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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.
Design
This is a feasibility study using Python programming language and Scrapy framework
for identifying web-scraping measurement domains.
Setting
We used the Rehabilitation Measures Database website (RMD; www.sralab.org/rehabilitation-measures) 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.
Participants
Not applicable.
Interventions
Not applicable.
Main Outcome Measures
Measurement domains were extracted and summarized using counts and percentages.
Results
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).
Conclusions
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.
Keywords
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Copyright
© 2022 Published by Elsevier Inc.
