ArticlePDF Available

The Influence of Partner's Behavior on Health Behavior Change The English Longitudinal Study of Ageing

Authors:

Abstract and Figures

Importance Couples are highly concordant for unhealthy behaviors, and a change in one partner’s health behavior is often associated with a change in the other partner’s behavior. However, no studies have explicitly compared the influence of having a partner who takes up healthy behavior (eg, quits smoking) with one whose behavior is consistently healthy (eg, never smokes).Objective To examine the influence of partner’s behavior on making positive health behavior changes.Design, Setting, and Participants We used prospective data from married and cohabiting couples (n, 3722) participating in the English Longitudinal Study of Ageing, a large population-based cohort of older adults (≥50 years). Studying men and women who had unhealthy behaviors in 3 domains at baseline (ie, smoking, physically inactive, or overweight/obese), we used logistic regression analysis to examine the influence of the partner’s behavior in the same domain on the odds of positive health behavior change over time.Main Outcomes and Measures Smoking cessation, increased physical activity, and 5% weight loss or greater.Results Across all domains, we found that when one partner changed to a healthier behavior (newly healthy), the other partner was more likely to make a positive health behavior change than if their partner remained unhealthy (smoking: men 48% vs 8%, adjusted odds ratio [OR], 11.82 [95% CI, 4.84-28.90]; women 50% vs 8%, OR, 11.23 [4.58-27.52]) (physical activity: men 67% vs 26%, OR, 5.28 [3.70-7.54]; women 66% vs 24%, OR, 5.36 [3.74-7.68]) (weight loss: men 26% vs 10%, OR, 3.05 [1.96-4.74]; women 36% vs 15%, OR, 3.08 [1.98-4.80]). For smoking and physical activity, having a consistently healthy partner also predicted positive change, but for each domain, the odds were significantly higher in individuals with a newly healthy partner than those with a consistently healthy partner (smoking: men OR, 3.08 [1.43-6.62]; women OR, 5.45 [2.44-12.16]) (physical activity: men OR, 1.92 [1.37-2.70]; women OR, 1.84 [1.33-2.53]) (weight loss: men OR, 2.28 [1.36-3.84]; women OR, 2.86 [1.55-5.26]).Conclusions and Relevance Men and women are more likely to make a positive health behavior change if their partner does too, and with a stronger effect than if the partner had been consistently healthy in that domain. Involving partners in behavior change interventions may therefore help improve outcomes.
Content may be subject to copyright.
Copyright 2015 American Medical Association. All rights reserved.
The Influence of Partner’s Behavior
on Health Behavior Change
The English Longitudinal Study of Ageing
Sarah E. Jackson, PhD; Andrew Steptoe, DSc; Jane Wardle, PhD
IMPORTANCE Couples are highly concordant for unhealthy behaviors, and a change in one
partner’s health behavior is often associated with a change in the other partner’s behavior.
However, no studies have explicitly compared the influence of having a partner who takes up
healthy behavior (eg, quits smoking) with one whose behavior is consistently healthy (eg,
never smokes).
OBJECTIVE To examine the influence of partner’s behavior on making positive health
behavior changes.
DESIGN, SETTING, AND PARTICIPANTS We used prospective data from married and cohabiting
couples (n, 3722) participating in the English Longitudinal Study of Ageing, a large population-
based cohort of older adults (50 years). Studying men and women who had unhealthy
behaviors in 3 domains at baseline (ie, smoking, physically inactive, or overweight/obese), we
used logistic regression analysis to examine the influence of the partner’s behavior in the same
domain on the odds of positive health behavior change over time.
MAIN OUTCOMES AND MEASURES Smoking cessation, increased physical activity, and 5%
weight loss or greater.
RESULTS Across all domains, we found that when one partner changed to a healthier
behavior (newly healthy), the other partner was more likely to make a positive health
behavior change than if their partner remained unhealthy (smoking: men 48% vs 8%,
adjusted odds ratio [OR], 11.82 [95% CI, 4.84-28.90]; women 50% vs 8%, OR, 11.23
[4.58-27.52]) (physical activity: men 67% vs 26%, OR, 5.28 [3.70-7.54]; women 66% vs 24%,
OR, 5.36 [3.74-7.68]) (weight loss: men 26% vs 10%, OR, 3.05 [1.96-4.74]; women 36% vs
15%, OR, 3.08 [1.98-4.80]). For smoking and physical activity, having a consistently healthy
partner also predicted positive change, but for each domain, the odds were significantly
higher in individuals with a newly healthy partner than those with a consistently healthy
partner (smoking: men OR, 3.08 [1.43-6.62]; women OR, 5.45 [2.44-12.16]) (physical activity:
men OR, 1.92 [1.37-2.70]; women OR, 1.84 [1.33-2.53]) (weight loss: men OR, 2.28 [1.36-3.84];
women OR, 2.86 [1.55-5.26]).
CONCLUSIONS AND RELEVANCE Men and women are more likely to make a positive health
behavior change if their partner does too, and with a stronger effect than if the partner had
been consistently healthy in that domain. Involving partners in behavior change interventions
may therefore help improve outcomes.
JAMA Intern Med. 2015;175(3):385-392. doi:10.1001/jamainternmed.2014.7554
Published online January 19, 2015.
Supplemental content at
jamainternalmedicine.com
Author Affiliations: Health
Behaviour Research Centre,
Department of Epidemiology and
Public Health, University College
London, London, England (Jackson,
Wardle); Psychobiology Group,
Department of Epidemiology and
Public Health, University College
London, London, England (Steptoe).
Corresponding Author: Jane Wardle,
PhD, Health Behaviour Research
Centre, Department of Epidemiology
and Public Health, University College
London, London WC1E 6BT, England
(j.wardle@ucl.ac.uk).
Research
Original Investigation
(Reprinted) 385
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
Modifiable lifestyles and health-related behaviors
are leading causes of morbidity and mortality
worldwide.
1-3
Smoking, poor diet, physical inactiv-
ity, and alcohol consumption have been identified
as particularly important risk factors, accounting for
over a third of all deaths in the United States in 2000.
2
Risk can be reduced by adopting healthier lifestyles,
4-11
but many people find it difficult to make lasting
changes.
12-14
A large body of evidence has shown that people tend to
exhibit health behaviors similar to those around them, in
particular their spouses. Concordance within couples has
been documented for a wide range of health-related factors,
including smoking,
15-21
alcohol consumption,
16-19,21-23
physi-
cal activity,
19,21,24
body mass index (BMI),
17,18,20,25,26
and
dietary intake.
18,27,28
Some of this concordance appears to
be a result of assortative mating, with individuals selecting
mates with behaviors similar to their own.
17-20
There is also
evidence that partners influence each other’s behavior. A
number of studies have shown that spousal behavior status
is a strong predictor of health behavior change; with people
more likely to improve their behavior if their partner’s
behavior is healthy, and more likely to adopt unhealthy
behaviors if their partner’s behavior is unhealthy.
15,22,25,29-39
For example, people are substantially more likely to
begin smoking, and less likely to quit, if their partner
smokes.
31,32
Concordance for health behavior change has also
been shown, with a change in one partner’s behavior
predicting change in the other’s behavior.
15,22,25,40-42
For
example, weight loss intervention studies have found evi-
dence of positive changes extending beyond treated indi-
viduals to spouses and other family members,
43-45
indicat-
ing that one partner changing their behavior can encourage
the other partner to change. However, the influence of a
partner who changes to a healthy behavior compared with
the influence of a consistently healthy partner is not known.
Given that couples tend to report similar readiness to
change health risk behaviors and express greater confidence
in their ability to change if their partner is also ready to
change,
46
one might expect to see more successful behavior
change in couples where both partners change together.
This study aimed to investigate whether people are
more likely to make a positive health behavior change
in a given domain if their partner also changes from
“unhealthy” to “healthy” in that domain than if their part-
ner has been consistently healthy (eg, whether a smoker is
more likely to quit if their partner quits smoking than if
their partner was always a nonsmoker). Using prospective
data from couples in a large cohort of English older adults,
we classified individuals according to their partner’s health
behavior (consistently healthy, consistently unhealthy,
became healthy, or became unhealthy) over 2 time points
and examined the influence of the partner’s behavior (or
change) on the odds of our index case becoming healthy
over the same interval. To test the effects robustly, we
examined changes in 3 domains: smoking, physical activity,
and body weight.
