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The Influence of Age-Related Cues on Health and Longevity


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Environmental cues that signal aging may directly and indirectly prime diminished capacity. Similarly, the absence of these cues may prime improved health. The authors investigated the effects of age cues on health and longevity in five very different settings. The findings include the following: First, women who think they look younger after having their hair colored/cut show a decrease in blood pressure and appear younger in photographs (in which their hair is cropped out) to independent raters. Second, clothing is an age-related cue. Uniforms eliminate these age-related cues: Those who wear work uniforms have lower morbidity than do those who earn the same amount of money and do not wear work uniforms. Third, baldness cues old age. Men who bald prematurely see an older self and therefore age faster: Prematurely bald men have an excess risk of getting prostate cancer and coronary heart disease than do men who do not prematurely bald. Fourth, women who bear children later in life are surrounded by younger age-related cues: Older mothers have a longer life expectancy than do women who bear children earlier in life. Last, large spousal age differences result in age-incongruent cues: Younger spouses live shorter lives and older spouses live longer lives than do controls. © The Author(s) 2010.
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Perspectives on Psychological
The online version of this article can be found at:
DOI: 10.1177/1745691610388762
2010 5: 632Perspectives on Psychological Science
Laura M. Hsu, Jaewoo Chung and Ellen J. Langer
The Influence of Age-Related Cues on Health and Longevity
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The Influence of Age-Related Cues on
Health and Longevity
Laura M. Hsu
, Jaewoo Chung
, and Ellen J. Langer
Department of Psychology, Harvard University, Cambridge, MA, and
MIT Media Lab, Massachusetts Institute of
Technology, Cambridge, MA
Environmental cues that signal aging may directly and indirectly prime diminished capacity. Similarly, the absence of these cues may
prime improved health. The authors investigated the effects of age cues on health and longevity in five very different settings. The
findings include the following: First, women who think they look younger after having their hair colored/cut show a decrease in
blood pressure and appear younger in photographs (in which their hair is cropped out) to independent raters. Second, clothing is
an age-related cue. Uniforms eliminate these age-related cues: Those who wear work uniforms have lower morbidity than do
those who earn the same amount of money and do not wear work uniforms. Third, baldness cues old age. Men who bald
prematurely see an older self and therefore age faster: Prematurely bald men have an excess risk of getting prostate cancer
and coronary heart disease than do men who do not prematurely bald. Fourth, women who bear children later in life are
surrounded by younger age-related cues: Older mothers have a longer life expectancy than do women who bear children earlier
in life. Last, large spousal age differences result in age-incongruent cues: Younger spouses live shorter lives and older spouses live
longer lives than do controls.
aging, age identity, social perception, health, longevity
Old age is commonly viewed as a period of inevitable cognitive
and physical decline (Langer, 1982). Research suggests that
negative perceptions of old age begin to develop around 6 years
of age (Isaacs & Bearison, 1986) and persist into old age
(Nosek, Banaji, & Greenwald, 2002).
The literature is replete with examples of negative stereo-
types about mature adults (e.g., Isaacs & Bearison, 1986; Langer
& Rodin, 1976; Levy, 1996; Nelson, 2002; Rodin & Langer,
1980). Because perceptions of old age are pervasive and
resistant to change, it becomes difficult to disentangle the extent
to which old age is necessarily a time of diminishing capacities
and the extent to which it is a function of negative premature
cognitive commitments or mindsets regarding age.
Research demonstrates that views of old age are automatic
and unconscious and influence a variety of behaviors congruent
with those views (e.g., Bargh, Chen, & Burrows, 1996; Levy,
1996; Levy, Hausdorff, Hencke, & Wei, 2000; Levy & Langer,
1994). Levy and Langer found that memory problems for older
adults were related to the premature cognitive commitments
people have about memory and aging. In Levy’s study examin-
ing age perception and cognitive performance, individuals
primed with negative stereotypes of old age (e.g., ‘‘senile,’
‘dependent’’) performed worse on memory tasks than did
those primed with positive stereotypes (e.g., ‘‘kind,’’ ‘‘alert’’).
Age perception also influences behavior. In Bargh et al.’s
study, individuals who had been primed with negative stereotypes
of old age walked more slowly down a hallway when leaving the
experiment than did control participants.
Although a number of studies illustrate how perceptions of
old age can lead to decrements in cognitive functioning and
physical behavior, research also shows priming positive
perceptions of aging can lead to improvements in cognitive
functioning and have far-reaching implications for one’s health
(Demakakos, Gjonca, & Nazroo, 2007; Knoll, Rieckmann,
Scholz, & Schwarzer, 2004; Kuper & Marmot, 2003;Levy, Slade,
Kunkel, & Kasl, 2002; Logan, Ward, & Spitze, 1992; Siegel,
Bradley, & Kasl, 2003; Uotinen, Rantanen, & Suutama, 2005;
Van Doorn & Kasl, 1998; Westerhof & Barrett, 2005;
Corresponding Author:
Ellen J. Langer, Department of Psychology, Harvard University, 33 Kirkland
Street, Cambridge, MA 02138
Perspectives on Psychological Science
5(6) 632–648
ªThe Author(s) 2010
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DOI: 10.1177/1745691610388762
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Westerhof, Barrett, & Steverink, 2003). Levy et al. discovered
in a longitudinal analysis that individuals with more positive
self-perceptions of aging measuredupto23yearsearlierlived
an average of 7.5 years longer than did individuals with less
positive self-perceptions of aging, after adjusting for the effect
of gender, socioeconomic status, loneliness, and functional
health. The researchers believed that it was self-perceptions
of aging that influenced physiological outcomes. In fact,
Vaupel et al. (1998) believed that as much as 75%of variation
in longevity may be attributed to nongenetic attributes, including
psychological and behavioralfactors. Although Levy et al. did not
measure actual health, there is considerable evidence that shows
self-rated health influences survival (Idler & Kasl, 1991; Idler,
Russell, & Davis, 2000; Kaplan & Camacho, 1983). In a cohort
study of 6,928 adults, perceived health was a better predictor of
mortality than actual health (Kaplan & Camacho), and in another
study, older adults who perceived their health as poor were six
times more likely to die than those who perceived their health
as excellent, regardless of their actual health status (Idler & Kasl).
Some of the individuals in Levy et al.’s (2002) study were
fortunate enough to defy common conceptions of aging even
though research on self-perception indicates that the majority
of older adults in Western societies internalize negative
attitudes about their own group; consider themselves as lower
in status; and are less likeable, unhappier, more dependent, and
less goal-oriented than younger adults (Nosek et al., 2002;
Zebrowitz & Montepare, 2000). A positive view about aging
appears to be important and adaptive for one’s physical
and mental health. We also believe, however, that there are
‘mindless’’ cues in the environment that can prime an older
or younger self, and the body may age accordingly regardless
of one’s views. These cues may put individuals in potentially
favorable or unfavorable positions, depending on the context.
The Importance of Contextual Cues
Context plays an important role in masking, muting, or magni-
fying age cues. We are constantly surrounded by age cues in the
environment that include physical signs of aging (e.g., wrinkles,
gray hair), the roles one occupies (e.g., grandmother), and the
interests and activities one has and does (Hendricks, 1987).
These age cues signal to ourselves and to others (a) what
age demographic others most likely fall into and (b) certain
assumptions that go along with those cues.
Contextual information has long been shown to be more
influential than chronological age information in judgments
of older adults (Kite & Wagner, 2002). For example, an older
person’s health status and performance are more important than
their actual age in influencing people’s judgments (Gekoski &
Knox, 1990; Reno, 1979). Being put in a context where one is
not expected to be, given her age (e.g., a woman in her 30s with
a young child), or where physical signs of aging are concealed
or muted (e.g., using Botox), may overturn or offset negative
perceptions of aging, and in effect, change the way our
bodies age.
How might this happen? Since the time of Descartes, as
humans we have mindlessly accepted mind/body dualism.
From this perspective the unanswered question has always
been ‘‘How do we get from the nonmaterial mind to the
material body?’’ Despite the absence of knowledge of the precise
mechanisms involved, there is ample evidence that our thoughts
have enormous influence over our bodily processes.
A series of studies by Langer and her colleagues illustrate
this mind/body phenomenon (Alexander, Langer, Newman,
Chandler, & Davies, 1989; Crum & Langer, 2007; Langer,
1989; Langer, Beck, Janoff-Bulman, & Timko, 1984; Langer
et al., 1988; Langer, Djikic, Pirson, Madenci, & Donahue, in
press; Langer & Rodin, 1976; Rodin & Langer, 1977). For
example, Langer and Rodin (1976) and Rodin and Langer
(1977) found that giving choice to elderly adults resulted in
increased longevity. In a more recent study, Crum and Langer
found that chambermaids who viewed their work as exercise
showed a decrease in weight, blood pressure, body fat,
waist-to-hip ratio, and body mass index. Measures were taken
4 weeks apart on diet, amount of exercise outside of work, and
typical workload (how many hotel rooms were cleaned each
day). At the end of 4 weeks, even though the actual workload
did not increase and participants did not report getting any
additional exercise outside of work or having a change in diet,
they perceived themselves as getting significantly more exer-
cise than before and showed improvements in various health
In another series of recent studies, Langer et al. (in press)
explored the effect of the mind on vision. The traditional eye
chart shows letters getting progressively smaller as one reads
down the chart, and thereby the eye chart creates the expecta-
tion that soon individuals will not be able to see. Most individ-
uals expect this to occur about two thirds of the way down the
chart. In one of the studies, the experimenters reversed the chart
so that the letters got progressively larger, creating the expec-
tation that soon individuals would be able to see. In another
study, the experimenters started the chart with smaller letters
that ordinarily would appear a third of the way down the chart.
