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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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:BACKGROUND:Frailty 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. METHODS:Data 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. FINDINGS:493 737 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. INTERPRETATION:Efforts 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. FUNDING:CSO Catalyst Grant and National Health Service Research for Scotland Career Research Fellowship.

Project description:Handgrip strength may provide an easily-administered marker of cognitive functional status. However, further population-scale research examining relationships between grip strength and cognitive performance across multiple domains is needed. Additionally, relationships between grip strength and cognitive functioning in people with schizophrenia, who frequently experience cognitive deficits, has yet to be explored.Baseline data from the UK Biobank (2007-2010) was analyzed; including 475397 individuals from the general population, and 1162 individuals with schizophrenia. Linear mixed models and generalized linear mixed models were used to assess the relationship between grip strength and 5 cognitive domains (visual memory, reaction time, reasoning, prospective memory, and number memory), controlling for age, gender, bodyweight, education, and geographical region.In the general population, maximal grip strength was positively and significantly related to visual memory (coefficient [coeff] = -0.1601, standard error [SE] = 0.003), reaction time (coeff = -0.0346, SE = 0.0004), reasoning (coeff = 0.2304, SE = 0.0079), number memory (coeff = 0.1616, SE = 0.0092), and prospective memory (coeff = 0.3486, SE = 0.0092: all P < .001). In the schizophrenia sample, grip strength was strongly related to visual memory (coeff = -0.155, SE = 0.042, P < .001) and reaction time (coeff = -0.049, SE = 0.009, P < .001), while prospective memory approached statistical significance (coeff = 0.233, SE = 0.132, P = .078), and no statistically significant association was found with number memory and reasoning (P > .1).Grip strength is significantly associated with cognitive functioning in the general population and individuals with schizophrenia, particularly for working memory and processing speed. Future research should establish directionality, examine if grip strength also predicts functional and physical health outcomes in schizophrenia, and determine whether interventions which improve muscular strength impact on cognitive and real-world functioning.

Dataset Information

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

Publications

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

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