A Bibliometric Analysis of Nonspecific Low Back Pain Research

BACKGROUND: Researchers are highly interested in the study of nonspecific low back pain (NSLBP). However, few have attempted to collect global data, analyze the emerging trends, and conduct reviews from the perspectives of visualization and bibliometrics. PURPOSE: We aimed to evaluate research situation and capture subsequent developmental dynamics regarding NSLBP via CiteSpace. METHODS: Publications on NSLBP in recent 19 years were retrieved from the Web of Science Core Collection (WoSCC). We used CiteSpace to analyze publication outputs, document types, countries, institutions, journals, authors, references, and keywords. Knowledge foundation, hot topics, and future direction were then stated. RESULTS: A total of 1099 papers were collected, and the trend of annual publications maintained growth with small fluctuations. Australia (188) and the University of Sydney (76) were the most prolific country and institution, respectively. The Netherlands (0.84) and the University of Sydney (0.47) had the maximum centrality, thus indicating that they have importance in this field. The journal Spine (publication: 87, cocitation counts: 942) ranked first in terms of the volume of publications and cocitation counts. Maher CG (52) who published the most papers and Waddell G (286) who was cited most frequently were the leading authors, thus making strong academic influences. “Motor control exercise” was the largest cluster, which contained most related research articles. 14 references with the strongest citation counts were cited until 2018, thus implying the future development trend. Current hotspots were treatment, meta-analysis, method, and risk factors. Spine, efficacy, adult, and meta-analysis can be regarded as research frontiers. CONCLUSION: This study offers insights into the trend of NSLBP to determine major research countries and institutions, core journals, pivotal authors, overall development tendency, hot topics, and research frontiers. Moreover, it will help researchers extract hidden valuable information for further study.