ArticlePDF AvailableLiterature Review

Beyond the “I” in the Obesity Epidemic: A Review of Social Relational and Network Interventions on Obesity


Abstract and Figures

Background: Recent research has shown the importance of networks in the spread of obesity. Yet, the translation of research on social networks and obesity into health promotion practice has been slow. Objectives: To review the types of obesity interventions targeting social relational factors. Methods: Six databases were searched in January 2013. A Boolean search was employed with the following sets of terms: (1) social dimensions: social capital, cohesion, collective efficacy, support, social networks, or trust; (2) intervention type: intervention, experiment, program, trial, or policy; and (3) obesity in the title or abstract. Titles and abstracts were reviewed. Articles were included if they described an obesity intervention with the social relational component central. Articles were assessed on the social relational factor(s) addressed, social ecological level(s) targeted, the intervention's theoretical approach, and the conceptual placement of the social relational component in the intervention. Results: Database searches and final article screening yielded 30 articles. Findings suggested that (1) social support was most often targeted; (2) few interventions were beyond the individual level; (3) most interventions were framed on behaviour change theories; and (4) the social relational component tended to be conceptually ancillary to the intervention. Conclusions: Theoretically and practically, social networks remain marginal to current interventions addressing obesity.
Content may be subject to copyright.
Hindawi Publishing Corporation
Journal of Obesity
Volume , Article ID ,  pages.//
Review Article
Beyond the ‘‘I’’ in the Obesity Epidemic: A Review of
Social Relational and Network Interventions on Obesity
Janette S. Leroux,1Spencer Moore,1,2 and Laurette Dubé3
1School of Kinesiology and Health Studies, Queen’s University, 28 Division Street Kingston, ON, Canada K7L 3N6
2Department of Community Health and Epidemiology, Queen’s University, Kingston, ON, Canada K7L 2N8
Correspondence should be addressed to Spencer Moore;
Received  April ; Accepted  July 
Academic Editor: Terry Huang
Copyright ©  Janette S. Leroux et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
networks and obesity into health promotion practice has been slow. Objectives.Toreviewthetypesofobesityinterventionstargeting
social relational factors. Methods. Six databases were searched in January . A Boolean search was employed with the following
sets of terms: () social dimensions: social capital, cohesion, collective ecacy, support, social networks, or trust; () intervention
type: intervention, experiment, program, trial, or policy; and () obesity in the title or abstract. Titles and abstracts were reviewed.
Articles were included if they described an obesity intervention with the social relational component central. Articles were assessed
on the social relational factor(s) addressed, social ecological level(s) targeted, the intervention’s theoretical approach, and the
conceptual placement of the social relational component in the intervention. Results. Database searches and nal article screening
yielded  articles. Findings suggested that () social support was most oen targeted; () few interventions were beyond the
individual level; () most interventions were framed on behaviour change theories; and () the social relational component tended
to be conceptually ancillary to the intervention. Conclusions. eoretically and practically, social networks remain marginal to
current interventions addressing obesity.
1. Introduction
health of our time []. Current intervention strategies meant
Addressing the magnitude of the obesity epidemic requires
the development of multilevel and cross-sectoral interven-
tions []. Genetic, biological, and psychological factors inter-
act with obesogenic environmental conditions to promote
inactivity, poor nutrition, and, resultantly, widespread weight
gain []. Social epidemiological research has highlighted
the importance of social determinants, such as gender, age,
socioeconomic status and ethnicity, on health. Interventions
on individual behaviors and choices fail, however, to account
for the social relational conditions that inuence personal
choices and behaviors and limit the eectiveness and impact
of obesity interventions [,]. ere is growing consensus on
the need to shi the paradigm for addressing the prevalence
e degree to which social relational constructs have been
integrated into obesity interventions remains unclear.
prominence in recent social epidemiological research on
obesity. ese constructs included social cohesion, collec-
networks. Social cohesion describes the trust, respect, and
participation within a community and has been conceptu-
alized as a social-structural, cultural condition that impacts
health through community integration []. Collective e-
cacy may refer to the norms and networks of relationships
that enable collective action and a culture of informal social
control and social cohesion, whereby people are united
and willing to act for the good of the community [].
Collective ecacy has been proposed as a constraint on
Journal of Obesity
unhealthy behaviors []andameansthroughwhicha
trust, security, and resources within society at large [].
Depending on the perspective, social capital can be consid-
ered as a communitarian- or network-driven phenomenon. A
communitarian denition would conceptualize social capital
as comprising elements of a sense of belonging, partici-
pation and civic engagement, reciprocity and cooperation,
and community trust. A network-based denition of social
capital would consider the availability and accessibility of
resources within an individuals social network. Independent
of these dierences in denitions and measurements, both
approaches have yielded associations with health outcomes
[], including obesity []. Social networks can be dened as
a web of social relationships and are characterized by overall
structure, as well as the individual ties of which it is com-
prised. More recent sophisticated methods of social network
analysis have revealed a social patterning of a number of
health outcomes. Christakis and Fowler [] demonstrated
data and validated old and new interest in harnessing the
potential of social networks in relation to population health.
Social support, which is categorized by instrumental and
nancial, informational, appraisal, and emotional forms of
support, is conceptualized as a psychosocial mechanism
which connects social relationships and individual health
through psychological, behavioral, and physiological path-
ways [].
Findings on the impact of social relationships on obesity
encourage the shi to interventions beyond the “I”, or
individual level and toward interpersonal dynamics by which
behaviours are shared, norms formed, and resources (e.g.,
information, support) exchanged. e objective of this review
is to examine the current state of social relational interven-
tions on obesity and characterize the degree to which these
interventions have addressed key social relational constructs
in intervention planning and implementation.
2. Methods
2.1. Search Strategy, Search Terms, and Search Criteria. To
identify the types of interventions targeting obesity from a
social inuence perspective, we conducted a systematic lit-
erature review on social relational interventions targeting
obesity. PubMed, Web of Knowledge, CINAHL, EMBASE,
TRoPHI, and OVID MEDLINE were all searched in January
. e searches were restricted to full-text, English-
language articles. A Boolean search strategy was employed
with the search designed to identify articles with the following
sets of terms in their title or abstract: () social dimensions:
social capital, social cohesion, collective ecacy, social sup-
port, social networks, or trust; () experimental conditions:
intervention, experiment, program, trial, or policy; and ()
2.2. Inclusion Criteria, Review Methods, and Data Synthesis.
From this pool, articles were included in the next stage if they
described an obesity-focused intervention among the general
population, and the social relational construct was central
enough to the intervention that it was included in the title
or abstract. Studies that were removed from the original pool
of articles included those that addressed eating disorders,
chronic diseases, or postpartum women. ese criteria were
applied independently by two researchers. Disagreements on
the inclusion of specic articles were discussed and resolved
by consensus.
e nal selection of studies was reviewed to assess
and characterize each study by () social relational con-
struct addressed, () social ecological level targeted, () the-
oretical approach used to guide the intervention, and ()
the placement of social relational construct on the inter-
vention’s conceptual pathway. e social relational con-
structs were social capital, social cohesion, collective e-
ecological model was used as a framework by which to
determine the social ecological level(s) targeted by the inter-
vention [] and included individual, interpersonal, orga-
nizational, community, and political levels. To distinguish
between interpersonal-level interventions and individual-
level interventions that included an interpersonal compo-
nent, the ensuing criteria were followed: a study was consid-
ered an interpersonal intervention if it involved one or more
members of a study participants existing social network. e
theoretical rationale for each intervention was garnered from
each study when provided.