In both cases, people were able to see what they previously
could not with the traditional eye charts because of a change
in expectations.
These studies support the mind/body hypothesis that where
the mind is, the body follows. We believe this phenomenon
also applies to the aging process. If we put the mind in a
younger place, surrounded by younger cues, physical measures
may reveal a younger body. This was the hypothesis that
guided the counterclockwise study.
In the counterclockwise study, Langer et al. (1988) brought
a group of men between the ages of 75 and 80 to a 5-day retreat.
The men were randomly assigned to one of two groups. The
first group was instructed to imagine they were actually
55 years old and live for the week as if it was 20 years earlier.
Thus, all conversation about the past was in the present tense.
The second group was told to reflect on their lives 20 years
before when they were 55 years old. For them, conversation
was in the past tense.
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Both groups were ‘‘experimental’’ in the sense that they
were taken from the contexts of their everyday lives to a retreat
where they were ‘‘brought back’’ to an earlier time. However,
the second group was referred to as the control group in order to
control for the experience of being at the retreat.
Pre- and post-measures were taken on physical strength,
perception, cognition, hearing, and visual thresholds, as well
as a self-report measure that assessed values and behavior.
Before and after photographs were also taken of men in both
groups. Between the first and last day of the retreat, experimen-
ters tried to recreate an atmosphere that would remind partici-
pants of events and experiences from 20 years earlier. Although
experimenters were instructed to recreate an atmosphere that
mimicked a previous time, they were blind to the hypothesis
of the study. The experimenters played popular music and
showed popular television shows of the time. They discussed
‘current’’ events such as the launch of the first U.S. satellite,
Explorer 1; the need for bomb shelters; and Castro’s advance
into Havana, Cuba. The men in both groups were asked not
to discuss anything that happened after September 1959 with
each other, but whereas the first group was asked to conduct all
conversations and discussions in the present, the second group
was asked to use the past tense. Experimenters also had
participants write autobiographical sketches about their lives
in 1959, with similar instructions to the first group and second
group to use the present and past tense, respectively.
By the end of the week, both groups looked younger by
about 3 years, as rated by independent judges who viewed
before-and-after photographs. Hearing and memory generally
improved for both groups. Both groups had some weight gain,
increased bideltoid and tricep skinfolds, and improved hand
strength. The fact that these changes were found in both groups
illustrates the importance of how a change in perception,
specifically a focus on being younger, can influence changes
in physiological measures even in a short period of time. There
were also significant differences between the two groups. Men
in the experimental group had better joint flexibility, finger
length (their arthritis diminished and they were able to
straighten their fingers more), better posture, greater increases
in tricep skinfold and bideltoid breadth, and improved vision,
compared with men in the control group. On intelligence tests,
63%of the experimental group improved their scores after the
retreat, compared with 44%of the control group. One might
argue that motivational differences accounted for the differ-
ences found between both groups. However, potential motiva-
tional differences do not account for the fact that men in both
groups appeared younger in photographs viewed by blind
The counterclockwise study demonstrates that the decline
in physical and mental health expected later in life may be a
product of assumptions about how one is supposed to age.
Supposedly irreversible signs of aging were altered as a result
of this psychological intervention. It should be noted that the
men in this study, particularly for men in the experimental
group, had to exercise some degree of mindfulness in order
to fully participate. They had to actively recontextualize their
mindset. While being at a retreat, socializing with other men
their age, and engaging in various activities might account for
some of the changes observed for the comparison group, we
believe the psychological change of mindset was at least par-
tially responsible for the differences between the two groups.
One group focused on being in the past while the second group
reflected on the past.
The following studies provide a conceptual replication of the
mind/body hypothesis: If one’s mindset is altered, one’s body
will change accordingly. Because this nondualist view is still
at odds with much current thinking, we present these data as
worthy of consideration in the spirit of possibility. Alternative
explanations, which we outline for each study, are also plausi-
ble. Each of the studies that follow was based on a priori hypoth-
eses: According to whether a younger or older self is primed, the
body will age accordingly. In this article, we discuss five studies
in which age markers are either muted or magnified (priming a
younger or older self, respectively) and how this may influence
health and longevity.
Field Study
If women change their appearance and think they look younger,
will others agree? Will the perception of being younger
translate into physical measures? In a recent investigation,
we were curious of whether women who dye their hair to cover
gray hair would feel younger than women around the same age
who just have their hair cut but not dyed.
We sampled 47 women (28 hair dyed, 19 no hair dye)
between the ages of 27 to 83 years of age (M¼42.7 years,
SD ¼10.9 years) at a local hair salon. Before each woman’s
hair appointment, we took photographs of just her face
and obtained her blood pressure. In addition, participants filled
out a brief questionnaire that asked the woman her age and
what age she thought she looked. After the woman’s hair
appointment, the same procedures were followed except that
in the postquestionnaire, in addition to asking what age the
woman felt she looked, she was also asked how satisfied she
was with the way she looked and how attractive she felt, rating
her response on a 5-point Likert-type scale ranging from 1 (not
at all)to5(completely). To determine whether women who got
their hair dyed appeared younger to independent raters, we
cropped photographs of the women so that their hair was not
showing. Independent raters (10 women, 8 men) ranging from
20 to 64 years of age (M¼44.9 years, SD ¼13.6 years) viewed
before-and-after photographs of each woman and were asked to
identify the photo in which the woman appeared younger.
The order of before-and-after photos were randomly switched
to control for order effects.
Results indicate that there were no significant differences
between women who had their hair dyed and women who did
not; however, when women, regardless of their hair procedure,
reported feeling younger after their hair appointment, other
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changes followed. Women who perceived themselves as
younger, regardless of whether they got their hair dyed,
appeared younger to independent raters compared with women
who felt the same age or older. Only 1 woman felt older after
her hair appointment, t(45) ¼–2.00, p< .05. This was the case
for both conditions despite the important fact that women did
not feel significantly more attractive or satisfied with how their
hair looked after their hair appointment. More important,
women who perceived themselves as younger after their hair
appointment showed a decrease in both systolic blood pressure
¼4.41, p< .05) and diastolic blood pressure (w
p< .05). This study showed that a change in self-perception of
age was associated with physiological changes for the partici-
pant and were even apparent to others unaware of the change
in mindset by participants. This study is unique because it exam-
ined the effect of antecedents of age perception on health as
opposed to generally exploring how age perception affects
health. The results of this exploratory study illustrate how a
common external change, such as getting one’s hair done, can
alter age perception and manifest itself in both outward (face)
and inward (blood pressure) physiological changes.
Archival Studies
The following studies present archival data. The researchers of
the studies described below do not mention age cues as a
possible reason for their outcomes. In fact, with respect to
baldness, the medical researchers state that ‘‘the precise mechan-
isms leading to the development of Male Pattern Baldness (MPB)
and prostate cancer are largely unknown’’ (Hawk, Breslow, &
Graubard, 2000, p. 523). Although there may be alternative
explanations, we believe that age-related cues and the mindsets
that accompany them may at least partially account for the
outcomes of all of these studies.
Uniforms and Morbidity
Clothing serves as an age-related cue (Twigg, 2007). It is
unlikely for a 50-year-old person, for example, to wear the
same outfits that are designed for a 25-year-old person. When
people wear uniforms at work, they are deprived of this age-
related cue. If one wears a uniform at a job from when he or she
is 25 years old until he or she is 65 years old, there is one less
cue suggesting that he or she is getting older. As such, it is
predicted that people who wear work uniforms will have better
health than those who do not wear work uniforms.
We examined morbidity data for 206 professions from the
National Health Interview Survey—which was conducted
between 1986 and 1994—to assess whether people in profes-
sions who wear uniforms have better health than those who
do not wear uniforms. We used an adjusted morbidity ratio
as our outcome variable, which controlled for age, gender, race
and ethnicity, and level of education (Lee et al., 2006). The
morbidity ratio was a weighted average of seven odds ratios
from logistic regressions predicting restricted activity days,
restricted bed days, work loss days as a result of illness or
injury, doctor visits, hospital stays, health status, and chronic
health conditions (Lee et al.). In our analysis of whether
uniform wearing significantly predicted morbidity, we also
controlled for income, level of physical activity a job may
demand, and level of happiness at one’s job. We controlled for
these factors because a higher income may afford more, and
better, access to health care (Lynch, Smith, Kaplan, & House,
2000) and a healthier lifestyle. The physical demand of a job
can be seen as a form of exercise (Crum & Langer, 2007).
Some jobs are more physically demanding than others (e.g.,
construction work vs. secretarial work). A job that involves a
fair amount of walking or lifting (e.g., farm work) can be
considered healthier for the cardiovascular system compared
with a sedentary desk job (e.g., receptionist). Last, happiness
has been linked to longevity (Davenport, 2005; Deeg & van
Zonneveld, 1989; Langer, 1989; Veenhoven, 2007), and thus
seemed important to control for as well.
Information on median salary from 1994 (the last year of the
National Health Interview Survey) was taken from the U.S.
Department of Labor, Bureau of Labor Statistics Web site.
Information on how happy people are in various professions
was taken from the General Social Survey by the National
Opinion Research Center at the University of Chicago (Smith,
2007). Independent raters coded whether people in each of the
206 professions typically wear uniforms (‘‘yes’’ or ‘‘no’’).
