Mobile healthcare solutions for biomedical applications

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Mobile healthcare solutions for biomedical applications

  1. 1. Mobile Health Solutionsfor BiomedicalApplicationsPhillip OllaMadonna University, USAJoseph TanWayne State University, USA Medical Information science reference Hershey • New York
  2. 2. Director of Editorial Content: Kristin KlingerSenior Managing Editor: Jamie SnavelyManaging Editor: Jeff AshAssistant Managing Editor: Carole CoulsonTypesetter: Larissa VinciCover Design: Lisa TosheffPrinted at: Yurchak Printing Inc.Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com/referenceand in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanbookstore.comCopyright © 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or byany means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identi.cation purposes only. Inclusion of the names of the products or companies doesnot indicate a claim of ownership by IGI Global of the trademark or registered trademark.Library of Congress Cataloging-in-Publication DataMobile 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 byusing 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 2008040451British Cataloguing in Publication DataA 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 notnecessarily of the publisher.
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  4. 4. Editorial Advisory BoardGeorge Demiris, University of Missouri, USANayna Patel, Brunel University, UKThomas M. Deserno, RWTH Aachen University, GermanyJyoti Choudrie, University of Hertfordshire, UKPaul Hu, University of Utah, USAPatrice Monthrope, University of West Indies, JamaicaRichard Hull, University of Newcastle upon Tyne, United KingdomElena Qureshi, Madonna University, USAFrancis Lau, University of Victoria, CanadaVenus Olla, Nottingham University, UKDave Parry, Auckland University of Technology, New ZealandMathew Guah, Erasmus University, The NetherlandsJim Warren, University of Auckland, New ZealandH. Joseph Wen, Southeast Missouri State University, USAYvette Miller, University of Toronto, CanadaYufei Yuan, McMaster University, CanadaDaniel Zeng, University of Arizona, USAKai Zheng, The University of Michigan, USAJacqueline Brodie, Napier University, ScotlandCarla Wiggins, Idaho State University, USABendik Bygstad, Norwegian School of IT, Norway
  5. 5. Table of ContentsPreface . ...............................................................................................................................................xiii Section I Mobile Health Applications and TechnologiesChapter IEvaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populationswith 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, USAChapter IIAccessing 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, PortugalChapter IIIPersonal Health Records Systems Go Mobile: Defining Evaluation Components............................... 45 . Phillip Olla, Madonna University, USA Joseph Tan, Wayne State University, USAChapter IVMedical 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
  6. 6. Chapter VA 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, SpainChapter VIManaging Mobile Healthcare Knowledge: Physicians’ Perceptions on KnowledgeCreation 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 DevicesChapter VIIPatient Monitoring in Diverse Environments ..................................................................................... 129 Yousef Jasemian, Engineering College of Aarhus, DenmarkChapter VIIIMonitoring Hospital Patients Using Ambient Displays....................................................................... 143 Monica Tentori, CICESE, Mexico Daniela Segura, CICESE, Mexico Jesus Favela, CICESE, MexicoChapter IXTowards 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 NetherlandsChapter XSensing of Vital Signs and Transmission Using Wireless Networks................................................... 180 Yousef Jasemian, Engineering College of Aarhus, Denmark
  7. 7. Chapter XITowards 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 SystemsChapter XIIA 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, UKChapter XIIITechnology Enablers for Context-Aware Healthcare Applications..................................................... 260 Filipe Meneses, Universidade do Minho, Portugal Adriano Moreira, Universidade do Minho, PortugalChapter XIVModeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270 Bjorn Gottfried, University of Bremen, GermanyChapter XVContext-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 NetherlandsCompilation of References................................................................................................................ 308About the Contributors..................................................................................................................... 332Index.................................................................................................................................................... 341
  8. 8. Detailed Table of ContentsPreface . ...............................................................................................................................................xiii Section I Mobile Health Applications and TechnologiesChapter IEvaluation of Two Mobile Nutrition Tracking Applications for Chronically Ill Populationswith 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, USAIn 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 chronicallyill population.Chapter IIAccessing 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, PortugalThe 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 toaccess a VEPR. This VEPR is being used within a university hospital by more than 1,000 doctors, on adaily 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 treatmentof their patients.