A conceptual typology was developed based on the role
of the social relational construct in the intervention. e
typology identied three potential roles for social relational
constructs to play in an obesity intervention: intervention tar-
get, delivery channel, and ancillary resource. e intervention
target was dened as a modiable social relational construct
lying directly on the intervention pathway. e delivery
channel was dened as the functional or structural means of
delivering the intervention, or a vehicle meant to facilitate
the intervention. e ancillary resource was dened as a
reinforcing but noncentral dimension of the study. Ancillary
resources might contribute to the uptake or success of the
intervention but was not a critical component of the delivery
channel or intervention target. For example, an ancillary
change health behavior and delivers the program in a group
setting which facilitates group cohesion and social support
among study participants.
3. Results
Database searches using title criteria yielded  titles.
Application of the inclusion criteria narrowed results to 
studies. Interrater reliability of the  full-text articles to the
 nal studies was calculated as Cohen’s kappa coecient
(Kappa = ., SE: .) []. Table  provides a comprehen-
sive overview of each study, organized by social relational
construct (type, modality, and measurement), intervention
type, theoretical explanation or reference, social ecological
level the intervention was targeting, and type of social rela-
tional construct conceptual pathway placement.
Journal of Obesity
T : Comprehensive overview of intervention studies found pertaining to social relational constructs and obesity.
Article Social relational constructs eory Social ecological level
Conceptual pathway
placement of SRC
Type Modality Measure
Lubans et al.,  []Socialsupport Textmessages Social cognitive
interpersonal Resource
et al.,  []Social support
Student workbook and teacher
manual including motivational
method and strategies (change
social inuence through modeling,
mobilizing social support)
eory of planned
Peterson and
Ward-Smith ,   [ ]Social support Community-based obesity support
group Social support questionnaire
Tra n s t heore t i c al
model, social
comparison theory,
and social support
Individual Channel
Gellertetal.,[] Social support Support group Stage-of-change
model Individual Resource
Kushneretal.,[]Socialsupport Cohesiveness between owners and
Social support and readiness
questionnaire Social learning theory Individual/
interpersonal Tar g e t
Rimmer et al.,  []Socialsupport
Professional advice, brochure
information, PA device, telephone
consultation, and monthly exercise
support group
Individual Channel
Activities to promote group
cohesion (ice breaker, potluck
dinners, outside activities, and
inclusion of friends and family)
Social support for eating and
exercise questionnaire
Social cognitive
theory, health belief
Individual Resource
et al.,  []Social support PA behavior change booklet, care at
obesity unit, and group sessions Tra n s t heore t i c al
model Individual Channel
Gold et al.,  []Socialsupport
Behaviour therapy with social
support lesson; group support Perceived social support scale Individual Channel
et al.,  []Social support Proposed mediator for changes in
HRQOL Family social support (Sallis) Social cognitive
theory Individual Resource
Gallagher et al.,
 []Social support Group sessions (behavioral
strategies to elicit social support) Behavioural processes subscale Social cognitive
theory Individual Resource
Pettman et al.,  []Socialsupport
Peer group setting incorporating
self-management programs,
establishing peer support networks;
information, shared experiences,
and outside interaction
eory of planned
behaviour Individual Channel
Kiernan et al.,  []Socialsupport Friend and family support for
Support subscales and sabotage
subscales; general supportive and
strained interactions with family
and friends subscales; qualitative
question on social support
Social support
measurement Individual Resource
Kalodner and DeLucia,
 []Social support Classmate interaction to facilitate
social cohesion and support Behaviour change Individual Resource
Journal of Obesity
T : Continu ed.
Article Social relational constructs eory Social ecological level
Conceptual pathway
placement of SRC
Type Modality Measure
Casazza and Ciccazzo,
 []Social support Computer-based education;
in-person lecture and pamphlets Social support survey Individual Resource
Interactive group sessions (group
support components, cohesive and
productive environment)
Client feedback questionnaire
includes “group support” as
potential component participants
found most useful
Individual Channel
Yancey et al.,  [] Socialsupport Inclusionofclosefriendorrelative Social ecological
interpersonal Resource
Cousins et al.,  []Socialsupport
Emphasized family-oriented
approach to health behaviors;
manual, inclusion of spouses, and
group support
interpersonal Resource
et al.,  []Social support Classroom/internet program Childrens dietary social support
scale Individual/
organizational Resource
Leblanc et al.,  []Socialsupport Structural social support provided by
group used in control arm of trial
Health at every size
Individual Channel
Bjelland et al.,  []
(i) Social
(ii) social capital
Classmate interaction to facilitate
social cohesion and support
(i) Perceived social support from
parents, friends, and teachers;
(ii) related to people in my
area/neighborhood: quality of
relationship with peers at school (in
+ out of classroom)
organizational Resource
Lee et al.,  []Group cohesion,
social support
Intervention group (shared goal,
working on team activities,
assigning team roles, encouraged to
contact each other outside of
intervention sessions)
Physical activity group environment
questionnaire; social support for
eating habits survey
Behavior change;
stage-of-change, and
social support
Individual Channel
De Niet et al.,  [] Family cohesion
Treatment team (psychologist,
dietician, pediatrician, and
physiotherapist) led information
sessions for parents
Family adaptability and cohesion
evaluation scales (FACES) III Social learning theory Individual/
interpersonal Resource
Leahey et al.,  [] Social cohesion Participation in group contingent
on weight loss Perceived cohesion scale Individual Resource
Participant recruitment through lay
health advisors social networks Community-based
participatory research Individual Channel
Leahey et al.,  []Socialnetworks
Media, newsletters, motivational
and educational activities, online
log, and encouraged team support
Reported social inuence for weight
Social inuence,
social learning theory,
and social modeling
Individual Resource
Gorin et al.,  []Socialnetworks
Instructional sessions to enhance
social support for weight loss eorts Individual Channel
Journal of Obesity
T : Continu ed.
