mobile solution for healthcare improvement

2. Mobile Health Solutions
for Biomedical
Applications
Phillip Olla
Madonna University, USA
Joseph Tan
Wayne State University, USA
Medical Information science reference
Hershey • New York

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Library of Congress Cataloging-in-Publication Data
Mobile health solutions for biomedical applications / Phillip Olla and Joseph Tan, editors.
p. ; cm.
Includes bibliographical references and index.
Summary: “This book gives detailed analysis of the technology, applications and uses of mobile technologies in the healthcare sector by
using case studies to highlight the successes and concerns of mobile health projects”--Provided by publisher.
ISBN 978-1-60566-332-6 (hardcover : alk. paper)
1. Telecommunication in medicine. 2. Mobile communication systems. 3. Wireless communication systems. 4. Cellular telephones. 5.
Medical technology. I. Olla, Phillip. II. Tan, Joseph K. H.
[DNLM: 1. Telemedicine. 2. Ambulatory Monitoring. 3. Cellular Phone. 4. Computers, Handheld. 5. Medical Records Systems, Com-
puterized. 6. User-Computer Interface. W 83.1 M6865 2009]
R119.9.M58 2009
610.28--dc22
2008040451
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not
necessarily of the publisher.

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5. Editorial Advisory Board
George Demiris, University of Missouri, USA
Nayna Patel, Brunel University, UK
Thomas M. Deserno, RWTH Aachen University, Germany
Jyoti Choudrie, University of Hertfordshire, UK
Paul Hu, University of Utah, USA
Patrice Monthrope, University of West Indies, Jamaica
Richard Hull, University of Newcastle upon Tyne, United Kingdom
Elena Qureshi, Madonna University, USA
Francis Lau, University of Victoria, Canada
Venus Olla, Nottingham University, UK
Dave Parry, Auckland University of Technology, New Zealand
Mathew Guah, Erasmus University, The Netherlands
Jim Warren, University of Auckland, New Zealand
H. Joseph Wen, Southeast Missouri State University, USA
Yvette Miller, University of Toronto, Canada
Yufei Yuan, McMaster University, Canada
Daniel Zeng, University of Arizona, USA
Kai Zheng, The University of Michigan, USA
Jacqueline Brodie, Napier University, Scotland
Carla Wiggins, Idaho State University, USA
Bendik Bygstad, Norwegian School of IT, Norway

6. Table of Contents
Preface . ...............................................................................................................................................xiii
Section I
Mobile Health Applications and Technologies
Chapter I
Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations
with Low Literacy Skills ........................................................................................................................ 1
Katie A. Siek, University of Colorado at Boulder, USA
Kay H. Connelly, Indiana University, USA
Beenish Chaudry, Indiana University, USA
Desiree Lambert, Trilogy Health Services, USA
Janet L. Welch, Indiana University School of Nursing, USA
Chapter II
Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture .......... 24
Ana Ferreira, Center for Informatics, Faculty of Medicine in Porto, Portugal
Luís Barreto, Instituto Politécnico de Viana do Castelo, Portugal
Pedro Brandão, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal
Ricardo Correia, Center for Informatics, Faculty of Medicine in Porto, Portugal
Susana Sargento, Universidade de Aveiro, Portugal
Luís Antunes, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal
Chapter III
Personal Health Records Systems Go Mobile: Defining Evaluation Components............................... 45
.
Phillip Olla, Madonna University, USA
Joseph Tan, Wayne State University, USA
Chapter IV
Medical Information Representation Framework for Mobile Healthcare ............................................ 71
Ing Widya,University of Twente, The Netherlands
HaiLiang Mei,University of Twente, The Netherlands
Bert-Jan van Beijnum,University of Twente, The Netherlands
Jacqueline Wijsman,University of Twente, The Netherlands
Hermie J. Hermens,University of Twente, The Netherlands

7. Chapter V
A Distributed Approach of a Clinical Decision Support System Based on Cooperation...................... 92
Daniel Ruiz-Fernández, University of Alicante, Spain
Antonio Soriano-Payá, University of Alicante, Spain
Chapter VI
Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge
Creation and Reuse.............................................................................................................................. 111
Teppo Räisänen, University of Oulu, Finland
Harri Oinas-Kukkonen, University of Oulu, Finland
Katja Leiviskä, University of Oulu, Finland
Matti Seppänen, The Finnish Medical Society Duodecim, Finland
Markku Kallio, The Finnish Medical Society Duodecim, Finland
Section II
Patient Monitoring and Wearable Devices
Chapter VII
Patient Monitoring in Diverse Environments ..................................................................................... 129
Yousef Jasemian, Engineering College of Aarhus, Denmark
Chapter VIII
Monitoring Hospital Patients Using Ambient Displays....................................................................... 143
Monica Tentori, CICESE, Mexico
Daniela Segura, CICESE, Mexico
Jesus Favela, CICESE, Mexico
Chapter IX
Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks........................... 159
Javier Espina, Philips Research Europe, The Netherlands
Heribert Baldus, Philips Research Europe, The Netherlands
Thomas Falck, Philips Research Europe, The Netherlands
Oscar Garcia, Philips Research Europe, The Netherlands
Karin Klabunde, Philips Research Europe, The Netherlands
Chapter X
Sensing of Vital Signs and Transmission Using Wireless Networks................................................... 180
Yousef Jasemian, Engineering College of Aarhus, Denmark

8. Chapter XI
Towards Wearable Physiological Monitoring on a Mobile Phone...................................................... 208
.
Nuria Oliver, Telefonica Research, Spain
Fernando Flores-Mangas, University of Toronto, Canada
Rodrigo de Oliveira, State University of Campinas, Brazil
Section III
Context Aware Systems
Chapter XII
A Framework for Capturing Patient Consent in Pervasive Healthcare Applications.......................... 245
Giovanni Russello, Imperial College London, UK
Changyu Dong, Imperial College London, UK
Naranker Dualy, Imperial College London, UK
Chapter XIII
Technology Enablers for Context-Aware Healthcare Applications..................................................... 260
Filipe Meneses, Universidade do Minho, Portugal
Adriano Moreira, Universidade do Minho, Portugal
Chapter XIV
Modeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270
Bjorn Gottfried, University of Bremen, Germany
Chapter XV
Context-Aware Task Distribution for Enhanced M-health Application Performance......................... 285
Hailiang Mei, University of Twente, The Netherlands
Bert-Jan van Beijnum, University of Twente, The Netherlands
Ing Widya, University of Twente, The Netherlands
Val Jones, University of Twente, The Netherlands
Hermie Hermens, , University of Twente, The Netherlands
Compilation of References................................................................................................................ 308
About the Contributors..................................................................................................................... 332
Index.................................................................................................................................................... 341

9. Detailed Table of Contents
Preface . ...............................................................................................................................................xiii
Section I
Mobile Health Applications and Technologies
Chapter I
Evaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populations
with Low Literacy Skills ........................................................................................................................ 1
Katie A. Siek, University of Colorado at Boulder, USA
Kay H. Connelly, Indiana University, USA
Beenish Chaudry, Indiana University, USA
Desiree Lambert, Trilogy Health Services, USA
Janet L. Welch, Indiana University School of Nursing, USA
In this chapter, the authors discuss two case studies that compare and contrast the use of barcode scanning,
voice recording, and patient self reporting as a means to monitor the nutritional intake of a chronically
ill population.
Chapter II
Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Architecture .......... 24
Ana Ferreira, Center for Informatics, Faculty of Medicine in Porto, Portugal
Luís Barreto, Instituto Politécnico de Viana do Castelo, Portugal
Pedro Brandão, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal
Ricardo Correia, Center for Informatics, Faculty of Medicine in Porto, Portugal
Susana Sargento, Universidade de Aveiro, Portugal
Luís Antunes, LIACC at Faculty of Science in Porto, R. Campo Alegre, Portugal
The main objective of this chapter is to model, develop and evaluate (e.g. in terms of efficiency, com-
plexity, impact and against network attacks) a proposal for a secure wireless architecture in order to
access a VEPR. This VEPR is being used within a university hospital by more than 1,000 doctors, on a
daily basis. Its users would greatly benefit if this service would be extended to a wider part of the hos-
pital and not only to their workstation, achieving this way faster and greater mobility in the treatment
of their patients.

