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. Author manuscript; available in PMC: 2019 Jan 1.
Published in final edited form as: Health Psychol. 2017 Oct 2;37(1):42–50. doi: 10.1037/hea0000549

An Examination of Dyadic Changes in Optimism and Physical Health over Time

William J Chopik 1, Eric S Kim 2, Jacqui Smith 3,4
PMCID: PMC5764777  NIHMSID: NIHMS909738  PMID: 28967767

Abstract

Objective

Having a partner high in optimism is associated with better health in older adults. However, partners, just like individuals, are not static entities and likely change considerably over time. The current study examined whether changes in one person's optimism was associated with corresponding changes in his/her partner's health over a four-year period.

Methods

We employed a sample of 2,758 heterosexual couples (5,516 individuals; Mage = 65.81, SD = 9.00), married an average of 36.06 years. Median level of education was at least a high school education (13.0% had less than a high school education; 56.1% had a high school education; 30.9% had at least some college education). Both couple members filled out measures of optimism and health twice over a four-year period.

Results

Having a partner high in optimism at baseline was associated with increases in an actor's optimism four years later (r = .08, p < .001). Baseline self-rated health and chronic illness of actors and their partners were mostly unrelated to changes in optimism. However, actor (rs > .05) and partner (rs > .03) changes in optimism were associated with changes in self-rated health and chronic illnesses over time. There was also some evidence for an interaction effect (rs > .03), such that the worst outcomes were experienced among couples in which both members decreased in optimism.

Conclusion

This is the first study to examine how changes in psychological characteristics are associated with changes in health within and across romantic partners. Possible mechanisms are discussed.

Keywords: optimism, relationships, Health and Retirement Study, older adults, lifespan development

Dispositional optimism, the generalized expectation that good things will happen in the future, has been linked with an array of positive health outcomes, including fewer sick days after major stressful events (e.g., death or major illness in family), enhanced immune response after an influenza vaccination, and a reduced risk of heart disease, stroke, and mortality (Boehm & Kubzansky, 2012; Chida & Steptoe, 2008; Kim, Park, & Peterson, 2011; Kim, Smith, & Kubzansky, 2014; Tindle et al., 2009). Having optimistic people in one's social network is also associated with better health and mobility over time (Kim, Chopik, & Smith, 2014). However, the people around us are not static entities and likely change considerably over time, particularly with respect to their perspectives about the future. How do these changes affect us? Are one person's changes in optimism associated with changes in another person's health? The current study examined these questions among older couples followed over a four-year interval.

Changes in Optimism between Partners

In a recent study, we demonstrated that there is a curvilinear relationship between age and optimism among older adults followed over a four-year period (Chopik, Kim, & Smith, 2015). Specifically, optimism increases until approximately age 70, before declining in later life. Many theoretical perspectives account for this relationship. For example, socioemotional selectivity theory states that, as people age, they become increasingly aware of their shortening time horizon. This awareness then leads older adults to prioritize emotional meaning, emotion regulation, and well-being (Carstensen, Isaacowitz, & Charles, 1999; Reed & Carstensen, 2012). As a result, older couples tend to collaborate more with each other, prioritize interpersonal enjoyment, and tend to view their spouse's behavior in an overly positive light (Berg et al., 2011; Rook, 2015; Story et al., 2007). However, as people age, they may ultimately be overwhelmed by declines in cognitive and physical abilities later in life. Thus, individuals may adjust their expectations about the future, which would predict declines in optimism in later life (Charles & Luong, 2013; Sweeny & Krizan, 2013). Thus, variability in optimism could be more closely tied to changes in mortality-related indicators, like health and chronic illnesses, which have been identified as factors leading to change in longitudinal studies at the individual level (Deci & Ryan, 2000; Gerstorf et al., 2008; Lang et al., 2013).