Methods
Study Population
Data are from couples in the English Longitudinal Study of Age-
ing (ELSA),
47
a population-based study of middle-aged and
older adults in the United Kingdom. The initial ELSA sample
was drawn from households with 1 or more member 50 years
or older responding to the Health Survey for England (HSE) in
1998, 1999, and 2001. All household members 50 years or older
plus partners who were younger than 50 years or had joined
the household since the HSE were invited for interview.
From 2002, ELSA participants have been followed up in
biennial waves with a computer-assisted interview and self-
administered questionnaires. Refreshment samples were re-
cruited at waves 3, 4, and 6. In addition to the data collected
at each wave, health examinations were conducted on alter-
nate waves, with nurses visiting the home to collect objective
measures of anthropometry. ELSA has received approval from
various ethics committees, including the London Multi-
Centre Research Ethics Committee, and full informed written
consent has been obtained from all participants.
Definition of Baseline and Follow-up Time Points
Smoking and physical activity status have been assessed in each
wave of ELSA to date (waves 1-6), and heights and weights have
been measured in even waves (waves 2, 4, and 6). We there-
fore assessed smoking cessation and increase in physical ac-
tivity over a 2-year interval and weight loss over a 4-year in-
terval. For each health domain, we used the first 2 consecutive
waves for which both partners had data available, with the first
wave constituting the baseline data and the second wavecon-
stituting the follow-up data.
Measures
Health Behaviors
Smoking status was assessed with the question “Do you smoke
cigarettes at all nowadays? (yes/no).” Among those answer-
ing yes at baseline, the mean (SD) number of cigarettes smoked
daily was 15.35 (9.50) in men and 14.26 (7.63) in women. Smok-
ing cessation was defined as answering yes at baseline and no
at follow-up.
Physical activity was assessed with a question adapted
from the Whitehall II study
48
:“Doyoutakepartinanysports
or activities that are (vigorous/moderately energetic/mildly en-
ergetic)?” Response options were “more than once a week,
“once a week,” “one to three times a month,” and “hardly ever
or never.” Weclassified partic ipantsas ac tive(moderate or vig-
orous activity at least once a week) vs inactive (less than this).
An increase in physical activity was defined as being inactive
at baseline and active at follow-up.
Weight was measured to the nearest 0.1 kg using THD-
305 portable electronic scales (Tanita Corporation). Height
was measured to the nearest millimeter using a portable sta-
diometer. At each assessment, the nurses who took the
measurements recorded any factors that could compromise
measurement reliability (eg, participant was stooped or
unwilling to remove shoes). We excluded measurements
Research Original Investigation Influence of Partner’s Behavior on Health Behavior
386 JAMA Internal Medicine March 2015 Volume 175, Number 3 (Reprinted) jamainternalmedicine.com
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
judged by the nurse to be unreliable. Body mass index (cal-
culated as weight in kilograms divided by height in meters
squared) was used to classify participants’ weight status as
normal (BMI <25), overweight (BMI 25.0-29.9), or obese (BMI
≥30). Weight loss was defined as a loss of at least 5% of base-
line body weight between baseline and follow-up in those
who were overweight or obese (hereinafter simply referred
to as overweight) at baseline.
Demographic Variables
Demographic information included each partner’s age and sex
and household nonpension wealth (a sensitive indicator of so-
cioeconomic status in this age group).
Health Conditions
In an older population, it is likely that health events may
prompt behavior change in both partners. If one partner has
a heart attack or develops lung cancer, both partners may
stop smoking. Health scares may motivate both members of
a couple to stop smoking, start exercising, or lose weight.
Weight loss may also be the result of illness. We therefore
included data on number of health conditions in our analy-
ses. Participants reported whether they had ever had
physician-diagnosed cancer, diabetes, coronary heart dis-
ease, stroke, and myocardial infarction at each wave. To
cover any conditions not included in this list, we also used
data on self-reported limiting longstanding illness, assessed
with 2 questions: (1) “Do you have any long-standing illness,
disability, or infirmity? By long-standing I mean anything
that has troubled you over a period of time or that is likely
to affect you over a period of time.” If they responded yes,
they were asked (2) “Does this illness or disability limit your
activities in any way?” Affirmation of a long-standing illness
and any form of limitation classified the participant as hav-
ing a limiting long-standing illness.
Inclusion Criteria
Participants were eligible for inclusion in the current analy-
ses if they reported being in a married or cohabiting couple and
had data on at least 1 health domain on at least 2 consecutive
time points (2 years apart for smoking and physical activity; 4
years apart for weight). Couples who split up during the study
interval were not included. Only opposite-sex couples were in-
cluded owing to the small number of same-sex couples meet-
ing the inclusion criteria (n = 26) and to allow analyses to be
stratified by sex within couples.
Statistical Analysis
Similarity between partners for smoking, physical inactivity,
and overweight status was examined in all included couples.
Two measures of partner similarity were calculated for each
behavior: pairwise concordance rates and tetrachoric corre-
lations. Tetrachoric correlations assume a latentbivariate nor-
mal distribution for each pair of dichotomous variables (in this
case, the behavior in each partner), with a threshold model for
the manifest variables (eg, moderate/vigorous activity at least
once a week vs less than this), and provide an indication of
effect size.
In couples with at least 1 partner with unhealthy behav-
ior at baseline (ie, who smoked, was physically inactive, or was
overweight), we used logistic regression to examine the odds
of positive health behavior change (smoking cessation, in-
crease in physical activity, or at least 5% weight loss) between
baseline and follow-up in relation to whether their partner had
consistently unhealthy behavior, consistently healthy behav-
ior, or unhealthy behavior at baseline and made a positive
health behavior change between baseline and follow-up(newly
healthy). For example, for smokers, we tested the odds of the
index person quitting smoking if their partner was a smoker
at both time points, a nonsmoker at both time points, or a base-
line smoker who had quit by follow-up.Individuals whose part-
ner moved to less healthy behavior between baseline and fol-
low-up (eg, started smoking) were not included in these
analyses owing to low numbers in this group.
We ran 2 models for each domain to investigate differ-
ences by partner behavior status; the first compared having
a consistently healthy or newly healthy partner with having
a consistently unhealthy partner, and the second model
compared having a newly healthy partner with having a
consistently healthy partner. Analyses were run separately
by sex to see if husbands were more affected by wives’
behavior than wives by husbands’ behavior. All models
adjusted for baseline wave, household wealth, and the age
of the outcome partner.
To test for confounding by changes in health status, we re-
peated these analyses, adjusting for the onset of the follow-
ing conditions between baseline and follow-up in either
partner: cancer, diabetes, coronary heart disease, stroke,
myocardial infarction, and self-reported limiting long-
standing illness.
In addition to exploring the influence of partner’s behav-
ior on positive change in the same domain, we also tested for
overlap across behaviors; for example whether one partner
quitting smoking predicted an increase in physical activity in
the other partner. These analyses were restricted to couples
with data for both partners in both health domains included
in the model at the same 2 consecutive time points. Accord-
ingly, models predicting smoking cessation from physical ac-
tivity and vice versa were conducted over 2 years, and those
that included weight status as a predictor or weight loss as an
outcome were conducted over 4 years.
Tetrachoric correlations were calculated using Stata soft-
ware, version 13.1 (StataCorp LP), and all other analyses were
performed using SPSS, version 20 (IBM Corporation). P<.05
determined statistical significance.
Results
Of 5746 couples participating in ELSA, 3722 were eligible for
inclusion in these analyses. Table 1 lists the participant char-
acteristics at baseline. Data on smoking status were available
at consecutive waves (baseline and follow-up) for both part-
ners in 3555 couples; data on physical activity were available
for 3520 couples; and data on weight were available for 1556
couples.
Influence of Partner’s Behavior on Health Behavior Original Investigation Research
jamainternalmedicine.com (Reprinted) JAMA Internal Medicine March 2015 Volume 175, Number 3 387
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
There was a strong correlation between partners’ smok-
ing status (r= 0.600), and moderate correlations for physi-
cal activity (r= 0.478) and weight status (r= 0.311) at base-
line. Concordance ranged from 67% (weight status) to 84%
(smoking status).
Over the study interval, 175 individuals (17% of smokers)
quit smoking, 1037 (44% of inactive individuals) became physi-
cally active, and 335 individuals who were overweight (15% of
overweight individuals) lost at least 5% of their baseline body
weight. Table 2 summarizes the logistic regression models test-
ing whether partner’s health behavior was associated with posi-
tive health behavior change in men and women who were un-
healthy at baseline.
