Document [original]

Changes in life satisfaction when losing

one’s spouse: individual differences in

anticipation, reaction, adaptation and

longevity in the German Socio-economic

Panel Study (SOEP)

FRANK J. INFURNA*, MAJA WIEST†, DENIS GERSTORF‡§||,

NILAM RAM§||¶, JÜRGEN SCHUPP§**, GERT G. WAGNER§¶††

and JUTTA HECKHAUSEN‡‡

ABSTRACT

Losing a spouse is among the most devastating events that may occur in people’s

lives. We use longitudinal data from , participants in the German Socio-eco-

nomic Panel Study (SOEP) to examine (a) how life satisfaction changes with the ex-

perience of spousal loss; (b) whether socio-demographic factors and social and

health resources moderate spousal loss-related changes in life satisfaction; and (c)

whether extent of anticipation, reaction and adaptation to spousal loss are associated

with mortality. Results reveal that life satisfaction shows anticipatory declines about

two and a half years prior to (anticipation), steep declines in the months surround-

ing (reaction) and lower levels after spousal loss (adaptation). Older age was

associated with steeper anticipatory declines, but less steep reactive declines.

Additionally, younger age, better health, social participation and poorer partner

health were associated with better adaptation. Higher pre-loss life satisfaction, less

steep reactive declines and better adaptation were associated with longevity. The dis-

cussion focuses on the utility of examining the interrelatedness among anticipation,

reaction and adaptation to further our understanding of change in life satisfaction in

the context of major life events.

KEY WORDS –anticipation of major life events, bereavement, hedonic treadmill,

subjective wellbeing, German Socio-economic Panel Study, SOEP.

* Arizona State University, Tempe, USA.

†Evangelische Hochschule (EHB), Berlin, Germany.

‡Humboldt University Berlin, Germany.

§ German Institute for Economic Research (DIW Berlin), Germany.

|| Pennsylvania State University, University Park, USA.

¶ Max Planck Institute for Human Development, Berlin, Germany.

** Free University, Berlin, Germany.

†† Berlin University of Technology (TUB), Berlin, Germany.

‡‡ University of California, Irvine, USA.

Ageing & Society ,,–. © Cambridge University Press 

doi:./SX



Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Introduction

Spousal loss is among those major life events that may shape individuals’

developmental trajectories (Baltes and Nesselroade ; Diener, Lucas

and Scollon ; Holmes and Rahe ). Spousal loss is a relatively

common phenomenon, particularly for women in old age. For example,

in the United States of America some  million Americans are widowed

at any given time (Elliott and Simmons ) and in Germany  per

cent of individuals aged  and older are widowed (Noll, Habich and

Schupp ). Several studies convincingly demonstrated that losing a

spouse is, on average, associated with dramatic declines in life satisfaction

(Bonanno et al. ; Lee and DeMaris ; Lucas et al. ; Ong,

Fuller-Rowell and Bonanno ) and is predictive of physical health

declines and earlier death (Elwert and Christakis ; Schulz and Beach

; Stroebe, Schut and Stroebe ). Although the majority of people

appear to be able to adjust to the loss, there are substantial individual differ-

ences in the extent to which individuals adapt to spousal loss (Bonanno

; Carr and Utz ; Infurna and Luthar in press).

Our study seeks to advance insight into the effects of spousal loss in three

ways. First, we explore the speciﬁc time-course of changes in life satisfaction

in relation to spousal loss. Second, we examine how socio-demographic

factors as well as social and health resources moderate changes in life satis-

faction in relation to spousal loss. Third, we examine the unique and shared

predictive effects of spousal loss-related changes in life satisfaction on mor-

tality. To do so, we capitalise on the strengths of longitudinal data from a

subset of bereaved participants in the German Socio-economic Panel

(SOEP) –a widely used long-running panel study that covers the full age

range of adulthood.

Change in life satisfaction with spousal loss

In the larger context of psychological research on life satisfaction, the

hedonic treadmill model (Brickman and Campbell )hasemergedas

an overarching model to examine whether major life events, such as

spousal loss, inﬂuence changes in wellbeing in adulthood and old age. The

hedonic treadmill model postulates that event-related changes in wellbeing

encompass reaction and adaptation (Diener, Lucas and Scollon ;

Frederick and Loewenstein ;Lucasa). Empirical reports focusing

on spousal loss are largely consistent with a distinction between reaction

and adaptation. For example, Lucas et al. () found that individuals typ-

ically exhibit sharp declines in life satisfaction in the year surrounding

spousal loss (reaction; −. standard deviation (SD)), with individuals

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

typically reporting sustained lower levels of life satisfaction following spousal

loss and evidence to suggest that life satisfaction takes up to eight years to

return back to pre-loss levels (adaptation). Similar results have been observed

with other facets of subjective wellbeing, such as depressive symptoms,

anxiety, as well as positive and negative affect (Carr et al. ; Lee and

DeMaris ; Wade and Pevalin ). For example, Ong, Fuller-Rowell

and Bonanno () observed that compared to a matched control group,

individuals who experienced spousal loss exhibited signiﬁcant declines in

positive emotions across a three-year period following spousal loss.

Conceptual models and empirical research on spousal loss and bereave-

ment suggest that changes in functioning as a result of losing a spouse

may be observable before the loss and may involve a certain extent of antici-

pation. The major approaches in this area agree that the months or years

prior to the spousal loss are characterised by pre-emptive changes (anticipa-

tion; see Glaser and Strauss ; Kastenbaum and Costa ), followed by

mourning and grief in the time surrounding spousal loss (reaction), and

culminating in a transformation involving both disengagement and connec-

tion (adaptation; Boerner and Heckhausen ). There are typically sub-

stantial between-person differences in such changes (Wortman and

Boerner ; Bonanno ; Infurna and Luthar in press; Wortman

and Silver ), with empirical evidence suggesting gender differences

in the ramiﬁcations of widowhood, which are typically the result of differ-

ences in the availability of resources (see Stevens ). For example,

men are more likely to show more profound declines in psychological well-

being (see Carr ; Naess, Blekesaune and Jakobsson ; Williams

), with possible explanations including men’s dependence on their

wives for emotional support and the maintenance of social contacts with

others and women’s generally stronger support networks (see Lee and

DeMaris ). We aim to integrate and substantiate these conceptual

models by directly modelling the speciﬁc time-course of changes in life sat-

isfaction through hypothesised stages (e.g. anticipation, reaction and adap-

tation), which promises to go above and beyond previous research that has

solely focused on modelling the reaction and adaptation (Lucas et al. ).

Initial empirical evidence suggests that individuals may show declines

from baseline levels of wellbeing prior to spousal loss (Kastenbaum and

Costa ; Lichtenstein et al. ; Ong, Fuller-Rowell and Bonanno

), but this research has been limited to only one or two observations

before spousal loss. In the present study, we aim to explore the existence

of pre-emptive changes in life satisfaction that we characterise as anticipa-

tory. The anticipatory period signiﬁes the process of changes in life satisfac-

tion (declines or stability) preceding spousal loss that may begin months or

even years prior to the death event (Carr and Utz ). One would expect

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

large between-person differences in the timing of the anticipatory period,

ranging from several years before losing one’s spouse (e.g. when dealing

with a long-term illness of the spouse) to no anticipatory changes at all. In

particular, we propose that anticipation could reﬂect either an adaptive

process of proactive coping or a compromising process of resource deple-

tion. Regarding anticipation as a proactive process, individuals may adjust

their goals and disengage from goals associated with the spouses survival

(Boerner and Heckhausen ; Haase, Heckhausen and Wrosch ;

Heckhausen, Wrosch and Schulz ). For example, individuals may rec-

ognise that their partner’s health is deteriorating and use goal disengage-

ment strategies to distance themselves from unrealistic goals (e.g. an

exotic vacation with the spouse; healing the illness) and through goal re-

engagement select and strive for more attainable goals with the spouse

(e.g. spend time together at home; manage the pain) and goals in other

domains (e.g. work or own health). This would operate to re-direct one’s

resources to manage stressful circumstances more effectively (Wrosch,

Amir and Miller ) and serve an adaptive purpose following spousal

loss through less reaction and better recovery. Second, anticipatory

changes could be the result of individuals having few resources in the

social, psychological and health domains that protect against decrements

due to spousal loss (e.g. vulnerability; Charles ). Stressors and burdens

associated with possible care-giving responsibilities may constrain one’s

emotion regulation capacities (Aneshenshel, Botticello and Yamamoto-

Mitani ; Charles ), resulting in lower levels and declines in life

satisfaction preceding spousal loss.

Timing of spousal loss-related change in life satisfaction

What is largely lacking in current research on spousal loss is the speciﬁca-

tion of the duration and the interrelatedness of change patterns such as an-

ticipation, reaction and adaptation. The timing and measurement of

observations may play a role in examining the patterning of change in life

satisfaction before and after spousal loss. Recent research shows that the

speciﬁcity of the time metric (e.g. months versus years) may impact the

amount of change that is observed empirically. For example, Uglanova

and Staudinger () observed that using granular time intervals (i.e.

yearly) may mask the change that accompanies negative life events and

that analyses based on three-month intervals provided additional speciﬁcity

for examinations of change in life satisfaction in relation to major life events

(see also Frijters, Johnston and Shields ).

To attain greater speciﬁcity in the timing and interrelatedness of anticipa-

tion, reaction and adaptation, we apply latent basis growth models

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

(see McArdle ; Ram and Grimm ) to longitudinal data from the

SOEP (Headey, Muffels and Wagner ; Wagner, Frick and Schupp

). Latent basis growth models permit description of and better articu-

lation of non-linear patterns of change in each hypothesised stage (see

Burke, Shrout and Bolger ) and examinination of whether there are

between-person differences in those non-linear patterns of change. This

will be done by isolating the observations for each stage and estimating

each stage using latent basis parameters and including factors to examine

whether they moderate such changes (see Myrskylä and Margolis ).