Article Social relational constructs eory Social ecological level
Conceptual pathway
placement of SRC
Type Modality Measure
Ashida et al.,  []Socialnetworks
Indentied encouragers for dietary
Social inuence (enumerated social
network members who “played
signicant role in life during past
year” and “have encouraged you to
eat more FVs/do PA”)
Social inuence Individual/
interpersonal Resource
Shaw-Perry et al.,
 []Social networks
Organized health programming
sessions transmitted to children
through social structures (home,
health class, school cafeteria, and
aer school)
organizational Channel
Gessell et al.,  []Socialnetworks
Social network evolution over
duration of intervention
Social network survey developed to
assess changes in social
Social network, social
interpersonal Tar g e t
Journal of Obesity
Table  provides a descriptive overview of these  stud-
e vast majority of studies (𝑁=22)featuredsocial
support as the social relational construct, whether alone
(𝑁=20) or in combination with social capital []or
social cohesion []. e ways that social support was incor-
porated into interventions ranged widely between interven-
tions related to the provisioning of professional advice and
telephone consultation, to motivational workbooks, to the
inclusion of a family or friend in the program itself, and
to instructional sessions or interactive group sessions. e
measurement of social support varied considerably across
interventions from no measures, formal survey instruments,
to informal qualitative assessments. Ten of the twenty studies
did not measure social support despite the fact that the
construct was included in the abstract or description of the
intervention. Two studies featured social cohesion, one of
which specically examined family cohesion []andthe
othersocialcohesioninaweight-lossgroup[]. Social
cohesion was measured in both studies with the use of
(dierent) questionnaire scales. Six studies featured social
the way in which social networks were incorporated varied
considerably. Social networks were observed to be used as
a study recruitment strategy [], a structure for transmit-
ting health programs and social inuence [], and a
changeable entity which might evolve in the intervention
[]. ree of the six studies measured the social network
component, which included a study-specic survey [], a
qualitative report of social inuence [], and a quantitative
report of social inuence []. ere were no studies that
addressed social trust, collective ecacy, or social capital
exclusively. Interventions focused primarily on the individual
example, a school-based intervention program tailored for
adolescent girls sent home four parent newsletters/progress
reports which reported their childrens time spent in physical
activity, sedentary behaviours, and self-reported fruit and
vegetable consumption. In addition, the newsletters included
information meant to increase awareness and encourage par-
ents to support their children’s physical activity and dietary
behaviors []. Such an intervention would be considered
primarily an intervention at the individual level with minimal
crossover into the interpersonal level. One study intervened
at the organizational level [], and no studies were found
to intervene at community or political levels. ere were
a number of studies which were seemingly conducted at
closer examination were in fact targeting individuals within
broader settings rather than targeting change at a higher
social ecological level itself. For example, the “Choose to
Move for +(Positive) Living” intervention drew participants
from a community-based “Stay the Course” obesity support
group and sought to determine the inuence of psychosocial
aspects of the (physical activity and heart healthy living)
program on increasing physical tness, perceived social
support and quality of life, and stage of health behaviour
change for physical activity. ese programs objectives were
T : Descriptive overview of intervention studies found per-
taining to social relational constructs and obesity.
Social relational construct 𝑁=30
Social support 
Social cohesion
Social network
Social trust
Collective ecacy
Social capital
Multiple social relational constructs
Social support-social cohesion
Social support-social capital
Social ecological level targeted 𝑁=30
Single level target
Individual 
Interpersonal environment
Organizational environment
Community —
Political environment
Multiple level target
Individual-interpersonal 
Individual-organizational 
Interpersonal-organizational —
Individual-interpersonal-organizational 
eory or model 𝑁=30
Health belief model  (+)
Stages of change (transtheoretical model)  (+)
Social learning theory (social cognitive theory)  (+)
eory of planned behaviour
Social support theory  (+)
Social comparison/inuence/modeling theory  (+)
Ecological approaches (CBPR, SEM)
Multiple theories, models, or approaches
No reference to theoretical rationale
Conceptual role of social relational construct 𝑁=30
Intervention channel 
Ancillary resource 
Intervention target
+ in theor y section indicates t he addition of partia l references of multiple the-
ories. SEM: social ecological model; CBPR: community-based participatory
individual-oriented, and although the intervention appeared
to operate as a holistic, community-based program, it did
not intervene at the community level []. Similarly, the
KeAno Ola: Moloka’i’s community-based healthy lifestyle
program was conducted in thecommunityandwasbasedon
principles of community-based participatory research. Yet,
the intervention targeted individual nutrition education [].
A number of interventions did not include a theoretical
rationale or explanation related to the social aspect of the
Journal of Obesity
intervention or program (𝑁=9). Most interventions refer-
enced the stages of change model (or transtheoretical model)
and social cognitive theory (social learning theory) (𝑁=4
and 𝑁=7, resp.). e typology was developed to identify the
in the interventions. e role that researchers considered the
social constructs to play in obesity prevention was reected
in the placement of the construct along the intervention
pathway. Two of the thirty studies reviewed featured social
relational constructs (social networks []andsocialsupport
[]) as intervention targets. Of the studies which featured
social support as the social relational construct, twelve of
these operationalized social support as an ancillary resource
with the remaining seven studies operationalizing social
support as a channel. e studies that featured social cohesion
lary resource. Social networks were mainly operationalized in
these studies as a channel to deliver the intervention itself [,
,]. Two studies included social networks as an ancillary
resource [,], and one exceptional study conceptualized
social networks as an intervention target [].
4. Discussion
sity interventions targeting social relational constructs and
characterize the degree to which these interventions have
addressed key social relational constructs in intervention
design and implementation. Social support was the predom-
inant social relational construct targeted [,,,
], treated as a mediator or channel [], or used as the
control treatment in a trial []. Social support was not
always clearly dened, with a diverse range of social support
(peer, family, group, and professional) being delivered either
inperson through peer groups or professional therapy or
remotely through such tools as handbooks, newsletters, or
electronic support messages. e measurement of social sup-
port also varied across interventions (e.g., perceived versus
inherent in any intervention that involved a support group.