They were told that some jobs require people to wear a uniform
for identification or professionalism purposes (e.g., those worn
by flight attendants) and/or as a safety regulation (e.g., a lab
coat worn by chemists). They were asked to code the presence
of uniform if people in that profession tend to wear a uniform
the majority of the time (especially when interacting with
clients or patients, as is the case with physicians). A dress code
(e.g., shirt and tie) did not qualify as a uniform because the
employee was able to purchase his or her own variation of
the required attire. For example, for a business-casual dress
code, someone could choose from a wide variety (e.g., color,
style, brand) of shirts and pants. In contrast, a work uniform
is usually provided by the company or organization for the
employee to wear (e.g., an apron or skirt for a waitress).
Physical activity was coded on a scale from 1 (light), such as
for a secretarial job, to 4 (heavy), such as for a construction job.
Coders were told (a) that some jobs involve more physical
activity than do others and (b) to make their best estimate on
a scale from 1 to 4 for how physically demanding each job is
(see Table 1 for list of professions and codes). If they were
unsure, they were told they could leave the field blank. Interrater
reliability was .89 for presence of uniforms and .75 for physical
To determine whether wearing a work uniform is a signifi-
cant predictor of morbidity, we conducted a multiple regression
analysis, with the adjusted morbidity ratio as the outcome
variable, and uniform and the selected control variables as the
predictor variables. We also tested for the interaction between
uniform and median income. Our reasoning was that clothing
might serve as a greater age-related cue for people with more
earning power. If wealthier people can afford to wear clothes
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Table 1. Occupations by Rank, From Worst to Best Health
Occupation Rank
morbidity Uniform
Median annual salary
(in U.S. dollars)
Physical activity
Social workers 1 1.42 No 26468 38.7 3
Inspectors, testers, graders 2 1.39 Yes 30108 36.6 2
Postal clerks (except mail carriers) 3 1.32 Yes 32708 35 2
Psychologists 4 1.3 No 33332 37.2 1
Grinding/abrading/buffing/polishing machine
5 1.28 Yes 20436 31.4 3
Nursing aides/orderlies and attendants 6 1.28 Yes 14300 28.3 3
Specified mechanics and repairers 7 1.23 Yes 25324 . 3
Inspectors, compliance officers (except
8 1.23 Yes 34684 22.3 3
Correctional institution officers 9 1.22 Yes 24908 26 3
Licensed practical nurses 10 1.22 Yes 23400 30.6 3
Punching and stamping press machine
11 1.21 Yes 20488 23.5 3
Mail carriers, postal service 12 1.21 Yes 33540 34.5 3
Actors and directors 13 1.2 No 28912 51 2
Guards and police, except public service 14 1.19 Yes 17628 23.8 3
Bill and account collectors 15 1.19 No 19656 . 1
Street and door-to-door sales workers 16 1.19 No 16796 25.2 3
Assemblers 17 1.18 Yes 17576 28.2 2
Purchasing managers 18 1.17 No 40144 . 1
Telephone operators 19 1.16 No 20384 25 1
Stationary engineers 20 1.16 No 30732 46.29 2
Dispatchers 21 1.16 No 21060 39 1
Personal service occupations, not elsewhere
22 1.16 Yes 15444 40 .
Administrators/officials, public administration 23 1.16 No 37076 36 2
Janitors and cleaners 24 1.16 Yes 15236 24.2 3
Teachers, special education 25 1.15 No 33436 52.6 3
Aerospace engineer 26 1.15 No 50232 46.29 1
Computer systems analysts and scientists 27 1.14 No 43992 38.7 1
Sheet metal workers 28 1.14 Yes 29172 . 3
Counselors, educational and vocational 29 1.14 No 35672 26.7 1
Messengers 30 1.14 Yes 17576 18.8 4
Health technologists and technicians 31 1.14 Yes 23972 . 3
Bus drivers 32 1.13 Yes 20384 34.29 1
Production inspectors, checkers, and
33 1.12 Yes 20332 22.3 2
Machinists 34 1.12 Yes 26988 30.1 3
Computer programmers 35 1.12 No 38376 30.1 1
Police and detectives, public service 36 1.12 Yes 33956 44 3
Technicians 37 1.11 Yes 27820 . 3
Welders and cutters 38 1.11 Yes 23920 20.8 3
Molding and casting machine operators 39 1.11 Yes 18824 31.4 3
Order clerks 40 1.11 24128 37.6 1
Bus, truck, and stationary engine mechanics 41 1.11 Yes 25532 14.5 4
Construction trades, not elsewhere classified 42 1.1 No 21736 22.1 4
Heating, air conditioning, and refrigeration
43 1.1 Yes 25688 26.5 3
Management analysts 44 1.1 No 41340 . 1
Health aides, except nursing 45 1.1 Yes 15288 . 3
Miscellaneous material moving equipment
46 1.1 No 23296 . 3
Data entry keyers 47 1.1 No 18876 25.4 1
Aircraft engine mechanics 48 1.09 Yes 36868 . 4
Engineering technicians 49 1.09 No 29380 . 2
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Occupation Rank
morbidity Uniform
Median annual salary
(in U.S. dollars)
Physical activity
Buyers, wholesale/retail trade except farm
50 1.09 No 26468 39.7 1
Stock and inventory clerks 51 1.09 No 20488 27.4 3
Mixing and blending machine operators 52 1.08 Yes 21476 31.4 3
Editors and reporters 53 1.08 No 31928 35.7 2
Clergy 54 1.08 Yes 27872 67.2 2
Taxicab drivers and chauffeurs 55 1.07 No 19448 32.39 1
Electrical and electronic equipment assemblers 56 1.07 Yes 17420 28.2 2
Supervisors, mechanics, and repairers 57 1.07 Yes 34996 . 1
Supervisors, cleaning and building service
58 1.07 No 18772 . 1
Drafting occupations 59 1.07 No 28548 29.2 1
Heavy equipment mechanics 60 1.06 Yes 28652 . 3
Teachers 61 1.06 No 32292 48.39 2
Not specified mechanics and repairers 62 1.06 Yes 24128 . 3
Designers 63 1.06 No 30576 31.4 2
Production coordinators 64 1.06 No 26416 24.7 .
Economists 65 1.05 No 46228 45.89 1
Industrial machinery repairers 66 1.05 Yes 27612 22.2 3
Advertising and related sales occupations 67 1.05 No 28808 42.29 1
Administrative support occupations 68 1.05 No 20384 27.5 1
Sheriffs/bailiffs/other law enforcement officers 69 1.05 Yes 26832 36.89 3
Records clerks 70 1.05 No 20696 28.2 1
Personnel/training/labor relations specialists 71 1.05 No 31772 . 2
Sales occupations, other business services 72 1.04 No 32864 . 2
Supervisors, general office 73 1.04 No 26520 . 1
Management-related occupations 74 1.04 No 31044 . 1
Laborers, except construction 75 1.04 No 16692 24.1 4
Miscellaneous machine operators 76 1.04 Yes 21216 31.4 3
Insurance adjusters, examiners, and
77 1.04 No 23712 28.6 1
Interviewers 78 1.04 No 18772 37.29 2
Industrial truck and tractor equipment
79 1.03 Yes 21944 25.8 2
Managers, medicine and health 80 1.03 No 32396 42.5 1
Teachers, prekindergarten and kindergarten 81 1.03 No 19656 37.1 3
Billing clerks 82 1.03 No 19396 33.6 1
Postsecondary teachers, subject unspecified 83 1.03 No 43628 38.89 3
Computer operators 84 1.03 No 21268 36.5 1
Receptionists 85 1.03 No 16016 33.89 1
Administrators, education and related fields 86 1.03 No 39936 45.39 1
Registered nurses 87 1.02 Yes 35464 36.29 3
Legal assistants 88 1.02 No 25636 22.8 2
Managers, properties and real estate 89 1.02 No 22568 37.6 2
Attendants, amusement and recreational
90 1.02 Yes 16900 15.7 3
Painters/sculptors/craft artists/artist
91 1.02 No 25532 31.7 2
File clerks 92 1.02 No 16172 . 1
Child care workers 93 1.02 No 13052 35.79 3
Typists 94 1.02 No 19240 29.4 1
Chemists, except biochemists 95 1.01 Yes 41236 26.4 1
Machine operators, not specified 96 1.01 Yes 20176 31.4 3
Bartenders 97 1.01 . 15548 19.6 3
Child care workers, private household 98 1.01 No 8216 33.39 3
Investigators and adjusters, except insurance 99 1.01 No 21320 42.89 1
Insurance sales occupations 100 1.01 No 31616 35.7 2
Truck drivers 101 1.01 No 24284 33.6 2
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Table 1 (continued)
Occupation Rank
morbidity Uniform
Median annual salary
(in U.S. dollars)
Physical activity
Freight, stock and material handlers, not else-
where classified
102 1.00 No 19136 20.8 3
Personnel and labor relations managers 103 1.00 No 35152 37.7 1
Traffic, shipping, and receiving clerks 104 1.00 Yes 19916 29.3 3
Electricians 105 1.00 Yes 29848 30.2 3
Teachers, secondary school 106 0.99 No 35880 36.1 3
Graders and sorters, except agricultural 107 0.99 Yes 14612 . 3
Supervisors, food preparation and service
108 0.99 Yes 16848 25.6 1
Mail clerks, except postal service 109 0.99 Yes 16744 37 2
Architects 110 0.99 No 36504 53.5 2
Automobile mechanics 111 0.99 Yes 22880 23.7 3
General office clerks 112 0.99 No 19344 32.89 1
Maids and housemen 113 0.99 Yes 12792 22.8 4
Payroll and timekeeping clerks 114 0.99 No 21268 21.9 1
Teachers, elementary school 115 0.98 No 32448 44.7 3
Electrical and electronic technicians 116 0.98 Yes 31252 36.7 3
Expediters 117 0.98 Yes 20124 24.3 1
Painting and paint-spraying machine operators 118 0.98 Yes 19240 30.7 3
Purchasing agents and buyers 119 0.98 No 31148 . 1
Librarians 120 0.98 No 31096 25.2 1
