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Influence of Personality and Health Behaviors on Fatigue in Late and Very Late Life

¹ Gerontology Program, Iowa State University, Ames.
² Gerontology Institute, University of Georgia, Athens.

Address correspondence to Peter Martin, Iowa State University, Gerontology Program, 1096 LeBaron Hall, Ames, IA 50011. E-mail: pxmartin{at}iastate.edu

	Abstract

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There is limited understanding about variables associated with fatigue in late and very late life. The purpose of this study was to explore the longitudinal influences of personality and health behaviors on changes in fatigue. One hundred and seventy-eight participants of the Georgia Centenarian Study were part of this study. Multiple regression analyses were computed to assess the influence of personality, physical activity, stress management, and nutritional risk on fatigue after initial fatigue, self-reported health, and psychosocial variables were controlled for. Anxiety, physical activity, and nutritional risk had significant longitudinal effects on fatigue. Older adults who were anxious, less engaged in physical activity, and who had higher levels of nutritional risk were more likely to show increased fatigue levels. The results suggest that more clinical and research attention should be paid to changes of fatigue in older populations.

Mental and physical fatigue can compromise optimal adaptation and life strengths in late life. Our earlier research suggested that fatigue increased over time (Martin, Long, & Poon, 2002), but there is no longitudinal evidence assessing predictors of fatigue over time. Therefore, our purpose in this investigation was to explore predictors of fatigue in late and very late life. We consider personality traits and health behaviors to be important resources or vulnerabilities predicting differential levels of fatigue.

Gallo, Rabins, and Anthony (1999) have argued that older adults often experience a phenomenon described as "depression without sadness." Others have described this as the "depletion syndrome" (Adams, 2001; Fogel & Fretwell, 1985; Newman, Engel, & Jensen, 1990, 1991; Newman, Klein, Jensen, & Essex, 1996). Unlike depression, which encompasses feelings of guilt, worthlessness, and self-blame in addition to feelings of low energy, depletion has little to no commonality with mood states (Adams). Depletion entails a general lack of interest in daily activities, as well as a feeling that everyday activity requires effort.

Gatz (1998) proposed a conceptual model of depressive and physical symptoms, which included fatigue as an important component. This particular model is based on a developmental diathesis–stress framework in which biological and physical vulnerability, stressful life events, and psychological diathesis (resilience) influence different trajectories across the life span (Gatz, Kasl-Godley, & Karel, 1996). Individuals are theorized as having long-standing resource deficits (e.g., greater physical limitations) stemming from stressful life experiences. Resource deficits occur under conditions of vulnerability (Gatz). In contrast, the use of protective factors may offset any decline in resources, and any age-related increase in fatigue could be offset by available resources.

Gatz (1998) also contends that older adults maintain an "affective reserve." The concepts of affective reserve and resilience share common features. Resilience refers to "patterns of desirable behavior in situations where adaptive functioning or development have been or currently are significantly threatened by adverse experiences or rearing conditions" (Masten, 1999, p. 283). Adverse experiences in later life include physical and functional impairment, as well as changes in social support, and sensory impairments (Martin, Poon, Kim, & Johnson, 1996). When an older adult is faced with these changes, depletion of the affective reserve brings the individual near or beyond a critical threshold. In extending this notion, Gatz contends that the correlation between physical and depressive symptoms is reflective of a common link conceptualized as fatigue. Fatigue denotes a lowered or depleted affective reserve (Gatz). It can be surmised that high levels of fatigue create limitations in resilience. Conversely, less fatigue results in stabilization or an increase in resiliency. Nonetheless, resiliency is a life strength that offers protection against decline, distress, and fatigue over time.

Fatigue may not only be seen as a vulnerability factor, but could in itself be an outcome on its own. Furthermore, resilient individuals may draw from internal (e.g., personality characteristics) and external (e.g., social support) resources when faced with adversity (Bergeman & Wallace, 1999; Wallace, Bisconti, & Bergeman, 2001). In this study, we investigate which specific individual characteristics may determine whether older adults are more likely to experience changes in fatigue. Examples of individual characteristics determining levels of strength or weakness include temperament and personality (individual differences) and self-regulating skills, such as impulse control and affect regulation (Masten & Powell, 2003). In this study, we focus on personality characteristics and health behaviors as self-regulating skills that may predict differential levels of fatigue.

Another approach in the resilience literature is concerned with stamina in later life. Stamina includes qualities of personal strength such as mental vigor, vitality, and endurance (Colerick, 1985). High levels of stamina entail "resilience and ‘staying power;’ the strength (physical and moral) to withstand disease, fatigue or hardship" (Colerick, p. 997). In this sense, stamina may well be the opposite end of fatigue.

