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Prospective associations between unforgiveness and physical health and positive mediating mechanisms in a nationally representative sample of older adults a

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Asani H. Seawell , Loren L. Toussaint & Alyssa C.D. Cheadle

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Department of Psychology, Grinnell College, Grinnell, IA, USA

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Department of Psychology, Luther College, Decorah, IA, USA

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Department of Psychology, University of California–Los Angeles, Los Angeles, CA, USA Accepted author version posted online: 23 Oct 2013.Published online: 22 Nov 2013.

To cite this article: Asani H. Seawell, Loren L. Toussaint & Alyssa C.D. Cheadle (2014) Prospective associations between unforgiveness and physical health and positive mediating mechanisms in a nationally representative sample of older adults, Psychology & Health, 29:4, 375-389, DOI: 10.1080/08870446.2013.856434 To link to this article: http://dx.doi.org/10.1080/08870446.2013.856434

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Psychology & Health, 2014 Vol. 29, No. 4, 375–389, http://dx.doi.org/10.1080/08870446.2013.856434

Prospective associations between unforgiveness and physical health and positive mediating mechanisms in a nationally representative sample of older adults Asani H. Seawella*, Loren L. Toussaintb and Alyssa C.D. Cheadlec

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Department of Psychology, Grinnell College, Grinnell, IA, USA; bDepartment of Psychology, Luther College, Decorah, IA, USA; cDepartment of Psychology, University of California–Los Angeles, Los Angeles, CA, USA (Received 8 May 2013; accepted 12 October 2013) Objective: This study examined the prospective association between unforgiveness and self-reported physical health and potential positive psychological mediators of this association. Design: Participants were a national sample of 1024 USA’s adults of ages 66 years and older. Data were collected at two time points separated by three years. Main Outcome Measures: Measures of trait unforgiveness, self-rated physical health, socio-demographics, health behaviours and positive psychological traits (e.g. life satisfaction, self-esteem) were included in a comprehensive survey known as the ‘Religion, Aging, and Health Survey.’ Results: The results indicated that unforgiveness was prospectively associated with declines in self-reported physical health three years later, and poor initial self-reported health status did not predict increases in unforgiveness across time. Furthermore, the prospective association of unforgiveness with selfreported health was mediated by a latent positive psychological traits variable. Conclusion: These results confirm cross-sectional findings suggesting that unforgiveness is related to health. The present study also suggests that unforgiveness has a prospective, but not reciprocal, association with self-reported physical health. Unforgiveness may have its association with self-reported physical health through its interruption of other positive traits that typically confer health benefits. Keywords: unforgiveness; older adults; physical health; positive psychological traits

Over the course of a lifetime, individuals are likely to perceive a number of interpersonal transgressions, many of which have the ability to impact health (Thoresen, Harris, & Luskin, 2000). Interpersonal transgressions can be stressful experiences since they involve the perception that another person has caused harm or offense (McCullough, Root, & Cohen, 2006). Though unproductive in many respects, individuals often respond to interpersonal transgressions with unforgiveness, the motivation to think, feel and act negatively towards a transgressor (McCullough et al., 1998). In the extant literature, unforgiveness is often conceptualised and measured in ways that *Corresponding author. Email: [email protected] © 2013 Taylor & Francis

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emphasise the negative motivational and emotional aspects of an individual’s response to injustice (Mauger, Perry, Freeman, & Grove, 1992; McCullough et al., 1998). Limited work has examined the detrimental effects of unforgiveness on health, but what research does exist connects unforgiveness to detriments in well-being (Lawler-Row et al., 2005; Worthington & Scherer, 2004). Hence, the purpose of this study was to examine the three-year prospective association between unforgiveness and physical health in a nationally representative sample of older adults.

