The Shape of Mobile Health: A Systematic Review of Health Visualization on Mobile Devices

Abstract

mHealth apps and tracking technology are becoming popular because they help people adopt a healthier lifestyle and form healthy habits. One way mHealth apps can help users is by presenting visuals to help them better understand their health data. Commendable efforts have been carried out to personalize health-promoting interventions to help users become more aware of their own health, and health practitioners for providing healthcare services. For example, digital self-tracking apps nowadays can be used to monitor physical activity, nutrition and sleep patterns. This systematic review aims to investigate the current trends, challenges, gaps, and opportunities in health visualizations on mobile devices. Peer-reviewed papers in English collected using online databases (ACM Digital Library, PubMed, and Web of Science) from 2012 to 2022 were considered, and 56 studies were selected out of 1,168 studies. Results showed that among 11 different health domains, general health and physical health were the most heavily studied. Relatedly, results also showed that physical fitness data is the most frequently collected data type automatically from sensors/trackers. Furthermore, bar and line charts are the most popular type of visualizations used for presenting a variety of health data, and while most apps present static visualizations, interactive visualizations, as well as a combination of both static and interactive visualizations, are becoming more common. Based on our results, we offer recommendations for future research as designers and researchers continue to improve the presentation of data visualizations in mHealth apps.

Keywords:

• Health data visualization
• mHealth
• visualization

Acknowledgement

This research was undertaken, in part, thanks to funding from the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP). We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant. The research is conducted as part of the Dalhousie University Persuasive Computing Lab.

Author contributions

Gerry Chan conceived and designed the research, analyzed the data, authored or reviewed drafts of the paper, approved the final draft. Emeka Nwagu conceived and designed the research, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft. Ifeanyi Odenigbo conceived and designed the research, analyzed the data, authored or reviewed drafts of the paper, approved the final draft. Alaa Alslaity conceived and designed the research, analyzed the data, authored or reviewed drafts of the paper, approved the final draft. Rita Orji conceived and designed the research, analyzed the data, authored or reviewed drafts of the paper, approved the final draft.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Google Fit, https://www.google.com/fit/.

Additional information

Funding

This research was undertaken, in part, thanks to funding from the Industrial Research Assistance Program (IRAP).

Notes on contributors

Gerry Chan

Gerry Chan is a postdoctoral researcher in the Persuasive Computing Lab at Dalhousie University, Canada. Gerry received his PhD in 2022 in Information Technology from Carleton University. His research interests are in the social and motivational aspects of games with a focus on player matching mechanisms and collaborative play.

Chukwuemeka Nwagu

Chukwuemeka Nwagu is a Master’s student at the Faculty of Computer Science at Dalhousie University, and a research assistant at the Persuasive Computing Lab.

Ifeanyi Odenigbo

Ifeanyi Odenigbo is a Master’s student at the Faculty of Computer Science at Dalhousie University. He is also a full stack developer with development experience in mobile, web, and desktop applications. His research interest is in using augmented reality and virtual reality technology to promote overall health and wellness in people.

Alaa Alslaity

Alaa Alslaity is an assistant professor at Trent University and an adjunct professor at Dalhousie University, Canada. He has a PhD in Computer Science, University of Ottawa. His thesis was nominated for the Best Thesis Award, and he received two Best Paper Awards. His research interests include persuasive technology, personalization, and HCI.

Rita Orji

Rita Orji is a Canada Research Chair in Persuasive Technology and a Computer Science Professor at Dalhousie University. She directs the Persuasive Computing Lab. Her research at the intersection of technology and human behavior focuses on user-centered approaches to designing technologies to improve lives and support people to achieve various self-improvement goals.