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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. 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. 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.

To quantify the association between daily total sitting and all-cause mortality risk and to examine dose-response relationships with and without adjustment for moderate-to-vigorous physical activity. Studies published from 1989 to January 2013 were identified via searches of multiple databases, reference lists of systematic reviews on sitting and health, and from authors' personal literature databases. We included prospective cohort studies that had total daily sitting time as a quantitative exposure variable, all-cause mortality as the outcome and reported estimates of relative risk, or odds ratios or hazard ratios with 95% confidence intervals. Two authors independently extracted the data and summary estimates of associations were computed using random effects models. Six studies were included, involving data from 595,086 adults and 29,162 deaths over 3,565,569 person-years of follow-up. Study participants were mainly female, middle-aged or older adults from high-income countries; mean study quality score was 12/15 points. Associations between daily total sitting time and all-cause mortality were not linear. With physical activity adjustment, the spline model of best fit had dose-response HRs of 1.00 (95% CI: 0.98-1.03), 1.02 (95% CI: 0.99-1.05) and 1.05 (95% CI: 1.02-1.08) for every 1-hour increase in sitting time in intervals between 0-3, >3-7 and >7 h/day total sitting, respectively. This model estimated a 34% higher mortality risk for adults sitting 10 h/day, after taking physical activity into account. The overall weighted population attributable fraction for all-cause mortality for total daily sitting time was 5.9%, after adjusting for physical activity. Higher amounts of daily total sitting time are associated with greater risk of all-cause mortality and moderate-to-vigorous physical activity appears to attenuate the hazardous association. These findings provide a starting point for identifying a threshold on which to base clinical and public health recommendations for overall sitting time, in addition to physical activity guidelines.

Driving is a common part of modern society, but its potential effects on health are not well understood. The present cross-sectional study (n = 37,570) examined the associations of driving time with a series of health behaviors and outcomes in a large population sample of middle-aged and older adults using data from the Social, Economic, and Environmental Factor Study conducted in New South Wales, Australia, in 2010. Multiple logistic regression was used in 2013 to examine the associations of usual daily driving time with health-related behaviors (smoking, alcohol use, diet, physical activity, sedentary behavior, sleep) and outcomes (obesity, general health, quality of life, psychological distress, time stress, social functioning), adjusted for socio-demographic characteristics. Findings suggested that longer driving time was associated with higher odds for smoking, insufficient physical activity, short sleep, obesity, and worse physical and mental health. The associations consistently showed a dose-response pattern and more than 120 minutes of driving per day had the strongest and most consistent associations with the majority of outcomes. This study highlights driving as a potential lifestyle risk factor for public health. More population-level multidisciplinary research is needed to understand the mechanism of how driving affects health.

AbstractObjectivesTo identify data availability, gaps, and patterns for population level prevalence of loneliness globally, to summarise prevalence estimates within World Health Organization regions when feasible through meta-analysis, and to examine temporal trends of loneliness in countries where data exist.DesignSystematic review and meta-analysis.Data sourcesEmbase, Medline, PsycINFO, and Scopus for peer reviewed literature, and Google Scholar and Open Grey for grey literature, supplemented by backward reference searching (to 1 September 2021)Eligibility criteria for selecting studiesObservational studies based on nationally representative samples (n≥292), validated instruments, and prevalence data for 2000-19. Two researchers independently extracted data and assessed the risk of bias using the Joanna Briggs Institute checklist. Random effects meta-analysis was conducted in the subset of studies with relatively homogeneous research methods by measurement instrument, age group, and WHO region.ResultsPrevalence data were available for 113 countries or territories, according to official WHO nomenclature for regions, from 57 studies. Data were available for adolescents (12-17 years) in 77 countries or territories, young adults (18-29 years) in 30 countries, middle aged adults (30-59 years) in 32 countries, and older adults (≥60 years) in 40 countries. Data for all age groups except adolescents were lacking outside of Europe. Overall, 212 estimates for 106 countries from 24 studies were included in meta-analyses. The pooled prevalence of loneliness for adolescents ranged from 9.2% (95% confidence interval 6.8% to 12.4%) in South-East Asia to 14.4% (12.2% to 17.1%) in the Eastern Mediterranean region. For adults, meta-analysis was conducted for the European region only, and a consistent geographical pattern was shown for all adult age groups. The lowest prevalence of loneliness was consistently observed in northern European countries (2.9%, 1.8% to 4.5% for young adults; 2.7%, 2.4% to 3.0% for middle aged adults; and 5.2%, 4.2% to 6.5% for older adults) and the highest in eastern European countries (7.5%, 5.9% to 9.4% for young adults; 9.6%, 7.7% to 12.0% for middle aged adults; and 21.3%, 18.7% to 24.2% for older adults).ConclusionProblematic levels of loneliness are experienced by a substantial proportion of the population in many countries. The substantial difference in data coverage between high income countries (particularly Europe) and low and middle income countries raised an important equity issue. Evidence on the temporal trends of loneliness is insufficient. The findings of this meta-analysis are limited by data scarcity and methodological heterogeneity. Loneliness should be incorporated into general health surveillance with broader geographical and age coverage, using standardised and validated measurement tools.Systematic review registrationPROSPERO CRD42019131448.