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Unforgiveness and physical health A small but emerging body of research has begun to focus on the role that unforgiveness may play in health outcomes since individuals commonly experience unforgiveness in response to interpersonal transgressions (Worthington, 2006; Worthington & Wade, 1999). As a negative response to interpersonal offense that commonly involves grudges, resentment and revenge (Mauger et al., 1992; McCullough et al., 1998; Van Oyen Witvliet, Ludwig, & Vander Laan, 2001), unforgiveness has been consistently related to poorer health in published research. For example, in a study by Witvliet and colleagues (2001), participants who were instructed to harbor grudges as compared to forgiveness showed significantly increased sympathetic nervous system activation, increased facial muscle tension indicative of negative emotion and cardiovascular reactivity. In a series of experimental studies, Lawler-Row, Hyatt-Edwards, Wuensch, and Karremans (2011), Lawler-Row, Karremans, Scott, Edlis-Mattiyahou, and Edwards (2008) and Lawler-Row et al. (2005) have shown that lower levels of unforgiveness are connected to improved physical health including lower systolic blood pressure, heart rate and decreased reactivity. For instance, in one set of their studies (2008, 2011), participants were asked to describe a time that they were betrayed by one of their parents. Unforgiveness was related to higher heart rate and systolic blood pressure during the interview and rest portions of the study. In sum, research has begun to document that unforgiveness is a common stress reaction that is detrimental to well-being (Worthington & Scherer, 2004). Unfortunately, most of these studies utilised young college students, used interview or imagined stimuli and examined only short-term health consequences of unforgiveness. As a result, researchers are limited in their ability to generalise the results of prior work to other age groups or stressors that participants have personally experienced. Finally, prior work makes it difficult to fully understand the longer term health outcomes that may arise as a result of unforgiveness. Unforgiveness and older adults Older adults may be an important population to study the links between unforgiveness and health. For one, the population of older adults in the USA and worldwide of age 65 and older is rapidly increasing; this increase is accompanied by a rise in certain physical illnesses (e.g. cancer, diabetes) in this population (Centers for Disease Control, 2003). Hence, there is a pressing need to understand factors related to the physical health and well-being of this population. In addition, research has indicated that we are likely to become more forgiving as we age (Enright & Gassin, 1992; Wink & Dillon, 2001). For instance, in a study conducted among a national sample of over 1400 individuals aged 18 and older (Toussaint, Williams, Musick, & Everson, 2001),

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forgiveness was higher among older adults and was more strongly related to mental and physical health relative to younger adults. These findings are consistent with the positivity effect observed in older adults (Mather & Carstensen, 2005), which suggests that as adults age, they are motivated to enhance positive and diminish negative emotion. A related theory, socioemotional selectivity theory, explains that as adults age, they selectively reduce their social partners but increase their emotional closeness to those partners (Carstensen, 1992). Taken together, this work suggests that though older adults may be more generally forgiving, unforgiveness may be more detrimental to them because of their greater closeness to those in their more limited social circle. Research by Ingersoll-Dayton, Toges, and Krause (2010) confirms the negative impact that unforgiveness may have for older adults. The results from their large national sample revealed that feeling unforgiven by others had negative influences on depressive symptoms. Finally, studies indicate that over the course of their lifetimes, older adults may experience many interpersonal transgressions that can be stressful particularly when responded to with unforgiveness (Worthington & Scherer, 2004). Research has noted the negative, accumulative effects that stress has on health (McEwen & Wingfield, 2003), suggesting that older adults who characteristically respond to interpersonal transgressions with unforgiveness may be particularly vulnerable to negative health outcomes. Psychological mechanisms A growing body of research supports the unforgiveness and health link, but unfortunately little is known about the mechanisms that might actually convey the potential effects of unforgiveness to health outcomes. Some research has examined variables that might account for the associations between unforgiveness and mental, but not physical, health. For instance, a model described by Toussaint and Webb (2005) suggests that negative psychological tendencies such as depressive rumination and low personal control as well as positive psychological tendencies such as social support, interpersonal functioning and health behaviours may account for the relationship between unforgiveness and depressive symptoms. To date, only the rumination hypothesis has been confirmed by research (Ingersoll-Dayton et al., 2010). Outside of this limited theory and research, there is little, if any, additional work examining the means by which feelings of resentment and a desire for revenge convey negative effects to physical health, and this is especially true of positive psychological traits. The lack of empirical evidence notwithstanding, several positive psychological variables, such as life satisfaction, optimism and well-being, have shown consistent relationships to physical health (Aldwin, Park, & Spiro, 2007) and both positive and negative affective states have been linked to unforgiveness (Kluwer & Karremans, 2009). The present model is based on the extant theoretical and empirical literature, and predicts that the prospective connection between unforgiveness and physical health is mediated by positive psychological characteristics. Present study Our study builds on prior unforgiveness-health research in a few significant ways. First, we chose to assess trait unforgiveness specifically using a measure that examines key