Plumbers, pipefitters, and steamfitters 121 0.97 Yes 27560 31 3
Photographers 122 0.97 No 25064 20.8 2
Teachers aides 123 0.97 No 13364 37 2
124 0.97 No 44252 40.89 1
Operations/systems researchers and analysts 125 0.97 No 40248 . 1
Miscellaneous food preparation occupations 126 0.96 Yes 11648 20.8 2
Cashiers 127 0.96 Yes 11856 24.5 2
Waiters and waitresses 128 0.96 Yes 13312 31.5 3
Library clerks 129 0.95 No 19136 . 2
Industrial 130 0.95 Yes . . 3
Accountants and auditors 131 0.95 No 32032 41.79 1
Musicians and composers 132 0.95 No . 41.79 1
Supervisors, construction 133 0.95 No 32396 . 2
Laundering and dry cleaning machine operators 134 0.95 No 13156 21.8 3
Securities and financial services sales
135 0.95 No 37336 39.6 1
Telephone installers and repairers 136 0.95 Yes 35308 37.89 3
Public relations specialists 137 0.94 No 29900 39.5 1
Sales workers, furniture and home furnishings 138 0.94 No 22204 25.7 2
Other financial officers 139 0.93 No 33124 33.89 1
Firefighting occupations 140 0.93 Yes 32708 57.2 3
Sales workers, motor vehicles and boats 141 0.93 No 27768 42.79 2
Managers and administrators 142 0.93 No 36556 . 1
Hand packers and packagers 143 0.93 No 14716 24.2 3
Operating engineers 144 0.93 Yes 27508 40.6 2
Cooks 145 0.93 Yes 13208 30.1 3
Clinical lab technologists and technicians 146 0.92 Yes 26988 33.2 2
Dressmakers 147 0.92 No 16588 28.7 2
Packaging and filling machine operators 148 0.92 No 15548 31.4 3
Butchers and meat cutters 149 0.92 Yes 17108 35.39 2
Groundskeepers and gardeners, except farm 150 0.92 No 14924 30.5 3
Carpenters 151 0.92 No 22048 33.6 4
Slicing and cutting machine operators 152 0.92 Yes 17212 13.6 3
Lawyers 153 0.92 No 58032 43 2
Private household cleaners and servants 154 0.91 No 10140 25.3 3
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Occupation Rank
morbidity Uniform
Median annual salary
(in U.S. dollars)
Physical activity
Bookkeepers, accounting, and auditing clerks 155 0.91 No 19448 38.95 1
Supervisors, production occupations 156 0.91 No 30368 . 1
Sales reps, mining/manufacturing/wholesale 157 0.91 No . . 2
Radiologic technicians 158 0.91 Yes 29432 34.6 2
Engineers 159 0.91 No 46644 46.29 2
Electrical and Electronic 160 0.91 Yes . . 3
Secretaries 161 0.91 No 19916 38.7 1
Food counter/fountain/related occupations 162 0.91 Yes 10608 . 3
Civil engineers 163 0.9 No 44720 40.89 2
Vehicle washers and equipment cleaners 164 0.9 Yes 14560 16.9 3
Sales workers, radio, TV, stereo, and appliances 165 0.9 Yes 20852 . 2
Timber cutting and logging occupations 166 0.9 No . . 4
Managers, farms, except horticultural 167 0.9 No . . 2
Pressing machine operators 168 0.89 No 14300 23.5 3
Hairdressers and cosmetologists 169 0.89 No 14820 32.2 2
Farmers, except horticultural 170 0.89 No . 32.39 4
Real estate sales occupations 171 0.89 No 30836 45.29 2
Roofers 172 0.89 No 19292 14.2 4
Garage and service-station-related occupations 173 0.89 Yes 13312 13.2 3
Electronic repairers, communication/industrial
174 0.88 No 28184 15.7 3
Sales workers, other commodities 175 0.88 No 15184 . 2
Printing press operators 176 0.88 No 22464 32.5 2
Stock handlers and baggers 177 0.88 Yes 13624 20.8 3
Supervisors, distribution, scheduling, and
178 0.88 No 29328 . 1
Supervisors, related agricultural occupations 179 0.87 No 21476 . 2
Mechanical 180 0.87 Yes . . 3
Brickmasons and stonemasons 181 0.87 No 25272 32.1 4
Bakers 182 0.87 Yes 17160 29.3 3
Transportation ticket and reservation agents 183 0.86 21164 56.5 1
Dental assistants 184 0.86 Yes 17108 36.6 2
Construction laborers 185 0.86 No 17576 18.8 4
Supervisors/proprietors, sales occupations 186 0.86 No 26052 . 1
Painters, construction and maintenance 187 0.85 Yes 19812 30.7 3
Waiters/waitresses assistances 188 0.85 Yes 11856 . 3
Financial managers 189 0.85 No 37336 41.29 1
Kitchen workers, food preparation 190 0.84 Yes 12064 20.8 3
Sales workers, building and hardware supplies 191 0.84 Yes 17316 55.89 2
Textile sewing machine operators 192 0.82 No 12324 32.29 2
Automobile body and related repairers 193 0.81 Yes 23660 16.4 3
Drywall installers 194 0.79 No 21788 . 4
Bank tellers 195 0.79 No 15340 33.29 2
Sales workers, parts 196 0.79 No 19656 . 2
Tool and die makers 197 0.78 Yes 34528 38.6 3
Sales counter clerks 198 0.77 Yes 13832 22.9 3
Sales workers, apparel 199 0.77 No 13780 29.1 3
Farm workers 200 0.75 No 13208 31.8 4
Driver-sales workers 201 0.74 Yes 23972 23 2
Dietitians 202 0.84 No 27924 . 1
Pharmacists 203 0.72 Yes 49608 24.7 2
Physicians 204 0.68 Yes 51792 43.89 2
Airplane pilots and navigators 205 0.61 Yes 52676 49.1 1
Dentists 206 0.53 Yes 49400 41.6 2
Note. From Lee et al. (2006).
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that are more varied and change their wardrobe more
frequently, they should experience more age-related cues. Thus,
the uniform effect should become more prominent at higher
income levels.
The correlations among the predictor variables and adjusted
morbidity are displayed in Table 2. People who wear work uni-
forms tend to be less happy at their jobs (r¼–0.21, p< .01),
and perhaps it is not surprising that people who earn more money
at their job were happier at their jobs (r¼0.48, p< .0001). The
results of the regression indicate that wearing a uniform is a sig-
nificant predictor of morbidity (t¼2.81, p< .01; see Table 3).
There were no main effects for happiness or physical activity
levels in predicting morbidity. The results also show a signifi-
cant interaction between uniform and median salary (t¼–3.05,
p< .01) such that people who earn less than $24,916 per year
and who wear work uniforms (e.g., waiters, waitresses) tend
to have poorer health (higher morbidity) than do people who
earn less than $24,916 per year and who do not wear work
uniforms (e.g., street and door-to-door sales workers). With
an annual income of more than $24,916, the trend reversed.
Individuals who earned more than $24,916 per year and who
did not wear work uniforms (e.g., engineers) had poorer health
than did their uniformed counterparts (e.g., chemists; see
Fig. 1). This finding is consistent with our hypothesis regarding
the lack of age-related cues a uniform provides. Dressing
appropriately for one’s age becomes somewhat irrelevant
when wearing work uniforms. Not only is the age cue absent
for workers who wear uniforms, butalsoitisabsentforthepeo-
ple with whom they work.
We may see the effect of uniform for people in higher socio-
economic statuses because clothing is a status symbol. Apart
from an age-related cue, having more money may mean having
the purchasing power to keep up with constantly changing
trends in fashion. A uniform at higher income levels may act
as a buffer for being all too aware of one’s age. In contrast,
people in higher earning brackets who do not wear uniforms
may continue to be aware of their age as they make daily
decisions about what they will wear and their work wardrobes
continually change over time.
The opposite effect of uniform for people with low earning
potential may be due to job control. Job control refers to
the amount of discretion and independence one has in
determining how and when work needs to be done. People of
low socioeconomic status usually have low job control (Bosma,
Stansfeld, & Marmot, 1998). In addition, people on the lower
end of the income spectrum tend to work high effort and low
reward jobs (Siegrist, Peter, Junge, Cremer, & Seidel, 1990).
The mismatch between a high workload and low control over
occupational status (e.g., job insecurity, poor promotion pros-
pects, status inconsistency) has been shown in several studies
to be associated with a higher incidence of coronary heart dis-
ease, even after controlling for major confounding behavioral
risk factors such as diet, exercise, cigarette smoking, and alco-
hol consumption (Bosma et al.; Peter et al., 2009; Siegrist
et al.). In an analysis that isolated the effects of job control
on coronary heart disease, psychological attributes such as
hostility, negative affectivity, minor psychiatric disorder, and
coping were also shown to have little effect on cardiovascular
disease (Bosma et al.). The personal characteristics were not
confounders, intermediate factors, or effect modifiers (Bosma
et al.). People of low socioeconomic status who wear uniforms
may experience less job control (as rated by the employee) than
those who do not wear uniforms. Wearing a uniform may be
seen as a way of being controlled, which may override any
effect the age cue could or could not have. In contrast, uniforms
worn by people with higher earning potential may be seen more
as a status symbol (e.g., doctors) compared with uniforms worn
by people with lower incomes (e.g., janitors).
