Number Of Lifestyle Risk Factors Research Articles

The associations of family atmosphere, religiosity and lifestyle with self-esteem and self-control among Saudi adolescents.

We assessed, with validated instruments, whether family atmosphere, religiosity or lifestyle were significant correlates of self-esteem and/or self-control among adolescents (Grades 7-12, n = 2067) in Saudi Arabia. Participants' mean age was 15.5 years; 64% were boys. Higher scores in family atmosphere and religiosity and having fewer lifestyle risk factors were significantly related to higher self-esteem and self-control scores (p < .05; adjusted linear regression models). The odds of scoring low (below median) in both self-esteem and self-control decreased incrementally across the increasing quartiles of family atmosphere and religiosity; the odds decreased incrementally across decreasing number of lifestyle risk factors (p < .05; adjusted multinomial regression). Programmes supporting healthy lifestyles, positive family environments and religiosity may boost self-esteem and self-control among adolescents.

Association between lifestyle factors and the risk of metabolic syndrome in the South Korea

This study aimed to examine the association between lifestyle factors and metabolic syndrome risk in South Korean adults. Korea National Health and Nutrition Examination Survey 2016–2018 data were used. The study included 6,995 subjects (2835 male; 4,160 female). Multiple logistic regression was used to estimate the relationship between the lifestyle factors, including sedentary time, sleep duration, alcohol consumption, smoking status, and dietary intake. Metabolic syndrome prevalence in healthy adults was 25.6% and 12.4% in men and women, respectively. Male with over four lifestyle risk factors had a higher OR for metabolic syndrome risk (over four lifestyle factors: OR 1.97, CI 1.18–3.27). Female with more than one lifestyle risk factor had a higher OR for metabolic syndrome risk (one lifestyle factor: OR 1.58, CI 1.10–2.28; two lifestyle factors: OR 2.08, CI 1.39–3.11; three lifestyle factors: OR 1.94, CI 1.20–3.13). In particular, female with more lifestyle factors had increased likelihood of abdominal obesity, hypertension, and high triglycerides. Male with more lifestyle factors had increased likelihood of high triglycerides. Sedentary time was significantly associated with increased metabolic syndrome in male and female. This study found a significant association between the number of lifestyle risk factors and the risk of metabolic syndrome in Korean adults. The greater the number of lifestyle risk factors, the higher the risk of metabolic syndrome in both sexes. People with a greater number of poor lifestyle behaviors tended to exhibit increased likelihood of especially elevated triglyceride levels.

The prevalence of five lifestyle risk factors in primary care physicians: A cross-sectional study in Switzerland

Having a healthy lifestyle is important not only for the health of physicians, but also for the realisation and effectiveness of counselling on patients. Information on lifestyle habits and the presence of health-related behaviours in primary care physicians (PCPs) is lacking.Using a cross-sectional study design, an anonymous questionnaire was sent to a random sample of 1′000 PCPs practicing in the seven Western cantons of Switzerland. In our sample, we assessed the presence of five lifestyle risk factors, namely current smoking, at risk alcohol consumption, insufficient physical activity, being overweight and insufficient hours of sleep.510 physicians participated in our study (51% participation rate). Respondents were 51% women, with a majority of general practitioners (67%), followed by paediatricians (19%) and gynaecologists (14%). 57% of PCPs had no or one lifestyle risk factor, 40% had two or three and 3% had four or all five. The average number of lifestyle risk factors was 1.39. Insufficient physical activity was the most prevalent lifestyle risk factor (40%), followed by excess weight and insufficient hours of sleep (32%), at risk drinking (25%) and current smoking (9%). Having ≥2 lifestyle risk factors was associated to being a man, working in a solo practice and for ≥7 half-days per week.Overall, a majority of Swiss PCPs have no or one lifestyle risk factor, but certain unfavourable health-related behaviours are present, notably insufficient physical activity. Developing strategies and courses to improve physicians’ lifestyles should be proposed early on in the medical curriculum.