Rather than imposing a speciﬁc functional form on the shape of change,

the algorithm quantiﬁes the pattern of change to emerge from the raw

data (more details are given in the methods section; for application at

other time scales, see Fortunato, Gatzke-Kopp and Ram ).

Individual differences in change in life satisfaction with spousal loss

Research has repeatedly demonstrated that there are large between-person

differences in how individuals anticipate, react and adapt to life-altering

events (Boerner and Wortman ; Wortman and Silver ; Zautra

et al. ). Spousal loss represents one of life’s signiﬁcant adversities that

can have substantial effects on many areas of life and tremendous individual

variability in these effects, with some individuals succumbing and showing

declines in functioning, whereas others being resilient and able to recover

from adversity (e.g. Bonanno ; Netuveli et al. ). The evidence is

mixed as to the degree to which individuals are able to adapt or recover

from spousal loss. The research on resilience most often uses measures of

distress and mental health and shows that the majority do not develop

mental health problems. Initial research showed that most individuals are

resilient, by showing stable, high levels of wellbeing (see Mancini,

Bonanno and Clark ). However, more recent research suggests that

most individuals show profound declines in wellbeing and mental health,

but are able to recover or (almost) return back to previous levels of func-

tioning (i.e. recovery; see Infurna and Luthar in press; Stone, Evandrou

and Falkingham ; Wade and Pevalin ). Following conceptual

notions of resilience that various factors are likely independent predictors

of better functioning following spousal loss and to understand the hetero-

geneity of individual change in life satisfaction surrounding spousal loss,

we examine whether socio-demographic factors and social and health

resources moderate the extent of changes within anticipation, reaction

and adaptation. In times of great life distress, people typically draw upon

resources to help protect against losses in domains, such as life satisfaction,

and these resources may operate differently during the various stages. Social

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

resources include one’s social network integration and the quality of social

relationships (Aldao ; Antonucci ). Health resources broadly

include presence of chronic illness, functional limitations and self-percep-

tions of health, as well as spousal health. Furthermore, socio-demographic

factors likely moderate change in life satisfaction with spousal loss and will

also be a focus in this study.

Anticipation. We hypothesise that age, participant health status and spousal

health will likely moderate changes in life satisfaction during anticipation.

Older age may result in an anticipatory period that involves strong declines

in life satisfaction preceding spousal loss. Very old adults may have fewer

resourcestodrawupontomitigatenewburdensencounteredwithwidowhood

(e.g. losses in key domains of cognitive, physical and social functions; Gerstorf,

Smith and Baltes ;JoppandSmith). Furthermore, individuals who

sufferfromdisabling conditionsoftenreportpoor life satisfaction,presumably

as a result of already challenged self-regulatory capacities (Charles ;

Infurna et al. ; Lucas b) that would be further taxed by spousal

loss. Functional limitations of the partner may increase the risk of providing

assistance and care on a regular basis, which could make death more likely

and thus predictable, resulting in anticipatory declines.

Reaction. We hypothesise that age, gender, social resources and spousal health

will have the most salient moderating effect on reaction. Experiencing spousal

loss in young adulthood or mid-life may result in larger decrements in life sat-

isfaction because more joint time is lost and it is an unexpected and off-time

event (Neugarten and Hagestad ). In a similar vein, older adults may

be better at accepting their partners and one’s own worsening health and

death and, therefore, are less likely to experience declines in life satisfaction.

Research on gender differences in bereavement-related change in life satisfac-

tion suggests that women report stronger increases in depressive symptoms in

the years surrounding spousal loss (Carr ; Lee and DeMaris ). Social

resources, such as social network integration and supportive relationships, may

protect against the negative impact of losing a spouse; a social network that

involves more supportive, emotionally meaningful relationships with a larger

pool of individuals to go to, to help cope and protect against declines in life

satisfaction (Bonanno ; Stroebe et al. ). Spousal health is viewed as

a proxy for the surviving spouse to possibly be involved in a care-giving role

due to a disability or chronic illness of their loved one. The care-giving litera-

ture shows that placement and passing of the care recipient oftentimes results

in event-related increases in the care-giver’s life satisfaction and other pertin-

ent psychological resources due to absence of care-giving-related duties and

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

decreases in care-giver burden (Gaugler, Pot and Zarit ;Schulzet al.

,).

Adaptation. We postulate that gender, education, social resources and

health resources will serve a vital role in providing individuals with the op-

portunity to recover following spousal loss. Men have been found to

report more profound declines in psychological wellbeing (see Carr ;

Naess, Blekesaune and Jakobsson ; Williams ), which could be

due to women being better integrated and having more supportive social

relationships beyond the spousal bond. More educated people may be

more likely to return back to their previous levels of functioning because

they tend to know and use more adaptive and compensatory strategies

(Adler et al. ). For example, educational attainment is associated

with psycho-social resources of perceived control that individuals can

utilise in stressful contexts to buffer against declines in life satisfaction

(Aneshensel, Botticello and Yamatoto-Mitani ; Lachman and Weaver

). Social integration and having supportive social relationships may

help individuals ﬁnd comfort in being with others, resulting in improve-

ments in life satisfaction following spousal loss (Bisconti, Bergeman and

Boker ; Stroebe et al. ). For example, social support may buffer

against adverse physiological processes that underlie lower levels of life sat-

isfaction (Stroebe et al. ). Poorer individual health may limit one’s

ability to adapt and recover one’s life satisfaction (Wiest et al. forthcoming),

whereas poor spousal health may lead to quicker recovery or adaptation due

to a greater expectation of spousal loss (Bonanno et al. ).

Mortality following spousal loss

Spousal loss often has long-term health implications (Schulz and Beach

; Stroebe, Schut and Stroebe ), including increased mortality

(Elwert and Christakis ; Roelfs et al. ; Stroebe, Schut and

Stroebe ). We examine whether life satisfaction levels and changes pre-

ceding (anticipation), surrounding (reaction) and following (adaptation)

spousal loss are associated with mortality. Previous research suggests that

both levels of and changes in various personality and psychological factors

can have health consequences (Infurna, Ram and Gerstorf ;

Mroczek and Spiro ; Zhang et al. ).

Levels of life satisfaction prior to spousal loss are a proxy for better overall

functioning across domains, including health (Pressman and Cohen ).

Similar to previous research from panel surveys, we would expect that

higher levels of life satisfaction prior to spousal loss promote longevity

(Danner et al. ; Wiest et al. ; Zhang et al. ). Declines in life

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

satisfaction that precede spousal loss (i.e. anticipatory changes) may provide

a protective function for mortality through prompting individuals to

partake in adaptive and coping strategies to maintain one’s health following

spousal loss (Heckhausen, Wrosch and Schulz ). Conversely, anticipa-

tory declines may be a proxy for poorer underlying health, resulting in an

increased likelihood of mortality following spousal loss. Too steep declines

in life satisfaction with spousal loss (reaction) may have serious conse-

quences for everyday functioning and living (Lyubormirsky et al. ).

Conversely, no changes in life satisfaction surrounding spousal loss could

be a sign of low emotional ﬂexibility, which can have negative health conse-

quences as well (Carstensen et al. ). Following spousal loss, sustained

lower levels of life satisfaction as indexed by less/no adaptation may have

detrimental effects on health due to its association with health behaviour

regulation and biological functioning (Steptoe, Wardle and Marmot

; Wiest et al. forthcoming; Williams ).

The present study

Our objective is to examine: (a) how life satisfaction changes with the experi-

ence of spousal loss; (b) whether socio-demographic factors and social and

health resources moderate spousal loss-related changes in life satisfaction;

and (c) whether the extent of anticipation, reaction and adaptation to

spousal loss are associated with mortality. We ﬁrst expect that changes

during the months and years preceding spousal losswillreﬂect an anticipation

period (years −to −prior to spousal loss), characterised by declines in life

satisfaction. Furthermore, based on previous evidence (see Lucas et al. ),

weexpectthatindividuals,onaverage,will experiencesteepdeclinesinlifesat-

isfaction in the months surrounding spousal loss (reaction), but will (almost)

return back to previous levels in the years following (adaptation; years –fol-

lowing spousal loss). Second, focusing on between-person differences and

factors that moderate change in life satisfaction with spousal loss, we hypothe-

sise that socio-demographic factors and social and health resources will serve

different functions during anticipation, reaction and adaptation. Third, we

expect that higher levels of life satisfaction prior to spousal loss and greater

ability to adapt will be associated with longevity.

Method

We examined these hypotheses using data from  waves (–)of

the SOEP (Headey, Muffels and Wagner ). Comprehensive informa-

tion about the design, participants, variables and assessment procedures

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

in the study is reported in Wagner, Frick and Schupp (). Details rele-

vant to the present analysis are given below.

Participants and procedure

The SOEP is a nationally representative annual panel study of private house-

holds and their inhabitants initiated in . The SOEP covers approxi-

mately , residents of Germany, including immigrants and resident

foreigners. Potential participants were randomly selected from a set of ran-

domly selected locations in Germany. Within each household, all family

members older than  years of age were eligible for personal participation.

Relatively high initial response rates (between  and %) and low longi-

tudinal attrition (about % for the second wave and less than % yearly

attrition across various sub-samples) provide for an overall sample that is

representative of the population living in private households (Kroh et al.

). Data were primarily collected via face-to-face interviews and self-

administered mail questionnaires.

For the present study, we analysed data from the , participants who (a)

were married at the outset of the study, (b) had a spouse who also participated

intheSOEP,(c)whoexperiencedspousallossoverthecourse ofthestudy,(d)

did not re-marry following spousal loss over the course of the study, and (e)

provided data on our measures of interest. Participants in this sub-sample

were, on average, . years of age at spousal loss (SD = .,range

–), had attained, on average, . years of education (SD = .,

range –)and per cent were women. We note that previous research

in the SOEP has shown that spousal loss is predictive of sample attrition.