For example, monthly meetings of overweight/obese individ-
uals who might share their challenges with healthy eating or
physical activity were considered to be inherently supportive
and equally available to all participants. As a result, many
interventions failed to measure whether participants actually
received social support. Only four of the  studies which
focused on social support included a theoretical rationale or
evidence for addressing social support in the intervention
[,,,]. e dierent functions of social support
(informational, emotional, t angible, and belonging) were out-
]. e nondierentiation of social support highlights the
atheoretical treatment of social support as an agent of change
in reducing obesity. Five studies mentioned social support
in combination with social cohesion as shared attributes of
peer support groups but did not distinguish between these
two dierent social relational constructs by denition or mea-
surement [,,,,]. Overall, social networks were
largely limited to methodological applications, as a means
of study recruitment or disseminating information related to
behavioural change. Little attention was given to the network
measures or the eects that social networks might have on
health. One exception was Gessell et al.’s [] study in which
they examined the evolution of social networks over the
duration of an obesity prevention intervention. In terms of
other social relational constructs, there were no studies which
discussed social trust, collective ecacy, or social capital. e
lack of interventions targeting these higher ecological social
network or relational variables suggests that there is still
much work to do in translating social capital work into actual
interventions, specically obesity. In addition, there may be
complex” social interventions in public health practice. Social
support was inconsistently dened, measured, and applied
in the current collection of the literature; this might imply
that health researchers are dierentially receptive to including
social support in an intervention, as compared to other social
relational constructs. Social support may seem intuitive and
most easily intervened on amidst the diering denitions
and approaches to measuring social capital; the sophisticated
methods of social network analysis; and the vagueness of
social cohesion and collective ecacy (and challenges of
e social ecological model provides a framework from
which to discern and compare the complexity of the dif-
ferent interventions examined in the current review. While
intraindividual factors, including beliefs, knowledge, and
skills, are important aspects in the behaviour change process,
interventions which are limited to targeting change at an indi-
vidual level fail to address the importance of broader social,
physical, economic and political contexts. e breakdown
of study types by social ecological level was shown to be
pyramid-shaped with the vast majority of studies focused on
the individual [,,,,,,]andafew
interventions that included components which spanned into
the interpersonal [,,,,,,] or organizational
realms [,,]. Within organizational realms, interven-
tions tended to target making nutritional or physical activity
resources available. For example, in a school setting, play-
grounds and school yards were made accessible for children
to play aer end of curricular program, and school canteens
were obliged to have fresh fruit and freshly made juices [].
Another study program modied the cafeteria food service
program (the contents of vending machines), and physical
education programs [] and another intervention included
implementing short PA breaks during lessons [].
e prominence of individual-level obesity interventions
was matched by the greater reliance on theoretical perspec-
tives built on individual psychosocial and behavioral models
largely centered on behavioral psychology, including social
cognitive theory, the transtheoretical model, and the theory
of planned behavior. e lack of social theory in intervention
planning limits the development of higher ecological level
interventions on obesity. For example, an obesity intervention
which is based solely on social cognitive theory would likely
lack the breadth to investigate or address the range of
Journal of Obesity
environmental factors that might impact persons odds of
being obese.
Furthermore, the frequent reference to self-ecacy in
the selected interventions requires additional attention. Self-
ecacy—which comprises an individual’s motivation, locus
of control, and behavioural choices, intentions, and actions
with respect to their goals, tasks, and challenges—was oen
included as a predictor, mediator, or moderator of overweight
on personal responsibility and control belies the use of
concepts related to social, political, and organizational change
[]. is is not to detract from the value of individually ori-
ented theories []. However, mounting evidence suggests that
innovative strategies for addressing and preventing obesity at
a population level should entail theories and approaches that
operate from an ecological perspective [].
ere were a range of outcomes found in the set of
interventions. Obesity-related outcomes included () anthro-
pometric indicators, such as body mass index or body fat
percentage, () physiological measures of cholesterol, blood
pressure, and blood sugar, and () behavioural risk factors
such as physical activity, dietary patterns and knowledge,
screen time, sedentary time, and smoking. A number of
studies included psychological and psychosocial outcomes,
such as depressive symptoms, self-ecacy, and motivation,
while some studies also included social indicators, such as
social support.
e conceptualization of a social relational construct as
an intervention target would suggest that the researchers view
the particular construct as integral to the obesity pathway.
Yet, within our sample of interventions, social relational
constructs were predominantly incorporated as a channel
through which to deliver the intervention, or a nonessential
intervention resource. Accordingly, these social relational
constructs may be seen as being useful but not amenable
characteristic in and of themselves. Although the fram-
ing of the rationale of some studies suggests a concep-
tual emphasis being put on the respective social relational
through in practice. When examining the studies collectively,
these ndings suggest either (i) a possible stagnation of
intervention research that builds on dierent social relational
constructs as they contribute to obesity or (ii) the idea that
the conceptualization, implementation, and evaluation of
interventions which incorporate social relational constructs
and theories beyond the individual are dauntingly complex
and inaccessible among researchers.
Despite the comprehensiveness of our search strategy, our
search criteria may have favored the discovery of smaller
scale interventions that would be communicated in more
traditional academic outlets. Accordingly, one limitation of
our study may have been the potential exclusion of broad-
er policy planning interventions that might target more
upstream social political determinants of obesity. Upstream
social interventions might consist of one or more social
relational constructs or address multiple levels of the social
ecological framework. Nevertheless, the lack of interventions
on social relational constructs suggests a limited landscape of
social relational interventions being implemented or incor-
porated in broader policy interventions.
5. Conclusion
To address the problem of obesity, there is a need for
public health programs to intervene at social ecological
levels beyond the individual. Intervening on interpersonal,
organizational, or community levels may be more eective
and sustainable in the long term in reducing individual risk
of obesity. e apparent lack of social network as opposed
to individual support interventions addressing obesity high-
lights a key gap existing between research and practice. While
social epidemiological research has examined the inuence
of social networks, social capital, and social environments
on obesity, this research has yet to be translated into the
design of social relational or network interventions that
address obesity. While social support may be an important
component of such interventions, there is a need to consider
more carefully the importance of social relationships and the
social environment on the onset and establishment of obesity.
e ndings of the current study suggest a vast potential for
methods and evidence from social health research to further
advances in addressing the obesity epidemic.
is paper was supported in part by the National Institutes of
Health Research (Grant no. ).
[] D. L. Katz, “Competing dietary claims for weight loss: nding
the forest through truculent trees,Annual Review of Public
Health, vol. , pp. –, .
[] S.K.Kumanyika,E.Obarzanek,N.Stettleretal.,“Population-
based prevention of obesity: the need for comprehensive pro-
motion of healthful eating, physical activity, and energy bal-
ance: a scientic statement from American Heart Association
Council on Epidemiology and Prevention, Interdisciplinary
Committee for prevention (formerly the expert panel on popu-
lation and prevention science),Circulation,vol.,no.,pp.
–, .
[] A. Jain, “Treating obesity in individuals and populations,Brit-
ish Medical Journal,vol.,no.,pp.,.
[] G. Egger and B. Swinburn, “An “ecological” approach to the
obesity pandemic,British Medical Journal,vol.,no.,
pp. –, .
[] T. A. Glass and M. J. McAtee, “Behavioral science at the cross-
roads in public health: extending horizons, envisioning the
future,Social Science and Medicine,vol.,no.,pp.,
and the built environment,JournaloftheAmericanDietetic
[] J.O.HillandJ.C.Peters,“Environmentalcontributionstothe
obesity epidemic,Science,vol.,no.,pp.,
[] M. B. Schwartz and K. D. Brownell, “e need for courageous
action to prevent obesity,” in Obesity Prevention and Public
Press, New York, NY, USA, .