10. Chapter III
Personal Health Records Systems Go Mobile: Defining Evaluation Components............................... 45
.
Phillip Olla, Madonna University, USA
Joseph Tan, Wayne State University, USA
This chapter provides an overview of Mobile Personal Health Record (MPHR) systems. A Mobile
personal health record is an eclectic application through which patients can access, manage, and share
their health information from a mobile device in a private, confidential, and secure environment. Specifi-
cally, the chapter reviews the extant literature on critical evaluative components to be considered when
assessing MPHR systems.
Chapter IV
Medical Information Representation Framework for Mobile Healthcare ............................................ 71
Ing Widya,University of Twente, The Netherlands
HaiLiang Mei,University of Twente, The Netherlands
Bert-Jan van Beijnum,University of Twente, The Netherlands
Jacqueline Wijsman,University of Twente, The Netherlands
Hermie J. Hermens,University of Twente, The Netherlands
This chapter describes a framework which enables medical information, in particular clinical vital signs
and professional annotations, be processed, exchanged, stored and managed modularly and flexibly in a
mobile, distributed and heterogeneous environment despite the diversity of the formats used to represent
the information.
Chapter V
A Distributed Approach of a Clinical Decision Support System Based on Cooperation...................... 92
Daniel Ruiz-Fernández, University of Alicante, Spain
Antonio Soriano-Payá, University of Alicante, Spain
This chapter presents an architecture for diagnosis support based on the collaboration among different
diagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinical
meetings in hospitals in which the members of a medical team share their opinions in order to analyze
complicated diagnoses.
Chapter VI
Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge
Creation and Reuse.............................................................................................................................. 111
Teppo Räisänen, University of Oulu, Finland
Harri Oinas-Kukkonen, University of Oulu, Finland
Katja Leiviskä, University of Oulu, Finland
Matti Seppänen, The Finnish Medical Society Duodecim, Finland
Markku Kallio, The Finnish Medical Society Duodecim, Finland

11. This chapter aims to demonstrate that mobile healthcare information system may also help physicians
to communicate and collaborate as well as learn and share their experiences within their work commu-
nity. Physicians’ usage of a mobile system is analyzed through a knowledge management framework
known as the 7C model. The data was collected through the Internet among all of the 352 users of the
mobile system. The results indicate that frequent use of the system seemed to improve individual physi-
cians’ knowledge work as well as the collective intelligence of a work community. Overall, knowledge
management seems to be a prominent approach for studying healthcare information systems and their
impact on the work of physicians.
Section II
Patient Monitoring and Wearable Devices
Chapter VII
Patient Monitoring in Diverse Environments ..................................................................................... 129
Yousef Jasemian, Engineering College of Aarhus, Denmark
This chapter intends to explore the issues and limitations concerning application of mobile health system
in diverse environments, trying to emphasize the advantages and drawbacks, data security and integrity
suggesting approaches for enhancements. These issues will be explored in successive subsections by
introducing two studies which were undertaken by the author.
Chapter VIII
Monitoring Hospital Patients Using Ambient Displays....................................................................... 143
Monica Tentori, CICESE, Mexico
Daniela Segura, CICESE, Mexico
Jesus Favela, CICESE, Mexico
In this chapter the authors explore the use of ambient displays to adequately monitor patient’s health
status and promptly and opportunistically notify hospital workers of those changes. To show the feasibil-
ity and applicability of ambient displays in hospitals they designed and developed two ambient displays
that can be used to provide awareness patients’ health status to hospital workers.
Chapter IX
Towards Easy-to-Use, Safe, and Secure Wireless Medical Body Sensor Networks........................... 159
Javier Espina, Philips Research Europe, The Netherlands
Heribert Baldus, Philips Research Europe, The Netherlands
Thomas Falck, Philips Research Europe, The Netherlands
Oscar Garcia, Philips Research Europe, The Netherlands
Karin Klabunde, Philips Research Europe, The Netherlands
Wireless Body Sensor Networks (BSNs) are an indispensable building stone for any pervasive healthcare
system. Although suitable wireless technologies are available and standardization dedicated to BSN
communication has been initiated, the authors identify key challenges in the areas of easy-of-use, safety,

12. and security that hinder a quick adoption of BSNs. To address the identified issues we propose using
Body-Coupled Communication (BCC) for the automatic formation of BSNs and for user identification.
They also present a lightweight mechanism that enables a transparent security setup for BSNs used in
pervasive healthcare systems.
Chapter X
Sensing of Vital Signs and Transmission Using Wireless Networks................................................... 180
Yousef Jasemian, Engineering College of Aarhus, Denmark
This chapter deals with a comprehensive investigation of feasibility of wireless and cellular telecom-
munication technologies and services in a real-time M-Health system. The chapter bases its investiga-
tion, results, discussion and argumentation on an already developed remote patient monitoring system
by the author.
Chapter XI
Towards Wearable Physiological Monitoring on a Mobile Phone...................................................... 208
.
Nuria Oliver, Telefonica Research, Spain
Fernando Flores-Mangas, University of Toronto, Canada
Rodrigo de Oliveira, State University of Campinas, Brazil
In this chapter, we present our experience in using mobile phones as a platform for real-time physiological
monitoring and analysis. In particular, we describe in detail the TripleBeat system, a research prototype
that assists runners in achieving predefined exercise goals via musical feedback, a glanceable interface
for increased personal awareness and a virtual competition. We believe that systems like TripleBeat will
play an important role in assisting users towards healthier and more active lifestyles.
Section III
Context Aware Systems
Chapter XII
A Framework for Capturing Patient Consent in Pervasive Healthcare Applications.......................... 245
Giovanni Russello, Imperial College London, UK
Changyu Dong, Imperial College London, UK
Naranker Dualy, Imperial College London, UK
In this chapter, the authors describe a new framework for pervasive healthcare applications where the
patient’s consent has a pivotal role. In their framework, patients are able to control the disclosure of their
medical data. The patient’s consent is implicitly captured by the context in which his or her medical data
is being accessed. Context is expressed in terms of workflows. The execution of a task in a workflow
carries information that the system uses for providing access rights accordingly to the patient’s consent.
Ultimately, the patient is in charge of withdrawing consent if necessary. Moreover, the use of workflow
enables the enforcement of the need-to-kwon principle. This means that a subject is authorised to access
sensitive data only when required by the actual situation.

13. Chapter XIII
Technology Enablers for Context-Aware Healthcare Applications..................................................... 260
Filipe Meneses, Universidade do Minho, Portugal
Adriano Moreira, Universidade do Minho, Portugal
This chapter focuses on how context and location can be used in innovative applications and how to
use a set of solutions and technologies that enable the development of innovative context and location-
aware solutions for healthcare area. It shows how a mobile phone can be used to compute the level of
familiarity of the user with the surrounding environment and how the familiarity level can be used in a
number of situations.
Chapter XIV
Modeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270
Bjorn Gottfried, University of Bremen, Germany
This chapter introduces spatial health systems, identifies fun¬damental properties of these systems, and
details for specific applications the methods to be applied in order to show how problems are solved in
this field. On the one hand, this chapter gives an overview of this area, on the other hand, it is written
for those who are interested in designing spatial health systems. The result is that different spatial scales
and pur¬poses require different representations for describing the spatiotemporal change of objects,
that is their spatiotemporal development, showing how fundamental aspects of spatial health systems
are dealt with.
Chapter XV
Context-Aware Task Distribution for Enhanced M-health Application Performance......................... 285
Hailiang Mei, University of Twente, The Netherlands
Bert-Jan van Beijnum, University of Twente, The Netherlands
Ing Widya, University of Twente, The Netherlands
Val Jones, University of Twente, The Netherlands
Hermie Hermens, , University of Twente, The Netherlands
As well as applying the traditional adaptation methods such as protocol adaptation and data prioritization,
the authors investigate the possibility of adaptation based on dynamic task redistribution. In this chapter,
the authors propose an adaptation middleware that consists of a task assignment decision mechanism
and a task redistribution infrastructure. The decision mechanism represents task assignment as a graph
mapping problem and searches for the optimal assignment given the latest context information. Once
a new assignment is identified, the member tasks are distributed accordingly by the distribution infra-
structure. A prototype implementation based on the OSGi framework is reported to validate the task
redistribution infrastructure.
Compilation of References................................................................................................................ 308
About the Contributors..................................................................................................................... 332
Index.................................................................................................................................................... 341