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aspects of this construct (i.e. resentment and bitterness; Worthington & Wade, 1999). Second, to date, much research has been cross-sectional in nature with small samples, making the results of prior work limited in generalisability and the capacity to speak to the direction and development of associations over time. Indeed, a need for large-scale prospective research remains (Worthington, Witvliet, Pietrini, & Miller, 2007). Third, the current study seeks to add to this small literature by determining which variables might in part account for the link between unforgiveness and physical health. The present study sought to address this gap in the literature by prospectively examining associations between trait unforgiveness of others and self-reported physical health using data from the (RAHS; Krause, 2006), a large archival data-set developed from a study of older USA’s adults that were followed over a three-year time period. A secondary aim was to test a number of positive psychological variables as potential mediators of this association. Based on our examination of prior work, our main hypothesis is that trait unforgiveness among older adults will predict poor self-reported physical health three years later, after controlling for socio-demographics and health behaviours. Further, we expect that trait unforgiveness will be inversely related to positive psychological variables (e.g. life satisfaction, self-esteem) that will, in turn, interfere with these variables’ usual protection against poor health. Method Participants Survey sample Data for the present investigation came from the RAHS, an archival dataset developed by Krause (2006) at the University of Michigan. This was a national sample of USA’s older adults of age 66 and older. Residents of Alaska and Hawaii were excluded. Krause (2006) originally decided to limit data collection to Christians to simplify item and scale generation. Participants were currently Christian, previously Christian or nonreligious. Data were weighted to adjust the sample so that it was comparable with the USA’s population with respect to age, sex, education and region of the country. Additional information on this sample and selection methods has been extensively described in previous studies (e.g. Krause & Ellison, 2003). Survey design The design of this study was longitudinal. The first wave of data collection occurred in 2001 and included 1500 Black and White Americans. The second wave of data collection took place three years later in 2004 and included 1024 re-interviews (68% retention). Of those not re-interviewed, 208 had died, 70 were too ill to participate, 112 could no longer be located, 11 had moved to nursing homes and 75 refused. Participants that were not re-interviewed did not differ from those who were re-interviewed or deceased on gender, race, region of residence, education or smoking/drinking status ( p < .05). Participants that were not re-interviewed were about the same age as those who were re-interviewed ( p > .05) and about 3.5 years younger than the deceased (F (2, 1483) = 27.38, p < .001). Participants that were not re-interviewed were also less likely to be married and more likely to be widowed than those who were re-interviewed (χ2 (8, N = 1486) = 43.24, p < .001).

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Sample socio-demographics

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The final sample for the present analyses consisted of 936 participants with complete data. Socio-demographic characteristics of the sample were as follows: (a) 36% male, (b) median age: 74 years, (c) marital status: 49% married, 2% separated, 8% divorced, 36% widowed and 5% never married, (d) 62% high school or greater education, (e) 46% black, 50% white and 4% other race, and (f ) USA’s region: 25% East, 21% Midwest, 14% West and 40% South.

