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Whatever Does Not Kill Us: Cumulative Lifetime Adversity, Vulnerability, and Resilience

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Exposure to adverse life events typically predicts subsequent negative effects on mental health and well-being, such that more adversity predicts worse outcomes. However, adverse experiences may also foster subsequent resilience, with resulting advantages for mental health and well-being. In a multiyear longitudinal study of a national sample, people with a history of some lifetime adversity reported better mental health and well-being outcomes than not only people with a high history of adversity but also than people with no history of adversity. Specifically, U-shaped quadratic relationships indicated that a history of some but nonzero lifetime adversity predicted relatively lower global distress, lower self-rated functional impairment, fewer posttraumatic stress symptoms, and higher life satisfaction over time. Furthermore, people with some prior lifetime adversity were the least affected by recent adverse events. These results suggest that, in moderation, whatever does not kill us may indeed make us stronger.
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Whatever Does Not Kill Us:
Cumulative Lifetime Adversity, Vulnerability, and Resilience
Mark D. Seery
University at Buffalo, The State University of New York
E. Alison Holman and Roxane Cohen Silver
University of California, Irvine
Exposure to adverse life events typically predicts subsequent negative effects on mental health and
well-being, such that more adversity predicts worse outcomes. However, adverse experiences may also
foster subsequent resilience, with resulting advantages for mental health and well-being. In a multiyear
longitudinal study of a national sample, people with a history of some lifetime adversity reported better
mental health and well-being outcomes than not only people with a high history of adversity but also than
people with no history of adversity. Specifically, U-shaped quadratic relationships indicated that a history
of some but nonzero lifetime adversity predicted relatively lower global distress, lower self-rated
functional impairment, fewer posttraumatic stress symptoms, and higher life satisfaction over time.
Furthermore, people with some prior lifetime adversity were the least affected by recent adverse events.
These results suggest that, in moderation, whatever does not kill us may indeed make us stronger.
Keywords: cumulative lifetime adversity, resilience, stress inoculation, toughening, mental health and
well-being
Despite the familiarity of the adage that whatever does not kill
us makes us stronger, the preponderance of empirical evidence
seems to offer little support for it. Histories of a given adverse
event (e.g., physical/sexual assault, parental loss, homelessness,
natural disasters) have all been associated with poorer mental
health outcomes (e.g., Edwards, Holden, Felitti, & Anda, 2003;
Emery & Laumann-Billings, 1998; Golding, 1999; Kendall-
Tackett, Williams, & Finkelhor, 1993). When adversities occur,
their negative effects can be long lasting. For example, disability
and unemployment have predicted lower life satisfaction that
persisted over at least several years (Lucas, 2007; Lucas, Clark,
Georgellis, & Diener, 2004). People can also recover quickly from
an adverse event or avoid severe disruption from it altogether (e.g.,
Bonanno, Wortman, et al., 2002; Bonanno, Moskowitz, Papa, &
Folkman, 2005; Gilbert, Pinel, Wilson, Blumberg, & Wheatley,
1998; Wortman & Silver, 1989), but mitigated negative conse-
quences in the near term do not necessarily contribute to building
future resilience to adversity. Nonetheless, in addition to the afore-
mentioned adage, theoretical and empirical work does suggest that
under the proper conditions, experiencing life adversity may foster
subsequent resilience. We sought to test this possibility in the
current investigation.
The Role of Adversity in Fostering Subsequent
Resilience
Resilience has been defined as successful adaptation or the
absence of a pathological outcome following exposure to stressful
or potentially traumatic life events or life circumstances. Thus, it
involves both the capacity to maintain a healthy outcome follow-
ing exposure to adversity and the capacity to rebound after a
negative experience (Rutter, 2007; Silver, 2009). Consistent with
this definition, part of our focus is on understanding the factors that
contribute to resistance to the potential negative effects of rela-
tively major adverse events. Although adversity can have negative
effects on people’s mental health and psychological well-being,
this does not occur for everyone, nor necessarily for an intermi-
nable duration (Silver & Wortman, 1980; Wortman & Silver,
1989). In fact, early research in child psychiatry suggests that
resilience is a common, rather than an extraordinary, phenomenon
(Rutter, 1985), even in the context of substantial adversity (e.g.,
Masten, 2001; Werner & Smith, 1992). Most people experience
some serious adversity in their lives (Bonanno, 2004); in the
absence of resilience or reparative mechanisms, this could seem-
ingly lead people to experience ongoing psychological distress, yet
most people do not live in such a chronic state. Gilbert et al. (1998)
suggested that a psychological immune system works to actively
dispel negative affect when people experience it. This promotes a
faster and easier return to baseline than individuals predict for
themselves in the face of a negative event.
This article was published Online First October 11, 2010.
Mark D. Seery, Department of Psychology, University at Buffalo, The
State University of New York; E. Alison Holman, Program in Nursing
Science, University of California, Irvine; Roxane Cohen Silver, Depart-
ment of Psychology and Social Behavior and Department of Medicine,
University of California, Irvine.
This research was supported by National Science Foundation Grants
BCS-9910223, BCS-0211039, and BCS-0215937 to Roxane Cohen Silver
and National Institute of Mental Health Grant T32 MH19958 to Mark D.
Seery. We thank Michael Poulin, Daniel McIntosh, Virginia Gil-Rivas, and
Judith Andersen for their assistance with study design and data collection
and the Knowledge Networks Government, Academic, and Non-profit
Research team of J. Michael Dennis, Rick Li, William McCready, and
Kathy Dykeman for granting access to data collected on Knowledge
Network panelists, preparing Web-based surveys, creating data files, and
providing general guidance on survey methodology.
Correspondence concerning this article should be addressed to Mark D.
Seery, Department of Psychology, University at Buffalo, SUNY, Park Hall,
Buffalo, NY 14260-4110. E-mail: mdseery@buffalo.edu
Journal of Personality and Social Psychology © 2010 American Psychological Association
2010, Vol. 99, No. 6, 1025–1041 0022-3514/10/$12.00 DOI: 10.1037/a0021344
1025
There is an important distinction, however, between recovering
back to baseline functioning (or largely avoiding decrements en-
tirely) after a negative event and deriving from it a greater capacity
for future resilience. Drawing on human and animal literature,
Dienstbier’s (1989, 1992) theory of psychophysiological tough-
ness provides an account that explains why facing adversity may
foster subsequent resilience. Similar ideas have been referred to by
others as stress inoculation (e.g., Lyons & Parker, 2007; Meichen-
baum, 1993), steeling (e.g., Rutter, 2006), thriving (e.g., Carver,
1998), and immunization (e.g., Basoglu et al., 1997). Dienstbier
argued that exposure to stressors has a positive toughening effect
when the exposure is limited, with an opportunity for recovery.
Toughness leaves individuals more likely to appraise situations
positively (i.e., perceiving them as more manageable), more emo-
tionally stable, and better able to cope psychologically and phys-
iologically with difficult stressors and minor challenges, relative to
nontoughened individuals. Once toughness develops, it can per-
meate across domains.
This is consistent with theories of anxiety that emphasize mas-
tery and control (Chorpita & Barlow, 1998; Mineka & Zinbarg,
2006). Early life experience of low perceived control and low
mastery facilitates interpreting subsequent situations as uncontrol-
lable. In contrast, the experience of high control and high mastery
has the opposite effect, facilitating future perceptions of control,
which in turn can enhance coping capabilities. For example, infant
monkeys who had the opportunity to control aspects of their
environment during development (i.e., delivery of food and water)
later exhibited less fear and more exploratory behavior than did
monkeys who lacked such opportunity to exert control (Mineka,
Gunnar, & Champoux, 1986). Coping with some amount of stress
could similarly promote perceptions of control and mastery. Thus,
mastery and toughness should substantially overlap; across both
perspectives, coping with stressors may promote subsequent ben-
efits.
It is important to note that Dienstbier (1989, 1992) further
argued that both sheltering from all stressors and continuous
exposure to stressors leads to lack of toughness. Although shelter-
ing from stressors may temporarily protect against distress, it
should not result in long-term advantages. Sheltering provides no
opportunity to develop toughness and mastery and is unlikely to
persist indefinitely, so when stressors are eventually encountered,
individuals are likely to be ill equipped to cope with them. For
example, in the face of work stressors in young adulthood, indi-
viduals who experienced prior work stress during adolescence did
not exhibit the negative effects on self-esteem, self-efficacy, and
depressed mood shown by individuals with little prior work stress
(Mortimer & Staff, 2004). The development of toughness and
mastery is analogous to the development of physical fitness from
aerobic exercise: Excessive exercise exerts a harmful toll on the
body, but fitness does not improve with inactivity.
Evidence also suggests that exposure to moderate levels of
adversity can predict better mental health and well-being than
exposure to either a high level of adversity or to no adversity.
Specifically, among Vietnam War veterans, Schnurr, Rosenberg,
and Friedman (1993) reported that peripheral exposure to combat
predicted improvements in psychological functioning (i.e., Minne-
sota Multiphasic Personality Inventory scores) from pre- to post-
military service, relative to veterans with both no exposure and
direct exposure to combat. Similarly, Fontana and Rosenheck
(1998) found an inverse U-shaped quadratic relationship when
degree of perceived threat from combat and degree of exposure to
death of others were used to predict self-reported psychological
benefits among veterans, such that moderate threat and exposure
predicted the greatest benefits.
Additional findings support the proposition that moderate levels
of adversity can contribute to nonspecific resilience to subsequent
stressors. In experimental studies, young monkeys exposed to an
intermittent stressor during development demonstrated greater re-
silience to subsequent novel stressors than did monkeys without
any exposure (Parker, Buckmaster, Schatzberg, & Lyons, 2004;
Parker, Buckmaster, Sundlass, Schatzberg, & Lyons, 2006). In
humans, children with moderate levels of early life adversity
exhibited lower cortisol activity than did others during a laboratory
stressor (Gunnar, Frenn, Wewerka, & Van Ryzin, 2009), a re-
sponse potentially consistent with Dienstbier’s (1989, 1992)
toughened physiological pattern.
In sum, resilience in response to adversity has been documented
in research spanning a variety of populations over the past several
decades. Although many researchers have sought to understand
personal characteristics or qualities that promote resilience, the
potential benefits of exposure to some adversity relative to no
adversity have received less attention. The reviewed theory and
findings suggest that adversity has the potential to foster future
resilience. Specifically, without adversity, individuals are not chal-
lenged to manage stress, so that the toughness and mastery they
might otherwise generate remains undeveloped. High levels of
adversity, on the other hand, are more likely to overwhelm indi-
viduals’ ability to manage stress, thereby disrupting toughness and
mastery. However, low/moderate adversity provides a more man-
ageable challenge than high levels of adversity, thus promoting the
development of toughness and mastery. This toughness and mas-
tery, in turn, should facilitate (a) greater resilience, manifested as
a less negative response when coping with subsequent adversity,
and (b) better mental health and well-being over time, regardless of
exposure to recent adversity.