Joint Associations of Multiple Lifestyle Factors With Risk of Active Tuberculosis in the Population: The Singapore Chinese Health Study.

Little is known about the joint associations of multiple lifestyle risk factors including smoking, low body mass index, physical inactivity, alcohol consumption, and low diet quality with risk of active tuberculosis. We analyzed data from the Singapore Chinese Health Study, a prospective cohort study of 63257 Chinese adults aged 45-74 years enrolled between 1993 and 1998. Incident cases of active tuberculosis were identified via linkage with the National TB Registry through 31 December 2016. Cox proportional hazards regression models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) of tuberculosis risk in relation to the combined scores of lifestyle risk factors. Compared with participants with none of the risk factors, the adjusted HRs (95% CI) of active tuberculosis for participants with 1, 2, 3, 4, and 5 risk factors were 1.24 (1.02-1.51), 1.84 (1.51-2.23), 2.52 (2.03-3.14), 4.07 (3.07-5.41), and 9.04 (5.44-15.02), respectively (Ptrend<.0001). The HR for those with 5 factors was ~1.5 times the product of individual risk estimates from the 5 factors on a multiplicative scale. The stepwise increase in risk of active tuberculosis with increasing number of lifestyle risk factors was significantly stronger in participants with diabetes than their counterparts without diabetes at recruitment (Pinteraction=.01). Multiple lifestyle risk factors were associated with risk of active tuberculosis in a synergistic manner. Our findings highlight the importance of public health programs and interventions targeting these factors simultaneously to reduce the tuberculosis burden among the general population.

Is lifestyle change around retirement associated with better physical performance in older age?: insights from a longitudinal cohort

A growing evidence base links individual lifestyle factors to physical performance in older age, but much less is known about their combined effects, or the impact of lifestyle change. In a group of 937 participants from the MRC National Survey of Health and Development, we examined their number of lifestyle risk factors at 53 and 60–64 years in relation to their physical performance at 60–64, and the change in number of risk factors between these ages in relation to change in physical performance. At both assessments, information about lifestyle (physical activity, smoking, diet) was obtained via self-reports and height and weight were measured. Each participant’s number of lifestyle risk factors out of: obesity (body mass index ≥ 30 kg/m2); inactivity (no leisure time physical activity over previous month); current smoking; poor diet (diet quality score in bottom quarter of distribution) was determined at both ages. Physical performance: measured grip strength, chair rise and standing balance times at both ages and conditional change (independent of baseline) in physical performance outcomes from 53 to 60–64 were assessed. There were some changes in the pattern of lifestyle risk factors between assessments: 227 (24%) participants had fewer risk factors by age 60–64; 249 (27%) had more. Reductions in risk factors were associated with better physical performance at 60–64 and smaller declines over time (all p < 0.05); these associations were robust to adjustment. Strategies to support reduction in number of lifestyle risk factors around typical retirement age may have beneficial effects on physical performance in early older age.

Discussions About Lifestyle Risk Factors Following a Cancer Diagnosis: Findings from a Sample of Australian Cancer Outpatients.

Individuals with a previous cancer diagnosis are at risk of cancer recurrence. However, many cancer survivors do not adhere to lifestyle recommendations to reduce cancer risk. Little is known about the extent to which cancer patients are asked about lifestyle risk factors by healthcare providers following diagnosis. The aim of this study is to determine among Australian cancer survivors the (1) proportion asked about smoking, alcohol consumption, nutrition and physical activity; (2) total number of lifestyle risk factors asked about; and (3) factors associated with being asked about fewer risk factors. A cross-sectional survey was conducted with cancer patients attending outpatient clinics. Eligible patients completed a baseline survey and a second survey 4weeks later. Data about demographic and disease characteristics, and whether participants had been asked about smoking, alcohol, physical activity and diet since being diagnosed with cancer, was collected. A total of 144 patients were included in the analyses. Following diagnosis, most had been asked about smoking (86%), alcohol consumption (85%), physical activity (80%) and diet (69%) by a healthcare provider. Sixty-one percent of participants reported being asked about all four risk factors; only 6% recalled being asked about none. After controlling for age, participants with a high school or lower education were more likely to be asked about fewer risk factors (OR 2.16; 95%CI 1.0 to 4.6; p = 0.04) compared with those with a trade, vocational or university-level education. Just over one-third of a sample of Australian cancer patients were not asked about all assessed lifestyle risk factors following their diagnosis of cancer. These findings suggest there is scope to improve identification of lifestyle risk factors among cancer survivors.