Because spousal loss is a predictor of sample attrition, the results obtained

can be regarded as conservative tests of our research question. Our results po-

tentially underestimate the amount of wellbeing loss because those who suffer

the most are the ones who are most likely to drop out.

Measures

Life satisfaction. Participants’reported on their life satisfaction annually,

answering the question ‘How satisﬁed are you with your life, all things con-

sidered?’, using a (totally unsatisﬁed) to  (totally satisﬁed) rating scale.

This item is considered a measure of cognitive-evaluative (as opposed to

emotional) aspects of wellbeing and has been used widely in psychological

research (for details, see Fujita and Diener ; Gerstorf et al. ).

Spousal loss. Spousal loss was determined by responses to the question ‘Has

your family situation changed since the beginning of year X[e.g. ]?’in

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

the category ‘spouse/partner has died’. If the individual indicated that their

family situation changed since the beginning of year X, then they were asked

a follow-up question regarding the month this event occurred. Timing of

spousal loss was deﬁned as the month and year the participant reported

their spouse/partner died. Spouse/partner month and year of death infor-

mation was used to re-align yearly reports of life satisfaction in the 

months prior to and  months following spousal loss –a time period

that, theoretically, spans the duration of anticipatory, reactive and adaptive

stages. Following procedures applied by Uglanova and Staudinger (),

we appropriated the monthly data into three-month (quarterly) intervals

(e.g. − to − months, − months to − months, and so on). For

example, if an observation was at  months prior to spousal loss, this was

grouped in the − to − month category. Similarly, changes for the

month of spousal loss (LS



) are embedded within the estimated changes

associated with LS



because this ‘window’covers the –months following

spousal loss. Table shows life satisfaction observations  months prior to

and  months following spousal loss and the average score for life satisfac-

tion. For example, life satisfaction reports were available for  of ,

participants for months − to −, with the average score being mean =

. (SD = .). We analysed our data in quarterly intervals as compared

to yearly intervals because previous research shows that this time interval

(a) provides the most efﬁcient trade-off of sample size and occasions

given the incompleteness in the data (i.e. covariance coverage; Uglanova

and Staudinger ), and (b) maintains the ability to align data with

respect to the timing of spousal loss. Participants, on average, provided

. life satisfaction observations (SD = ., range –) within the 

months of interest.

Moderators. The socio-demographic and social and health resource vari-

ables that were examined as potential moderators of anticipation, reaction

and adaptation are shown in Table (along with descriptives). Social partici-

pation was measured using a four-item index given regularly in the SOEP

that assessed frequency of involvement in or attendance at social networking

and community activities, including politics, honorary activities in clubs/

groups, sports and attendance of cultural events (see Headey, Muffels and

Wagner ; Infurna et al. ). The scale is highly similar in structure

to other instruments used to assess social participation (see Parslow et al.

). Participants answered each item on a scale from (each week) to

(never). Responses were reverse coded and averaged to obtain an index

with higher scores indicating greater social participation. Repeated observa-

tions of an individual’s social participation obtained in the available period

prior to spousal loss were averaged, so that social participation scores

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

TABLE .Descriptive statistics for life satisfaction in relation to spousal loss

Time to/from spousal

loss (months) Number of observations Mean SD

Level − to −  . .

− to −  . .

− to −  . .

Level − to −  . .

Anticipation − to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

− to −  . .

−to −  . .

Anticipation −to −  . .

Reaction −to   . .

Reaction to   . .

Adaptation to   . .

to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

 to   . .

Adaptation  to   . .

Notes:N=, participants provided , observations. Participants, on average, provided

. (standard deviation (SD) = ., range –) life satisfaction observations over this time

period. The number of observations column refers to the number of life satisfaction observa-

tions we have for this time period in the study across the participants. The mean column

refers to the average score of life satisfaction, based on a – scale, during that time period

in the study in relation to spousal loss. The SD column refers to the SD or distribution of

scores for life satisfaction during that time period in the study in relation to spousal loss. For

example, for months − to −, we have  life satisfaction observations out of the ,

participants who were included in this study, the average score at this time period is .,

with SD = ..

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

indicate the level of social resources individuals bring to spousal loss.

Disability status of both the individual and the spouse were derived from a

single item asking each year whether the respondent was ‘ofﬁcially

certiﬁed as having a reduced capacity to work or being severely handi-

capped’(for details, see Infurna et al. ; Lucas b). Thus, disability

indicators were based on self-reports, but referred to ofﬁcial certiﬁcations.

These repeated assessments were used as a time-invariant measure that indi-

cates whether the individual or spouse was disabled at any point prior to

spousal loss. In follow-up analyses, the pattern of ﬁndings did not differ

when we included the length of disability prior to spousal loss or whether

the person of focus became disabled during the ﬁve years ( months)

prior to spousal loss as a moderator. Of the , individuals who experi-

enced spousal loss in our sample,  were not disabled and  were dis-

abled prior to spousal loss. Of the , spouses who died,  were not

disabled and  were disabled prior to death.

Mortality. Mortality status and year of death for deceased participants who

lost their spouse was obtained either (a) by interviewers at the yearly assess-

ments (i.e. from household members or, in the case of one-person house-

holds, neighbours) or (b) from city registries. Of the , participants

included in our analysis,  (%) have died. On average, deceased parti-

cipants were . years of age at the time of spousal loss (SD = ., range

–) and died . years later (SD = ., range –). We did not have

access to cause of death information and, therefore, our mortality analyses

focus on all-cause mortality. Research by Elwert and Christakis () shows

that mortality following the event of spousal loss does not vary substantially

TABLE .Means, standard deviations (SD) and intercorrelations among

moderators included in the present study

Mean SD 

. Age at spousal loss

(– years) . . –

. Women (%) . . −.*–

. Education (– years) . . −.*−.*–

. Social participation

(–). . −. −.*.*–

. Disability: individual

(= disabled) . . .*−.*−.*−. –

. Disability: partner

(= disabled) . . .*.*−.*−.*.*–

Note:N=, ( men,  women).

Signiﬁcance level:*p<..

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

by causes of death, suggesting that there is no increased likelihood of one

cause of death over another.

Analysis procedures

Multi-phase latent basis growth model. To examine inter-individual differ-

ences in the hypothesised pattern of change, we used a multi-phase latent

basis growth model (see Ram and Grimm ; Singer and Willet ).

Figure graphically illustrates the structural equation model that we

applied to our data (top panel) and how it maps on to hypothesised

spousal loss-related changes in life satisfaction (bottom panel): level

(months − to −), anticipation (months − to −), reaction

(months −to +) and adaptation (months +to +).

Figure . Illustration of the structural equation model (top panel) and components of change

that life satisfaction follows in relation to spousal loss. Level components of life satisfaction refer

to how individuals may report varying levels of life satisfaction in the several years prior to

spousal loss. Anticipation reﬂects changes in life satisfaction in the years leading up to spousal

loss. Reaction refers to how individuals may display differential rates of change with the

incidence of spousal loss. Lastly, differential rates of change may be exhibited in the years

following spousal loss, which is referred to as adaptation; adaptation may be immediate (one

year following spousal loss) or take several years.

Note: Each line in the ﬁgure displays a hypothetical trajectory of change for individuals who

experience spousal loss. The factor loadings for level are all set to . The factor loadings for

anticipation, reaction and adaptation that are not labelled are freely estimated. See the text for

further explanation of the ﬁgure.

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

The repeated measures of life satisfaction (rectangles in Figure ) were

modelled as a function of the four latent growth factors (ellipses; level, an-

ticipation, reaction and adaptation) and a set of unique/error factors

(circles; assumed to have homogeneous variance). The factor loadings con-

necting each growth factor to the yearly measures of life satisfaction (arrows

connecting ellipses to rectangles) were estimated in a manner similar to

conﬁrmatory factor analysis and indicate the shape of the trajectory of life

satisfaction (see e.g. Fortunato, Gatzke-Kopp and Ram ; Ram and

Grimm ).

An individual-speciﬁc intercept factor score quantiﬁes the expected level

of life satisfaction prior to spousal loss, g

i

.Anticipation scores (g

i

) and

the parameters of the vector A



[t] indicate the extent of change in life sat-

isfaction in the years preceding spousal loss (year − months to month

−). Reaction scores (g

i

) and the parameters of the vector A



[t] indicate

the extent of short-term change in the months surrounding spousal loss

(month −to month +). Adaptation scores (g

i

) and the parameters of

the vector A



[t] indicate the extent of recovery in life satisfaction in the

years following spousal loss (month +to month +). The factor means

(not depicted in ﬁgure) describe the level and extent of anticipation, reac-

tion and adaptation for a prototypical widower and the factor variances

(small double-headed arrows on each of the ellipses) indicate the extent

of between-person differences in each component of the change process.

In subsequent analyses, the models were expanded in two ways. First, to

further examine the interrelatedness of the anticipation, reaction and adapta-

tion, we tested a mediation model where the correlations amongst these factors

were replaced by regressions. The reaction factor (g



) was regressed on the an-

ticipation factor (g



) and the adaptation factor (g



) was regressed on the antici-

pation and reaction factors. From the parameters we then determined, using

the Sobel test (Sobel ), whether reaction mediated the association

between anticipation and adaptation. Second, we regressed socio-demographic

factors and social and health resources on level (g



), anticipation (g



), reaction



) and adaptation (g



) to examine the extent to which these between-person

difference factors moderated each component of the change process.

All models were estimated using MPlus (Muthén and Muthén ), with

incomplete data accommodated under missing at random assumptions at

the within-person level, and, to retain longitudinal data, missing completely

at random at the between-person level (Little and Rubin ; set-ups pro-

vided in Ram and Grimm ).

Survival analysis. In a ﬁnal step, we outputted the estimated factor scores

from the (unconditional) multi-phase growth model, and used Cox propor-

tional hazard regression models (Cox ) to model the hazard for the

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

event of mortality in the post-spousal loss period (SAS PROC PHREG; see

Allison ) as a function of these factor scores, socio-demographic

factors, and social and health resource variables.