Investigators have reported that physical activity, anxiety, stress, and nutritional intake are linked to self-perceived fatigue in older adults (Avlund, Damsgaard, & Schroll, 2001; Chen, 1986; Liao & Ferrell, 2000). In particular, older adults who remain physically inactive, exhibit high levels of neuroticism, function within stressful environments, and maintain poor nutritional habits are more likely to feel a lack of energy (Avlund et al.; Chen; Liao & Ferrell). Although anxiety symptoms tend to demonstrate relative longitudinal stability and increase vulnerability to life stressors, evidence supports that health behaviors related to exercise and proper nutritional intake may actually buffer or lessen the perception of fatigue among older adult populations (Chen; Penninx et al., 2002; Wetherell, Gatz, & Pedersen, 2001). In essence, personality traits and health behaviors associated with physical exercise and nutritional risk may prove to be salient resources or vulnerabilities over time.

The impact of personal characteristics such as personality and health behaviors on fatigue has to be measured against alternative variables that may be related to fatigue. Such covariates would be demographic characteristics, such as age, gender, ethnicity, and education (Colerick, 1985), as well as health-related influences (Colerick), such as subjective health assessments, level of disability, sleep disturbance, and depression. Finally, because religiosity is related to mortality (McCullough, Hoyt, Larson, Koenig, & Thoresen, 2000) and also serves as an important internal resource for older adults (Martin, Rott, Poon, Courtenay, & Lehr, 2001), we also included it as a potential confound in the relation between internal resources and fatigue.

Numerous researchers have investigated fatigue from the perspective of chronic fatigue syndrome and have reported an association between fatigue and sleep, medical treatment, or medication (Buxton, Frizelle, Parry, Pettigrew, & Hopkins, 1992; Galindo-Ciocon & Ciocon, 1997; Smets, Garrssen, Bonke, & de Haes, 1995). Relatively little effort has been made, however, to address fatigue as a mental state. Hooker and McAdams (2003) noted that personality states may be uniquely suited to capture intraindividual personality processes. In this study, we conceptualized fatigue as a state characterized by a general lack of energy or vigor. By linking personality traits (anxiety and extraversion) and health behaviors with changes in fatigue, we address the question of whether trait structures can influence personality state processes. Therefore, we attempt to answer the following question: Do personality and health behaviors predict changes in fatigue in advanced life? The results of this study should shed light on the question of whether personality and health behaviors are associated with high levels of fatigue.

	METHODS

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Sample participants for this study originated from the Georgia Centenarian Study (Poon et al., 1992); 321 participants (217 women, 104 men) classified as sexagenarians (n = 91), octogenarians (n = 93), and centenarians (n = 137), as well as 178 longitudinal survivors from a follow-up testing, participated in this study. Demographic characteristics of the sample are covered in Table 1. During our initial testing we only included community-dwelling and cognitively intact participants, but we contacted every participant again for the follow-up measure. (We considered cognitively intact participants to be those with a score of 23 or higher on the Mini-Mental State Examination, as discussed by Folstein, Folstein, & McHugh, 1975, or a score of 2 or lower on the Global Deterioration Scale, as discussed by Reisberg, Ferris, de Leon, & Crook, 1982.)

To assess differences between participants in the first and second wave of the study, we computed cross-tabulations and univariate analyses of variance. The results suggested that younger adults were more likely to participate in both waves of the study, ²(N = 321) = 18.86, p <.001, but there were no gender or ethnicity differences, ²(N = 321) = 1.59, p =.21 and ²(N = 321) = 2.61, p =.11, respectively. We also obtained differences for Time 1 only and longitudinal participants for fatigue, F(1, 273) = 5.02, p <.05, health, F(1, 315) = 4.20, p <.05, and physical activity, F(1, 314) = 10.58, p <.01. This indicated that longitudinal participants reported lower levels of fatigue, reported higher levels of subjective health, and exercised more than dropouts of the study. We obtained no mean differences for anxiety, F(1, 308) = 0.76, p =.39, extraversion, F(1, 309) = 0.02, p =.89, stress management, F(1, 291) = 2.62, p =.11, and nutritional risk, F(1, 291) = 0.29, p =.59.

Our main objective in this study was to explore longitudinal predictors of changes in fatigue. To achieve this goal, we analyzed data by using hierarchical multiple regression analyses. We assessed personality (i.e., anxiety and extraversion) and health behaviors (i.e., physical activity, stress management, and nutritional risk) as predictors of fatigue after we controlled for covariates of fatigue at Time 1.

