Multiple Lifestyle Risk Behaviours Research Articles

Longitudinal patterns of lifestyle risk behaviours among UK adults with established cardiovascular disease: a latent transition analysis.

People with cardiovascular disease (CVD) need to engage in healthy lifestyle behaviours. However, there is a gap in identifying longitudinal patterns of change in lifestyle behaviours among people with CVD. This study aimed to identify clustering of lifestyle risk behaviours and their 4 ± year changes among UK adults with CVD, and to determine the associated factors. We used the UK Biobank data collected at two time points (2006-2010/baseline data = T0 and 2014+/third visit data = T4). Six key lifestyle risk behaviours were assessed: smoking, high alcohol intake, poor fruit and vegetable consumption, physical inactivity, poor sleep balance (<7 or >8 h/night) and prolonged sitting. A random intercept latent transition analysis was performed to identify patterns of lifestyle risk behaviours at T0 and their changes from T0 to T4. We included 5,304 participants with CVD whose data on lifestyle risk behaviours were collected at two-time points. Alcohol intake and current smoking were 75.7% and 5.4% at baseline, respectively, and 67.4% and 3.0% at follow-up. Three latent classes emerged: Latent class (LC) 1-"high alcohol intake, poor sleep balance and poor fruit and vegetable intake", LC2-"high alcohol intake and poor fruit and vegetable intake", and LC3-"high alcohol intake". Most adults remained in the same LC over the 4 + years (range: 83.9%-100.0%). After 4 + years, 3.5% from LC3 and 10.4% from LC2 at baseline moved into LC1. The odds of transitioning to LC2 relative to staying in LC1 and LC3 were 2.22 and 4.13 times higher for males than for females, respectively. A single-year increase in participants' age was associated with a 1.16 times increase in the odds of moving to LC1 relative to staying in LC2. People with CVD did not show improvement in lifestyle risk behaviours, and interventions targeting multiple lifestyle risk behaviours are needed to improve CVD.

Health4Life eHealth intervention to modify multiple lifestyle risk behaviours among adolescent students in Australia: a cluster-randomised controlled trial

Lifestyle risk behaviours are prevalent among adolescents and commonly co-occur, but current intervention approaches tend to focus on single risk behaviours. This study aimed to evaluate the efficacy of the eHealth intervention Health4Life in modifying six key lifestyle risk behaviours (ie, alcohol use, tobacco smoking, recreational screen time, physical inactivity, poor diet, and poor sleep, known as the Big 6) among adolescents. We conducted a cluster-randomised controlled trial in secondary schools that had a minimum of 30 year 7 students, in three Australian states. A biostatistician randomly allocated schools (1:1) to Health4Life (a six-module, web-based programme and accompanying smartphone app) or an active control group (usual health education) with the Blockrand function in R, stratified by site and school gender composition. All students aged 11-13 years who were fluent in English and attended participating schools were eligible. Teachers, students, and researchers were not masked to allocation. Primary outcomes were alcohol use, tobacco use, recreational screen time, moderate to vigorous physical activity (MVPA), sugar-sweetened beverage intake, and sleep duration at 24 months, measured by self-report surveys, and analysed in all students who were eligible at baseline. Latent growth models estimated between-group change over time. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000431123). Between April 1, 2019, and Sept 27, 2019, we recruited 85 schools (9280 students), of which 71 schools with 6640 eligible students (36 schools [3610 students] assigned to the intervention and 35 [3030 students] to the control) completed the baseline survey. 14 schools were excluded from the final analysis or withdrew, mostly due to a lack of time. We found no between-group differences for alcohol use (odds ratio 1·24, 95% CI 0·58-2·64), smoking (1·68, 0·76-3·72), screen time (0·79, 0·59-1·06), MVPA (0·82, 0·62-1·09), sugar-sweetened beverage intake (1·02, 0·82-1·26), or sleep (0·91, 0·72-1·14) at 24 months. No adverse events were reported during this trial. Health4Life was not effective in modifying risk behaviours. Our results provide new knowledge about eHealth multiple health behaviour change interventions. However, further research is needed to improve efficacy. Paul Ramsay Foundation, the Australian National Health and Medical Research Council, the Australian Government Department of Health and Aged Care, and the US National Institutes of Health.

Sixteen-year trends in multiple lifestyle risk behaviours by socioeconomic status from 2004 to 2019 in New South Wales, Australia.