Clustering of four major lifestyle risk factors among Korean adults with metabolic syndrome

The purpose of this study was to investigate the clustering pattern of four major lifestyle risk factors—smoking, heavy drinking, poor diet, and physical inactivity—among people with metabolic syndrome in South Korea. There were 2,469 adults with metabolic syndrome aged 30 years or older available with the 5th Korean National Health and Nutrition Examination Survey dataset. We calculated the ratio of the observed to expected (O/E) prevalence for the 16 different combinations and the prevalence odds ratios (POR) of four lifestyle risk factors. The four lifestyle risk factors tended to cluster in specific multiple combinations. Smoking and heavy drinking was clustered (POR: 1.86 for male, 4.46 for female), heavy drinking and poor diet were clustered (POR: 1.38 for male, 1.74 for female), and smoking and physical inactivity were also clustered (POR: 1.48 for male). Those who were male, younger, low-educated and living alone were much more likely to have a higher number of lifestyle risk factors. Some helpful implications can be drawn from the knowledge on clustering pattern of lifestyle risk factors for more effective intervention program targeting metabolic syndrome.

Retirement-A Transition to a Healthier Lifestyle?: Evidence From a Large Australian Study.

Population aging is associated with a rising burden of non-communicable disease, profoundly impacting health policy and practice. Adopting and adhering to healthy lifestyles in middle or older age can protect against morbidity and mortality. Retirement brings opportunities to reconfigure habitual lifestyles and establish new routines. This study examines the longitudinal association between retirement and a range of lifestyle risk behaviors among a large population-based sample of Australian adults. Study sample included working adults aged ≥45 years at baseline (2006-2009, N=23,478-26,895). Lifestyle behaviors, including smoking, alcohol use, physical activity, diet, sedentary behavior, and sleep, were measured at both baseline and follow-up (2010). Logistic regression models estimated the odds of having each risk factor at follow-up and multiple linear regression models calculated the change in the total number of risk factors, adjusted for baseline risk and other covariates. Sociodemographic characteristics and reasons for retirement were tested as potential effect modifiers. During the 3.3-year follow-up, about 11% of respondents retired. Retirement was associated significantly with reduced odds of smoking (AOR=0.74); physical inactivity (AOR=0.73); excessive sitting (AOR=0.34); and at-risk sleep patterns (AOR=0.82). There was no significant association between retirement and alcohol use or fruit and vegetable consumption. Change in the total number of lifestyle risk factors differed significantly by reason for retirement. In a large population-based Australian cohort, retirement was associated with positive lifestyle changes. Health professionals and policymakers should consider developing special programs for retirees to capitalize on the healthy transitions through retirement.

Concurrent lifestyle risk factors: Clusters and determinants in an Australian sample

ObjectiveTo examine clustering among three major lifestyle risk factors for chronic disease (smoking, alcohol, and physical inactivity) and define sociodemographic subgroups with elevated risks of multiple lifestyle risk factors. MethodData on 6052 adults aged 28–32, 48–52, and 68–72 from wave 3 (2007–2010) of the PATH Through Life Cohort Study, Australia, were used to estimate prevalence of individual and combinations of risk factors, and multinomial regression analysis undertaken to examine demographic factors associated with number of risks. ResultsClustering of risks varied by age and gender, with more people than expected having none or all of the risk factors. Smoking clustered with harmful alcohol use, as well as physical inactivity. No relationship was observed between physical inactivity and alcohol use. Several sociodemographic characteristics were associated with the number of lifestyle risk factors including partner status, gender, age, education, and physical and mental health related quality of life. ConclusionsThe tendency for lifestyle risk factors to aggregate in different subgroups has meaningful implications for health promotion strategies. Better insight in the more vulnerable subpopulations that are at higher risk of displaying multiple lifestyle risk factors is of importance if we wish to reduce the population propensity for chronic disease.