Measures Because the data for this study was collected by Krause (2006) in the Religion, Aging, and Health Survey, the present assessments were limited to those that he and his team decided to include in their data collection project. In 2001, the first year of the study, forgiveness research was just starting to burgeon and few brief, valid, measures existed. Krause was also interested in measuring a broad range of religious, spiritual, psychosocial, health and socio-demographic variables, and consequently, due to time constraints on the survey, utilised brief assessments (i.e. one to three items) for most constructs. He utilised a comprehensive approach to develop the items with several phases of development including: (1) focus groups, (2) in-depth interviews, (3) input from quantitative studies, (4) development of preliminary quantitative measures, (5) expert review, (6) cognitive interviews, (7) pilot study, (8) nationwide survey and (9) psychometric testing. This process led to an item set that offered broad coverage of multiple relevant constructs but did so with efficiency. In the past decade or so, Krause has published dozens of studies showing adequate psychometric properties of many of these scales (Ingersoll-Dayton et al., 2010; Krause & Ellison, 2003).

Unforgiveness Unforgiveness of others was measured at both waves of data collection using two items that specifically tap resentment and grudges, two key aspects of the motivational and emotional nature of unforgiveness (Mauger et al., 1992; McCullough et al., 1998; Van Oyen Witvliet et al., 2001). These items were: (a) ‘How often do you feel resentful toward others for things they have done?’ and (b) ‘How often do you hold a grudge?’ Items were responded to on a Likert-type response scale of 1 (Never) to 4 (Very often). Higher scores represent greater levels of unforgiveness. Internal consistency of the unforgiveness scale and all subsequent scales was estimated using Cronbach’s alpha. Because there were only four ordinal response options on all of the items, we also estimated ordinal alpha using the statistics package R following the guidelines provided by Gadermann, Guhn, and Zumbo (2012). When items are considered ordinal in nature, coefficient alpha has been shown to provide a negatively biased estimate of reliability (Zumbo, Gadermann, & Zeisser, 2007). Therefore, ordinal alpha is an appropriate measure of internal consistency, but as it is still a very new metric, we provide both alpha and ordinal alpha. Coefficient alpha is designated by ‘α’ and ordinal alpha by ‘α0’. Internal consistency for the unforgiveness scale α = .68, α0 = .78 at time 1 and α = .68, α0 = .84 at time 2.

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Test-retest reliability was r = .28. Though this might be considered on the low side of stability for a trait, recent work (Maiden, Peterson, Caya, & Hayslip, 2003) has shown that even traits that are widely considered to be more stable, such as extraversion, neuroticism and openness, show a substantial amount of variability (test-retest rs: N = .35, E = .28, O = .41) in oldest-old adults (mean age = 74). This research also showed that changes in personality in older adults are accounted for by changes in health, social and resource changes. Trait unforgiveness is likely influenced by changing dynamics in these realms as well. As a result, our expectations of stability need to be adjusted in the context of older adults. In sum, these items refer to the frequency of experienced unforgiveness and broadly capture one’s tendencies to engage in unforgiving responses to transgression and even though there is considerable change, there is also real stability and trait-like consistency to this construct.

Self-reported physical health Health was measured at both waves of data collection using three items previously used to form an index of self-rated physical health (McFarland, Smith, Toussaint, & Thomas, 2012). The items were: (1) ‘How would you rate your overall health at the present time?’, (2) ‘Would you say your health is better, about the same or worse than most people your age?’ and (3) ‘Do you think your health is better, about the same or worse than it was a year ago?’ The first item was responded to on a Likert-type response scale of 1 (Excellent) to 4 (Poor). The second and third items are responded to on a Likert-type response scale of 1 (Better), 2 (About the same) and 3 (Worse). Internal consistency for this scale was α = .61, α0 = .65 at time 1 and α = .72, α0 = .79 at time 2. Items were standardised and summed so that higher scale scores represent poorer self-reported physical health. These items are commonly used in epidemiological studies of health in the USA (e.g. National Health Interview Survey). Recently, a number of investigations have examined brief measures of a variety of health-related constructs and found them to be efficient, useful and valid (Bush et al., 2010; Hoeppner, Kelly, Urbanoski, & Slaymaker, 2011; Kavanaugh & Schwarz, 2009; Stiel, Kues, Krumm, Radbruch, & Elsner, 2011). Likewise, a recent investigation has shown that selfreported physical health has good predictive validity in predicting all-cause mortality (Haring et al., 2011).

