The five-factor model of personality and physical inactivity: A meta-analysis of 16 samples
Introduction
The World Health Organization (WHO, 2015) estimates that approximately 31% of the world’s population is physically inactive. Physical inactivity, defined as insufficient physical activity or minimal body movements, is the pole of the activity spectrum most detrimental to health (Dietz, 1996, Must and Tybor, 2005, Schmid et al., 2015). Those who are classified as insufficiently active fail to reach the recommended 150 min of moderate intensity (or 75 min of vigorous intensity) activity per week. This includes activity accumulated during leisure or work time, active transportation, household chores, sport, play, or regular exercise (WHO, 2010). Such inactivity is associated with increased risk for obesity, cardiovascular disease, type 2 diabetes, breast and colon cancers, and mortality (Healy et al., 2008, Hu et al., 2003, Jakes et al., 2003, Lee et al., 2012). The distinction between frequency of physical activity and the relative absence of physical movements reflects evidence that level of physical activity and time spent inactive are independent predictors of health outcomes (Biswas et al., 2015, Dietz, 1996, Schmid et al., 2015). For example, even among individuals who engage in some physical activity also engaging in activities that are more sedentary, such as time spent sitting or watching television, doubles the risk for cardiovascular mortality and increases risk for all-cause mortality by 50% (Matthews et al., 2012). Many factors contribute to an inactive lifestyle, including psychological, as well as contextual factors (Bauman et al., 2012). A better understanding of the psychological correlates of physical inactivity will inform more effective prevention and intervention programs to increase physical activity.
Among the factors associated with lifestyle behaviors, an individual’s characteristic ways of thinking, feeling, and behaving are associated consistently with greater frequency of physical activity (Rhodes and Smith, 2006, Wilson and Dishman, 2015). Several of the traits that define the Five Factor Model of personality (McCrae & Costa, 2008) are routinely implicated in engaging in more physical activity. Individuals who are high in Neuroticism (the tendency to experience negative emotions and stress) tend to avoid physical activity, whereas individuals who are high in Extraversion (the tendency to experience positive emotions and be outgoing) and Conscientiousness (the tendency to be organized and disciplined) tend to engage in more physical activity (Rhodes & Smith, 2006). Trait Openness (the tendency to be open-minded and creative) has recently also been associated with greater physical activity (Wilson & Dishman, 2015). In contrast to the other traits, Agreeableness (the tendency to be cooperative) tends to be unrelated to physical activity. Less is known, however, about the risk of physical inactivity and sedentary behavior associated with personality. That is, the personality correlates of physical inactivity may or may not mirror the correlates associated with physical activity.
To that end, we report a meta-analysis of 16 large-scale studies from the US, the UK, Germany, the Netherlands, Australia, and Japan, totaling more than 125,000 participants. None of these samples were included in previous meta-analyses of personality and physical activity. Many large-scale national panel and cohort studies now include brief measures of both personality and physical activity. We address whether it is possible to detect a signal between personality and physical inactivity even with such rudimentary measures. We address the relation between personality and physical (in)activity in three ways. First, we focus on lack of physical activity because of the high worldwide prevalence of inactivity (Hallal et al., 2012). In addition, this group tends to be at the greatest risk for poor health outcomes and has the most to gain by incorporating even light physical activity into their daily routines (Lee et al., 2012, Powell et al., 2011). Second, as a point of comparison, we examine the association between personality and amount of physical activity typically engaged in. Third, in a subset of five of the 16 studies, we examine how personality traits are associated with measures of sedentary behavior (e.g., amount of time spent sitting). Across all analyses, we test whether these associations are moderated by sex or age.
Section snippets
Participants and procedure
Participants were drawn from 16 national surveys. The studies included in the analysis were the Health and Retirement Study (HRS), the Midlife in the United States (MIDUS) study, the Wisconsin Longitudinal Study Graduate sample (WLS-G) and Sibling sample (WLS-S), the National Longitudinal Survey of Youth-Children and Young Adult (NLSY-CYA) study, the National Study of Adolescent to Adult Health (Add Health), the National Health, Aging, and Trends Study (NHATS), the Midlife in Japan (MIDJA)
Results
The descriptive statistics for the demographic variables and for physical inactivity are shown in Table 1. Similar to the WHO estimate, across all samples, 31.44% of participants on average were inactive. There was considerable variability across the samples; however, comparisons across samples were unwarranted given the differences in the question used and the characteristics of the samples. Despite this variability, the pattern of associations between personality and inactivity was quite
Discussion
Consistent with previous research on physical activity, the traits that define the Five Factor Model of personality were also associated with an increased risk of a physically inactive lifestyle. The results were remarkably consistent across 16 large national datasets. For every standard deviation difference in the trait, there was up to a 27% increased risk of being physically inactive. Higher Neuroticism and lower Conscientiousness were further associated with more time spent in actual
Acknowledgements
This research was supported by a Grant from the Eunice Kennedy Shriver National Institute of Child Heath and Human Development (1R15HD083947) to Angelina R. Sutin. Add Health: This research uses data from Add Health funded by grant P01-HD31921, with funding from 23 other federal agencies and foundations. Information about the Add Health data is available at http://www.cpc.unc.edu/addhealth. There was no direct support from P01-HD31921 for this analysis. HRS: The Health and Retirement Study is
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