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Algorithm-Defined Muscle Dysmorphia Estimates Across Weighting and Case-Definition Strategies in a Gender-Balanced

Christopher Zaiser1, Nora M Laskowski1, Georg Halbeisen2

  • 1Clinic for Psychosomatic Medicine and Psychotherapy, LWL-University Clinic, Ruhr-University Bochum, Bochum, Germany.

The International Journal of Eating Disorders
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PubMed
Summary

Estimates for muscle dysmorphia (MD) in adults vary significantly based on how data is weighted and defined. Transparent reporting of these methods is crucial for understanding the prevalence of this condition.

Keywords:
body imagecase definitioneating disordersepidemiologygender differencesmuscle dysmorphiaonline sampleprevalencepsychometricsweighting

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Area of Science:

  • Psychology
  • Epidemiology
  • Public Health

Background:

  • Epidemiological data on muscle dysmorphia (MD) is scarce.
  • Self-report algorithm-defined prevalence estimates for MD can be influenced by sampling methods and case definitions.
  • Understanding the variability in MD estimates is essential for accurate public health assessments.

Purpose of the Study:

  • To investigate how algorithm-defined muscle dysmorphia (MD) prevalence estimates and correlates differ across various weighting and case-definition scenarios.
  • To analyze these variations within a gender-balanced German online sample.
  • To assess the impact of different analytical approaches on MD prevalence.

Main Methods:

  • A cross-sectional web-based study involving 1468 German adults.
  • Self-report measures were used to estimate algorithm-defined MD.
  • Estimates were compared across four scenarios: unweighted vs. weighted (age/gender) and global vs. gender-specific criterion A cutoffs.
  • Logistic regression models examined correlates across different analytical specifications.

Main Results:

  • Algorithm-defined MD estimates showed substantial variation depending on the operationalization used.
  • Prevalence estimates ranged from 6.2% (unweighted global) to 2.6% (weighted gender-specific).
  • Lower BMI and higher identity disturbance were consistent correlates of MD; female gender showed lower odds in pooled models.

Conclusions:

  • Self-report algorithm-defined MD affects a notable portion of the adult population.
  • Prevalence estimates are highly sensitive to the weighting strategy and case definition employed.
  • Transparent reporting of algorithmic operationalizations is necessary for reliable MD research.