Positive psychological mediators Four positive psychological mediators were measured including life satisfaction, self-esteem, optimism and personal control. Life satisfaction was assessed using a fouritem scale (α = .75, α0 = .79). An example item was, ‘As I look back on my life, I am fairly well satisfied’. Self-esteem1 was measured using a three-item scale (α = .91). An example item was, ‘I take a positive attitude toward myself’. Optimism was measured using a three-item scale (α = .80, α0 = .85). An example item was, ‘I’m optimistic about my future’. Finally, personal control was measured using a three-item scale (α = .82, α0 = .89). An example item was, ‘I have a lot of influence over most things that happen in my life’. All four of these variables were measured on a 1 (Strongly disagree) to 4 (Strongly agree) response scale.

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Socio-demographics and health behaviour control variables To control for the possibility that associations between unforgiveness and health could be confounded by socio-demographic factors or health behaviours, the following variables were included in all path analytic models (dummy coding for categorical variables is in parentheses): (a) gender (1 = female), (b) age, (c) marital status (1 = separated, divorced, widowed, never married; 0 = married), (d) education, (e) race (1 = black, other race; 0 = white) and (f ) region of residence (three dummy variables representing East, Midwest and West. South was the reference category). The survey also contained two rudimentary assessments of two key health behaviours. Health behaviour items were: (a) ‘Do you smoke cigarettes now?’ and ‘Do you ever drink beer, wine, or liquor?’ Responses to both health behaviour items were ‘Yes’ (1) and ‘No’ (0). Health risks of smoking are evident, but some controversy exists regarding the health effects of moderate alcohol consumption (Nova, Baccan, Veses, Zapatera, & Marcos, 2012). Nevertheless, even moderate consumption by elderly adults has been shown to be related to some increased risks for cardiovascular problems and adverse drug reactions (Mukamal et al., 2005; Onder et al., 2002). For these reasons, it is important to control for variation due to alcohol use in our analyses. Analytic plan To establish factorial validity of the present item sets representing the various constructs, we designed a confirmatory factor analysis in which each of the study constructs were modelled as the latent variables and each of the respective items were indicator variables. This amounts to a measurement only model for the present study with no structural paths. Model fit was evaluated using χ2, RMSEA < .08, RMR < .05, CFI > .90 (Kline, 2005; Schermelleh-Engel, Moosbrugger, & Müller, 2003). Loadings were examined for their magnitude and statistical significance. To test our hypotheses, two theoretical models were examined using cross-lagged path analyses. Cross-lagged effects of unforgiveness and self-reported physical health were examined in a first model to explore our primary study hypotheses. In a second model, potential mediating factors were examined to determine if significant indirect effects were present. Path coefficients reported for all analyses are standardised betas. Model fit was evaluated as described above and all paths were evaluated for significance at the p < .05 level. Results The measurement model was tested using MLE in AMOS 19. Loadings ranged from .34 to .91 and all were statistically significant (p < .001). Model fit was acceptable, χ2 = 482.16, p < .001 CFI = .95, RMSEA = .05. Correlations between factors varied widely and ranged from r = −.44 to r = .65. Average factor correlation was r = .04. The results of the measurement model suggest that the conceptualisation of the constructs fit the item data well. This provides additional psychometric support for scales developed by Krause (2002), and offers some indication of the factorial validity of the scales used in testing our hypotheses below. The cross-lagged unforgiveness and self-reported physical health model (see Figure 1) was tested using maximum likelihood estimation (MLE) in AMOS 19.

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Figure 1. Cross-lagged effects of unforgiveness and physical health, controlling effects of gender, age, marital status, education, race, current smoking status, current drinking status and geographic region of residence. * p < .05, **p < .01, ***p < .001.

