Older Adults With Diabetes and Osteoarthritis and Their Spouses: Effects of Activity Limitations,...

SUSANNE OLSEN ROPER Brigham Young University

JEREMY B. YORGASON Brigham Young University*

Older Adults With Diabetes and Osteoarthritis and

Their Spouses: Effects of Activity Limitations,

Marital Happiness, and Social Contacts on

Partners’ Daily Mood

Using daily diary data from 28 later lifecouples where one spouse had diabetes andosteoarthritis, we examined crossover effects oftarget spouses’ daily activity limitations andtheir partners’ daily mood. On days whentarget spouses’ daily activity limitations werehigher than average, partners’ positive mooddecreased and negative mood increased; whentarget wives’ limitations were higher thanaverage, husbands’ positive mood was higher.Marital happiness and frequency of telephoneconversations of target spouses buffered somerelations. Results advance our understandingof daily health processes within later lifemarriages by identifying crossover effects ofactivity limitations of an ill spouse with themood of their partner and underscore the role

School of Family Life, 2089 Joseph F. Smith Building,Brigham Young University, Provo, UT 84602([email protected]).∗School of Family Life, 2079 Joseph F. Smith Building,Brigham Young University, Provo, UT 84602.

Both authors contributed equally to this research.

Key Words: caregiving of the elderly, marital quality, mood,multiple chronic illnesses, social support networks.

of marital happiness and social contacts inbuffering these associations.

A growing literature has examined specificdiseases and their effects on married individualsand their spouses (Berg & Upchurch, 2007).Little research, however, has investigated themarital experiences of individuals living withmultiple chronic illnesses. This oversight iscostly, given that over half of adults aged 65 andolder report having at least two chronic illnesses(Hoffman, Rice, & Sung, 1996), and that averagelife expectancy continues to rise (Yang, 2007),increasing the likelihood of contracting age-related chronic conditions.

Most studies of health and marriage examinehealth events across several months and years.Although major health events, such as hospitalvisits or surgery, can be influential, daily healthevents such as illness symptoms, pain, mobil-ity limitations, and medicinal side effects areoften overlooked. After diagnosis, the hospitalstay, or surgery come the daily, microhealth pro-cesses that accompany management of chronichealth conditions and shape individual and part-ner well-being. ‘‘Daily event methodologies’’have been suggested as an appropriate approach

460 Family Relations 58 (October 2009): 460–474

Partner Mood, Diabetes, and Osteoarthritis 461

to studying illness effects (Larsen & Kasimatis,1991).

The purpose of this study is to examine dailyhealth processes of couples wherein one spousehas two age-related chronic illnesses (diabetesand osteoarthritis). This study adds to previ-ous research by (a) addressing important issuesfor a growing segment of the U.S. popula-tion, (b) focusing on daily health limitations ofage-related chronic illnesses that require dailymanagement, (c) addressing ways that healthlimitations are associated with both positiveand negative affect (Fredrickson, 2001), and(d) addressing the effects of comorbid healthconditions on family relationships (Roberto,Gigliotti, & Husser, 2005). Examining activitylimitations is especially relevant to those withchronic illnesses, because the personal implica-tions of long-term illness (such as how limitedan individual feels because of a chronic illness)may take on greater meaning than illness symp-toms alone. By examining effects of the dailyhealth of one spouse on his or her partner, as wellas contextual influences such as marital happi-ness and social contacts, we address a gap in pastresearch and provide findings that can be usefulto practitioners working with older individualsexperiencing multiple chronic illnesses.

Osteoarthritis and Diabetes

Among older adults, arthritis is the mostprevalent chronic condition, affecting 21% ofcommunity-living adults ages 65 and older(National Center for Health Statistics, 2008).Arthritis is a leading cause of activity limitations(Rohlfsen & Kronenfeld, 2008), and osteoarthri-tis is the most common form of arthritis. Type 2diabetes (hereafter referred to as ‘‘diabetes’’)is the most common form of diabetes in olderadults, affecting approximately 12 million adultsover age 60 (National Diabetes InformationClearinghouse, 2007). Diabetes managementinvolves a complex lifestyle that includes glu-cose monitoring, appropriate diet, and regularexercise. Without proper management, dia-betes can lead to vision, kidney, nerve, dental,and cardiovascular problems (National DiabetesInformation Clearinghouse). Both arthritis anddiabetes require daily management, with symp-toms or management issues causing discomfortand disrupting activities.

Illness Spillover in Marriage

Health conditions might affect spousal well-being through a ‘‘spillover’’ process from the illspouse to his or her partner (Westman, 2001).This occurs when positive or negative expe-riences in one area affect other areas in aperson’s life. For individuals living with chronichealth problems, daily health limitations mayspill over to affect their interpersonal relation-ships, and, therefore, their spouse. This processcan occur through an emotional transmission(Larson & Almeida, 1999), or otherwise stated,through a ‘‘crossover’’ effect where distress inindividuals affects their partners (Westman &Vinokur, 1998). Westman and Vinokur sug-gested three potential mechanisms operating inpartner crossover that can be applied to ill-ness situations: (a) through negative interactionsbetween spouses, (b) by one spouse empathizingthe distress of the other, or (c) by both partnersresponding to a common stress. In the case ofhealth limitations, when a person’s health causesphysical discomfort, requires management timeand attention, or limits the ability to accom-plish goals, he or she can become irritated orfrustrated. Changes in mood might influencecommunication with a partner, and, thereby,cross over to affect the well-being of the partner.Alternatively, the cross over might result fromone partner responding in an emotionally empa-thetic way to the distress of the other or by bothspouses being impacted by the illness.