Model 1 compared the 3 groups with the consistently un-
healthy partner as the reference group for each domain. For
smokers, having a consistently healthy (nonsmoking) partner
was associated with significantly higher odds of quitting smok-
ing (odds ratio [OR] range for men and women, 2.06-3.84)
(Figure, A). For inactive individuals, having a consistently ac-
tive partner was associated with higher odds of becoming physi-
cally active (OR range, 2.75-2.92) (Figure, B). However, having
an unhealthy partner in either of those domains who became
healthy was associated with even higher odds of positivechange
(smoking OR range, 11.23-11.82;physic al activity OR range,5.28-
5.36). For individuals who were overweight, having a partner
whose BMI was consistently in the normal range did not in-
crease the odds of losing weight, but having an overweightpart-
ner who lost weight was associated with 3 times higher odds of
weight loss (OR range, 3.05-3.08) (Figure, C).
Model 2 specifically tested whether the influence of a part-
ner who became healthy was statistically stronger than the in-
fluence of a partner who was consistently healthy. For each
health behavior, men and women weresignific antly more likely
to make positive changes if their partner also changed their
health behavior over the same period than if their partner was
consistently healthy (OR range, 1.84-5.45). Adjusting for the
onset of chronic health conditions in either partner did not
change the results (eTable 1 in the Supplement).
Analyses testing for crossover between the differenthealth
behaviors revealed that a change in partner’s behavior in one
domain did not predict positive change in other domains in
either men (eTable 2 in the Supplement) or women (eTable 3
in the Supplement), indicating that the effects were behavior
specific.
Discussion
Using prospective data from a large population-based sample
of older adults, this study examined the influence of one part-
ner’s health behavior change on the likelihoodof the other part-
ner making positive changesto the same health behaviors com-
pared with having a partner who had consistently healthy
behavior. Analyses covered 3 domains: smoking, physic al ac-
tivity, and weight status.
Consistent with a wealth of previous research,
15-28
there
was moderate to strong concordance within couples for each
domain at baseline. There was also an influence of partner’s
behavior on change over time. Having a partner who was con-
sistently healthy was associated with greater likelihood of posi-
tive change for smoking and physical activity, although it had
no significant effect on weight loss. However, having a part-
ner who made a positive change to their behavior was associ-
ated with substantially higher likelihood of the index partici-
pant doing so as well. Although concurrent changes in health
behaviors in couples have been reported by a number of other
studies,
22,40-45
only 2 prior studies have examined the influ-
ence of consistently healthy vs newly healthy partners on
health behavior change (one in physical activity and the other
across multiple behaviors)
40,41
; and neither study specifi-
cally tested the difference between these partner behavior pat-
terns. In one of these studies,
41
a pattern of results similar to
those in the present study was observed for changes in smok-
ing, with higher odds of change in individuals whose partner
stopped smoking than in those whose partner had never
smoked. However, neither study showed a substantial differ-
Table 1. Indexand Partner Participant Characteristics at Baseline
a
Characteristic
Men
(n = 3722)
Women
(n = 3722)
Demographics (n = 3722)
Age, mean (SD), y 63.05 (8.54) 60.60 (8.25)
Wealth quintile
b
1 (Poorest) 490 (13.2) 490 (13.2)
2 616 (16.6) 616 (16.6)
3 769 (20.7) 769 (20.7)
4 884 (23.8) 884 (23.8)
5 (Richest) 963 (25.9) 963 (25.9)
Health Behaviors
c
Smoking status (n = 3555)
Nonsmoker 3061 (86.1) 3028 (85.2)
Smoker 494 (13.9) 527 (14.8)
Physical activity (n = 3520)
Active 2421 (68.8) 2271 (64.5)
Inactive 1099 (31.2) 1249 (35.5)
Weight (n = 1556)
BMI, mean (SD) 27.85 (3.93) 27.74 (5.08)
Normal weight 353 (22.7) 504 (32.4)
Overweight/obese 1203 (77.3) 1052 (67.6)
Comorbid conditions (n = 3722)
Cancer 154 (4.1) 203 (5.5)
Diabetes 310 (8.3) 174 (4.7)
Coronary heart disease 409 (11.0) 171 (4.6)
Stroke 135 (3.6) 62 (1.7)
Myocardial infarction 229 (6.2) 61 (1.6)
Limiting long-standing illness 1114 (29.9) 1082 (29.1)
Abbreviation: BMI, body mass index (calculated as weight in kilograms divided
by height in meters squared).
a
Unless otherwise indicated, data are reported as mean number (percentage)
of index participants.
b
Wealth is calculated at the household level so does not differ between
partners.
c
Information on all health behaviors was not available for all participants so
numbers may not sum to the total sample number. Valid percentages are
presented for ease of interpretation.
Research Original Investigation Influence of Partner’s Behavior on Health Behavior
388 JAMA Internal Medicine March 2015 Volume 175, Number 3 (Reprinted) jamainternalmedicine.com
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
ence between having newly healthy and consistently healthy
partners for physical activity.
40,41
We observed higher concordance for smoking than for
physical activity or weight status at baseline and found part-
ners’ behavior to be a much stronger influence on men’s and
women’s smoking than on the other behaviors. That the ef-
fect was larger for smoking is not surprising because it is a more
cue-associated behavior than the others. An individual try-
ing to quit smoking while their partner continues to smokemay
find it more difficult owing to the constant exposure to the be-
havior they are trying to avoid; whereas for someone trying
to be more active or lose weight, seeing their partner not ex-
ercising or staying the same weight may be less salient. Dif-
ferences in effect size across behaviors were also observed in
a previous study of partner influence on multiple health be-
haviors, which found a greater influence of partners’ behav-
ior on change in more cue-associated behaviors (ie, smoking
and drinking vs exercising).
41
Another difference between the behaviors examined in the
present study was that having a partner with a healthy BMI at
both times was not associated with higher odds of weight loss,
while having a consistently healthy partner predicted change
in both other behaviors. This might be because having a non-
overweight partner is less salient than having one who does
not smoke, or it might be because weight loss was the only be-
havior that could not be changed instantaneously. It is also pos-
sible that having a partner with a consistently healthy BMI in-
fluenced weight at a subthreshold level(ie, was assoc iatedw ith
<5% weight loss), and a significant association would become
evident over a longer follow-up.
Why might having a partner who becomes healthy be more
influential than a partner who is consistently healthy? One pos-
sibility is that partners make a decision to change together. A
recent study assessed married couples’ readiness to eat more
healthily, lose weight, and exercise more, and their confi-
dence in their ability to make these changes.
46
Men and women
who indicated readiness to change their behavior were less con-
fident that they could change if their spouse was in a lowerstage
of readiness to change, suggesting that people feel more able
to change their behavior if their partner is also motivated to
change. Alternatively, successful behavior change in one part-
ner may encourage the other to try to change their behavior.
Table 2. LogisticRegression Models Examining the Influence of Partner’s Health Behavior on Positive Health Behavior Change
Among Index Men and Women With Unhealthy Behavior at Baseline
Partner’s
Health Behavior
a
Index Men Index Women
Tot al ,
No.
Changed to
Healthier
Behavior, % OR (95% CI)
P
Value
Tot al ,
No.
