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. Author manuscript; available in PMC: 2018 Mar 1.
Published in final edited form as: Psychol Aging. 2017 Mar;32(2):131–138. doi: 10.1037/pag0000158

Personality and Actigraphy-Measured Physical Activity in Older Adults

Ashley Artese 1, Desirae Ehley 1, Angelina R Sutin 1, Antonio Terracciano 1
PMCID: PMC5369413  NIHMSID: NIHMS851263  PMID: 28287783

Abstract

Most studies on personality and physical activity have relied on self-report measures. This study examined the relation between Five Factor Model personality traits and objective physical activity in older adults. Sixty-nine participants (Mage=80.2; SD=7.1) wore the ActiGraph monitor for 7 days and completed the NEO Personality Inventory–3 First Half. Extraversion, Agreeableness, and Conscientiousness were associated with more moderate physical activity and more steps per day, while Neuroticism was inversely related to these physical activity measures (Betas>.20). The associations for Neuroticism and Conscientiousness were attenuated by about 20% to 40% when accounting for disease burden and body mass index but were essentially unchanged for Extraversion and Agreeableness. These findings confirm self-report evidence that personality traits are associated with physical activity levels in older adults.

Keywords: Physical activity, personality, older adults, accelerometer

There are many benefits to a physically active lifestyle. Individuals who are physically active have better fitness and functionality, weight management, psychological wellbeing, and reduced risk for chronic disease and mortality (Warburton, Nicol, & Bredin, 2006). Despite these benefits, less than half of adults in the United States (U.S.) engage in the recommended 150 minutes of moderate physical activity (PA) per week (Schoenborn, Adams, & Peregoy, 2013). PA levels are highest in young adults, with approximately 55.4% of those aged 18–24 meeting the guidelines (Schoenborn et al., 2013). The prevalence of PA declines as age increases, with only 37.1% of U.S. adults aged 65–75 and 24.3% of those over 75 years meeting this recommendation (Schoenborn et al., 2013). Regular PA is especially important for older adults as they are at a greater risk for chronic diseases, declines in physical function, and loss of independence. Among this population, PA has been shown to be an effective means of managing chronic diseases, improving cardiovascular fitness, increasing strength, reducing bone loss, lowering fall risk, and increasing psychological well-being (Chodzko-Zajko et al., 2009).

There are many reasons why older adults may or may not be physically active, such as health status, the environment, physician advice, and knowledge of PA benefits (Balde, Figueras, Hawking, & Miller, 2003; Cohen-Mansfield, Marx, & Guralnik, 2003; Schutzer & Graves, 2004). In addition to these factors, there is a significant psychological component that contributes to PA. In particular, an individual’s characteristic ways of thinking, feeling, and behaving, that is, his/her personality traits, have been associated consistently with self-report measures of PA (Sutin et al., 2016). Personality, as defined by the Five Factor Model (McCrae & Costa, 2008), is characterized by five primary traits: Neuroticism (the tendency to be experience negative emotions), Extraversion (the tendency to be outgoing and sociable), Openness (the tendency to be creative and unconventional), Agreeableness (the tendency to be modest and trusting), and Conscientiousness (the tendency to be organized and disciplined). The traits with the most consistent associations with PA tend to be Extraversion, Conscientiousness, and Neuroticism: Individuals who are high in Extraversion and Conscientiousness are more likely to engage in greater levels of PA while those who are high in Neuroticism are less likely to engage in PA (Courneya, Bobick, & Schinke, 1999; Courneya & Hellsten, 1998; Rhodes & Smith, 2006; Wilson & Dishman, 2015). The evidence tends to be more mixed for Openness and Agreeableness (Courneya et al., 1999; Courneya & Hellsten, 1998; Rhodes & Smith, 2006; Sutin et al., 2016; Wilson & Dishman, 2015)