14. xiii
Preface
Pervasive healthcare environment, focusing on the integration of mobile and ubiquitous technology to
reform working and living conditions for individuals and organizations in the healthcare sector, sets the
stage for an innovative emerging research discipline. Healthcare systems are experiencing a variety of
challenges including the prevalence of life-style related conditions, growing consumerism in healthcare,
the need to empower patients with information for better decision making, requests for better tools for
self-care and management of deteriorating health conditions, the need for seamless access for healthcare
services via the Internet and mobile devices, and the growing costs of providing healthcare.
Mobile health (m-health) is an integral and significant part of the emerging pervasive healthcare field.
M-Health contains three core components integrated into the healthcare environment. The first component
is the availability of a reliable wireless architecture; the second component is the integration of medical
sensor or wearable devices for monitoring; and the final component is a robust application and services
infrastructure. M-Health relates to applications and systems such as telemedicine, telehealth, e-health,
and biomedical sensing system. The rapid advances in information communication technology (ICT),
nanotechnology, bio-monitoring, mobile networks, pervasive computing, wearable systems, and drug
delivery approaches are transforming the healthcare sector and fueling the m-health phenomenon. M-
Health aims to make healthcare accessible to anyone, anytime, and anywhere by elimination constraints
such as time and location in addition to increasing both the coverage and quality of healthcare.
Mobile and wireless concepts in healthcare are typically related to bio-monitoring and home moni-
toring; however, more recently the trend to incorporate mobile technology has become more prevalent
across almost the entire healthcare data acquisition task domains. Bio monitoring using mobile networks
includes physiological monitoring of parameters such as heart rate, electrocardiogram (ECG), electro-
encephalogram (EEG) monitoring, blood pressure, blood oximetry, and other physiological signals.
Alternative uses include physical activity monitoring of parameters such as movement, gastrointestinal
telemetry fall detection, and location tracking. Using mobile technology, patient records can be accessed
by healthcare professionals from any given location by connecting the institution’s internal network.
Physicians now have ubiquitous access to patient history, laboratory results, pharmaceutical data, in-
surance information, and medical resources. These mobile healthcare applications improve the quality
of patient care. Handheld devices can also be used in home healthcare, for example, to fight diabetes
through effective monitoring. A comprehensive overview of some of these mobile health applications
and research has been presented in this book.
This book provides an international perspective on the benefits of mobile health technology to illus-
trate different examples and applications implemented in the global healthcare sector. The work features
32 contributing authors representing six countries including the United States, United Kingdom, Spain,
Portugal, Italy, and Denmark. Even though the healthcare policies and governance of healthcare systems

15. xiv
in these countries differ, the benefits to be realized from a future of implementations of mobile health
technology are not inconsistent among the countries.
The book may be divided into three major sections:
1. Mobile Health Applications and Technologies
2. Patient Monitoring and Wearable Devices
3. Context Aware Systems in Healthcare
The first section “Mobile Health Applications and Technologies” provides an analysis of the technol-
ogy. Case studies highlighting the successes and challenges of mobile health projects offer real-world
illustrations of applications and uses of mobile technologies in the healthcare sector. M-Health is a
broad area transcending multiple disciplines and utilizing a broad range of technologies. “Evaluation of
Two Mobile Nutrition Tracking Applications for Chronically Ill Populations with Low Literacy Skills,”
authored by Katie A. Siek, Kay H. Connelly, Beenish Chaudry, Desiree Lambert, and Janet L. Welch,
discusses two case studies that compare and contrast the use of barcode scanning, voice recording, and
patient self reporting as a means to monitor the nutritional intake of a chronically ill population.
Chapter II “Accessing an Existing Virtual Electronic Patient Record with a Secure Wireless Archi-
tecture” by Ana Ferreira, Luís Barreto, Pedro Brandão, Ricardo Correia, Susana Sargento, and Luís
Antunes presents the concept of a virtual electronic patient records system that enables the integration
and sharing of healthcare information within heterogeneous organizations. The VEPR system aims to
alleviate the constraints in terms of physical location as well as technology in order to access vital patient
records. The use of wireless technology attempts to allow access to patient data and processing of clinical
records closer to the point of care. The ubiquitous access to information can minimize physical as well
as time constraints for healthcare, enhancing users’ mobility within the institution. The next chapter in
this section entitled “Personal Health Records Systems Go Mobile: Defining Evaluation Components”
is authored by Phillip Olla and Joseph Tan. It provides an overview of Mobile Personal Health Record
(MPHR) systems. A Mobile personal health record is an eclectic application through which patients can
access, manage and share their health information from a mobile device in a private, confidential, and
secure environment.
Chapter IV focusing on “Medical Information Representation Framework for Mobile Healthcare”
was written by Ing Widya, HaiLiang Mei, Bert-Jan van Beijnum, Jacqueline Wijsman, and Hermie
Hermens. This chapter describes a framework which enables medical information such as clinical, vital
signs and professional annotations to be manipulated in a mobile, distributed and heterogeneous envi-
ronment despite the diversity of the formats used to represent the information. It further proposes the
use of techniques and constructs similar to the internet to deal with medical information represented in
multiple formats. Chapter V is “A Distributed Approach of a Clinical Decision Support System Based
on Cooperation,” authored by Daniel Ruiz-Fernández and Antonio Soriano-Payá. This chapter discusses
an architecture that supports diagnosis based on the collaboration among different diagnosis-support
artificial entities or agents and the physicians themselves. The proposed systems architecture, which
was tested in a melanoma and urological dysfunctions diagnosis, combines availability, cooperation and
harmonization of all contributions in a diagnosis process. Chapter VI, the final chapter in this section,
“Managing Mobile Healthcare Knowledge: Physicians’ Perceptions on Knowledge Creation and Reuse”
was authored by Teppo Räisänen, Harri Oinas-Kukkonen, Katja Leiviskä, Matti Seppänen, and Markku
Kallio. This chapter focuses on mobile access to medical literature and electronic pharmacopoeias, aim-
ing to demonstrate that using these recourses effectively may help physicians to communicate and col-

16. xv
laborate as well as learn and share their experiences within their user community. The chapter presents
a case study of the users of Duodecim mobile healthcare information system.
The second section presents research on Patient Monitoring and Wearable Devices. Chapter VII, the
first chapter in this section, is titled “Patient Monitoring in Diverse Environments” and is authored by
Yousef Jasemian. This chapter discusses the benefits of recording of physiological vital signs in patients’
real-life environment by a mobile health system. This approach is useful in the management of chronic
disorders such as hypertension, diabetes, anorexia nervosa, chronic pain, or severe obesity. The author
explored the issues and limitations concerning the application of mobile health system in diverse envi-
ronments, emphasizing the advantages and drawbacks, data security and integrity while also suggesting
approaches for enhancements. The following chapter, Chapter VIII, is titled “Monitoring Hospital Patients
using Ambient Displays” authored by Monica Tentori, Daniela Segura, and Jesus Favela. This chapter
explores the use of ambient displays to promptly notify hospital workers of relevant events related to
their patients. To highlight the feasibility and applicability of ambient displays in hospitals, this chapter
presents two ambient displays aimed at creating a wearable connection between patients and healthcare
providers. The authors also discuss issues and opportunities for the deployment of ambient displays
for patient monitoring. Chapter IX is titled “Towards Easy-to-uUse, Safe, and Secure Wireless Medical
Body Sensor Networks” and is authored by Javier Espina, Heribert Baldus, Thomas Falck, Oscar Garcia,
and Karin Klabunde. This chapter discusses the use of wireless body sensor networks (BSNs), which
are an integral part of any pervasive healthcare system. It discusses suitable wireless technologies and
standardization dedicated to BSN communication and highlights key challenges in the areas of easy-
of-use, safety, and security that hinder a quick adoption of BSNs. To address the identified challenges,
the authors proposed the use of body-coupled communication (BCC) for the automatic formation of
BSNs and for user identification and presented a lightweight mechanism that would enable a transparent
security setup for BSNs used in pervasive healthcare systems.
Chapter X is titled “Sensing of Vital Signs and Transmission Using Wireless Networks” and is authored
by Yousef Jasemian. This chapter investigated the feasibility using wireless and cellular telecommu-
nication technologies and services in a real-time m-health system. He based his investigation, results,
discussion and argumentation on an existing remote patient monitoring system. His results indicated
that the system functioned with a clinically acceptable performance, and transferred medical data with
a reasonable quality, even though the system was tested under totally uncontrolled circumstances during
the patients’ daily activities. Both the patients and the healthcare personnel who participated expressed
their confidence in using the technology. The author also suggested enhancing features for more reliable,
more secure, more user-friendly and higher performing M-Health system in future implementations.
Chapter XI, “Towards Wearable Physiological Monitoring on a Mobile Phone” by Nuria Oliver,
Fernando Flores-Mangas, and Rodrigo de Oliveira discusses the experience gained from using mobile
phones as a platform for real-time physiological monitoring and analysis. The authors presented two
mobile phone-based prototypes that explore the impact of real-time physiological monitoring in the daily
life of users. The first prototype is called HealthGear; this is a system to monitor users while they are
sleeping and automatically detect sleep apnea events; the second is TripleBeat, a prototype that assists
runners in achieving predefined exercise goals via musical feedback and two persuasive techniques: a
glanceable interface for increased personal awareness and a virtual competition.
The third and last section focuses on research and on the theme of Context Aware Systems in the
healthcare arena. Chapter XII, the first chapter in this section, is titled “A Framework for Capturing
Patient Consent in Pervasive Healthcare Applications.” It is authored by Giovanni Russello, Changyu
Dong, and Naranker Dualy and describes a new framework for pervasive healthcare applications where
the patient’s consent plays a pivotal role. In the framework presented, patients are able to control the