Results

Change in life satisfaction with spousal loss

In a preliminary step, we calculated the intraclass correlation, which was

., indicating that  per cent of the total variance in life satisfaction

was between-person variance and  per cent was within-person variance.

The repeated measures of life satisfaction thus appeared to contain both

substantial amounts of between-person differences and within-person vari-

ation over time; this suggests that individuals are more likely to differ

from themselves over time than their average differs from that of other

persons.

Results from the multi-phase latent basis growth model examining

changes in life satisfaction in relation to spousal loss are shown in

Table . Each column displays the latent basis coefﬁcients (i.e. parameters

of A

[t] indicate the shape of the trajectory), which specify the proportion

of change that has occurred up to that point for each phase of the

process: anticipation, reaction and adaptation. Figure shows the model-

implied trajectory of life satisfaction across the study period (black line)

for the prototypical participant (average score for each moderator), over-

laid on model-implied trajectories for a sub-sample of  participants

(grey lines). Life satisfaction was characterised by average levels at months

− to − (μ

g

=.,p<.). For anticipation, during the months and

years preceding spousal loss (months − to −), individuals typically

experienced declines in life satisfaction (μ

g

=−.,p<.,d=−.).

The mean of the anticipation factor indicates that during the time interval

of − months to −months prior to spousal loss, the average amount of

decline in life satisfaction was −. points on a – scale. For reaction, indi-

viduals, on average, experienced a sizeable decrease in life satisfaction in the

six months surrounding spousal loss (μ

g

=−.,p<.,d=−.). The

latent basis coefﬁcient for −months prior to spousal loss was not reliably

different from zero (A



[−]=.), suggesting that reaction does not

occur in the months preceding spousal loss, but only in the three

months following spousal loss A



[+months] = −.). Following spousal

loss, the average pattern of change was characterised by gradual increase

post-reaction (A



[t] increase from . at months to . at  months

post-loss). However, on average life satisfaction levels did not approach

pre-loss levels (baseline level μ

g

=. versus level at  months

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

TABLE .Fixed and random effects and latent basis estimates for examining change in life satisfaction to/from spousal loss

Pattern of change (latent basis)

Speciﬁc time-course of change in life satisfaction

Level, g



Anticipation, g



Reaction, g



Adaptation, g



Factor loadings for

change factors A

[t]LS

−

=. LS

−

=.*(.)LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=. LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=. LS



=.*(.)

−

=. LS



=.*(.)

−

=. LS



=.*(.)

…LS



=.*(.)



=. LS



=.

Change factor:

Factor means (SE) μ

g

=.*(.)μ

g

=−.*(.)μ

g

=−.*(.)μ

g

=.*(.)

Factor variances and correlations:

Level, g



.*(.)

Anticipation, g



−.*.*(.)

Reaction, g



−.*−.*.*(.)

Adaptation, g



−. . −.*.*(.)

Fit statistics:

CFI .

RMSEA .

Notes:N=,. Number of observations = ,. Residual variance = ., standard error (SE) = ..LS

= life satisfaction observation at tmonths in

relation to spousal loss. Intraclass correlation = .. Pseudo-R



=.. CFI: Comparative Fit Index. RMSEA: Root Mean Square Error of Approximation.

The number in parentheses represent the standard errors for the speciﬁc parameter.

Signiﬁcance level:*p<..

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

post-loss = .,−. lower, d=−.), indicating there is not full recovery

(adaptation, μ

g

=.,p<.,d=.). This suggests that from three

months to ﬁve years following spousal loss, life satisfaction went up, on

average, . points, which is still lower than the . points that it declined

from months years prior to spousal loss to three months following spousal

loss (μ

g

=−. +μ

g

=−. =−.).

Finally, the estimated correlations in Table indicate how anticipation,

reaction and adaptation were interrelated (in this sample). The correlation

of anticipation with reaction and adaptation was r

gg

=−. (p<.) and

gg

=. (p>.), respectively. This suggests that exhibiting stronger an-

ticipatory declines in life satisfaction was associated with less-reactive

declines, but was not related to the extent of adaptation. The correlation

between reaction and adaptation was r

gg

=−. (p<.), suggesting

that stronger declines in life satisfaction immediately surrounding the

Figure . Model-implied mean (black line) for change in life satisfaction in relation to spousal

loss with predicted scores from our latent basis model from Table for a sub-sample of 

participants (grey lines). Population mean for the  German Socio-economic Panel Study

(SOEP) sample is depicted to illustrate how individuals’life satisfaction compares to mean

population average. Changes in life satisfaction are characterised by a multi-stage pattern.

SOEP participants who experienced spousal loss, on average, reported declines in life

satisfaction in the months and years preceding spousal loss (anticipation), substantial declines

in the months surrounding spousal loss (reaction) and did not return back to previous levels

following spousal loss (adaptation).

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

spousal loss were associated with more-extensive adaptation. Furthermore,

the correlations of level with anticipation, reaction and adaptation were

gg

=−. (p<.), r

gg

=−. (p<.)andr

gg

=−. (p>.),

respectively, suggesting that the level of life satisfaction ﬁve years prior to

spousal loss was associated with the extent of change during anticipation

and reaction, but not during adaptation. Individuals who are at lower

levels of life satisfaction at the baseline are less likely to show further

declines in life satisfaction with spousal loss, but with no implication for

what happens during post-loss recovery.

We tested an additional model that replaced the correlations among antici-

pation, reaction and adaptation with regressions (i.e. regress reaction on an-

ticipation, regress adaptation on anticipation and reaction). This was done to

determine whether reaction mediated the effect of anticipation on adapta-

tion. We found that anticipation predicted reaction (a=−.,standard

error (SE) = .,p<.), suggesting that stronger declines in life satisfac-

tion during anticipation were associated with less steep declines in reaction.

We also found that both anticipation (c′=−.,SE=.,p<.) and re-

action (b=−.,SE=.,p<.) predicted adaptation, such that stron-

ger declines in life satisfaction during anticipation and reaction were

associated with better adaptation or quicker recovery of life satisfaction follow-

ing spousal loss. The Sobel test conﬁrmed that reactionmediated the effect of

anticipation on adaptation (estimate = .,p<.).

Individual differences in change in life satisfaction with spousal loss

With the variance components and predicted trajectories in Figure (grey

lines) suggesting that there were meaningful between-person differences in

the extent of anticipation (σ

g

=.,p<.), reaction (σ

g

=.,

p<.) and adaptation (σ

g

=.,p<.) to spousal loss, we pro-

ceeded to examine whether socio-demographic factors and social and

health resources moderated the extent of change. Results are shown in

Table . Participants who were older (β



=−.,p<.) were more

likely to exhibit declines in life satisfaction preceding spousal loss (anticipa-

tion). Participants who were older (β



=.,p<.) and reported lower

levels of social participation (β



=−.,p<.) were more likely to

exhibit less steep declines in life satisfaction in the months surrounding

spousal loss (reaction). Lastly, younger age (β



=−.,p<.), greater

social participation (μ



=.,p<.), not being disabled (μ



=−.,

p<.) and partner disability (μ



=−.,p<.) were each associated

with better adaptation following spousal loss (adaptation). Figure illus-

trates the age differences, indicating that participants who were older

when their spouse died tended to report higher life satisfaction to begin

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

TABLE .Fixed and random effects and latent basis estimates for examining change in life satisfaction to/from spousal loss:

the effect of socio-demographic and social and health resources

Pattern of change (latent basis)

Speciﬁc time-course of change in life satisfaction

Level, g



Anticipation, g



Reaction, g



Adaptation, g



Factor loadings for change factors A

[t]LS

−

=. LS

−

=.*(.)LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=. LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=. (.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=.*(.)LS



=.*(.)

−

=. LS

−

=. LS



=.*(.)

−

=. LS



=.*(.)

−

=. LS



=.*(.)

…LS



=.*(.)



=. LS



=.

Change factor:

Factor means (SE) μ

g

=.*(.)μ

g

=−.*(.)μ

g

=−.*(.)μ

g

=.*(.)

Effects of moderators on change factors (SE):

Age β



=.*(.)β



=−.*(.)β



=.*(.)β



=−.*(.)

Women β



=−. (.)β



=. (.)β



=−. (.)β



=. (.)

Education β



=−. (.)β



=−. (.)β



=. (.)β



=. (.)

Social participation β



=.*(.)β



=. (.)β



=−.*(.)β



=.*(.)

Disability status of individual β



=−.*(.)β



=−. (.)β



=. (.)β



=−.*(.)

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

TABLE .(Cont.)

Pattern of change (latent basis)

Speciﬁc time-course of change in life satisfaction

Level, g



Anticipation, g



Reaction, g



Adaptation, g



Disability status of partner β



=−. (.)β



=−. (.)β



=. (.)β



=.*(.)

Residual factor variances and correlations:

Level, g



.*(.)

Anticipation, g



−.*.*(.)

Reaction, g



−.*−.*.*(.)

Adaptation, g



. . −.*.*(.)

Pseudo R



. . . .

Fit statistics:

CFI .

RMSEA .

Notes: N =,. Number of observations = ,. Residual variance = ., standard error (SE) = ..LS

= life satisfaction observation at tmonths in

relation to spousal loss. Intraclass correlation = .. CFI: Comparative Fit Index. RMSEA: Root Mean Square Error of Approximation. The number in

parentheses represent the standard errors for the speciﬁc parameter.

Signiﬁcance level:*p<..

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

with, but also experienced stronger declines in life satisfaction preceding

spousal loss (anticipation), whereas younger participants were more likely

to experience stronger declines in life satisfaction in the months immediately

surrounding spousal loss (reaction), but were more likely to recover in the

years thereafter (adaptation).