Crossover effects of health declines withinmarriage have been supported by studies of gen-eral health, daily health symptoms, and arthritissymptoms. Declines in spousal health in generalhave been linked to poorer marital quality (Booth& Johnson, 1994), with health to marital qualityassociations being mediated by psychologicaldistress (Yorgason, Booth, & Johnson, 2008).Furthermore, in a study of daily processes (Yor-gason, Almeida, Neupert, Spiro, & Hoffman,2006), higher average daily health symptomsamong husbands were associated with higherlevels of wife negative mood. These researchers,however, did not test whether daily mood of thepartner reporting health symptoms mediated thelink between their symptoms and partner mood.The role of mood or psychological distressappears especially relevant given findings fromstudies specific to arthritis. Stephens, Martire,Cremeans-Smith, Druley, and Wojno (2006)indicated that higher arthritis symptom levels

462 Family Relations

are linked to higher partner mental distress. Onepurpose of the current study was to examine themechanisms involved in daily illness crossoverby controlling for the ill partner’s daily mood.

Health and Partner Affect. Historically, moodhas been conceptualized as a ‘‘unitary, bipolarconstruct’’ (McCrae et al., 2008, p. 43), eventhough some research has shown that negativeand positive affective systems are independentin their operations (Cacioppo & Berntson,1994; Watson, Clark, & Tellegen, 1988) and‘‘empirically separable’’ (Billings, Folkman,Acree, & Moskowitz, 2000, p. 132). Watson(1988) delineated the differences: positive affectmeasures individuals’ ‘‘pleasurable engagementwith the environment’’ (p. 1020), whereasnegative affect assesses a range of aversive moodstates and denotes subjective distress.

Ryff and Singer (2000) have argued that toadequately study well-being, it should not bedefined merely as the absence of negative effectsrelated to mental or physical illness. Positiveemotions can assist individuals in placing theevents in their lives in broader contexts; theyalso have long-lasting consequences, includingbroadened psychological resilience, which cancontribute to enhanced emotional well-being(Fredrickson, 2001). Because negative well-being has been the focus of the bulk of pastresearch (Billings et al., 2000), a gap exists incurrent literature regarding predictors of positiveaffect of partners of individuals with chronicillnesses.

Buffering Effects of Marital Happiness andSocial Contacts

Social processes, such as marital happiness andsocial contacts, might modify the link betweenhealth limitations and partner well-being. In mar-riages where one spouse has a functional disabil-ity, emotional closeness and ‘‘relationship talk’’act as buffers to chronic illness distress (Badr &Acitelli, 2005; Mancini & Bonanno, 2006). Fur-thermore, marital support is associated with bet-ter physical and mental health outcomes amongindividuals with diabetes (Trief et al., 2006), andmarital satisfaction modifies the negative effectsof pain (Roberto, Gold, & Yorgason, 2004). Incontrast, negative marital interactions in laterlife couples are associated with ‘‘more physicalhealth symptoms, chronic health problems, andphysical disability’’ (Bookwala, 2005, p. 85).

Regarding social contacts, research withadults with diabetes indicates that they benefitmore from support networks that offer encour-agement and reassurance, rather than specifichelp with tangible aspects of diabetes man-agement, and that higher perceived levels ofsupport are related to higher levels of physicalfunctioning (Connell, Davis, Gallant, & Sharpe,1994). Additionally, when individuals with dia-betes perceive that they receive adequate levelsof support, those who report more illness-relatedfunctional disabilities are less depressed (Little-field, Rodin, Murray, & Craven, 1990). Othercross-sectional studies of older adults indicatethat in addition to the quality of support pro-vided, more extensive support networks alsopredict fewer depressive symptoms (Antonucci,Fuhrer, & Dartigues, 1997). Because social sup-port is often considered to be a function of thesocial network (Ertel, Glymour, & Berkman,2009) and contact is necessary for support tooccur, in this study we consider social contacts,as assessed by frequency of telephone conver-sations with friends, relatives, or others, as wellas how these social contacts moderate the linkbetween health limitations and partner mood.

Gender Differences

Studies indicate that women experience higherlevels of contact among friends and rela-tives as well as higher levels of depressionthan men (Turner & Marino, 1994). Addition-ally, wives are more likely to be receiversthan senders of emotional transmission (Lar-son & Almeida, 1999), suggesting that spousalhealth may impact wives more than hus-bands (see Yorgason et al., 2006). Less isknown about husbands whose wives expe-rience multiple chronic illnesses and whosestrain may be less than their wives, but stillsubstantial.

Hypotheses

On the basis of previous research, we testedthree research hypotheses:

Hypothesis 1: Greater limitations in daily activitiesby the target spouse will be associated with lowerpartner positive daily mood and greater negativedaily mood, and the effect of activity limitationswill be mediated by the target spouse’s daily


mood. There will be more associations betweenhealth limitations and negative mood.Hypothesis 2: Higher target spouse maritalhappiness will buffer the relationship between theiractivity limitations and partner mood.Hypothesis 3: Greater number of telephoneconversations by the target spouse will buffer therelationship between their activity limitations andpartner mood.

METHOD

Participants

Twenty-eight married couples were recruitedthrough a diabetes management clinic and dia-betes education classes at two hospitals in ametropolitan county in a western state. Thetwo hospitals (two of five in the county) areamong the largest in the area and serve patientsof varying socioeconomic levels. Couples wererecruited where (a) one spouse had been diag-nosed with both diabetes and osteoarthritis,(b) the individual was married and over age 59,and (c) both spouses were willing to participatein the study.

A computerized search of diabetes clinic med-ical records identified 35 individuals who hadboth arthritis and diabetes and were married, andrecruitment letters were mailed to these individu-als, inviting them and their spouse to participate.Letters were followed by telephone calls, result-ing in 8 of the 35 couples being recruited(response rate for eligible couples = 42%).

Twenty additional couples were recruitedfrom diabetes education classes and supportgroups provided by the diabetes managementclinic. Many class participants did not qual-ify for the study because they were too young,unmarried, or did not have osteoarthritis; approx-imately 20% who met the criteria declined par-ticipation. The final sample included 28 couplesin which one spouse (15 wives; 13 husbands)was diagnosed with both osteoarthritis and dia-betes (the target spouse). In eight couples, bothspouses had osteoarthritis and diabetes. For thosecouples, the spouse with more severe diabetesor arthritis or both symptoms was chosen as thetarget spouse. In each of these cases, there wasa clear difference in severity reports.