17. xvi
disclosure of their medical data. The patient’s consent is implicitly captured by the context in which
his or her medical data is being accessed. Context is expressed in terms of workflows. The execution
of a task in a workflow carries information that the system uses for providing access rights accord-
ingly to the patient’s consent. Ultimately, the patient is in charge of withdrawing consent if necessary.
Chapter XIII is titled “Technology Enablers for Context-Aware Healthcare Applications” authored by
Filipe Meneses and Adriano Moreira. This chapter discusses how context and location can be used in
innovative applications and how to use a set of solutions and technologies that enable the development
of innovative context and location-aware solutions for healthcare area. The chapter highlights how a
mobile phone can be used to compute the level of familiarity of the user with the surrounding environ-
ment and how the familiarity level can be used in a number of situations. The increasing availability
of mobile devices and wireless networks, and the tendency for them to become ubiquitous in our dally
lives, creates a favourable technological environment for the emergence of new, simple, and added-value
applications for healthcare. Chapter XIV is titled “Modeling Spatiotemporal Developments in Spatial
Health Systems” is authored by Bjorn Gottfried and discusses Spatial health systems and the support
these systems can provide to disabled people and the elderly in dealing with everyday life problems.
The author also addresses every kinds of health related issues that can develop in space and time. The
work focuses on how spatial health systems monitor the physical activity of people in order to determine
how to support the monitored individuals. Chapter XV, the final chapter in this section, titled, “Context-
Aware Task Distribution for Enhanced M-Health Application Performance” authored by Hailiang Mei,
Bert-Jan van Beijnum, Ing Widya, Val Jones, Hermie Hermens. This chapter describes the importance
of context-aware mobile healthcare systems. Due to the emergence of new medical sensor technologies,
the fast adoption of advanced mobile systems to improve the quality of care required by today’s patients
context aware healthcare systems is clearly needed . The authors propose an adaptation middleware that
consists of a task assignment decision mechanism and a task (re-) distribution infrastructure. The deci-
sion mechanism represents task assignment as a graph mapping problem and searches for the optimal
assignment given the latest context information.
The research presented in this book is important due to the emergence of pervasive computing and
health care systems that provide quality patient care services. By reviewing the diverse chapters pre-
sented a healthcare provider or practitioner will learn about the potential applications that will become
the norm in the future.

19. Section I
Mobile Health Applications
and Technologies

20. Chapter I
Evaluation of Two Mobile
Nutrition Tracking Applications
for Chronically Ill Populations
with Low Literacy Skills
Katie A. Siek
University of Colorado at Boulder, USA
Kay H. Connelly
Indiana University, USA
Beenish Chaudry
Indiana University, USA
Desiree Lambert
Trilogy Health Services, USA
Janet L. Welch
Indiana University School of Nursing, USA
ABSTRACT
In this chapter, the authors discuss two case studies that compare and contrast the use of barcode
scanning, voice recording, and patient self reporting as a means to monitor the nutritional intake of a
chronically ill population. In the first study, they found that participants preferred unstructured voice
recordings rather than barcode scanning. Since unstructured voice recordings require costly transcrip-
tion and analysis, they conducted a second case study where participants used barcode scanning or an
integrated voice response system to record nutritional intake. The authors found that although the latter
input method provided participants with a faster method to input food items, participants had difficulty
using the system despite training.
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
a

21. Evaluation of Two Mobile Nutrition Tracking Applications
INTRODUCTION select a picture. Health professionals could eas-
ily administer the intervention and evaluate data
Chronic diseases, such as chronic kidney disease without intermediate steps of electronic transcrip-
(CKD) and heart disease, are among the leading tion. The low literacy chronically ill participants
causes of death and disability in the world. At least benefit from using the application because they
half of the chronic disease related deaths could can use the application anytime they consumed a
be prevented by adopting a healthy lifestyle, such food item, receive immediate visual feedback on
as good nutrition, increased physical activity, and their nutritional intake, and make decisions on a
cessation of tobacco use. Researchers believe that prospective basis. In addition, the interface and
the world must put a higher priority on interven- content could be customized for populations with
tions to help prevent and successfully manage varying literacy and computing skills.
chronic illness (Preventing Chronic Diseases: A In this chapter, as part of a larger study, we
Vital Investment, 2005). will compare and contrast the use of barcode scan-
Current interventions to help chronically ill ning, integrated voice response system (IVRS),
populations improve their nutritional health and and patient self reporting as a means to monitor
self-manage therapeutic diets include paper- their nutritional intake relative to their dietary
based food diaries, 24 hour recalls, and food prescription of CKD patients. In the first case study
frequency questionnaires (Dwyer, Picciano, we found that participants preferred unstructured
Raiten, 2003; Resnicow et al., 2000). Patients who voice recordings rather than barcode scanning.
use these interventions must have high literacy Unstructured voice recordings are difficult to
and memory recall skills. Unfortunately, over a automatically parse and require transcription. We
quarter of the United States population do not had to find out if patients would use a menu-based
have the necessary literacy or numeracy skills structured voice input system, such as IVRSs for
needed to successfully self-monitor themselves automated recognition. In the second case study,
(Kirsch et al., 1993). If people cannot self-moni- we explored participant use of an IVRS and found
tor themselves, they cannot manage their chronic although the system provided participants with a
conditions (HRSA Literacy) and may lead them to quicker way to input food items, participants had
worse health outcomes (Schillinger et al., 2002). difficulty using the system and some could not
In addition, to administer current interventions use the system despite training. We will discuss
medical professionals must spend a significant the methodology and findings from these two
amount of time evaluating the data from paper- case studies. We will conclude the chapter with
based forms. lessons learned during the user study and provide
We are currently developing a mobile handheld considerations for future areas of research.
application to assist CKD patients on hemodialy-
sis monitor and maintain their nutritional intake.
Initially, we thought a personal digital assistant RELATED WORK
(PDA) would be the best solution for health pro-
fessionals and patients (Connelly, Faber, Rogers, PDAs with scanner input and mobile phones
Siek, Toscos, 2006). Participants could scan used for IVRS input gather information in many
barcodes on food items for their primary input or domains. PDAs and scanners have been used
select items from an interface as a secondary input. to show clinicians videos about specific unit
These input mechanisms are ideal for low literacy appliances (Brandt, Björgvinsson, Hillgren,
populations because there is no reading required Bergqvist, Emilson, 2002), save and search
– participants only have to identify a barcode or for information about food products, music, and
TRCTRT