Although these approaches offer an explanation of why an individual's perspective about the future might change, the influence of a partner's characteristics on changes in optimism is unclear. For example, it is unclear whether having an optimistic partner enhances someone else's optimism over time. Longitudinal studies examining how emotions and psychological states spread across social networks of interconnected individuals suggest that one person's optimism may affect their partner (Cacioppo, Fowler, & Christakis, 2009; Fowler & Christakis, 2008). Indeed, positive, constructive, and optimistic responses to a partner's disclosure of good news predicted greater relationship satisfaction and stability over time in a study of couples followed over 2 months (Gable, Gonzaga, & Strachman, 2006). In another study, optimists perceived that their partners engaged more constructively during conflicts, which led to greater conflict resolution in couples and predicted relationship longevity 1 year later (Srivastava et al., 2006). Many of the benefits of optimism in close relationships stem from the feelings of support optimists receive from their partner. Srivastava and colleagues (2006) suggest that optimists' tendencies to see their relationship in a positive light leads to more optimistic responses between people and better relationship outcomes. Thus, we hypothesized that having a partner high in optimism would be associated with increases in an individual's optimism over time.

A partner's health status may also affect how individuals think about the future. Changes in health status often correspond to changes in people's personalities and may affect their expectations about the future (Jokela et al., 2014). Further, the declining health of one person could also precipitate changes in optimism in their partner (Hoppmann, Gerstorf, & Hibbert, 2011). The prospect of growing old without their partner may lead individuals to adopt more pessimistic outlooks. Alternatively, there is some evidence that optimism could be resilient to changes in health status, in that a partner's health status would not change a person's optimism over time (Schou et al., 2005; Stanton & Snider, 1993; Stiegelis et al., 2003). Thus, we examined if individual and partner health statuses were associated with changes in optimism over time.

Partner Associations with Changes in Health in Older Adulthood

We were also interested in the question of whether these changes in one person's optimism were then associated with changes in health in their partner. We hypothesized that when one partner increases in optimism, their partner will also have better health over time.

Extant research demonstrates the large influence that a partner's characteristics can have on individual health and well-being in cross-sectional studies of couples (Orth, 2013). For example, individuals have the ability to pressure their partner to adhere to medical treatments, leading to faster recovery over time (Stephens et al., 2009). The dispositions and coping styles of individuals also lead to important outcomes for partners. In a study of 1,970 couples, having a partner high in optimism at baseline was associated with better self-rated health, greater mobility, and fewer chronic illnesses for individuals over time (Kim, Chopik, et al., 2014).

Many studies examining the effect of partner characteristics on individual outcomes often examine how partner characteristics at baseline affect changes in an individual's health over time (Kenny, Kashy, & Cook, 2006). However, the ways in which individual characteristics change over time may also exert a large influence on a partner's health. In fact, couples show a large degree of coordinated changes over time—they tend to change in similar ways with respect to their social activities, physical limitations, cognition, health, and happiness (e.g., Hoppmann & Gerstorf, 2009; Hoppmann et al., 2011). Changes in psychological characteristics within couples also predict relationship satisfaction and stability over time (Erol & Orth, 2014). When one person initiates a positive health change, their partner is quick to follow, whether it be quitting smoking, drinking less, exercising more, going for a cholesterol screening, getting a flu shot, or losing weight (Jackson, Steptoe, & Wardle, 2015). Despite evidence suggesting that the health behaviors of couples are intimately related, it is unclear if changes in one person's disposition (i.e., optimism) are associated with changes in his or her partner's health.