Few studies have examined trends in inequalities related to lifestyle risk behaviours. This study examined 1) 16-year (2004-2019) trends of individual lifestyle risk factors and a combined lifestyle risk index and 2) trends in socioeconomic inequalities in these risk factors, in New South Wales (NSW; Australia) adults. Data was sourced from the NSW Adult Population Health Survey, an annual telephone survey of NSW residents aged ≥16 years, totalling 191,905 completed surveys. Excessive alcohol consumption, current smoking, insufficient physical activity, insufficient fruit and/or vegetable consumption, sugar-sweetened beverage [SSB] consumption, and a combined lifestyle risk index (overall high-risk lifestyle defined as total number of lifestyle risk behaviours ≥2) were examined. Socioeconomic status was assessed using education attainment, postal area-level disadvantage measured by Index of Relative Socioeconomic Disadvantage (IRSD), and remoteness based on Accessibility-Remoteness Index of Australia Plus (ARIA+). Socioeconomic inequalities were examined as prevalence difference for absolute inequalities and prevalence ratio for relative inequalities. The prevalence of lifestyle behaviours by levels of each socioeconomic status variable were estimated using predicted probabilities from logistic regression models. After adjusting for covariates, there was a decrease in prevalence over time for most lifestyle risk behaviours. Between 2004 and 2019, the prevalence decreased for current smoking from 21.8% to 17.1%, insufficient physical activity from 39.1% to 30.9%, excessive alcohol consumption from 15.4% to 13.7%, daily SSB consumption from 29.9% to 21.2%, and overall high-risk lifestyle from 50.4% to 43.7%. Socioeconomic inequalities, based on one or more of the socioeconomic variables, increased over time for current smoking, insufficient physical activity, daily SSB consumption, and an overall high-risk lifestyle. Overall, the health behaviours of the NSW population improved between 2004 and 2019. However, some socioeconomic inequalities increased during this time, highlighting the need for effective public health strategies that seek to improve health behaviours among the most socioeconomically disadvantaged.

Multiple lifestyle risk behaviours and hierarchical dimensions of psychopathology in 6640 Australian adolescents.

Physical inactivity, sugar sweetened beverage consumption, alcohol use, smoking, poor sleep and excessive recreational screen time (the 'Big 6' lifestyle risk behaviours) often co-occur and are key risk factors for psychopathology. However, the best fitting latent structure of the Big 6 is unknown and links between multiple lifestyle risk behaviours and hierarchical dimensions of psychopathology have not been explored among adolescents. This study aimed to address these gaps in the literature. Confirmatory factor analysis, latent class analysis and factor mixture models were conducted among 6640 students (Mage = 12.7 years) to identify the latent structure of the Big 6 lifestyle risk behaviours. Structural equation models were then used to examine associations with psychopathology. A mixture model with three classes, capturing mean differences in a single latent factor indexing overall risk behaviours, emerged as the best fitting model. This included relatively low-risk (Class 1: 30%), moderate-risk (Class 2: 67%) and high-risk (Class 3: 3%) classes. Students high on externalizing demonstrated significantly greater odds of membership to the high-risk class (odds ratio = 8.75, 99% confidence interval = [3.30, 23.26]) and moderate-risk class (odds ratio = 2.93, 99% confidence interval = [1.43, 5.97]) in comparison to the low-risk class. Similarly, students high on internalizing demonstrated significantly higher odds of membership to the high-risk class (odds ratio = 1.89, 99% confidence interval = [1.06, 3.37]) and the moderate-risk class (odds ratio = 1.66, 99% confidence interval = [1.03, 2.67]) in comparison to the low-risk class. Associations between lower order factors of psychopathology and lifestyle risk behaviours were mostly accounted for by the more parsimonious higher order factors. Classes representing differences in probabilities of the Big 6 lifestyle risk behaviours relate to varying levels of hierarchical dimensions of psychopathology, suggesting multiple health behaviour change and transdiagnostic intervention approaches may be valuable for reducing risk of psychopathology.

195The Health4Life Initiative: An eHealth intervention targeting multiple lifestyle risk behaviours among Australian adolescents

Abstract Background Physical inactivity, poor diet, sedentary recreational screen time, poor sleep, alcohol use and smoking (the “Big 6”) are key lifestyle risk factors for chronic disease. The Big 6 typically emerge during adolescence, co-occur and continue into adulthood. To improve short- and long-term health, early and effective prevention is critical. Methods A cluster randomised controlled trial (ACTRN12619000431123) is being conducted in 71 schools across NSW, QLD and WA. Schools are randomised to the Health4life intervention or health education as usual. The intervention has three components: 1) An online, school-based program delivered to all Year 7 students; 2) An accompanying smartphone application (all students); 3) Booster content, delivered to at-risk students in Years 8 and 9. Students complete questionnaires on five occasions over three years. Results Preliminary analyses including 6716 students (49% female) indicated that 22% of participants met national guidelines for physical activity, 27% reported drinking 2+ cups of sugar-sweetened beverages/week, 3% had consumed a standard alcoholic drink in the prior 6 months, 2% had tried smoking, 86% engaged in excessive recreational screen time, and 32% had inadequate sleep. The final dataset will be analysed for the conference. Conclusions Many Australian adolescents are engaging in lifestyle behaviours that put them at risk of chronic disease. Effective and scalable preventive interventions that simultaneously target these behaviours are needed. Key messages This is the first trial of an eHealth intervention to concurrently target the Big 6 among adolescents. Health4Life has the potential to improve physical and mental health in adolescence and beyond.