One reason some people wear work uniforms is for safety
regulations (e.g., machine operators); some professions are
more risky than others. We were only able to identify incidence
of injury statistics for 89 of the 206 occupations from the 1994
Survey of Occupational Injuries and Illnesses. Moreover,
incidence of injury statistics was only available for private sec-
tor jobs. Information on public sector jobs (e.g., policemen,
firefighters) was only available by state. Because we could not
find incidence of injury statistics for the majority of the
occupations, we ran a separate regression analysis with just the
89 professions, adding injury as another predictor variable.
Injury did not have an effect. Nevertheless, one of the measures
Lee et al. (2006) collected was lost days from work as a result
of illness or injury, so injury seemed to be accounted for in the
adjusted morbidity ratio.
Male Pattern Baldness and Disease
As men age, baldness increases. Thus, balding is a cue for older
age. We hypothesized that premature balding would signal an
older self and would thus result in other signs of premature
Research on social perceptions of bald men shows that bald
men are perceived as being older than their real age compared
with men who are not bald (Henss, 2001; Muscarella &
Cunningham, 1996). Men who are prematurely bald are likely
to be perceived as older than their real age because they are not
expected to bald until later in life. The most revealing signs of
aging are physical. Gray hair, wrinkles, and balding are usually
apparent indications that one is getting older. Because physical
Table 2. Correlations Between Predictors and Adjusted Morbidity
Variable 1 2 3 4 5
1. Adjusted morbidity .0398 .0133 –.0091 –.0224
2. Uniform –.1270
–.2147** –.3729***
3. Median salary .4823*** –.2851***
4. Happiness Index –.3118***
5. Physical activity
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cues are the most salient to others, these cues are usually auto-
matically associated with stereotypes of old age. Baldness may
be a blatant reminder of one’s perceived age and thus cues a
physically older self. To test this premise, we sought informa-
tion on premature balding and health. The only data available
were on prostate cancer and coronary heart disease.
Balding and prostate cancer are more likely to occur in older
men (Kwabi-Addo et al., 2007). A longitudinal study, however,
that tracked 4,421 men between the ages of 25 and 75 years of
age with no history of prostate cancer for up to 21 years,
showed that prematurely bald men had a 50%excess risk of
being diagnosed with prostate cancer than did men who were
not bald (Hawk et al., 2000). Data were taken from the Epide-
miologic Follow-up Study of the first National Health and
Nutrition Examination Survey, a nationally representative
cross-sectional survey. Incident cases of prostate cancer were
identified by interviews, medical records, and death certificates.
Age-standardized incidence rates and proportional hazards mod-
els were used to examine the association between male pattern
baldness and clinical prostate cancer.
Male pattern baldness is a clearly observable trait that
generally precedes the diagnosis of clinical prostate cancer
by decades (Hawk et al., 2000). In the longitudinal study by
Hawk et al., male pattern baldness was reported as early as
25 years of age. However, the rate of baldness was more than
50%for men between 45 and 55 years old and more than
70%for men by age 80. In comparison, the percentages of
having male pattern baldness in the general population are
24.5%in one’s 50s, 34.3%in one’s 60s, and 46.9%in one’s
70s. The percentage of men who have prostate cancer are
14%in one’s 50s, 37%in one’s 60s, and 41%in one’s 70s. The
percentages indicate a correlation between baldness and
prostate cancer. At the beginning of 60 years of age, men who
were prematurely bald had a consistently higher incidence of
prostate cancer—a 50%excess risk compared with men who
were not prematurely bald (see Fig. 2).
Prostate cancer is the most common cancer diagnosed
among American men, and the likelihood of being diagnosed
increases with age (Kwabi-Addo et al., 2007). Because the only
visible difference between men with a higher rate of diagnosis
and those with a lower rate is baldness, bald men may perceive
themselves as older before contracting cancer or heart disease,
and we believe this perception may at least partially account for
how their body ages. Hawk et al. (2000) stated that balding
and prostate cancer share epidemiological and biological risk
factors, including aging, heritable genetic factors, and andro-
genic metabolism. However, they admitted that the precise
mechanisms leading to the development of balding and prostate
cancer are largely unknown.
In another study on baldness and disease, men with rapid
hair loss had a greater risk of coronary heart disease than did
Table 3. Parameter Estimates From the Regression of Morbidity on Uniform, Controlling for Selected Background Characteristics
Predictors Model 1: Baseline Model 2: Uniform Model 3: Interaction
Intercept 1.0348*** 1.0294*** 0.9498***
Control variables
Median income –.0004 –.0006 .0022
Happiness Index –.0000 .0004 .0005
Physical activity 2 –.0539
Physical activity 3 –.0054 –.0077 –.0024
Physical activity 4 –.0601 –.0601 –.0484
Question predictors
Uniform .0017 .1667**
Interaction of uniform and median income –.0067**
3.41 3.61 9.29
F1.08 0.93 2.17*
Note. Median income was divided by $1,000.
yp< .10.
*p< .05.
**p< .01.
***p< .001.
Fig. 1. Intersection of uniform and annual median salary at
$24,916. Median income was divided by $1,000.
Age-Related Cues and Health 641
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men with no to moderate progression of hair loss (Herrera,
D’Agostino, Gerstmann, Bosco, & Belanger, 1995). Herrera
et al. examined 2,017 men for degree of hair loss and grouped
them into one of three groups: ‘‘mild or no progression,’
‘moderate progression,’’ or ‘‘rapid progression.’’ Participants
were biennially followed prospectively for up to 30 years for
new occurrences of coronary heart disease, coronary heart
disease death, cardiovascular disease, and death as a result of
any cause. The researchers assessed the relations between the
extent and progression of baldness and coronary heart disease
using a Cox proportional hazards model, adjusting for age
and other known cardiovascular disease risk factors. The
results show that although the degree of baldness was not asso-
ciated with examined heart disease, the amount of progression
of baldness was associated with coronary heart occurrence
(risk rate ¼2.4), coronary heart disease mortality (risk ¼3.8), and
all-cause mortality (risk ¼2.4).
Together, these findings provide support for the mind/body
hypothesis: Prematurely bald men are likely to perceive them-
selves as older, and this self-perception may influence adverse
health outcomes later in life. Studies show there is a link
between perceived age and cardiovascular health. In Levy
et al.’s (2000) study, individuals primed with negative stereo-
types of old age had heightened cardiovascular responses, even
though they rated tasks of the experiment (i.e., memory tasks,
mathematical problems) as equally stressful as did individuals
primed with positive age stereotypes. In another study that
linked perceptions of age and health, the chronological age at
which individuals perceived as the end of ‘‘middle age’’ was
associated with a wide array of cardiovascular and functional
health outcomes, including mortality from coronary heart
disease in a 7-year follow-up (Kuper & Marmot, 2003). These
studies demonstrate that there are visceral responses that
accompany associations of old age, which may then have
negative consequences for health over time. We believe a
similar process is in effect for men who are prematurely bald.
Over time, the cumulative effects turn into something larger and
more visible (in this case, a disease); the internalized belief that
one is older, and the negative associations that may result,
become part of one’s identity, and it may have important physio-
logical consequences down the line.
Taken together, these findings indicate that baldness, and
the rate of its progression, may serve as an early and visible risk
factor for both prostate cancer and coronary heart disease.
Although biological and genetic factors likely play a role in the
higher incidence of prostate cancer and cardiovascular disease
among prematurely bald men, we believe that the feelings asso-
ciated with being older than one’s chronological age account
for some of these health outcomes.
Optimal Age for First-Time Mothers
Child rearing provides numerous age-related cues. One of the
most salient of these is the age of other mothers one encounters
in school meetings, in parks, on television, and in magazines.
The median age of first-time mothers in the United States from
1989 to 1999 was 25 years (Ventura, Martin, Curtin, Mathews,
& Park, 2000; Ventura, Martin, Curtin, Menacker, & Hamilton,
2001). The social world one experiences during this time of life
is typically that of a woman in her mid-20s. If a woman has her
first child later in life, she likely lives a life much like a younger
mother does. On the basis of these premises, we predicted that
women who have children later in life will live longer than
those who have children at the more normative time.
A study by Mirowsky (2005) shows that women who bear
their first child between the ages of 29 and 34 years have better
Fig. 2. Cumulative incidence of prostate cancer by baldness in the Epidemiologic Follow-
up Study of the First National Health and Nutrition Examination Survey, 1971–1974
(baseline) through 1992. The darker line represents no baldness; the lighter line represents
any baldness. From Hawk, Breslow, and Graubard (2000).
642 Hsu, Chung, and Langer
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long-term health outcomes compared with women younger
than 29 years of age and women older than 42 years of age.
Mirowsky sampled 2,215 women from the 1986 U.S. Survey
of Americans’ Changing Lives, and its 8-year mortality
follow-up of deaths recorded on the National Death Index
(House, 2003). Women provided information on a health
problem index that had seven subscales:
1. The respondent’s rating of her own health on a 5-point
Likert-type scale ranging from very healthy to very
2. The interviewer’s rating of the respondent’s health on a
similar 5-point Likert-type scale, made at the end of the
3. A count of six potentially fatal conditions the respondent
reported having within the past 12 months, including lung
disease, hypertension, heart disease, diabetes, cancer, and
4. A count of four nonfatal conditions in the past 12 months,
including arthritis or rheumatism, foot problems (e.g.,
corns, calluses, poor circulation), broken or fractured
bones, and urinary incontinence.