Preliminary analyses indicated that coupleswhere both spouses had diabetes and arthritistended to have slightly higher levels of daily neg-ative mood (t = −3.41, df = 696, p = .001)than the remainder of the sample. There were no

differences in age, activity limitations, or posi-tive mood. Observed differences were viewed asminor, so all couples were included in the finalmodels. The mean ages of the target spousesand their partners were 67.89 years (SD = 7.47)and 68.14 years (SD = 8.04), respectively. Tar-get spouses had more illnesses (M = 4.93,SD = 2.06) than their partners (M = 3.38,SD = 2.19; t = −2.72, df = 54, p = .009).For the target spouse, average time since diag-nosis of diabetes was 11.90 years (SD = 10.71)and 16.31 years (SD = 14.53) for osteoarthritis.On average, target spouses rated the severity oftheir arthritis as fairly bad (M = 2.65, SD =0.85) and diabetes as about the same (M =2.63, SD = 0.79, where 1 = not bad at all,2 = not too bad, 3 = bad, 4 = really bad, and5 = as bad as it could possibly be).

Six (21%) target spouses and seven (25%) oftheir partners were currently employed. Coupleswere married on average 42.16 years (SD =13.53). Congruent with the demographics ofthe area where the sample was recruited, themajority of couples were Caucasian (92%).

Procedures

Trained interviewers met with couples in theirhomes, obtained informed consent, and lefta baseline questionnaire for couples to com-plete. Approximately 1 week later interviewersreturned, picked up the baseline questionnaire,conducted an interview with the couple, andexplained the daily diary procedures. Partic-ipants were asked to independently completedaily diaries in the evening for 14 consecutivedays. Investigators provided a reminder tele-phone call each evening, and participants mailedtheir diaries to the researchers after completingthem. Participants received a $50 gift card fortheir participation.

Measures

Baseline frequency of telephone conversations.Target spouses’ frequency of telephone con-versations was measured using one questionthat asked how many times they had talked onthe telephone with friends, relatives, or othersduring the past week. Responses ranged fromnot talking on the phone with anyone duringthe week (coded as 0) to talking with othersmore than once a day (coded as 8). Althoughthis measure does not differentiate positive


from negative interactions, it provided someindication of social connection with others.

Baseline marital happiness. Target spouses’perceptions of marital happiness were assessedwith a 10-item measure (Johnson, White,Edwards, & Booth, 1986), using a scale anchoredby (3) Very happy and (1) Not too happy, withhigher scores indicating higher marital happi-ness (α = .74). A sample item included, ‘‘Howhappy are you with the amount of love andaffection you receive?’’

Daily positive and negative mood. Husbandsand wives completed the Positive and NegativeAffect Schedule (PANAS; Watson et al., 1988),which consists of a 10-item positive mood scaleand a 10-item negative mood scale. Each itemin the scale represents an emotion or feeling(e.g., distressed, excited); participants indicatedto what extent they felt each emotion on the daythey completed the survey using a scale anchoredby (1) Very slightly or not at all to (5) Extremely.Internal consistency reliabilities for the scale aretypically high (e.g., .84 to .90, Watson et al.).Reliability coefficients calculated within a three-level model (items nested within days nestedwith persons; see Shavelson & Webb, 1991) inthe current sample were .91 for positive and .86negative daily mood.

Daily activity limitations. Couples alsocompleted a five-item instrument that measuredhow much they had to limit their activities eachday as a result of their physical health. Four itemswere modified from the SF36 health survey,which asked if respondents accomplished lessthan they would like to, spent less timeworking, felt limitations in what they coulddo, and had difficulty performing tasks on agiven day (Hays, Sherbourne, & Mazel, 1993).The fifth item identified whether respondentsexperienced side effects from the medicinesthey were taking that required them to changetheir activities. For all five items, the scale was(1) No, (2) Yes, Slightly, (3) Yes, Very Much,with higher scores indicating higher activitylimitations. A multilevel analysis revealed scalereliability of .86.

Analysis

Using SAS (PROC MIXED), multilevel models(Raudenbush & Bryk, 2002) were estimatedto examine the associations between the target

spouses’ daily limitations in activity andtheir partners’ positive and negative mood(Tables 1 and 2). In constructing the models, weassumed that positive and negative affect wereindependent. The Level 1, or within-couple,portion of our models examined variationand fluctuation across time, investigating thecovariation of the target spouses’ limitationsin activities and their partners’ mood on agiven day. Such an approach does not includetime as a predictor in the model and isnot considered a growth curve analysis. Toconfirm this, we ran models with linear andquadratic time coefficients, and neither trendwas statistically significant. In some cases whererepeated measures from both spouses are used, athree-level model is needed (repeated measuresnested within individuals, individuals nestedwithin couples; see Lyons & Sayer, 2005). Thisapproach is necessary when the same measuresare used for both partners in the analyses and amultivariate model is estimated. Our analysis isdifferent, using outcomes from only one partnerin each couple. The multilevel aspect of themodels accounts for nonindependence in thedata resulting from repeated measures from thedaily diary (Singer & Willett, 2003). There wasrelatively little data missing in the sample (arange of 9% to 19% of missing data in eachmodel; see couples/occasions in Tables 1 and 2).In SAS Proc Mixed missing data are not deletedlistwise, and the program takes advantage of allpresent data.