Longevity following spousal loss

In a ﬁnal model, we examined whether level, anticipation, reaction and

adaptation were associated with post-loss longevity. We outputted the

factor scores for each individual from the model in Table and used

those estimated factor scores as predictors in a Cox regression model of

mortality risk, controlling for socio-demographics, and social and health

resources. Results from Table indicate that pre-spousal loss levels, reaction

and adaptation were each associated with longevity. Higher levels of life sat-

isfaction prior to spousal loss, less steep declines in life satisfaction in the

months surrounding spousal loss and better adaptation were each asso-

ciated with an increased likelihood of longevity following spousal loss.

Figure . Illustration of the moderating role of age for change in life satisfaction in relation to

spousal loss. Older age at spousal loss was associated with an increased likelihood of

experiencing stronger life satisfaction declines preceding spousal loss (anticipation) and less

steep declines surrounding spousal loss (reaction), whereas younger age at spousal loss was

associated with maintenance of life satisfaction prior to spousal loss (anticipation), steeper

declines surrounding spousal loss (reaction) and quicker recovery in the years thereafter

(adaptation).

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

The parameter estimates in Table are interpreted in regards to how each

one-unit increase in the factor of interest is associated with mortality risk.

For example, the parameter estimate for reaction was ., each one-

point less steep change (decline) in life satisfaction surrounding spousal

loss from the mean of −. (e.g. reaction estimate = −.) was associated

with a  per cent decreased likelihood of mortality. Finally, each one unit

higher level in adaptation (e.g. adaptation estimate = .), signifying better

or quicker recovery, was associated with a  per cent decreased likelihood

of mortality. Figure illustrates the expected differences in mortality risk

between individuals who exhibited more-extensive adaptation (solid line;

lower likelihood for mortality following spousal loss) and individuals who

exhibited less-extensive adaptation (dashed line; higher likelihood for mor-

tality following spousal loss). Additional factors that were related to

decreased mortality risk were younger age, being a woman, social participa-

tion and partner disability.

Discussion

The objective was to examine (a) how life satisfaction changes with the experi-

ence of spousal loss; (b) whether socio-demographic factors and social and

health resources moderate spousal loss-related changes in life satisfaction;

and (c) whether extent of anticipation, reaction and adaptation to spousal

loss are associated with mortality. We observed that changes in life satisfaction

TABLE .Likelihood of longevity in the years following spousal loss as a

function of level, anticipation, reaction and adaptation of life satisfaction

Hazard ratio %CI

Intercept .*.,.

Anticipation . .,.

Reaction .*.,.

Adaptation .*.,.

Age .*.,.

Women .*.,.

Years of education . .,.

Social participation .*.,.

Disability status: individual . .,.

Disability status: partner .*.,.

Model ﬁt statistics:

Degrees of freedom 



.

Notes:N=, with  recorded deaths in the post-spousal loss observation period.

CI: conﬁdence interval.

Signiﬁcance level:*p<..

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

in relation to spousal loss were characterised by a multi-stage pattern. On

average, life satisfaction began to decline two and a half years preceding

spousal loss (anticipation), steeply dropped during the six months surround-

ing spousal loss (reaction), with individuals’life satisfaction recovering, but

not returning back to pre-loss levels (adaptation). We also found substantial

heterogeneity in the extent of changes in life satisfaction. Older adults were

less likely to report declines in life satisfaction in the months surrounding

spousal loss (reaction), whereas younger age was associated with better recov-

ery following spousal loss (adaptation). Better health, partner disability (indi-

cative of possible care-giving role prior to spousal loss) and greater social

participation were additionally associated with better adaptation following

spousal loss. In a ﬁnal step, we found that higher levels of life satisfaction

ﬁve years prior to, less steep declines surrounding spousal loss (reaction)

and better adaptation in the ﬁve years following were each associated with

increased longevity. Our discussion focuses on the interrelatedness of the

time-course through which life satisfaction changes with spousal loss,

factors that moderate between-person differences and pathways through

which life satisfaction inﬂuences longevity following spousal loss.

Change in life satisfaction with spousal loss

Our aim was to provide an interpretation of the theories on spousal loss and

bereavement by modelling change in life satisfaction using three-month

Figure . Illustration of the predictive effects of adaptation for survival following spousal loss.

Adaptation refers to changes in life satisfaction in the months and years following spousal loss

and whether life satisfaction levels are able to recover. More-extensive adaptation following

spousal loss was associated with increased likelihood of survival in the years following spousal

loss.

Note: SD: standard deviation.

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

intervals to decipher the speciﬁc time-course that life satisfaction may follow

when experiencing spousal loss, anticipation, reaction and adaptation. For

reaction, our ﬁndings are similar to previous research showing that spousal

loss typically results in substantial declines in life satisfaction (Lee and

DeMaris ; Lucas et al. ; Ong, Fuller-Rowell and Bonanno ;

Wiest et al. forthcoming). For adaptation, we observed that, on average, indi-

viduals recovered, but their life satisfaction did not reach the levels reported

ﬁve years prior to spousal loss. Our ﬁndings are similar to previous research

showing adaptation to spousal loss, within panel surveys, may take up to

eight years (Burke, Shrout and Bolger ; Lucas et al. ; Wiest et al.

forthcoming).

A novel contribution of our study to the larger context of the hedonic

treadmill model and how life satisfaction changes as a function of major

life events is that individuals may anticipate the incidence of an event.

Anticipation of a major life event is characterised by changes in functioning

preceding the event (could be months or years) and not just in the months

surrounding and years following. Previous conceptual models on spousal

loss and bereavement have discussed possible anticipatory changes, but

have been vague in when this may begin. Kastenbaum and Costa () dis-

cussed that anticipatory grief may begin years leading up to death of a loved

one. Our focus was to model directly anticipation that has previously been

theorised, and go above and beyond previous research that has solely

focused on the reaction and adaptation stages (Lucas et al. ; Wiest

et al. forthcoming). To move in this direction, we used multi-phase latent

basis growth models and modelled change in life satisfaction over three-

month intervals (as opposed to annual intervals). We were able to provide

evidence to suggest pre-emptive (anticipatory) changes in life satisfaction

around two and a half years prior to spousal loss, with a more substantial

drop in life satisfaction in the months surrounding spousal loss. We note

that we had to a priori specify the parameters of our model for anticipation

and that there are large between-person differences as to when this period

may begin. Anticipation can either be adaptive (i.e. proactive self-regulation,

bracing oneself) or maladaptive (passive depleted resources before loss

even happens). Stronger declines in life satisfaction prior to spousal loss

were associated with less steep declines in life satisfaction as a result of

spousal loss (reaction) and better adaptation or recovery of life satisfaction

following spousal loss.

In accordance with the line of reasoning that considers anticipation of

major life events being an active process, the Motivational Theory of Life-

span Development (Heckhausen, Wrosch and Schulz ) discusses that

as a developmental deadline is approaching or, in this case, a major life

event, individuals may engage in various strategies to optimise development

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

leading up to and/or following the event. As discussed in the Introduction,

an anticipatory strategy may serve this optimising function. Anticipation

may be a proactive process that involves the re-directing of one’s resources

to manage stressful circumstances more effectively and may serve an adap-

tive purpose following spousal loss through less reaction and thus easier re-

covery (Heckhausen, Wrosch and Schulz ; Wrosch, Amir and Miller

). In the case of spousal loss, substantial declines in life satisfaction

leading up to spousal loss (anticipation) may protect an individual against

further declines surrounding spousal loss (reaction) and thereafter (adap-

tation). Our empirical results are in line with these hypotheses; we found

that stronger declines preceding spousal loss was associated with better re-

covery/adaptation following spousal loss and this was mediated via less

strong reactive declines in life satisfaction in the months surrounding

spousal loss.

This study is only an initial step in the direction of broadening our view of

developmental change in relation to major life events by incorporating and

directly modelling an anticipatory period and its implications for later recov-

ery. It is upon future research to evaluate further whether anticipatory

changes are salient in other major life events, such as chronic illness and

its functional implications. For example, previous research has shown that

although life satisfaction shows substantial changes surrounding disability,

the data and results suggest that disability may be foreshadowed by

decreases in life satisfaction in the years preceding the event (Lucas

a). Direct modelling of anticipatory changes in life satisfaction preced-

ing major life events and using more ﬁne-grained time intervals (e.g.

monthly versus yearly) will allow researchers to examine the course of life sat-

isfaction (and other domains) change in relation to major life events more

thoroughly.

Individual differences in life satisfaction change with spousal loss

Similar to previous conceptual work and empirical reports on resilience to

signiﬁcant life adversity (Boerner, Wortman and Bonanno ; Bonanno

; Wortman and Silver ), we found that there were large between-

person differences in the extent to which life satisfaction changed in rela-

tion to spousal loss. This was in line with our expectations that individuals

may follow different paths towards resilience to and recovery from spousal

loss, with most individuals displaying substantial declines (Infurna and

Luthar in press; Netuveli et al. ; Stone, Evandrou and Falkingham

). To address this, we examined whether social and health resources

and socio-demographic factors moderated such changes. The age of the

participant moderated the life satisfaction change during anticipation,

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

reaction and adaptation. First, individuals who were older experienced

steeper anticipatory declines in life satisfaction preceding spousal loss.

One interpretation is that at higher ages, the death of a spouse/partner

may be drawn out or expected, resulting in more gradual life satisfaction

declines in the years leading up to spousal loss (Bonanno and Kaltman

). Second, younger age at the time of spousal loss, on average, was asso-

ciated with steeper declines in life satisfaction in the months surrounding

spousal loss. Greater life satisfaction declines with spousal loss in younger

ages may be the result of not having a model for mastering the loss, of

the fact that plans for the future with one’s spouse can no longer be realised

and, in the case of having young children, the difﬁculty of raising the chil-

dren with one parent can be signiﬁcant (Carr et al. ). Lastly, younger

age was associated with better adaptation or quicker recovery following

spousal loss. Individuals in young adulthood and mid-life may have more

resources to draw upon to promote life satisfaction, have a more-extended

future life perspective, and may therefore bounce back more quickly follow-

ing spousal loss. We note that the experience of spousal loss in young adult-

hood, mid-life and old age is likely to be qualitatively different. As shown in

Figure , there may also be initial level differences in life satisfaction

between participants who were older and younger at the time of spousal

loss.