5. A two-item index of difficulty walking or using stairs
(none, a little, some, a lot, cannot do).
6. A two-item index of difficulty seeing or hearing, even with
glasses or a hearing aid (very well, quite well, somewhat
well, not too well, or not at all well).
7. A four-item index of malaise in the past week (feeling that
everything was an effort, feeling unable to get going,
having restless sleep, and not feeling like eating—hardly
ever, some of the time, most of the time).
These seven factors have been known to predict mortality risk
independently, adjusting for age and socioeconomic status
(Idler & Benyamini, 1997, as cited in Mirowsky, 2005).
Mirowsky’s (2005) analysis, which controlled for race/
ethnicity and level of education, shows that the longer women
delayed first childbirth, the lower the relative hazard of death
over the 8 years of follow-up. Examining other health factors
that predict mortality, Mirowsky discovered that the optimal
age at first birth for mothers’ long-run health occurs about two
decades after the median end of puberty, around the age of
34 years. Thirty-four years of age is about 13 years after the age
at which pregnant women have the lowest risk of spontaneous
abortion, ectopic pregnancy, stillbirth, and obstetric problems.
In contrast, it is within a few years of mothers’ age at birth
associated with the minimum risk of infant mortality, and a
decade short of the median end of fecundity (Mirowsky).
Perls, Alpert, and Fretts (1997) found similar effects to
Mirowsky’s (2005) study. Perls et al. examined two groups
of women born in 1896: (a) those who lived to 100 years of age
and (b) those who lived to 73 years of age. The women did not
differ significantly with respect to race, religion, or level of
education. Perls et al. found that women who lived to at least
age 100 were four times more likely to have had children while
in their 40s than women who survived only to age 73. His study
did not differentiate which numbered child the women had in
their 40s, but this study nevertheless supports Mirowsky’s
findings that having children later in life is linked to longevity.
One might think a woman having her fourth child in her 30s
should benefit just the same as a woman having her first child
in her 30s. A mother with multiple children, however, has
already had a same-age cohort raising her older children.
Having a first child versus a fourth child in one’s 30s may be a
very different experience. In this investigation, we first wanted
to control for the number of children an older mother had as a
basis of comparison between younger and older mothers.
Second, the activities and lifestyle that the older mother experi-
enced before having her first child were likely different from
those of a mother having her fourth child later in life. Suddenly,
the older, first-time mother is put into a world of young children
and young mothers with whom she can now identify. In contrast,
the older mother with her fourth child has already gone through
rounds of changing diapers, taking her children to the park, and
setting up play dates with other mothers. Although Perls et al.
(1997) did not differentiate which numbered child women in his
study had in their 40s, Mirowsky (2005) found a clear effect for
women who had their first child later in life. Regardless of
whether it was a first or fourth child, though, the findings of these
two studies nevertheless show that having a child later in life is
beneficial to a woman’s long-term health.
The findings from Mirowsky’s (2005) study have been
explained by both biological and social factors that could have
just as easily predicted the reverse finding. At a young age, a
woman suffers the risk of complications during pregnancy and
birth, such as prematurity and low birth weight (Mirowsky).
Pregnancy puts strain on the body, which can also lead to poor
health outcomes later in life. Childbirth in late life has its own
set of physical risks. Young mothers, particularly under the age
of 20 years, have also been linked to single motherhood,
poverty, and low education, and they tend to seek prenatal care
less often than do older mothers (Mirowsky). Sociologists (e.g.,
Stein & Susser, 2000) have argued that older women’s social
advantages allow them to care for themselves and their children
more competently. Compared with younger women, older
women tend to be more financially stable, having had more
time to establish a career. This, Stein and Susser have argued,
will allow older women to provide more resources for them-
selves and their children, such as better health care. Although
these are all legitimate explanations (and considering that hav-
ing a fourth child by the time a woman is in her 30s drains more
resources over time), Mirowsky’s study controlled for level of
education. We acknowledge that the older mother has had more
time to amass work experience and has probably saved up more
money by the time she is in her 30s. Alternatively, biologically
it is counterintuitive to expect older women who bear children
to live longer; their bodies are already aging, and pregnancy
puts more strain on the body. The older woman may not have
the requisite energy needed for child rearing and may not feel
like part of the in-group.
There are many plausible explanations for both predictions,
some of which may account for the findings. However, we also
Age-Related Cues and Health 643
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believe that perception of age influences one’s health out-
comes. Youthful cues signal a younger person. Suddenly the
older mother’s world is surrounded by younger cues, including
younger mothers and their young children. The older mother
will engage in the same kinds of activities as the other mothers
will, which include playing with her young child. In addition,
the older mother will interact with the other mothers (a) at
parent–teacher association meetings, (b) at school events
(e.g., plays, performances), (c) while dropping off and picking
up her child from school, and (d) at play dates. The period of
time the older mother raises a young child shifts her to an ear-
lier age thatis associatedwith younger mothers in their 20s. In the
longer term, the older mother will invest more time in her child
until the child turns 18 and she will become an empty nester later
in life. This will also cue a younger self, given that her life transi-
tions will occur later than most of her same-age peers.
One might argue that an older, first-time mother is just as
likely to feel older and not part of the in-group as one could feel
younger and part of the in-group. In response, there is research
to suggest that individuals who interact with each other more
over time perceive themselves as more similar to each other.
In studies on interpersonal attraction, researchers have found
that perceived similarity is greater than actual similarity in
predicting interpersonal attraction (e.g., Morry, 2007).
Although the age difference might initially be obvious, it could
eventually become less prominent as time progresses and more
pertinent similarities revolving around children emerge.
Women also talk with each other a great deal about their
bodies (Paulson & Willig, 2008). The conversations an older
mother may have about her body may be more similar to a
younger mother who just had a child than with another woman
her own age who is lamenting about her body ‘‘breaking
down.’’ Furthermore, the knowledge that the older mother was
able to have a child at her age may reinforce the fact that she
was healthy enough to do so, which may confirm the feeling
that she is younger. Similar to Langer et al.’s (1988) study,
which effectively put 75-year-old men in a younger mindset,
having a child at an older age in effect ‘‘rewinds’’ a woman’s
age to reflect a younger woman’s self. We believe that this
change in self-perception, in part, contributes to a longer life.
Age Differences in Marriage and Longevity
As we age in early adulthood, we grow personally, professionally,
and financially. Thus, being the older spouse in early adulthood
typically means being the more dominant partner (Klinger-
Vartabedian & Wispe, 1989). When there is a large age difference
between spouses, the life lived is likely to mirror that of the older
and more dominant partner. Thus, we predict that those who
marry older people will live older lives sooner and consequently
have shorter life spans. Conversely, older spouses will benefit
from being more dominant and being around the younger spouse
and thus are predicted to live longer.
Klinger-Vartabedian and Wispe (1989) examined age dif-
ferences in marriage and female longevity for 437 women, and
they found that women who marry men up to 14 years their
senior have shorter life expectancies than do women who marry
men roughly the same age and up to 14 years younger. This
finding signifies that there is something about the social
dynamic between an older spouse and a younger spouse that
makes it more biologically advantageous for the older spouse
and less advantageous for the younger spouse.
Klinger-Vartabedian and Wispe (1989) analyzed the 1968
portion of the National Mortality Followback Survey (U.S.
National Center for Health Statistics, 1970) and the 1970
U.S. Census (U.S. Bureau of the Census, 1972), which con-
tained all marriages in the United States by age difference of
spouse. The National Morbidity Followback Survey provided
individual records; however, the census data necessary to gen-
erate age-specific death rates were classified in 5-year inter-
vals. Therefore, the researchers grouped the ages of all
deceased wives and the age of all surviving husbands into
cohorts containing 5-year intervals. The researchers acknowl-
edge a more precise grading would have been desirable for the
same age designation, but they explained that 5-year age group-
ings are commonly used in mortality estimates (Stockwell &
Groat, 1984, as cited in Klinger-Vartabedian & Wispe).
Klinger-Vartabedian and Wispe (1989) used a standard
mortality ratio (SMR) to make comparisons between couples
with different age differentials. The SMR represents the ratio
of the number of actual or observed deaths to the expected
number in each specific cohort. Representing fluctuation from
the base rate of 100, the SMR was only 84 for wives with hus-
bands 4–14 years younger, whereas it was 125 for women
SMRs were lowest for women married to men about 6 years
younger, they rose above 100 for women married to men who
were 10 or more years younger. This difference in the SMR
for the age differential shows there is a limit to how young
(or old) a spouse needs to be in order to see an effect. If a
spouse is much too young, the older spouse will start to feel
too old and vice versa. This effect may differ by gender, but
the effect is in the same predicted direction (see Fig. 3).
Several reasons have been offered to account for the find-
ings from this study. They include younger spouses having to
take care of the older spouse in old age, which may, over time,
be emotionally and physically taxing on the younger spouse.
If and when the older spouse dies before the younger spouse,
the younger spouse is left to grieve, and studies show that after
a spouse dies, the other spouse dies soon after, a phenomenon
referred to as widowhood effect (Lillard & Panis, 1996; Lillard
& Waite, 1995).