Cumulative Adversity
Much research investigating life adversity focuses on single
events (e.g., reactions to a divorce) or single types of events (e.g.,
prior combat experience vs. not). However, adversities often co-
occur (Dong et al., 2004; Green et al., 2010), making it difficult to
isolate the impact associated with any single event, especially over
a lifetime. Furthermore, a given type of event can have general
effects across domains of psychological functioning, thus overlap-
ping with the effects of other adversity types (McMahon, Grant,
Compas, Thurm, & Ey, 2003). Such findings suggest the merits of
attempting to assess individuals’ overall history of adversity. This
has led to measurement of cumulative adversity—the total amount
of adversity experienced by a person—which has also been asso-
ciated with negative outcomes. Assessments of cumulative adver-
sity typically involve counts of negative events experienced over a
period of time (e.g., one’s lifetime). Increases in the nonspecific
total predict incrementally worse outcomes, including greater psy-
chiatric symptoms and disorder risk (e.g., Breslau, Chilcoat,
Kessler, & Davis, 1999; Cabrera, Hoge, Bliese, Castro, & Messer,
2007; Turner & Lloyd, 1995, 2004) and greater risk behavior and
physical disease (Felitti et al., 1998; Sledjeski, Speisman, &
1026 SEERY, HOLMAN, AND SILVER
Dierker, 2008). It is important to note that adversities that fail to
meet typical definitions of “trauma” nonetheless contribute to
predicting psychiatric disorder onset (Lloyd & Turner, 2003) and
symptoms (Galea et al., 2008; Silver, Holman, McIntosh, Poulin,
& Gil-Rivas, 2002).
As described above, experiencing low levels of adversity should
be most likely to foster mastery and toughness, thereby leading to
subsequent resilience. In terms of cumulative lifetime adversity, a
low level of adversity should equate to a low but nonzero number
of total lifetime adverse events. The specific characteristics of
given events could certainly vary across individuals on dimensions
relevant for developing mastery and toughness, but on average, a
low but nonzero total count should be most likely to lead to
subsequent mastery and toughness. Any measure of adversity must
be sensitive enough to differentiate low adversity from a history of
no adversity and a history of high adversity, both of which should
promote less mastery and toughness than low adversity. This
should yield a quadratic, curvilinear relationship between contin-
uous cumulative adversity count and outcome measures. Consis-
tent with prior evidence for linear relationships in which greater
cumulative adversity predicts worse outcomes, an overall linear
trend in this direction could still emerge, but with a reversal of this
relationship between no adversity and low adversity (cf. May &
Bigelow, 2005). Whereas the precise number of events that is
optimally “low” is not particularly meaningful for testing hypoth-
eses, establishing the presence of such a reversal within the sam-
ple’s range is crucial.
Quadratic effects of lifetime adversity have rarely been tested
(Schilling, Aseltine, & Gore, 2008). If high adversity predicts the
worst outcomes, an expected linear relationship could easily
emerge and limit further investigation (May & Bigelow, 2005).
Moreover, measures of cumulative adversity commonly assess
only a few types of stressors and often exclude multiple instances
of the same type, thereby limiting measurement sensitivity. The
fewer adversities measured, the more likely that people with his-
tories of no adversity and low adversity will show the same total,
thereby obscuring the critical differences between them.
Hypotheses
We expected that low levels of cumulative lifetime adversity
would predict greater resilience than having experienced no prior
adversity or high levels of adversity. Using a national survey panel
(N2,398) assessed repeatedly from 2001–2004, we tested for
quadratic relationships between lifetime adversity and a variety of
longitudinal measures of mental health and well-being: global
distress, functional impairment, posttraumatic stress symptoms,
and life satisfaction. We generated two specific hypotheses:
Hypothesis 1: Cumulative lifetime adversity was expected to
predict subsequent mental health and well-being outcomes in
a quadratic fashion, such that a history of low adversity would
predict better outcomes than histories of no or high adversity.
This is consistent with the reasoning that low levels of prior
adversity should be most likely to foster toughness and mas-
tery. Because toughness and mastery should generalize across
domains and facilitate coping with both large and small
stressors, recent life adversity need not have occurred to elicit
this pattern of results.
Hypothesis 2: Cumulative lifetime adversity was expected to
moderate the relationship between recently experienced ma-
jor adversity and subsequent mental health and well-being
outcomes in a quadratic fashion, such that low prior lifetime
adversity would protect against the negative effects of recent
adversity, relative to no and high prior lifetime adversity. In
other words, low lifetime adversity should predict resilience
in the face of recent adversity.
Method
Data Collection
The sample was drawn from a nationally representative Web-
enabled research panel established by Knowledge Networks, Inc.
(KN). Panel members were randomly selected to participate in our
research (Holman et al., 2008; Silver et al., 2002; Silver et al.,
2006). The KN panel is developed using traditional probability
methods for creating national survey samples and, at the time of
our study, was recruited using stratified random-digit-dial tele-
phone sampling. Random-digit-dial sampling provides a known
nonzero probability of selection for every U.S. household having a
telephone. The distribution of the KN panel closely tracks the
distribution of census counts for the U.S. population on age, race,
Hispanic ethnicity, geographical region, employment status, in-
come, and education. To ensure representation of population seg-
ments that would not otherwise have Internet access, KN provided
panel households with an Internet connection and Web TV to serve
as a computer monitor. Panel members participate in brief Internet
surveys three to four times a month in exchange for free Internet
access or other compensation if the household is already Web-
enabled. Unlike typical Internet panels, in which people who
already have Internet access choose to opt in, no one can volunteer
for the KN panel.
When members are assigned a survey, they receive notice of its
availability in their individual password-protected e-mail accounts.
Surveys are confidential, self-administered, and accessible any
time for a designated period; panelists can complete a survey only
once. Responding to any given survey is voluntary, and the pro-
vision of Internet service is not dependent on completion of any
specific survey. Even though panel members complete surveys
regularly, there are no significant differences over time in re-
sponses given by “seasoned” participants from “naı¨ve” ones (Den-
nis, 2001).
Design
Starting in September, 2001, our research team collected longi-
tudinal data from a national sample of the adult U.S. population
randomly selected from the KN panel (Holman et al., 2008; Silver
et al., 2002, 2006). A total of 2,398 respondents reported their
lifetime exposure to negative events and some outcome measures.
The sample closely resembled the national population.
Data collection for the current analyses occurred at five points:
when respondents entered the KN panel (prior to September,
2001), Wave 1 (around September, 2002), Wave 2 (around March,
2003), Wave 3 (around September, 2003), and Wave 4 (around
September, 2004). All initial assessments were administered by
KN online; subsequent surveys were administered by KN online or
1027
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
via paper-and-pencil surveys mailed to respondents if they had
already left the KN panel. Additional waves of data were collected
on subsamples of these respondents before Wave 1 (see Silver et
al., 2006); because these waves provide restricted sample sizes and
are not relevant for current analyses, they are not discussed further.
Measures
Background characteristics and individual differences.
KN administered several surveys to panelists upon entry into the
KN panel, including a demographic questionnaire and a mental
and physical health history. Demographic information collected
included gender, ethnicity (U.S. Census categories), age, income,
marital status, household size (coded 1 if living alone, 0 if not), and
education. An index of physician-diagnosed mental health prob-
lems with values of 0 (no diagnoses)or1(depression, anxiety, or
both) and a count of 35 physician-diagnosed physical health ail-
ments were created. At Wave 1, respondents reported their current
employment status, coded as 1 if they endorsed being currently
employed (i.e., paid employee, self-employed, owner/partner in
small business/practice/farm, or working at least 15 hr per week
without pay in family business/farm) and 0 if they chose any of the
other five possible options (i.e., unemployed but looking for work,
retired, disabled, homemaker, or other). At Wave 2, respondents
completed the Ten-Item Personality Inventory (Gosling, Rentfrow,
& Swann, 2003), a brief measure of the Big Five personality
dimensions (e.g., McCrae & Costa, 1999; 1–7 response scale).
Cumulative lifetime adversity. By Wave 1, lifetime expo-
sure to cumulative adversity was assessed by asking respondents
whether they ever experienced each of 37 negative events and the
age(s) at which they occurred. The list of events included seven
categories: own illness or injury, loved one’s illness or injury,
violence (e.g., physical assault, forced sexual relations), bereave-
ment (e.g., parent’s death), social/environmental stress (e.g., seri-
ous financial difficulties, lived in dangerous housing); relationship
stress (e.g., parents’ divorce); and disaster (e.g., major fire, flood,
earthquake, or other community disaster). The measure was mod-
ified from the Diagnostic Interview Schedule trauma section (Rob-
ins, Helzer, Croughan, Williams, & Spitzer, 1981), expanded to
include a wider variety of stressful events using primary care
patients’ reports of lifetime stress (Holman, Silver, & Waitzkin,
2000), and has provided rates of negative events comparable to
those in other community samples (Breslau et al., 1998; Kessler,
Sonnega, Bromet, & Nelson, 1995). Up to four instances of each
event were tallied, regardless of duration. We treated the total
number of instances of adversity as a continuous variable.
Recent adversity. At Wave 2, respondents reported their
exposure to the same list of negative events over the previous 6
months only. Events reported at this assessment were distinct from
those included in respondents’ assessment of lifetime adversity.
We treated the total number of recent events as a continuous
variable.
Mental health and well-being. Respondents completed out-
come measures at Waves 1– 4. Measures assessed four aspects of
mental health and well-being. Global distress was assessed using
the Brief Symptom Inventory (␣⫽.92 to .93; 0 4 response scale),
a well-validated standardized scale that measures prior-week
symptoms of psychological distress (depression, anxiety, and so-
matization; Derogatis, 2001). Functional impairment was assessed
with four items adapted from the SF–36 Health Survey (i.e., extent
physical/emotional health interfered with social/work activities;
␣⫽.85 to .87; 1–5 response scale; Ware & Sherbourne, 1992).
Life satisfaction was assessed with a five-item scale (␣⫽.90 to
.93; 1–7 response scale; Diener, Emmons, Larsen, & Griffin,
1985). Posttraumatic stress (PTS) symptoms were assessed using
the PTSD Checklist (PCL; ␣⫽.92 to .94; 1–5 response scale;
Weathers, Litz, Herman, Huska, & Keane, 1993). Unlike our other
measures of mental health and well-being, PTS symptoms were
assessed specifically in reference to a recent collective trauma—
the terrorist attacks of September 11, 2001 (9/11)—that generated
psychological responses nationwide, including among people who
were not directly exposed (Silver et al., 2002, 2006). Given find-
ings demonstrating that PTS symptoms in response to a prior event
can be exacerbated by other negative events (i.e., recent events;
Classen et al., 2002), we expected the same pattern of results for
PTS symptoms as the other outcome measures.