Measures
Demographic variables
The demographic variables we used in this study included age, gender, ethnicity, and education. We asked participants to report the following: (a) their current age; (b) whether they were male or female; (c) whether they were Caucasian, African American, Asian, Hispanic, American Indian, or other; (d) whether their educational achievement included 0–4 years of school, 5–8 years of school, the attendance of some high school, the completion of high school, the attendance of business or trade school, 1–3 years of college, the completion of college, or the attendance of graduate school.

Personality
We assessed anxiety and extraversion by using the second-order factors of the 16PF Personality Factor Inventory (Cattell, Eber, & Tatsuka, 1957; Krug & Johns, 1986). The 16PF is a classic measure of personality that assesses 16 primary personality traits, as well as 5 second-order traits. In this study, we included the second-order factors Anxiety and Extraversion of the 16PF in the analyses, because these traits have received the most theoretical and empirical attention in relation to subjective well-being (Diener, Suh, Lucas, & Smith, 1999).

The Extraversion trait consists of the first-order factors Warmth (10 items), Enthusiasm (13 items), Boldness (13 items), and Self-sufficiency (10 items). Sample items for this scale include, "If asked to work with a charity drive, I would accept or politely say I'm too busy" or "I find it easy to mingle among people at a social gathering." Anxiety consisted of the first-order factors Emotional Stability (recoded, 12 items), Shyness (13 items), Suspiciousness (10 items), Insecurity (13 items), Self-Conflict (10 items), and Tension (13 items). Sample items for this scale include, "I have sometimes been troubled by people's saying bad things about me behind my back, with no grounds at all" or "Once in a while I have a sense of a vague danger or sudden dread for reasons that I do not understand." Original test–retest reliabilities for these scales ranged from.54 to.89. For the current study, Cronbach's alpha for Extraversion and Anxiety was = 0.62 and = 0.68, respectively. Additional second-order factors are Tough Poise, Independence, and Superego–Control, but these dimensions had less desirable reliability ( < 0.60).

Health behaviors
We assessed health behaviors in the areas of physical activity, stress management, and nutritional risk. We measured physical activity by using one single-item indicator from the Older Americans Resources and Services instrument (OARS; Fillenbaum, 1988). Participants were asked to answer "yes" or "no" to the following question: "Do you regularly participate in any vigorous sports activity such as hiking, jogging, tennis, biking, or swimming?"

We assessed stress management by using one single item indicator: "Do you consciously take steps to control or reduce the stress in your life?" Participants were asked to respond 1 = yes, take steps to control; 2 = no, don't take steps; and 3 = not sure. We recoded this item so that a high score indicated high levels of stress management.

The nutritional assessment is a summary score of 16 nutritional-risk measures, such as "trouble biting food" or "lack of appetite" (Wolinsky, Pendergast, Miller, Coe, & Chavez, 1985). Participants were asked to respond to each nutrition risk factor with 1 = yes (have the risk) or 0 = no (don't have the risk). A high score indicated high levels of nutrition risk. In our study, Cronbach's alpha for this measure was = 0.60.

Fatigue
We assessed fatigue with the Fatigue Scale as part of the Eight State Questionnaire (Institute for Personality and Ability Testing, 1975). The scale consists of 12 items. The fatigue personality state is conceptualized as a dimension of short-term, more or less reversible intraindividual change or variability within normal everyday behavior. Persons who score high on fatigue describe themselves as being exhausted, having no energy, being sluggish, being tired, needing rest, being weary, and performing below par. These expressions of fatigue may not be regarded as somatic symptoms of a psychiatric disorder. The reliability coefficient for immediate retest in the standardization sample was r =.92. In this sample, Cronbach's alpha for the fatigue scale was = 0.82.

Covariates
Covariates included subjective health assessments, level of disability, sleep disturbance, depression, and religiosity. We assessed subjective health, level of disability, and sleep disturbance with measures from the OARS (Fillenbaum, 1988). We assessed self-rated health with the following question: "How would you rate your overall health at the present time—excellent (3), good (2), fair (1) or poor (0)?" We assessed disability with the following question: "Do you have any physical disabilities such as total or partial paralysis, missing or nonfunctional limbs, or broken bones?" We recoded items to 0 = no disabilities and 1 = disabilities. We assessed sleep disturbance with this question: "Is your sleep fitful and disturbed?" (yes or no). We used the Geriatric Depression Scale (Yesavage et al., 1983) to control for depressive symptoms. Items summarize to a global depression score that ranges from 0 to 30. Cronbach's alpha in the validation study was = 0.94 (Yesavage et al., 1983). In our sample, the internal consistency was = 0.86. Finally, we used the Religiosity in 5-D assessment (Faulkner & DeJong, 1966) to control for religiosity. This is a 23-item questionnaire including the five dimensions of ideological, intellectual, ritual, experiential, and influential religiosity. In this study, we used the overall summary score of the scale. Faulkner and DeJong reported a coefficient of reproducibility of.90 or higher for all five dimensions. Cronbach's alpha for the total scale in this study was = 0.68.

