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A Novel Mobile Tool (Somatomap) to Assess Body Image Perception Pilot Tested With Fashion Models and Nonmodels: Cross-Sectional Study
- Source :
- JMIR Mental Health
- Publication Year :
- 2019
- Publisher :
- JMIR Publications, 2019.
-
Abstract
- Background Distorted perception of one’s body and appearance, in general, is a core feature of several psychiatric disorders including anorexia nervosa and body dysmorphic disorder and is operative to varying degrees in nonclinical populations. Yet, body image perception is challenging to assess, given its subjective nature and variety of manifestations. The currently available methods have several limitations including restricted ability to assess perceptions of specific body areas. To address these limitations, we created Somatomap, a mobile tool that enables individuals to visually represent their perception of body-part sizes and shapes as well as areas of body concerns and record the emotional valence of concerns. Objective This study aimed to develop and pilot test the feasibility of a novel mobile tool for assessing 2D and 3D body image perception. Methods We developed a mobile 2D tool consisting of a manikin figure on which participants outline areas of body concern and indicate the nature, intensity, and emotional valence of the concern. We also developed a mobile 3D tool consisting of an avatar on which participants select individual body parts and use sliders to manipulate their size and shape. The tool was pilot tested on 103 women: 65 professional fashion models, a group disproportionately exposed to their own visual appearance, and 38 nonmodels from the general population. Acceptability was assessed via a usability rating scale. To identify areas of body concern in 2D, topographical body maps were created by combining assessments across individuals. Statistical body maps of group differences in body concern were subsequently calculated using the formula for proportional z-score. To identify areas of body concern in 3D, participants’ subjective estimates from the 3D avatar were compared to corresponding measurements of their actual body parts. Discrepancy scores were calculated based on the difference between the perceived and actual body parts and evaluated using multivariate analysis of covariance. Results Statistical body maps revealed different areas of body concern between models (more frequently about thighs and buttocks) and nonmodels (more frequently about abdomen/waist). Models were more accurate at estimating their overall body size, whereas nonmodels tended to underestimate the size of individual body parts, showing greater discrepancy scores for bust, biceps, waist, hips, and calves but not shoulders and thighs. Models and nonmodels reported high ease-of-use scores (8.4/10 and 8.5/10, respectively), and the resulting 3D avatar closely resembled their actual body (72.7% and 75.2%, respectively). Conclusions These pilot results suggest that Somatomap is feasible to use and offers new opportunities for assessment of body image perception in mobile settings. Although further testing is needed to determine the applicability of this approach to other populations, Somatomap provides unique insight into how humans perceive and represent the visual characteristics of their body.
- Subjects :
- Multivariate analysis
Waist
body image
media_common.quotation_subject
body perception
Population
digital health
03 medical and health sciences
0302 clinical medicine
Rating scale
Perception
mobile app
medicine
body image disorder
education
mobile health
media_common
education.field_of_study
Original Paper
business.industry
Usability
Visual appearance
medicine.disease
3. Good health
030227 psychiatry
Psychiatry and Mental health
eating disorder
Body dysmorphic disorder
body representation
Psychology
business
030217 neurology & neurosurgery
mental health
Cognitive psychology
Subjects
Details
- Language :
- English
- ISSN :
- 23687959
- Volume :
- 6
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- JMIR Mental Health
- Accession number :
- edsair.doi.dedup.....73cb583c8b08cb98336c9a84f9ec3300