  9. 9. Chapter IIIPersonal Health Records Systems Go Mobile: Defining Evaluation Components............................... 45 . Phillip Olla, Madonna University, USA Joseph Tan, Wayne State University, USAThis chapter provides an overview of Mobile Personal Health Record (MPHR) systems. A Mobilepersonal health record is an eclectic application through which patients can access, manage, and sharetheir 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 whenassessing MPHR systems.Chapter IVMedical 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 NetherlandsThis chapter describes a framework which enables medical information, in particular clinical vital signsand professional annotations, be processed, exchanged, stored and managed modularly and flexibly in amobile, distributed and heterogeneous environment despite the diversity of the formats used to representthe information.Chapter VA 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, SpainThis chapter presents an architecture for diagnosis support based on the collaboration among differentdiagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinicalmeetings in hospitals in which the members of a medical team share their opinions in order to analyzecomplicated diagnoses.Chapter VIManaging Mobile Healthcare Knowledge: Physicians’ Perceptions on KnowledgeCreation 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
  10. 10. This chapter aims to demonstrate that mobile healthcare information system may also help physiciansto 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 frameworkknown as the 7C model. The data was collected through the Internet among all of the 352 users of themobile 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, knowledgemanagement seems to be a prominent approach for studying healthcare information systems and theirimpact on the work of physicians. Section II Patient Monitoring and Wearable DevicesChapter VIIPatient Monitoring in Diverse Environments ..................................................................................... 129 Yousef Jasemian, Engineering College of Aarhus, DenmarkThis chapter intends to explore the issues and limitations concerning application of mobile health systemin diverse environments, trying to emphasize the advantages and drawbacks, data security and integritysuggesting approaches for enhancements. These issues will be explored in successive subsections byintroducing two studies which were undertaken by the author.Chapter VIIIMonitoring Hospital Patients Using Ambient Displays....................................................................... 143 Monica Tentori, CICESE, Mexico Daniela Segura, CICESE, Mexico Jesus Favela, CICESE, MexicoIn this chapter the authors explore the use of ambient displays to adequately monitor patient’s healthstatus 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 displaysthat can be used to provide awareness patients’ health status to hospital workers.Chapter IXTowards 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 NetherlandsWireless Body Sensor Networks (BSNs) are an indispensable building stone for any pervasive healthcaresystem. Although suitable wireless technologies are available and standardization dedicated to BSNcommunication has been initiated, the authors identify key challenges in the areas of easy-of-use, safety,
  11. 11. and security that hinder a quick adoption of BSNs. To address the identified issues we propose usingBody-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 inpervasive healthcare systems.Chapter XSensing of Vital Signs and Transmission Using Wireless Networks................................................... 180 Yousef Jasemian, Engineering College of Aarhus, DenmarkThis 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 systemby the author.Chapter XITowards 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, BrazilIn this chapter, we present our experience in using mobile phones as a platform for real-time physiologicalmonitoring and analysis. In particular, we describe in detail the TripleBeat system, a research prototypethat assists runners in achieving predefined exercise goals via musical feedback, a glanceable interfacefor increased personal awareness and a virtual competition. We believe that systems like TripleBeat willplay an important role in assisting users towards healthier and more active lifestyles. Section III Context Aware SystemsChapter XIIA 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, UKIn this chapter, the authors describe a new framework for pervasive healthcare applications where thepatient’s consent has a pivotal role. In their framework, patients are able to control the disclosure of theirmedical data. The patient’s consent is implicitly captured by the context in which his or her medical datais being accessed. Context is expressed in terms of workflows. The execution of a task in a workflowcarries 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 workflowenables the enforcement of the need-to-kwon principle. This means that a subject is authorised to accesssensitive data only when required by the actual situation.