Changed to
Healthier
Behavior, % OR (95% CI)
P
Value
Smoking
Model 1
Stable smoker 194 7.7 1 [Reference] NA 195 8.2 1 [Reference] NA
Stable nonsmoker 262 23.3 3.84 (2.09-7.06) <.001 293 17.4 2.06 (1.13-3.77) .02
Quit smoking 31 48.4 11.82 (4.84-28.90) <.001 30 50.0 11.23 (4.58-27.52) <.001
Model 2
Stable nonsmoker 262 23.3 1 [Reference] NA 293 17.4 1 [Reference] NA
Quit smoking 31 48.4 3.08 (1.43-6.62) .004 30 50.0 5.45 (2.44-12.16) <.001
Physical Activity
Model 1
Stable inactive 363 25.9 1 [Reference] NA 356 24.4 1 [Reference] NA
Stable active 350 54.0 2.75 (1.98-3.81) <.001 480 52.1 2.92 (2.12-4.00) <.001
Became active 266 67.3 5.28 (3.70-7.54) <.001 273 65.6 5.36 (3.74-7.68) <.001
Model 2
Stable active 350 54.0 1 [Reference] NA 480 52.1 1 [Reference] NA
Became active 266 67.3 1.92 (1.37-2.70) <.001 273 65.6 1.84 (1.33-2.53) <.001
Weight
Model 1
Stable overweight 718 9.9 1 [Reference] NA 759 14.8 1 [Reference] NA
Stable normal weight 269 12.6 1.34 (0.86-2.08) .20 133 15.8 1.08 (0.65-1.80) .77
Overweight
and lost weight
151 25.8 3.05 (1.96-4.74) <.001 110 35.5 3.08 (1.98-4.80) <.001
Model 2
Stable normal weight 269 12.6 1 [Reference] NA 133 15.8 1 [Reference] NA
Overweight
and lost weight
151 25.8 2.28 (1.36-3.84) .002 110 35.5 2.86 (1.55-5.26) .001
Abbreviations: NA, not applicable; OR, odds ratio.
a
Model 1 compared all 3 groups, with the consistently unhealthy partner group
as the reference category; model 2 compared the consistently healthy and
newly healthy partner groups, with the consistently healthy partner group as
the reference category. All models are adjustedfor baseline wave, household
wealth, and the index partner’s age.
Influence of Partner’s Behavior on Health Behavior Original Investigation Research
jamainternalmedicine.com (Reprinted) JAMA Internal Medicine March 2015 Volume 175, Number 3 389
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
This could be the result of an active effort on the part of the
“follower,” with the “leader” inspiring them to change, or it
could be a passive effect whereby the follower changes with-
out consciously trying to, for example losing weight through
eating the same lower-calorie meals as the leader. The find-
ing that having a consistently healthy partner was associated
with greater likelihood of behavior change suggests that a
leader-follower model may be true in some couples, with the
unhealthy partner changing their behavior to match the healthy
partner. However, in our study, having a partner who took up
healthy behavior was a much stronger influence on behavior
change across all behaviors, suggesting that the majority of
couples change together—whether the change is initiated by
one partner and the other follows suit or it is a mutual deci-
sion to become more healthy.
The present findings have implications for the design
and delivery of interventions aimed at reducing the risk of
morbidity and mortality. Given that partners have a mutual
influence on one another’s behavior, behavior change inter-
ventions could be more effective if they targeted couples as
opposed to individuals. Consistent with this, findings from
the weight loss literature indicate that involvement of
spouses in behavior change interventions may improve
effectiveness.
49
Our results suggest that similar benefits
could be obtained by involving partners in interventions to
help people quit smoking or become more physically active.
In addition, a “halo” effect has been shown in studies treat-
ing only one person in a couple, with spouses of individuals
randomized to lifestyle interventions achieving significant
weight loss and making positive dietary changes that were
not observed in spouses of controls.
43-45
Significant weight
loss and improved eating behavior have also been docu-
mented in obese spouses of patients who have undergone
bariatric surgery.
50
This has important implications for
assessment of cost-effectiveness of interventions, since pro-
viding treatment or support to help one individual to
change their behavior may have a no-cost impact on their
partner’s behavior.
51
Our study had a number of limitations. Although weight
status was objectively measured, we relied on self-reports of
smoking and physical activity. Changes were analyzed over
several years, so we would have missed short-lived changes
during the interval. Concordance for weight change would be
underestimated for partners who lost less than 5% of their
body weight. It was not possible to determine whether
couples who both changed their behavior did so at the same
time, or whether one partner changing their behavior
prompted subsequent change in the other. Data were not
available on behavior prior to marriage or cohabitation, so we
were unable to assess whether mate selection itself was due
to unseen traits that might have influenced change (and
response to spousal change) later in life. To study change in
behavior over time, our analyses were limited to couples with
data for both partners on at least 2 consecutive waves.
Couples in which a partner had died, dropped out of the
study, or did not have data on the relevant health domain
were therefore not included. Also excluded were couples
who split up over the study interval, which may have influ-
enced our results, since previous research has found that
social relationships in which members are dissimilar are
more likely to dissolve than if members are similar.
52,53
The
analyzed samples were slightly younger and wealthier than
the total ELSA sample, in line with retention in other longitu-
dinal studies,
54
so the results may not be representative of all
couples in this age range. In addition, because we used an
older sample, our results may not apply to younger couples.
Figure. Proportions of Index Study Participants Who Changed From Unhealthy to Healthier Behaviorin Each of 3 Domains by Partner’s Domain Status
Consistent smoker
Partner’s smoking status
Consistent nonsmoker
Quit smoking
Consistent inactive
Partner’s physical activity status
Consistent active
Became active
Consistent overweight
Partner’s weight status
Consistent normal weight
Overweight and lost weight
60
70
50
40
30
20
10
0
Index Participants Who Quit Smoking, %
Male Smokers Female Smokers
70
80
60
50
40
30
20
10
0
Index Participants Who Became Active, %
Inactive Men Inactive Women
45
20
25
30
35
40
15
10
5
0
Index Participants Who Lost Weight, %
Overweight Men Overweight Women
Smoking
A
Physical inactivity
B
Overweight
C
A-C, “Consistent” indicates the same domain status at follow-upas at baseline; all values are mutually adjusted for baseline wave, household wealth,andageofthe
index partner; error bars indicate 95% CIs. C, “Lost weight” indicates that the partner lost at least 5% of baseline body weight.
Research Original Investigation Influence of Partner’s Behavior on Health Behavior
390 JAMA Internal Medicine March 2015 Volume 175, Number 3 (Reprinted) jamainternalmedicine.com
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
Conclusions
We foundthat men and women are strongly influenced by their
partner’s behavior in relation to making health behavior
changes. Individuals whose partner’s behavior became healthy
were significantly more likely to improve their own behavior
than those with a partner who was always healthy. This sug-
gests that people may be more successful in changingtheir be-
havior if their partner does it with them.
ARTICLE INFORMATION
Published Online: January 19, 2015.
doi:10.1001/jamainternmed.2014.7554.
Author Contributions: Dr Jackson had full access
to all the data in the study and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design: Jackson, Wardle.
Acquisition, analysis, or interpretation of data:
Jackson, Steptoe, Wardle.
Drafting of the manuscript: Jackson, Wardle.
Critical revision of the manuscript for important
intellectual content: Jackson, Steptoe, Wardle.
Statistical analysis: Jackson.
Obtained funding: Steptoe.
Administrative, technical, or material support:
Steptoe.
Study supervision: Steptoe, Wardle.
Conflict of Interest Disclosures: None reported.
Funding/Support: The ELSA was developed by a
team of researchers based at University College
London, the Institute of Fiscal Studies, and the
National Centre for Social Research. The funding is
provided by the US National Institute on Aging
(grants 2RO1AG7644-01A1 and 2RO1AG017644)
and a consortium of United Kingdom government
departments coordinated by the Office for National
Statistics. The data are lodged with the United
Kingdom Data Archive. Dr Jackson is supported by
ELSA funding. Dr Steptoe is supported by the
British Heart Foundation. Dr Wardle is supported by
Cancer Research UK.
Role of the Funder/Sponsor:The funding
institutions had no role in the design and conduct of
the study; collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.
Additional Contributions: The authors thank the
staff and participants of ELSA for their important
contributions, and Martin Jarvis, DSc, University
College London, and our reviewers for their useful
comments. No contributors to this study received
compensation for their contributions beyond that
provided in the normal course of their employment.
REFERENCES
1. Ford ES, Bergmann MM, Kröger J, Schienkiewitz
A, Weikert C, Boeing H. Healthy living is the best
revenge: findings from the European Prospective
Investigation Into Cancer and Nutrition-Potsdam
study.Arch Intern Med. 2009;169(15):1355-1362.
2. Mokdad AH, Marks JS, Stroup DF, Gerberding JL.
Actual causes of death in the United States, 2000.
JAMA. 2004;291(10):1238-1245.
3. McGinnis JM, Foege WH. Actual causes of death
in the United States. JAMA. 1993;270(18):2207-2212.
4. Peto R, Darby S, Deo H, Silcocks P, Whitley E,
Doll R. Smoking, smoking cessation, and lung
cancer in the UK since 1950: combination of
national statistics with two case-control studies. BMJ.
2000;321(7257):323-329.
5. Critchley JA, Capewell S. Mortality risk reduction
associated with smoking cessation in patients with
coronary heart disease: a systematic review. JAMA.