Journal of Obesity
[] K. Glanz and D. B. Bishop, “e role of behavioral science
theory in development and implementation of public health
interventions,Annual Review of Public Health,vol.,pp.
, .
[] L. Cohen, D. P. Perales, and C. Steadman, “e O word: why the
focus on obesity is harmful to community health,California
[] B. Swinburn and G. Egger, “Preventive strategies against weight
gain and obesity,Obesity Reviews,vol.,no.,pp.,
[] R. G. Wilkinson, Unhealthy Societies: e Aictions of Inequal-
[] D.A.Cohen,B.K.Finch,A.Bower,andN.Sastry,“Collective
ecacy and obesity: the potential inuence of social factors on
health,Social Science and Medicine,vol.,no.,pp.,
[] R. J. Sampson, S. W. Raudenbush, and F. Earls, “Neighborhoods
and violent crime: a multilevel study of collective ecacy,
[] I.Kawachi,B.P.Kennedy,andR.Glass,“Socialcapitalandself-
rated health: a contextual analysis,American Journal of Public
[] S. Moore, M. Daniel, C. Paquet, L. Dub´
e, and L. Gauvin, “Asso-
ciation of individual network social capital with abdominal
adiposity, overweight and obesity,Journal of Public Health,vol.
[] N.A.ChristakisandJ.H.Fowler,“espreadofobesityina
large social network over  years,e New England Journal of
[] B. N. Uchino, “Social support and health: a review of physiolog-
ical processes potentially underlying links to disease outcomes,
Journal of Behavioral Medicine,vol.,no.,pp.,.
health behavior,” in Health Behavior and Health Education:
eory, Research, and Practice,K.Glanz,B.K.Rimer,andK.
Viswanath, Eds., Jossey-Bass, San Francisco, Calif, USA, th
edition, .
[] M. J. Wood, “Understanding and computing Cohens Kappa: a
tutorial,” , WebPsychEmpiricist, .
[] M.Bjelland,I.H.Bergh,M.Grydelandetal.,“Changesinado-
lescents’ intake of sugar-sweetened beverages and sedentary
behaviour: results at  month mid-way assessment of the
HEIA study—a comprehensive, mu lti-component school-bas ed
randomized trial,International Journal of Behavioral Nutrition
and Physical Activity,vol.,article,.
[] R. E. Lee, D. P. O’Conner, R. Smith-Ray et al., “Mediating eects
of group cohesion on physical activity and diet in women of
color: health is power,AmericanJournalofHealthPromotion,
vol. , no. , pp. e–e, .
[] J. De Niet, R. Timman, C. Rokx, M. Jongejan, J. Passchier, and
E. van den Akker, “Somatic complaints and social competence
predict success in childhood overweight treatment,Interna-
[] T.M.Leahey,J.G.omas,J.G.LaRose,andR.R.Wing,“A
randomized trial testing a contingency-based weight loss inter-
vention involving social reinforcement,Obesity,vol.,no.,
pp. –, .
[] S. Kim, D. Koniak-Grin, J. H. Flaskerud, and P. A. Guarnero,
“e impact of lay health advisors on cardiovascular health
promotion: using a community-based participatory approach,
e Journal of Cardiovascular Nursing,vol.,no.,pp.,
“Teammates and social inuence aect weight loss outcomes in
a team-based weight loss competition,Obesity,vol.,no.,
pp. –, .
[] A.A.Gorin,R.R.Wing,J.L.Favaetal.,“Weightlosstreatment
inuences untreated spouses and the home environment: evi-
dence of a ripple eect,International Journal of Obesity,vol.,
pp. –, .
[] S. Ashida, A. V. Wilkinson, and L. M. Koehly, “Social inuence
and motivation to change health behaviors among Mexican-
origin adults: implications for diet and physical activity,” Ameri-
can Journal of Health Promotion,vol.,no.,pp.,.
[] M. Shaw-Perry, C. Horner, R. P. Trevi˜
dez, and A. Bhardwaj, “NEEMA: a school-based diabetes risk
prevention program designed for African-American children,
Journal of the National Medical Association,vol.,no.,pp.
–, .
[] S. B. Gessell, K. D. Bess, and S. L. Barkin, “Understanding the
social networks that form within the context of an obesity pre-
vention intervention,Journal of Obesity,vol.,ArticleID
, p. , .
[] D. R. Lubans, P. J. Morgan, A. D. Okely et al., “Preventing
obesity among adolescent girls,Archives of Pediatric Adolescent
[] P. D. Angelopoulos, H. J. Milionis, E. Grammatikaki, G.
Moschonis, and Y. Manios, “Changes in BMI and blood pres-
sure aer a school based intervention: the CHILDREN study,
European Journal of Public Health,vol.,no.,pp.,
[] J. A. Peterson and P. Ward-Smith, “Choose to move for positive
living: physical activity program for obese women,Holistic
Nursing Practice, vol. , no. , pp. –, .
[] K. S. Gellert, R. E. Aubert, and J. S. Mikami, “Ke`
eAno Ola:
es community-based healthy lifestyle modication
program,American Journal of Public Health,vol.,no.,pp.
–, .
[] R. F. Kushner, D. J. Blatner, D. E. Jewell, and K. Rudlo, “e
PPET study: people and pets exercising together,Obesity,vol.
, no. , pp. –, .
[] J. H. Rimmer, A. Rauworth, E. Wang, P. S. Heckerling, and
B. S. Gerber, “A randomized controlled trial to increase phys-
ical activity and reduce obesity in a predominantly African
American group of women with mobility disabilities and severe
obesity,Preventive Medicine,vol.,no.,pp.,.
[] M. R. Stolley, L. K. Sharp, A. Oh, and L. Schier, “A weight loss
intervention for African American breast cancer survivors,
,Preventing Chronic Disease,vol.,no.,articleA,.
[] E. Hemmingsson, M. Hell´
enius, U. Ekelund, J. Bergstr¨
om, and
S. R¨
ossner, “Impact of social support intensity on walking in the
severely obese: a randomized clinical trial,Obesity,vol.,no.
, pp. –, .
[] R. T. Anderson, A. King, A. L. Stewart, F. Camacho, and W.
J. Rejeski, “Physical activity counseling in primary care and
patient well-being: do patients benet?” Annals of Behavioral
Medicine, vol. , no. , pp. –, .
[] K. I. Gallagher, J. M. Jakicic, M. A. Napolitano, and B. H. Mar-
cus, “Psychosocial factors related to physical activity and weight
loss in overweight women,Medicine and Science in Sports and
Exercise, vol. , no. , pp. –, .