The relation between personality traits and PA is fairly established, but there are two significant limitations in this literature. First, most studies that have examined this association used self-reported PA questionnaires (Courneya et al., 1999; Courneya & Hellsten, 1998) with only a few studies using objective PA measures (Ohmori et al., 2007; Wilson, Das, Evans, & Dishman, 2015). While self-report measures are used for purposes of practicality, low cost, reduced participant burden, and general acceptance, there are limitations in their accuracy (Dishman, Washburn, & Schoeller, 2001). Objective measures of PA may provide a more accurate assessment of PA time, frequency, and intensity. Such measures also have no method overlap with personality assessment. The few studies that have used objective measures of PA, however, have relied on select and younger populations to assess the relation between personality and PA. Ohmori et al. (2007) examined the link between PA measured by accelerometers in Japanese middle-aged individuals who were part of a behavioral intervention for obesity and found that females who were low in Neuroticism had lower step counts and PA levels. Wilson et al. (2015) also assessed PA using accelerometers in young women. Wilson and colleagues found the expected negative association between Neuroticism and step counts, but in contrast to results from self-report measures, Extraversion was unrelated to PA in this sample (the other three traits were not measured). Second, there is a lack of evidence for the association between personality and PA specifically in older adults. It is not known, for example, if the established relation between personality traits and PA is altered as individuals age. To our knowledge, no study has examined the association between the five personality traits and actigraphy-assessed PA in older adults. We expect that personality traits are likely to play a significant role in older adults’ lifestyle. Especially after retirement, personality-driven choices may have greater influences on the pattern of PA than when life is more structured around work and raising children. Furthermore, with aging there is a decline in energy reserve and mobility (Schrack, Simonsick, & Ferrucci, 2010) that is likely to be more accentuated for individuals who are high in Neuroticism and low in Extraversion and Conscientiousness given their long-held preference for a sedentary lifestyle (Sutin et al. 2016).

The purpose of this study was therefore to examine the relation between the five factors of personality and objective indicators of PA in older adults. Specifically, to gain a better understanding of how personality is associated with intensity and frequency of PA, we examined the relation between personality and actigraphy-assessed time spent in moderate-to-vigorous PA (MVPA), light activity, sedentary behavior, and total step counts. By using an assessment method other than self-reported PA, this research can enrich the multi-method evidence in support of a link between personality and PA in older adults. In turn, this evidence is relevant for lifespan models of personality and health, in which PA may mediate the association between the psychological profile of a person and health outcomes. There is robust evidence that personality predicts health status, such as obesity and Alzheimer’s disease (Terracciano et al., 2014), measured with a variety of assessment methods. By using actigraphy, this research will provide much-needed objective evidence in support of the link between personality and PA in older adults. It will also further elucidate the pathways through which personality differences matter for the health and well-being of older adults. Based on the large self-report literature (Sutin et al., 2016; Wilson & Dishman, 2015), we hypothesized that those who score high in Conscientiousness and Extraversion and score low in Neuroticism will have greater levels of objectively assessed PA. Furthermore, given that personality and PA are both related to health and body weight (Chapman, Roberts, & Duberstein, 2011; Hampson, Goldberg, Vogt, & Dubanoski, 2006; He & Baker, 2004; Sutin & Terracciano, 2016), we further examine whether BMI and disease burden to account for the associations between personality and PA.

In addition to the five broad factors, this study also examined the association between the more specific facets of each trait and PA. To our knowledge, this is the first study to assess facets of personality and actigraphy. Such in-depth assessment provides a more detailed understanding of the relation between personality and PA. Compared to the five factors, the facets are less heterogeneous constructs and often have greater predictive power for narrowly-defined outcomes (Paunonen, Haddock, Forsterling, & Keinonen, 2003; Terracciano et al., 2009). For example, among the facets of Extraversion, we expect to find step count to be more strongly associated with Activity (tendency to be fast-paced, energetic, and vigorous) than the Warmth facet (tendency to be friendly and affectionate).

Methods

Participants

Data were drawn from the Florida Longitudinal Study of Aging. Eighty-three older adults were recruited from two retirement communities through the use of flyers and announcements at community meetings and events. Participants were all independent living residents of the retirement communities and were between the ages of 67 and 95 years (Mean = 80.2: SD = 7.1). Of the 83 individuals who consented to participate, 69 were included in the analysis (52 females and 17 males). Seven of the 83 participants were excluded because they did not wear the activity monitor (n = 4) or did not have at least one valid day of activity (n = 3). An additional seven participants were excluded because they were missing twenty or more responses on the NEO Personality Inventory.

