Comparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal

TitleComparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal
Publication TypeJournal Article
Year of Publication2014
AuthorsAshutosh Jadhav, Donna Andrews, Alexander Fiksdal, Ashok Kumbamu, Jennifer McCormick, Andrew Misitano, Laurie Nelsen, Euijung Ryu, Amit Sheth, Stephen Wu, Jyotishman Pathak
JournalJournal of Medical Internet Research
KeywordseHealth, health information search, health search log, health seeking behavior, mHealth, mobile health, online health information seeking, search query analysis

Background: The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. Objective: The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. Methods: Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic consumer health information website. We performed analyses on Queries with considering repetition counts (QwR) and Queries without considering repetition counts (QwoR). The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. Results: Query strings used for health information searching via PCs and SDs differ by almost 50%. The most searched health categories are Symptoms (1 in 3 search queries), Causes, and Treatments & Drugs. The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. Conclusions: This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed.

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