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Coronary heart disease (CHD) development is associated to a combination of lifestyle and genetic factors. A number of lifestyle risk factors is well defined, while genetic factors have not yet been well determined.

TCF7L2 type 2 diabetes risk variant, lifestyle factors, and incidence of prostate cancer.

Variation in transcription factor 7-like 2 (TCF7L2), the strongest genetic risk factor for type 2 diabetes (T2D), may play a role in prostate cancer (PCa) depending on lifestyle factors. The aims of this study were to determine if TCF7L2 rs7903146 is associated with risk of PCa and if the association is modified by lifestyle factors independently of T2D status. We prospectively followed 8,558 men in the Malmö Diet and Cancer Study from baseline 1991-1996 until end of 2009. Cox regression models were used to assess the association between rs7903146 T2D-risk allele (T) and PCa. Effect modification by incident T2D status, fasting glucose levels, dietary, and lifestyle risk factors were tested. During follow-up 855 incident PCa cases were registered. We observed a non-significant tendency for the TCF7L2 variant to associate with higher risk of PCa, which was unaffected by adjustment for incident T2D (HR = 1.24; 95% CI: 0.96, 1.60; P = 0.079) but more pronounced among subjects who developed T2D (HR = 1.91, 95% CI: 0.88, 4.14; P = 0.064). In a sub-sample of hyperglycemic men we observed an increased risk of PCa among T-allele carriers (HR = 2.72, 95% CI: 1.22, 6.04; P = 0.014; P(interaction) = 0.056). T-allele carriers with higher number of lifestyle risk factors had an increased risk of PCa (P(interaction) = 0.006). We found no independent association between TCF7L2 rs7903146 and PCa risk. However, among hyperglycemic men we observed that the risk allele may increase risk of PCa. The association between rs7903146 and PCa risk may also be modified by lifestyle factors.

Commentary

Mika Kivimaki initiated the Individual Participant Data (IPD) Meta-Analysis Consortium, which currently has 50 members. The Consortium recently published several research reports on the relationship between job strain (high psychological demands and low decision latitude at work), on the one hand, and cardiovascular disease and its risk factors, on the other hand. Since IPD repre- sents a novel way to conduct epidemiological research collaboration and as some of the findings from the IPD Consortium have been criticized, this commentary aims to address the rationales behind the approach and discuss some of the main criticisms of the Consortium.Researchers must tackle many problems when inter- preting associations between a psychosocial work envi- ronment factor and a health outcome. First of all, work environment factors belong to a "distal" rather than a "proximal" group. In other words, the closer one gets to a biological mechanism relevant for disease development, the more likely it is that a relevant association will be strong. For instance, small samples are needed to establish an individual "brain" factor associated with depression or emotional exhaustion - simply because the brain factor is more or less depression. Factors related to work orga- nization, on the other hand, are more "distal" since there are many factors that influence the relationship between the environment and the body's organs. Accordingly, it is sometimes difficult to obtain sufficient statistical power for the establishment of an undisputable association. For instance, the long "distance" between job strain and the outcome, myocardial infarction (MI), explains why we should expect a weaker association than in the study of "proximal" factors, for instance myocardial metabolism in relation to MI. Nevertheless, on a societal level, job strain is very important since it affects many working people, with a prevalence in the working population (in the IPD Consortium study's operational definition) of around 15%. Accordingly, if an unequivocal association is established, it is of major importance to those respon- sible for work organization and interventions designed to improve working conditions. However, large samples are needed to establish unequivocal proof.Since Karasek introduced his demand-control model (1), there have been many studies of the association between job strain and risk of MI. These studies have become increasingly sophisticated over the years. In addi- tion, there is accumulated indirect evidence from longitu- dinal studies of the relationship between job strain, on the one hand, and blood pressure variations and endocrine, metabolic, and immunological parameters, on the other hand. The results from these studies give us a plausible physiological explanation of the assumed relationship between job strain and MI risk.A reason why this research field has attracted strong attention is that MI is an undisputable illness outcome. The study of MI risk, therefore, serves as a good scientific model for studying the relationship between job strain and adverse health outcomes in general.Establishment of and rationale behind the IPD ConsortiumThere have been divided opinions about the relationship between job strain and risk of MI. The main reason for the controversy has been that, despite the relatively large size of several of the published cohort studies with number of observation years often in the range of 50 000, the statistical power has been too small for an unequivocal establishment of an association. As a result, Mika Kivimaki invited a number researchers, who had included psychosocial job factors in their study protocols and had or had not published results on the relationship between job strain and MI risk, to establish the IPD Consortium. Including unpublished cohorts was important as this provided a possibility to address the problem of publication bias - the tendency of research- ers and journals to publish only positive findings that can lead to inflated associations. …