We observed that better individual health, poorer partner health and

greater social participation each moderated life satisfaction change

during the adaptation stage. First, individuals who were disabled were less

likely to experience recovery of life satisfaction following spousal loss.

This could be a result of their self-regulation system already being at its

limits and not having the resources to recover from life satisfaction declines

with spousal loss (Charles ; Infurna, Gerstorf and Ram ; Lucas

b). Second, partner disability was linked to better adaptation or

quicker recovery following spousal loss. Partner disability may be a proxy

for the surviving spouse being involved in care-giving-related tasks that

may constrain and undermine emotion regulation capacities (Charles

), and bereavement may operate as a relief (Schulz et al. ).

Third, greater social participation prior to spousal loss is an important re-

source individuals can draw upon to recover from spousal loss in terms of

life satisfaction levels returning back to previous levels more quickly

(Isherwood, King and Luszcz ; Zautra et al. ). Social participation

may operate through various pathways, including cognitive appraisal,

coping strategies and health behaviours. Individuals who are more embed-

ded in their social network may be better able to recover from spousal loss

through decreasing feelings of social loneliness that can help alleviate and

compensate for loss of a spouse (Stroebe et al. ). Social participation

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

may also operate via having the necessary coping strategies more readily

available to buffer against the impact of the stressful life event and move

forward in terms of ﬁnding a sense of meaning and purpose in life

(Bonanno et al. ). Furthermore, our measure of social participation

included sport activity and this may help individuals maintain their health

and social interactions in the face of spousal loss, leading to better life satis-

faction (Shahar et al. ).

Based on previous empirical evidence (Carr ; Lee and DeMaris

; Naess, Blekesaune and Jakobsson ; Williams ), we expected

that gender would moderate changes in life satisfaction before and after

spousal loss. More speciﬁcally, we hypothesised that women would show

greater declines in life satisfaction during the time surrounding spousal

loss (reaction), but would have the ability to bounce back or recover

more quickly (adaptation). However, we did not ﬁnd evidence that

gender moderated changes in life satisfaction. Differences with the prior lit-

erature include the large adult lifespan sample, with spousal loss possibly

being more detrimental in different stages of the lifespan. For example,

older men may show stronger declines in wellbeing following spousal loss

due to being less socially integrated. Second, our measure of life satisfaction

is representative of cognitive-evaluative components of wellbeing, whereas

previous research largely focused on affective components, such as depres-

sive symptoms. There may be differences in the extent to which men and

women report depressive symptoms in the context of spousal loss. Lastly,

the nature of our analyses was at the more ﬁne-grained time-scale

whereas previous research examined mean-level change across fewer assess-

ments (i.e. two or three time assessments).

Mortality following spousal loss

Our objective was to assess whether one’s life satisfaction in the context of

spousal loss has mortality implications. Reporting higher levels of life satis-

faction, less steep reactive declines surrounding spousal loss and one’s

ability to adapt were each associated with longevity. Our ﬁndings are consist-

ent with previous research from longitudinal surveys that have observed

higher levels of life satisfaction may operate as a protective resource

against mortality and be indicative of better overall health (Carstensen

et al. ; Danner, Snowdon and Friesen ; Wiest et al. ; Zhang

et al. ). Levels of life satisfaction prior to spousal loss may be linked

to longevity through psycho-social functioning and better overall health.

First, feeling greater joy over life relates to perceiving that one’s actions,

behaviours and efforts can shape life circumstances to attain desirable out-

comes (Infurna et al. ; Lyubomirsky et al. ). Second, higher levels

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

of overall life satisfaction are protective against declines in health through

partaking in more health-promoting behaviours and buffering against the

negative effects of stress on biological functioning (Pressman and Cohen

). Focusing on the reaction stage, the maintenance of one’s life satis-

faction, despite spousal loss, was protective against mortality and may be in-

dicative of resilience in the face of this potentially traumatic event (Mancini,

Bonanno and Clark ). Too steep declines of life satisfaction after

spousal loss may lead to declines in one’s engagement and frequency of par-

taking in activities that sustain one’s subjective wellbeing and overall health

(Lyubomirsky and Layous ). Furthermore, too steep declines may

negatively impact one’s biological systemic integrity (e.g. broken heart syn-

drome; Wittstein et al. ), leading to increased likelihood of morbidity

and mortality (Steptoe and Kivimäki ).

Similar to previous research, we observed that individuals who were able

to adapt to or recover from spousal loss attained greater longevity (Wiest

et al. forthcoming). Remaining at low levels of life satisfaction for an

extended period of time may result in long-term dysfunction. Adaptation

may be linked to longevity through engaging with one’s social network,

health behaviours and compensatory strategies. First, quicker recovery fol-

lowing spousal loss may be a proxy for individuals utilising their social

network resources to buffer against the negative impact of spousal loss

(Antonucci ; Berkman et al. ). Second, spousal loss can result in

declines in health-promoting behaviours and deterioration of nutritional

status (Shahar et al. ; Wilcox et al. ); one’s ability for life satisfac-

tion to recover following spousal loss can enable individuals to maintain par-

ticipation in health-promoting behaviours and better cardiovascular and

immune system functioning (Pressman and Cohen ; Steptoe, Wardle

and Marmot ; Wrosch and Schulz ). Similarly, positive affect

and life satisfaction may be associated with one’s ability to engage in adap-

tive goal (dis)engagement strategies following spousal loss, leading to

increased likelihood of longevity (Haase, Poulin and Heckhausen ;

Wrosch, Schulz and Heckhausen ). We were not able to test the

speciﬁc mechanisms involved in how life satisfaction is associated with lon-

gevity following spousal loss. Future studies are needed involving both panel

surveys and sub-groups who experienced widowhood to evaluate the pro-

posed mechanisms underlying such associations.

Limitations and outlook

We note several limitations of our study. First, we acknowledge that there are

a myriad of other potential moderators, both in terms of personality and

social and health resources, that we were unable to examine due to

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

non-availability of data. For example, previous work has shown that higher

levels of perceived control and emotional support are associated with more

positive life satisfaction in the years following bereavement (Aneshensel,

Botticello and Yamamoto-Mitani ). Also, developmental heuristics of

optimised goals choice and effective goal engagement and disengagement,

as well as purpose and meaning in life and personality traits, may help indi-

viduals to anticipate and adapt to widowhood (Boyce and Wood ;

Heckhausen, Wrosch and Schulz ; King and Hicks ). In a

similar vein, ﬁnancial resources, such as a high(er) income and labour

force participation, as well as supportive social relations can potentially

help individuals be resilient to spousal loss. The lack of ﬁnancial resources

and supportive social relationships can impose additional stressors and com-

plicate adaptation to spousal loss. Second, we do not have a sufﬁciently large

number of respondents with cause of death information (SOEP did not

collect cause of death information prior to ;see Infurna et al. ).

It is likely that cause of death for spouses differed by age, gender and edu-

cation, possibly leading to differences in the stages of change in life satisfac-

tion with spousal loss. Future research thus should examine whether cause

of death of spouse and care-giving characteristics and burden has implica-

tions for the life satisfaction and survival of the spouse. Third, there are lim-

itations in our statistical models and pattern of observations in life satisfaction.

In particular, we modelled change in life satisfaction based on three-month

intervals in that each person was not measured every three months and we

do not have data in the three ‘missing’observations between each yearly as-

sessment. Fourth, we also imposed where the start and end of each stage was

and that each person was required to follow the same (non-parametric) func-

tional form within a stage (e.g. we did not test multiple-group models based on

age, gender and education differences). Additional work and data are

needed to understand if and how the timing of transitions between, length

and pattern of change of each phase may differ across individuals. Fifth, an

additional route to extend our statistical models further would be by articulat-

ing and testing speciﬁc non-linear functions of change (e.g. exponential; see

Wiest et al. forthcoming) rather than the freely estimated form used in our

study. This would require more precise articulation of what the patterns of

change look like in each stage, what equilibrium is and the appropriate

time-scale that these patterns emerge (see Grimm, Ram and Hamagami

). Lastly, research examining life satisfaction change in relation to

major life events has largely used a single-item measure of life satisfaction.

Other components of wellbeing, including positive affect and negative

affect, may show a differential pattern, with stronger changes in the time sur-

rounding the event (reaction) and a quicker return back to previous levels

following the event (adaptation).

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

In sum, spousal loss results in substantial declines in life satisfaction that can

be characterised by anticipation, reaction and adaptation, with large between-

person differences in the extent of change experiences. Intriguingly, anti-

cipation was found to buffer the reaction to spousal loss and thus beneﬁt

long-term adaptation. Older adults are more likely than adults in young adult-

hood and mid-life to experience greater declines in life satisfaction prior to

spousal loss, but less steep declines in the months surrounding spousal loss,

and younger persons adapt more quickly to this major life event. Social par-

ticipation is associated with better adaptation in the years following spousal

loss. Furthermore, our study showed that life satisfaction levels prior to, as

well as reaction and adaptation to spousal loss, are each associated with lon-

gevity. Future research should focus on (additional) personality differences in

self-regulatory capacities and social resources that may moderate anticipatory,

reactive and adaptation changes associated with major life events and the

pathways through which changes in life satisfaction are associated with

longevity.

Acknowledgements

Denis Gerstorf and Nilam Ram gratefully acknowledge the support provided by NIA

(RC-AG,R-AG,R-AG), NICHD (R-HD,R-

HD); the National Center for Advancing Translational Sciences (UL-

TR), the DIW Berlin (German Institute for Economic Research) and the

Social Science Research Institute at the Pennsylvania State University. Denis

Gerstorf gratefully acknowledges the support provided by the German Research

Foundation (DFG, GE /-). Additional support was provided by Gert

G. Wagner’s fellowship from the Max Planck Society. The content is solely the re-

sponsibility of the authors and does not necessarily represent the ofﬁcial views of

the funding agencies.