A longitudinal study of multiple lifestyle health risk behaviours among nursing students and non-nursing peers.

The aim of this paper is to compare the evolution of health risk behaviours between undergraduate nursing and social work students. Nursing includes the promotion of health and the shaping of healthy behaviours. An important determinant for providing lifestyle advice is the lifestyle of nurses themselves. Longitudinal comparative study. We compared lifestyle risk behaviours (binge drinking, cannabis/hashish/marijuana use, smoking, oral hygiene/toothbrushing, breakfast/fruit/vegetable consumption, physical activity and screen time/sedentary behaviours) using a self-administered standardized questionnaire in nursing (n = 121) and social work (n = 140) students at the beginning (2012) and the end of their studies (2015). Adjusted multivariable logistic/Poisson regression models were performed. There were no statistically significant differences between the departments in most risk factors in both assessments. However, in relation to their first year, both nursing and social work students displayed higher relative risk of engaging in more behavioural risk factors at the end of their studies (in delivery/junk food consumption, sunburns, hashish/marijuana use and multiple sexual partners). Social work students displayed better behaviours in physical activity and breakfast intake. Nursing students share the patterns of their nonnursing peers in behavioural risk factors compromising their future health and health-promoting role. We need strategies to safeguard the professional nursing practice.

Study protocol of the Health4Life initiative: a cluster randomised controlled trial of an eHealth school-based program targeting multiple lifestyle risk behaviours among young Australians

IntroductionLifestyle risk behaviours, including alcohol use, smoking, poor diet, physical inactivity, poor sleep (duration and/or quality) and sedentary recreational screen time (‘the Big 6’), are strong determinants of chronic disease....

Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis

Lifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours. In this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163. Of 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56·2% were female, with a mean age of 13·41 years (SD 1·52). eHealth school-based multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0·11, 95% CI 0·03 to 0·19; p=0·007) and both accelerometer-measured (0·33, 0·05 to 0·61; p=0·02) and self-reported (0·14, 0·05 to 0·23; p=0·003) physical activity, and reduced screen time (-0·09, -0·17 to -0·01; p=0·03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low. eHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects. Paul Ramsay Foundation and Australian National Health and Medical Research Council.

The impact of physical activity and an additional behavioural risk factor on cardiovascular disease, cancer and all-cause mortality: a systematic review

BackgroundRegular physical activity improves overall health, and has the capacity to reduce risk of chronic diseases and death. However, better understanding of the relationship between multiple lifestyle risk behaviours and disease outcomes is pertinent for prioritising public health messaging. The aim of this systematic review is to examine the association between physical inactivity in combination with additional lifestyle risk behaviours (smoking, alcohol, diet, or sedentary behaviour) for cardiovascular disease, cancer, and all-cause mortality.MethodsWe searched Ovid Medline, EMBASE, and the Cochrane Register from 1 January 2010 to 12 December 2017, for longitudinal observational studies of adults (18+ years) in the general population with a publication date of 2010 onwards and no language restriction. Main exposure variables had to include a physical activity measure plus at least one other lifestyle risk factor. In total, 25,639 studies were identified. Titles, abstracts and full-text articles of potentially relevant papers were screened for eligibility. Data was extracted and quality assessment was completed using a modified Newcastle-Ottawa Scale (NOS).ResultsAcross the 25 eligible studies, those participants who reported being physically active combined with achieving other health behaviour goals compared to those who were categorised as physically inactive and did not achieve other positive lifestyle goals, were at least half as likely to experience an incident cardiovascular disease (CVD) event, die from CVD, or die from any cause. These findings were consistent across participant age, sex, and study length of follow-up, and even after excluding lower quality studies. We also observed a similar trend among the few studies which were restricted to cancer outcomes. Most studies did not consider epidemiological challenges that may bias findings, such as residual confounding, reverse causality by pre-existing disease, and measurement error from self-report data.ConclusionsHigh levels of physical activity in combination with other positive lifestyle choices is associated with better health outcomes. Applying new approaches to studying the complex relationships between multiple behavioural risk factors, including physical activity, should be a priority.