22. Evaluation of Two Mobile Nutrition Tracking Applications
books (Bernheim, Combs, Smith, Gupta, 2005), dinner. The nutritional analysis is given on a
and obtain information about an environment separate screen. Researchers at Indiana University
from embedded barcodes (Fitzmaurice, Khan, studied how three people with CKD used Diet-
Buxton, Kurtenback, Balakrishnan, 2003). MatePro to monitor nutritional consumption over
Mobile phones used for IVRSs have been used a three-month period. They found participants had
for patient counseling to enhance time spent difficulty navigating standard PDA menu naviga-
with health professionals (Glasgow, Bull, Piette, tion and preferred using a large PDA screen with
Steiner, 2004) and assess patient status with touch sensitive icons (Dowell Welch, 2006).
chronic illnesses such as depression, cancer, Sevick and colleagues evaluated how five CKD
heart failure, and diabetes (Piette, 2000). In this participants used BalanceLog over a four-month
section, we discuss specifically how PDAs and period. They found that participants improved
mobile phones have been used for interventions their dietary intake using the electronic nutrition
and nutritional monitoring. monitoring system (Sevick et al., 2005). Both
applications evaluated in these studies required
PDA Nutrition Monitoring significant literacy and cognitive skills.
Interventions Stephen Intille et al. created a proof-of-concept
PDA application that provides users with a way to
Currently, there are many PDA applications that scan food items and obtain nutritional information
can assist with the self-monitoring of nutritional to assist users in making healthy choices (Intille,
intake. The United States Department of Agri- Kukla, Farzanfar, Bakr, 2003). The application
culture (USDA) has a PDA nutrient database that did not have an extensive UPC/nutrition database
provides people with a mechanism for looking up because none are freely available. Although the
the nutritional information of foods. Users must application does not allow users to save intake
correctly type the first few letters of a food item information, the application shows that integration
they are looking for into a search box and then click of scanners and nutrition information is possible
through a series of menus to find the appropriate given enough resources.
food item based on portion size and preparation Researchers at Microsoft created a generic
(“USDA Palm OS Search,” 2008). barcode look-up system that gave participants
DietMatePro ( http://www.dietmatepro.com) the opportunity to look up product information
and BalanceLog (http://www.healthetech.com/) available online about specific food items. During
use the USDA database along with other fast food their five-week study with twenty participants
nutritional information to create a PDA program familiar with PDA technology, they found par-
that provides users with a way to save consump- ticipants had mixed reactions to the system in
tion information for a set of specific nutrients. terms of enjoyment and usefulness. Similar to a
CalorieKing (http://www.calorieking.com/) uses recent mobile phone study at Georgia Tech (Patel,
its own nutritional database and provides users Kientz, Hayes, Bhat, Abowd, 2006), participants
the ability to save consumption information. In in the Microsoft study did not always bring the
addition, it has a nutritional tracking application PDA with them despite being enthusiastic PDA
specific to diabetic populations. The applications owners (Bernheim et al., 2005).
are similar to the USDA database in that users In addition to PDA monitoring of nutrition,
must be able to spell the first few letters of food there have been great strides in mobile phone
items. Unlike the USDA database, users must nutrition monitoring applications. Those who
type in portion size. Food items are also broken use the commercial application myFoodPhone
up into three subsections - breakfast, lunch, and take pictures of foods they are consuming with

23. Evaluation of Two Mobile Nutrition Tracking Applications
their mobile phone and post the pictures to an needs among 207 homeless adults, finding some
online food journal to receive feedback from evidence of greater disclosure of risky behaviors
a nutritionist (http://www.myfoodphone.com/). with IVRS.
However, users must have access to a computer Long-term IVRS usage has had mixed report-
and be able to properly upload the information. ing rates and health-related quality of life benefits.
Tsai and colleagues developed a mobile phone A 91 day coital study by Schroder et al. (2007)
application where participants input food items found a significant decrease in self-reports over
via the keypad and immediately receive feedback time, while a two-year study with daily reports of
on caloric balance on the phone screen. During alcohol consumption by Helzer et al. (2006) had
the month-long feasibility study with 15 college- a 91.7% reporting rate, but compensated partici-
educated participants, they found participants pants per call. Daily alcoholism reports among
preferred the mobile phone input system to tra- HIV patients found a decrease in drinking over
ditional paper and pen journaling methods (Tsai time (Aharonovich et al., 2006). In contrast, an
et al., 2006). These applications use mobile phone IVRS intervention with diabetes patients found
input via pictures or key presses, but a more natu- no measurable effects on anxiety or health-related
ral input interaction would be voice recognition quality of life (Piette et al., 2000).
software. In the next subsection, we discuss the Disease management IVRSs that act as diaries
use of IVRSs in health interventions. have improved participant satisfaction over paper
diaries (Hays et al., 2001). Two recent studies have
Integrated Voice Response Systems challenged this result (Weiler, Christ, Woodworth,
in Interventions Weiler, Weiler, 2004; Stuart, Laraia, Ornstein,
Nietert, 2003). Weiler et al. (2004) conducted a
IVRSs in healthcare have been used for reminders, 3-week, 3-way, cross-over trial including 87 adults
surveys, screening and assessments, and disease with allergic rhinitis recording daily through
management (Lavigne, 1998). A review of IVRS an IVRS or paper diary. A majority (85%) of
feasibility studies in populations with chronic ill- the participants preferred the paper instrument,
nesses such as depression, cancer, heart failure, whereas only 4% preferred the IVRS. Stuart et
and diabetes led Piette to conclude that IVRSs are al. (2003) conducted a year-long study with 642
feasible for chronically ill populations, including patients to enhance antidepressant medication
populations that have mental health problems compliance. One of three different treatment
or low-income (Piette, Weinberger, McPhee, strategies included a 12-week IVRS component,
2000). According to Mundt et al. (2002), IVRSs yet no significant differences in patient compli-
benefit healthcare because they ensure procedural ance were found and 50% of the 232 patients
standardization, automatic data scoring, direct assigned to the IVRS component either never
electronic storage, and remote accessibility from used the system or stopped before the 12 weeks
multiple locations. were completed.
Long-term alcoholism and coital studies have IVRSs in healthcare typically limit response
supported the feasibility of interventions (Aharo- input to yes/no or numeric responses (Levin
novich et al., 2006; Helzer, Badger, Searles, Rose, Levin, 2006). Recent work exploring how
Mongeon, 2006; Mundt et al., 2002; Hays, IVRS vocabulary is expanded in a two week
Irsula, McMullen, Feldblum, 2001; Schroder pain monitoring study by Levin et al. found that
et al., 2007), though the populations are well edu- number of sessions per subject ranged from 1 to
cated and technically savvy. Notably Aiemagno 20, accumulating 171 complete sessions and 2,437
et al. (1996) assessed substance abuse treatment dialogue turns. Only 2% of responses recorded
RS

24. Evaluation of Two Mobile Nutrition Tracking Applications
Table 1. Overview of case study 1
Study Length of
Motivating Research Question(s)
Phase # Phase
Phase 1 1 week 1. Can participants find, identify, and successfully scan barcodes on food items?
Break 3 weeks
Phase 2 2 weeks 1. Will participants remember how to use this application after a 3 week break?
2. Will participants actively participate without meeting with researchers every other day?
were out-of-vocabulary. Though volunteers in participants input food items into an electronic
the evaluation were not trained, the results sug- intake monitoring application. The study required
gested that training sessions could have significant that participants complete PDA application train-
value and that IVR-based data collection is not ing exercises, meet with researchers during di-
a replacement for existing data collection, but alysis sessions three times per week, and use the
simply another option for healthcare providers Barcode Ed application during two study phases
and researchers. for a total of three weeks. Table 1 shows that there
Whereas the research discussed in this section was a three week break between the two phases
primarily focuses on how well educated, techni- that allowed researchers to evaluate the data and
cally savvy users interact with various technology decide on future directions for the application. All
interventions for monitoring in their everyday interactions with participants were done during
lives, our work deals with how non-technical us- dialysis treatment in an urban, hospital-based,
ers with varying literacy skills use two different outpatient dialysis unit. We documented how
types of input mechanisms. The IVRS literature we conducted user studies in a dialysis ward in
especially shows how compliance is studied with previous work (Siek Connelly, 2006).
this technology, but it does not research if partici-
pants could use the system and how the system Methodology
can be improved. We are iteratively studying input
mechanisms because our target population will In this section, we discuss why we selected the
depend on the application for their personal health hardware and application used for this case
and thus will have to find using the application study.
efficient and enjoyable for long-term adoption.
This chapter details two case studies that provided Hardware
insight into finding the ideal input mechanism for
nutrition monitoring. We chose an off-the-shelf Palm OS Tungsten
T3 PDA for our study. The Tungsten T3 has an
expandable screen, large buttons, voice recorder,
C STUDY 1: BARCODE AND SDIO slot, 52 MB of memory, and Bluetooth. We
UNSTRUCTURED VOICE chose an off-the-shelf PDA so the results could
RING be useful to the consumer health informatics
community for future studies.
In this section we present our initial formative The Socket In-Hand SDIO card scanner
study that examines what, when, and how CKD (Socket Scanner) was chosen as the barcode scan-
SSTBRCSTRCTRC