 Journal of Obesity
[] T. L. Pettman, G. M. H. Misan, K. Owen et al., “Self-
management for obesity and cardio-metabolic tness: descrip-
tion and evaluation of the lifestyle modication program of a
randomised controlled trial,International Journal of Behavioral
Nutrition and Physical Activity,vol.,article,.
[] M. Kiernan, S. D. Moore, D. E. Schoman et al., “Social support
for healthy behaviors: scale psychometrics and prediction of
weight loss among women in a behavioral program,Obesity,
vol. , no. , pp. –, .
[] C. R. Kalodner and J. L. DeLucia, “e individual and combined
eects of cognitive therapy and nutrition education as additions
to a behavior modication program for weight loss,Addictive
Behaviors, vol. , no. , pp. –, .
[] K. Casazza and M. Ciccazzo, “e method of delivery of nutri-
tion and physical activity information may play a rolein eliciting
behavior changes in adolescents,Eating Behaviors,vol.,no.,
[] P. Hajek, K. Humphrey, and H. McRobbie, “Using group
support to complement a task-based weight management pro-
gramme in multi-ethnic localities of high deprivation,Patient
Education and Counseling,vol.,no.,pp.,.
[] A.K.Yancey,W.J.McCarthy,G.G.Harrison,W.K.Wong,J.
M. Siegel, and J. Leslie, “Challenges in improving tness: results
of a community-based, randomized, controlled lifestyle change
intervention,Journal of Women’s Health,vol.,no.,pp.
, .
[] J. H. Cousins, D. S. Rubovits, J. K. Dunn, R. S. Reeves, A. G.
Ramirez, and J. P. Foreyt, “Family versus individually oriented
intervention for weight loss in Mexican American women,
Public Health Reports,vol.,no.,pp.,.
Berino, “Minimal in-person support as an adjunct to internet
obesity treatment,Annals of Behavioral Medicine,vol.,no.,
[] V. Leblanc, V. Provencher, C. B´
egin, L. Corneau, A. Tremblay,
and S. Lemieux, “Impact of a Health-At-Every-Size intervention
on changes in dietary intakes and eating patterns in pre-
menopausal overweight women: results of a randomized trial,
Clinical Nutrition,vol.,pp.,.
[] D.A.Williamson,C.M.Champagne,D.W.Harshaetal.,“Eect
of an environmental school-based obesity prevention program
on changes in body fat and body weight: a randomized trial,
[] T. T.-K. Huang and T. A. Glass, “Transforming research strate-
gies for understanding and preventing obesity,Journal of the
American Medical Association,vol.,no.,pp.,
... The occurrence of obesity epidemics is associated with an environment that promotes excessive food intake and insufficient levels of physical activity [64]. Social, economic, and behavioral aspects also contribute to the establishment and perpetuation of the obese phenotype [65]. As a consequence of the peaking rates related to the obesity-associated metabolic syndrome, liver disorders caused by this derangement show climbing statistics [66]. ...
Full-text available
Liver cancer is one of the most lethal malignancies and is commonly diagnosed as hepatocellular carcinoma (HCC), a tumor type that affects about 90% of patients. Non-alcoholic steatohepatitis (NASH) and obesity are both risk factors for this disease. HCC initiation and progression are deeply linked with changes in the hepatic microenvironment, with cytokines playing key roles. The understanding of the pathogenic pathways that connect these disorders to liver cancer remains poor. However, the inflammasome-mediated cytokines associated with both diseases are central actors in liver cancer progression. The release of the pro-inflammatory cytokines IL-1β and IL-18 during inflammasome activation leads to several detrimental effects on the liver microenvironment. Considering the critical crosstalk between obesity, NASH, and HCC, this review will present the connections of IL-1β and IL-18 from obesity-associated NASH with HCC and will discuss approaches to using these cytokines as therapeutic targets against HCC.
... Generally speaking, social intervention measures is a social coping mechanism, which refer to social forces such as governments, private institutions, and social organizations that borrow various measures before and after panic buying events to help people solve actual needs, restore psychological balance, and alleviate panic buying behavior. With regard to social intervention, scholars have conducted research from the perspectives of the government (9), enterprises (10), and individuals (11), but they mostly focus on other social issues, for example, curb information dissemination (12), reduce social loneliness (13), etc. There are few intervention studies on panic buying, and most of them are qualitative analysis, which lack of quantitative discussion. ...
Full-text available
COVID-19 that broke out at the end of 2019 continues to spread globally, with frequent occurrence of variant disease strains, thus epidemic prevention and control become a kind of routine job. At present, due to the prevention and control measures such as maintaining social distance and community blockades, there is a boom in material purchases in many places, which not only seriously endangers social order and public environmental safety, but also easily leads to the interruption of the supply chain and the shortage of social materials. This article aims to study the intervention methods to curb the spread and spread of panic buying behavior. Firstly, through crawler technology and LDA (Latent Dirichlet Allocation) topic model, this article analyzes the intervention measures taken by various social forces in China to curb the spread of panic buying, and summarizes the multi-channel intervention measures including online and offline forms. Secondly, through the multi-Agent Monte Carlo method, the targeted intervention mechanism is supplemented in each propagation link of the panic buying propagation model, and a new social intervention model of panic buying under sudden epidemic is constructed. Then, through MATLAB modeling and simulation, the main factors affecting panic buying intervention are discussed. The simulation results show that: (1) The single plan with the best intervention effect is the supply monitoring. While the official response can play an immediate inhibitory effect, but it is affected by credibility and timeliness. The intervention effect of psychological counseling is limited, and it generally needs to be used in combination with other measures. (2) The combination strategy with the best intervention effect is “supply monitoring + official response + psychological counseling,” and the worst is “information review and guidance + psychological counseling.” Supply monitoring is a key measure to curb panic buying. At the same time, “information review and guidance” will have a certain counter-effect in the combined strategy. Finally, the effectiveness and universality of the proposed model are verified by examples of China and Britain.
... L'anxiété, la peur ou la crainte de se noyer sont des sentiments à ne pas sous-estimer chez les pratiquants qui débutent le longe-côte (Amou, 2016). Par conséquent, il est nécessaire de prendre en considération les bons comme les mauvais effets de l'environnement de pratique sur l'individu (Leroux, Moore & Dubé, 2013). ...
Le longe-côte est une activité initialement destinée à servir de préparation physique pour les pratiquants d’aviron. Depuis 2010, elle s’est progressivement transformée en une activité de bien-être. Si certaines études ont pu démontrer les bénéfices physiologiques d’une activité aquatique comme le longe-côte, il n’en demeure pas moins qu’aucune étude n’a démontré l’effet et les représentations du milieu marin sur les craintes, les motifs de pratique ou encore les attentes du pratiquant au cours des premières séances. Pourtant, l’environnement dans lequel le longe-côte est pratiqué joue un rôle fondamental sur la perception du pratiquant. Cette étude associant l’observation de terrain aux entretiens semi-directifs tente d’analyser les connexions qui peuvent être générées dans le cadre de ce sport en plein essor. L’approche empirique mise en place dans cette recherche est basée sur des observations et des entretiens semi-directifs permettant de connaître plus en détail les déterminants d’une pratique hybride du longe-côte basée sur l’éveil des sens, l’aventure et le nomadisme. La prise de l’individu sur son milieu de pratique génère une perception et une attention particulières vis-à-vis du risque perçu et des situations d’apprentissage.
