Inroduction
The benefits of preventive health behaviours (e.g. exercise, eating fruit and vegetables), and the adverse outcomes associated with risky health behaviours (e.g. smoking, excessive alcohol consumption), in terms of health and mortality have been the subject of considerable research [
1]. Typically, these behaviours were examined separately, which may be an overly simplistic approach as there is evidence that they co-occur as lifestyle patterns within population sub-groups [
2‐
4]. This co-occurrence appears to create synergistic effects, with increasing risk of premature mortality from cancer, cardiovascular disease and all-cause mortality beyond the expected additive effects of the separate behaviours [
5‐
7]. Prior studies have demonstrated transfer effects, whereby health-promoting and health-harmful behaviours tend to be highly correlated within behaviour groupings, or clusters, but not between these groupings [
8]. Furthermore, campaigns targeting just one health behaviour can have unintended consequences for modifying other health behaviours that co-occur [
1]. Consequently, there is a growing emphasis on managing multiple health-risk behaviours as opposed to single risk factors, to increase the efficacy and lower costs of interventions across the population [
9], an approach that has been encouraged by the World Health Organization [
10].
Co-occurring health behaviours are described as “health lifestyles” [
11] that are to some extent socially determined, through factors such as income and education, and have consequences for one’s ongoing health status. Understanding the components and correlates of health behaviour lifestyles is therefore important for: (1) identifying groups whose health lifestyles place them at greater risk for future ill health [
12]; (2) designing holistic approaches to health promotion [
2,
11]; and (3) targeting groups most likely to benefit from particular health campaigns or health services [
13]. Since health lifestyles comprise a unique constellation of different attributes, cluster analysis is most suited for identifying different lifestyle groups within a given population [
9] and for identifying characteristics of cluster membership that determine responses to health interventions. Lifestyle group membership has generally been related to self-rated health [
2,
14], greater mortality risk [
15], body mass index [
16,
17], depression [
14,
18,
19], quality of life [
2] and to the differential effects of dietary interventions [
20,
21]. Research employing cluster analysis of health lifestyles has typically focused on younger populations of adolescents and college students [
12,
22,
23]. Much less is known about health lifestyle clusters across the full adult age range, but limited evidence suggests that the number and type of clusters vary across age groups [
24], possibly reflecting different social and physical contexts or different rates of health behaviours across ages. Older people, for example, have higher rates of cancer screening than younger age groups and lower rates of smoking [
25,
26].
In the context of rapidly ageing societies, increasing life expectancies and burgeoning costs of health care [
27,
28], there is a need to understand how older people cluster in terms of their engagement in health-related behaviours in order to inform the design of age-appropriate health promotion interventions. Since adherence to a healthy lifestyle is associated with delayed onset of disability, slower functional decline and less cognitive impairment, information about health clusters is likely to be a key factor for promoting positive ageing [
29,
30].
Only two studies have utilised a statistical clustering approach to specifically target older individuals: (1)
N = 2,002 Germans over 50 years [
4]; and (2)
N = 5,880 Taiwanese over 60 years [
14]. The German study focused on four health-related behaviours (i.e. smoking, alcohol consumption, exercise, diet) [
4], identifying five clusters: (1) No risk behaviours; (2) Physically inactive individuals; (3) Fruit and vegetable avoiders; (4) Smokers with risk behaviours; and (5) Drinkers with risk behaviours. The Taiwanese study [
14], included health check-ups, but not diet, and clustered behaviour across time separately for males and females, with gender affecting the number of cluster trajectories, but there was a relatively small healthy lifestyle grouping for both males and females. One further study (
N = 4,165 older Koreans) [
31], that did not use statistical clustering but divided the sample into 16 groups according to level of adherence to guidelines for smoking, drinking, physical activity and weight, found that only 11.7 % met recommendations for all four behaviours.