The Current Study

The current study examined whether changes in one person's optimism were associated with changes in his or her partner's health. We began by examining whether having an optimistic partner predicts increases in optimism over time. Then, we employed a variation of the actor-partner interdependence model (Kenny et al., 2006) to predict changes in self-rated health and chronic illnesses from individual (i.e., actor) and partner changes in optimism. A large sample of older couples (N = 2758) from the Health and Retirement Study completed measures of optimism and health at two waves, spaced four years apart. Based on research demonstrating that psychological states spread across groups of individuals over time (Fowler & Christakis, 2008), we hypothesized that having an optimistic partner would be associated with increases in optimism over a four-year period. We also hypothesized that increases in optimism would be associated with higher levels of subjective health and fewer chronic illnesses over time. We expected these hypotheses to be true of both actors and partners. This second hypothesis was based on research demonstrating that changes in psychological characteristics in one person are linked to changes in physical health among close others over time (Hoppmann & Gerstorf, 2009; Hoppmann et al., 2011). We also test alternative explanations for how optimism and health are related over time, such as whether baseline health status facilitates changes in optimism. Thus, the current study departs notably from previous examinations of actor and partner optimism and health in two important ways (Kim, Chopik, et al., 2014). First, we examine the antecedents of changes in optimism within couples, particularly how initial levels of optimism and health of both actors and partners facilitate changes in optimism over time. Second, we examine the implications of changes in optimism for health both within and across actors and partners. In Kim et al. (2014), optimism was assumed to not vary over time in conjunction with health and a later study found evidence to the contrary at the individual level(Chopik et al., 2015). Hence, in the current study, we relax this assumption by examining concurrent interpersonal changes in optimism and health in couples over a four-year period. This work closely resembles studies on correlated change between psychological characteristics and health conducted at the individual level (Allemand, Schaffhuser, & Martin, 2015; Chopik et al., 2015; Takahashi et al., 2013). However, this is the first study to examine these changes processes within a dyadic context.

Method

Sample and Procedure

The Health and Retirement Study (HRS) is a nationally representative and prospective panel study that has surveyed more than 22,000 Americans aged 50+ every two years (Sonnega et al., 2014). The University of Michigan's Institute for Social Research is responsible for the study and provides extensive documentation about the protocol, instrumentation, sampling strategy, and statistical weighting procedures. The current study's analyses were considered exempt by Michigan State University's Institutional Review Board.

In 2006, a random 50% of HRS respondents were selected and then visited for an enhanced face-to-face interview. In 2008, the remaining 50% of HRS respondents were visited for an enhanced face-to-face interview. At the end of each interview, respondents received a self-report psychosocial questionnaire every other wave (every four years) that they completed and mailed to the University of Michigan. Thus, two distinct cohorts were formed that had two psychosocial assessments four years apart (i.e., Cohort 1:Assessed in 2006 (Wave 1) and 2010 (Wave 2); Cohort 2:Assessed in 2008 (Wave 1) and 2012 (Wave 2)). The cohorts were combined into one sample for the present analyses to increase statistical power and precision (as done in Chopik et al., 2015); in line with findings from past studies, interaction terms with cohort showed that cohort source did not moderate any of the effects reported below. As such, the current sample also includes many more couples than the previous study by Kim and colleagues (2014), which only examined one cohort of older adults.

The current study considered data from any couples who reported complete information on: optimism and health measures (self-rated health and chronic conditions) at both time points, as well as covariate information at baseline. Hence, 1,654 couples were removed from the analyses because of missing data on these variables and the final sample consisted of 2,758 heterosexual couples (5,516 individuals). The current sample differed from the broader sample, albeit these differences were small or negligible. Specifically, the current sample, compared to the broader HRS sample, was more optimistic (dWave1 [dW1] = .11; dWave2 [dW2] = .08), had higher self-rated health (dW1 =.28; dW2 = .17), had fewer chronic conditions (dW1 = .21; dW2 = .05), and was younger on average (dW1 = .30). The final analytic sample had a mean age of 65.81 years (SD = 9.00). The couples were married an average of 36.06 years (SD = 15.38). Median level of education was at least a high school education (13.0% had less than a high school education; 56.1% had a high school education; 30.9% had at least some college education).

Measures

Optimism

Optimism was measured at both waves using the Life Orientation Test-Revised (LOT-R). Studies have shown that the LOT-R has good reliability and validity (Scheier, Carver, & Bridges, 1994; Tindle et al., 2009). A sample item is, “In uncertain times, I usually expect the best.” Participants are asked to rate the extent to which they agree with each item on a scale ranging from 1 (strongly disagree) to 6 (strongly agree). In total, six items were used to assess optimism (αW1=.75; αW2=.76). Three negatively worded items were reverse scored, and then all of the items were averaged to create a scale for optimism.