Clustering of Lifestyle Risk Factors and Poor Physical Function in Older Adults: The Hertfordshire Cohort Study

To examine the relationship between number of lifestyle risk factors (out of low physical activity, poor diet, obesity, smoking) and physical function in older community-dwelling men and women. Cross-sectional study, Hertfordshire, United Kingdom. Men (n = 1,682) and women (n = 1,540) aged 59 to 73. Physical activity was assessed using an administered questionnaire with a score from 0 to 100; low activity was defined as a score of 50 or less. Diet was assessed using a food frequency questionnaire; diet quality was assessed according to a score for a principal component analysis-defined "healthy" dietary pattern. Poor diet was categorized as a dietary pattern score in the lowest quarter of the distribution. Obesity was defined as a body mass index of 30.0 kg/m(2) or more. Physical function was assessed according to self-report (SF-36); poor function was defined as a score in lowest quarter of the distribution. A subgroup of participants had objective assessments of physical function (Timed Up-and-Go Test, timed 3-m walk, chair rises, one-legged standing balance). There was a graded increase in prevalence of poor self-reported physical function in men and women with increasing number of risk factors (men, adjusted odds ratio (AOR) for 3 or 4 risk factors vs none = 3.79, 95% confidence interval (CI) = 2.31-6.21; women, AOR = 5.37, 95% CI = 2.66-10.84). With the exception of balance, the objective assessments also showed graded relationships with number of risk factors, such that more risk factors was associated with poorer physical function. These modifiable lifestyle risk factors are linked to marked differences in risk of poorer physical function in older adults. Efforts to encourage healthy lifestyles have the potential to improve physical function and to promote healthier ageing.

Combined Influence of Lifestyle Risk Factors on Body Fat in Spanish Adolescents – the AVENA Study

Objective: To examine the combined influence of four lifestyle risk factors related to physical activity, television viewing, sleep duration, and meal frequency on body fat (BF) in adolescents. Method: This cross-sectional study comprised 1,310 Spanish adolescents (age 13–18.5 years). Lifestyle variables were self-reported and BF indicators (weight, height, six skinfold thicknesses, waist circumference) measured during the years 2000–2002. Lifestyle risk factors were: physically inactive, ≧3 h/day watching television, <8 h/day sleep duration, and <5 meals a day. The number of lifestyle risk factors was calculated for each participant, ranging from 0 to 4. Results: The number of lifestyle risk factors was positively associated with sum of six skinfolds, %BF, waist circumference, and waist-height ratio (all p < 0.001). The odds ratios (95% confidence interval) of overweight (including obesity) for groups with 1, 2, and 3–4 lifestyle risk factors compared with those with 0 were 2.86 (1.77–4.62), 3.61 (2.16–6.04), and 5.81 (3.07–10.99), respectively (p for trend <0.001). All the observations were independent of age, gender, race, socioeconomic status, and fat free mass. Conclusion: The combined influence of four lifestyle risk factors is positively associated with BF and an approximately sixfold risk of overweight in adolescents.

