This study provides a snapshot of the prevalence of major lifestyle risk factors by age and gender in Chinese urban adults. Current daily smoking was most prevalent among our male subjects aged 40-49 years. Men aged 18-39 years had a significantly lower smoking prevalence than men in the older age groups. As reported by Yang et al, the smoking rate among Chinese young adults aged 15-40 years in 2010 Global Adult Tobacco Survey (GATS) showed a slight downward trend compared to the results in the national surveys of 1996 and 2002 . The distribution of PA level across the age groups was different from that observed in the US and the European Union, where young adults were more physically active than older adults [50–52]. In our survey, adults aged 50-64 years were the most physically active after men aged 18-29 years. Men aged 30-49 years and women aged 18-39 years were the least active. Almost half of the men and four-fifths of the women in our sample population did not work or were retired. Older people generally had more free time than younger ones and were more concerned about their health status . The frequency of out-of-home eating decreased with age. This result was similar to that in other populations [10, 15]. Although accompanying conditions were more prevalent among older people and self-evaluation of health worsened with advanced age , older people lived a healthier lifestyle than younger people in China.
As with results from other populations [17, 21–23, 54–57], women in our study engaged in more positive lifestyle behaviours, including less smoking, more fruit consumption, and less eating out. This may be a result of social role differentiation [58–60]. Less frequent smoking among Chinese women can be explained by the special culture of gender relations in China . Younger women were significantly less active than younger men, which may be explained at least in part by physiological differences, such as muscle strength and endurance . However, women aged over 40 years lived a more active life than younger women. A significantly higher proportion of women than men kept a moderate level of physical activity after the age of 50.
Thus far, the analysis of lifestyle risk factors in China has been limited to individual risk factors and their relationship with socioeconomic factors. This was one of the few studies to explore the socio-demographic differences in patterns of health-related lifestyle behaviours using a holistic approach in Chinese urban adults. Three distinct clusters were identified based upon four lifestyle behaviours of interest. About one-quarter of the sample was characterised as having an unhealthy lifestyle, and nearly one-third of the sample was characterised as moderately healthy. Four socio-demographic variables, age, sex, education, and asset index, had significant and independent roles to distinguish these three clusters of adults. Our findings indicate that high-risk profiles are more prevalent among men in the 40-49 age group and those with lower levels of education. In contrast, people over age 50 are more likely than young and middle-aged adults to live healthy lifestyles.
Some studies have shown that multiple risk factors were more prevalent among those with low income levels, and healthy lifestyles were more prevalent among those with higher income levels [24, 56, 62]. However, in our study, people with a high asset index were more likely to have moderately healthy lifestyles than healthy ones. This may be the case generally in urban China. People with higher income levels may be occupied with busy work and engaged in more social activities, like eating out for business or with friends. It is difficult to make the healthiest choices on the menu and resist the temptation to overeat. Meanwhile, social smoking and drinking have become part of the inveterate culture in China [63, 64]. Of course, it is also important to recognise the limitation of using the asset index. The asset index is better thought of as a proxy for long-term household wealth rather than current per capita consumption . The strong correlation between asset index and money metric measures like income and expenditure was not consistently supported .
A few studies have examined the clustering of multiple lifestyle risk factors and their association with socio-demographic characteristics. However, it is difficult to make direct comparisons, as these studies focused on different (combinations of) lifestyle risk factors and used different measures and/or cut-off points, different study populations, and different analytical techniques [17, 20–29]. Our sample size was not large enough to allow a deep exploration of different combinations of lifestyle risk factors in different subpopulations. We did not set cut-off points arbitrarily for some variables that were continuous in nature, like the number of days of FV consumption and the total number of times of eating out. We did not analyse socio-demographic distribution based simply on the number of risk factors. We instead relied on two-step cluster analysis, which can handle both continuous and categorical variables, to assign individuals into natural clusters based on multiple lifestyle risk factors.
The current study may have important implications for health policy and practice in China. The findings show that Chinese people who are middle aged, men, and less educated are most likely to be part of the cluster with a high-risk profile. This provides us with an opportunity to identify subgroups of the population in which the future burden of disease may lie. These subgroups should be targeted for early prevention programs. In recent years, population-wide prevention efforts on chronic disease in China have relied mainly on interventions based on health education and operated by doctors in community health centres (CHCs) or public health professionals in local Centres for Disease Control and Prevention (CDCs). The elderly are the main attendees for the intervention activities, including health lectures held in community rooms or in the CHCs and community events, and they are the people who are most concerned about health posters and bulletin board displays in the community . Young and middle-aged adults are not engaged in intervention activities as significantly as the elderly. Health promotion and interventions in the workplace offer a valuable approach for reaching working-age adults and should be a part of future intervention plans.
Furthermore, the trend towards clustering of multiple behaviour risk factors in particular subgroups, as found in our results and in other studies, emphasises the need for targeting multiple behaviours with comprehensive and integrated programs. Multiple-behaviour interventions may not only have a much greater impact on public health than single-behaviour interventions , but they may also be more effective and efficient at achieving these goals . Identifying subgroups of the population with a cluster of lifestyle risk factors could lead us to understand the mechanisms by which societal factors affect development of risk factors and thus lead to a radical population-wide approach to prevent the development of risk factors. This kind of population-based approach aims to remove the underlying impediments to healthier behaviours and to control the adverse pressures . In other words, it aims to improve the aspects of the physical, social, and economic environment that predispose people to an unhealthy lifestyle.
The strength of this study is its ability to capture a comprehensive panel of major risk behaviours, including smoking, physical inactivity, FV consumption, and out-of-home eating, for a representative random sample of the urban population aged 18-64 in China. The individual response rate for the survey was about 72% of all eligible households. The distribution of age and gender of surveyed subjects was comparable to that of the eligible population in the sampled households. We analysed the age distribution by dividing the 18-64 age range into four groups, which helped us identify the significant differences in lifestyles at different stages of life.
One of the limitations of this study was that the prevalence of some lifestyle risk factors by age does not necessarily mean that the prevalence of these behaviours changes as individuals age. The age effect, cohort effect, and period effect are usually thought to be jointly responsible for the prevalence trend across age observed in the cross-sectional analyses . Furthermore, the detailed mechanisms by which the socio-demographic factors relate to lifestyle risk factors could not be fully determined from this study. Nonetheless, this study was useful for identifying groups that are generally more at risk and developing tailored intervention activities. The second limitation is that dietary habit is a complex behaviour. Several dietary factors, including energy, fat, sugar, salt, and FV consumption, have been proven to be associated with risks of major chronic diseases . However, ours was a survey with multiple objectives, and limited space was allotted for dietary questions. It was difficult to quantitatively measure the intakes of energy, fat, sugar, and salt. Because people eating out were more likely to eat foods high in fat, salt, and sugar and to eat more, we used the frequency of out-of-home eating to reflect these dietary factors. Third, lifestyle risk factors were self-reported. Studies have shown that self-reports tend to underestimate smoking status  and overestimate physical activity levels [72, 73]. Surveys using food frequency questionnaires reported mixed situations of overestimating as well as underestimating of food and nutrient intakes . Self-reported data were potentially subject to information bias when the primary concern was the absolute level of lifestyle risk factors. However, when the main purpose was to rank and categorise subjects according to their relative level (as in this study), self-reported data were shown to have reasonable validity with the benefit of greater accessibility in large epidemiological studies [75, 76]. In spite of these potential limitations, the evidence derived from our results should be helpful for health policy makers' decisions on where to put resources in their efforts to tackle the growing chronic disease epidemic in China.