25. Evaluation of Two Mobile Nutrition Tracking Applications
ner because it was small, easy to use, and gave PDA beeps and shows appropriate feedback when
visual and audio feedback to users. Participants participants have successfully scanned a barcode.
must press the predefined scanning button, line Previous studies have shown that CKD patients
up the scanning light perpendicular to the bar- can use the Tungsten T3 and Socket Scanner
code, and hold the PDA and object steady. The (Moor, Connelly, Rogers, 2004)
Figure 1. Screen shots from Barcode Ed. (a) Home Screen; (b-c) Voice recording and playback screens;
(d-e) Barcode Scanning feedback screens

26. Evaluation of Two Mobile Nutrition Tracking Applications
Application Design If the food item was not successfully scanned, a
red “X” would appear on the Barcode scanning
We created a simple application, Barcode Ed, unsuccessful page and participants could decide
because we wanted to isolate participants’ abil- whether to scan again or return to the home screen
ity to scan and yet have an alternative input and voice record the item instead.
mechanism (e.g., voice input) to record all food The application recorded the time the par-
items consumed. In initial interviews, half of the ticipant first pressed a Scan or Voice button,
CKD patients said they did not eat any foods with the barcode number or voice recording, and the
barcodes. However, once they were prompted, time the recording was saved. We also recorded
we found they primarily ate frozen, canned, and how many times participants played back their
prepared foods. Thus, for participants to use voice recordings. We did not record how many
an easy input mechanism like scanning, they failed barcode scans were attempted because it
would have to learn how to identify barcodes was difficult to differentiate when a participant
and use the scanner. We only used scanning and was scanning the same object or gave up and
voice recording in this study because we did attempted to scan a new object during the same
not want to overburden novice computer users period of time. Also, participants sometimes did
with a complex interface because they may have not use the scan button on the Barcode scanning
decreased cognitive function during treatment unsuccessful page - instead they went to the Home
(Martin-Lester, 1997). screen and then pressed the scan button again.
Barcode Ed consists of five screens as shown The times recorded assisted us in determining
in Figure 1. Since our user group had low literacy when participants recorded what they consumed.
skills, we relied on icons 11mm large with some Recording the number of voice recording play-
text for navigation. We found these CKD patients backs gave us insight into how participants used
could view icons 10mm or larger (Moor et al., the application.
2004). When participants turned on the PDA,
they would view the Home screen. Participants Participants
could choose to voice record by pressing the
Voice button or scan a barcode by pressing the Participants were asked to participate in the study
Scan button. As soon as participants pressed during their dialysis session. They had to be (1)
the Voice button, the application would begin over 21 years of age, (2) able to make their own
voice recording and show participants how many food or have the ability to go out and purchase
minutes and seconds they recorded on the Voice food, (3) willing to meet with researchers during
recording screen. When participants were finished each dialysis session during the week, and (4)
recording, they could press the Stop button and willing to carry the PDA and scanner with them
play back their recording on the Voice recording and input food items consumed. Ten participants
play back screen. When participants were satis- volunteered for the study. During the first phase,
fied with their recording, they could return to one participant could not participate anymore
the Home screen. When participants pressed the because of a medical emergency and another
Scan button, participants could see a red laser participant dropped out because he did not want
line emitted by the scanner. Participants lined the to record what he was eating (n = 8). We lost two
scanner line perpendicularly across the barcode participants during phase two for similar reasons
they were attempting to scan. If the food item was (n = 6).
successfully scanned, a green check mark would The average age of participants was 52 years
appear on the Barcode scanning success screen. old (s.d. = 16.28). Half of the participants were

27. Evaluation of Two Mobile Nutrition Tracking Applications
male; all of the participants were black. One a food item that could have had a barcode. Par-
participant completed an associate degree, four ticipants returned the PDAs at the end of each
participants graduated from high school, and one phase of the study, talked to researchers about
participant completed 10th grade. Participants had their experience, and verbally completed a modi-
been receiving dialysis treatments on average of fied Questionnaire for User Interface Satisfaction
five years (s.d. = 3.5 years). (QUIS) (Chin, Diehl, Norman, 1988) survey.
Only four participants reported using a Participants received ten dollars (U.S.) for every
computer. Usage frequency ranged from every time they met with researchers for a total of thirty
couple of months to once a week for a half hour. dollars during phase 1. For phase 2, participants
Participants primarily played games and surfed received five dollars each time they met with the
the Internet. Only two of the participants owned researcher for a total of fifteen dollars.
a mobile phone that they used for emergencies Competency skills tests were administered at
only. the end of the second and fourth meeting of the
The participants were equally divided about first phase and during the first and last meeting of
how many food items they consumed had bar- the second phase to test basic Barcode Ed skills
codes - some thought all and some did not think - turning the PDA on; inserting the scanner; scan-
any food items had barcodes. Five patients said ning three to five objects with different physical
they did not have to monitor any nutrients or qualities; voice recording with play back; and do
fluid. However, by the end of the first phase, the a combined barcode scanning and voice record-
researcher had established a trusting relationship ing sequence. The items participants had to scan
with the participants and found that all of them ranged from a cardboard soup mix box that is easy
had to monitor fluid and nutrients such as sodium, to scan because of the material; a can of chips that
potassium, phosphorus, and protein. None of the is somewhat difficult to scan because of material
patients recorded their fluid or nutrient consump- and barcode orientation; and a bag of candy that
tion prior to the study. is difficult to scan because it is amorphous and
made of shiny material. Researchers measured
Design and Procedure how many times it took participants to success-
fully complete each task. We measured the time
We met with participants during dialysis sessions it took to complete each competency skill with
four times during each phase of the study for ap- the Barcode Ed application.
proximately 30 minutes. During the first session, Participants were instructed to scan or voice
we collected background information and taught record food items when they consumed the
participants how to turn the PDA on, insert the items. Participants were instructed to scan the
scanner, and use the application. Participants barcodes on food items first and voice recording
practiced scanning various food items and voice items only if they could not scan the barcode or
recording messages. Researchers met with par- if a food item did not have a barcode. When par-
ticipants during the study sessions to discuss any ticipants mastered scanning and voice recording,
problems participants may have had with the researchers encouraged participants to note via
PDA, retrain participants how to do certain tasks voice recording how much they were consuming
(e.g., barcode scanning), and collect recordings and the portion size. Each participant was given
and barcodes from the PDAs via Bluetooth. The a phone number of a researcher to contact if they
researchers played back the voice recordings to had any questions during the study. Participants
ensure the correct information was transcribed were given a visual state diagram of the applica-
and informed participants if they voice recorded tion to assist them with any questions regarding

28. Evaluation of Two Mobile Nutrition Tracking Applications
use of the application that had images similar to Barcode Scanning and Voice
those shown in Figure 1. Recording Frequency
Findings One of the motivating factors for the first phase
of the Barcode Education study was to teach
The key findings of our study were: participants how to identify and scan barcodes.
In Figure 2, we see that there was a learning
• Participants preferred voice recording once curve associated with identifying and scanning
they mastered the application barcodes during the first study phase. Participants
• Participants with low literacy skills needed voice recorded more individual food items during
extra instruction on how to sufficiently the first few days of the study because they were
describe food items for voice recordings either unsure of where the barcode was located on
• Participants reported more individual food the food item or were unable to scan the barcode.
items with the Barcode Ed application than Gradually during the week, we noticed an increase
what they thought they consumed of barcode scans up until the last day of the first
• Electronic monitoring provides researchers study phase when participants barcode scanned
with ways to identify participant compli- more than they voice recorded.
ance A goal of the second study phase was to see
if this trend of increased barcode scans would
In this section, we present the results in more persist and if participants would continue actively
detail. participating in the study without meeting with
Figure 2. Graph of the number of voice recordings and barcode scans participants input over the two
barcode education study phases (dotted line denotes study break). Faces underneath each day denote
when researchers met with participants