Whilst informative, these findings are based on relatively small samples of homogeneous populations, and may not generalise to health lifestyles of individuals in vastly heterogeneous and multicultural societies, such as Australia and the USA. One USA-based study [
15] (
N = 19,662) reported the existence of 12 health profiles amongst older (>50 years) adults based on permutations of only three health behaviours (smoking, drinking, physical activity). Smoking and heavy drinking were associated with the greatest mortality risk, with inactivity also associated with increased mortality. However, these groupings were based on an ad hoc approach, rather than employing a more statistically rigorous cluster analytic technique.
A notable limitation of the Korean-, US- and German-based studies was the exclusion of cancer screening, which is regarded as a key component of the preventive health approach, particularly for older adults [
32]. Current guidelines in developed nations vary according to specific age recommendations to commence cancer screening, and the recommended between-screening intervals, but there is consensus that regular screening for cancers (e.g. bowel, breast) reduces both cancer-related mortality and morbidity [
32,
33].
Sociodemographic factors are also regarded as key variables on which health lifestyles are clustered [
11]. For example, age- and gender- distinguished clusters in the German [
4] and Taiwanese [
14] studies, whereby even amongst older adults, being younger and male decreased the odds of being in the healthy lifestyle category. Gender was likewise linked to clustering in studies of all-age samples from Ireland [
2], Australia [
34] and Belgium [
35]. Having a marital partner was another factor associated with healthy lifestyle cluster membership in the German study [
4]. Moreover, socio-economic status has consistently been associated with cluster membership with healthier/low risk lifestyle clusters more likely to emerge in higher socio-economic status groups [
26]; however, reported socio-economic status typically uses only individual levels of income and education, neglecting other factors that might affect health status. For example, residential location influences health status through the effect of relative access to healthcare, fresh food, etc., with neighbourhood socio-economic status impacting all-cause mortality beyond the effect of an individual’s socio-economic status [
36]. Work status (i.e. full-time vs. part-time vs. retired or not working) is also a potential economic factor to consider when undertaking clustering analyses of health lifestyles, especially in the over-50 age group for whom the transition into retirement is regarded as a significant life event [
37]. In summary, the rapid ageing and increasing life expectancy seen in many countries has heightened the need to understand how to promote positive ageing both for the benefit of individuals who are living longer and to reduce burgeoning national spending on health care [
28]. However, little is known of the health lifestyles of this ageing population. The initial aim of the current study was to extend earlier work by employing a cluster analytic approach to identify health behaviour lifestyle groups within a heterogeneous society, using a large Australian population cohort of older adults. These data were drawn from more than 92,000 people who were enrolled in the 45 and Up Study, the largest cohort panel in the Southern Hemisphere [
38]. Addressing limitations of earlier work, we included cancer screening behaviour as a clustering factor, along with exercise, smoking, alcohol consumption, and diet. A second aim was to investigate the association between cluster membership and biological (body mass index and physical functioning) and psychological (self-rated quality of life and psychological distress) states, as well as a wide range of socio-economic variables (age, gender, income, marital status, education, neighbourhood location and work status). Consistent with the majority of prior research, it was hypothesised that cluster groupings would be characterised primarily by within-group similarities (i.e. healthy behaviours with other healthy behaviours; unhealthy behaviours with unhealthy behaviours). It was further hypothesised that unhealthy behaviour cluster membership would be associated with lower socio-economic status, greater psychological dysfunction and poorer physical functioning than healthy clusters.
Discussion
This study addressed the need to identify and describe health lifestyle clusters evident in older populations. Understanding the factors that promote healthy and positive ageing has become increasingly important in the face of longer life expectancies and the growing burden on national health budgets [
27]. A second aim was to investigate the association between cluster membership and biological (body mass index and physical functioning) and psychological (self-rated quality of life and psychological distress) states, as well as a wide range of socio-economic variables (age, gender, income, marital status, education, neighbourhood location and work status).