Health Measurements

Self-rated health was assessed with a single item, “Would you say your health is excellent, very good, good, fair, or poor?” Participants rated their health on a scale ranging from 1 (poor) to 5 (excellent). An index of chronic illnesses (ranging from 0-8) was computed at each wave. Participants were asked to report if he or she was diagnosed (yes/no) by a physician with any of the following: (1) high blood pressure, (2) diabetes, (3) cancer or a malignant tumor of any kind, (4) lung disease, (5) coronary heart disease including heart attacks, angina, and congestive heart failure, (6) emotional, nervous, or psychiatric problems, (7) arthritis or rheumatism, and (8) stroke. Higher values reflect more health problems.

Closeness

Marital closeness was assessed at Wave 1 and was allowed to vary across participants rather than creating a couple-level variable for closeness (as perceptions of closeness might vary within a relationship). Closeness was assessed with a single item, “How close is your relationship with your partner or spouse?” Participants rated their feelings of closeness on a four-point scale ranging from 1 (not at all close) to 4 (very close).

Results

To account for the interdependence of individuals within dyads, we used multilevel modeling (MLM) procedures recommended for dyadic data analysis (Kenny et al., 2006). MLM estimates both actor effects (associations between a person's own changes in optimism and his/her own health) and partner effects (associations between a person's changes in optimism and his/her partner's health) while accounting for the statistical non-independence of members in a couple. Further, these analyses enabled us to model actor and partner effects simultaneously, such that a significant partner effect can be interpreted as a contribution over-and-above the contribution of actor effects. Following recommended procedures, gender was contrast-coded (-1 = men, 1 = women) and other variables (partner/actor optimism) were grand-mean centered. Changes in self-rated health and chronic illnesses served as the dependent measures.

Preliminary Analyses

Descriptive statistics and correlations among primary study variables are presented in Table 1 for husbands (lower diagonal) and wives (upper diagonal) separately. Actor and partner optimism were significantly correlated at each time point. The correlation between actor and partner optimism was not moderated by age at Wave 1 (p = .75) or Wave 2 (p = .76), suggesting that the similarity of optimism between partners in a couple was constant across ages. Actor optimism at each wave was associated with better self-rated health and fewer chronic illnesses at each wave. Partner optimism was also associated with better self-rated health and fewer chronic illnesses at each wave. Health and chronic illnesses were correlated across waves. Ostensibly, little linear change was observed from wave 1 to wave 2 judging by the closeness of the mean values of optimism across time (see Table 1). However, this closeness masks curvilinear changes and individual differences in change that occur across the lifespan, which is further supported by the small amount of shared variance arising from the stability of optimism over time.

Table 1.

Correlations among primary study variables.

Wave 1 Wave 2
1 2 3 4 5 6 7 8 M SD
Wave 1 1. Actor Optimism .29* .30* -.15* .62* .26* .28* -.17* 4.64 .96
2. Partner Optimism .29* .18* -.09* .25* .62* .19* -.11* 4.50 .93
3. Health .30* .17* -.46* .30* .17* .65* -.47* 3.46 1.01
4. Chronic Illnesses -.15* -.06* -.43* -.16* -.11* -.41* .87* 1.77 1.30
Wave 2 5. Actor Optimism .62* .26* .28* -.16* .29* .33* -.19* 4.62 .95
6. Partner Optimism .26* .62* .16* -.09* .29* .20* -.13* 4.47 .93
7. Health .26* .15* .62* -.41* .29* .15* -.48* 3.37 1.01
8. Chronic Illnesses -.16* -.08* -.43* .86* -.17* -.10* -.45* 2.14 1.40
M 4.50 4.64 3.36 1.94 4.47 4.62 3.22 2.36
SD .93 .96 1.03 1.36 .93 .95 1.04 1.43
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Note. Husbands are in the lower diagonal; wives are in the upper diagonal. N = 2755 to 2758.

**

p < .01.

Models of Dyadic Change

We hypothesized that having a partner high in optimism at Wave 1 would lead to increases in actor optimism, over-and-above the contribution of an actor's optimism at Wave 1. We also examined whether initial health status of both actors and partners were associated with changes in actor optimism. This analysis constitutes a test of whether health status (of both an actor and his/her partner) affects peoples' dispositions about the future (i.e., their optimism).

