# Research hotspots and future trends of insomnia in Parkinson’s disease: a bibliometric and visualization analysis from 1973 to 2024

## TL;DR

This study analyzes global research trends on insomnia in Parkinson’s disease from 1973 to 2024, identifying key contributors and emerging research directions.

## Contribution

The study provides a comprehensive bibliometric and visualization analysis of insomnia research in Parkinson’s disease, revealing new research hotspots and future trends.

## Key findings

- The United States leads in research output on PD and insomnia, followed by China.
- Burst keyword analysis highlights growing interest in diagnostic tool validation and scale development since 2019.
- Future research is expected to integrate artificial intelligence and personalized medicine for improved treatment precision.

## Abstract

Background and objectives: Despite the growing body of research on Parkinson’s disease (PD) and insomnia, comprehensive analysis of overall research trends remains limited. This study aims to evaluate these trends and identify research hot spots using bibliometric analysis.

Publications on PD and insomnia from 1973 to 2024 were extracted from the Web of Science Core Collection. Analytical tools such as VOSviewer, CiteSpace, and R 4.3.3 were employed for evaluation.

A total of 610 publications are analyzed, demonstrating a consistent upward trend. The United States leads the field with 150 articles, representing 24.59% of total output, followed by China with 79 publications. Harvard University is the top contributing institution with 44 articles. Movement Disorders ranks as the leading journal, publishing 28 papers (4.59% of the total) and also securing the top spot in total citations. The most prolific author is Lima Marcelo M. S., with 15 publications and 50 recorded collaborations. Burst keyword analysis revealed increasing interest in terms such as “validation,” “index,” and “scale” since 2019.

Research on PD and insomnia exhibits a clear upward trend, reflecting increasing academic interest. Future studies are expected to focus on the validation of diagnostic tools, the development of scales, and the integration of artificial intelligence and personalized medicine for improved treatment precision.