Sedentary behaviour (SB) has been proposed as an ‘independent’ risk factor for chronic disease risk, attracting much research and media attention. Many countries have included generic, non-quantitative reductions in SB in their public health guidelines and calls for quantitative SB targets are increasing. The aim of this narrative review is to critically evaluate key evidence areas relating to the development of guidance on sitting for adults. We carried out a non-systematic narrative evidence synthesis across seven key areas: (1) definition of SB, (2) independence of sitting from physical activity, (3) use of television viewing as a proxy of sitting, (4) interpretation of SB evidence, (5) evidence on ‘sedentary breaks’, (6) evidence on objectively measured sedentary SB and mortality and (7) dose response of sitting and mortality/cardiovascular disease. Despite research progress, we still know little about the independent detrimental health effects of sitting, and the possibility that sitting is mostly the inverse of physical activity remains. Unresolved issues include an unclear definition, inconsistencies between mechanistic and epidemiological studies, over-reliance on surrogate outcomes, a very weak epidemiological evidence base to support the inclusion of ‘sedentary breaks’ in guidelines, reliance on self-reported sitting measures, and misinterpretation of data whereby methodologically inconsistent associations are claimed to be strong evidence. In conclusion, public health guidance requires a consistent evidence base but this is lacking for SB. The development of quantitative SB guidance, using an underdeveloped evidence base, is premature; any further recommendations for sedentary behaviour require development of the evidence base and refinement of the research paradigms used in the field.

Self-reported rates of participation in sport vary by country. In the UK, about 40% of men and women aged 16 years or older participate in at least one sport every week. Although few data exist to assess trends for participation in sport, there is little evidence of change in the past decade among adults. Large cohort studies suggest that such participation in sport is associated with a 20–40% reduction in all-cause mortality compared with non-participation. Randomised trials and crossover clinical studies suggest that playing sport is associated with specific health benefits. Some sports have relatively high injury risk although neuromuscular training programmes can prevent various lower extremity injuries. Clinicians can influence a large number of patients through brief interventions that promote physical activity, and encouragement toward participation in sport for some physically inactive patients qualifies as evidence-based therapy. Exercise might also be considered as a fifth vital sign and should be recorded in patients' electronic medical records and routine histories.

Background Social engagement forms the basis of social relationships by providing a sense of belonging, social identity, and fulfillment. Previous research demonstrates that social engagement was associated with positive health behaviors among older adults. However, the results have been different across health-related behaviors, and mostly based on data from high-income countries. For example, studies from the US and UK showed that social engagement was protective against smoking, while others found social engagement encouraged more smoking in many Asian cultures. In this study, we aim to examine the association between social engagement and a range of health-related behaviors and subjective well-being among older adults in six low- to middle-income countries. Methods Data from the WHO Study on Global Ageing and Adult Health (SAGE Wave 1) were used. A total of 33,338 individuals aged 50 and older in China, Russia, India, Ghana, South Africa, and Mexico were included. Social engagement, tobacco use, alcohol consumption, fruit and vegetable intake, physical activity, sedentary behavior, sleep duration, depression symptoms, self-rated health status, and quality of life were assessed using established self-reported measures. Multiple logistic regression models were used to examine the relationship between social engagement and nine outcome variables, adjusting for socio-demographic characteristics. Results Lower levels of social engagement were positively related to physical inactivity, prolonged sitting time, unhealthy sleep duration, perceived depression, poor self-rated health, and low quality of life. However, the associations between social engagement and tobacco use, excessive drinking, and insufficient fruit and vegetable intake were mixed across countries. Conclusion This international study found high social engagement as a potential health-promoting factor in some low- to middle-income countries. Although the impacts of social engagement on tobacco and alcohol use and diet were complicated and culture-specific, interventions at both individual and community levels should encourage healthy lifestyles through positive social engagement.

In the past few decades, the field of physical activity has grown and evolved in scope, depth, visibility and impact around the world. Global progress has been observed in research and practice in physical activity regarding surveillance, health outcomes, correlates/determinants, interventions, translation and policy. The 2012 and 2016 Lancet series on physical activity provide some of the most comprehensive global analysis on various topics within physical activity. Based on the Lancet series and other key developments in the field, literature searches, and expert group meetings and consultation, we provide a global summary on the progress of, gaps in and future directions for physical activity research in the following areas: (1) surveillance and trends, (2) correlates and determinants, (3) health outcomes and (4) interventions, programmes and policies. Besides lessons learnt within each specific area, several recommendations are shared across areas of research, including improvement in measurement, applying a global perspective with a growing emphasis on low-income and middle-income countries, improving inclusiveness and equity in research, making translation an integral part of research for real-world impact, taking an ‘upstream’ public health approach, and working across disciplines and sectors to co-design research and co-create solutions. We have summarised lessons learnt and recommendations for future research as ‘roadmaps’ in progress to encourage moving the field of physical activity towards achieving population-level impact globally.

To contextualise the trend in exercise, we contrasted the GRSR of ‘exercise’ with ‘television show’ as a proxy for population-level interest in television viewing, a common sedentary activity expected to increase during the lockdown. [...]population-level interest in exercise in April 2020 was at an all-time high since GT records began in January 2004. Furthermore, GT data could not distinguish whether searches emanated from habitually active individuals or from exercise novices. [...]the actual change in the population prevalence of physical activity is not yet known. During the COVID-19 lockdown, some governments halted routine data collection (eg, New South Wales Population Health Surveys).

Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Understanding why people are physically active or inactive contributes to evidence-based planning of public health interventions, because effective programmes will target factors known to cause inactivity. Research into correlates (factors associated with activity) or determinants (those with a causal relationship) has burgeoned in the past two decades, but has mostly focused on individual-level factors in high-income countries. It has shown that age, sex, health status, self-efficacy, and motivation are associated with physical activity. Ecological models take a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails. New areas of determinants research have identified genetic factors contributing to the propensity to be physically active, and evolutionary factors and obesity that might predispose to inactivity, and have explored the longitudinal tracking of physical activity throughout life. An understanding of correlates and determinants, especially in countries of low and middle income, could reduce the effect of future epidemics of inactivity and contribute to effective global prevention of non-communicable diseases.