	RESULTS

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Using Time 1 predictor variables, we computed a blockwise hierarchical regression analysis to compare and assess changes in fatigue at Time 2. The results are summarized in Table 3. We computed three models. The first model included fatigue scores at Time 1 to assess the stability of fatigue over time. The second model included the following possible confounders: gender, ethnicity, education, age, subjective health, disability, religiosity, sleep disturbance, and depression. The third model added individual resources (i.e., personality and health behaviors) to the model. We used blockwise hierarchical regression analyses to assess whether each model was a significant improvement over the previous one.

Model 3 was a significant improvement over Model 2, F(5, 111) = 5.26, p <.001. Fatigue at Time 1, education, age group, religiosity, anxiety, physical activity, and nutritional risk were all significant predictors of fatigue changes. More highly educated participants, older adults, more religious participants, more anxious persons, and those who were less physically active and scored higher on nutritional risk were more likely to show increased fatigue levels at Time 2. (We also recomputed Model 3 to include the second-order personality variables Tough Poise, Independence, and Superego–Control into the equation. None of these personality variables were significant predictors of changes in fatigue: ß = 0.09, p >.05 for Tough Poise, ß = 0.02, p >.05 for Independence, and ß = –0.02, p >.05 for Superego–Control.)

To assess the direction of effect for fatigue and individual characteristics, we used fatigue at Time 1 as a predictor of individual characteristics at Time 2 after we controlled for the stabilities of individual characteristics and the demographic confounders. Results are summarized in Table 4. Fatigue at Time 1 only predicted nutritional risk at Time 2. Participants with high scores on fatigue at Time 1 were higher on changes in nutritional risk at Time 2.

	DISCUSSION

TOP Abstract Methods Results Discussion References

We found age, anxiety, and health behaviors (physical activity and nutritional risks) to be the consistent predictors of changes in fatigue over time. This evidence suggests that personality and physical activity are relevant predictors of fatigue changes and may be important risk or resilience factors in late and very late life.

Age was a strong predictor of changes in fatigue in the longitudinal model, even after we controlled for other predictors. Age may be a strong force for fatigue changes over time, and vulnerability to fatigue may be elevated over time. In support of this interpretation, Newman and colleagues (1996) reported that depletion of energy does show a strong linear increase across age. In other words, individuals who reach advanced old age are more likely than younger persons to display feelings of weakness, tiredness, or fatigue. Thus, the oldest-old may be more susceptible to normative age-related effects that lead to changes in fatigue.

Anxiety has a stronger influence on fatigue changes than does extraversion. Wetherell and colleagues (2001) posited that personality traits such as neuroticism may serve as a source of increasing vulnerability among older adults. As a personality trait, anxiety in advanced later life may contribute to overall increasing fatigue and the loss of energy over time. There are a number of reasons why anxiety appears to play an important role in predicting fatigue. Individuals who score high on anxiety may be more likely to report higher levels of fatigue; alternatively, the constant worry and concern shown by anxious people may simply "wear people out," thereby increasing fatigue levels. Future research should investigate physiological and biological mechanisms that help to explain why anxious people appear to be more fatigued.

Physical activity is an important predictor of changes in fatigue, but fatigue did not predict changes in physical activity. Chen (1986) found that physically inactive older adults are more than twice at risk for being afflicted with fatigue. This finding acknowledges that physical and functional inactivity in later life leads toward an increased vulnerability to becoming fatigued. Health behaviors adopted by older adults may help alleviate the risk of experiencing depressive symptoms related to fatigue. Results from this study demonstrated that health behaviors, especially active engagement in physical activity, are protective of possible fatigue changes. According to Penninx and associates (2002), aerobic exercise can reduce vulnerability to more general depressive symptoms. Consequently, exercise may play a crucial role in reducing fatigue in old and very old adults.