Lifetime Fruit and Vegetable Consumption and Arterial Pulse Wave Velocity in Adulthood

Background— The relationships between childhood lifestyle risk factors and adulthood pulse wave velocity (PWV) have not been reported. We studied whether childhood and adulthood lifestyle risk factors are associated with PWV assessed in adulthood. Methods and Results— The study cohort comprised 1622 subjects of the Cardiovascular Risk in Young Finns Study followed up for 27 years since baseline (1980; aged 3 to 18 years) with lifestyle risk factor data available since childhood. Arterial PWV was measured in 2007 by whole-body impedance cardiography device. Vegetable consumption in childhood was inversely associated with adulthood PWV (β=−0.06, P =0.02), and this association remained significant (β=−0.07, P =0.004) when adjusted for traditional risk factors (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, systolic blood pressure, body mass index, and smoking). Vegetable consumption was also an independent predictor of PWV in adulthood when adjusted for lifestyle or traditional risk factors (β=−0.08, P =0.002 and β=−0.07, P =0.0007, respectively). Persistently high consumption of both fruits and vegetables from childhood to adulthood was associated with lower PWV compared with persistently low consumption ( P =0.03 for both). The number of lifestyle risk factors (the lowest quintile for vegetable consumption, fruit consumption, physical activity, and smoking) in childhood was directly associated with PWV in adulthood ( P =0.001). This association remained significant when adjusted for the number of lifestyle risk factors in adulthood ( P =0.003). Conclusions— These findings suggest that lifetime lifestyle risk factors, with low consumption of fruits and vegetables in particular, are related to arterial stiffness in young adulthood.

Hypertension and Risk Factors Among Traders in Enugu, Nigeria

Aim: To determine the prevalence of hypertension and the associated socio-demographic and lifestyle risk factors for hypertension among traders in Enugu, Nigeria. Materials and Methods: The study design was cross sectional and systematic sampling method was used to select the 451 traders studied. Data was collected in February 2006 with a pretested questionnaire and also by direct measurement of respondents' blood pressure, height and weight. Results: The prevalence of hypertension among these traders was 39.5% and it did not vary by gender; 37.9% for females and 40.5% for males. The hypertension rate among respondents increased with increasing age from 13.4% in ≤ 29year olds to 87.5% in ≥ 50 year olds and with decreasing educational attainment from 13.0% in the none-educated to 76.9% in the tertiary educated. Hypertension prevalence was highest in respondents who were separated, divorced or widowed (76.9%) and lowest in the unmarried (19.3%) while a positive family history of hypertension was associated with greater hypertension rate. Hypertension rate was more in tobacco users but did not vary with use of alcohol. Hypertension prevalence decreased with increasing physical activity of traders ranging from 49.8% in sedentary traders to 22.4% in those engaged in adequate physical activity. The hypertension rate was positively correlated with the BMI of respondents ranging from 29.0% for BMI ≤ 24 kgm2 to 73.5% for BMI ≥ 30 kgm2. Hypertension was positively associated with the number of lifestyle risk factors found in respondents varying from 22.2% in those without risks to 52.4% in traders with all the 4 risks present. Conclusion: The rate of hypertension was high among the study group while the associated risk factors were many. Efforts should be made to reduce the modifiable risk factors present among these traders through health education and other specific health programmes Keywords : Nigeria, hypertension, hypertensive risk factors, lifestyle, traders Journal of College of Medicine Vol. 13 (2) 2008: pp.111-115

Seasonal variations in coronary heart disease.

Coronary heart disease exhibits a winter peak and summer trough in incidence and mortality, in countries both north and south of the equator. In England and Wales, the winter peak accounts for an additional 20,000 deaths per annum. It is likely that this reflects seasonal variations in risk factors. Seasonal variations have been demonstrated in a number of lifestyle risk factors such a physical activity and diet. However, a number of studies have also suggested a direct effect of environmental temperature on physiological and rheological factors. We review the available evidence on seasonal variations in coronary heart disease and possible explanations for them.