Several fit indices were examined to evaluate the overall fit of the model: χ2 (1, N = 940) = .549, p > .05; CFI = 1.00; RMR = .001; RMSEA = .000. Taken together, these fit indices suggest that the hypothesised model was an acceptable fit for the data in this study. Path coefficients for the model are standardised beta weights and are net of control for the effects of all socio-demographic and health behaviour control variables. Unforgiveness at time 1 predicted poorer self-reported physical health at time 2 (B = .062, p < .05), but the reverse was not true (B = .004, p > .05). Unforgiveness at time 1 predicted unforgiveness at time 2 (B = .261, p < .001) and poor self-reported physical health at time 1 predicted poor health at time 2 (B = .374, p < .001). The mediating effect of positive psychological traits on the association of unforgiveness and self-reported physical health was tested in a subsequent model (see Figure 2).

Figure 2. Cross-lagged mediation model of unforgiveness, positive traits and physical health. Controlling effects of: Gender, age, marital status, education, race, current smoking status, current drinking status and geographic region of residence. Control variables/paths and unanalysed correlations of exogenous variables not shown. ** p < .01, ***p < .001.

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The four variables chosen to represent the broader latent construct of positive psychological characteristics were first evaluated to determine if a single positive psychological latent construct underlie the four separate variables. A confirmatory factor analysis of the four observed variables was conducted using MLE. The fit of the one-factor model proved to be acceptable χ2 (2, N = 940) = 1.804, p > .05; CFI = 1.00; RMR = .003; RMSEA = .000. Standardised loadings for life satisfaction, self-esteem, optimism and personal control were .561, .683, .733 and .551, respectively. All indicators loaded at a statistically significant level (p < .001). The structural model was subsequently estimated. Unforgiveness predicted decreased levels of positive psychological traits (B = −.258, p < .001) and these traits, in turn, showed protective associations with future poor health (B = −.097, p < .01). The indirect effect of unforgiveness through positive psychological traits on later self-reported physical health was statistically significant (B = .020, p < .05) and small in size according to Kenny’s rubric (Kenny, 2012). The reverse-causal indirect effect of positive psychological traits through unforgiveness on later self-reported physical health was not statistically significant (B = −.012, p > .05). The direct effect of unforgiveness on later self-reported physical health was no longer significant after inclusion of the latent mediator (B = .041, p > .05, not shown). The fit of this model was acceptable χ2 (57, N = 940) = 119.088, p < .05; CFI = .970; RMR = .020; RMSEA = .034. Given that the direct effect of unforgiveness on self-reported physical health was not significant in the mediation model, this path was removed and the model was re-estimated. This resulted in a model that had an equally good fit to the data as indicated by a virtually equivalent chi-square change fit statistic after conducting a nested one-degree of freedom model test Δχ2 (58, N = 940) = 1.631, p > .05. In sum, one important mechanism by which unforgiveness acts on future health status is through interference with concurrent positive psychological traits that are protective against poor health. The indirect effect entirely accounts for the direct effect of unforgiveness on poor health. Discussion Three-year prospective associations between unforgiveness and physical health The untoward associations between unforgiveness and health are just beginning to be documented (e.g. Toussaint & Cheadle, 2009; Witvliet et al., 2008). This literature, while continuing its early development, remains relatively small in size and scope. Theoretical and conceptual models have been offered that guide predictions regarding unforgiveness and health (Toussaint & Webb, 2005; Worthington et al., 2007), but few empirical studies have provided evidence of this connection. The present study supplements this limited empirical literature by specifically examining unforgiveness and its connection to self-reported physical health among a large, national sample of older adults. As one of only a handful of studies of its type, the present results offer at least three important insights into the connection between unforgiveness and physical health. First, we demonstrate that unforgiveness prospectively correlates with self-reported physical health. This confirms findings from cross-sectional studies that show that unforgiveness is associated with physical health conditions such as heart disease (Toussaint & Cheadle, 2009) and offers the possibility that the non-salutary potential of unforgiveness may reach outside the cardiovascular realm to impact physical health more broadly.

