Assessing mobile health applications with twitter analytics

Rajesh R. Pai, Sreejith Alathur

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Introduction: Advancement in the field of information technology and rise in the use of Internet has changed the lives of people by enabling various services online. In recent times, healthcare sector which faces its service delivery challenges started promoting and using mobile health applications with the intention of cutting down the cost making it accessible and affordable to the people. Objectives: The objective of the study is to perform sentiment analysis using the Twitter data which measures the perception and use of various mobile health applications among the citizens. Methods: The methodology followed in this research is qualitative with the data extracted from a social networking site “Twitter” through a tool RStudio. This tool with the help of Twitter Application Programming Interface requested one thousand tweets each for four different phrases of mobile health applications (apps) such as “fitness app” “diabetes app” “meditation app” and “cancer app”. Depending on the tweets, sentiment analysis was carried out, and its polarity and emotions were measured. Results: Except for cancer app there exists a positive polarity towards the fitness, diabetes, and meditation apps among the users. Following a system thinking approach for our results, this paper also explains the causal relationships between the accessibility and acceptability of mobile health applications which helps the healthcare facility and the application developers in understanding and analyzing the dynamics involved the adopting a new system or modifying an existing one.

Original languageEnglish
Pages (from-to)72-84
Number of pages13
JournalInternational Journal of Medical Informatics
Publication statusPublished - 05-2018

All Science Journal Classification (ASJC) codes

  • Health Informatics


Dive into the research topics of 'Assessing mobile health applications with twitter analytics'. Together they form a unique fingerprint.

Cite this