Gait analysis in older adults with mild cognitive impairment: a bibliometric analysis of global trends, hotspots, and emerging frontiers - Zhong, Yuan; Huang, Siqi; Zou, Meixia; Chen, Yiming; Shen, Peifeng; He, Yanan; Li, Yuanchao; Liu, Chunlong; Chen, Zhibiao (Frontiers in Aging, 2025)
BackgroundGait analysis has emerged as a critical non-invasive tool for early identification and monitoring of mild cognitive impairment (MCI) in aging populations, particularly given its potential to predict dementia progression. This bibliometric analysis synthesizes two decades of research to map the evolution of gait analysis in MCI, identify interdisciplinary collaborations, and highlight emerging frontiers in MCI-related mobility research.MethodsLiterature related to gait analysis in MCI was retrieved from the Web of Science Core Collection. The search spanned publications from 2005 to 2024 and was executed in a single search session on 15 December 2024. CiteSpace and VOSviewer software were used to analyze publications, authorship, institutional affiliations, journals, keywords, and cited references. Burst detection and timeline analyses of keywords and references were conducted to identify emerging trends and temporal patterns.ResultsA total of 1,223 articles were identified. Annual publication trends indicate sustained scholarly interest over the past 5 years. The United States contributed the most publications (392 articles, 32.05%), with Western University (Canada, 65 articles) as the leading institution. Journals publishing these studies primarily focus on Alzheimer’s disease (AD), gerontology, and neurology, while prolific authors like Verghese J (USA) and Montero-odasso M(Canada) shaped the field’s trajectory. Emerging research frontiers include dementia progression, AD, and Parkinson’s disease, with 2024 priorities emphasizing “dual-task walking”, “digital biomarkers” and “working groups”. Additionally, validity and reliability assessments of gait analysis for MCI diagnosis and intervention represent a growing research trend.ConclusionThis study provides a comprehensive overview of the current landscape, hotspots, and trends in gait analysis for MCI management. By delineating its transformation from a descriptive tool to a predictive framework, we highlight persistent challenges such as methodological heterogeneity and small sample sizes. However, advances in machine learning and multicenter collaborations present opportunities to standardize protocols. Future high-quality studies are expected to establish gait-derived biomarkers as clinically actionable tools in MCI stratification and therapeutic monitoring.