References

Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L. and

Syme, S. L. . Socioeconomic status and health: the challenge of the gradient.

American Psychologist,,,–.

Aldao, A. . The future of emotion regulation research: capturing context.

Perspectives on Psychological Science,,,–.

Allison, P. D. .Survival Analysis Using the SAS(R) System: A Practical Guide. SAS

Institute, Cary, North Carolina.

Aneshensel, C. S., Botticello, A. L. and Yamamoto-Mitani, N. . When caregiving

ends: the course of depressive symptoms after bereavement. Journal of Health and

Social Behavior,,,–.

Antonucci, T. C. . Social relations: an examination of social networks and social

support, and sense of control. In Birren, J. E. (ed.), Handbook of the Psychology of

Aging. Fifth edition, Academic Press, San Diego, California, –.

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Baltes, P. B. and Nesselroade, J. R. . History and rationale of longitudinal re-

search. In Nesselroade, J. R. and Baltes, P. B. (eds), Longitudinal Research in the

Study of Behavior and Development. Academic Press, New York, –.

Berkman, L. F., Glass, T. A., Brisette, I. and Seeman, T. E. . From social integra-

tion to health: Durkheim in the new millennium. Social Science & Medicine,,,

–.

Bisconti, T. L., Bergeman, C. S. and Boker, S. M. . Social support as a predictor

of variability: an examination of the adjustment trajectories of recent widows.

Psychology and Aging,,,–.

Boerner, K. and Heckhausen, J. . To have and have not: adaptive bereavement

by transforming mental ties to the deceased. Death Studies,,,–.

Boerner, K., Wortman, C. B. and Bonanno, G. A. . Resilient or at risk? A -year

study of older adults who initially showed high or low distress following conjugal

loss. Journals of Gerontology: Psychological Sciences,B,,P–.

Bonanno, G. A. . Loss, trauma, and human resilience: have we underestimated

the human capacity to thrive after extremely adverse events? American Psychologist,

,,–.

Bonanno, G. A., Galea, S., Bucciarelli, A. and Vlhaov, D. . What predicts psycho-

logical resilience after disaster? The role of demographics, resources, and life

stress. Journal of Consulting and Clinical Psychology,,,–.

Bonanno, G. A. and Kaltman, S. . Toward an integrative perspective on bereave-

ment. Psychological Bulletin,,,–.

Bonanno, G. A., Wortman, C. B., Lehman, D. R., Tweed, R. G., Haring, M.,

Sonnega, J., Carr, D. and Nesse, R. M. . Resilience to loss and chronic grief:

a prospective study from preloss to -months postloss. Journal of Personality and

Social Psychology,,,–.

Boyce, C. J. and Wood, A. . Personality prior to disability determines adaptation:

agreeable individuals recover loss life satisfaction faster and more completely.

Psychological Science,,,–.

Brickman, P. and Campbell, D. T. . Hedonic relativism and planning the good

society. In Appley, M. H. (ed.), Adaptation Level Theory: A Symposium. Academic

Press, New York, –.

Burke, C. T., Shrout, P. E. and Bolger, N. . Individual differences in adjustment

to spousal loss: a nonlinear mixed model analysis. International Journal of Behavioral

Development,,,–.

Carr, D. S. . Gender, preloss marital dependence, and older adults’adjustment

to widowhood. Journal of Marriage and Family,,,–.

Carr, D., House, J. S., Kessler, R. C., Nesse, R. M., Sonnega, J. and Wortman, C. .

Marital quality and psychological adjustment to widowhood among older adults: a

longitudinal analysis. Journals of Gerontology: Social Sciences,B,,S–.

Carr., D., House, J. S., Wortman, C., Nesse, R. and Kessler, R. C. . Psychological

adjustment to sudden and anticipated spousal loss among older widowed persons.

Journals of Gerontology: Social Sciences,B,,S–.

Carr, D. and Utz, R. . Late-life widowhood in the United States: new directions

in research and theory. Ageing International,,,–.

Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershﬁeld, H., Samanez-

Larkin, G. R., Brooks, K. P. and Nesselroade, J. R. . Emotional experience

improves with age: evidence based on over  years of experience sampling.

Psychology and Aging,,,–.

Charles, S. T. . Strength and vulnerability integration: a model of emotional life

satisfaction across adulthood. Psychological Bulletin,,,–.

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Cox, P. R. . Regression models and life tables. Journal of the Royal Statistical Society

Series B (Methodological),,,–.

Danner, D. D., Snowdon, D. A. and Friesen, W. V. . Positive emotions in early

life and longevity: ﬁndings from the Nun Study. Journal of Personality and Social

Psychology,,,–.

Diener, E., Lucas, R. E. and Scollon, C. N. . Beyond the hedonic treadmill: re-

vising the adaptation theory of life satisfaction. American Psychologist,,,–.

Elliott, D. B. and Simmons, T. . Marital events of Americans: . American

Community Survey Reports, ACS-, US Census Bureau, Washington DC.

Elwert, F. and Christakis, N. A. . The effect of widowhood on mortality by the

causes of death of both spouses. American Journal of Public Health,,,–.

Floyd, F. J., Seltzer, M. M., Greenberg, J. S. and Song, J. . Parental bereavement

during mid-to-later life: pre- and postbereavement functioning and intrapersonal

resources for coping. Psychology and Aging,,,–.

Fortunato, C. K., Gatzke-Kopp, L. M. and Ram, N. . Associations between re-

spiratory sinus arrhythmia reactivity and internalizing and externalizing symptoms

are emotion speciﬁc. Cognitive, Affective, & Behavioral Neuroscience,,,–.

Frederick, S. and Loewenstein, G. . Hedonic adaptation. In Kahneman, D.,

Diener, E. and Schwarz, N. (eds), Life Satisfaction: The Foundations of Hedonic

Psychology. Sage, New York, –.

Frijters, P., Johnston, D. W. and Shields, M. A. . Life satisfaction dynamics with

quarterly life event data. Scandinavian Journal of Economics,,,–.

Fujita, F. and Diener, E. . Life satisfaction set point: stability and change. Journal

of Personality and Social Psychology,,,–.

Gaugler, J. E., Pot, A. M. and Zarit, S. H. . Long-term adaptation to institution-

alization to dementia caregivers. The Gerontologist,,,–.

Gerstorf, D., Ram, N., Estabrook, R., Schupp, J., Wagner, G. G. and Lindenberger, U.

. Life satisfaction shows terminal decline in old age: longitudinal evidence

from the German Socio-Economic Panel Study (SOEP). Developmental Psychology,

,,–.

Gerstorf, D., Smith, J. and Baltes, P. B. . A systemic-wholistic approach to differ-

ential aging: longitudinal ﬁndings from the Berlin Aging Study. Psychology and

Aging,,,–.

Glaser, B. G. and Strauss, A. L. .Time for Dying. Aldine, Chicago.

Grimm, K. J., Ram, N. and Hamagami, F. . Nonlinear growth curves in develop-

mental research. Child Development,,,–.

Haase, C. M., Heckhausen, J. and Wrosch, C. . Developmental regulation across

the life span: toward a new synthesis. Developmental Psychology,,,–.

Haase, C. M., Poulin, M. and Heckhausen, J. . Happiness as a motivator: positive

affect predicts primary control striving for career and educational goals. Personality

and Social Psychology Bulletin,,,–.

Headey, B., Muffels, R. and Wagner, G. G. . Long-running German panel survey

shows that personal and economic choices, not just genes, matter for happiness.

Proceedings of the Academy of Sciences of the United States of America,,,–.

Heckhausen, J., Wrosch, C. and Schulz, R. . A motivational theory of life-span

development. Psychological Review,,,–.

Holmes, T. H. and Rahe, R. H. . The social readjustment rating scale. Journal of

Psychosomatic Research,,,–.

Infurna, F. J., Gerstorf, D. and Ram, N. . The nature and correlates of change in

depressive symptoms with cancer diagnosis: reaction and adaptation. Psychology

and Aging,,,–.

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Infurna, F. J., Gerstorf, D., Ram, N., Schupp, J. and Wagner, G. G. . Long-term

antecedents and outcomes of perceived control. Psychology and Aging,,,

–.

Infurna, F. J., Gerstorf, D., Ram, N., Sprangers, M. A. G., Schupp, J. and Wagner, G. G.

. Linking concurrent self-reports and retrospective informant-reports about

the last year of life: a prevailing picture of life satisfaction decline. Journals of

Gerontology: Psychological and Social Sciences,B,,–.

Infurna, F. J. and Luthar, S. S. Resilience to major life stressors is not as common as

thought. Perspectives on Psychological Science, in press.

Infurna, F. J., Ram, N. and Gerstorf, D. . Level and change in perceived control

predict -year mortality: ﬁndings from the Americans’Changing Lives Study.

Developmental Psychology,,,–.

Isherwood, L. M., King, D. S. and Luszcz, M. A. . A longitudinal analysis of social

engagement in late-life widowhood. International Journal of Aging and Human

Development,,,–.

Jopp, D. and Smith, J. . Resources and life-management strategies as determi-

nants of successful aging: on the protective effect of selection, optimization, and

compensation. Psychology and Aging,,,–.

Kastenbaum, R. and Costa, P. T. . Psychological perspectives on death. Annual

Review of Psychology,,,–.

King, L. A. and Hicks, J. A. . Detecting and constructing meaning in life events.

The Journal of Positive Psychology,,,–.

Kroh, M., Pischner, R., Spiess, M. and Wagner, G. G. . On the treatment of non-

original sample members in the German Household Panel Study (SOEP): tracing

the weighting. Methoden –Daten –Analysen,,,–.