2003;290(1):86-97.
6. Anthonisen NR, Skeans MA, Wise RA ,
Manfreda J, Kanner RE, Connett JE; Lung Health
Study Research Group. The effects of a smoking
cessation intervention on 14.5-year mortality:
a randomized clinical trial. Ann Intern Med. 2005;
142(4):233-239.
7. Esposito K, Pontillo A, Di Palo C, et al. Effect
of weight loss and lifestyle changes on vascular
inflammatory markers in obese women:
a randomized trial. JAMA. 2003;289(14):
1799-1804.
8. Hamman RF, Wing RR, Edelstein SL, et al. Effect
of weight loss with lifestyle intervention on risk of
diabetes. Diabetes Care. 2006;29(9):2102-2107.
9. Birks S, Peeters A, Backholer K, O’Brien P, Brown
W. A systematic review of the impact of weight loss
on cancer incidence and mortality. Obes Rev.2012;
13(10):868-891.
10. Estruch R, Martínez-González MA, Corella D,
et al; PREDIMED Study Investigators. Effects of a
Mediterranean-style diet on cardiovascular risk
factors: a randomized trial. Ann Intern Med. 2006;
145(1):1-11.
11. Knoops KT, de Groot LC, Kromhout D, et al.
Mediterranean diet, lifestyle factors, and 10-year
mortality in elderly European men and women:
the HALE project. JAMA. 2004;292(12):1433-1439.
12. Anderson JW, Konz EC, Frederich RC, Wood CL.
Long-term weight-loss maintenance:
a meta-analysis of US studies. Am J Clin Nutr.2001;
74(5):579-584.
13. Weiss EC, Galuska DA, Kettel Khan L, Gillespie C,
Serdula MK. Weight regain in U.S. adults who
experienced substantial weight loss, 1999-2002.
Am J Prev Med. 2007;33(1):34-40.
14. Centers for Disease Control and Prevention
(CDC). Quitting smoking among adults: United
States, 2001-2010.MMWR Morb Mor tal Wkly Rep.
2011;60(44):1513-1519.
15. Christakis NA, Fowler JH. The collective
dynamics of smoking in a large social network.
N Engl J Med. 2008;358(21):2249-2258.
16. Reynolds CA, Barlow T, Pedersen NL. Alcohol,
tobacco and caffeine use: spouse similarity
processes. Behav Genet. 2006;36(2):201-215.
17. Monden C. Partners in health? exploring
resemblance in health between partners in married
and cohabiting couples. Sociol Health Illn. 2007;29
(3):391-411.
18. Meyler D, Stimpson JP, Peek MK. Health
concordance within couples: a systematic review.
Soc Sci Med. 2007;64(11):2297-2310.
19. Wilson SE. The health capital of families: an
investigation of the inter-spousal correlation in
health status. Soc Sci Med. 2002;55(7):1157-1172.
20. Di Castelnuovo A, Quacquaruccio G, Donati
MB, de Gaetano G, Iacoviello L. Spousal
concordance for major coronary risk factors:
a systematic review and meta-analysis. Am J
Epidemiol. 2009;169(1):1-8.
21. Jurj AL , WenW, Li H-L, et al. Spousal
correlations for lifestyle factors and selected
diseases in Chinese couples. Ann Epidemiol. 2006;
16(4):285-291.
22. Rosenquist JN, Murabito J, Fowler JH,
Christakis NA. The spread of alcohol consumption
behavior in a large social network. Ann Intern Med.
2010;152(7):426-433,W141.
23. Meiklejohn J, Connor JL, Kypri K. Drinking
concordance and relationship satisfaction in New
Zealand couples. Alcohol Alcohol. 2012;47(5):
606-611.
24. Simonen RL , Perusse L, Rankinen T, Rice T, Rao
DC, Bouchard C. Familial aggregation of physical
activity levels in the Québec Family Study. Med Sci
Sports Exerc. 2002;34(7):1137-1142.
25. Christakis NA, Fowler JH. The spread of obesity
in a large social network over 32 years. N Engl J Med.
2007;357(4):370-379.
26. Brown H, Hole AR , RobertsJ. Going the same
“weigh”: spousal correlations in obesity in the
United Kingdom. Appl Econ. 2014;46(2):153-166.
27. Pachucki MA , JacquesPF, Christakis NA. Social
network concordance in food choice among
spouses, friends, and siblings. Am J Public Health.
2011;101(11):2170-2177.
28. Brummett BH, Siegler IC, Day RS, Costa PT.
Personality as a predictor of dietary quality in
spouses during midlife. Behav Med. 2008;34(1):
5-10.
29. Dollar KM, Homish GG, Kozlowski LT,Leonard
KE. Spousal and alcohol-related predictors of
smoking cessation: a longitudinal study in a
community sample of married couples. Am J Public
Health. 2009;99(2):231-233.
30. Holahan CJ, North RJ, Holahan CK, Hayes RB,
Powers DA, Ockene JK. Social influences on
smoking in middle-aged and older women. Psychol
Addict Behav. 2012;26(3):519-526.
31. Cobb LK, Mc Adams-DeMarcoMA , Huxley RR,
et al. The association of spousal smoking status
with the ability to quit smoking: the Atherosclerosis
Risk in Communities Study. Am J Epidemiol.2014;
179(10):1182-1187.
32. Daly KA, Lund EM, Harty KC,Ersted SA. Factors
associated with late smoking initiation in Minnesota
women. Am J Public Health. 1993;83(9):1333-1335.
33. Homish GG, Leonard KE. Spousal influence on
general health behaviors in a community sample.
Am J Health Behav. 2008;32(6):754-763.
34. Kahn RS, Certain L, Whitaker RC.
A reexamination of smoking before, during, and
after pregnancy. Am J Public Health. 2002;92(11):
1801-1808.
Influence of Partner’s Behavior on Health Behavior Original Investigation Research
jamainternalmedicine.com (Reprinted) JAMA Internal Medicine March 2015 Volume 175, Number 3 391
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
Copyright 2015 American Medical Association. All rights reserved.
35. Monden CWS, de Graaf ND, Kraaykamp G. How
important are parents and partners for smoking
cessation in adulthood? an event history analysis.
Prev Med. 2003;36(2):197-203.
36. Homish GG, Leonard KE. Spousal influence on
smoking behaviors in a US community sample of
newly married couples. Soc Sci Med. 2005;61(12):
2557-2567.
37. LeonardKE , DasEiden R . Husband’sand wife’s
drinking: unilateral or bilateral influences among
newlyweds in a general population sample. J Stud
Alcohol Suppl. 1999;13:130-138.
38. Leonard KE, Mudar P. Husbands’ influence on
wives’ drinking: testing a relationship motivation
model in the early years of marriage. Psychol Addict
Behav. 2004;18(4):340-349.
39. Moos RH, Finney JW, Cronkite RC. Alcoholism
Treatment: Context, Process, and Outcome. Oxford,
England: Oxford University Press; 1990.
40. Li K-K, Cardinal BJ, Acock AC. Concordance of
physical activity trajectories among middle-aged
and older married couples: impact of diseases and
functional difficulties. J Gerontol B Psychol Sci Soc Sci.
2013;68(5):794-806.
41. FalbaTA, Sindelar JL. Spousal concordance in
health behavior change. Health Serv Res. 2008;43
(1 Pt 1):96-116.
42. Franks MM, Pienta AM, Wray LA. It takes two:
marriage and smoking cessation in the middle
years. J Aging Health. 2002;14(3):336-354.
43. Gorin AA , WingRR , Fava JL, et al; Look AHEAD
Home Environment Research Group. Weight loss
treatment influences untreated spouses and the
home environment: evidence of a ripple effect. Int J
Obes (Lond). 2008;32(11):1678-1684.
44. Golan R, Schwarzfuchs D, Stampfer MJ, Shai I;
DIRECT group. Halo effect of a weight-loss trial on
spouses: the DIRECT-Spouse study. Public Health
Nutr. 2010;13(4):544-549.
45. Schierberl Scherr AE, Mc Clure BrenchleyKJ,
Gorin AA. Examining a ripple effect: do spouses’
behavior changes predict each other’s weight loss?
J Obes. 2013;2013(4):297268.
46. Franks MM, Shields CG, Lim E, Sands LP,
Mobley S, Boushey CJ. I will if you will: similarity in
married partners’ readiness to change health risk
behaviors. Health Educ Behav. 2012;39(3):
324-331.