29. Evaluation of Two Mobile Nutrition Tracking Applications
researchers every other day. The first two days of recordings. Since the participants were unable to
the second study phase were promising because read the name on the food item, they were not able
participants were scanning everything they con- to say what they were eating (e.g., Lucky Charms
sumed and only voice recorded items without cereal). Instead, participants said, “I had cereal for
barcodes (e.g., fresh produce). However, after the breakfast.” When we met with participants and
second day, participants realized everything had played the recordings for transcription, we were
barcodes and were overwhelmed with the amount able to suggest ways to be more descriptive (e.g.,
of time it took to scan each individual food item. describe what is on the box) to help us identify the
Thus, during the third and fourth day of the study, food items. After two to three sessions, the low
participants began voice recording food items they literacy participants recorded more descriptive
had previously scanned to save time. input (e.g., I ate the cereal with the leprechaun and
The lack of items input at the end of phase one rainbow on the box) and it was easier to identify
shown in Figure 2 can be attributed to not seeing a what they were eating. However, even with de-
study researcher to encourage them to participate scriptive input, we were unable to identify three
at the end of the week. Indeed, three participants of the items mentioned in the 195 recordings.
acknowledged that they had forgotten to input
foods on more than one occasion because they had Barcode Ed vs. Self Reported Food
not been visited by a researcher. Participants were Items
more likely to forget to input foods on weekends
(days six, seven, thirteen, and fourteen). In pre-study interviews, participants told us they
During the second week of the second study had good and bad days that affected how much
phase, participants rarely scanned barcodes and they consumed and discussed how many meals
typically voice recorded what they consumed. The they typically consumed on each of these days.
voice recordings listed multiple food items in an The participants usually had a good and bad day
unstructured manner. For example, one partici- fairly recently and could easily describe to us
pant recorded, “I ate a small apple, a lunch meat the exact number of items they consumed. We
sandwich, and a boost for lunch. I ate … eggs, asked participants if they had a good or bad day
and bacon for breakfast. Tonight for dinner I am each time we met during the first study phase.
planning on eating…” We then compared how many items they elec-
When we asked participants why they scanned tronically input to how many items they said they
more on the 13th day of the study, they told us would consume, including the type of day they
that they had remembered they would see a re- were having in the calculation. Participants ate
searcher on the following day to finish the study. more than they estimated for an average of three
Of course, the researchers called the participants days (s.d. = 2.875) during the seven day period.
to remind them to bring the PDAs to the last day When participants did consume more than they
of the study. estimated, they typically consumed on average
3.5 more items than estimated – nearly doubling
Voice Recording Food Items their normally recorded intake of 4.4 items (s.d.
= 3.27)1.
We thought voice recording food items was an
easy alternative input method when participants Participant Compliance
could not scan. However, participants with low
literacy skills were initially unable to give suf- For this study, we loosely defined compliance as
ficient identifying information in their voice inputting at least one food item a day. Similar
10

30. Evaluation of Two Mobile Nutrition Tracking Applications
Figure 3. Example of voice recordings, barcode scans, and voice recordings that should have been bar-
code scans (wrong record) a participant made during the first phase. The participant did back filling as
shown by the green circle and increased input during the end of the study. The dotted lines denote the
next day. Faces denote when researchers met with participants
to traditional monitoring methods, participants and increases participation in hopes the researcher
could back fill and modify their compliance re- will not notice.
cord. However, unlike traditional methods, with We discussed earlier that once participants
electronic nutrition monitoring, researchers can realized everything had a barcode on it, partici-
identify this behavior more quickly. For example, pants began to voice record more. We see this
a participant back filled entries in Figure 3 (green behavior in Figure 3– the participant starts to
circle) by recording what he had consumed for scan items, but then starts to hoard consumption
the last two days since he had not actively par- information in one voice recording a day. The
ticipated. Another indicator of back filling is the participant told us in a post-study interview that
number of times a participant recorded a food reporting everything he ate in one voice recording
item that could be scanned during a short time was more time efficient.
interval since participants cannot scan items that
have been consumed and discarded.
Participants were unaware that we were record- CASE STUDY 2: BARCODE AND
ing the date and time of inputs and thus assumed IVR
if they said, “Today, on February 11, I ate…” the
researcher would not know that it was recorded In this section we present our follow-up study that
on February 12. When we showed participants examines what, when, and how CKD participants
similar graphs as shown here, participants at- input food items into an electronic intake moni-
tempted to decrease backfilling or were more toring application and an IVRS with a borrowed
truthful in disclosing lack of participation. In mobile phone. Similar to the first case study,
addition to backfilling, we see in Figure 3 an participants complete PDA application and mobile
example of End-Of-Study compliance where the phone training exercises, meet with researchers
participant realizes the end of the study is near during dialysis sessions, and use either the PDA
11
CSSTBRCR

31. Evaluation of Two Mobile Nutrition Tracking Applications
barcode monitoring application or the mobile We provided participants with a Nokia 6682
phone IVRS over a two week period. Participants mobile phone to provide participants the ability
were recruited and trained at the same dialysis to record food at any time. The phone has a high-
unit from the first case study. resolution color screen and large buttons. As with
the PDAs, we provided soft leather cases with
Methodology belt clips to the participants. We programmed the
phone so that pressing any button would dial the
In this section, we discuss the hardware selected number for recording their food items.
for the study and design of the applications used
for capturing participant input. Application Design
Hardware The scanning application was similar to the Bar-
code Ed application used in the first case study.
We designed an application to run on a PDA with The only difference in the application was that
an attached barcode scanner to test participants’ participants did not have the ability to record
ability to scan barcodes of food items. For the unstructured voice recordings. If the food item
PDA, we chose an off-the-shelf Pocket PC from did not have a barcode, the participant could not
Hewlett Packard: the iPAQ hx2495b. We decided record the food item.
to use an iPAQ for the second case study because We implemented an IVRS that could be ac-
the Windows CE operating system provides a cessed with any phone to test participants’ ability
better rapid prototyping environment with Visual to use structured voice input. As Figure 4 shows,
Studio .NET CF. The iPAQ hardware includes a we implemented the IVRS by transferring a call
large, color, touch screen, stylus and large buttons. through a Session Initiation Protocol (SIP) gate-
We used the same SDIO In-Hand Scan Cards way to Voxeo, an IVRS platform provider. The
(SDSC Series 3E). caller identifier was then submitted to our web
Figure 4. Integrated voice response system overview
12

32. Evaluation of Two Mobile Nutrition Tracking Applications
server where a CGI script selected participant before completion. Two people dropped out after
grammar files (Nuance GSL Grammar Format), the second day due to lack of interest and one
returning a VoiceXML form to collect items. person was forced to drop out at the end of the
The initial grammar included 152 food items first week because she had to undergo emergency
and 2 command operators, ‘done’ and ‘wrong.’ surgery and remained in the hospital during the
The same grammar was available at every prompt. second week of the study. This high dropout rate
‘Done’ submitted the results and terminated the is consistent with our previous studies and is a
call. ‘Wrong’ incremented a counter, such that if result of working with this type of chronically ill
said twice without an intervening positive rec- population. Here, we report on the six participants
ognition, the participant was prompted to voice who completed the study (n=6).
record the item for addition to the grammar. With The participants’ average age was 55 years,
food items, 45 were single words (e.g., bagel), with a standard deviation of 10.9 years. The
12 were compound words (e.g., fish sticks), 27 youngest participant was 36 and the oldest was
used optional phrase operators where a portion 65. Four of the participants were female. Five
need not be uttered (e.g., French fries; French is participants identified themselves as Black or
conditional) and 50 optional phrase operators African American, and one as White. One
initially existed. There were 4 subset uses of the participant had a ninth grade education, two
disjunction operator [] (e.g., ([green baked] beans) had completed high school and three had some
is valid for ‘green beans’ or ‘baked beans’). community college.
We updated the grammar throughout the study One participant had undergone dialysis for 23
based on participant interviews and the items voice years. The remaining participants ranged from
recorded through IVRS interaction. The Voxeo 2-5 years of dialysis treatment. Two participants
platform also provided detailed logs of each call, said they did not try to keep track of their nutrient
identifying the caller and the interaction sequence or fluid consumption. Two participants did not
between the participant and VoiceXML prompts. keep track of nutrients, but attempted to limit
The interaction sequence logs included timeouts, their fluid intake by either not drinking liquids
grammar recognition errors labeled No Match, over the weekend or “staying conscious” of how
prompts, and recognitions. much they drank. Two participants claimed to
With a completed call, two lists of items and keep track of both nutrients and fluid. One used
counter variables were submitted to a MySQL a journal and was conscious of portion sizes; the
Database—a list for food items misinterpreted other could not describe their method of moni-
by the IVRS when identified as wrong by the toring but said they carefully monitored sodium
participant and a list of identified food items. and potassium intake. We have found in previous
When a participant recorded an item for addition studies that participants in this population often
to their grammar, the WAV file was submitted to tell researchers what they think they want to hear
our web server, written to disk, and a VoiceXML in regards to their nutrient and fluid consumption,
file returned to continue prompting for additional regardless of the reality.
food items. Two participants were very familiar with com-
puters. One took surveys on the Internet, while
Participants the other used his laptop daily, including bringing
it to the dialysis sessions. One participant had
We used the same criteria for selecting participants some familiarity with computers. This partici-
as we described in case study one. Nine people pant had a computer at home, but did not use it
volunteered for the study, but three dropped out very often. The final three participants said they
13