Using data from one of the largest samples that has ever contributed to a cluster analysis of health behaviours, six clear clusters emerged: smokers, non-screeners, higher risk ex-smokers, lower risk ex-smokers, sedentary non-smokers and active non-smokers. The clustering of several unhealthy behaviours together, particularly in the smoker, non-screener, higher risk ex-smoker and sedentary non-smoker groupings, is consistent with other research demonstrating within-group similarities and transfer effects of unhealthy behaviours, but which did not include screening behaviours in their analyses [e.g. Laaksonen et al. (Finland; [
46]); Héroux et al. (Canada; [
47]); Hsu et al. (Taiwan; [
14]); Tobias et al. (New Zealand; [
48])]. Since smoking and heavy drinking have been shown to substantially elevate risk for mortality for middle-aged (51–65 years) and older (66+ years) adults, along with inactivity among non-smokers [
15], the “unhealthy” behaviour clusters identified in the present study represent individuals who are at increased mortality risk, warranting future intervention to modify these health-damaging behaviours within the cluster groupings. The Higher Risk Ex-smoker clustering represents an example of healthy and unhealthy behaviours in combination, suggesting a compensatory effect similar to that described by Knäuper et al. [
49], whereby individuals engaging a health-harmful activity such as high alcohol intake compensate somewhat for this unhealthy practice by adopting a health activity (i.e. screening, giving up smoking). While engagement in at least one healthy behaviour is promising, the ongoing adoption of unhealthy behaviours continues to place these individuals at increased risk for future ill health, warranting the need for interventions to increase the healthy, and decrease the unhealthy behaviours of these groups.
The characteristics of the clusters identified in the present study differed somewhat from the German study of older people [
4], whose clusters were focused on the dimensions of physical activity, diet, smoking and alcohol intake. These differences may reflect the inclusion of cancer screening in the present study, which was found to be a significant factor that identified a unique cluster. Given its relevance for older individuals [
32], understanding the characteristics of the non-screened group of individuals is an important contribution of the current study and should be used to inform future screening promotion approaches. The differences between the studies may also reflect broad cultural differences in health-related behaviours across nations. For example, there were lower rates of smoking (7 %) in the Australian sample compared to 15 % of smokers in both the German and Korean studies, and 17 % in Shaw and Agahi’s sample of US citizens [
15]. However, in all samples, smokers were more likely to be male and younger. The current study also distinguished current from previous smokers. This distinction is important as while quitting smoking decreases risks for certain diseases (e.g. asthma and respiratory-related diseases; [
50]), previous smoking still places individuals at increased risk of developing other chronic and life-threatening conditions [
51‐
54].
As in other health cluster studies [
2,
4,
34], and consistent with the concept of transfer from healthy behaviours to other healthy behaviours [
8], one overall positive or “ideal” health behaviour cluster was found with individuals characterised by being physically active non-smokers who had a healthy diet, low to moderate alcohol intake and were cancer screeners. These findings are also consistent with limited findings suggesting increased positive health behaviours amongst individuals undergoing screening for disease risk [
55]. However, the relative size (20 %) of this healthy cluster, while larger than the Korean sample [
31] of older people (12 %) was somewhat smaller than in other studies, where for example, 25 % of older Germans [
4], 34 % of older Americans [
15], and approximately 29 % of Taiwanese [
14] were in the healthy profile groups, as were 45 % of participants in the all-age Western Australian study [
34]. Although past research indicates that older people may engage in fewer risky health behaviours [
56], future research should aim to confirm if they also engage in lower rates of positive health behaviour.
As found in other health behaviour cluster studies [
2,
4,
34,
35], the clusters differed significantly in terms of the sociodemographic profile of their members, highlighting the importance of considering such factors when addressing health behaviour. Consistent with Shaw and Agahi [
15] and Lee et al. [
31], women dominated the two non-smoking profiles with the lowest alcohol intake, and as found by Hsu et al. [
14], education level was associated with cluster membership.