The age that both spouses feel may be a reflection of the
interests and activities of their spouse. In marriage, there is
often an ‘‘exchange’’ and mutual participation of lifestyle
activities and interests for both spouses over time (Kalmijn &
Bernasco, 2001). Aside from one’s perceived age, the other
spouse may constantly be reminded of his or her partner’s
chronological age and internalize feelings when with him or
her accordingly. For example, people who have younger part-
ners sometimes say, ‘‘He or she makes me feel ‘young’ or ‘alive’
again.’’ The opposite is not often heard: ‘‘He or she makes me
644 Hsu, Chung, and Langer
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feel mature,’’ or worse, ‘‘morbid’!According to Greenberg,
Schimel, and Mertens (2002), one reason that society harbors
negative attitudes toward older adults is because older adults
remind us of the prospects of diminishing beauty, health, sensa-
tion, and ultimately, death. On the one hand, the older spouse
serves as an age prime for the younger spouse. On the other hand,
the younger spouse remains something of a buffer to the older
spouse because he or she constantly reminds the older spouse
of youth. These experiences and mindsets can be translated into
physiological changes as both spouses interact.
Zajonc, Adelmann, Murphy, and Niedenthal (1987) have
examined physiological reactions in marital interaction over
time. They discovered that spouses tend to look more alike with
age. It is generally easier for spouses to look more alike when
they are closer in age. Zajonc et al. also commented on the
influence of shared emotions on facial expressions. They
explained that emotional states are reflected nonverbally
through smiles, frowns, grimaces, and other facial expressions,
and that these outward expressions are mimicked by those
intimate to us as they share our emotions. Thus, one product
of a long-term relationship is that partners develop similar
smile or frown lines, postures, and movements. Zajonc
et al.’s research also suggested that facial expressions help
produce emotions by altering blood flow to the brain, which,
in turn, regulates the release of various mood-altering neuro-
chemicals. It is thereby proposed that outward appearances and
inward feelings are reciprocally influenced, and because people
interact with their spouses frequently, their appearances will
have a great effect on one another.
Age difference in marriage and longevity may be similarly
intertwined, a phenomenon that Klinger-Vartabedian and
Wispe (1989) referred to as the mortality mean. Neugarten
(1968) suggested that as human beings, we are deeply influ-
enced by ‘‘social clocks’’—we gauge our lives by the implicit
belief that there is a ‘‘right age’’ for certain behaviors or
attitudes. Klinger-Vartabedian and Wispe proposed that it is
possible that marital partners set their own social or biological
clocks in accordance with their spouse’s age, thus creating a
mortality mean. In this hypothetical averaging of ages, the older
person becomes ‘‘younger’’ and lives longer than expected, and
the younger person becomes ‘‘older’’ and dies sooner than
Evidence across five very different domains supports the
general mind/body hypothesis that when a younger mind is
primed, a younger body can accompany it. In the studies pre-
sented in this article, age was primed by a beauty treatment,
clothing, premature baldness, late-in-life child bearing, and
spousal age differences. In each case, health and longevity pre-
dictably followed the age-related cue. As with the retreat study
described earlier (Langer et al., 1988), one could ask, ‘‘How did
it really happen?’’ How did changing one’s psychological state
result in such unambiguous physical changes? Although we do
not know the precise mechanisms involved, researchers in
previous studies who have studied the relation between percep-
tions of aging and survival may offer some insights. For
example, in the longitudinal study by Levy et al. (2002), they
found that the ‘‘will to live’’—defined as a judgment that the
perceived benefits of one’s life outweigh the perceived hard-
ships—partially mediated perceptions about aging and survival.
Levy et al. said—but did not test whether—other mediators are
likely involved to account for the outcomes in their study.
Another possible mechanism is a heightened cardiovascular
response to stress about aging. As reported earlier, participants
who were primed with negative stereotypes of old age had
heightened cardiovascular responses, even though they rated
tasks of an experiment as equally stressful as did individuals
who were primed with positive age stereotypes (Levy et al.,
2000). These studies demonstrate that individuals can have phy-
siological responses to ideas about aging. It is thus conceivable
that real-life situations such as being prematurely bald or marry-
ing an older spouse can make one either unconsciously or con-
sciously aware of old age and set in motion a series of
physiological processes that can have real effects on short-term
Fig. 3. Standard mortality ratios (SMRs) of women and men
married to younger and older spouses. Numbers above 100
mean that more deaths occurred than expected, and numbers
below 100 mean that fewer deaths occurred. From Klinger-
Vartabedian and Wispe (1989).
Age-Related Cues and Health 645
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and long-term health. Although many experienced ailments may
be a natural part of aging, many may not be, and instead may be a
function of one’s mindsets about old age and the cues that signal
diminishing capacity.
Many predictions follow from the mind/body hypothesis.
For example, we would predict that childcare workers who take
care of young children all day would have better health than
senior care workers who look after older adults all day. As
Greenberg et al. (2002) reasoned, older adults can remind us
of the prospects of diminishing health. Therefore, individuals
who are around older adults may be influenced by such
thoughts that could affect their health over time. Another pre-
diction is that people who teach students of the same age every
year (i.e., students are not aging, per se) and who do not have
children themselves should age better than people who teach
students of the same age year after year, but who have children.
A third prediction is that individuals who feel younger after a
cosmetic procedure, such as Botox or a surgical face lift, should
test younger.
Because wearing work uniforms, having male pattern
baldness, being an older mother, and marrying a spouse who
is older or younger than themselves are not uncommon
phenomena, longitudinal studies should be conducted on these
different phenomena that include measures of actual and sub-
jective age, self-rated health, and actual health to see how much
subjective age accounts for these health outcomes, as we
believe they do. The major caution to researchers testing this
hypothesis is that the belief has to be complete for it to work.
As with placebos, many mindsets regarding disease and aging
are strong and thus resistant to change.
The mind/body hypothesis as articulated and tested here
need not relate only to aging. We know this from the vast liter-
ature on placebos and studies on emotion. For example, Cohen,
Doyle, Turner, Alper, and Skoner (2003) have shown the
effects of thoughts on prevention/cure from the common cold,
and Rozin and his colleagues (Rozin & Fallon, 1987; Rozin,
Haidt, & McCauley, 2000) show the effect of thoughts, such
as drinking a drop of urine, on our disgust response. If we were
to cue weight loss (e.g., Crum & Langer, 2007), improved
vision (Langer et al., 1988; Langer et al., in press), or a host
of other physical and psychological phenomena (e.g., compe-
tence), we may find that many of our presumed limits are
self-limiting and self-fulfilling. It may be time to question our
own mindsets and consider the possibility that psychology has
even more to offer the medical world than we had previously
In the last century, the average American’s life span has
increased by 27 years (Rogers, Hummer, & Nam, 2000).
The extended life expectancy has led to prolonged periods
of time spent in various work and family roles (Gee,
1987). People are getting married and having children later,
and more adults are going into higher education (Arnett,
2000). Therefore, age norms are starting to change or are,
at least, extending in accordance with societal trends. These
changes may lead to changes in age-related cues, which
may, in turn, affect health outcomes. Research should be
ongoing in this area to monitor how self-perceptions of age
change with societal trends.
1. Lee et al. (2006) also provided an unadjusted morbidity ratio that
did not control for age, race/ethnicity, and education for the
206 professions. We chose to use the adjusted morbidity ratio to
control for these potentially confounding factors.
The authors thank Julie Bracamontes for assisting with data collection
and Ryan Williams, Natasha Kravchenko, and Byeongseok Kim for
their comments on this article.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with respect
to their authorship or the publication of this article.
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... In consumer settings, prior studies have found, for example, that older consumers who feel younger are particularly interested in fashion-related products (Lin and Xia 2012) and inclined to try new brands (Gwinner and Stephens 2001;Stephens 1991). Other studies (Guido, Amatulli, and Peluso 2014;Hsu, Chung, and Langer 2010) have investigated the effect of contextual factors on older consumers' feel-age and provided preliminary evidence that it may be malleable. These studies indicate the need to systematically investigate the contextual effects and underlying mechanisms. ...
... Theoretically, we contribute to prior studies on older adults' feel-age by shedding light on the processes underlying its malleability as a function of social cues. Building on prior research documenting older consumers' tendency to feel younger (Barak et al. 1988; Barnhart and Peñaloza 2013;Van Auken and Barry 1995) and its malleability (Guido et al. 2014;Hsu et al. 2010), we investigate how and why older consumers' feel-age is contingent on age-related social cues. By doing so, we provide a more comprehensive understanding of the ways in which people's feel-age is context-dependent. ...
How do social cues in the immediate environment affect older consumers' tendency to feel younger? And what is the impact of this tendency on consumption? This research investigates the malleability of older consumers' feel-age and the underlying mechanisms by focusing on the influence of contextual social cues and the downstream effects on consumption behavior. Five studies provide evidence that the mere presence of young social cues triggers an identity threat for older consumers; and feeling younger is a way to protect the self from negative stereotypes associated with aging. By contrast, young consumers are relatively immune to age-related social cues. Whereas the presence of young social cues magnifies older consumers' tendency to feel younger, this effect is attenuated when the young social cues are less desirable or when the older consumers possess higher self-esteem. The greater tendency to feel younger in the presence of young social cues increases older consumers' choice of contemporary over traditional products, especially among those with lower self-esteem. Theoretical insights and practical implications are discussed. © The Author(s) 2018. Published by Oxford University Press on behalf of Journal of Consumer Research, Inc. All rights reserved.