Covariates in supplementary analyses. In primary analyses,
statistical models did not include covariates. However, to provide
evidence that any observed effects were not attributable to demo-
graphic and other characteristics, we repeated analyses controlling
for the following: gender, ethnicity, age, income, marital status,
household size, education, employment status, mental and physical
health history, and degree of exposure to the 9/11 attacks (partic-
ularly relevant for the measure of PTS symptoms). We accounted
for degree of exposure to the attacks using items modified from
prior research on disaster exposure (Holman & Silver, 1998;
Koopman, Classen, & Spiegel, 1994). Individuals were catego-
rized into three levels: direct exposure (in the World Trade Center
or Pentagon, seeing or hearing the attacks in person, or having a
close relationship with someone in the targeted buildings or air-
planes), live media exposure (watching the attacks on television
live as they occurred), or no live exposure (seeing video replay or
learning of the attacks only after they had occurred). We included
these covariates to rule out alternative explanations based on their
expected association with adversity (e.g., health history) and sub-
sequent mental health and well-being (e.g., 9/11 exposure). This
created a conservative test, in that only the unique relationships
between adversity and mental health were tested for significance.
To the extent that analyses with and without covariates yield
comparable results, it should increase confidence in interpreting
our findings. Analyses without these covariates are reported in
detail and presented in the tables and figures. The pattern of results
remained consistent when including covariates, which is summa-
rized in the Supplementary Analyses sections.
Analytic Strategy
Repeated-measures approach. Analyses were conducted
with generalized estimating equations (GEE), a population-
averaged analysis appropriate for repeated-measures longitudinal
data that accommodates missing assessments and provides neces-
sary adjustments of standard errors. The analysis yields a single
significance test for each predictor across all assessments of a
given outcome variable. Appropriate for our research question,
GEE focuses on between-subject effects (i.e., differences between
respondents with different levels of cumulative adversity), rather than
within-subject effects (i.e., trajectories within individuals over time).
Coefficients from GEE models thus have analogous meaning to
1028 SEERY, HOLMAN, AND SILVER
coefficients from standard multiple regression. Analyses were con-
ducted with STATA 9.2 (StataCorp, College Station, TX), specifying
the robust option and including dummy-coded variables representing
assessment wave. In addition to quadratic and interaction terms,
models included appropriate linear and other component terms.
Transformations. The following variables were highly pos-
itively skewed: physical health diagnoses (included only in covari-
ate analyses), lifetime and recent adversity counts, and all outcome
variables except life satisfaction. In primary analyses, inverse or
natural logarithmic transformations were performed on these vari-
ables to create distributions that more closely approximated nor-
mal and to decrease the influence of extreme scores (Tabachnick &
Fidell, 1996). To confirm that transformations did not create
spurious nonlinear relationships, we conducted supplementary
analyses using (a) untransformed outcome variables; (b) recoded
untransformed lifetime adversity, setting the most extreme cases
(approximately 10% with adversity count greater than 15) to 15,
thus addressing extreme scores without altering data at low levels
of adversity, which were most important for our hypotheses; and,
for Hypothesis 2, (c) dichotomized recent adversity count (expe-
rienced one or more events vs. none; it was otherwise continuous),
which similarly addressed extreme scores, given that recent ad-
verse events were relatively low in frequency compared with
lifetime adverse events. Analyses using transformed variables are
reported in detail and presented in the tables and figures. The
pattern of results remained consistent using untransformed vari-
ables; these findings are summarized in the Supplementary Anal-
yses sections.
Coefficient reporting. To make results more interpretable,
adversity counts were divided by their standard deviations (SD
1) and outcome variables were converted to zscores (M0, SD
1) using the standard deviation of all observations across all waves
of data collection. Coefficients thus reflect effect sizes in units of
standard deviations: Bs represent the number of standard devia-
tions of change in the outcome variable predicted for each standard
deviation change in the predictor. Because GEE uses maximum
likelihood estimation, traditional measures of effect size based on
variance accounted for cannot be calculated.
Prospective tests. To create prospective tests, our models
included outcome variables measured in waves subsequent to
those in which predictors were assessed. This helped to rule out the
possibility that observed relationships could be explained by con-
temporaneous reporting of predictors and outcomes. Analyses
testing Hypothesis 1 (i.e., those that did not include recent adver-
sity) used assessments from Waves 2– 4 as outcome variables.
When testing Hypothesis 2 (i.e., moderation of recent adversity),
we included recent events assessed only at Wave 2 as a predictor
and outcomes from only Waves 3– 4. Thus, lifetime adversity
(from Wave 1), recent adversity (Wave 2), and outcomes (Waves
3– 4) were measured at distinct times.
Results
Sample Characteristics
At the start of the study, the sample ranged in age from 18 to 101
years old (M49.3, SD 16.1) and was 51.3% female. Almost
74% of the sample self-identified as White (non-Hispanic), 10.0%
as Hispanic, 8.7% as African American (non-Hispanic), and 7.4%
as “other,” which included Asian. Median household income was
$40,000 –$49,999. Approximately 63% of the sample was married,
15.7% was divorced or separated, 14.6% was single, and 7.1% was
widowed. Almost 15% of the sample lived alone. Just over 8% of
the sample attained less than a high school degree, 33.7% held a
high school degree, 29.7% attended some college, and 28.5% held
a college or advanced degree. Approximately 62% of the sample
was employed (see Table 1 for a correlation matrix).
The total number of lifetime adverse events experienced ranged
from0to71(M7.69, SD 6.04; Mdn 7, interquartile
range 6); it is important to note that a nontrivial number of
respondents (8.1%) reported experiencing no adverse events. Be-
reavement was the most frequently reported event (39.5% of all
events reported by participants), followed by a loved one’s illness
or injury (15.0% of events reported), relationship stress (12.7%),
violent events (11.4%), social/environmental stress (8.9%), own
illness or injury (6.9%), and disaster (5.5%). The total number of
recent adverse events (i.e., experienced during the 6 months prior
to Wave 2) ranged from 0 to 9 (M0.66, SD 1.08; Mdn 0,
interquartile range 1).
Attrition
A substantial majority of the sample (n1,994, 83.2%) pro-
vided longitudinal data over Waves 2– 4. To predict dropout after
Wave 1, we first tested the variables that were included as covari-
ates in supplementary analyses (gender, ethnicity, age, income,
marital status, household size, education, employment status, men-
tal and physical health history, and degree of exposure to the 9/11
attacks). In a simultaneous logistic regression model, age was the
only significant predictor, such that older respondents were less
likely to drop out (odds ratio [OR] 0.966, p.001). Next, in a
logistic regression with linear lifetime adversity as the predictor,
higher adversity was associated with lower likelihood of dropout
(OR 0.848, p.002). However, when quadratic lifetime
adversity was added to this model, it did not significantly predict
attrition. To further evaluate whether respondents who dropped out
differed from those who remained, we examined dropout with each
Wave 1 outcome (global distress, functional impairment, PTS
symptoms, and life satisfaction) as the predictor in a separate
logistic regression. Neither Wave 1 outcomes themselves nor their
interactions with linear or quadratic lifetime adversity significantly
predicted attrition.
Hypothesis 1: Quadratic Cumulative
Lifetime Adversity
Primary analyses. We first sought to demonstrate the typical
finding for adversity: a linear effect such that a history of more
lifetime adversity is associated with poorer outcomes for mental
health and well-being. Effects of this nature emerged for all four
longitudinal outcome variables: greater cumulative lifetime adver-
sity predicted significantly higher global distress (B0.174, p
.001), functional impairment (B0.136, p.001), and PTS
symptoms (B0.090, p.001), and significantly lower life
satisfaction (B– 0.059, p.007).
To investigate the hypothesized quadratic relationship between
lifetime adversity and longitudinal outcomes, we tested models
with linear and quadratic lifetime adversity terms. Consistent with
1029
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
predictions, results revealed significant quadratic relationships be-
tween cumulative lifetime adversity and each outcome (i.e., a
significant interaction term for Lifetime Adversity Lifetime
Adversity, ps.001).
Although a positive Bcoefficient for the quadratic term is
consistent with a curvilinear, U-shaped curve (hypothesized for
negatively valenced global distress, functional impairment, and
PTS symptoms) and a negative Bcoefficient is consistent with an
inverse U-shaped curve (hypothesized for positively valenced life
satisfaction), these curves need not be U-shaped within the sam-
ple’s range. In other words, a significant quadratic term could also
reflect only the right-hand side of a Ushape, such that greater
lifetime adversity predicts exponentially worse outcomes, without
any protective effect at low adversity relative to no adversity.
Thus, to assess the extent of any protective influence of lifetime
adversity, we tested the simple slope of the quadratic curve at zero,
which represented a history of no adversity. This is analogous to
testing simple slopes within an interaction between two variables
in regression, assessing the effect of one variable at a high or low
value of the other variable. Essentially treating lifetime adversity
as though it was interacting with itself, we used zero adversity as
the reference point to assess the momentary linear slope at that
point on the quadratic curve. We chose zero as the reference point
because it was within the sample’s range and conceptually, the
simple slope at zero most clearly represented the difference be-
tween a history of no adversity versus some adversity. As pre-
dicted, this simple slope was negative and significant for negative
outcomes and positive and significant for life satisfaction ( ps
.001): People with low lifetime adversity reported better outcomes
over time than did people who had experienced no adversity.
To establish the reversal of this relationship, such that additional
adversity predicted worse outcomes instead of better ones, we
tested the simple slope at a high level of lifetime adversity. We
used a relatively high value that was within the sample’s range
(mean 1SD;87th percentile) as a reference point and assessed
the momentary linear slope on the quadratic curve at that point.
This simple slope was significant and in the opposite direction of
the no-adversity slope for all outcomes ( ps.001), such that high
lifetime adversity predicted worse outcomes over time than did
low adversity (see Figure 1; for statistical details, see Table 2).