Multi-level models were constructed in which an actor's optimism at Wave 2 was regressed on actor and partner optimism at Wave 1, actor and partner self-rated health at Wave 1, actor and partner chronic illnesses at Wave 1, age, gender, length of marriage, marital closeness, and education. The residual variance in an outcome at time 2 after controlling for initial levels of that variable at time 1 can be interpreted as change in that outcome (Kenny et al., 2006). As seen in Table 2, actor optimism at Wave 1 was associated with actor optimism at Wave 2. Having a partner high in optimism at Wave 1 was also associated with higher actor optimism at Wave 2. Actor and partner chronic illnesses and partner self-rated health were unrelated to changes in actor optimism. However, actor self-rated health was positively related to changes in actor optimism, such that healthier actors reported increases in optimism over time.

Table 2.

Dyadic models for changes in optimism.

Optimism at Wave 2
b SE(b) Z p r
Intercept 1.40 .12
Gender .04 .01 4.42 < .001 .08
Age -.002 .002 -1.17 .24 .02
Actor Optimism Wave 1 .56 .01 47.43 < .001 .55
Partner Optimism Wave 1 .06 .01 5.57 < .001 .08
Actor Health Wave 1 .07 .01 6.23 < .001 .09
Partner Health Wave 1 .01 .01 .43 .67 .01
Actor Illnesses Wave 1 -.01 .01 -1.58 .11 .02
Partner Illnesses Wave 1 -.02 .01 -1.88 .06 .03
Relationship Length .001 .001 1.60 .11 .03
Closeness .02 .02 1.32 .19 .02
Education
 High School .17 .04 4.67 < .001 .07
 Some college .07 .03 2.20 .03 .03
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Note. N = 5516. Effects are reported as unstandardized regression coefficients; Gender: -1 = men, 1 = women. The reference group for education is having less than a high school education.

An important consideration in research using the actor-partner interdependence model involves testing whether actor and partner effects are “distinguishable” (i.e., moderated) by gender, such that the relationships observed may be stronger among husbands or wives (Kenny et al., 2006). To test for this distinguishability, the moderating role of actor gender on actor optimism and partner optimism was included in the model. We also examined whether any actor/partner associations were moderated by actor age, such that actor/partner optimism might be associated with optimism more so among the young-old or the oldest-old. None of these effects were moderated by actor gender (ps>.10) or actor age (ps>.27). The only exception was a significant age × actor optimism effect (r=.03, p=.03) in which individuals increased in optimism below the age of 70 and decreased thereafter, reproducing the findings of Chopik et al. (2015).

Associations between Actor/Partner Changes in Optimism and Changes in Health

Our main research question was whether partner changes in optimism were associated with changes in actor self-rated health and chronic conditions. We constructed a multi-level model in which change scores were regressed on changes in health/illnesses. Actor and partner change scores (for optimism, health, and illnesses) were computed by predicting Wave 2 scores from Wave 1 scores and saving the residuals. Residual change scores partial out initial differences at baseline to isolate only change from one time point to another. Two models were then fit regressing changes in self-rated health (Table 3) and chronic illnesses (Table 4) from actor changes in optimism, partner changes in optimism, and the interaction between these two change scores. Age, gender, length of marriage, marital closeness, and education were added as covariates. Actor increases in optimism were associated with better self-rated health and fewer chronic illnesses over time, reproducing previous research (Chopik et al., 2015). Further, partner increases in optimism were also associated with better self-rated health and fewer chronic illnesses over time. Lastly, there was a significant actor optimism changes × partner optimism changes interaction for changes in self-rated health but not chronic illnesses (although this association was marginally significant). As seen in Figure 1, spousal increases in optimism buffered the negative effect of actor decreases in optimism.

Table 3.

Dyadic models for changes in health.