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Second, unforgiveness is associated with prospective health changes above and beyond traditional socio-demographic and health behaviour predictors demonstrating its unique effect. These findings support theoretical and empirical work that has pointed to an important role of unforgiveness in health (e.g. Ingersoll-Dayton et al., 2010; Lawler-Row et al., 2005; Toussaint & Webb, 2005). The third useful insight into the connections between unforgiveness and health that the present findings afford is that unforgiveness is associated with self-reported health in an older adult sample. As noted above, the average age of respondents in this sample was about 75 years and with age comes the potential to feel the unmitigated effects of accumulating stress from years of interpersonal offenses. The accumulated effects of chronic stress are well-documented (McEwen & Wingfield, 2003) and undoubtedly manifested in poorer physical health. As the national and international aged population continues to swell over the coming decades (Centers for Disease Control, 2003), our ability to meet the needs of this growing segment of the population will certainly require healthcare providers to consider complementary and alternative options in protecting health. For instance, among elderly adults with terminal cancer, forgiveness therapy resulted in improvements in psychological well-being including increases in forgiveness and quality of life, and reductions in anger (Hansen, Enright, Baskin, & Klatt, 2009). Hence, interventions aimed at reducing unforgiveness such as psycho-education (Worthington, 2005) could certainly go a long way toward helping to manage the toll of accumulated unforgiveness issues for the aged and consequently may contribute to improved physical health. What are the mechanisms? Utilising the Religion, Aging and Health Survey (RAHS; Krause, 2006) offers the distinct advantage of being able to examine potential mediating mechanisms that might explain why unforgiveness predicts self-reported physical health. We tested the mediating effects of a latent positive psychological mediator representing the observed indicators of self-esteem, life satisfaction, optimism and personal control. This latent variable possessed strong psychometric characteristics and offered a parsimonious and statistically efficient means of examining a mediational model. The results of our analyses showed that positive psychological traits do convey the effects of unforgiveness to physical health and this was supported by a statistically significant indirect effect. Importantly, two other findings from our mediation model warrant mentioning. First, there was no reverse causality present in the model. That is, while positive psychological traits mediated the association of unforgiveness with health, unforgiveness did not mediate the reverse causal association between positive psychological traits and health. Second, after including positive psychological traits in the model, the direct effect of unforgiveness on health was reduced to zero, and further, its removal from the model did not significantly decrease the model’s fit to the data. These statistical outcomes are important because they suggest that unforgiveness acts entirely through disruption or contamination of other positive psychological characteristics and, in effect, suppresses their otherwise salutary connections with health. Furthermore, this is not a threevariable tautology because indirect effects of unforgiveness through positive psychological traits are present but indirect effects of positive psychological traits through unforgiveness are not. In short, the statistical findings support the notion that there is a