47. Steptoe A, Breeze E, Banks J,Nazroo J. Cohort
profile: the English Longitudinal Study of Ageing.
Int J Epidemiol. 2013;42(6):1640-1648.
48. Marmot MG, Smith GD, Stansfeld S, et al.
Health inequalities among British civil servants:
the Whitehall II study.Lancet. 1991;337(8754):
1387-1393.
49. McLean N, Griffin S, Toney K, Hardeman W.
Family involvement in weight control, weight
maintenance and weight-loss interventions:
a systematic review of randomised trials. Int J Obes
Relat Metab Disord. 2003;27(9):987-1005.
50. Woodard GA, Encarnacion B, Peraza J,
Hernandez-Boussard T, Morton J. Halo effect for
bariatric surgery: collateral weight loss in patients’
family members. Arch Surg. 2011;146(10):
1185-1190.
51. Christakis NA. Social networks and collateral
health effects. BMJ. 2004;329(7459):184-185.
52. O’Malley AJ, Christakis NA. Longitudinal
analysis of large social networks: estimating the
effect of health traits on changes in friendship ties.
Stat Med. 2011;30(9):950-964.
53. Noel H, Nyhan B. The “unfriending” problem:
the consequences of homophily in friendship
retention for causal estimates of social influence.
http://opensiuc.lib.siu.edu/pnconfs_2010/14.
Accessed December 1, 2014.
54. Mendes de Leon CF. Aging and the elapse of
time: a comment on the analysis of change.
J Gerontol B Psychol Sci Soc Sci. 2007;62(3):
S198-S202.
Research Original Investigation Influence of Partner’s Behavior on Health Behavior
392 JAMA Internal Medicine March 2015 Volume 175, Number 3 (Reprinted) jamainternalmedicine.com
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archinte.jamanetwork.com/ by a University College London User on 03/03/2015
... There have been studies of sibling effects on substance use (Rende et al., 2005) and Blok et al. (2013, p. 667) argued that "the spread of unhealthy behavior shows marked similarities with infectious diseases," exhibiting patterns of social diffusion. Perry et al. (2016) reported partner concordance for physical activity, fruit and vegetable and fast food consumption, and Jackson et al. (2015) reported that when one partner changed to a healthier behavior (e.g., smoking cessation, physical activity, and weight loss), the other partner was also more likely to make a similar behavior change. ...
... There is growing interest in whether behavioral health and, for that matter, behaviors that affect behavioral health (Jackson et al., 2015) are contagious (e.g., Katz, Beach & Joiner, Jr., 1999) in that they are correlated among spouses, close network connections, and roommates. The interest in contagion or spillover in health reflects the reality that studies that consider only the effects of factors affecting behavioral health on a single, focal individual can underestimate costs and other effects if, in fact, the behavioral health of one individual affects the health of connected others. ...
Article
Full-text available
The empirically related psychopathologies of stress and depression exact an enormous economic toll and have many physical and behavioral health effects. Most studies of the effects of stress and depression focus on their causes and consequences for a single, focal individual. We examine the extent to which depression, as indicated by filling antidepressant prescriptions (SSRI and Benzodiazepines), co-occurs across spouses, constituting a negative spillover effect. To better understand the conditions that affect within-household contagion of depression, we examine whether the stress and uncertainty occasioned by job change and financial stress (net worth) increases spillover effects among spouses. We use panel data from various Danish administrative registers from the year 2001–2015 with more than 4.5 million observations on more than 900,000 unique individuals and their spouses from Danish health registers. Spouses in a household with their partner using antidepressants have a 62.1% higher chance of using antidepressants themselves, with the one year lagged effect being 29.3% and a two-year lagged effect of 15.1%. The effects become larger by 14.8% contemporaneously and 20% in the two-year lagged model if the focal individual changed employers. There was also a substantively unimportant effect of lower financial wealth to increase inter-spousal contagion.
... Our results are well aligned with previous reports that found male cancer survivors to be less likely successfully adopt or maintain healthy lifestyles [39]. Moreover, individuals who had a non-smoking spouse were more likely to attempt to quit or be successful in tobacco product smoking [72,73]. Previous studies also report that marriage was a significant transitional moment in reduction of risky alcohol use and drinking problems became moderate after marriage [74]. ...
... Previous studies also report that marriage was a significant transitional moment in reduction of risky alcohol use and drinking problems became moderate after marriage [74]. Given these findings, incorporating spouses or partners in behavior change programs should be considered for cancer survivors [72]. ...
Article
Full-text available
Purpose Unhealthy lifestyle behaviors are associated with inferior health outcomes among cancer survivors, including increased mortality. It is crucial to identify vulnerable subgroups, yet investigations have been limited. Thus, this study aimed to examine sociodemographic and clinical characteristics associated with risky health behaviors among cancer survivors. Methods We used national, cross-sectional survey data (Health Information National Trends Survey, HINTS 2017–2020) for 2579 cancer survivors. We calculated the prevalence of risky alcohol use, current cigarette smoking, e-cigarette use, and not meeting physical activity guidelines. We performed weighted logistic regression to obtain multivariable-adjusted odds ratios (OR) for the association between each unhealthy behavior with sociodemographic and clinical characteristics. Results Overall, 25% showed risky alcohol use, 12% were current cigarette smokers, 3% were current e-cigarette users, and 68% did not meet physical activity guidelines. Cancer survivors who were males, non-Hispanic Whites or African Americans, without a college education, not married and with comorbidities or psychological distress were more likely to have unhealthy behaviors. Those with lung disease or depression were 2 times as likely to smoke cigarette or e-cigarettes and those with psychological distress were 1.6 times as likely to be physically inactive. Moreover, risky drinkers (OR = 1.75, 95% CI = 1.22–2.52) and e-cigarette smokers (OR = 16.40, 95% CI 3.29–81.89) were more likely to be current cigarette smokers. Conclusions We identified vulnerable subpopulations of cancer survivors with multiple unhealthy lifestyle behaviors. Implications for Cancer Survivors Our findings inform clinicians and program and policy makers of the subgroups of cancer survivors to target for multiple health behavior interventions.
... This pragmatic strategy would result in cessation among two rather than one smoker. Indeed, as with other tobacco products (e.g., cigarettes) one partner quitting may increase the chance that the other partner will quit (Falba & Sindelar, 2008;Jackson et al., 2015). Whether efficacy of interventions differ by intervening with one or both members of dual and single smoker couples remains an important area of research. ...
Article
Full-text available
Negative health effects of waterpipe tobacco smoking (WTS) are likely more pronounced in dual rather than single smoker couples. Data on how smokers’ perceived harms for self and partner differ between couple types and how these perceptions are associated with motivation to quit are needed. We examined these associations by surveying one member of dual smoker (i.e., both partners smoke) and single smoker (i.e., one partner smokes) couples who engages in WTS. We enrolled online adults ages 18–32 who engaged in WTS during the last month and were in a committed relationship of at least six months. Participants rated their harm to self and, when relevant, to partner, how much they were harming their partner due to their WTS, and partner’s smoke exposure. Participants reported their motivation to quit. Of the 323 participants, 215 (67%) were in dual smoker couples. Participants in dual smoker couples reported lower own perceived risk, which correlated highly with perceived partner risk, than participants in single smoker couples; they also reported harming their partners more even though they downplayed how frequency of smoke exposure was harming the partner. Motivation to quit did not differ by couple type. Across couple types, motivation to quit increased with greater perceived harms. Smokers in dual compared to single smoker couples downplay their risks and perceived harms their smoking causes their partner. Interventions focused on harms to self and partner may be effective to increase motivation to quit and cessation in both couple types.
... Couples with unfavourable lifestyles may be able to correct their lifestyles and prevent illness by competing with and encouraging each other. Since most couples of a similar age have similar health statuses, it may be possible to prevent cardiometabolic-related diseases by actively encouraging one another to attend health checks (primary prevention) and disease screenings (secondary prevention) [32][33][34]. ...