... Sociometry, with an already well-established literature, and SNA provide possibilities for mapping these influential social connections of the adolescents (Mérei 1971;Jones 2006;Grunspan et al. 2014). This type of information is only rarely used in health promotion aiming at adolescents, presumably partly because of its limited accessibility (Leroux et al. 2013;Járomi and Vitrai 2017). The present study aims to investigate the relations between the social connections in school classes and the health behaviour of the students, with the tools used to analyse complex systems, and thus help design and implement more targeted and efficient behaviour change interventions. ...
Full-text available
Aim The aim of the study was to investigate what kind of social networks can be identified in the class communities of 13–14-year-old students and how these networks can be utilized in students’ health behaviour change. Subject and methods A joint analysis of the results of a national, representative, cross-sectional study on the health behaviour of Hungarian 7th graders and a social network analysis on a sub-sample (40 classes, 680 students) was performed. The random network walk method was applied to identify social networks in classrooms. The assortativity, measuring the similarity of students’ health behaviour within classroom networks, was examined using Moran’s autocorrelation coefficient. Permutation was used to define whether the connections of a given network influence the similarities found. Results Classroom networks based on sympathy, rejection and popularity were detected, but only sympathy networks played a role in generating similarities in health behaviours, such as the assessment of health determinants, risk-taking behaviours, online gaming, eating in fast-food restaurants and being on a diet. However, no such link was observed either in connection with regular smoking, alcohol consumption, physical exercising or related to the intake of cola, energy drinks or sweets. Conclusion Because sympathy connections depend on individual preferences, the efficiency of interventions aiming at individuals is presumably higher where the role of sympathy relations was verified. Where this was not proven, presumably, community-level interventions are also needed for behaviour change. These results, gained by the innovative use of network analysis methods, can help health professionals implement more targeted and thus more effective behaviour change interventions.
... Studies on multi-person households point to family meals as promoting greater consumption of fresh produce, less fast food, and less sugar-sweetened beverages [26,27]. Leroux, Moore, and Dube reviewed obesity interventions targeting social relational factors, such as social support and social networks [28], and recommend addressing social-ecological levels when analyzing health interventions. ...
Full-text available
The aim of this study is to ascertain if the living environment (type of residential neighborhood and number of household members) will elucidate differences in obesity risk reduction behaviors and self-efficacy in Chinese Americans. A cross-sectional survey design was used to recruit participants from Los Angeles County and New York City metropolitan areas. A total of 650 adults were recruited from diverse socioeconomic backgrounds. Descriptive statistics were measured for 19 behaviors reflecting food intake and portion size control and items measuring self-efficacy and attitudes. T-tests were applied for the two categories of living environment. The mean age of the sample was 36.3 years. The ‘high income’ neighborhood group indicated a greater frequency of behaviors, including choosing steamed over fried foods (p < 0.01) and using small amounts of oil (p < 0.05). In general, this group exhibited more favorable attitudes and stronger self-efficacy to perform health behaviors. Multiple regression analyses point to the impact of self-efficacy in predicting behaviors. Nutrition professionals must assess client’s living environments in the adoption of obesity prevention behaviors and the fostering of behavioral confidence.
... 35 Egy 2013-ban készített kutatás a rendszertudományi szemlélet meglétét vizsgálta az elhízás visszaszorítását célzó beavatkozásokban. 36 Annak ellenére, hogy az áttekintett vizsgálatok a viselkedésváltoztatás elméletére alapultak és céljaik között szerepelt a társas támogatás erősítése, alig tekintettek az egyén szintjén túl. A szerzők következtetésként levonták, hogy a társas kapcsolatok bevonása még igen elhanyagolt az elhízás megelőzésben, pedig ennek erősítése kiemelt feladat kellene, hogy legyen. ...
Full-text available
A közlemény célja, hogy áttekintse az utóbbi években egyre több területen elfogadottá váló hálózatkutatási módszerek népegészségügy területén történő alkalmazásának lehetőségeit és tapasztalatait, valamint hogy bátorítsa a szakembereket és kutatókat a módszer minél szélesebb körben való felhasználására. A bemutatott közleményeket áttekintve egyértelműnek látszik, hogy ennek az újfajta elemzési módszernek helye van a modern népegészségügyben. Az így keletkező információk fontos segítséget nyújthatnak a szakembereknek a lakosság egészség-magatartásának megértésében és ezáltal hatásos népegészségügyi beavatkozások megvalósításában.
While associations between stress and hypertension have been documented, little research has examined the association between coping and hypertension, especially in the context of understanding racial disparities. Utilizing data from the CHDS-DISPAR study, we examine the association between avoidant coping and hypertension among adults age 50 while assessing for potential differences across (1) coping in response to the general stress and discrimination and (2) African American and White racial groups. Coping was measured using a 9-item scale with an avoidant coping subscale (e.g., drinking alcohol). Mean avoidance coping scores were calculated for both general stress and discrimination. No racial differences in avoidant coping were found. Within our sample (n = 414), there was a high burden of hypertension among African American respondents compared to White respondents (50.3% vs. 22.6%). Models assessed associations between avoidant coping and hypertension adjusted for sociodemographic factors, obesity, and either experience of stress or discrimination depending on the coping domain examined. Avoidant coping in response to the general stress and discrimination was associated with increased hypertension among White respondents (PR: 1.63 [95%CI 1.01, 2.24]; PR: 1.69 [95%CI 1.12, 2.26], respectively) and no associations among African American respondents (PR: 0.83 [95%CI 0.57, 1.09]; PR: 0.82 [95%CI 0.52, 1.12], respectively). This research suggests that racial disparities in hypertension may not be attributable to individual-level coping behaviors.
Full-text available
Background: Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity. Objective: This study aims to examine web-based social factors in relation to obesity research. Methods: We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed. Results: In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users' connection to the real world. Conclusions: We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user's web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle.