Supplementary analyses. We further conducted two sets of
supplementary analyses. First, when we repeated the analyses with
covariates (gender, ethnicity, age, income, marital status, house-
Table 1
Correlation Matrix of Lifetime Adversity Count, Age at First Adverse Event, Background Characteristics, and Wave 1 Mental Health
and Well-Being Outcomes
Variable 1234567
1. Lifetime adversity count
2. Age at first adversity .41
ⴱⴱⴱ
3. Female gender .05
.01 —
4. White ethnicity .03 .05
.00 —
5. Age .14
ⴱⴱⴱ
.18
ⴱⴱⴱ
.03 .20
ⴱⴱⴱ
6. Income .07
ⴱⴱ
.01 .07
ⴱⴱⴱ
.07
ⴱⴱ
.09
ⴱⴱⴱ
7. Never married .08
ⴱⴱⴱ
.09
ⴱⴱⴱ
.03 .12
ⴱⴱⴱ
.39
ⴱⴱⴱ
.07
ⴱⴱⴱ
8. Living alone .09
ⴱⴱⴱ
.02 .07
ⴱⴱ
.00 .12
ⴱⴱⴱ
.19
ⴱⴱⴱ
.17
ⴱⴱⴱ
9. College degree or higher .04 .01 .00 .03 .06
ⴱⴱ
.27
ⴱⴱⴱ
.02
10. Number physical health ailments .30
ⴱⴱⴱ
.05
.11
ⴱⴱⴱ
.10
ⴱⴱⴱ
.28
ⴱⴱⴱ
.06
ⴱⴱ
.13
ⴱⴱⴱ
11. Depression/anxiety diagnosis .23
ⴱⴱⴱ
.11
ⴱⴱⴱ
.12
ⴱⴱⴱ
.05
.03 .06
ⴱⴱ
.01
12. Currently employed .10
ⴱⴱⴱ
.06
ⴱⴱ
.14
ⴱⴱⴱ
.13
ⴱⴱⴱ
.49
ⴱⴱⴱ
.25
ⴱⴱⴱ
.14
ⴱⴱⴱ
13. Global distress .22
ⴱⴱⴱ
.12
ⴱⴱⴱ
.09
ⴱⴱⴱ
.06
ⴱⴱ
.06
ⴱⴱ
.15
ⴱⴱⴱ
.05
14. Functional impairment .24
ⴱⴱⴱ
.08
ⴱⴱⴱ
.07
ⴱⴱ
.03 .03 .19
ⴱⴱⴱ
.01
15. Life satisfaction .13
ⴱⴱⴱ
.08
ⴱⴱⴱ
.03 .10
ⴱⴱⴱ
.14
ⴱⴱⴱ
.21
ⴱⴱⴱ
.11
ⴱⴱⴱ
16. PTS symptoms .16
ⴱⴱⴱ
.05
.10
ⴱⴱⴱ
.09
ⴱⴱⴱ
.01 .11
ⴱⴱⴱ
.01
M7.69 12.43 0.51 0.74 49.28 10.37 0.15
SD 6.04 10.53 0.50 0.44 16.09 3.90 0.35
N2,398 2,198 2,398 2,351 2,398 2,391 2,357
Note. PTS posttraumatic stress. The following variables were dichotomous, with the named category coded as 1 and all others coded as 0: female
gender, White ethnicity, never married, living alone, college degree or higher, depression/anxiety diagnosis, and currently employed. All other variables
were continuous; untransformed values are reported.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
Figure 1. The quadratic relationship between cumulative lifetime adver-
sity and four standardized longitudinal mental health and well-being out-
comes. Life satisfaction is positively valenced. On the adversity scale, “0”
represents no lifetime adversity and “High” represents M1SD. Both
points are within the sample’s range. Observations exist past the “High”
point but are not displayed because predicted values are based on progres-
sively fewer observations. PTS posttraumatic stress.
1030 SEERY, HOLMAN, AND SILVER
hold size, education, employment status, mental and physical
health history, and degree of exposure to the 9/11 attacks), the
results presented in Table 2 remained substantively unchanged.
For all four outcome variables, the quadratic term ( ps.002),
simple slope at no adversity ( ps.017), and simple slope at high
adversity ( ps.001) were significant and in the same shape and
direction as in analyses without these covariates. Because greater
age could increase the opportunity for any given adversity to have
occurred at some previous time or may impact the way that later
life events are interpreted (cf. Poulin & Silver, 2008), we also
tested models with quadratic age terms, thereby paralleling qua-
dratic lifetime adversity; doing so yielded essentially identical
results.
Second, when using untransformed variables (as described
above), all four quadratic terms remained significant and in the
same shape as when using transformed variables ( ps.001). The
simple slope at no adversity was significant for functional impair-
ment, PTS symptoms, and life satisfaction ( ps.007) but did not
reach significance for global distress ( p.16). The simple slope
at high adversity was significant for all outcomes ( ps.001). In
sum, these supplementary analyses suggest that neither the covari-
ates nor statistical artifact arising from variable transformations
can account for the observed pattern of findings for cumulative
lifetime adversity. Figure 2 presents the raw values for the out-
come variables when treating each level of adversity count as a
separate category (i.e., 0, 1, 2, etc.). These raw values reflect the
mean of each outcome across Waves 2– 4.
Exploring a history of no adversity. To further investigate
the differences between respondents with a history of no adverse
events versus those with a low adversity count, we divided adver-
sity count into four categories: zero adverse events, 1 event, 2– 4
events, and 5events. We used these cut points because 1 event
is as close as possible to zero, and respondents with a history of
2– 4 adverse events tended to report the best outcomes across the
four outcome variables (see Figure 2), so this approach allowed us
to focus on the most meaningful distinctions between zero and low
adversity counts. In a series of separate standard regressions for
continuous variables and logistic regressions for dichotomous vari-
ables, we used this categorical adversity variable to examine any
differences in background characteristics or individual difference
variables between groups. Categorical adversity was dummy
coded with zero as the reference group. Table 3 presents descrip-
tive statistics and results of analyses comparing the zero adversity
group with other categories.
We considered three ways in which background characteristics
could potentially explain meaningful differences between respon-
dents in the zero-adversity group versus those with low adversity
in particular. First, respondents in the zero group may have been
younger than others, allowing them less time to experience adverse
life events. However, no significant differences emerged between
the zero group and the 1-event and 2– 4 groups.
Second, respondents in the zero group may have avoided ad-
versity because they were more socially isolated than others.
Having fewer close relationships could lessen the chances of
negative events happening to a close other (e.g., injury, illness,
death), as well as in the context of one’s relationship with a close
other (e.g., divorce and domestic abuse). We used marital status as
one marker of social isolation. We dichotomized marital status into
“never married” versus all others, based on the logic that having
never been married is more likely to be a better marker of a history
of social isolation than being currently divorced, separated, or
widowed (i.e., currently unmarried but having been so in the past).
Because marital status does not capture cohabitating couples or
most same-sex relationships, we used household size (i.e., living
8 9 10 11 12 13 14 15 16
.04 —
.04
.03 —
.04
.01 .32
ⴱⴱⴱ
.03 .12
ⴱⴱⴱ
.24
ⴱⴱⴱ
.08
ⴱⴱⴱ
.06
ⴱⴱ
.07
ⴱⴱⴱ
.17
ⴱⴱⴱ
.26
ⴱⴱⴱ
.06
ⴱⴱ
.05
.09
ⴱⴱⴱ
.26
ⴱⴱⴱ
.28
ⴱⴱⴱ
.18
ⴱⴱⴱ
.67
ⴱⴱⴱ
.07
ⴱⴱⴱ
.11
ⴱⴱⴱ
.10
ⴱⴱⴱ
.19
ⴱⴱⴱ
.03 .42
ⴱⴱⴱ
.38
ⴱⴱⴱ
.04 .04
.11
ⴱⴱⴱ
.16
ⴱⴱⴱ
.06
ⴱⴱ
.64
ⴱⴱⴱ
.42
ⴱⴱⴱ
.24
ⴱⴱⴱ
0.15 0.28 3.43 0.15 0.62 0.36 1.40 4.49 1.35
0.36 0.45 3.15 0.35 0.48 0.50 0.68 1.48 0.52
2,058 2,381 2,390 2,390 2,398 2,356 2,354 2,354 2,353
1031
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
alone vs. not) as an additional marker of social isolation. For both
marital status and household size, no significant differences
emerged between the zero group and the 1-event and 2– 4 groups.
Third, rather than being socially isolated in particular, it is
possible that individuals with a history of no adversity are less
likely to seek out opportunities in other life domains. Failing to
attempt new things could provide shelter from potential negative
consequences (i.e., adverse events). We reasoned that current
employment status could be one indicator of such a tendency.
Specifically, employment represents an opportunity for gain (e.g.,
income, prestige) but also for loss (e.g., unwelcome changes to
daily routine, being fired from the job). If individuals in the zero
adversity group were less likely to engage in opportunities in life,
they may have been less likely than others to be currently em-
ployed. In contrast, analyses revealed no significant differences
between the zero, 1, and 2– 4 groups.
We also tested for differences in endorsement of Big Five
personality dimensions (e.g., McCrae & Costa, 1999). We rea-
soned that extraversion and agreeableness could be inversely re-
lated to tendency for social isolation. However, no significant
differences emerged between respondents with a history of no
adversity and respondents in the 1-event and 2– 4 groups. We
further reasoned that openness to experience could be related to
seeking life opportunities, but no significant differences emerged
between respondents in the zero group and others. We next tested
for differences in the remaining two dimensions: conscientious-
ness and emotional stability (neuroticism when reverse scored).
Individuals in the zero group endorsed significantly lower consci-
entiousness and emotional stability than those in the 2– 4 group.
Given that personality dimensions may be particularly likely to
be stable over time, we repeated longitudinal analyses, adding the
five dimensions as covariates. These analyses revealed that includ-
ing personality covariates did not substantively affect the relation-
ship between continuous lifetime adversity and mental health and
well-being outcomes. As reported above, controlling for back-
ground characteristics in longitudinal analyses also had no sub-
stantive effect. Thus, findings fail to support the idea that back-
ground characteristics and personality dimensions meaningfully
contribute to differentiating respondents with a history of no
versus low adversity and further fail to support alternative expla-
nations based on age, social isolation, and seeking life opportuni-
ties.