Lachman, M. E. and Weaver, S. L. . The sense of control as a moderator of social

class differences in health and well-being. Journal of Personality and Social Psychology,

,,–.

Lee, G. R. and DeMaris, A. . Widowhood, gender, and depression: a longitu-

dinal analysis. Research on Aging,,,–.

Lichtenstein, P., Gatz, M., Pedersen, N. L., Berg, S. and McClearn, G. E. . A co-

twin-control study of response to widowhood. Journals of Gerontology: Psychological

Sciences,B,,P–.

Little, R. J. A. and Rubin, D. B. .Statistical analysis with missing data. New York,

NY: Wiley.

Lucas, R. E. a. Adaptation and the set-point model of subjective well-being: does

happiness change after major life events? Current Directions in Psychological Science,

,,–.

Lucas, R. E. b. Long-term disability is associated with lasting changes in subject-

ive well-being: evidence from two nationally representative longitudinal studies.

Journal of Personality and Social Psychology,,,–.

Lucas, R. E., Clark, A. E., Georgellis, Y. and Diener, E. . Reexamining adaptation

and the set point model of happiness: reactions to changes in marital status.

Journal of Personality and Social Psychology,,,–.

Lyubomirsky, S., King, L. and Diener, E. . The beneﬁts of frequent positive

affect: does happiness lead to success?. Psychological Bulletin,,,–.

Lyubomirsky, S. and Layous, K. . How do simple positive activities increase

well-being?. Current Directions in Psychological Science,,,–.

Mancini, A. D., Bonanno, G. A. and Clark, A. E. . Stepping off the hedonic

treadmill: individual differences in response to major life events. Journal of

Individual Differences,,,–.

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

McArdle, J. J. . Latent variable modeling of differences and changes with longi-

tudinal data. Annual Review of Psychology,,,–.

Mroczek, D. K. and Spiro, A., III . Personality change inﬂuences mortality in

older men. Psychological Science,,,–.

Muthén, L. K. and Muthén, B. O. .Mplus User’s Guide (th ed.). Los Angeles,

CA: Author.

Myrskylä, M. and Margolis, R. . Happiness: before and after the kids.

Demography,,,–.

Naess, S., Blekesaune, M. and Jakobsson, N. . Marital transitions and life satisfac-

tion: evidence from longitudinal data from Norway. Acta Sociologica,,,–.

Netuveli, G., Wiggins,R. D., Montgomery, S. M., Hildon, Z. andBlane, D. .Mental

health and resilience at older ages: bouncing back after adversity in the British

HouseholdPanelSurvey.JournalofEpidemiologyandCommunityHealth,,,–.

Neugarten, B. L. and Hagestad, G. O. . Age and the life course. In Binstock, R. E.

and Shanas, E. (eds), Handbook of Aging and Social Sciences. Van Nostrand Reinhold,

New York. –.

Noll, H.-H., Habich, R. and Schupp, J. .Datenreport  –Ein Sozialbericht für die

Bundesrepublik Deutschland. Statistisches Bundesamt, Bundeszentrale für politische

Bildung, Wissenschaftszentrum Berlin für Sozialforschung (WZB), Berlin.

Ong, A. D., Fuller-Rowell, T. E. and Bonanno, G. A. . Prospective predictors of

positive emotions following spousal loss. Psychology and Aging,,,–.

Parslow, R. A., Jorm, A. F., Christensen, H. and Mackinnon, A. . An instrument

to measure engagement in life: factor analysis and associations with sociodemo-

graphic, health, and cognition measures. Gerontology,,,–.

Pressman, S. D. and Cohen, S. . Does positive affect inﬂuence health?

Psychological Bulletin,,,–.

Ram, N. and Grimm, K. . Using simple and complex growth models to articulate

developmental change: matching theory to method. International Journal of

Behavioral Development,,,–.

Roelfs, D. J., Shor, E., Curreli, M., Clemow, L., Burg, M. M. and Schwartz, J. E. .

Widowhood and mortality: a meta-analysis and meta-regression. Demography,,,

–.

Schulz, R. and Beach, S. R. . Caregiving as a risk factor for mortality: the

Caregiver Health Effects Study. Journal of the American Medical Association,,

,–.

Schulz, R., Belle, S. H., Czaja, S. J., McGinnis, K. A., Stevens, A. and Zhang, S. .

Long-term care placement of dementia patients and caregiver health and well-

being. Journal of American Medical Association,,,–.

Schulz, R., Mendelsohn, A. B., Haley, W. E., Mahoney, D., Allen, R. S., Zhang, S.,

Thompson, L. and Belle, S. H. . End-of-life care and the effects of bereave-

ment on family caregivers of persons with dementia. New England Journal of

Medicine,,,–.

Shahar, D. R., Schultz, R., Shahar, A. and Wing, R. R. . The effect of widowhood

on weight change, dietary intake, and eating behavior in the elderly population.

Journal of Aging and Health,,,–.

Singer, J. D. and Willett, J. B. .Applied Longitudinal Data Analysis: Modeling

Change and Event Occurrence. New York, NY: Oxford University Press.

Sobel, M. E. . Asymptotic conﬁdence intervals for indirect effects in structural

equation models. Sociological Methodology,,,–.

Steptoe, A. and Kivimäki, M. . Stress and cardiovascular disease. Nature Reviews

Cardiology,,,–.

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Steptoe, A., Wardle, J. and Marmot, M. . Positive affect and health-related

neuroendocrine, cardiovascular, and inﬂammatory processes. Proceedings of the

Academy of Sciences of the United States of America,,,–.

Stevens, N. . Gender and adaptation to widowhood in later life. Ageing & Society,

,,–.

Stone, J., Evandrou, M. and Falkingham, J. . The transition to living alone and

psychological distress in later life. Age and Ageing,,,–.

Stroebe, M., Schut, H. and Stroebe, W. . Health outcomes of bereavement.

Lancet,,,–.

Stroebe, W., Stroebe, M., Abakoumkin, G. and Schut, H. . The role of loneliness

and social support in adjustment to loss: a test of attachment versus stress theory.

Journal of Personality and Social Psychology,,,–.

Stroebe, W., Zech, E., Stroebe, M. S. and Abakoumkin, G. . Does social support

help in bereavement? Journal of Social and Clinical Psychology,,,–.

Uglanova, E. A. and Staudinger, U. M. . Zooming in on life events: is hedonic

adaptation sensitive to the temporal distance from the event. Social Indicators

Research,,,–.

Wade, T. J. and Pevalin, D. J. . Marital transitions and mental health. Journal of

Health and Social Behavior,,,–.

Wagner, G. G., Frick, J. R. and Schupp, J. . Enhancing the power of household

panel studies: the case of the German Socio-Economic Panel Study (SOEP).

Schmollers Jahrbuch,,–.

Wiest, M., Huxhold, O., Wurm, S., Tesch-Römer, C. and Schüz, B. Individual differ-

ences in adaptation to critical life events predict mortality. Forthcoming.

Wiest, M., Schüz, B., Webster, N. and Wurm, S. . Subjective well-being and mor-

tality revisited: differential effects of cognitive and emotional facets of well-being

on mortality. Health Psychology,,,–.

Wilcox, S., Evenson, K. R., Aragaki, A., Wassertheil-Smoller, S., Mouton, C. P. and

Loevinger, B. L. . The effects of widowhood on physical and mental health,

health behaviors, and health outcomes: the Women’s Health Initiative. Health

Psychology,,,–.

Williams, K. . Has the future of marriage arrived? A contemporary examination

of gender, marriage, and psychological well-being. Journal of Health and Social

Behavior,,,–.

Williams, K. . The transition to widowhood and the social regulation of health:

consequences for health and health risk behavior. Journals of Gerontology: Social

Sciences,B,,S–.

Wittstein, L. S., Thiemann, D. R., Lima, J. A. C., Baughman, K. T., Schulman, S. P.,

Gerstenblith, G., Wu, K. C., Rade, J. J., Bivalacqua, T. J., Champion, H. C. .

Neurohumoral features of myocardial stunning due to sudden emotional stress.

New England Journal of Medicine,,,–.

Wortman, C. B. and Boerner, K. . Reactions to the death of a loved one: Myths

of coping versus scientiﬁc evidence. In Friedman H. S. and Silver R. C. (eds),

Foundations of Health Psychology, New York, NY: Oxford. –.

Wortman, C. B. and Silver, R. C. . The myths of coping with loss. Journal of

Consulting and Clinical Psychology,,,–.

Wrosch, C., Amir, E. and Miller, G. E. . Goal adjustment capacities, coping, and

subjective well-being: the sample case of caregiving for a family member with

mental illness. Journal of Personality and Social Psychology,,,–.

Wrosch, C. and Schulz, R. . Health-engagement control strategies and -year

changes in older adults’physical health. Psychological Science,,,–.

Spousal loss-related change in life satisfaction

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core

Wrosch, C., Schulz, R. and Heckhausen, J. . Health stresses and depressive

symptomatology in the elderly: a control-process approach. Current Directions in

Psychological Science,,,–.

Zautra, A. J., Hall, J. S., Murray, K. E. and the Resilience Solutions Group .

Resilience: a new integrative approach to health and mental health research.

Health Psychology Review,,,–.

Zhang, J-P., Kahana, B., Kahana, E., Hu, B. and Pozuelo, L. . Joint modeling of

longitudinal changes in depressive symptoms and mortality in a sample of commu-

nity-dwelling elderly people. Psychosomatic Medicine,,,–.

Accepted December ;ﬁrst published online  April 

Address for correspondence:

Frank J. Infurna,

Arizona State University,

Department of Psychology,  S. McAllister Ave.,

Tempe, AZ ,USA

E-mail: [email protected]

 Frank J. Infurna et al.

Downloaded from https://www.cambridge.org/core. Universitaetsbibliothek, on 26 Oct 2017 at 12:40:35, subject to the Cambridge Core