Article
Full-text available
Background Previous observational studies have shown similarities in cardiometabolic risk factors between spouses. It is still possible that this result reflects the age similarity of spouses rather than environmental factors of spouses (e.g. cohabitation effect). To clarify the importance of mate cardiometabolic risk factors for similarity of environmental factors, it is necessary to examine whether they are observed in random male-female pairs while maintaining the age of the spousal pairs. This study aimed to determine whether the similarities found between spousal pairs for cardiometabolic risks were also observed between random male-female pairs. Methods This cross-sectional study included 5,391 spouse pairs from Japan; data were obtained from a large biobank study. For pairings, women of the same age were randomly shuffled to create new male-female pairs of the same age as that of the original spouse pairs. Similarities in cardiometabolic risk factors between the random male-female pairs were analysed using Pearson’s correlation or age-adjusted logistic regression analyses. Results The mean ages of the men and women were 63.2 and 60.4 years, respectively. Almost all cardiometabolic risk factors similarities were not noted in cardiometabolic risk factors, including the continuous risk factors (anthropometric traits, blood pressure, glycated haemoglobin level, and lipid traits); lifestyle habits (smoking, drinking, and physical activity); or diseases (hypertension, type 2 diabetes mellitus, and metabolic syndrome) between the random male-female pairs. The age-adjusted correlation coefficients ranged from − 0.007 for body mass index to 0.071 for total cholesterol. The age-adjusted odds ratio (95% confidence interval) for current drinkers was 0.94 (0.81 − 1.09); hypertension, 1.07 (0.93 − 1.23); and type 2 diabetes mellitus, 1.08 (0.77 − 1.50). Conclusion In this study, few similarities in cardiometabolic risk factors were noted among the random male-female pairs. As spouse pairs may share environmental factors, intervention strategies targeting lifestyle habits and preventing lifestyle-related diseases may be effective.
... Gerstorf, & Hibbert, 2011;Hoppmann, Gerstorf, Willis, et al., 2011;Hoppmann & Gerstorf, 2009). Further, when one person initiates a positive health change, their partner is quick to follow, whether it be quitting smoking, drinking less, exercising more, going for a cholesterol screening, getting a flu shot, or losing weight (Falba & Sindelar, 2007;S. E. Jackson et al., 2015). ...
Article
Full-text available
Previous studies show consistent associations between conscientiousness and health outcomes. However, less is known about how various facets of conscientiousness, of both individuals and their partners, are associated with changes in health in older adults over time. Applying the actor–partner interdependence model, we examined dyadic associations of broader conscientiousness and its six facets and changes in health, health behavior, and well-being in middle-aged and older couples. With a sample of 3,271 couples (N=6,542) from the Health and Retirement Study, we found that actor conscientiousness, orderliness, and industriousness were most reliably associated with better health outcomes over time. Partner orderliness was associated with better health and more positive health behavior. The remaining associations were near-zero in their effect sizes. Many of these associations persisted over the 10-year period of the study, and there was little evidence for gender differences or multiplicative interactions.
... These findings are in line with a large body of observational research conducted in the general population showing familial concordance in alcohol consumption [34,35]. Together, these findings suggest that involving relatives in interventions designed to reduce alcohol consumption in people LWBC could help improve outcomes [36]. For example, interventions could encourage the formation of collaborative implementation intentions with family members to reduce alcohol consumption. ...
Article
Full-text available
Purpose Social support facilitated healthy behaviours in people living with and beyond cancer (LWBC) before the COVID-19 pandemic. Little is known about how social support impacted their health behaviours during the pandemic when social restrictions were imposed. The aim of this study was to qualitatively explore how social support was perceived to impact the health behaviours of people LWBC during the COVID-19 pandemic. Methods Semi-structured interviews were conducted via telephone with 24 adults living with and beyond breast, prostate and colorectal cancer. Inductive and deductive framework analysis was used to analyse the data. Results Five themes developed. These were (1) Companionship and accountability as motivators for physical activity, (2) Social influences on alcohol consumption, (3) Instrumental support in food practices, (4) Informational support as important for behaviour change and (5) Validation of health behaviours from immediate social networks. Conclusion This study described how companionship, social influence, instrumental support, informational support and validation were perceived to impact the health behaviours of people LWBC during the COVID-19 pandemic. Interventions for people LWBC could recommend co-participation in exercise with friends and family; promote the formation of collaborative implementation intentions with family to reduce alcohol consumption; and encourage supportive communication between partners about health behaviours. These interventions would be useful during pandemics and at other times. Government policies to help support clinically extremely vulnerable groups of people LWBC during pandemics should focus on providing access to healthier foods.
Article
The purpose of this study is to assess how one spouse's behavior change can influence their partner's successful behavior changes in smoking, drinking and physical activity. We used data from 10-wave prospective annual surveys of 9417 married couples (discrete-time person-years = 118,876) aged 50–59 years in the Longitudinal Survey of Middle-aged and Elderly Persons in Japan. A logistic generalized estimating equation model with discrete-time design was used among individuals who smoked at baseline to examine the impact of their spouse's health behaviors (i.e. quit smoking, stable non-smoker, or started smoking in reference to stable smoker) on changes in their own behavior (quitting smoking) which lasted one year or more. Similarly, reducing alcohol intake and starting physical activity were individually analyzed. Partners of spouses who had quit smoking had higher odds of quitting smoking themselves than partners of spouses who were stable smokers. The multivariable odds ratios[95%CI] in men and women were 1.94[1.23–3.07] and 2.89[1.81–4.52]. An association was found in partners of spouses who had been stable non-smokers (OR:1.64[1.33–2.03] and 2.20[1.66–2.94]), but not after spouses had started smoking (OR:1.29[0.71–2.36] and 1.27[0.54–2.99]). Similar associations were found for reducing alcohol intake and starting physical activity although for physical activity, the association was still found after the spouse had become physically inactive. Couples affect each other's health behaviors. Both male and female participants had higher odds of adopting positive health behavior changes if these changes had previously been made by their spouse.
Article
Background Complicated pregnancies by gestational diabetes mellitus (GDM) and hypertensive disorder of pregnancy (HDP) are relatively common worldwide. The evidence is still inconclusive regarding the role of GDM and HDP as spousal risk factor of diabetes (DM) and hypertension (HTN). This study aimed to determine the spousal risk of development of DM and/or HTN in the context of GDM and/or HDP. Methods This population-based cohort study involved couples who participated in Tehran Lipid and Glucose Study. A total of 3650 pairs of spouses were identified, and among them, 2820 met the inclusion criteria. Included participants, followed up 3-year intervals visits from 1999 to 2018. All pairs underwent standard data collection. GDM and HDP were the main exposure of interest in females, and DM and HTN were the main outcomes in both females and their spouses. Cox proportional hazard regression models were used for both females and their spouses, adjusting for age, consanguinity, waist-to-height ratio, physical activity, smoking, and parity. Results Of 2820 females, 558 (19.79 %) had histories of GDM or HDP, and 72 (2.55 %) experienced both. Among females who experienced GDM and HDP, 24 (33 %) and 31 (33 %) developed DM and HTN during the follow-up. The corresponding numbers were 89 (16 %) and 191 (34 %) for those who experienced GDM or HPD, and 274 (13 %) and 623 (28 %) for the non-risk factors group. The incidences of DM were 9 (12 %), 100 (18 %), and 373 (17 %) for males whose spouses experienced both GDM and HDP, either one or none of them, respectively. Among males in these groups, 20 (28 %), 150 (27 %), and 630 (29 %) developed HTN, respectively. Females who never had history of GDM and HDP have 34 % (95 % CI: 21, 45) less hazard of being diabetic than their spouses if they have the same age and waist to hip ratio. In cases with histories of both GDM and HDP, the risk of females increases to 3.05 (95 % CI: 1.43, 6.52) times of their spouses. Also, females who had experienced GDM (HR: 3.51, 95 % CI: 2.23, 5.53), or HDP (HR: 2.80, 95 % CI: 1.72, 4.56) were at higher risk of developing DM compared with females who never had GDM or HDP. We found that females with neither GDM nor HDP were more likely than males to be hypertensive in the future by the hazard ratio of 1.21 (95 % CI: 1.06, 1.39). Conclusions Complicated pregnancies by GDM and/or HDP were associated with increased risk of development DM and HTN in later life of females and their spouses. Further studies are required to confirm these results. Preventive care programs should be considered pregnancy complications as couple-based risk factors for subsequent DM and HTN.