Hypothesis 2: Moderation of Recent Adversity by
Quadratic Cumulative Lifetime Adversity
Primary analyses. We investigated the impact of exposure to
recent adversity on subsequent mental health and well-being out-
comes. Specifically, we used the number of adverse events re-
ported at Wave 2 as having been experienced in the previous 6
Table 2
Quadratic Relationship Between Cumulative Lifetime Adversity and Longitudinal Mental Health and Well-Being
Outcome variable and model
terms
At no lifetime adversity At high lifetime adversity (M1SD)
B95% CI pB95% CI p
Global distress
a
Wave 3 0.108 0.148, 0.067 .001 —
Wave 4 0.027 0.070, 0.016 .215
Lifetime adversity 0.227 0.352, 0.103 .001 0.451 0.355, 0.546 .001
Quadratic lifetime adversity 0.101 0.070, 0.131 .001 —
Functional impairment
b
Wave 3 0.002 0.047, 0.042 .913
Wave 4 0.015 0.032, 0.061 .540
Lifetime adversity 0.358 0.483, 0.234 .001 0.476 0.391, 0.561 .001
Quadratic lifetime adversity 0.124 0.095, 0.153 .001 —
Life satisfaction
c
Wave 3 0.004 0.040, 0.032 .825
Wave 4 0.015 0.053, 0.023 .428
Lifetime adversity 0.340 0.211, 0.470 .001 0.334 0.435, 0.233 .001
Quadratic lifetime adversity 0.100 0.132, 0.068 .001 —
PTS symptoms
d
Wave 3 0.103 0.063, 0.143 .001 —
Wave 4 0.158 0.118, 0.198 .001 —
Lifetime adversity 0.352 0.491, 0.213 .001 0.396 0.287, 0.504 .001
Quadratic lifetime adversity 0.111 0.077, 0.146 .001 —
Note. CI confidence interval; PTS posttraumatic stress. Each outcome variable was tested in a separate analysis. Assessment wave was dummy coded
with Wave 2 as the reference group. For each outcome, quadratic lifetime adversity refers to the model term for Lifetime Adversity Lifetime Adversity.
Within this quadratic effect, lifetime adversity refers to the momentary linear simple slope of the quadratic curve at either no adversity or high adversity.
In the no lifetime adversity column, Bcoefficients reflect when zero lifetime adversity was used as the reference point for assessing the simple slope of
the quadratic curve; in the high lifetime adversity column, Bcoefficients reflect when M1SD was used as the reference point. Only information that
is not redundant with the no adversity column is presented in the high adversity column. For global distress, functional impairment, and PTS symptoms,
negative Bs for the simple slope at no lifetime adversity indicate that as adversity increases from 0, outcomes improve (i.e., symptoms decrease); positive
Bs at high lifetime adversity indicate that as adversity increases from 1 SD above the mean, outcomes worsen (i.e., symptoms increase). For positively
valenced life satisfaction, the direction of these relationships is reversed (i.e., a positive Bindicates improving outcomes).
a
Model
2
(4, N1993) 137.83, p.001.
b
Model
2
(4, N1992) 126.91, p.001.
c
Model
2
(4, N1993) 42.31, p.001.
d
Model
2
(4, N1994) 111.38, p.001.
1032 SEERY, HOLMAN, AND SILVER
months since Wave 1—thus distinct from our measure of lifetime
adversity—to predict outcomes in Waves 3 and 4. To focus on
changes in mental health and well-being from Wave 1 (i.e., before
the recent adversity had occurred), we controlled for Wave 1
values when analyzing each outcome variable.
We first confirmed a linear relationship between greater recent
adversity and worse outcomes. Effects of this nature emerged for
all four longitudinal outcome variables: Greater recent adversity
predicted significantly higher global distress (B0.110, p
.001), functional impairment (B0.087, p.001), PTS symp-
toms (B0.090, p.001), and significantly lower life satisfac-
tion (B– 0.051, p.003). Adding a term representing linear
cumulative lifetime adversity to these models did not substantively
change the results.
We hypothesized that cumulative lifetime adversity would mod-
erate the above relationships between recent adversity and longi-
tudinal outcomes in a quadratic fashion, such that low lifetime
adversity would protect against the negative effects of recent
adversity, relative to no and high lifetime adversity. We tested
models that included the interaction between recent adversity and
quadratic lifetime adversity (as well as the appropriate lower order
model terms). Consistent with predictions, results revealed signif-
icant U-shaped quadratic moderation of the effect of recent adver-
sity on negative outcomes (i.e., a significant interaction term for
Recent Adversity Lifetime Adversity Lifetime Adversity,
predicting global distress, functional impairment, and PTS symp-
toms) and an inverse Ushape for life satisfaction ( ps.016).
Thus, the magnitude of recent adversity’s negative impact on
longitudinal outcomes depended on previous lifetime adversity
history.
To explore the nature of this moderation effect at no versus high
lifetime adversity, we tested terms representing the Lifetime Ad-
versity Recent Adversity simple interaction. Analogous to the
simple slopes tested for Hypothesis 1, this term allowed us to
assess the shape of the quadratic moderation effect at the reference
points of no versus high adversity. Specifically, for the negative
outcome variables, we expected the deleterious impact of recent
adversity to significantly decrease in magnitude as lifetime adver-
sity increased from zero (i.e., the slope of recent adversity on the
negative outcome should become smaller). This would result in a
Lifetime Adversity Recent Adversity simple interaction term
that was negative in sign and significant at zero lifetime adversity.
We also expected the deleterious impact of recent adversity to
significantly increase in magnitude as lifetime adversity increased
to the high value (i.e., the slope of recent adversity should become
larger). This would result in a simple interaction term that was
positive in sign and significant at high lifetime adversity. For
positively valenced life satisfaction, the opposite pattern should
Figure 2. The mean value of four standardized mental health and well-being outcomes at each discrete level
of cumulative lifetime adversity count. “15” represents a count of 15 or higher. Mean values reflect the mean
of each outcome across Waves 2– 4. Life satisfaction is positively valenced. PTS posttraumatic stress.
1033
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
occur. The simple interaction terms at reference points of no and
high adversity thus provide the best tests of our hypothesis because
they reveal the curvilinear pattern of lifetime adversity’s influence
on the slope between recent adversity and the outcome variables.
At a history of no adversity, the Lifetime Adversity Recent
Adversity simple interaction term was in the predicted direction
for all outcomes and was significant ( ps.05) for all outcomes
except PTS symptoms, which approached significance ( p.103).
At high adversity, the simple interaction term was significant and
in the predicted direction for all outcomes ( ps.004). Thus,
across the outcome variables as a whole, people with low lifetime
adversity were less negatively affected by recent adversity than
people who had experienced either no or high lifetime adversity
(see Figure 3; for statistical details, see Table 4).
Supplementary analyses. As with Hypothesis 1, we con-
ducted two sets of supplementary analyses. First, when we added
covariates (in addition to Wave 1 outcome values) to the models,
all of the Recent Adversity Lifetime Adversity Lifetime
Adversity interaction terms remained significant ( ps.019) and
in the same form as the results presented in Table 4. At a history
of no adversity, the Lifetime Adversity Recent Adversity simple
interaction term was in the same direction as in the primary
analyses for all outcomes and was significant ( ps.034) for all
outcomes except PTS symptoms, which approached significance
(p.099). At high adversity, the simple interaction term was
significant and in the same direction as in the primary analyses for
all outcomes ( ps.019). We also tested models with quadratic
age terms (paralleling quadratic lifetime adversity), which yielded
essentially identical results.
Second, when using untransformed variables, the results pre-
sented in Table 4 were substantively unchanged: All of the highest
order interaction terms were again significant ( ps.019), the
simple interaction terms at no lifetime adversity were significant
Table 3
Comparing Respondents With Zero Versus Other Categorical Levels of Cumulative Lifetime Adversity on Additional Characteristics
Variable
Number of lifetime adverse events
0 1 2–4 5
Background characteristics (n) 194 94 459 1,651
Female gender (%) 44.33 42.55 51.42 52.51
White ethnicity (%) 60.54 62.64 73.51
ⴱⴱ
76.26
ⴱⴱⴱ
Age (Myears) 44.34 41.45 44.94 51.51
ⴱⴱⴱ
Income (M) 9.56 ($30,000–$34,999) 10.14 ($35,000–$39,999) 10.96
ⴱⴱⴱ
($40,000–$44,999) 10.31
($35,000–$39,999)
Never married (%) 21.86 27.96 18.28 11.92
ⴱⴱⴱ
Living alone (%) 11.95 11.25 9.23 16.68
College degree or higher (%) 26.06 26.88 33.41 27.47
Number physical health ailments (M) 1.92 2.26 2.51
3.93
ⴱⴱⴱ
Depression/anxiety diagnosis (%) 8.76 8.51 9.17 17.51
ⴱⴱ
Currently employed (%) 68.56 76.60 67.97 59.12
Big Five personality dimensions (n) 115 55 294 1,179
Extraversion (M) 3.98 3.74 4.00 4.01
Agreeableness (M) 4.88 4.81 5.03 5.18
ⴱⴱ
Conscientiousness (M) 5.08 5.21 5.44
ⴱⴱ
5.53
ⴱⴱⴱ
Emotional stability (M) 4.72 4.63 5.02
4.94
Openness to experiences (M) 4.68 4.64 4.67 4.86
Note. Means are reported for continuous variables and percentages are reported for dichotomous variables. To explore the differences between
respondents with a history of no adverse events versus others, the four adversity categories were dummy coded with zero adversity as the reference category.
Each of the other three categories was then tested for a significant difference relative to zero adversity. Standard regression was conducted for continuous
variables and logistic regression for dichotomous variables. The nrefers to the maximum nin each adversity category when predicting the variables in each
set (within each set, actual nfluctuated slightly from variable to variable as a function of missing data). The following variables were dichotomous, with
the named category coded as 1 and all others coded as 0: female gender, White ethnicity, never married, living alone, college degree or higher,
depression/anxiety diagnosis, and currently employed. All other variables were continuous. Household income was reported on a 1–19 scale (ranging from
less than $5,000 to $175,000 or more) and was treated as a continuous variable; the label most closely corresponding to the mean value is displayed in
parentheses. Big Five personality dimensions were assessed with a 1–7 scale.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
Figure 3. The quadratic relationship between cumulative lifetime adversity and
the slope of recent adverse events on four standardized longitudinal mental health
and well-being outcomes. Life satisfaction is positively valenced. Plotted values
represent Bcoefficients (slopes) for the relationship between recent adversity and
outcome at the given level of cumulative lifetime adversity (rather than predicted
values of the outcome). A slope of zero indicates that recent adversity did not
predict the outcome, whereas slopes with greater absolute values (positive or
negative) indicate greater change in the outcome for each unit change in recent
adversity. On the adversity scale, “0” represents no lifetime adversity and “High”
represents M1SD. Both points are within the sample’s range. Observations
exist past the “High” point but are not displayed because predicted values are based
on progressively fewer observations. PTS posttraumatic stress.
1034 SEERY, HOLMAN, AND SILVER
(ps.05) except for PTS symptoms ( p.150), and all simple
interaction terms at high lifetime adversity were significant ( ps
.005). In sum, these supplementary analyses suggest that neither
the covariates nor statistical artifact arising from variable transfor-
mations can account for the observed pattern of findings.