The Level 2 portion of the models speci-fied main effects of between-person differencesin activity limitations, target spouses’ maritalhappiness and frequency of telephone conver-sations, Level 2 interactions between the twomoderators and average activity limitations, andcross-level interactions of the two moderatorswith target spouses’ daily deviation from theiraverage activity limitations. Marital happinessand frequency of telephone conversations weregrand-mean centered at values near the meansfor both husbands and wives (1.5 for maritalhappiness; six phone conversations per week) tofacilitate interpretation of the intercepts. Eachmodel included a random intercept, but addinga random slope coefficient did not improve themodels sufficiently to warrant their inclusion(as per maximum likelihood deviance tests).The following equation illustrates the multilevelmodel of associations among daily activity lim-itations, marital happiness, and partner mood.

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Level 1 Model:

Moodti = β0i + β1i(Deviation from Daily Average

Activity Limitationsti) + eti,

where the predicted value for the outcome ofpartners’ mood, for each person ‘‘i’’ on agiven day ‘‘t’’, is a function of the partners’average mood (intercept; β0i), plus the targetspouses’ deviation from their average dailyactivity limitations (the daily activity limitationsminus the persons’ average activity limitations;β1i), plus variation in the outcome for a givenpartner on a given day (eti).

Level 2 Model:

β0i = V00 + V01(Mean Activity Limitationsi)

+ V02(Marital Happinessi)

+ V03(Marital Happinessi

∗Mean Activity Limitationsi) + U0i

β1i = V10 + V11(Marital Happinessi),

where the average mood (intercept) is afunction of the sample average mood (V00),plus a given target spouses’ average activitylimitations (V01), plus a given target spouses’baseline marital happiness (V02), plus a Level2 interaction between marital happiness andaverage activity limitations (V03), plus randomdeviation from the sample average (randomintercept; U0i). The effect of the target spouses’deviation from average daily activity limitations(β1i) is a function of the average sampledeviation (V10), plus the target spouses’ baselinemarital happiness (V11). When Level 1 andLevel 2 equations are combined, a cross-levelinteraction between baseline marital happinessand deviation from average daily activitylimitations is included in the model.

RESULTS

Four baseline models were estimated usinga compound symmetry covariance matrix toexamine variability in the four dependentvariables: husband partners’ positive mood,wife partners’ positive mood, husband partners’negative mood, and wife partners’ negativemood (Model 1 of Tables 1 and 2). Intraclasscorrelation coefficients (ICCs) were calculatedfrom these initial models to determine the ratioof within- versus between-person variability.

The ICCs for the daily variables were .67for wife partners’ positive mood (indicating67% between-person variability, 33% within-person variability), .64 for husband partners’positive mood (64% between-person variability,36% within-person variability), .75 for wifepartners’ negative mood (75% between-personvariability, 25% within-person variability), and.53 for husband partners’ negative mood (53%between-person variability, 47% within-personvariability). These ICCs indicate variability thatcould be accounted for by both Level 1 andLevel 2 predictors.

Next, equations that included target spouseactivity limitation variables were estimated(Model 2 of Tables 1 and 2). The remainderof the models used a Toeplitz error struc-ture, which implies that days closer togetherare more strongly correlated than days that arefarther apart (Singer & Willett, 2003). Third,equations that included the main effects and a tar-get spouse moderator variable (either telephoneconversation frequency or marital happiness)were estimated (see Models 3 and 5 of Tables 1and 2). Fourth, equations that included interac-tion effects for between-person activity limita-tions and the moderator and within-person activ-ity limitations and the moderator were estimated(Models 4 and 6 in Tables 1 and 2). Mediatingeffects of the target spouse’s daily mood andbaseline depression also were estimated in thefinal models (depression coefficients not shown).

Negative Mood

Table 1, upper panel, indicates that in Models2 to 5, no significant relations were found forthe target wives’ variables and their partners’negative mood. Model 6 shows that on dayswhen target wives had higher than averageactivity limitations, their husbands reportedhigher negative mood. Specifically, a 1-unitincrease in wife difference from average activitylimitations was associated with about a 4-pointincrease in husband negative mood (equal toabout half of the standard deviation in husbandnegative mood). The significant interaction(Figure 1, upper left panel) indicates that fortarget wives who talked on the telephonemore frequently, lower activity limitations wereassociated with more negative mood. For targetwives who talked less frequently on the phone,however, their lower activity limitations wereassociated with less negative mood reported by


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husbands. Table 1, Models 4 and 6 (top panel),shows that target wife negative daily moodpredicted husband negative mood, yet did notaccount for the effects of activity limitation.

When wives’ negative mood was the outcomevariable, there was only one significant find-ing. When target husbands reported higher thanaverage activity limitations, their wives reportedhigher negative mood (Table 1, bottom panel).Specifically, a 1-unit increase in target husbands’average activity limitations was associated withan approximate 13-point increase in wife part-ner negative mood (equal to nearly two standarddeviations in negative mood). As seen in Models4 and 6 of Table 1 (bottom panel) target husbandpositive daily mood predicted wife partner pos-itive mood, yet did not account for the effects ofactivity limitations.

Positive Mood

No significant relations were found in Model 2(Table 2, upper panel). Models 3 and 4 show thatwhen target wives reported higher than averageactivity limitations and reported higher maritalhappiness, their husbands had significantlyhigher positive mood (1-unit increases inwives’ average activity limitations and maritalhappiness were associated with 10- and 12-point increases in husbands’ positive mood,respectively, which is equal to approximatelyone standard deviation in husband positivemood). The interaction effect between wives’marital happiness and activity limitations(Figure 1, upper right panel), indicates thathusband positive mood was lower for coupleswhen the wife reported lower marital happiness,and even lower for these couples on days thetarget wife reported higher activity limitations.Alternately, with higher wife marital happiness,husband positive mood was not associated withwife activity limitations.

On days when target wives experienced higherthan average activity limitations, their husbandsexperienced lower positive mood (Table 2,Model 6 in upper panel). Specifically, a 1-unitincrease in a target wife’s difference from heraverage activity limitations was related to a 7-point drop in husband positive mood (equal toapproximately two thirds of a standard deviationin mood). The significant interaction (Figure 1,lower left panel) indicates that husband positivemood did not differ for target wives whoreported to talk more frequently on the phone.

