Research Article
Understanding Barriers of Missing Data in Personal Informatics Systems
@INPROCEEDINGS{10.1007/978-3-031-34586-9_40, author={Nannan Wen and Aditi Mallavarapu and Jacob Biehl and Erin Walker and Dmitriy Babichenko}, title={Understanding Barriers of Missing Data in Personal Informatics Systems}, proceedings={Pervasive Computing Technologies for Healthcare. 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2023}, month={6}, keywords={Personal Informatics (PI) missing data visualization synthetic data}, doi={10.1007/978-3-031-34586-9_40} }- Nannan Wen
Aditi Mallavarapu
Jacob Biehl
Erin Walker
Dmitriy Babichenko
Year: 2023
Understanding Barriers of Missing Data in Personal Informatics Systems
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-34586-9_40
Abstract
Advanced personal informatics (PI) tools enable users to collect and reflect on a wide range of personal data. Researchers consider missing data, or discontinuous (sparse) data caused by device malfunctions or human errors, an important barrier for adopting PI tools in their daily routines. While a lot is known about why missing data occurs, less is known about its impact on user reflection or how tools can be designed to mediate/reduce its negative effects in PI systems. In this work, we focused on exploring the importance and impact missing data has on user reflection and extracting insights to improve the design of PI reflection tools. We present a semi-structured interview to investigate the impact of missing data on users’ daily usage on two user groups, trainees and maintainers. We then provide design implications for incorporating visualization of estimated data (synthetic data) in the reflection stage, as a potential solution to the missing data problem. In this work, we provided data-driven implications for the design of future PI tools to help users reflect upon and mitigate missing data in their tracking activities.