Discussion
Consistent with prior research on the impact of adversity, linear
effects emerged in our results, such that more lifetime adversity
was associated with higher global distress, functional impairment,
and PTS symptoms, as well as lower life satisfaction. However,
our results also yielded quadratic, U-shaped patterns, demonstrat-
ing a critical qualification to the seemingly simple relationship
between lifetime adversity and outcomes. Supporting Hypothesis
1, our findings revealed that a history of some lifetime adversity—
relative to both no and high adversity—predicted lower global
distress, lower functional impairment, lower PTS symptoms, and
higher life satisfaction. Supporting Hypothesis 2, across these
same longitudinal outcome measures, people with a history of
some lifetime adversity appeared less negatively affected by recent
adverse events than did other individuals. It is also important to
note that the observed U-shaped patterns are not completely sym-
metrical. In many cases, outcomes at the high end of adversity
appear more negative than those at zero adversity, and some curves
Table 4
Cumulative Lifetime Adversity as Quadratic Moderator of the Relationship Between Recent Adversity and Changes in Mental Health
and Well-Being
Outcome variable and model terms
At no lifetime adversity At high lifetime adversity (M1SD)
B95% CI pB95% CI p
Global distress
a
Wave 4 0.076 0.031, 0.122 .001 —
Global distress at Wave 1 0.606 0.566, 0.646 .001 —
Recent adversity 0.268 0.107, 0.428 .001 0.120 0.075, 0.164 .001
Lifetime adversity 0.090 0.038, 0.219 .166 0.040 0.140, 0.060 .431
Quadratic lifetime adversity 0.019 0.051, 0.012 .226
Recent Lifetime Adversity 0.221 0.343, 0.098 <.001 0.132 0.074, 0.191 <.001
Recent Quadratic Lifetime Adversity 0.052 0.029, 0.076 <.001 —— —
Functional impairment
b
Wave 4 0.023 0.025, 0.071 .346
Functional impairment at Wave 1 0.510 0.470, 0.550 .001 —
Recent adversity 0.234 0.041, 0.428 .018 0.086 0.039, 0.134 .001
Lifetime adversity 0.009 0.159, 0.142 .908 0.026 0.085, 0.137 .646
Quadratic lifetime adversity 0.005 0.031, 0.041 .778
Recent Lifetime Adversity 0.185 0.329, 0.040 .012 0.097 0.033, 0.160 .003
Recent Quadratic Lifetime Adversity 0.042 0.014, 0.070 .003 —— —
Life satisfaction
c
Wave 4 0.017 0.058, 0.024 .413
Life satisfaction at Wave 1 0.669 0.635, 0.704 .001 —
Recent adversity 0.120 0.292, 0.053 .173 0.069 0.110, 0.028 .001
Lifetime adversity 0.014 0.157, 0.129 .852 0.044 0.061, 0.149 .413
Quadratic lifetime adversity 0.009 0.026, 0.043 .625
Recent Lifetime Adversity 0.132 0.008, 0.257 .037 0.102 0.157, 0.048 <.001
Recent Quadratic Lifetime Adversity 0.035 0.058, 0.011 .003 —— —
PTS symptoms
d
Wave 4 0.055 0.014, 0.097 .009
PTS symptoms at Wave 1 0.642 0.604, 0.681 .001 —
Recent adversity 0.120 0.057, 0.297 .184 0.111 0.065, 0.157 .001
Lifetime adversity 0.058 0.210, 0.094 .457 0.004 0.090, 0.098 .933
Quadratic lifetime adversity 0.009 0.025, 0.043 .594
Recent Lifetime Adversity 0.113 0.248, 0.023 .103 0.107 0.046, 0.169 <.001
Recent Quadratic Lifetime Adversity 0.033 0.006, 0.059 .015 —— —
Note. CI confidence interval; PTS posttraumatic stress. Model terms of primary interest are in boldface. Each outcome variable was tested in a
separate analysis. Assessment wave was dummy coded with Wave 3 as the reference group. For each outcome, Recent Quadratic Lifetime Adversity
refers to the model term for Recent Adversity Lifetime Adversity Lifetime Adversity. Within this quadratic interaction, Recent Lifetime Adversity
reflects the momentary rate of change in the slope between recent adversity and the outcome variable, at either no or high lifetime adversity. In the no
lifetime adversity column, Bcoefficients reflect when zero lifetime adversity was used as the reference point for assessing the nature of the quadratic
influence of lifetime adversity (i.e., does the deleterious effect of recent adversity increase or decrease in magnitude as lifetime adversity increases); in the
high lifetime adversity column, Bcoefficients reflect when M1SD was used as the reference point. Only information that is not redundant with the no
adversity column is presented in the high adversity column. For global distress, functional impairment, and PTS symptoms, negative Bs for the simple
interaction at no lifetime adversity indicate that as lifetime adversity increases from 0, the negative impact that recent events have on outcomes decreases;
positive Bs at high lifetime adversity indicate that as lifetime adversity increases from 1 SD above the mean, the negative impact of recent events increases.
For positively valenced life satisfaction, the direction of these relationships is reversed (i.e., a positive Bindicates decreasing negative impact).
a
Model
2
(7, N1527) 1,414.20, p.001.
b
Model
2
(7, N1526) 1,011.50, p.001.
c
Model
2
(7, N1526) 1,697.45, p
.001.
d
Model
2
(7, N1525) 1,406.38, p.001.
1035
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
could be described as more J-shaped than U-shaped (cf. May &
Bigelow, 2005; e.g., global distress in Figures 1 and 3 and func-
tional impairment in Figure 3). But across all the outcomes, the
curvilinear pattern is clearly established. Moreover, this pattern
appears using both modeled data (see Figures 1 and 3) and aver-
aged raw data (see Figure 2). Although these data cannot establish
causation, the evidence for both hypotheses is consistent with the
proposition that in moderation, experiencing lifetime adversity can
contribute to the development of resilience.
Findings for PTS symptoms paralleled findings for other out-
comes, despite the fact that PTS symptoms were assessed specif-
ically in reference to the terrorist attacks of September 11, 2001,
whereas the other outcomes were not tied to a specific event. This
fits with previous research in which PTS symptoms were exacer-
bated by other negative events (Classen et al., 2002). Given
the consistent pattern across all measures— despite their differing
foci— our results may best be viewed in terms of implications for
general mental health and well-being, which overlaps conceptually
with each of the four specific outcomes assessed.
Alternative Explanations
Our reported analyses and study design allow us to address a
variety of alternative explanations for our quadratic effects for
cumulative lifetime adversity. It is possible that demographic
characteristics (gender, ethnicity, age, income, marital status,
household size, and education) and other individual difference
variables (personality; mental and physical health; employment
status; and 9/11-related exposure, which is particularly relevant for
PTS symptoms) could explain relationships between lifetime ad-
versity and mental health and well-being outcomes. However,
when we repeated analyses with these variables included as co-
variates, the results were substantively unchanged. We also re-
peated analyses without using logarithmic or inverse transforma-
tions, which, although statistically appropriate for our purposes
(Tabachnick & Fidell, 1996), could conceivably introduce a spu-
rious nonlinear relationship between adversity and the outcomes.
Again, the results were substantively unchanged. The fact that
these supplementary analyses yielded the same pattern of findings
as our primary analyses should increase confidence in our inter-
pretation of the results.
Given the critical differences in longitudinal outcomes between
individuals with a history of no lifetime adverse events versus
those with a history of only a few events, we further explored other
ways in which these groups might differ on background charac-
teristics and individual difference variables. Possible alternative
explanations for our primary findings differentiating respondents
with a history of no versus low lifetime adversity are that individ-
uals with no adversity were younger, more socially isolated, or less
likely to seek out opportunities in life. None of these alternatives
were supported by our supplementary analyses. Results did reveal
that respondents with low adversity endorsed greater emotional
stability (i.e., lower neuroticism) and conscientiousness than re-
spondents with no adversity. Dienstbier (1989, 1992) suggested
that toughness should be associated with emotional stability, which
fits the difference that emerged in our data between individuals
with no versus low adversity.
In a longitudinal investigation, it is possible that differential
rates of attrition could color results if the attrition is related to key
variables in the analysis. Respondent attrition was predicted by age
and linear lifetime adversity. It is important to note, however, that
quadratic lifetime adversity, outcome measures reported at Wave 1
(before any dropout), and interactions between Wave 1 outcomes
and linear and quadratic lifetime adversity all failed to predict
attrition significantly. Hence, there was no evidence that the pat-
tern of attrition overlapped with the pattern of substantive findings,
or that respondents with relatively high or low levels of mental
health and well-being were disproportionately likely to drop out of
the study. This leaves little reason to believe that differential
attrition can account for our results.
Because respondents reported cumulative adversity retrospectively,
it is possible that mental health and well-being outcomes experienced
at Wave 1 could have biased recall of prior lifetime adversity, or that a
general reporting bias could have led to an association between high
adversity and poor outcomes. However, several aspects of our design
and analyses minimize the likelihood that any such bias can explain
our findings. First, data collection was longitudinal rather than cross-
sectional, decreasing the probability of confounding predictors and
outcomes. Second, analyses testing quadratic moderation of recent
events (Hypothesis 2) controlled for outcomes experienced at Wave 1,
before the recent events had occurred. This would have accounted for
a spurious association between lifetime adversity recall and stable
differences in reporting of mental health and well-being outcomes.
Even if respondents who consistently reported high levels of negative
outcomes across assessment waves were also biased to recall more
lifetime adversity, including Wave 1 outcome values as covariates
should have yielded effects above and beyond such bias. Third, the
observed quadratic relationships— unlike linear relationships—are
difficult to attribute to recall or reporting biases (e.g., trait negative
affectivity; Watson & Pennebaker, 1989). Although these biases po-
tentially explain why reports of high adversity and high levels of
negative outcomes might co-occur, a relationship in the opposite
direction also emerged in our data. Inconsistent with a recall or
reporting bias, respondents who reported no previous adversity re-
ported worse outcomes than respondents who reported some adver-
sity. In sum, given these elements of our design and analyses, it seems
difficult to attribute our findings to biases in recall or reporting.
Assessing Adversity
Our adversity measure represented the number of events expe-
rienced rather than detailed characteristics of events, so meaning-
ful variability in adversity may not have been captured. However,
simple counts avoid potential ambiguities. For example, isolating
effects of single adversities is difficult, given that events are not
experienced in a vacuum but, rather, in the context of individuals’
adversity history (Dong et al., 2004; Green et al., 2010). Attempt-
ing to rate event severity objectively can be problematic because
not everyone experiences adversities identically (Silver & Wort-
man, 1980). For example, what may seem discrete or limited to
observers may become chronic or more severe if individuals
ruminate about it. Relying on individuals to judge severity for
themselves potentially confounds severity with individuals’ re-
sponse to adversity, which is the outcome of interest (Kessler,
1997). For example, rating an event as severe in magnitude could
reflect “objective” qualities of disruptiveness and severity of the
event itself, the rater’s lack of resilience in responding to the event,
or a combination of the two.