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clear unidirectional effect of unforgiveness through positive psychological traits on self-reported physical health, and this indirect association fully explains the prospective association between unforgiveness and later physical health. The mediation model offers insights into the impact that unforgiveness might have on otherwise salutary psychological traits. This extends previous theory and empirical work, which has tended to emphasise connections between unforgiveness and negative psychological traits such as rumination (e.g. Ingersoll-Dayton et al., 2010; Toussaint & Webb, 2005). Future work might examine positive psychological traits such as gratitude, humility or other positive psychological variables, to determine what contribution these variables might add to understanding the connection between unforgiveness and health. Indeed, unforgiveness may well impact adversely an entire constellation of positive psychological characteristics that have otherwise been shown to offer defense against poor mental and physical health (e.g. Peterson, Stark, & Seligman, 2005; Seligman, Steen, Park, & Peterson, 2005). Other possibilities exist to examine mechanisms of the unforgiveness and health connection. Rumination and anger have been shown to be important mediators, but in only a few studies (Lawler-Row et al., 2008; Burnette, Taylor, Worthington, & Forsyth, 2007). These mechanisms were not tested in the present analyses and may offer equally important insight into explaining this relationship. Furthermore, constructs such as social support/loneliness, spiritual transcendence and God-image, and other psychosocial and religious/spiritual variables have received very little attention as possible mechanisms. Future work might do well to incorporate a broader assessment of these types of variables and consider them simultaneously as opportunities for these types of designs arise. Limitations Limitations of this research include the use of two-item and three-item measures with modest alphas and limited information about validity. More precise measurements of forgiveness and positive psychological variables certainly exist such as the Enright Forgiveness Inventory; (Enright, Rique, & Coyle, 2000) and the Coopersmith SelfEsteem Inventory (Coopersmith, 1981), but these instruments are of 50–60 items or more in length. Because of this, brief assessments are fairly common in large-scale, population-based data collection due to survey time constraints. This limitation notwithstanding, the unforgiveness measure assesses key features of unforgiveness, resentment and grudges (Mauger et al., 1992; McCullough et al., 1998), and items selected for health and positive psychological variables were carefully chosen by the original designers of the survey to ensure validity (Krause, 2002). Second, while we are enthused by the prospective association between unforgiveness and self-reported physical health, we are fully aware that variable omission could have biasing effects on our model. That is, by omitting key variables we may not have fully accounted for variance in either our predictors or outcomes and this may result in our estimates being biased from the true population effect. Though we have attempted to limit this possibility, nothing short of experimental designs will allow for causal conclusions. Given that experimental unforgiveness paradigms for investigating effects on global physical health may be short-term, contrived, lack ecological validity or lack generalisability, prospective survey designs may offer the best insights into the temporal connections between unforgiveness and health.

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A third issue may trouble some readers, and this has to do with the absolute size of our unforgiveness-health direct and indirect effects. There are a few reasons to consider the value of these statistically significant, though small, associations. First, Schmidt (1996) has argued that small effects may be important if shown to be replicable. The present findings add to a small but consistent body of work showing that the unforgiveness-health association is indeed replicable. Second, Abelson (1985) has suggested that small effects may be important if these effects accumulate over time. Indeed, our hypothesis is not that the solitary effect of unforgiveness on health over a three-year time period is crucial to one’s long-term well-being, but that the small yet real direct and indirect deleterious effects of unforgiveness will accumulate over years of life and take an increasingly negative health toll. Given that the median age of the sample was 74 years, and a 75-year old individual can expect to live approximately another 10 years (Aries, 2012), the deleterious impact of unforgiveness on self-reported physical health could continue to compound. Furthermore, a common issue of concern is whether unforgiveness is prospectively associated with and is not simply concomitant with poor health. Theoretically, this is a critically important issue to resolve that has heretofore not been sufficiently addressed empirically. Theoretical models predict that unforgiveness is a factor in health (e.g. Toussaint & Webb, 2005) and the present findings support this contention. Conclusion Though a small body of work has shown cross-sectional associations between unforgiveness and physical health, to date, we are not aware of any studies demonstrating a prospective association in a representative older adult sample. This study provides a first glimpse at these prospective associations and tests a latent mediating mechanism. Future work should aim to replicate these results and address additional mechanisms of action. Continued attention to the impact of unforgiveness on physical health in older adults may offer new and innovative approaches to integrative and complementary care for older adults facing declining health in an era where effective prevention of illness and promotion of well-being will be paramount to meet the challenges of healthcare in the foreseeable future in the USA and worldwide. Note 1.

Ordinal alpha could not be computed for the self-esteem variable because responses on this variable created a non-conformable array and the polychoric correlation matrix could not be computed. This is a known drawback of computing ordinal alpha from polychoric correlation matrices that are computationally intensive to estimate and can result in non-positive definite matrices (Gadermann et al., 2012).

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Prospective associations between unforgiveness and physical health and positive mediating mechanisms in a nationally representative sample of older adults.

This study examined the prospective association between unforgiveness and self-reported physical health and potential positive psychological mediators...
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