For target wives who spoke less frequentlyon the phone, fewer activity limitations wereassociated with higher husband positive mood,and more limitations were linked with lowerpositive mood.

Daily change from target husbands’ averageactivity limitations was associated with wives’positive mood (Table 2, Models 2 and 3, lowerpanel). Wives had higher positive mood ondays when their husband had fewer activitylimitations than his average (a 1-unit drop fromaverage activity limitations in target husbandswas associated with a 3-point increase in wifepositive mood, which is equal to about one thirdof a standard deviation in wife mood). The cross-level interaction between husband within-personactivity limitations and marital happiness wasalso significant (Model 4, Table 2). Specifically,wives’ positive mood was higher for coupleswhere the target husband reported higher maritalhappiness (Figure 1, lower right panel). Theinteraction indicates that for couples where thetarget husband reports lower marital happiness,his wife’s positive mood is about the sameregardless of husband activity limitations. Incontrast, for couples where the target husbandreports higher marital happiness, fewer husbandactivity limitations were associated with higherpositive mood in their wives. As seen in Models4 and 6 of Table 2 (bottom panel), target husbandpositive mood was a significant predictor of wifepositive mood, yet did not account for the effectsof activity limitations on wife positive mood.

DISCUSSION

The purpose of this study was to examineways in which daily health limitations in atarget spouse with diabetes and osteoarthritiswere related to the well-being of his or hermarital partner. Crossover effects from the targetspouses to their partners were confirmed tobe important. In partial support of our firsthypothesis, wife partners’ positive mood waslower on days when their target husbands’daily activity limitations were higher thanaverage. Further, wives’ negative mood washigher when the target husbands reported higheractivity limitations. In support of the emotionaltransmission paradigm (Larson & Almeida,1999), health events of the target spouses, aswell as their mood, were predictors of partnermood in many cases. Target spouses’ mood,however, did not mediate the effects of activity


limitations. Models were tested separately forpartner husbands and partner wives, and, ashypothesized, marital happiness and frequencyof telephone conversations tended to buffer thecrossover of activity limitations.

Interpretation of Effects With the Literature

Partially supporting our first hypothesis, whentarget husbands’ daily activity limitations werehigher than average, wife partners reportedhigher negative and lower positive daily mood.This effect of health limitations was in additionto the effect of husbands’ daily mood, sug-gesting that health limitations are not affectingspousal mood solely through negative partnerinteractions, but possibly also through part-ner empathy or through feelings of a sharedstressor (Westman & Vinokur, 1998). Berg andUpchurch (2007) provided support of this expla-nation, suggesting that ‘‘illness ownership’’ (i.e.,illness belongs to the target spouse or it is‘‘shared’’ with the partner) may shape howactivity limitations are interpreted and handled.Thus, a possible explanation of our results isthat if the illness is ‘‘owned’’ by the couple,rather than just the ill partner, increases in ahusband’s activity limitations may contribute toless positive and increased negative daily affectof his wife. Alternatively, wife partners mayinterpret their husbands’ increased daily limi-tations as being associated with higher needsand increased dependency, which could possiblyresult in additional responsibilities on their part.Past research (Barusch & Spaid, 1996; Yorgasonet al., 2006) suggests that wives who are care-givers may experience higher levels of burden.Thus, the anticipated additional amount of workinvolved in the care of her husband, because ofhis activity limitations, may influence a wife’snegative and positive mood.

In contrast, when target wives’ activity limi-tations were higher than average (in cases wherewives reported higher marital happiness), theirhusbands’ positive mood was higher. Theseresults are in line with the ideas of Berg andUpchurch (2007), who theorized that amongolder adults, marital quality may impact couples’daily management of the chronic illness. Previ-ous research with cancer patients has also shownthat marital satisfaction buffers the effects of illspouses’ physical impairments on their partner’sdistress (Fang, Manne, & Pape, 2001). It may

be that under conditions of higher marital happi-ness, when target wives experience higher thanaverage limitations, their husbands offer to assistthem with more tasks and with greater frequency.As a result of performing these tasks, husbandsmight experience a greater sense of contribution,which may translate into an increase in positivemood (Kramer, 1997).

In support of our second hypothesis, resultsindicated that higher target wife marital hap-piness was related to higher husband posi-tive mood. It is possible that individuals whoare happy in their marriages may communi-cate better and feel more emotional closenesswith their spouse, which may buffer the influ-ence of their limitations in activities on theirspouses’ mood (Badr & Acitelli, 2005; Mancini& Bonanno, 2006).

It also appears that wife partners’ positivemood drops on days of high health limitationswhen target husbands have higher marital happi-ness. Yet, when husbands experience low maritalhappiness, there is little difference in wife care-giver positive mood on days of high versuslow limitations. When husbands report highermarital happiness, the positive mood of wivesmay drop if higher health limitations contributeto fewer positive couple interactions, increasednegative interactions, or a combination of both(Yorgason et al., 2006). In essence, couples withhigher marital happiness are not immune to dailyhealth challenges.

Similar patterns were found for the mod-erating effect of both marital happiness andfrequency of telephone conversations on targetwives’ limitations in activities and husband care-givers’ positive mood. When target wives talkedmore frequently on the telephone or were happyin their marriages, there were only minor differ-ences in caregiver husbands’ positive mood ondays of low versus high limitations in her activ-ities. In contrast, when target wives reportedtalking less frequently on the telephone orlow marital satisfaction, their husbands reportedeven lower positive mood on days of higherhealth limitations.