33. Evaluation of Two Mobile Nutrition Tracking Applications
were not familiar with computers, although one Participants were paid ten dollars (US) at the
had three years of typing experience and said she end of each week of the study, for a total of twenty
could use a keyboard. Three participants owned dollars. Payment did not depend on the number
mobile phones. of times they recorded food items
Design and Procedure Findings
For most participants, the study lasted a total of The key findings of our second case study
two weeks. However some participants had extra were:
time with one of the applications because bad
weather caused them to miss the dialysis session • Participants spent less time recording input
in which they were supposed to change technol- with the IVRS
ogy. For these participants, we extended the total • Participants performed better with the scan-
length of the study to ensure they had a minimum ner application on non-dialysis days and
of one week with each technology. better with the IVRS on dialysis days
We primarily used the same methods described • Participants can record more items consumed
in the first study. In this section, we describe ad- with the IVRS, but the scanner application
ditions we made to the methods. For the phone is more usable for a larger audience
application, we taught participants how to turn the • Input mechanism preference is not always
phone on and off, how to dial the number to record linked with the participants’ performance
their meals and how to record food items with with the technology
the voice recognition application, making sure to
speak one food item at a time very clearly. Barcode Scanning and IVRS
During each session, the researcher asked Frequency of Use
participants about any problems they were hav-
ing with the application, if there were any food Despite participants using each technology for at
items they did not record, why they did not least seven days, we found that in reality partici-
record a food item, when and how they used the pants used the PDA to scan items on average only
application and their general opinions about its five days (s.d. = 1.4 days) and the mobile phone
usefulness. In addition, we asked participants to to input items with the IVRS on average of 4.5
list the foods they had eaten in the last 24 hours days (s.d. = 2.95 days). We found that participants
so that we could compare their recall with what who used the technologies on most of the study
they recorded with the applications. days did so because they enjoyed using the ap-
Similar to the first study, competency tests plication systems and wanted to tinker with the
were given to participants during all but the final technology to identify breaking points. In addition,
day of the study. For the mobile phone, partici- participants mentioned a desire to help advance
pants were asked to record their last meal, which medical research to help themselves and their
required them to turn the mobile phone on, dial peers. Participants also mentioned the compensa-
the number, and follow the prompts to record the tion rewards, although the compensation was not
meal. We recorded the number of times partici- dependent on frequency of use. Participants who
pants attempted to complete each task and noted did not use the technologies regularly in the study
any difficulties they were having. If necessary, sometimes forgot the PDA in their homes and
we retrained and retested the participant. expressed a reluctance to integrate technologies
14

34. Evaluation of Two Mobile Nutrition Tracking Applications
Table 2. Number and length of time (minutes:seconds) of sessions for each device. Averages are cal-
culated per week
PDA CP
#sessions (avg.) length (avg.) #session (avg.) length (avg.)
1 18 (2.57) 72:23 (4:01) 10 (1.43) 24:10 (2:25)
PDA 2 16 (2.29) 29:07 (1:49) 25 (3.57) 28:19 (1:08)
3 4 (0.57) 5:27 (1:22) 4 (0.57) 0:04 (0:01)
4 19 (2.71) 48:48 (2:34) 22 (3.14) 15:26 (0:42)
CP 5 6 (0.86) 9:17 (1:33) 13 (1.86) 17:41 (1:28)
6 7 (1.00) 16:14 (2:19) 8 (1.14) 0:52 (0:07)
into their daily routines. We found no correlations be to use these systems in their everyday lives. If
between personal computer and mobile phone us- technology is going to take too much time, then
age outside of the study and their willingness to individuals will not be willing to use it. We see in
incorporate the technology into their lives. Table 2 that participants spent less time on input
We examined usage patterns more closely by sessions when using the IVRS in comparison to
looking at participant input sessions. We defined the PDA scanning application. Scanning took
an input session for the PDA scanner application more time because (1) occasionally the scanner
as events that occurred within 10 minutes of each popped out of the SDIO card holder and had to
other because we found participants took longer be replaced multiple times and (2) participants
to scan items in realistic situations (e.g., cooking were multitasking during scanning sessions and
meals). We defined an input session for the IVRS input food items as they were doing an activity
as any time a participant called into the system. (e.g., cooking a meal) instead of input all at once
When we analyzed usage of each technology later on (e.g., right after eating). Participants’ who
on a per input session basis, we found participants multi-tasked with the PDA application showed that
overall had more input sessions with the IVRS they are willing to integrate the technology into
than with the PDA (13.67 input sessions versus their lives. However, it also shows that raw input
11.67 input sessions), but they had similar amount times may not be the best measure of efficient
of input sessions when averaged over the week usage of the PDA application.
(1.95 input sessions versus 1.67 input sessions).
In Table 2, we show the total and average num- Performance
ber of sessions each participant had with each
device, and the total and average time spent in Besides the actual usage of the technologies in
each session. Participants 1-3 had the PDA the this study, we wanted to study the participant
first week of the study, while participants 4-6 had performance with each input mechanism. For
the mobile phone. this study, we defined performance as the ratio of
Looking at the time participants spent on unsuccessful to successful attempts at recording
input gives us insight into how realistic it would food items. We observed that performance was
15

35. Evaluation of Two Mobile Nutrition Tracking Applications
not consistent on all days. The ratio of unsuccess- Electronic Input vs. Self Reported Food
ful to successful barcode scans on dialysis days Items
was two times higher than on non-dialysis days
(2.43 to 1.11). Conversely, we found participants We asked participants to recall all of the food
performed better with voice recording on dialysis they ate in the last 24 hours each time we met
days – they had better performance on three out of with them. We then compared their 24 hour
the four non-dialysis days. Thus, on non-dialysis recall to the foods they electronically input into
days participants performed better with the scan- either the scanning program or IVRS with Venn
ner application and on dialysis days, participants diagrams shown in Figures 5 and 6 . The relative
performed better using the IVRS. ratios between these three numbers provide us
We also studied how participants interacted insight into how participants used the electronic
with the IVRS. Unlike the first study, participants application.
would have to say items one at a time and use The Venn diagrams for voice and scanning
command operators to record food items. We show that participants did not record everything
found on average that 53% of the time participants they ate. Indeed, participants were somewhat
did not use command operators correctly during limited with their ability to electronically record
IVRS sessions. Participants did not say, “Wrong,” because the scanning application required all
when items were not recognized by the IVRS for recorded items to have barcodes and the IVRS
27% of the total calls. Participants did not say, required the items be in the database to be rec-
“Done,” when they finished their calls 26% of ognized. We found that sometimes participants
the total calls. These errors effect how the IVRS electronically recorded items they did not eat.
interprets the input and thus could affect giving One participant in particular recorded non-food
participants feedback on their food consumption items. Overall, it appears that participants can
in future implementations. capture more items they consume with the IVRS.
Figure 5. Venn diagram of food items in 24 hour recall and items scanned
16