The study was approved by the University Institutional Review Board. All participants signed an informed consent before participating. Researchers met individually with each participant for an interview and physical assessment. The interview consisted of cognitive testing and an assessment of autobiographical memory. Physical assessment included the assessment of height, weight, blood pressure, grip strength, and peak expiratory flow. Participants were provided with an activity monitor and a take-home questionnaire. Participants were instructed to return the monitor and questionnaire (which included the personality measure, along with a medical history, well-being, creativity, spirituality, and other psychosocial measures) approximately one week later.

Physical Activity Measures

Accelerometer

PA was assessed via accelerometry using the ActiGraph ActiSleep (ActiGraph, Pensacola, FL) monitor. Participants were asked to wear the monitor for a total of seven days on the dominant wrist. ActiGraph data were analyzed in the ActiLife (ActiGraph Manufacturing Technology Inc., FL) Software. Freedson Adult (1998) cut-points in one-minute epochs were used to establish the intensity of activity throughout each day with MVPA defined as ≥ 1952 counts per minute (Freedson, Melanson, & Sirard, 1998). A non-wear time was defined as 60 minutes of continuous zero counts. A day with a minimum of 15 hours of wear-time constituted a valid day. A day with less than 15 hours of wear-time was not counted as a valid day and was excluded from the analysis. Fifteen complete hours of recorded wear time (900 total valid minutes) on all valid days, starting at the time the participant awoke, were counted to determine each participant’s daily activity. These values were used to calculate the average MVPA and step counts. Participants who did not have at least one valid day of wear-time were excluded from the analysis. Compliance with wearing the ActiGraph was high with 71% of the 69 participants having 6 or more valid days. Two participants had one valid day, one participant had 3 valid days, and the rest had between 4 and 8 valid days of assessment. The inter-day reliability of the accelerometry data was generally good in this sample. For MVPA, the Spearman-Brown coefficient for the first 4 days was 0.93 (ICC = .93; [95%CI = .89 – .95]) and for the first 6 days was 0.95 (ICC = .95; [95%CI = .93 – .97]). For step counts, the Spearman-Brown coefficient for the first 4 days was 0.92 (ICC = .92; [95%CI = .89 – .95]) and for the first 6 days was 0.93 (ICC = .94; [95%CI = .91 – .96]). Similar reliability coefficients were obtained for sedentary and light activity. The accelerometry data were about normally distributed, with skewness values within ± 1.

Personality

Participants completed the NEO Personality Inventory – 3 First Half (NEO-PI-3FH) (McCrae & Costa, 2007), a questionnaire that consisted of the first 120 items of the NEO Personality Inventory-3. The NEO is a common measure of the Five Factor Model personality traits: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. In addition, the NEO-PI-3FH assessed six facets that composed each of the five factors. Participants responded to items on a 5-point Likert scale that ranged from strongly disagree to strongly agree. Raw scores were standardized as T-scores (Mean = 50, SD = 10) using combined-sex norms reported in the manual.

Statistical Analysis

Hierarchical regression analyses were used to examine the association between personality traits and the PA measures. In model M1, we control for age, gender, and number of valid days the accelerometer was worn. In model M2, we further control for body mass index (BMI) and an index of disease burden. BMI was derived from staff-assessed weight and height. A detailed medical history was used to calculate a disease burden index as the sum of 14 clinical conditions, including cancer, myocardial infarction, heart disease, peripheral vascular disease, stroke, hypertension, diabetes, chronic pulmonary disease, liver disease, kidney disease, connective tissue disease (e.g., arthritis), Parkinson’s disease, muscular dystrophy, and multiple sclerosis. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23 software (Chicago, IL). Significance was accepted at p ≤ 0.05 (two-tail).