Changes in Health
b SE(b) Z p r
Intercept .29 .11
Gender .03 .01 2.46 .01 .05
Age -.01 .002 -4.23 < .001 -.07
Actor Changes in Optimism .11 .01 7.49 < .001 .10
Partner Changes in Optimism .04 .01 2.38 .02 .03
Actor × Partner Changes in Optimism -.04 .02 -2.26 .02 -.04
Relationship Length .0001 .001 .14 .89 .003
Closeness .04 .02 2.37 .02 .04
Education
 High School .17 .04 4.69 < .001 .07
 Some college .07 .03 2.07 .04 .03
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Note. N = 5516. Effects are reported as unstandardized regression coefficients; Gender: -1 = men, 1 = women. The reference group for education is having less than a high school education.

Table 4.

Dyadic multi-level models for changes in chronic illnesses.

Changes in Illnesses
b SE(b) Z p r
Intercept -.17 .10
Gender -.03 .01 -2.89 .004 -.05
Age .003 .001 2.21 .03 .04
Actor Changes in Optimism -.04 .01 -3.24 .001 -.05
Partner Changes in Optimism -.03 .01 -2.08 .04 -.03
Actor × Partner Changes in Optimism .03 .02 1.68 .09 .03
Relationship Length -.0002 .001 -.25 .80 -.005
Closeness .003 .02 -.22 .83 -.003
Education
 High School -.08 .03 -2.38 .02 -.02
 Some college -.02 .03 -.69 .49 -.004
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Note. N = 5516. Effects are reported as unstandardized regression coefficients; Gender: -1 = men, 1 = women. The reference group for education is having less than a high school education.

Figure 1.

Figure 1

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Actor × partner optimism changes predicting self-rated health.

Note. Regression lines are plotted at one standard deviation above and below the means of partner changes in optimism. Among participants whose partners increased in optimism, the slope of actor changes in optimism on changes in health is positive and significant (b = .08, Z = 3.86, p < .001). Among participants whose partners decreased in optimism, the slope of actor changes in optimism on changes in health is positive and significant as well (b = .14, Z = 7.03, p < .001). Among actors who increased in optimism, the slope of partner changes in optimism was not significant (b = .003, Z = .16, p = .87). Among actors who decreased in optimism, the slope of partner changes in optimism was significant and positive (b = .07, Z = 3.31, p = .001).

We ran a series of four supplementary analyses. First, we also ran models identical to those in Tables 3 and 4, but also controlling for Wave 1 health/illnesses. The difference between these two analyses is that, in the main analysis, variance in the Wave 2 is partialled out prior to being added as an outcome. In these analyses, the association between the other variables and Wave 1 health/illnesses are controlled for. The results of these analyses did not vary from those presented in Tables 3 and 4. Second, we ran an additional analysis in which baseline (wave 1) actor/partner optimism were controlled for, reducing the statistical bias of change scores (Gollwitzer, Christ, & Lemmer, 2014; Iida & Shrout, 2015). The results from these analyses do not differ from those reported in Tables 3 and 4. Third, we also ran a series of latent difference score models using AMOS in which changes in optimism were allowed to covary with changes in health/illnesses. These analyses are also consistent with those reported in Tables 3 and 4—actor and partner optimism increases were associated with better self-rated health and fewer chronic illnesses over time (see Supplementary Table 1). Finally, analyses were conducted using the optimism subscale (the three positively worded items) and the pessimism subscale (the three negatively worded items). Results were substantively identical to the results reported above. A copy of the results from these supplementary analyses can be requested from the first author.

Discussion

In the current study, we found that higher levels of optimism and increases in optimism over time were associated with better health for both couple members. Specifically, we examined a cross-over between psychological characteristics and health in romantic partners over time—whether one person changing in optimism affected another person's health. Because health behaviors and changes in health behaviors are often coordinated and correlated within couples (Jackson et al., 2015), it is important to examine the psychological antecedents of health behaviors and how these antecedents change over time (Chopik et al., 2015).