Multiple lifestyle risk behaviours and excess weight among adolescents in Barcelona, Spain

ObjectiveWe aimed to analyse the prevalence of having multiple lifestyle risk behaviours (LRB) and the potential relationship between excess weight (including overweight and obesity) and cumulative multiple LRB among adolescents in Barcelona, Spain. MethodsA cross-sectional study was performed among a representative sample of 3,114 secondary school students in Barcelona. Height and weight were objectively measured and excess weight was defined in accordance with World Health Organization criteria. Information on screen time, breakfast, physical activity and sleep duration was obtained through a self-administered questionnaire. ResultsMore than 80% of the students had at least two LRBs. In compulsory schooling, the adjusted prevalence ratio (aPR) of excess weight increased with a higher number of reported LRBs (four LRBs: aPR=1.56; 95% confidence interval: 1.19-2.05). ConclusionsThese results highlight the importance of a multiple-behaviour approach in preventive programmes aimed at reducing adolescent obesity.

A Widening Gap? Changes in Multiple Lifestyle Risk Behaviours by Socioeconomic Status in New South Wales, Australia, 2002-2012.

BackgroundSocioeconomic inequalities in health outcomes have increased over the past few decades in some countries. However, the trends in inequalities related to multiple health risk behaviours have been infrequently reported. In this study, we examined the trends in individual health risk behaviours and a summary lifestyle risk index in New South Wales, Australia, and whether the absolute and relative inequalities in risk behaviours by socioeconomic positions have changed over time.MethodsUsing data from the annual New South Wales Adult Population Health Survey during the period of 2002–2012, we examined four individual risk behaviours (smoking, higher than recommended alcohol consumption, insufficient fruit and vegetable intake, and insufficient physical activity) and a combined lifestyle risk indicator. Socioeconomic inequalities were assessed based on educational attainment and postal area-level index of relative socio-economic disadvantage (IRSD), and were presented as prevalence difference for absolute inequalities and prevalence ratio for relative inequalities. Trend tests and survey logistic regression models examined whether the degree of absolute and relative inequalities between the most and least disadvantaged subgroups have changed over time.ResultsThe prevalence of all individual risk behaviours and the summary lifestyle risk indicator declined from 2002 to 2012. Particularly, the prevalence of physical inactivity and smoking decreased from 52.6% and 22% in 2002 to 43.8% and 17.1% in 2012 (p for trend<0.001). However, a significant trend was observed for increasing absolute and relative inequalities in smoking, insufficient fruit and vegetable consumption, and the summary lifestyle risk indicator.ConclusionsThe overall improvement in health behaviours in New South Wales, Australia, co-occurred with a widening socioeconomic gap.ImplicationsGovernments should address health inequalities through risk factor surveillance and combined strategies of population-wide and targeted interventions.

Characteristics of interventions targeting multiple lifestyle risk behaviours in adult populations: a systematic scoping review.

BackgroundModifiable lifestyle risk behaviours such as smoking, unhealthy diet, physical inactivity and alcohol misuse are the leading causes of major, non-communicable diseases worldwide. It is increasingly being recognised that interventions which target more than one risk behaviour may be an effective and efficient way of improving people’s lifestyles. To date, there has been no attempt to summarise the global evidence base for interventions targeting multiple risk behaviours.ObjectiveTo identify and map the characteristics of studies evaluating multiple risk behaviour change interventions targeted at adult populations in any country.MethodsSeven bibliographic databases were searched between January, 1990, and January/ May, 2013. Authors of protocols, conference abstracts, and other relevant articles were contacted. Study characteristics were extracted and inputted into Eppi-Reviewer 4.ResultsIn total, 220 studies were included in the scoping review. Most were randomised controlled trials (62%) conducted in the United States (49%), and targeted diet and physical activity (56%) in people from general populations (14%) or subgroups of general populations (45%). Very few studies had been conducted in the Middle East (2%), Africa (0.5%), or South America (0.5%). There was also a scarcity of studies conducted among young adults (1%), or racial and minority ethnic populations (4%) worldwide.ConclusionsResearch is required to investigate the interrelationships of lifestyle risk behaviours in varying cultural contexts around the world. Cross-cultural development and evaluation of multiple risk behaviour change interventions is also needed, particularly in populations of young adults and racial and minority ethnic populations.

Clustering of unhealthy behaviours over time: An analysis of health and wellbeing surveys in Southeast England, 2003 and 2012

Background Most chronic diseases are strongly associated with four modifiable behaviours: smoking, alcohol consumption, low fruit and vegetable consumption and physical inactivity. Most of the research is directed into understanding how a single behaviour affects health; and relatively little is known about how these behaviours cluster together in the population, and how the prevalence of multiple lifestyle risk behaviours have changed over time. The objective of this study was to examine the clustering …