First Advisor
Nirupama Bulusu
Date of Award
Spring 6-14-2024
Document Type
Thesis
Degree Name
Bachelor of Science (B.S.) in Computer Science and University Honors
Department
Computer Science
Language
English
Subjects
quantified self, mHealth, n=me, research ethics
Abstract
The adoption of an application of new technology always comes with a bias, this is never more true for the case of human behavioral analytics within higher education. While movements such as the quantified self movement make strides to reinterpret the realm of data analytics, psychology, and computer science, there are inevitably limitations to the adoption and application of such approaches within the standard realm of research. Herein is presented a case where an effort to evaluate the prospect of use of mobile phone data as secondary indicators of personal mental health through the lens of data analysis was put at odds with institutional knowledge especially that of the institutional review board and expectation both for the disciplines of psychology, computer science, data science and that of software engineering.
Recommended Citation
Lazaras, Julian E., "The Institutional Challenges of a Quantified Self Study An attempt to ascertain how data collected from a mobile device can be an indicator of personal mental health over time." (2024). University Honors Theses. Paper 1485.
Included in
Applied Behavior Analysis Commons, Databases and Information Systems Commons, Data Science Commons, Experimental Analysis of Behavior Commons, Health Psychology Commons, Other Computer Sciences Commons, Quantitative Psychology Commons, Software Engineering Commons, Theory and Philosophy Commons