1036 SEERY, HOLMAN, AND SILVER
Nonetheless, more detailed measures of cumulative adversity
could provide other important information. We did not consider
the specific type of prior adverse event and the impact of different
types of experiences on outcomes over time. It is possible that
some aversive life events tend to be more “strengthening” than
others (cf. Silver & Wortman, 1980). In contrast, some life expe-
riences, such as chronic exposure to social or environmental stres-
sors, may be particularly taxing. The repeated experience of a
particular type of traumatic event (e.g., childhood sexual abuse)
may have different long-term implications than repeated exposure
to illness or loss, perhaps because of the larger questions of
unfairness and injustice such events may trigger or the increased
amount of self-blame they may engender (Silver & Wortman,
1980). For example, evidence supports that prior experience with
natural disasters may mitigate negative outcomes when an indi-
vidual is subsequently exposed to the same type of natural disaster,
such as floods (Norris & Murrell, 1988) or earthquakes (Knight,
Gatz, Heller, & Bengtson, 2000). But prior occurrences of other
types of adversity do not necessarily lead to this pattern. Luhmann
and Eid (2009) found that although a second divorce predicted less
negative effects on life satisfaction than a first divorce, repeated
unemployment predicted progressively lower life satisfaction.
Other aspects of adversity may be important for developing
toughness and mastery. For example, Hamburg and Adams (1967)
suggested that the success of one’s prior coping efforts should be
a key determinant in responses to future adversity. Given that
manageable stressors should be more likely than overwhelming
ones to result in successful coping, this notion is consistent with
successful coping in particular contributing to mastery and control
(Chorpita & Barlow, 1998; Mineka & Zinbarg, 2006) and tough-
ness (Dienstbier, 1989, 1992). Dienstbier’s (1989, 1992) theory
further emphasizes the relevance of the timing between adverse
events, in that adequate opportunity for recovery between stressors
should promote toughness development. Although it is challenging
to reliably and precisely define the end of a single adverse event
and its psychological effects (see the preceding discussion), doing
so could provide meaningful insight when assessing adversity in
future work, as could incorporating measures of coping success.
To test differences between a history of no cumulative lifetime
adversity and low adversity effectively, it was particularly impor-
tant to assess a wide range of events and facilitate maximal
reporting of them. Otherwise, respondents with no and low adver-
sity would have been more likely to yield identical totals, making
it impossible to differentiate between them. Self-report assess-
ments of life events are not without criticism (Dohrenwend, 2006),
but sensitive topics are more likely to be acknowledged in self-
report assessments than in interviews. By decreasing social desir-
ability concerns, Web-based data collection improves accuracy of
reports over less anonymous methods (Schlenger & Silver, 2006).
We assessed more types of adversity than is typical in research
investigating adversity history, and we allowed respondents to
endorse multiple instances of each type, which should have further
helped to differentiate no versus low adversity. This likely con-
tributed to revealing quadratic relationships between adversity
history and mental health and well-being outcomes, as opposed to
only the typical linear relationships identified in both prior re-
search and our own data. When no and low adversity are not
sufficiently differentiated, it is tantamount to collapsing across
individuals with no and low adversity. This should be more likely
to obscure the protective influence of having experienced some
prior adversity, as well as enhance the linear relationship between
adversity and outcomes.
Furthermore, some types of adverse events may be particularly
likely to cluster together (e.g., Green et al., 2010). If only events in
the cluster are assessed, individuals will disproportionately en-
dorse either most or none of the events. This should force a
comparison similar to the one described above, between histories
of high adversity versus a combination of no and low adversity. In
contrast, assessing a wide variety of events that better represents
the full range to which people may be exposed (i.e., including
those outside the cluster) should maximize the opportunity to
reveal individuals who fall between either extreme of cluster
endorsement. This should better differentiate no versus low adver-
sity history, thereby facilitating discovery of evidence for resil-
ience.
Despite its relatively extensive nature, our adversity measure did
not necessarily include all possible or relevant events. Events that
do not meet standard definitions of trauma can still make important
contributions to adversity counts (Lloyd & Turner, 2003). Simi-
larly, minor challenges faced in the vicissitudes of life may also
play a role in building toughness and mastery (Chorpita & Barlow,
1998; Dienstbier, 1989, 1992; Mineka & Zinbarg, 2006). In part
for these reasons, it is likely impossible to identify a precise
“ideal” number of prior adverse events that are protective of future
mental health difficulties (although Figure 2 suggests that experi-
encing on average around three events may be a turning point).
Our purpose was not to identify such a number but, instead, to
demonstrate the theoretically meaningful distinction between
“some” versus “none.” Future research can further investigate the
range of life experiences that may be initially undesirable and
disruptive but nonetheless facilitate subsequent mental health.
Fostering Resilience and Mental Health and
Well-Being
Our results suggest that previous research does not paint a
complete picture of adversity’s role in building resilience and,
more broadly, mental health and well-being. Resilience involves
having psychological and social resources that help people tolerate
adversity (Rutter, 2007), but coping with adversity may itself
promote development of subsequent resilience (see, e.g., Aldwin,
Sutton, & Lachman, 1996; Carver, 1998; Egeland, Carlson, &
Sroufe, 1993). Although our data are correlational and therefore
cannot establish causality, it is possible to speculate how causal
mechanisms could function. Experiencing low but nonzero levels
of adversity could teach effective coping skills, help engage social
support networks, create a sense of mastery over past adversity,
foster beliefs in the ability to cope successfully in the future, and
generate psychophysiological toughness (e.g., Chorpita & Barlow,
1998; Dienstbier, 1989, 1992; Mineka & Zinbarg, 2006; Silver &
Wortman, 1980). All of these qualities should contribute to resil-
ience in the face of subsequent major adversity. Such qualities
should also make subsequent minor daily hassles seem more
manageable rather than overwhelming, leading to benefits for
overall mental health and well-being. For example, regular work-
place demands formerly experienced as stressful could be reap-
praised as trivial. However, higher levels of adversity could negate
these benefits by overtaxing coping skills and support networks,
1037
RESILIENCE FOLLOWING EXPOSURE TO ADVERSITY
creating feelings of hopelessness and loss of control, and disrupt-
ing the development of toughness. Resilience to subsequent major
adversity should be inhibited, and minor hassles should be more
likely to seem overwhelming, exerting a toll on mental health and
well-being.
We did not directly assess these and other possible mechanisms
and mediators of our observed effects. For example, self-
enhancement (Bonanno, Field, Kovacevic, & Kaltman, 2002),
positive emotions (Folkman & Moskowitz, 2000; Fredrickson,
2001), and directing attention away from negative emotions (Coif-
man, Bonanno, Ray, & Gross, 2007) have been associated with
resilience in the face of adversity. It seems reasonable that such
predictors of resilience could also be associated with mastery and
toughness, and—in turn—low but nonzero levels of lifetime ad-
versity. Assessing behavioral and physiological mediators could
provide further insight into the underlying mechanisms. Indeed,
Dienstbier’s (1989) review explicitly incorporates a psychophysi-
ological component to the toughness construct. There seem to be
many avenues for future empirical work to expand this area of
research.
We believe that the current investigation has important theoret-
ical implications. The concept of posttraumatic or adversarial
growth (e.g., Linley & Joseph, 2004; Tedeschi & Calhoun, 2004)
also addresses the potential positive consequences of major life
adversity (see also Updegraff & Taylor, 2000). Adversarial growth
refers to when the process of coping with adversity leads to higher
levels of psychological functioning and well-being than previously
experienced. Affleck and Tennen (1996) concluded that after ex-
periencing a major medical problem (e.g., HIV infection; Upde-
graff, Taylor, Kemeny, & Wyatt, 2002), people commonly report
benefits or gains, such as improved social relationships, new and
valued life priorities, and developing greater patience and courage.
Tedeschi and Calhoun (2004) suggested that only adversity of
large enough magnitude to be considered “seismic” should sub-
stantially disrupt individuals’ existing beliefs about the world,
which allows such beliefs to be reconstructed in a way that yields
personal growth. However, in keeping with Dienstbier’s (1989,
1992) perspective, Aldwin and Levenson (2004) maintained that
relatively minor stressors also promote growth. Conceptually,
then, adversarial growth seems to overlap with the development of
mastery and toughness, and specifically suggests that adversity
contributes to such development. For example, after a serious
illness, other adversities and stressors may seem less critically
important and overwhelming by comparison.
Although empirical evidence supports the existence of adver-
sarial growth, Bonanno (2005) argued that this evidence does not
establish whether reported growth is real or simply perceived,
given that these findings are limited to retrospective, postadversity
reports in mostly cross-sectional designs, without preadversity
assessments. Our investigation was not designed to test adversarial
growth in particular, but the benefits of experiencing some adver-
sity may represent a form of adversarial growth (Linley & Joseph,
2004), consistent with a lesser, rather than greater, challenge to
one’s beliefs (see Aldwin & Levenson, 2004; Tedeschi & Calhoun,
2004). Alternatively, prior adversity may predict adversarial
growth after a specific subsequent major event, such that dramatic
growth should be unlikely to occur among already resilient people
(Bonanno, 2005).
The idea of adversarial growth may also shed light on the
experience of people with a history of no adversity, who essen-
tially have not had the opportunity to appreciate benefits in adver-
sity. For these individuals, distress they experience may be diffi-
cult to explain, justify, or find meaning in, given the absence of
negative life events. This lack of compelling reason for their
distress may prove even more distressing, relative to people who
have experienced some adversity. Most directly relevant for this
possibility are the results from Hypothesis 2, which revealed that
respondents with no prior adversity were more negatively affected
by recent adversity than were individuals with low lifetime adver-
sity. At least in the relative short term (i.e., within the 2 years we
followed our sample), the experience of new adversity served no
obvious benefit for people with a history of no prior adversity.
However, it may be the case that such benefits take longer to
emerge, such as after the acute effects of recent adversity have
faded.
Adversity history may be relevant for other theories focusing on
coping resources, such as the reserve capacity model (Gallo,
Bogart, Vranceanu, & Matthews, 2005; Gallo, Espinosa de los
Monteros, & Shivpuri, 2009), which uses psychosocial factors to
explain the relationship between lower socioeconomic status and
worse physical health outcomes. Low but nonzero lifetime adver-
sity may promote the constellation of resources referred to as
reserve capacity, which in turn predicts better physical health.
Given that coping with stress permeates widely through social
psychological phenomena—ranging from being the target of prej-
udice and discrimination to rejection in close relationships and
threats to self-esteem—the beneficial effects of prior adversity
have the potential to generate new research across many areas of
study.
