---
_id: '13880'
abstract:
- lang: eng
text: "Objective\r\nEpidemiological 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.\r\nMethod\r\nIn 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.\r\nResults\r\nAlgorithm-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.\r\nDiscussion\r\nSelf-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.\r\n"
article_number: eat.70170
author:
- first_name: Christopher
full_name: Zaiser, Christopher
last_name: Zaiser
- first_name: Nora M.
full_name: Laskowski, Nora M.
last_name: Laskowski
- first_name: Georg
full_name: Halbeisen, Georg
id: '85780'
last_name: Halbeisen
orcid: 0000-0002-9529-2215
- first_name: Marietta
full_name: Lieb, Marietta
last_name: Lieb
- first_name: Georgios
full_name: Paslakis, Georgios
last_name: Paslakis
citation:
ama: Zaiser C, Laskowski NM, Halbeisen G, Lieb M, Paslakis G. Algorithm‐Defined
Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in
a Gender‐Balanced Adult Online Sample. International Journal of Eating Disorders.
Published online 2026. doi:10.1002/eat.70170
apa: Zaiser, C., Laskowski, N. M., Halbeisen, G., Lieb, M., & Paslakis, G. (2026).
Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition
Strategies in a Gender‐Balanced Adult Online Sample. International Journal
of Eating Disorders, Article eat. 70170. https://doi.org/10.1002/eat.70170
bjps: Zaiser C et al. (2026) Algorithm‐Defined Muscle Dysmorphia Estimates
Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online
Sample. International Journal of Eating Disorders.
chicago: Zaiser, Christopher, Nora M. Laskowski, Georg Halbeisen, Marietta Lieb,
and Georgios Paslakis. “Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting
and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample.” International
Journal of Eating Disorders, 2026. https://doi.org/10.1002/eat.70170.
chicago-de: Zaiser, Christopher, Nora M. Laskowski, Georg Halbeisen, Marietta Lieb
und Georgios Paslakis. 2026. Algorithm‐Defined Muscle Dysmorphia Estimates Across
Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample.
International Journal of Eating Disorders. doi:10.1002/eat.70170,
.
din1505-2-1: 'Zaiser, Christopher
; Laskowski, Nora M. ; Halbeisen,
Georg ; Lieb, Marietta ;
Paslakis, Georgios: Algorithm‐Defined
Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in
a Gender‐Balanced Adult Online Sample. In: International Journal of Eating
Disorders. New York, NY , Wiley (2026)'
havard: C. Zaiser, N.M. Laskowski, G. Halbeisen, M. Lieb, G. Paslakis, Algorithm‐Defined
Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in
a Gender‐Balanced Adult Online Sample, International Journal of Eating Disorders.
(2026).
ieee: 'C. Zaiser, N. M. Laskowski, G. Halbeisen, M. Lieb, and G. Paslakis, “Algorithm‐Defined
Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in
a Gender‐Balanced Adult Online Sample,” International Journal of Eating Disorders,
Art. no. eat. 70170, 2026, doi: 10.1002/eat.70170.'
mla: Zaiser, Christopher, et al. “Algorithm‐Defined Muscle Dysmorphia Estimates
Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online
Sample.” International Journal of Eating Disorders, eat. 70170, 2026, https://doi.org/10.1002/eat.70170.
short: C. Zaiser, N.M. Laskowski, G. Halbeisen, M. Lieb, G. Paslakis, International
Journal of Eating Disorders (2026).
ufg: 'Zaiser, Christopher u. a.: Algorithm‐Defined Muscle Dysmorphia Estimates
Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online
Sample, in: International Journal of Eating Disorders (2026).'
van: Zaiser C, Laskowski NM, Halbeisen G, Lieb M, Paslakis G. Algorithm‐Defined
Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in
a Gender‐Balanced Adult Online Sample. International Journal of Eating Disorders.
2026;
date_created: 2026-07-28T08:39:30Z
date_updated: 2026-07-31T08:51:40Z
department:
- _id: DEP1500
doi: 10.1002/eat.70170
keyword:
- body image
- case definition
- eating disorders
- epidemiology
- gender differences
- muscle dysmorphia
- online sample
- prevalence
- psychometrics
- weighting
language:
- iso: eng
place: 'New York, NY '
publication: International Journal of Eating Disorders
publication_identifier:
eissn:
- 1098-108X
issn:
- 0276-3478
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition
Strategies in a Gender‐Balanced Adult Online Sample
type: scientific_journal_article
user_id: '83781'
year: '2026'
...