Results

Table 1 shows descriptive data for participants in the study. Participants had an average of 8832 (SD = 2917) step counts per day with 39% averaging 10,000 or more daily step counts. The average time spent in sedentary activity was 329.1 (SD = 105.4) minutes per day, while the average time spent in MVPA was 113.3 (SD = 64.9) minutes per day. All time spent in MVPA was moderate activity, with zero minutes spent in vigorous activity. There were age differences in both MVPA and daily step counts; relatively older participants tended to be less active than younger participants (MVPA: r = −0.35; step counts: r = −0.28; p < .05). Women had significantly more MVPA than men (Women: Mean = 125.4 min; SD = 65.3; Men: Mean = 76.5 min; SD = 48.8; p < .05), but this was partly due to the women being younger (79 vs. 82 years old).

Table 1.

Participant Characteristics

Variable Mean ± SD or % Min-Max
Age (years) 80.2 ± 7.1 67 – 95
Sex (female) 75.4% --
BMI (kg/m2) 27.5 ± 5.0 18.4 – 44.7
Disease burden 3.19 ± 1.74 0 – 9
Average # days participant wore ActiGraph 5.8 ± 1.3 1.0 – 8.0
Sedentary Activity (min) 329.1 ± 105.4 151.7 – 727.3
Light Activity (min) 455.1 ± 79.9 164.8 – 625.8
MVPA (min) 113.3 ± 64.9 7.8 – 267.2
Steps (steps per day) 8832 ± 2917 1795 – 15,112
Neuroticism (score) 41.6 ± 9.8 21.2 – 67.4
Extraversion (score) 52.2 ± 11.2 28.6 – 77.5
Openness (score) 53.5 ± 13.7 17.3 – 86.7
Agreeableness (score) 50.1 ± 10.6 23.2 – 73.9
Conscientiousness (score) 48.4 ± 11.7 23.2 – 71.0
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Values are means ± standard deviations. BMI: Body mass index. N=69

MVPA: Moderate-Vigorous Physical Activity.

In Figure 1 we plotted the step counts with each of the five factors along with zero-order regression lines. Table 2 displays the standardized coefficients Beta from the regression analyses. In model M1 that controlled for age, gender, and number of days of accelerometer worn, there was no relation between any of the five personality factors and either sedentary or light activity. There was, however, a significant association between each personality trait (except Openness) and both MVPA and total step count. Consistent with our hypothesis, participants who scored lower in the general tendency to experience negative emotions (Neuroticism) and higher in the general tendencies to be outgoing (Extraversion) and disciplined (Conscientiousness) had higher levels of PA, as assessed by MVPA and step counts. The association between Agreeableness and MVPA and step counts was somewhat surprising. In a regression model M2 that further controlled for BMI and disease burden, the association between Neuroticism and Conscientiousness and MVPA and step counts was reduced roughly by 20% to 40% to non-significance. The association between Extraversion and Agreeableness and MVPA and step counts was reduced less by BMI and disease burden and remained significant. Given the small number of male participants, we conducted sensitivity analyses by excluding males and obtained similar findings in the female only sample as compared to the full sample.

Figure 1.

Figure 1

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Scatterplot of step counts and the five major dimensions of personality with zero-order regression lines.

Table 2.

