Late Breaking Research Poster 1828753| Volume 103, ISSUE 3, e30, March 2022

A Novel Web Scraping Approach to Identify Stroke Outcome Measures: A Feasibility Study

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      Research Objectives

      Web scraping is an innovative, efficient and automatic software application to extract large amounts of website data, commonly used for price tracking, product comparison, weather monitoring, and tracking online presence. While this approach provides a promising method to identify and summarize text-based online data, it has yet been utilized in rehabilitation research. This study applies a web-scraping method to rehabilitation research by investigating the scope of existing outcome instruments for stroke patients from one website.


      This is a feasibility study using Python programming language and Scrapy framework for identifying web-scraping measurement domains.


      We used the Rehabilitation Measures Database website (RMD; to extract information on stroke outcome instruments. The RMD provides instrument information, such as the International Classification of Functioning, Disability and Health (ICF) and measurement domains, cost, and administration time.


      Not applicable.


      Not applicable.

      Main Outcome Measures

      Measurement domains were extracted and summarized using counts and percentages.


      Less than fifteen minutes were taken for accessing each RMD page to store measurement domains of instruments for stroke patients in a csv formatted file by running python programming queries. Among 124 instruments identified for stroke patients, motor (38.5%, n=65) was the most frequently measured domain, followed by activities of daily living (23.1%, n=39), general health (12.4%, n=21), cognition (10.7% n=18), emotion (7.7%, n=13), sensory (5.9%, n=10), and participation (1.8%, n=3).


      This study demonstrated that a web-scraping method could be a convenient tool to retrieve publicly available online information for clinical or research purposes. A web-scraping method allows users to obtain target information in analytically friendly formats without requiring laborious manual efforts. Future rehabilitation research studies could leverage web scraping to support making efficient clinical decisions, classifying rehabilitation data, evaluating research impact, and exploring online attitudes, sentiment and behaviors.

      Author(s) Disclosures

      All authors listed in the abstract do not have any conflicts or lack thereof.


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