... Against this backdrop, the direct experimental manipulation of subjective age promises an additional and maybe more direct manipulation of FTP. Certain visual cues, such as early baldness or hair color (Hsu, Chung, & Langer, 2010), were shown to influence expectations and manifestations of longevity indicators and, consequently, to promise to increase the individual's self-relevance with regard to the embodiment of a virtual old age avatar. As chronological age cannot actually be influenced, earlier approaches relied on the induction effect of self-relevance and emotional salience toward strongly content-driven hypothetical vignettes. ...
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Objectives: Socio-emotional selectivity theory implies that an individual's motives change over their lifespan, starting with a focus on information seeking and shifting toward the motivation of maintaining emotionally meaningful social relationships in old age. The concept of future time perspective serves as an underlying mechanism for this phenomenon. Methods: This study aimed to capture how social motivation changes as a result of the manipulation of one's own visual appearance. Thus, the explicit age stereotypes of N = 74 participants were assessed, among other covariates. The following intervention consisted of a virtual reality (VR) scenario in which the experimental group embodied an old age avatar and the control group a young age avatar. Results: Changes in social motivation were assessed using the concept of socio-emotional selectivity based on imagined situational preferences. Results with strong effect sizes indicate that changes in social motivation commonly connected with old age might be caused by visual cues when actively embodying a virtual avatar.
... That is to say, for the aged people in the nursing homes, how to live is the most basic life theme in their eyes; the so-called "successful aging" [11] is only a pursuit of higher life goals in the elders' world. If the researchers still focus only on the negative influences of environmental cues towards aging [12], the positive effect of motivated cognition during aging [13], from successful aging to esteem of the fourth age [14] instead of understanding their loneliness and the strong desire of living a longer life, which therefore separates the mainstream world from the elders. Even under the Chinese culture that puts a high value on providing for the elders, there still is such deviation in research. ...
... In a broader perspective, the present findings contribute to the evidence that activation of the aging stereotype-even without awareness-can lead people to behave in stereotype-consistent ways with effects on behavior, performance, and cardiovascular responses (e.g., Bargh et al. 1996;Dijksterhuis et al. 2001;Hausdorff et al. 1999;Hsu et al. 2010;Levy 1996;Levy et al. 2000). However, there is a controversy on whether it is necessary to belong to the stereotype-relevant group in order to behave consistently with an activated stereotype. ...
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Based on the Implicit-Affect-Primes-Effort model and evidence that aging is associated with cognitive difficulties, this experiment investigated the effect of masked age primes on young adults’ effort-related cardiovascular response during a mental arithmetic task. We predicted that elderly primes should activate the aging stereotype and thus render the performance difficulty concept accessible, while youth primes should activate the performance ease concept—similarly, as affect primes do. The accessible difficulty or ease concepts, in turn, should influence experienced demand and thus effort-related cardiovascular response during cognitive performance. A neutral prime control condition should fall into these conditions. We found the expected effects on performance-related responses of heart rate (HR) and diastolic blood pressure (DBP): For both measures, the elderly primes led to the strongest reactivity, the youth primes led to the weakest reactivity, and the neutral-prime control condition fell in between these conditions.
... Although, old age is linked to both positive (e.g., wise) and negative (e.g., senile) stereotypes, negative attributes clearly outweigh positive ones (Hummert, 1990;Kite & Johnson, 1988). Both the activation of age stereotypes and age-related cues influence performance and behavior and are related to health and longevity (Hess, 2006;Horton, Baker, & Deakin, 2007;Hsu, Chung, & Langer, 2010). ...
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Organizational citizenship behaviour (OCB) is an employee's discretionary behaviour, which influences the organizational effectiveness and employee well-being by lubricating the social machinery of the organization. Several studies have been conducted to identify the consequences and antecedents of this kind of extra-role behaviour. In this line of research, the present study is an effort to explore the relationship of trait emotional intelligence and work-family culture with organizational citizenship behaviour. The study was carried out on 117 front level executives of Indian organizations. Three standardized psychometric measures namely TEIQue-SF, Work- Family Culture Scale and OCB Scale were used for data collection. Obtained data was analyzed by using correlation and regression analysis. Results of the correlational analysis indicate that trait emotional intelligence was significantly and positively associated with OCB and its dimensions. Managerial support was significantly and positively correlated with courtesy and altruism whereas career consequences and organizational time demand was significantly and negatively associated with all the dimensions and overall OCB except sportsmanship dimensions. Results of Regression analysis (simultaneous) supported the results of the correlational analysis in terms of directions, but not exactly in the terms of degree of relationship.
... Although, old age is linked to both positive (e.g., wise) and negative (e.g., senile) stereotypes, negative attributes clearly outweigh positive ones (Hummert, 1990;Kite & Johnson, 1988). Both the activation of age stereotypes and age-related cues influence performance and behavior and are related to health and longevity (Hess, 2006;Horton, Baker, & Deakin, 2007;Hsu, Chung, & Langer, 2010). ...
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The present study was conducted to translate and validate the Toronto Alexithymia Scale (Taylor, Bagby & Parker, 1994) for the purpose of having a culturally equivalent, linguistically accurate Urdu version with theoretically replicable factor structure to use in Pakistan. The forward-backward translation method was used for translation of this scale. Urdu translated version of the scale was applied on 300 participants of different age groups (M= 35.5, SD= 12.1). Based on a confirmatory factor analysis (CFA), results confirmed that a three-dimensional model with the factors difficulty describing feelings, difficulty identifying feelings and externally oriented thinking, provided an excellent fit to the data. Its Cronbach alpha reliability coefficient was .82. The cross-language validity determined on a sample of 60 participants showed highly satisfactory validity indices. Convergent validity of the scale was proved by finding positive correlation of Toronto Alexithymia Scale with anxiety (r = .51, p < .001) and negative relationship with emotional intelligence (r = -.35, p < .001), which revealed its divergent validity.
Older people use more drugs than any other age group. The prescription strategy constitutes a major challenge. Polypharmacy, inappropriate prescribing and drug-related problems in older people are important problem of public health as a link exists with significant morbidity and mortality and with a large waste of health resources. The main target is the balance between an unsustainable number of different prescription drugs to treat various chronic diseases and the failure to take preventive measures in these older patients. In this review, the difficulties in prescribing in the older population and the identification, prevention and optimization of inappropriate prescribing and drug related problems in older people are respectively discussed. Medication reconciliation, medication review, tools for detection of potentially inappropriate prescribing, use of health information technology, adherence optimization patient-tailored pharmacotherapy en judicious drug cessation) are highlighted as a part of collaborative and integrative approaches.
The Cambridge Handbook of Successful Aging - edited by Rocío Fernández-Ballesteros January 2019
Respondents at an Internet site completed over 600,000 tasks between October 1998 and April 2000 measuring attitudes toward and stereotypes of social groups. Their responses demonstrated, on average, implicit preference for White over Black and young over old and stereotypic: associations linking male terms with science and career and female terms with liberal arts and family. The main purpose was to provide a demonstration site at which respondents could experience their implicit attitudes and stereotypes toward social groups. Nevertheless, the data collected are rich in information regarding the operation of attitudes and stereotypes, most notably the strength of implicit attitudes, the association and dissociation between implicit and explicit attitudes, and the effects of group membership on attitudes and stereotypes. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
Studies on the health effects of income inequality have generated great interest. The evidence on this association between countries is mixed,1-4 but income inequality and health have been linked within the United States,5-11 Britain,12 and Brazil.13 Questions remain over how to interpret these findings and the mechanisms involved. We discuss three interpretations of the association between income inequality and health: the individual income interpretation, the psychosocial environment interpretation, and the neo-material interpretation. Summary points Income inequality has generally been associated with differences in health A psychosocial interpretation of health inequalities, in terms of perceptions of relative disadvantage and the psychological consequences of inequality, raises several conceptual and empirical problems Income inequality is accompanied by many differences in conditions of life at the individual and population levels, which may adversely influence health Interpretation of links between income inequality and health must begin with the structural causes of inequalities, and not just focus on perceptions of that inequality Reducing health inequalities and improving public health in the 21st century requires strategic investment in neo-material conditions via more equitable distribution of public and private resources
Analysis of 1968 mortality data and comparable 1970 census data for women showed that women married to younger men tended to live longer than expected, while women married to older men tended to die sooner than expected. Representing fluctuation from the base rate of 100, the summary SMR (standard mortality ratio) for women with spouses 4 years older to 14 years younger was 84, while the average SMR for women with spouses up to 14 years older was 125. Thus the mortality risk associated with marriage to a younger man was clearly less than that associated with marriage to an older man. Two possible explanations are discussed: (a) mortality outcomes are predetermined by mate selection, or (b) psychological, social, and/or biological interaction within marriage influences longevity.
The factors influencing the identification of oneself as “old” have been studied extensively for people in their later life. This study extends the analysis to the middle years of life, when transitions may occur from age identities of young to middle-aged, as well as from middle-aged to old. It replicates previous findings for the effects of chronological age and poor health on older age identity. The study also demonstrates that having children is associated with a middle-aged (rather than young) identification, while not being married (largely due to widowhood or divorce) is associated with labeling oneself as old (rather than middle-aged). In the comparisons both of persons who identify themselves as young rather than middle-aged, and middle-aged rather than old, the older category of people have less happiness and lower life satisfaction, suggesting that these transitions are experienced negatively.
Because leisure activities are often viewed as optional, their value to people with disabilities may not be recognized. This study explored the benefits of leisure activities for eight young people who are blind. These activities provided them with supportive relationships, a desirable identity, experiences of power and control, and experiences of social justice. They enabled the young people we studied to thrive despite adversity.