Association Between Personality Traits and Physical Activity Measures

Trait Sedentary Light MVPA Step Counts
M1a M2b M1 M2 M1 M2 M1 M2
Neuroticism .17 .07 −.06 .02 −.22* −.15 −.25* −.14
Extraversion −.05 −.02 −.19 −.23 .33* .32* .28* .25*
Openness .13 .15 −.26 −.30* .09 .09 −.02 −.03
Agreeableness −.23 −.14 .02 −.06 .37* .32* .37* .28*
Conscientiousness −.16 −.07 .00 −.07 .28* .22 .25* .14
N1: Anxiety .07 .03 .10 .13 −.21 −.18 −.14 −.09
N2: Angry Hostility .19 .10 −.19 −.13 −.12 −.04 −.26* −.16
N3: Depression .20 .09 −.07 .02 −.25* −.18 −.28* −.16
N4: Self-Consciousness .12 .08 .03 .07 −.23* −.21* −.15 −.12
N5: Impulsiveness .10 −.06 −.09 .02 −.09 .03 −.14 .05
N6: Vulnerability .10 .05 −.07 −.04 −.08 −.03 −.14 −.08
E1: Warmth −.14 −.09 .01 −.05 .25* .22* .28* .22*
E2: Gregariousness −.04 −.02 −.12 −.14 .23* .22 .23* .21*
E3: Assertiveness .07 .02 −.19 −.15 .10 .15 .01 .07
E4: Activity −.28* −.24* .05 .01 .40* .38* .43* .39*
E5: Excitement Seeking .20 .18 −.30* −.30* .05 .09 −.09 −.05
E6: Positive Emotions −.05 .05 −.13 −.22 .23* .17 .22 .12
O1: Fantasy .19 .21 −.20 −.23 −.10 −.09 −.10 −.10
O2: Aesthetics .15 .16 −.22 −.24 .06 .06 .01 .01
O3: Feelings −.04 −.04 −.08 −.10 .13 .15 .05 .07
O4: Actions .12 .15 −.24 −.27* .04 .02 −.03 −.06
O5: Ideas −.01 .01 −.10 −.13 .09 .09 −.01 −.01
O6: Values .08 .10 −.17 −.19 .108 .08 −.01 −.03
A1: Trust −.14 −.12 .09 .06 .11 .11 .15 .14
A2: Straightforwardness −.12 −.06 −.03 −.07 .22* .17 .22 .15
A3: Altruism −.13 −.03 −.03 −.12 .22 .15 .23 .12
A4: Compliance −.20 −.15 .05 .02 .24* .19 .23 .16
A5: Modesty −.12 −.09 .11 .08 .11 .08 .12 .09
A6: Tender-mindedness −.09 −.01 −.09 −.18 .35* .31* .31* .24*
C1: Competence .05 .13 −.19 −.25 .18 .12 .10 .01
C2: Order −.19 −.12 .09 .04 .20 .14 .21 .13
C3: Dutifulness −.16 −.17 .00 .01 .27* .25* .19 .18
C4: Achievement-Striving −.05 .01 −.04 −.09 .16 .12 .11 .05
C5: Self-Discipline −.17 −.05 .04 −.06 .25* .17 .27* .13
C6: Deliberation −.09 −.04 .04 −.01 .14 .12 .11 .08
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Note. N=69. MVPA: Moderate-Vigorous Physical Activity.

a

M1 controls for age, sex, and days of activity monitor wear.

b

M2 controls for M1 covariates, BMI, and disease burden.

*

p < .05.

Several of the facets were also associated with MVPA and step count, but there was almost no association between the facets and either sedentary or light activity. Among the facets of Neuroticism, N3: Depression had the strongest association with both MVPA and step counts. N2: Angry Hostility and N4: Self-Consciousness were also associated with fewer step counts and less MVPA, respectively. Most of these associations became non-significant in model 2, which further controlled for BMI and disease burden. Within the Extraversion domain, as expected the E4: Activity had the strongest associations with both MVPA and step counts. In addition, E4: Activity was also the only one of the 30 facets with a significant negative association with sedentary behavior. E5: Excitement Seeking was the facet with the strongest negative association with light activity. E1: Warmth, E2: Gregariousness, and E6: Positive Emotion were associated positively with either MVPA or step count. Most associations in the Extraversion domain remained significant even after accounting for BMI and disease burden. There were no associations between the Openness facets and the accelerometer measures. Within the Agreeableness domain, A6: Tender-Mindedness had the strongest associations with both MVPA and step count. A2: Straightforwardness and A4: Compliance were also associated with MVPA, but these association were not significant after accounting for BMI and disease burden. Finally, within the Conscientiousness domain, C5: Self-Discipline was associated with both MVPA and step counts. C3: Dutifulness was associated with MVPA, an effect that remained significant after accounting for BMI and disease burden.

