Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample
- 1Clinic for Psychosomatic Medicine and Psychotherapy LWL‐University Clinic, Ruhr‐University Bochum Bochum Germany
- 2Faculty of Business Administration and Economics OWL University of Applied Sciences and Arts Lemgo Germany
Abstract
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.
Related
- A hybrid metaheuristic algorithm for efficient de novo drug design using SELFIES and adaptive search strategies
- Sex and weighting in abbreviated DASI-based perioperative risk prediction
- Age as a significant prognostic factor in patients meeting the global ARDS definition but not the Berlin definition
- Acyclovir and ganciclovir exposure and short-term outcomes in hospitalized children with EBV-associated mononucleosis-like illness: a retrospective cohort study using inverse probability of treatment weighting
- Intraoperative failure in valve-sparing aortic root replacement: technique, experience, and the definition of success.
BibTeX
@article{zaiser2026algorithmdefined,
title = {{Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample}},
author = {Christopher Zaiser and Nora M. Laskowski and Georg Halbeisen and Marietta Lieb and Georgios Paslakis},
journal = {The International Journal of Eating Disorders},
year = {2026},
month = jul,
doi = {10.1002/eat.70170},
url = {https://doi.org/10.1002/eat.70170},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample
AU - Christopher Zaiser
AU - Nora M. Laskowski
AU - Georg Halbeisen
AU - Marietta Lieb
AU - Georgios Paslakis
PY - 2026
DA - 2026/07/09
JO - The International Journal of Eating Disorders
DO - 10.1002/eat.70170
UR - https://doi.org/10.1002/eat.70170
AB - 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.
SN - 0276-3478
SN - 1098-108X
ER -