Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample

<jats:title>ABSTRACT</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Method</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Discussion</jats:title> <jats:p>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.</jats:p> </jats:sec>