Discussion

Consistent with findings from self-report measures (Courneya et al., 1999), using accelerometer data and a comprehensive measure of the five factor model of personality, we found that older adults who scored higher in Conscientiousness and Extraversion and lower in Neuroticism have greater levels of PA. We also found high Agreeableness associated with more PA. Among the facets, the strongest effect was found for the Activity facet of Extraversion. Several aspects of these findings make significant contributions to the literature. First, contrary to expectation that associations based solely on self-report measures would be biased upwards due to share measurement variance, we found similar or larger effect sizes for the associations between personality and accelerometer-based indices of PA. For example, a recent meta-analyses of the published literature (Wilson & Dishman, 2015) and one based on large national databases (Sutin et al., 2016) both found associations of about r = .10 for Extraversion and Conscientiousness and r = −.07 for Neuroticism. In this study, we found larger associations between these traits and MVPA and step counts (Betas > 0.20). Second, the association between Conscientiousness and Neuroticism and PA was reduced by 20% to 40% (to non-significance) after accounting for BMI and disease burden, while the association between PA and Extraversion and Agreeableness were mostly unchanged in the fully adjusted model. It is worth noting that Conscientiousness and Neuroticism are the personality traits that are more consistently associated with health conditions (Chapman et al., 2011; Hampson et al., 2006; Sutin & Terracciano, 2016; Terracciano et al., 2014). Lifespan models of personality and health posit that traits have pervasive influences on behavior and lifestyles, including physical inactivity, poor diet, and other health-risky behaviors that may lead to morbidity and mortality (Chapman et al., 2011; Friedman & Kern, 2014). There is empirical support for this model: Conscientiousness, for example, predicts changes in BMI and risk of obesity in several samples (Hampson et al., 2006; Sutin & Terracciano, 2016; Terracciano et al., 2009). In contrast, there is little evidence that BMI predicts change in the five major dimensions of personality in longitudinal studies (Sutin et al., 2013). From this theoretical perspective, controlling for BMI or disease burden might be an overcorrection since personality dispositions are thought to drive the level of PA and ultimately influence the risk of obesity and morbidity. Still, BMI and some diseases may have an impact on PA and personality and therefore may partly explain their link. Third, similar to a previous study that found personality associated with peak aerobic performance but not resting metabolic rate (Terracciano et al., 2013), the associations were generally stronger with MVPA compared to sedentary or light activity, which suggests that personality is most relevant when older adults are engaged in demanding activities. Next we discuss the findings for each trait.

Neuroticism

Older adults who scored higher in Neuroticism took fewer steps than those with lower scores on this trait. This finding is consistent with evidence from self-report measures and a previous study that used actigraphy in college women (Wilson et al., 2015), but contrary to the results of a study in Japanese middle-age women (Ohmori et al., 2007). Individuals high in Neuroticism are less likely to find exercise enjoyable, perceive more barriers to exercise (Courneya & Hellsten, 1998) and are more likely to discontinue an exercise program compared to those lower in Neuroticism (Potgieter & Venter, 1995). This personality trait may further impact physical health and quality of life with increasing age as older adults higher in Neuroticism are more likely to report greater disability and have lower physical function (Jang, Mortimer, Haley, & Graves, 2002) and lower muscular strength (Tolea, Terracciano, Milaneschi, Metter, & Ferrucci, 2012). We also found that the Depression facet of Neuroticism was associated with both lower MVPA and total step count. Other scales that measure depression and other negative emotions have been previously linked to lower levels of PA (Hassmén, Koivula, & Uutela, 2000). PA can have beneficial effects on depressive symptoms, hostility, and other negative states (Lavie & Milani, 1999; Salmon, 2001), which underscore the need for exercise interventions specifically for older individuals with these traits.

Extraversion

Extraversion was associated positively with more PA and higher step counts in older adults. While Wilson et al. (2015) did not find an association between Extraversion and objective PA measures, Extraversion has been consistently related to self-reported PA. Those who score high in Extraversion are energetic, prefer a fast paced life, and enjoy going to large social gatherings (McCrae & John, 1992). They are motivated to exercise in part because of the social engagement, and prefer exercising in a group (Courneya & Hellsten, 1998). Furthermore, high Extraversion may serve to protect older adults against losses in physical function as those with lower Extraversion have been found to experience a more rapid decline in motor performance (Buchman et al., 2013). Within the Extraversion domain, the strongest associations indicate that individuals who scored high on the Activity facet engaged in more PA and spent less time in sedentary activities. By being physically active, these individuals may satisfy their need and preference for vigorous movements and social interactions, which can further enhance their mood and energy (Berger & Motl, 2000). Other facets of Extraversion, such as Warmth and Gregariousness, had also robust associations with measures of PA. Of interest, individuals with a tendency to seek excitement and stimulation were more likely to engage in light activity.