Talking on the telephone also moderatedthe relation between target wives’ activitylimitations and their husbands’ negative mood.On days with fewer limitations, husbands wereless negative if their wives had talked lessfrequently on the phone and more negative iftheir wives talked on the phone more frequently.It may be that if target wives are not experiencing


a lot of health limitations, caregiver husbandsfeel irritated with their spouse for talking on thephone when they could be doing other things,which may impact their mood. Alternately,on days target wives experienced high healthlimitations, their husbands’ negative mood wasslightly higher if the target wife had talkedless frequently on the telephone, and slightlylower if the target wife talked more frequentlyon the telephone. Apparently, husbands are lessnegative if the target wife has someone to talk toon days where she is experiencing more healthlimitations. One explanation for this finding isrelated to spillover; if the target wife is talking onthe phone to someone else, she may spend lesstime in negative interaction with her husband,which, in turn, could contribute to his moodbeing less negative. On the other hand, thepeople she talks to on the phone could beoffering her support in terms of how to manageher illnesses (Denham, Manoogian, & Schuster,2007), which could be seen by the husband asa help. Findings related to the helpfulness ofmarital happiness and telephone conversationsunderscore the importance of marital and socialfactors for husbands who may be involved incare (Boerner, Chulz, & Horowitz, 2004).

Limitations and Directions for Future Research

Several limitations in the research suggestdirections for future investigations. First, ourmeasurement of daily processes and socialconnection has limitations. Regarding dailydiary assessments, the initial variability modeledin the outcome variables (see the ICCs) assumesthat measurement error remained constant acrossdays and that all of the variation was due to‘‘true’’ variability in the concepts of interest.Also, we only used one item that measuredthe frequency of telephone conversationsto assess social connection. We did notassess participants’ perceptions of support, northe helpfulness or quality of the telephoneconversations. Future research that is morefine-grained could shed light on various errorvariance structures in daily mood and on wayssocial connections and support contribute todaily processes, both positively and negatively.

Second, the investigation of other aspects ofthe marital relationship, in addition to maritalhappiness, should be explored as moderatorsin future studies. Couple communication,interaction, and marital conflict are other features

of marital relationships that could possibly beassociated with links between health and partnermood (Sandberg, Miller, & Harper, 2002).

Third, the sample for the study was small, andparticipants had sought treatment at a diabetesmanagement clinic, which may not be repre-sentative of the population. Participants may bemore or less healthy than individuals who are notreceiving medical services. In addition, couplesin this study were mostly Caucasian. Previousresearch indicates that minority caregivers maybe different from Caucasians (Wallsten, 2000),which has important implications since non-Caucasian older adults have higher incidencerates of diabetes (National Diabetes Informa-tion Clearinghouse, 2007). Future studies couldexplore the effects of both health status andrace to better understand how health limitationsinfluence daily mood.

The current study provides a template forlooking at other combinations of comorbid ill-nesses. For example, diabetes is often comor-bid with cardiovascular and cerebrovasculardiseases (Fillenbaum, Pieper, Cohen, Cornoni-Huntley, & Guralnik, 2000). Other prevalentillnesses that often have comorbid conditions,such as cancer, HIV/AIDS, COPD, and hyper-tension, could be examined in ways similar tothe approach used in this study by examiningcrossover effects between ill spouses and theirpartners.

Finally, future studies of comorbid illnessesmight build upon recent research with olderadults with arthritis, investigating how posi-tive affect modifies the relation between healthlimitations and negative affect (Zautra, Smith,Affleck, & Tennen, 2001) or how positive affectacts as a source of resilience for individualsexperiencing health limitations (Zautra, John-son, & Davis, 2005). Potential mediators, suchas empathy, could also be examined in an effortto better understand emotional transmission pro-cesses (Westman, 2001). Continued use of dailydiary methodologies will be instrumental inunderstanding health mechanisms.

Implications for Practitioners

Health professionals who work with older adultsand couples need to be aware of daily healthprocesses between spouses and the cumulativeeffects that multiple illnesses can have withinmarriage. Clinicians and educators can assessnot only the daily ins and outs related to multiple


chronic illnesses but also how partners perceivesuch events as activity limitations. Resultsfrom this study also point to the importanceof targeting both the person with chronicillnesses and his or her partner for interventions(Schmaling & Sher, 2000). Most research onosteoarthritis has not focused on partners andthe important role they serve in helping theirspouse manage (Martire, Schulz, Keefe, Rudy,& Starz, 2007). Some research, however, hasindicated that if interventions target both theindividuals with osteoarthritis and their partners,intervention efficacy may be enhanced (Keefeet al., 2002; Martire, Lustig, Schulz, Miller,& Helgeson, 2004). Particularly, the caregiverpartners’ role in helping an ill spouse cope withthe arthritis, their attitude, sense of well-being,and sense of mastery in providing assistanceand support may improve through interventionefforts (Martire et al., 2007). Finally, given thebenefits and protective effects of positive affect(Folkman & Moskowitz, 2000; Fredrickson,2001), interventions directed toward improvingmarriage, as well as generating and sustainingpositive affect under stressful conditions, couldcontribute to enhanced couple well-being(Folkman & Moskowitz).

Attention to marital well-being seems espe-cially important when gender of the ill partneris taken into account. Although we did nottest gender effects statistically, results hint thatthese daily processes may operate differentlywhen target spouses are husbands versus wives.Specifically, the crossover of daily health chal-lenges may be buffered for husbands when theirwives have someone to talk with on the phone,and when their marital happiness is high. Thisbuffer does not seem to operate the same waywhen husbands are ill, however. In fact, wifepositive mood appears lower on poor health daysof their husbands, especially when marital happi-ness is high. Thus, when a wife is ill, assessmentof social contacts by practitioners may be par-ticularly helpful. Similarly, efforts to assess andenhance marital happiness may prove benefi-cial. When husbands suffer from chronic healthconditions, practitioners might normalize thenegative impact of poor health days, especiallywhen they report to be happy in their marriage.Happily married couples could be encouragedto appreciate the good health days, with hus-bands paying particular attention to ways thattheir health likely influences their partner’smood.