The results extend prior work by showing that not only do clusters differ by age, gender, marital status, education and income but they also differ in terms of work status and residential locality, which explained unique variance in membership even after controlling for all other factors. Retirement transition (through part-time work, for example) and full retirement (cessation of all paid work) are important milestones that are unique to the older population. The finding that the non-screeners cluster had the highest level of full-time workers (holding age constant) suggests that lack of time may be an issue related to their non-compliance. The results of this study also showed that living in a poorer, disadvantaged neighbourhood with larger numbers of unemployed and disabled people not only increased the likelihood of membership in less healthy behaviour clusters for those who were themselves less educated, had a lower income, or less work but also increased the likelihood of being in the less healthy clusters for the more highly educated, wealthy and fully employed who nevertheless lived in these disadvantaged areas. This association between neighbourhood socio-economic status and cluster membership regardless of an individual’s socio-economic status supports arguments that health interventions need to consider social context [
57,
58].
Clusters were significantly different in terms of key physical and psychological indicators, with the active non-smokers having the most “positive” indicators of all groups, the higher risk ex-smokers having poor physical characteristics (higher body mass index and lower physical functioning) and the smokers having poor psychological characteristics (lower quality of life and higher distress). This reflects emerging evidence that in those who successfully quit smoking are psychologically healthier (i.e. on measures of overall quality of life, health-related quality of life and positive emotions), than their smoking counterparts [
59].
Study Limitations
Although these results provide support for the expected benefits and problems associated with health lifestyles [
10,
11], this was a cross-sectional study. Future research needs to investigate the longitudinal outcomes of cluster membership, and whether or not changing cluster membership can alter such outcomes.
A limitation of the current study is that, like many investigations of health behaviour, it relied on self-reported data. Whilst the risk of socially desirable or inaccurate responses exists, self-report data from the 45 and Up Study have been validated against actual measurements (e.g. of body mass index) and found to be highly correlated (
r = .95) [
60]. In addition, the measure of health behaviours was at a fairly broad level. For example, although one of few studies to distinguish ex-smokers from those who had never smoked, the quantity of smoking and number of years smoked was not considered, even though such information may provide a more fine-grained distinction between those in the smoking cluster. Likewise, the measure of dietary behaviour only assessed fruit and vegetable consumption whereas a more comprehensive analysis would have included fat and sugar intake, for example.
The study’s strengths include the large sample size, which enabled cross validation of the results. The broad representation of adults in this age group also contributes to confidence in the stability of the clusters. Nonetheless, the recent study by Hsu et al. [
14] indicates that older people also follow different trajectories across time in terms of changing engagement in health behaviour. Future research tracking the stability of cluster membership is needed to understand the factors that trigger change.
Conclusions
This study lends important insight into the range of health lifestyle profiles or clusters evident in an older culturally heterogeneous population. Previous research had demonstrated the importance of considering multiple behaviours and cluster membership on health status and long-term survival. This study extended this earlier work by delineating lifestyle clusters that incorporated cancer screening behaviours (a key component of the preventive health approach currently advocated), a more fine-grained analysis of smoking incorporating both current and past smoking behaviours, as well as drinking, diet and physical activity variables that have typically been included in past investigations. Moreover, the role of socio-economic variables on cluster membership was more comprehensively assessed than in previous reports, incorporating work status and neighbourhood socio-economic status as factors beyond the typical range of socio-economic status indicators. Result indicated that clusters characterised by the most harmful behaviours are more likely to include men, those who are living alone and people with a lower income and who live in a deprived neighbourhood. Importantly, members of these clusters are also more likely to report having a lower quality of life and being more distressed.
From a public health perspective, these findings are important as they highlight the need to consider beyond the typical factors of smoking, drinking and physical exercise when characterising health behaviours for older adults, to incorporate factors such as screening behaviours that are critical components of preventive health approaches for this age group. Our study focused specifically on cancer screening behaviours, but future research could incorporate other forms of disease screening such as cholesterol monitoring. Inclusion of a greater range of health behaviours might also enable the identification of “gateway behaviours,” whereby intervention on a key behaviour effects positive change in other behaviours not directly subjected to intervention [
61]. Within the context of a rapidly ageing population, this type of information can inform future campaigns to target multiple health behaviour changes, rather than focusing on individual risk factors [
62]. Audience segmentation approaches utilising cluster analytic techniques will be crucial, since a message for young to older adults regarding the role of work status and screening behaviours on health would be very different from a message for older individuals who have long been retired.