Full-text available
PurposeThis study sought to develop a psychometrically sound measure to assess effective and ineffective forms of input from others regarding eating, physical activity, and weight in higher-weight people, namely, the Weight-Related Interactions Scale (WRIS).Methods Participants (n = 736) were adults in the overweight/obese weight ranges who completed the WRIS and measures of weight-specific social support, emotional eating, weight stigma, eating-specific self efficacy, and social desirability.ResultsExploratory and confirmatory factor analyses of the WRIS supported a three-factor solution of ‘Criticism’, ‘Minimization’, and ‘Collaboration’ as forms of weight-related input from others. Support was found for the reliability and the concurrent, convergent, and divergent validity of the WRIS.Conclusions The WRIS is a promising new instrument for comprehensively assessing the input of others in relation to eating, physical activity, and weight among higher-weight individuals.Level of evidenceLevel III. Evidence obtained from well-designed cohort or case–control analytic studies.
Full-text available
Purpose This study sought to develop a psychometrically-sound measure to assess effective and ineffective forms of input from others for managing one’s weight, namely, the Weight-Related Interactions Scale (WRIS). Methods Participants (n = 736) were adults in the overweight/obese weight range who completed the WRIS and measures of weight-specific social support, emotional eating, weight-related stigma, eating-specific self-efficacy, and social desirability. Results Exploratory and confirmatory factor analyses of the WRIS supported a three-factor solution of ‘Criticism’, ‘Minimization’, and ‘Collaboration’ as forms of weight-related input from others. Support was found for the reliability and the concurrent, convergent, and divergent validity of the WRIS. Conclusions The WRIS is a promising new instrument for comprehensively assessing the input of others in relation to managing one’s weight, eating, and physical activity. Level of evidence Level III. Evidence obtained from well-designed cohort or case-control analytic studies.
Full-text available
Focusing on the obese and overweight individual alone and is not helping us address the broader social and economic issues that influence people's lives. This paper discusses strategies to remove us from a focus on the O word and from blaming the individual for their condition. In recent years, newspapers, magazines, and the electronic media have covered obesity and overweight extensively. The "O" word -obesity -seems to be everywhere. Indeed, media coverage of obesity almost quadrupled from January 1999 to April 2005 in the U.S. (International Food Information Council (IFIC) Foundation, 2005). This intense coverage even led the Center for Consumer Freedom (2005), a restaurant and food industry supported group, to label the obesity coverage as "hype" and an "obesity-mortality myth". Regardless of the controversy over the exact number of deaths associated with overweight and obesity (, 2005), clearly, the National Center for Health Statistics data shows the doubling of obese adults and the tripling of overweight young people (ages 6-19) over the last 30 years (National Center for Health Statistics, 2004). Hidden in this confusing rhetoric is an important message that many will find startling: while there are real concerns related to 60 million obese adults and 9 million overweight youth, the single-minded focus on weight results in prejudice towards the obese and overweight and negatively impacts community health overall.
Full-text available
Background. Antiobesity interventions have generally failed. Research now suggests that interventions must be informed by an understanding of the social environment. Objective. To examine if new social networks form between families participating in a group-level pediatric obesity prevention trial. Methods. Latino parent-preschool child dyads (N = 79) completed the 3-month trial. The intervention met weekly in consistent groups to practice healthy lifestyles. The control met monthly in inconsistent groups to learn about school readiness. UCINET and SIENA were used to examine network dynamics. Results. Children's mean age was 4.2 years (SD = 0.9), and 44% were overweight/obese (BMI ≥ 85th percentile). Parents were predominantly mothers (97%), with a mean age of 31.4 years (SD = 5.4), and 81% were overweight/obese (BMI ≥ 25). Over the study, a new social network evolved among participating families. Parents selectively formed friendship ties based on child BMI z-score, (t = 2.08; P < .05). This reveals the tendency for mothers to form new friendships with mothers whose children have similar body types. Discussion. Participating in a group-level intervention resulted in new social network formation. New ties were greatest with mothers who had children of similar body types. This finding might contribute to the known inability of parents to recognize child overweight.
Introduction: Breast cancer survival rates are lower for African American women than for white women. Obesity, high-fat diets, and lack of regular physical activity increase risk for breast cancer recurrence, comorbid conditions, and premature death. Eighty-two percent of African American women are overweight or obese, partly because of unhealthy eating and exercise patterns. Although successful weight loss and lifestyle interventions for breast cancer survivors are documented, none has considered the needs of African American breast cancer survivors. This study assessed the feasibility and impact of Moving Forward, a culturally tailored weight loss program for African American breast cancer survivors. Methods: The study used a pre-post design with a convenience sample of 23 African American breast cancer survivors. The 6-month intervention was theory-based and incorporated qualitative data from focus groups with the targeted community, urban African American breast cancer survivors. Data on weight, body mass index (BMI), diet, physical activity, social support, and quality of life were collected at baseline and at 6 months. Results: After the intervention, we noted significant differences in weight, BMI, dietary fat intake, vegetable consumption, vigorous physical activity, and social support. Conclusion: This is the first published report of Moving Forward, a weight loss intervention designed for African American breast cancer survivors. Although a randomized trial is needed to establish efficacy, the positive results of this intervention suggest that this weight loss intervention may be feasible for African American breast cancer survivors. Lifestyle interventions may reduce the disparities in breast cancer mortality rates.
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.
Objective To evaluate the impact of a 12-month multicomponent school-based obesity prevention program, Nutrition and Enjoyable Activity for Teen Girls among adolescent girls. Design Group randomized controlled trial with 12-month follow-up. Setting Twelve secondary schools in low-income communities in the Hunter and Central Coast regions of New South Wales, Australia. Participants Three hundred fifty-seven adolescent girls aged 12 to 14 years. Intervention A multicomponent school-based intervention program tailored for adolescent girls. The intervention was based on social cognitive theory and included teacher professional development, enhanced school sport sessions, interactive seminars, nutrition workshops, lunch-time physical activity sessions, handbooks and pedometers for self-monitoring, parent newsletters, and text messaging for social support. Main Outcome Measures Body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), BMI z score, body fat percentage, physical activity, screen time, dietary intake, and self-esteem. Results After 12 months, changes in BMI (adjusted mean difference, −0.19; 95% CI, −0.70 to 0.33), BMI z score (mean, −0.08; 95% CI, −0.20 to 0.04), and body fat percentage (mean, −1.09; 95% CI, −2.88 to 0.70) were in favor of the intervention, but they were not statistically different from those in the control group. Changes in screen time were statistically significant (mean, −30.67 min/d; 95% CI, −62.43 to −1.06), but there were no group by time effects for physical activity, dietary behavior, or self-esteem. Conclusions A school-based intervention tailored for adolescent girls from schools located in low-income communities did not significantly reduce BMI gain. However, changes in body composition were of a magnitude similar to previous studies and may be associated with clinically important health outcomes. Trial Registration Identifier: 12610000330044
The Choose to Move for + (Positive) Living program was implemented to increase physical activity among obese women. A holistic approach was used to promote stage of health behavior change, social support, and quality of life and reduce depression. Within 6 months, physical fitness improved and depressive symptoms decreased.