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Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014
 

Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014

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Presentation of the results of the ANAMIA Project by Paola Tubaro "Body Image and Eating Disorders Behavior. The Effect of Personal Networks"

Presentation of the results of the ANAMIA Project by Paola Tubaro "Body Image and Eating Disorders Behavior. The Effect of Personal Networks"

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    Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014 Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014 Presentation Transcript

    • Body image and eating disorders behaviors. The effects of personal networks Paola Tubaro! University of Greenwich, London & CNRS, Paris! Francesca Pallotti! University of Greenwich, London! Antonio A. Casilli! Telecom ParisTech & EHESS, Paris! Thomas W. Valente! University of Southern California, Los Angeles!
    • INTRODUCTION Eating disorders and body image •  Eating disorders are characterized by abnormal eating behaviors with either insufficient or excessive food intake and exercising to the detriment of physical and mental health. •  Eating disorders are accompanied by distorted perceived body image.
    • Body image and behavior Self-perceived image affects individuals’ behavior Source: A Brazilian campaign against anorexia
    • Body image and peer effects Networks, peer effects and body image •  Peer pressure is know to be a significant contributor to body image concerns and eating attitudes (Costa-Font and Jofre-Bonetpeer, 2011). •  "Teen girls' concerns about their own weight, about how they appear to others and their perceptions that their peers want them to be thin are significantly related to weight-control behavior“ (Mackey and La Greca, 2008) •  We use a network perspective to look more closely at the social environment to establish the extent to which interpersonal relationships affect the perception of individuals with respect to their body image
    • Our approach A personal networks approach •  We use ego-centric network data collected through a web survey of 284 English- and French-speaking respondents with eating disorders (Research project: Anamia). •  Network data include information on their broadly defined personal networks including face-to face and Internet-based networks.
    • The online questionnaire Questionnaire interface
    • General personal networks: using a visual sociogram •  In the online questionnaire, a graphical ego network applet enabled participants to draw their personal networks! •  Two graphical networks each! –  general personal network (school, family, workplace etc.)! –  online personal network (forums, blogs etc.)! The ANAMIA study
    • Network structure and composition •  Each graphical interface enabled participants to:! –  Add alters, specifying name, gender, and qualification (friend, family member etc.)! –  Position emotionally closest alters next to ego, and less close alters further away! –  Draw ties between alters;! –  Group alters in social circles (schoolmates, member of a sports team, participants in a forum etc.)! The ANAMIA study
    • Personal networks as drawn by respondents Some examples of personal networks Personal network data
    • Data Ego-networks •  We reconstructed 265 ego-networks •  Two types of network variables: 1. Structural : –  Adjusted density – taking into account social circles –  Size – the number of all alters 2. Compositional: –  –  –  –  Online and offline alters (multiplexity) (Blau index) Level of intimacy (proportion) Gender (proportion and variance) Qualification (Blau index)
    • Egos’ characteristics •  Social, demographic and economic characteristics! –  •  age, gender, geographic location, employment, qualifications, income, family structure, profession of parents, etc.;! Body and health ! –  –  –  –  Body image (how they see themselves, how others see them, how they would like to look);! Body characteristics (current weight; minimum and maximum weight achieved; desired weight; weight believed to be healthy for them; height; BMI); ! Exercise and sports practice; ! Health (what eating disorder, if any; whether currently or previously under treatment and if yes, for what diagnosis, with which professionals, and since when; the importance of health for them, for their families, and for their friends);! Some basic descriptive statistics:! •  36 FR; 148 UK! 1 •  ender: 95% women;! G •  ge: Average 21.7 (both FR and UK);! A •  tudents: Average FR 66%, UK 69%;! S •  orkers: Average FR 34%, UK 41%;! W •  MI is normal for 53.96% of participants; B underweight for 27.55, overweight for 18.49%;! •  ating disorders: EDNOS (FR 47%, UK 45%); E Anorexia nervosa (FR 17%, UK 34%); bulimia nervosa (FR 28%, UK 16%); Binge eating (FR 8%, UK 5%);! •  reatment: over 50% of the participants who T reported an eating disorder are being treated now!
    • Self-perception & Body image Figure Rating Scale 1 2 3 4 5 6 7 8 9 1.  If I had to describe myself, I would say that I look like (perceived body image - D) 2.  If I could choose, I would like to look as (ideal body image - C) 3.  People usually say that I look like (mediated body image - O) Each of these variables is measured on a scale ranging from 1 – very thin silhouette – to 9 – corpulent silhouette.
    • Data Dependent variables •  •  D – C: Difference between self-description and how one would choose to look;
 D – O: Difference between self-description and how people think others see them. A positive difference would indicate that the respondent’s current body image is heavier than his/her ideal or mediated body image. A negative difference would indicate the opposite.
    • MODEL Analysis •  Two-stages econometric analysis. As a first step, discrepancies in the two forms of body image are modeled for the whole sample. Secondly, we evaluate the determinants of discrepancies in body image for individuals in different BMI categories. •  Model: bivariate ordered probit model to account for the joint distribution of the two forms of body image distortions, as a function of both individual attributes and personal network characteristics. •  For the analysis we used the bioprobit Stata program written by Sajaia (2008). •  As our sub-samples consist of a limited number of observations, we check for the robustness of our models by applying a simple stochastic re-sampling procedure based on bootstrapping techniques.
    • Results: •  djusted density has an effect on D-C. A This result is also confirmed for the normal weight and overweight subsamples. •  ize is significant only for over- and S under-weight individuals. •  elational proximity matters for D-C, R for overweight only. •  eterogeneity in sex also matters for H over- and under-weight individuals. •  roportion of females: negative effect P for the underweight, positive effect for normal weight
    • Also: •  ositive effect of BMI (difference P between individual BMI and country-level average BMI): The more corpulent I am relative to my general social environment, the more dissatisfied I am with my body image. •  ositive effect of medical treatment on P both D – C and D – O. •  egative effect of age: older people N seem to have narrower gaps. •  ositive effect of doing sports on both D P – C and D – O.
    • Conclusions Effects of network structure and composition •  ffects of individual variables confirm findings from the literature (peer effects E from social environment broadly interpreted) • n addition, we find evidence of network effects on body image distortions I
    • THANKS! For more information: www.anamia.fr/en Contact: coordination@anamia.fr, p.tubaro@gre.ac.uk, f.pallotti@gre.ac.uk Twitter: @anamia