Few prospective effects were found (i.e., baseline optimism/health predicting changes in health/optimism), suggesting that actor/partner optimism and health are more coordinated over time, changing in concert with one another. Nevertheless, it is difficult to isolate which changes precede other changes. Do actor/partner changes in optimism lead to changes in health? Or do changes in health lead to changes in actor/partner optimism? Although we have argued for the former process, the latter process is a distinct possibility. Extant research suggests that individual and partner health may predict changes in psychological characteristics (Jokela et al., 2014). For example, Hoppmann and colleagues (2011) found that changes in functional limitations in one person were associated with changes in depression in their partner. The lack of relation between partner health and an actor's optimism could be attributed to the stability of optimism and its endurance to changes in individual health status (Schou et al., 2005; Stanton & Snider, 1993; Stiegelis et al., 2003). Given optimists' proactive coping tendencies (Nes & Segerstrom, 2006), they may be less likely to view changes in their partner's health status as an indicator of a grim future (e.g., lowering their optimism as a result); rather, they may use this news as an opportunity to engage in better health behaviors with their partner, whether it be exercising or cutting bad habits (Giltay et al., 2007), eating healthier (Kelloniemi, Ek, & Laitinen, 2005), or helping them adhere to medication regimens (Kronstrom et al., 2014). The finding that actor self-rated health was but actor chronic illnesses were not related to changes in an actor's optimism is more perplexing. Because optimism has been shown to be resilient to changes in objective health (i.e., chronic illness diagnoses), self-rated health could be capturing aspects of a person beyond his or her objective health status. For example, findings from other studies show that self-rated health ratings are informed by functional health and presence of disease or symptoms, but also by positive feelings and a sense of vitality—both factors that are also correlated with optimism (Benyamini et al., 2000; Mora et al., 2008). Additionally, although self-rated health is a strong predictor of mortality (Idler & Benyamini, 1997), it is sometimes correlated more strongly with some chronic illnesses but not others (Wu et al., 2013) and may implicate evaluations of other aspects of health (e.g., a good prognosis; Jylhä, 2009). Thus, feeling good about one's health may be more related to forecasts about the future rather than objective indicators of illness.

Mechanisms Linking Changes in Actor Optimism to Changes in Partner Health

Mechanisms linking changes in optimism to changes in health across actors could exert their influence at several different stages. For example, there could be a direct effect of an actor's changing optimism on their partner's health behavior, which would lead to better health. People who become more optimistic might be more likely to provide an encouraging word or suggestion that their partner exercise more frequently or eat healthier, enhancing their partner's health. Alternatively, there could be an indirect effect where people's changing optimism causes enhancements in their own health behaviors, which then influence a partner's health behaviors. Perhaps seeing a partner engage in healthier behavior will motivate the other partner to engage in that health behavior as well, either for their own benefit or to be supportive of their partner's efforts. Optimists feel as though they have a greater degree of control over their environment and lives (Scheier et al., 1994); thus, they may also feel a greater degree of control over the behavior of their partner and optimistically believe that positive health changes will lead to good outcomes for their partner. Increases in optimism might also increase perceptions of control, making actors more likely to initiate healthier behaviors for both themselves and their partners (Scheier & Carver, 1992). Although changes in psychological characteristics have been linked to better health at the individual level (Mroczek & Spiro, 2007), the mechanisms that link such changes to health have proved elusive. Although the most likely mechanisms involve facilitating changes in health behavior (Takahashi et al., 2013), it could also be possible that such changes are also associated with more proximate outcomes like metabolic syndrome or other markers of allostatic load and inflammation (Human et al., 2013; Luchetti et al., 2014; Stephan et al., 2016). Future research can also integrate these more objectively measured mechanisms and model them in the relationship between actor/partner optimism changes and health and mortality.

Finally, because changes in individual optimism partially depended on the optimism of one's partner, it could be the case that partner changes in optimism are linked to better health over time through the psychological changes they instigate for individuals (e.g., changes in individual optimism). Thus, the mechanisms linking partner effects of psychological change to health benefits might be simpler in that positive health behavior change is initiated by individuals who experienced an increase in optimism from being married to an optimistic partner. Dyadic mediation models are rarely tested, especially dyadic sequential change mediation models; however, this possible pathway provides an exciting future direction for researchers interested in how couple characteristics influence health over time (Manne et al., 2014).

