[{"user_id":"83781","citation":{"bjps":"<b>Zaiser C <i>et al.</i></b> (2026) Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample. <i>International Journal of Eating Disorders</i>.","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;","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. <i>International Journal of Eating Disorders</i>. doi:<a href=\"https://doi.org/10.1002/eat.70170\">10.1002/eat.70170</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Zaiser, Christopher</span> ; <span style=\"font-variant:small-caps;\">Laskowski, Nora M.</span> ; <span style=\"font-variant:small-caps;\">Halbeisen, Georg</span> ; <span style=\"font-variant:small-caps;\">Lieb, Marietta</span> ; <span style=\"font-variant:small-caps;\">Paslakis, Georgios</span>: Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample. In: <i>International Journal of Eating Disorders</i>. 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).","ufg":"<b>Zaiser, Christopher u. a.</b>: Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample, in: <i>International Journal of Eating Disorders</i> (2026).","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.” <i>International Journal of Eating Disorders</i>, 2026. <a href=\"https://doi.org/10.1002/eat.70170\">https://doi.org/10.1002/eat.70170</a>.","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,” <i>International Journal of Eating Disorders</i>, Art. no. eat. 70170, 2026, doi: <a href=\"https://doi.org/10.1002/eat.70170\">10.1002/eat.70170</a>.","mla":"Zaiser, Christopher, et al. “Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample.” <i>International Journal of Eating Disorders</i>, eat. 70170, 2026, <a href=\"https://doi.org/10.1002/eat.70170\">https://doi.org/10.1002/eat.70170</a>.","apa":"Zaiser, C., Laskowski, N. M., Halbeisen, G., Lieb, M., &#38; Paslakis, G. (2026). Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample. <i>International Journal of Eating Disorders</i>, Article eat. 70170. <a href=\"https://doi.org/10.1002/eat.70170\">https://doi.org/10.1002/eat.70170</a>","short":"C. Zaiser, N.M. Laskowski, G. Halbeisen, M. Lieb, G. Paslakis, International Journal of Eating Disorders (2026).","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. <i>International Journal of Eating Disorders</i>. Published online 2026. doi:<a href=\"https://doi.org/10.1002/eat.70170\">10.1002/eat.70170</a>"},"_id":"13880","place":"New York, NY ","date_updated":"2026-07-31T08:51:40Z","author":[{"first_name":"Christopher","full_name":"Zaiser, Christopher","last_name":"Zaiser"},{"full_name":"Laskowski, Nora M.","last_name":"Laskowski","first_name":"Nora M."},{"orcid":"0000-0002-9529-2215","last_name":"Halbeisen","full_name":"Halbeisen, Georg","first_name":"Georg","id":"85780"},{"first_name":"Marietta","full_name":"Lieb, Marietta","last_name":"Lieb"},{"first_name":"Georgios","full_name":"Paslakis, Georgios","last_name":"Paslakis"}],"department":[{"_id":"DEP1500"}],"publication_identifier":{"eissn":["1098-108X"],"issn":["0276-3478"]},"publication_status":"published","status":"public","language":[{"iso":"eng"}],"date_created":"2026-07-28T08:39:30Z","article_number":"eat.70170","quality_controlled":"1","type":"scientific_journal_article","title":"Algorithm‐Defined Muscle Dysmorphia Estimates Across Weighting and Case‐Definition Strategies in a Gender‐Balanced Adult Online Sample","publication":"International Journal of Eating Disorders","year":"2026","publisher":"Wiley","abstract":[{"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","lang":"eng"}],"keyword":["body image","case definition","eating disorders","epidemiology","gender differences","muscle dysmorphia","online sample","prevalence","psychometrics","weighting"],"doi":"10.1002/eat.70170"}]