Designing an Internet-of-Things (IoT) and sensor-based in-home monitoring system for assisting diabetes patients: iterative learning from two case studies

ABSTRACT

The ageing of the global population is creating a crisis in chronic disease management. In the USA, 29 million people (or 9.3% of the population) suffer from the chronic disease of diabetes; according to the WHO, globally around 200 million people are diabetic. Left unchecked, diabetes can lead to acute and long-term complications and ultimately death. Diabetes prevalence tends to be the highest among those aged 65 and older (nearly 20.6%), a population which often lacks the cognitive resources to deal with the daily self-management regimens. In this paper, we discuss the design and implementation of an Internet-of-Things (IoT) and wireless sensor system which patients use in their own homes to capture daily activity, an important component in diabetes management. Following Fogg’s 2009 persuasion theory, we mine the activity data and provide motivational messages to the subjects with the intention of changing their activity and dietary behaviour. We introduce a novel idea called “persuasive sensing” and report results from two home implementations that show exciting promise. With the captured home monitoring data, we also develop analytic models that can predict blood glucose levels for the next day with an accuracy of 94%. We conclude with lessons learned from these two home case studies and explore design principles for creating novel IoT systems.

KEYWORDS:

• Internet-of-Things
• persuasive systems
• design science research
• diabetes
• mobile phones
• neural networks

Editor Ken Peffers Associate Editor Marcus Rothenberger

Editor Ken Peffers Associate Editor Marcus Rothenberger

Acknowledgements

We acknowledge Miles Moore for helping us recruit subjects and many reviewers who have helped improve this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project was partially supported by an EAGER grant from the National Science Foundation CNS [Award no.: 1048366]