Limitations

There were some notable limitations to the current study. We only had two time points available to examine changes in optimism, precluding us from employing more complicated modeling procedures (Choi, Yorgason, & Johnson, 2015; Hoppmann et al., 2011). Further, the use of two time points forces an assumption of linear change over time, despite evidence that optimism and other positive psychological characteristics typically show curvilinear changes across the lifespan (Chopik et al., 2015; Roberts, Walton, & Viechtbauer, 2006). On a related note, the four-year interval in the current study may provide only a small snapshot into how optimism changes over time; longer follow-ups with multiple assessment points will allow us to map larger and more precise changes in optimism (and their relation to changes in health) across the lifespan. Test-retest correlations of optimism over a few months to a few years are generally high (∼.59-.75; see Abraham, 2007 for a summary). However, it could be the case that changes in optimism are larger and perhaps even more influential among older adults. Few studies have examined life course changes in optimism to definitively answer this question.

Related, it is also possible that the benefits of increases in optimism and the mechanisms linking changes in optimism or changes in health over time might be specific to older adults and less applicable to younger adults. Because we expect that the benefits of optimism likely accumulate slowly over time, the same processes might not operate (1) in samples in which health is not a major concern or (2) among couples that have not been together long enough for the interdependence required to elicit these effects to be forged. Researchers are just now examining the life course sensitivity of the effects of couple characteristics on health and well-being and coordination between couples (Hoppmann & Gerstorf, 2009; Wrosch, Jobin, & Scheier, 2016). The time course in which interdependence forms to allow for cross-partner benefits for health is a crucial step for future research.

Further, many of the effects in the current study are relatively small in magnitude. With large sample sizes, smaller effects can be captured and more precise effect sizes can be estimated. The current study utilized a large sample of couples to examine how changes in psychological constructs across individuals have implications for their health and well-being. However, large sample sizes may yield findings that are statistically significant but of little practical significance (Cohen, 1990). Thus, there is also a counter-narrative in the current manuscript in which partner changes in optimism are largely unrelated to changes in individuals' health and are dwarfed by the size of just having a partner initially high in optimism. Nevertheless, it is important to quantify the size of the contribution that the psychological characteristics of partners have on health and well-being. There is likely variability in effect sizes of partner optimism when predicting different domains of health and health behavior. For example, partner optimism might have a small effect on health status but a larger effect on more proximal health behaviors that lead to better health. Future research can examine this possibility by including a broader array of health and health behavior indices.

A substantial portion of older couples were not included in the analyses because of missing data on one of the key variables. As such, our findings might not extend to all substrata of the population. Future research can further examine these questions in a more representative sample of couples across the lifespan. Differences in optimism scales might also lead to different results. For example, the particular response scale used in our study (a 6-point scale) differs from the often-employed 5-point response scale for the LOT-R, which might have led to an increased likelihood of finding changes in optimism or attenuated the correlation between partners' scores on optimism. This difference in response format is non-trivial as additional response options and the possibility of a “neutral” or “neither agree nor disagree” option does change the amount of information gleaned from a set of items (Bradburn, Sudman, & Wansink, 2004; Nardi, 2006). Little research has been conducted on how response formats affect estimates of intra-individual changes (Barlas & Thomas, 2012). Nevertheless, it is important to acknowledge the response option of the scale as a limitation of the current research as it may have affected our ability to estimate changes in optimism over time. Future research should examine these possibilities.

Conclusion

The current study is an important first step in examining how changes in optimism are associated with better health for both individuals and their loved ones. Changes in both actor and partner optimism were associated with better self-rated health and fewer chronic illnesses over time. Future research can examine the antecedents of dyadic changes in optimism and the specific mechanisms that enhance the health of close relationships and the people in them.

Supplementary Material

1

Acknowledgments

This research was supported by a grant from the National Institute of Aging awarded to the first author (1 R03 AG054705-01A1). Eric Kim's contribution was partially supported by the National Heart, Lung, and Blood Institute (T32 HL 098048). The Health and Retirement Study is sponsored by the National Institute on Aging (NIA U01AG09740) and is conducted by the University of Michigan. Jacqui Smith's contribution was partially funded by the National Institute on Aging (U01AG009740). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

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