Conscientiousness

Similar to Extraversion, we found that Conscientiousness was associated positively with PA levels and step counts in older adults. Courneya and Hellsten (1998) concluded that Conscientiousness was one of the two personality traits that was most consistently negatively related to exercise barriers such as lack of energy, lack of motivation, and embarrassment. Hall et al. (2014) also found that Conscientiousness was a significant predictor of PA and positive health behaviors. Conscientiousness is associated with physical function in older adults as those with higher Conscientiousness are less likely to experience declines in walking speed over time (Tolea, Costa, et al., 2012). Facets within the Conscientiousness dimension, such as self-discipline, have been found to be associated with better health behaviors (Hall et al., 2014). Likewise, we found that self-discipline and dutifulness were two facets related to greater MVPA in older adults. Those who score higher in self-discipline may perceive themselves as having a greater ability to control their engagement in an exercise program (Hoyt, Rhodes, Hausenblas, & Giacobbi, 2009). Furthermore, dutifulness relates to an individual’s tendency to uphold social commitments and obligations (Roberts, Chernyshenko, Stark, & Goldberg, 2005), and these individuals are more likely to follow recommendations about PA. Dutifulness and Self-Discipline have also been related to walking speed (Terracciano et al., 2013) and several metabolic and inflammatory markers (Sutin et al., 2010).

Agreeableness

Surprisingly, the strongest association at the domain level was found for Agreeableness for both MVPA and step counts. This contradicts findings from previous studies that found no association between Agreeableness and PA motivation or behavior (Courneya & Hellsten, 1998; Rhodes & Smith, 2006; Wilson & Dishman, 2015) and a weak association between Agreeableness and self-reported PA (Sutin et al., 2016). This association may be due to the age of our population and the nature of the retirement community in which they live. Those who are high in Agreeableness tend to be considerate, kind, and compassionate. Therefore, in an older population where functional limitations and chronic disease are more prevalent, individuals high in Agreeableness may be more likely to help or assist other individuals in the community who are less functional, accumulating more PA in doing so.

Openness

We found no relationship between Openness and PA. While this is consistent with some previous studies (Courneya & Hellsten, 1998; Rhodes & Smith, 2006), it contradicts more recent findings (Sutin et al., 2016; Wilson & Dishman, 2015). Openness has been shown to be related positively to performance of instrumental activities of daily living (Puente, Lindbergh, & Miller, 2015), physical function (Duberstein et al., 2003), and walking speed in older adults (Terracciano et al., 2013). While the mean age in our sample was higher compared to studies that observed a positive association between physical function measurements, we may have not observed a relation between Openness and PA in our sample because all participants were living independently and had few functional limitations. Other studies have found mixed evidence on Openness and walking speed in a healthy population of older adults (Terracciano et al., 2013; Tolea, Costa, et al., 2012). Perhaps this relation may be more apparent in an older population with more functional impairments.

Conclusions

Strengths of this study include the use of objective measures to quantify PA in older adults and the use of the NEO Personality Inventory to assess both the five main personality factors along with the facets of each trait. Indeed, this is the first study to assess the relation between personality traits and their individual facets with actigraphy measures in older adults. There are some limitations as well. First, the sample size recruited for this study was small. Additional research with a larger sample size may be helpful in determining the relation between PA and personality in this population. The participants in this study were residents of retirement communities that emphasize active living. While it is encouraging that residents of these communities maintain a high level of activity at an average age of 80 years old, most of them were more physically active than the majority of older adults living in the United States, making this study not completely representative of the older adult population. In addition, this study was cross-sectional, so the potential reciprocal relations between personality and PA (Stephan, Sutin, & Terracciano, 2014) could not be assessed. Despite these limitations, it is of note that the results were consistent with self-report measure of PA and extend the relation to more detailed measures of both personality and PA in an older population.

Acknowledgments

The authors are grateful to the individual participants and organizations who generously shared their time for the purpose of this project.

Funding: This work was supported in part by the National Institute on Aging Grant R03AG051960.

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