Another health-related behavior that poses important implications for fatigue in later life is nutrition. In this study, we found nutritional risk to have an effect on changes in fatigue, and fatigue predicted changes in nutritional risk at Time 2. Other studies have shown that nutritional risks, such as low calcium intake and body mass, can pose a greater risk for fatigue (Chen, 1986). Nutrition may also influence fatigue through nutritional deficiencies that cause anemia. Deficiencies of iron, vitamin B12, and other nutrients are associated with increased risk for anemia, and anemia is associated with fatigue (Beghé, Wilson & Ershler, 2004). Anemia is one of the nutritional risk factors in the instrument we used (Wolinsky et al., 1985).

The predictors of fatigue remain significant after a number of important covariates are controlled for. For example, Colerick (1985) reported that past health predicted stamina in later life. In our data, subjective health was not related to changes in fatigue.

Zarit, Femia, Gatz, and Johansson (1999) contended that, among very old populations, resources mediate how older persons recover from negative conditions. Personality and health behaviors are important personal resources or vulnerabilities for optimal adaptation and survivorship in late and very late life and may support the notion that stamina plays an important role in late life adaptation (Colerick, 1985). Feelings of tiredness often reflect a decline in various reserve capacities (i.e., physical or functional health; cf. Avlund et al., 2001). In essence, fatigue may stem from the depletion and disablement of physical and functional health. Antonucci and colleagues (2002) have provided conclusive evidence that high resource deficits do result in negative outcomes in later life. As older individuals experience increasing decline and losses with advanced age, fatigue may become a more salient mental state and somatic complaint. Successful survivorship often entails how well old and very old persons use available resources to facilitate and implement strategies of resilience in response to loss (Lang, Rieckmann, & Baltes, 2002). However, resilience to fatigue is dependent on the amount of reserved resources that older adults possess.

Whereas resource-rich survivors display more energy and active engagement in everyday life, resource-poor survivors spend more time in less active or passive pursuits (Lang et al., 2002). Relative to fatigue, it can be argued that resilience among old and very old survivors has two forms: First, resilience is framed by increased resource expenditure with advancing age. Second, resource conservation is used to regain or to stabilize resiliency. In effect, resource availability (e.g., personality, health behaviors) in advanced old age can result in an increased or lower susceptibility to fatigue in later life. Still, the very acts of resiliency can also increase or decrease vulnerability among older adult survivors who must expend resources when threatened by feelings of fatigue. Furthermore, some of the risk or resilience factors may be more modifiable than others. For example, it may be easier to strengthen health behaviors such as physical activity, but it may be more difficult to change the relatively stable trait of anxiety.

This study is not without limitations. The sample is restricted to a Southern older population, and participants were in reasonably good health. Although we had two time points for the longitudinal analysis, additional data points, carefully selected for intervals between measures, would have enhanced the study. Thus, the results reflect a rather unsystematic time frame for the predictors of fatigue. It will be important to assess different time lags to understand how long or after what time interval individual resources or risk factors exert an effect. Additional time points would also allow for the assessment of individual growth curves and for nonlinear changes in fatigue. To the extent that fatigue may be a more fluctuating state concept, multiple assessments may also help researchers to understand the dynamic changes of fatigue in older adults (Hooker & McAdams, 2003). Furthermore, additional time points may help researchers to understand how changes in predictors may influence changes in fatigue. An ordered time sequence would also allow for better specification of directionality (e.g., do changes in health behavior influence fatigue, or does fatigue lead to changes in health behavior).

Finally, the measures used in this study could be improved in future research. For example, this study assessed nutritional risk, which is the risk of having a nutritional problem; it did not actually assess nutrient intake. Measures of nutrient intake might be more sensitive indicators of fatigue. Furthermore, only single-item indicators were available to assess physical activity and stress management. There may be considerable overlap in constructs of physical activity, personality, and fatigue. Physical activity and personality items that emphasize vigorous exercise or activity may also tap into energy, vitality, and energy. Anxious individuals may also overreport health ailments, because other studies have shown a significant association between neuroticism and poor health (Duberstein et al., 2003). Future studies should also consider objective indicators of physical health. Finally, the relatively low reliabilities of the personality variables might have compromised our efforts to detect stronger effects.

Nonetheless, this study succeeded in establishing several key predictor variables of changes in fatigue in very late life. Researchers should continue to increase their understanding of the underlying dimensions of fatigue in later life. Particularly, researchers should seek to further explore and model the development of fatigue over time, to understand the influence of fatigue on adaptation, and to examine changes in resiliency and fatigue versus changes in later life resources.

	Acknowledgments

 
This research was supported in part by Grants RO1 43435-10 and 1 PO1 AG17553-01A1 from the National Institutes of Health.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication February 15, 2005. Accepted for publication October 20, 2005.

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