Our results should not be construed to endorse intentional
trauma exposure or to deny that adversity can have negative
consequences for mental health and well-being, especially in the
short term. Instead, they highlight that people are not doomed to be
damaged by adversity. Beyond recovering to past levels of func-
tioning in the aftermath of adversity, we found evidence consistent
with people actually benefiting from the experience of some ad-
versity. Ultimately, a richer understanding of how adversity con-
tributes to positive mental health and well-being and resilience
may suggest ways to promote them. To modify an adage, in
moderation, whatever does not kill us may indeed make us
stronger.
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Received February 6, 2010
Revision received July 1, 2010
Accepted August 25, 2010
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... Often, empirical research on human resilience seeks to identify and demonstrate a range of protective factors that help individuals overcome hardship (Laird, Lavretsky, Wu, Krause, & Siddarth, 2019). Yet, more research is needed to examine the underlying mechanisms that explain how some individuals who experience adversity and stress may adapt and strengthen due to these stressful experiences (Dienstbier, 2015a;Ellis, Bianchi, Griskevicius, & Frankenhuis, 2017;Seery, Holman, & Silver, 2010;Yerkes & Dodson, 1908). Studies on the potential neurocognitive benefits from low-to-moderate levels of stress are scarce in psychological science. ...
... In the hormetic process, resilience is built during the strengthening phase and is manifested in the buffering/protective phase, in which the maladaptive impact of stress is mitigated. For example, Seery et al. (2010) found quadratic relations that followed a hormetic pattern between cumulative lifetime adversity and mental health and well-being. ...
... Human studies involving stress hormones have also found blunted cortisol activity from moderate but not low or severe early life stress in 10-12 year-olds (Gunnar, Frenn, Wewerka, & Van Ryzin, 2009) and lower hair cortisol concentration from moderate chronic adversity than low and high levels at age 17 (Ouellet-Morin et al., 2021). In addition, Seery et al. (2010) found that a history of some lifetime adversity seemed to impart mental health and well-being benefits in adults, which was not reflected in those with zero or high levels of lifetime adversity. Furthermore, they found that some degree of cumulative lifetime adversity provided a buffer against contemporary adversity (Seery et al., 2010). ...
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Extensive research documents the impact of psychosocial stress on risk for the development of psychiatric symptoms across one's lifespan. Further, evidence exists that cognitive functioning mediates this link. However, a growing body of research suggests that limited stress can result in cognitive benefits that may contribute to resilience. The hypothesis that low-to-moderate levels of stress are linked to more adaptive outcomes has been referred to as hormesis. Using a sample of young adults from the Human Connectome Project (N = 1,206, 54.4% female, Mage = 28.84), the present study aims to test the hormetic effect between low-to-moderate perceived stress and psychopathological symptoms (internalizing and externalizing symptoms), as well as to cross-sectionally explore the intermediate role of cognitive functioning in this effect. Results showed cognitive functioning as a potential intermediating mechanism underlying the curvilinear associations between perceived stress and externalizing, but not internalizing, behaviors. This study provides preliminary support for the benefits of limited stress to the process of human resilience.
... Humans need to encounter physical and mental challenges in order to develop resilience (Haidt and Lukianoff, 2018). Seery et al. (2010) found that that individuals whom had experienced a moderate amount of adversity had better mental health as opposed to those whom had no such experience, or too much experience with hardship. Additionally, those with a moderate experience of adversity not only reported having better mental health, but they were also less likely to be affected by a recent adverse event (Seery et al., 2010). ...
... Seery et al. (2010) found that that individuals whom had experienced a moderate amount of adversity had better mental health as opposed to those whom had no such experience, or too much experience with hardship. Additionally, those with a moderate experience of adversity not only reported having better mental health, but they were also less likely to be affected by a recent adverse event (Seery et al., 2010). The restriction of exposure to challenges by intervening before a child has struggled or failed undermines the child's effort to establish healthy regulatory strategies (Perry et al., 2018). ...
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... Resilience is a process and an interactive concept, rather than a fixed trait, that could be influenced by both personality dispositions and external systems [85]. Therefore, we can speculate that resilience could be improved by training, especially given that evidence has proven that a certain amount of adversity experience [86] and appropriate intervention [87] are conducive to the improvement of individuals' psychological resilience. In Study 2, the online resilience intervention program we adopted had the characteristics of an individualized intervention under the same framework. ...
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The experience of long-term exposure to environmental contaminants, or chronic environmental contamination (CEC), is an increasingly common environmental hazard with deleterious physical and mental health outcomes. CEC is also an environmental justice issue, as communities of color and low-income communities disproportionately face such hazards. Research on environmental issues in psychology has largely focused on acute hazards such as natural disasters, and on abstract hazards such as climate change. While there has been limited research on more intermediate hazards like CEC in psychology, we assert that psychological methods and theories have much to add to interdisciplinary collaborations concerning stress, resilience, and collective action in the context of CEC. In the present paper, we first situate CEC relative to other environmental hazards that have received more attention in psychology. We then review literature on the psychological health impacts of CEC, as well as relevant disparities in negative outcomes associated with CEC. We then recommend ways for psychological researchers to engage in interdisciplinary and community-based participatory research on this topic. We finish with suggested future directions for research that documents and intervenes on the impacts of CEC using psychologically-informed interdisciplinary research.
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Objectives: Using nationally representative data among U.S. adults, we assess age differences in changes in mental health both from 2018 to May 2020 and during the pandemic. We also examine factors explaining age differences in mental health.Methods: We analyzed 2018 General Social Survey data (N = 2,348; age 18-89) and three waves of COVID Response Tracking Study data (N = 2,279; age 20-94) spanning May-August 2020. Outcomes included happiness, loneliness, stress, positive affect, and negative affect.Results: U.S. adults reported greater loneliness and less happiness in May 2020 versus 2018. Only loneliness and negative affect changed significantly from May to August 2020, showing declines. Mental health trajectories did not differ significantly by age. Overall, older adults reported lower loneliness, stress, and negative affect than younger adults during 2020. Older age was associated with two factors linked with better mental health: less likelihood of COVID-19 exposure and greater satisfaction with social activities and relationships. However, none of the factors examined herein explained age differences in mental health.Conclusion: Although mental health trajectories during the pandemic were similar across ages, older adults tended to report better mental health than younger adults. Future research should identify factors that explain age differences in mental health that persisted into 2020.
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By now, it should be apparent that something is happening to behavior therapy. Its “learning theory” basis is being challenged and is being replaced, in part by a cognitive orientation. The conceptual basis of “learning theory” that provided the framework and heuristic background for a variety of behavior therapy procedures is being oppugned on both theoretical grounds (e.g., Bandura, 1974; Breger & McGaugh, 1965; McKeachie, 1974) and empirical grounds (e.g., Brewer, 1974; Mahoney, 1974; Meichenbaum, 1974a). Such time-honored concepts as the automaticity of reinforcement and the continuity assumption between overt and covert events are being seriously questioned. As Bandura stated in his presidential address to the American Psychological Association: So-called conditioned reactions are largely self-activated on the basis of learned expectations rather than automatically evoked. The critical factor, therefore, is not that events occur together in time, but that people learn to predict them and to summon up appropriate reactions. (1974, p. 860)
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The terrorist attacks of September 11, 2001, exposed every person in the USA to an experience that, in recent decades, was unprecedented in its scope and traumatic impact. Perhaps over 100,000 individuals directly witnessed these events, and many others viewed the attacks and their aftermath via the media (Yehuda, 2002). It has been argued that this national trauma “influenced and will continue to influence the clinical presentation of patients seeking health care services” in the USA (Yehuda, 2002, p. 108). A wide range of responses can be expected following traumatic life events. Research conducted after the Oklahoma City, OK, bombing indicates that responses to a terrorist attack are likely to be highly variable (North et al., 1999). Research in the broader field of stress and coping has also demonstrated considerable variability in emotional and cognitive responses to stressful experiences (Silver and Wortman, 1980;Wortman and Silver, 1989, 2001). Despite advances in understanding reactions to traumatic events, our understanding of responses to community-level events in general, and terror attacks in particular, is limited. Progress in understanding the social and psychological process following such occurrences requires examination of how responses to a variety of stressful events are similar and different at both the group and individual level. Research has matured to the point that large-scale, prospective, longitudinal studies with the scope to examine mediators and moderators of adjustment processes are not only possible, but also necessary (North and Pfefferbaum, 2002). Moreover, the threat of future terrorist attacks demands that a higher level of urgency and research sophistication be directed not only at understanding the effects of such attacks, but also at the individual and social variables that predict psychological outcomes to such events over time.
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The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity. The most surprising conclusion emerging from studies of these children is the ordinariness of resilience. An examination of converging findings from variable-focused and person-focused investigations of these phenomena suggests that resilience is common and that it usually arises from the normative functions of human adaptational systems, with the greatest threats to human development being those that compromise these protective systems. The conclusion that resilience is made of ordinary rather than extraordinary processes offers a more positive outlook on human development and adaptation, as well as direction for policy and practice aimed at enhancing the development of children at risk for problems and psychopathology. The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity.
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This article describes the concept of posttraumatic growth, its conceptual foundations, and supporting empirical evidence. Posttraumatic growth is the experience of positive change that occurs as a result of the struggle with highly challenging life crises. It is manifested in a variety of ways, including an increased appreciation for life in general, more meaningful interpersonal relationships, an increased sense of personal strength, changed priorities, and a richer existential and spiritual life. Although the term is new, the idea that great good can come from great suffering is ancient. We propose a model for understanding the process of posttraumatic growth in which individual characteristics, support and disclosure, and more centrally, significant cognitive processing involving cognitive structures threatened or nullified by the traumatic events, play an important role. It is also suggested that posttraumatic growth mutually interacts with life wisdom and the development of the life narrative, and that it is an on-going process, not a static outcome.
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This article reports the development and validation of a scale to measure global life satisfaction, the Satisfaction With Life Scale (SWLS). Among the various components of subjective well-being, the SWLS is narrowly focused to assess global life satisfaction and does not tap related constructs such as positive affect or loneliness. The SWLS is shown to have favorable psychometric properties, including high internal consistency and high temporal reliability. Scores on the SWLS correlate moderately to highly with other measures of subjective well-being, and correlate predictably with specific personality characteristics. It is noted that the SWLS is suited for use with different age groups, and other potential uses of the scale are discussed.
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In this article, the author describes a new theoretical perspective on positive emotions and situates this new perspective within the emerging field of positive psychology. The broaden-and-build theory posits that experiences of positive emotions broaden people's momentary thought-action repertoires, which in turn serves to build their enduring personal resources, ranging from physical and intellectual resources to social and psychological resources. Preliminary empirical evidence supporting the broaden-and-build theory is reviewed, and open empirical questions that remain to be tested are identified. The theory and findings suggest that the capacity to experience positive emotions may be a fundamental human strength central to the study of human flourishing.