail, 185 360 (38%) pre-frail, and 291 839 (59%) not frail. Frailty was significantly associated with multimorbidity (prevalence 18% [4435/25 338] in those with four or more long-term conditions; odds ratio [OR] 27·1, 95% CI 25·3-29·1) socioeconomic deprivation, smoking, obesity, and infrequent alcohol consumption. The top five long-term conditions associated with frailty were multiple sclerosis (OR 15·3; 99·75% CI 12·8-18·2); chronic fatigue syndrome (12·9; 11·1-15·0); chronic obstructive pulmonary disease (5·6; 5·2-6·1); connective tissue disease (5·4; 5·0-5·8); and diabetes (5·0; 4·7-5·2). Pre-frailty and frailty were significantly associated with mortality for all age strata in men and women (except in women aged 37-45 years) after adjustment for confounders.InterpretationEfforts to identify, manage, and prevent frailty should include middle-aged individuals with multimorbidity, in whom frailty is significantly associated with mortality, even after adjustment for number of long-term conditions, sociodemographics, and lifestyle. Research, clinical guidelines, and health-care services must shift focus from single conditions to the requirements of increasingly complex patient populations.FundingCSO Catalyst Grant and National Health Service Research for Scotland Career Research Fellowship.

Dataset Information

The association between driving time and unhealthy lifestyles: a cross-sectional, general population study of 386 493 UK Biobank participants.

Background

Methods

Results

Conclusions

Publications

The association between driving time and unhealthy lifestyles: a cross-sectional, general population study of 386 493 UK Biobank participants.

Similar Datasets

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