Article
Objective Relationship partners’ impacts on health are not fully captured by existing measures. A measure that applies to a prevention context and accounts for both partners’ perspectives is needed. This work developed and assessed the psychometric properties of the novel Partner Investment in Health scale (PI-H). Design A cross-sectional design assessed participants (N = 261) using an online survey. Exploratory factor analyses were used to determine the PI-H factor structure. Main Outcome Measures Items assessed the person’s investment in their partner’s health and their perception of their partner’s investment in their health. Results A 2 factor structure underlying 24 items on the PI-H scale was supported. Factors represented 1) the respondent’s investment and 2) the respondent’s perception of their partner’s investment. The PI-H significantly correlated with related measures (e.g. relationship satisfaction, dyadic and communal coping; p < .05). Conclusion A full PI-H scale, two subscales, and a short version of the scale (8 items) are presented. Correlations demonstrated convergent validity and suggested the PI-H is distinct from existing constructs. Theoretical implications and applications are discussed.
Article
Full-text available
The adoption and maintenance of healthy behaviors contribute for its accumulation throughout life, which require more than information disclosure and recommendations. Biopsychosocial factors may work as barriers to adherence to healthier behaviors, and yet have been underexplored. The objective was to investigate the factors related to the accumulation of healthy behavior among older adults attending Primary Health Care. Cross-sectional analysis with 201 older adults from baseline of Longitudinal Investigation of Functioning Epidemiology (LIFE) was performed in a Southeastern Brazilian city. The Healthy Behavior Score (HBS), ranging from 0 to 8, was calculated by the sum of the following habits: Physical activity practice, healthy eating, water consumption, night sleep time, not smoking, not drinking alcohol, frequent social relations, and spirituality. A linear multivariate regression was performed to test the influence of biopsychosocial aspects on HBS, with 95% confidence interval. Higher number of healthy behaviors was related to high social support, better cognitive status, less depressive symptoms and lower functional performance. Additionally, age and resilience score were correlated to healthy behaviors, which were higher among women and those with sufficient income. Multivariate analysis revealed depressive symptoms, functional performance and education as independent predictors of HBS. Depressive symptoms, functional performance and education are predictors of accumulation of health behaviors, independently of health status, contextual and sociodemographic aspects. Higher social support partially contributed to the higher number of healthy behaviors, and should be considered in public health policies for healthy longevity.
Article
Full-text available
Background: Including spouses in obesity treatment has been found to promote weight loss. We assessed whether spouses' diet and activity changes impacted each other's weight loss when both members attended an active weight loss program (TOGETHER) or only the primary participant attended treatment (ALONE). Methods: Heterosexual couples (N = 132) enrolled in an 18-month randomized controlled weight loss trial were weighed and completed measures of dietary intake and physical activity at baseline and 6 months. We conducted dyadic data analyses using the Actor-Partner Interdependence Model. Results: Participants' weight loss was not predicted by their partners' behavior changes. However, partners' weight loss was predicted by their participants' changes in calorie and fat intake. When partners were coupled with a participant who did not reduce their own calorie and fat intake as much, these partners had higher weight loss when treated in the TOGETHER group but lower weight loss when they were untreated in the ALONE group. There were no reciprocal effects found with physical activity changes. Conclusions: Direct treatment had the greatest impact on participants and partners who were treated. Untreated partners' weight losses were positively impacted by their spouses' dietary changes, suggesting a ripple effect from treated spouses to their untreated partners.
Article
Full-text available
Objectives.This study examined spousal concordance of physical activity trajectories among middle-aged and older married couples and the influences of recent diseases and functional difficulties on individuals' trajectories and those of their spouses'.Method.Participants included 5,074 married couples aged 50 or older in the Health and Retirement Study in 2004-2010. Participants were categorized into 4 physical activity trajectories (i.e., stable active, adopters, relapsers, and stable sedentary) using confirmatory latent class growth analysis. Individuals' trajectory memberships were predicted by their spouses' memberships, together with recent diseases and functional difficulties of both couple members. In the main, corresponding husbands' trajectories predicted wives' trajectories and vice versa. More functional difficulties predicted higher likelihoods of unfavorable trajectories among individuals but not of their spouses'. Among wives, more recent diseases predicted slightly more physical activity in subsequent data waves but not trajectory memberships.Discussion.Results supported spousal concordance in physical activity trajectories. The negative impact of functional difficulties was considerably contained within individuals. Increases in physical activity after acquiring diseases among wives were small and short lived. More research is needed to understand the underlying processes, which can be used to improve the design of future physical activity interventions directed toward women, men, and couples.
Article
The Whitehall study of British civil servants begun in 1967, showed a steep inverse association between social class, as assessed by grade of employment, and mortality from a wide range of diseases. Between 1985 and 1988 we investigated the degree and causes of the social gradient in morbidity in a new cohort of 10 314 civil servants (6900 men, 3414 women) aged 35-55 (the Whitehall 11 study). Participants were asked to answer a self-administered questionnaire and attend a screening examination. In the 20 years separating the two studies there has been no diminution in social class difference in morbidity: we found an inverse association between employment grade and prevalence of angina, electrocardiogram evidence of ischaemia, and symptoms of chronic bronchitis. Self-perceived health status and symptoms were worse in subjects in lower status jobs. There were clear employment-grade differences in health-risk behaviours including smoking, diet, and exercise, in economic circumstances, in possible effects of early-life environment as reflected by height, in social circumstances at work (eg, monotonous work characterised by low control and low satisfaction), and in social supports. Healthy behaviours should be encouraged across the whole of society; more attention should be paid to the social environments, job design, and the consequences of income inequality.
Article
Background: Alcohol consumption has important health-related consequences and numerous biological and social determinants. Objective: To explore quantitatively whether alcohol consumption behavior spreads from person to person in a large social network of friends, coworkers, siblings, spouses, and neighbors, followed for 32 years. Design: Longitudinal network cohort study. Setting: The Framingham Heart Study. Participants: 12 067 persons assessed at several time points between 1971 and 2003. Measurements: Self-reported alcohol consumption (number of drinks per week on average over the past year and number of days drinking within the past week) and social network ties, measured at each time point. Results: Clusters of drinkers and abstainers were present in the network at all time points, and the clusters extended to 3 degrees of separation. These clusters were not only due to selective formation of social ties among drinkers but also seem to reflect interpersonal influence. Changes in the alcohol consumption behavior of a person's social network had a statistically significant effect on that person's subsequent alcohol consumption behavior. The behaviors of immediate neighbors and coworkers were not significantly associated with a person's drinking behavior, but the behavior of relatives and friends was. Limitations: A nonclinical measure of alcohol consumption was used. Also, it is unclear whether the effects on long-term health are positive or negative, because alcohol has been shown to be both harmful and protective. Finally, not all network ties were observed. Conclusion: Network phenomena seem to influence alcohol consumption behavior. This has implications for clinical and public health interventions and further supports group-level interventions to reduce problematic drinking.
Article
The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.
Article
The obesity epidemic has received widespread media and research attention. However, the social phenomenon of obesity is still not well understood. Data from the British Household Panel Survey (BHPS) show positive and significant correlations in spousal body mass index (BMI). This article explores the three mechanisms of matching in the marriage market, social learning and shared environment to explain this correlation. We apply a novel method of testing for social learning by focusing on how the addition of individual and partner health and marriage length affects the correlation in spousal BMI. Results show the importance of matching in the marriage market in explaining correlated BMI outcomes. There is significant correlation in partner BMI even after controlling for own health, spouse health, marriage length and regional effects, suggesting evidence of a social influence. However, it does not appear to be a learning effect as the spouse health and marriage length are insignificant.
Article
Smoking is the leading cause of preventable death in the United States. Studies have shown that smoking status tends to be concordant within spouse pairs. This study aimed to estimate the association of spousal smoking status with quitting smoking in US adults. We analyzed data from 4,500 spouse pairs aged 45-64 years from the Atherosclerosis Risk in Communities Study cohort, sampled from 1986 to 1989 from 4 US communities and followed up every 3 years for a total of 9 years. Logistic regression with generalized estimating equations was used to calculate the odds ratio of quitting smoking given that one's spouse is a former smoker or a current smoker compared to a never smoker. Among men and women, being married to a current smoker decreased the odds of quitting smoking (for men, odds ratio (OR) = 0.37, 95% confidence interval (CI): 0.29, 0.46; for women, OR = 0.54, 95% CI: 0.43, 0.68). Among women only, being married to a former smoker increased the odds of quitting smoking (OR = 1.26, 95% CI: 1.04, 1.53). In conclusion, spouses of current smokers are less likely to quit, whereas women married to former smokers are more likely to quit. Smoking cessation programs and clinical advice should consider targeting couples rather than individuals.