The International journal of eating disorders · 2026
Algorithm-Defined Muscle Dysmorphia Estimates Across Weighting and Case-Definition Strategies in a Gender-Balanced Adult Online Sample.
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Zaiser Christopher, Laskowski Nora M, Halbeisen Georg, Lieb Marietta, Paslakis Georgios
Аннотация
OBJECTIVE: Epidemiological evidence on muscle dysmorphia (MD) remains limited, and self-report algorithm-defined estimates may depend on sampling and case definitions. We examined how algorithm-defined MD estimates and exploratory correlates varied across weighting and case-definition scenarios in a gender-balanced German online sample.
METHOD: In this cross-sectional web-based study, 1468 adults from Germany completed self-report measures: 739 (50.3%) men, 706 (48.1%) women, 21 (1.4%) nonbinary/diverse participants, and 2 (0.1%) who preferred not to disclose their gender. Algorithm-defined MD was estimated using a self-report algorithm derived from prior epidemiological work. Estimates were compared across four scenarios combining unweighted versus age- and gender-weighted analyses with global versus gender-specific criterion A cutoffs. Logistic regression models examined correlates across weighted and unweighted analytic specifications.
RESULTS: Algorithm-defined estimates varied substantially across operationalizations. The unweighted global algorithm yielded an estimate of 6.2% overall and 11.0% in men. The weighted gender-specific scenario yielded 2.6% overall, 3.5% in men, and 1.7% in women. Across regression specifications, lower BMI and higher identity disturbance were the most consistent correlates of algorithm-defined MD. Female gender showed lower odds in pooled models, particularly under the global cutoff, but this finding should be interpreted cautiously.
DISCUSSION: Self-report algorithm-defined MD may affect a meaningful minority of adults, but estimates depend strongly on weighting strategy and case definition. These findings highlight the need for transparent reporting of algorithmic operationalizations.
METHOD: In this cross-sectional web-based study, 1468 adults from Germany completed self-report measures: 739 (50.3%) men, 706 (48.1%) women, 21 (1.4%) nonbinary/diverse participants, and 2 (0.1%) who preferred not to disclose their gender. Algorithm-defined MD was estimated using a self-report algorithm derived from prior epidemiological work. Estimates were compared across four scenarios combining unweighted versus age- and gender-weighted analyses with global versus gender-specific criterion A cutoffs. Logistic regression models examined correlates across weighted and unweighted analytic specifications.
RESULTS: Algorithm-defined estimates varied substantially across operationalizations. The unweighted global algorithm yielded an estimate of 6.2% overall and 11.0% in men. The weighted gender-specific scenario yielded 2.6% overall, 3.5% in men, and 1.7% in women. Across regression specifications, lower BMI and higher identity disturbance were the most consistent correlates of algorithm-defined MD. Female gender showed lower odds in pooled models, particularly under the global cutoff, but this finding should be interpreted cautiously.
DISCUSSION: Self-report algorithm-defined MD may affect a meaningful minority of adults, but estimates depend strongly on weighting strategy and case definition. These findings highlight the need for transparent reporting of algorithmic operationalizations.