{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T05:55:25Z","timestamp":1790920525899,"version":"4.1.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"\n Background<\/jats:title>\n The Balance Evaluation Systems Test (BESTest) and its stroke-specific short version (S-BESTest) are widely used to evaluate balance after stroke. However, clinically meaningful classifications of balance ability derived from these instruments remain unavailable.<\/jats:p>\n <\/jats:sec>\n \n Objectives<\/jats:title>\n To examine the dimensionality of the BESTest and S-BESTest and establish Rasch-based classifications of balance ability in individuals with stroke.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this cross-sectional study, 335 individuals with stroke from two tertiary care hospitals completed the BESTest and S-BESTest. Exploratory factor analysis (EFA) was performed to identify the underlying factor structure of each instrument. Principal component analysis of Rasch residuals (PCAR) and disattenuated correlations between Rasch person measures were subsequently used to evaluate essential unidimensionality. Rasch analysis using the Andrich Rating Scale Model was then conducted to derive balance ability classifications and corresponding raw-score cutoffs. Known-groups validity of the proposed classifications was examined using lower extremity Fugl-Meyer Assessment (FM-LE) scores.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n EFA identified a three-factor solution for the BESTest and a two-factor solution for the S-BESTest. However, PCAR and disattenuated correlations demonstrated that both instruments functioned as essentially unidimensional measures of balance ability. Rasch analysis showed acceptable-to-excellent reliability and yielded clinically interpretable balance ability classifications with corresponding raw-score cutoff values. The BESTest was classified into six balance impairment levels, ranging from very severe to very mild impairment, whereas the S-BESTest was classified into four levels, ranging from very severe to mild impairment. FM-LE scores differed significantly across all classification levels for both instruments, supporting good known-groups validity.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Although exploratory factor analysis identified multiple factors, the BESTest and S-BESTest functioned as essentially unidimensional measures of balance ability in individuals with stroke. The proposed Rasch-based classifications provide clinically interpretable balance ability levels that may facilitate communication, facilitate individualized rehabilitation planning, and support clinical decision-making.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1889177","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T05:35:49Z","timestamp":1790919349000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Rasch-based classification of post-stroke balance ability: dimensionality and clinical validation of the BESTest and S-BESTest"],"prefix":"10.3389","volume":"18","author":[{"given":"Thitimard","family":"Winairuk","sequence":"first","affiliation":[{"name":"Faculty of Physical Therapy, Srinakharinwirot University","place":["Nakhon Nayok, Thailand"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco Y. 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