Sleep Breath. 2026 Sep 5;30(5):254. doi: 10.1007/s11325-026-03802-z.
ABSTRACT
BACKGROUND: This study conducted a bibliometric and visualization analysis of research on stroke-related sleep disorders from 2015 to 2026. It aims to elucidate the current research landscape, identify key hotspots, and discern potential future trends within this field.
METHODS: We retrieved relevant publications on stroke-related sleep disorders from the Web of Science Core Collection database for the period from January 1, 2016, to December 31, 2025. Using Citespace (Version 6.2.R4), we generated and visualized knowledge maps encompassing analyses of authors, countries, institutions, keywords, co-cited references, co-cited authors, and co-cited journals.
RESULTS: A total of 607 publications were ultimately included. The annual publication count demonstrated a general upward trend over the past decade. Publication output peaked in 2025 with a transient decline in 2019. The United States emerged as the most productive country, followed closely by China. Network analyses revealed strong collaborative relationships among countries, institutions, and authors, shaping influential research clusters represented by scholars such as Devin L. Brown and Ronald D. Chervin. Among institutions, Harvard University contributed the most publications and exhibited a notably high centrality (0.06). Keyword co-occurrence analysis identified eight major thematic clusters and 20 keywords with the strongest citation bursts, with "ischemic stroke" and "apnea" emerging as the predominant core keywords.
CONCLUSIONS: Sleep apnea, particularly obstructive sleep apnea (OSA) and insomnia, remains the most extensively investigated sleep disorder in stroke populations. Hypertension emerged as one of the most frequent keywords among the identified influencing factors. Acute ischemic stroke represented the most common stroke type linked to sleep disturbances. Future investigations are likely to emphasize the identification of underlying pathophysiological mechanisms, systematic exploration of influencing factors, and the development of robust risk prediction models, ultimately aiming to inform clinical decision-making and improve long-term patient outcomes.
PMID:42700285 | DOI:10.1007/s11325-026-03802-z