Designing an Internet-of-Things (IoT) and Sensor-based In-home Monitoring System for Assisting Diabetes Patients: Iterative Learning from Two Case Studies

Samir Chatterjee, Jongbok Byun, Kaushik Dutta, Rasmus Ulslev Pedersen, Akshay Pottathil, Harry (Qi) Xie

Research output: Contribution to journalJournal articleResearchpeer-review

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.
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.
LanguageEnglish
JournalEuropean Journal of Information Systems
Volume27
Issue number6
Pages670-685
Number of pages16
ISSN0960-085X
DOIs
StatePublished - 2018

Bibliographical note

CBS Library does not have access to the material

Keywords

  • Design science research
  • Diabetes
  • Internet-of-Things
  • Mobile phones
  • Neural networks
  • Persuasive systems

Cite this

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title = "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 = "Design science research, Diabetes, Internet-of-Things, Mobile phones, Neural networks, Persuasive systems, Design science research, Diabetes, Internet-of-Things, Mobile phones, Neural networks, Persuasive systems",
author = "Samir Chatterjee and Jongbok Byun and Kaushik Dutta and {Ulslev Pedersen}, Rasmus and Akshay Pottathil and Xie, {Harry (Qi)}",
note = "CBS Library does not have access to the material",
year = "2018",
doi = "10.1080/0960085X.2018.1485619",
language = "English",
volume = "27",
pages = "670--685",
journal = "European Journal of Information Systems",
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Designing an Internet-of-Things (IoT) and Sensor-based In-home Monitoring System for Assisting Diabetes Patients : Iterative Learning from Two Case Studies. / Chatterjee, Samir; Byun, Jongbok; Dutta, Kaushik; Ulslev Pedersen, Rasmus; Pottathil, Akshay; Xie, Harry (Qi).

In: European Journal of Information Systems, Vol. 27, No. 6, 2018, p. 670-685.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Designing an Internet-of-Things (IoT) and Sensor-based In-home Monitoring System for Assisting Diabetes Patients

T2 - European Journal of Information Systems

AU - Chatterjee,Samir

AU - Byun,Jongbok

AU - Dutta,Kaushik

AU - Ulslev Pedersen,Rasmus

AU - Pottathil,Akshay

AU - Xie,Harry (Qi)

N1 - CBS Library does not have access to the material

PY - 2018

Y1 - 2018

N2 - 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.

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