Conclusion

Findings from this study extend the extant lit-erature by (a) addressing marital relationshipsin the context of multiple chronic illnesses,(b) investigating how ill spouses’ daily limi-tations in activities are associated with theirpartners’ positive and negative mood, and(c) exploring the ways that marital happiness andsocial connections modify the health to partnermood links. With increasing numbers of indi-viduals living longer and spending more time infamily relationships and the greater prevalenceof chronic health problems among older adults,further work is needed to better understand theinfluence of positive and negative supports toill husbands or wives, and to explore the influ-ence of multiple dimensions of marital quality.In addition, further examination of daily cou-ple processes in context of chronic illness inlater life will continue to unravel the micro-processes that account for long-term maritaltrends.

NOTE

We express our thanks to the couples who participated inthis study and to Brandan Wheeler and Elise McDonaldfor their assistance with data management. We also expressour appreciation to Dawn Higley, R.N., David Donaldson,M.D., and staff for their assistance in recruiting. Fundingwas provided through the Marjorie Pay Hinckley Chair,the Family Studies Center, and the Gerontology Center atBrigham Young University.

REFERENCES

Antonucci, T. C., Fuhrer, R., & Dartigues, J. F. (1997).Social relations and depressive symptomatologyin a sample of community-dwelling French olderadults. Psychology and Aging, 12, 189 – 195.

Badr, H., & Acitelli, L. K. (2005). Dyadic adjustmentin chronic illness: Does relationship talk matter?Journal of Family Psychology, 19, 465 – 469.

Barusch, A. S., & Spaid, W. M. (1996). Spousecaregivers and the caregiving experience: Doescognitive impairment make a difference? Journalof Gerontological Social Work, 25, 93 – 105.

Berg, C. A., & Upchurch, R. (2007). A developmental-contextual model of couples coping with chronicillness across the adult life span. PsychologicalBulletin, 133, 920 – 954.

Billings, D. W., Folkman, S., Acree, M., & Mosko-witz, J. T. (2000). Coping and physical healthduring caregiving: The roles of positive andnegative affect. Journal of Personality and SocialPsychology, 79, 131 – 142.


Boerner, K., Schulz, R., & Horowitz, A. (2004).Positive aspects of caregiving and adaptationto bereavement. Psychology and Aging, 19,668 – 675.

Bookwala, J. (2005). The role of marital quality inphysical health during the mature years. Journal ofAging and Health, 17, 85 – 104.

Booth, A., & Johnson, D. R. (1994). Declining healthand marital quality. Journal of Marriage and theFamily, 56, 218 – 223.

Cacioppo, J. T., & Berntson, G. G. (1994).Relationship between attitudes and evaluativespace: A critical review, with emphasis on theseparability of positive and negative substrates.Psychological Bulletin, 115, 401 – 423.

Connell, C. M., Davis, W. K., Gallant, M. P., &Sharpe, P. A. (1994). Impact of social support,social cognitive variables, and perceived threaton depression among adults with diabetes. HealthPsychology, 13, 263 – 273.

Denham, S. A., Manoogian, M. M., & Schuster,L. (2007). Managing family support and dietaryroutines: Type 2 diabetes in rural Appalachianfamilies. Families, Systems, & Health, 25, 36 – 52.

Ertel, K. A., Glymour, M., & Berkman, L. F.(2009). Social networks and health: A lifecourse perspective integrating observational andexperimental evidence. Journal of Social andPersonal Relationships, 26, 73 – 92.

Fang, C. Y., Manne, S. L., & Pape, S. J.(2001). Functional impairment, marital quality,and patient psychological distress as predictorsof psychological distress among cancer patients’spouses. Health Psychology, 20, 452 – 457.

Fillenbaum, G. G., Pieper, C. F., Cohen, H. J.,Cornoni-Huntley, J. C., & Guralnik, J. M. (2000).Comorbidity of five chronic health conditions inelderly community residents: Determinants andimpact on mortality. Journal of Gerontology:Medical Sciences, 55A, M84 – M89.

Folkman, S., & Moskowitz, J. T. (2000). Thecontext matters. Personality and Social PsychologyBulletin, 26, 150 – 151.

Fredrickson, B. L. (2001). The role of positiveemotions in positive psychology: The Broaden-and-Build Theory of Positive Emotions. AmericanPsychologist, 56, 218 – 226.

Hays, R. D., Sherbourne, C. D., & Mazel, R. M.(1993). The RAND 36-item health survey 1.0.Health Economics, 2, 217 – 227.

Hoffman, C., Rice, D., & Sung, H. Y. (1996).Persons with chronic conditions. Their prevalenceand costs. The Journal of the American MedicalAssociation, 276, 1473 – 1479.

Johnson, D. R., White, L., Edwards, J., & Booth, A.(1986). Dimensions of marital quality. Journal ofFamily Issues, 7, 31 – 49.

Keefe, F. J., Smith, S. J., Buffington, A. L. H.,Gibson, J., Studts, J. L., & Caldwell, D. S.

(2002). Recent advances and future directionsin the biopsychosocial assessment and treatmentof arthritis. Journal of Counseling and ClinicalPsychology, 70, 640 – 655.

Kramer, B. J. (1997). Differential predictors of strainand gain among husbands caring for wives withdementia. The Gerontologist, 3, 239 – 249.

Larsen, R. J., & Kasimatis, M. (1991). Day-to-dayphysical symptoms: Individual differences in theoccurrence, duration, and emotional concomitantsof minor daily illnesses. Journal of Personality,59, 387 – 423.

Larson, R. W., & Almeida, D. M. (1999). Emotionaltransmission in the daily lives of families: A newparadigm for studying family process. Journal ofMarriage and the Family, 61, 5 – 20.

Littlefield, C. H., Rodin, G. M., Murray, M. A.,& Craven, J. L. (1990). Influence of functionalimpairment and social support on depressivesymptoms in persons with diabetes. HealthPsychology, 9, 737 – 749.

Lyons, K. S., & Sayer, A. G. (2005). Longitudinaldyad models in family research. Journal ofMarriage and Family, 67, 1048 – 1060.

Mancini, A. D., & Bonanno, G. A. (2006). Maritalcloseness, functional disability, and adjustment inlate life. Psychology and Aging, 21, 600 – 610.

Martire, L. M., Lustig, A. P., Schulz, R., Miller,G. E., & Helgeson, V. S. (2004). Is it beneficialto involve a family member? A meta-analysisof psychosocial interventions for chronic illness.Health Psychology, 23, 599 – 611.

Martire, L. M, Schulz, R., Keefe, F. J., Rudy, T. E.,& Starz, T. W. (2007). Couple-oriented educationand support intervention: Effects on individualswith osteoarthritis and their spouses. RehabilitationPsychology, 52, 121 – 132.

McCrae, C. S., McNamara, J. P., Rowe, M. A.,Dzierzewski, J. M., Dirk, J., Marsiske, M., &Craggs, J. G. (2008). Sleep and affect in olderadults: Using multilevel modeling to examinedaily associations. Journal of Sleep Research, 17,42 – 53.

National Center for Health Statistics. (2008). FastStats: Arthritis. Retrieved November 26, 2008,from http://www.cdc.gov/nchs/fastats/arthrits.htm.

National Diabetes Information Clearinghouse. (2007).National diabetes statistics, 2007. RetrievedNovember 26, 2008, from http://diabetes.niddk.nih.gov/dm/pubs/statistics/index.htm#what.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchi-cal linear models: Applications and data analysismethods (2nd ed.). Thousand Oaks, CA: Sage.

Roberto, K. A., Gigliotti, C. M., & Husser, E. K.(2005). Older women’s experiences with multiplehealth conditions: Daily challenges and carepractices. Health Care for Women International,26, 672 – 692.


Roberto, K. A., Gold, D. T., & Yorgason, J. B. (2004).Chronic illness and marital relationships: The influ-ence of osteoporosis on the lives of older couples.Journal of Applied Gerontology, 23, 443 – 456.

Rohlfsen, L., & Kronenfeld, J. J. (2008). Functionalability and disability among older adults witharthritis: The impact of age, duration of arthritis,and severity of arthritis. In J. J. Kronenfeld (Ed.),Care for major health problems and populationhealth concerns: Impacts on patients, providersand policy: Research in the Sociology of HealthCare, 26 (pp. 41 – 64). Bingley, UK: EmeraldGroup Publishing Limited.

Ryff, C. D., & Singer, B. (2000). Interpersonalflourishing: A positive health agenda for the newmillennium. Personality and Social PsychologyReview, 4, 30 – 44.

Sandberg, J. G., Miller, R. B., & Harper, J. M.(2002). A qualitative study of marital process anddepression in older couples. Family Relations, 51,256 – 264.

Schmaling, K. B., & Sher, T. G. (2000). Thepsychology of couples and illness: Theory,research, & practice. Washington, DC: AmericanPsychological Association.

Shavelson, R. J., & Webb, N. M. (1991). Generaliz-ability theory: A primer. Newbury Park, CA: Sage.

Singer, J. D., & Willett, J. B. (2003). Applied longi-tudinal data analysis: Modeling change and eventoccurrence. New York: Oxford University Press.

Stephens, M. A. P., Martire, L. M., Cremeans-Smith,J. K., Druley, J. A., & Wojno, W. C. (2006). Olderwomen with osteoarthritis and their caregivinghusbands: Effects of pain and pain expression onhusbands’ well-being and support. RehabilitationPsychology, 51, 3 – 12.

Trief, P. M., Morin, P. C., Izquierdo, R., Teresi, J.,Starren, J., Shea, S., & Weinstock R. S. (2006).Marital quality and diabetes outcomes: TheIDEATel project. F amilies, Systems, and Health,24, 318 – 331.

Turner, R. J., & Marino, F. (1994). Social support andsocial structure: A descriptive epidemiology. Jour-nal of Health and Social Behavior, 35, 193 – 212.

Wallsten, S. S. (2000). Effects of caregiving, gender,and race on the health, mutuality, and socialsupports of older couples. Journal of Aging andHealth, 12, 90 – 111.

Watson, D. (1988). Intraindividual and interindivid-ual analyses of positive and negative affect: Theirrelation to health complaints, perceived stress, anddaily activities. Journal of Personality and SocialPsychology, 54, 1020 – 1030.

Watson, D., Clark, L. A., & Tellegen, A. (1988).Development and validation of brief measures ofpositive and negative affect: The PANAS scales.Journal of Personality and Social Psychology, 54,1063 – 1070.

Westman, M. (2001). Stress and strain crossover.Human Relations, 54, 717 – 751.

Westman, M., & Vinokur, A. D. (1998). Unravelingthe relationship of distress levels within cou-ples: Common stressors, empathic reactions, orcrossover via social interaction? Human Relations,51, 137 – 156.

Yang, Y. (2007). Long and happy living: Trendsand patterns of happy life expectancy in theU.S., 1970 – 2000. Social Science Research, 37,1235 – 1252.

Yorgason, J. B., Almeida, D., Neupert, S.,Spiro, A., & Hoffman, L. (2006). A dyadic exam-ination of daily health symptoms and emotionalwell-being in later life couples. Family Relations,55, 613 – 624.

Yorgason, J. B., Booth, A., & Johnson, D. (2008).Health, disability, and marital quality: Is theassociation different for younger versus oldercohorts? Research on Aging, 60, 623 – 648.

Zautra, A. J., Johnson, L. M., & Davis, M. C. (2005).Positive affect as a source of resilience for womenin chronic pain. Journal of Consulting and ClinicalPsychology, 73, 212 – 220.

Zautra, A., Smith, B., Affleck, G., & Tennen, H.(2001). Examinations of chronic pain and affectrelationships: Applications of a dynamic modelof affect. Journal of Consulting and ClinicalPsychology, 69, 786 – 795.

Older Adults With Diabetes and Osteoarthritis and Their Spouses: Effects of Activity Limitations,...

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