  12. 12. Chapter XIIITechnology Enablers for Context-Aware Healthcare Applications..................................................... 260 Filipe Meneses, Universidade do Minho, Portugal Adriano Moreira, Universidade do Minho, PortugalThis chapter focuses on how context and location can be used in innovative applications and how touse 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 offamiliarity of the user with the surrounding environment and how the familiarity level can be used in anumber of situations.Chapter XIVModeling Spatiotemporal Developments in Spatial Health Systems.................................................. 270 Bjorn Gottfried, University of Bremen, GermanyThis chapter introduces spatial health systems, identifies fun¬damental properties of these systems, anddetails for specific applications the methods to be applied in order to show how problems are solved inthis field. On the one hand, this chapter gives an overview of this area, on the other hand, it is writtenfor those who are interested in designing spatial health systems. The result is that different spatial scalesand pur¬poses require different representations for describing the spatiotemporal change of objects,that is their spatiotemporal development, showing how fundamental aspects of spatial health systemsare dealt with.Chapter XVContext-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 NetherlandsAs 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 mechanismand a task redistribution infrastructure. The decision mechanism represents task assignment as a graphmapping problem and searches for the optimal assignment given the latest context information. Oncea 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 taskredistribution infrastructure.Compilation of References................................................................................................................ 308About the Contributors..................................................................................................................... 332Index.................................................................................................................................................... 341
  13. 13. xiiiPrefacePervasive healthcare environment, focusing on the integration of mobile and ubiquitous technology toreform working and living conditions for individuals and organizations in the healthcare sector, sets thestage for an innovative emerging research discipline. Healthcare systems are experiencing a variety ofchallenges 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 forself-care and management of deteriorating health conditions, the need for seamless access for healthcareservices 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 componentis the availability of a reliable wireless architecture; the second component is the integration of medicalsensor or wearable devices for monitoring; and the final component is a robust application and servicesinfrastructure. 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 drugdelivery 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 constraintssuch 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 prevalentacross almost the entire healthcare data acquisition task domains. Bio monitoring using mobile networksincludes 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, gastrointestinaltelemetry fall detection, and location tracking. Using mobile technology, patient records can be accessedby 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 qualityof patient care. Handheld devices can also be used in home healthcare, for example, to fight diabetesthrough effective monitoring. A comprehensive overview of some of these mobile health applicationsand 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 features32 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
  14. 14. xivin these countries differ, the benefits to be realized from a future of implementations of mobile healthtechnology are not inconsistent among the countries. The book may be divided into three major sections:1. Mobile Health Applications and Technologies2. Patient Monitoring and Wearable Devices3. 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-worldillustrations of applications and uses of mobile technologies in the healthcare sector. M-Health is abroad area transcending multiple disciplines and utilizing a broad range of technologies. “Evaluation ofTwo 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, andpatient 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 LuísAntunes presents the concept of a virtual electronic patient records system that enables the integrationand sharing of healthcare information within heterogeneous organizations. The VEPR system aims toalleviate the constraints in terms of physical location as well as technology in order to access vital patientrecords. The use of wireless technology attempts to allow access to patient data and processing of clinicalrecords closer to the point of care. The ubiquitous access to information can minimize physical as wellas time constraints for healthcare, enhancing users’ mobility within the institution. The next chapter inthis 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 canaccess, manage and share their health information from a mobile device in a private, confidential, andsecure 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 HermieHermens. This chapter describes a framework which enables medical information such as clinical, vitalsigns 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 theuse of techniques and constructs similar to the internet to deal with medical information represented inmultiple formats. Chapter V is “A Distributed Approach of a Clinical Decision Support System Basedon Cooperation,” authored by Daniel Ruiz-Fernández and Antonio Soriano-Payá. This chapter discussesan architecture that supports diagnosis based on the collaboration among different diagnosis-supportartificial entities or agents and the physicians themselves. The proposed systems architecture, whichwas tested in a melanoma and urological dysfunctions diagnosis, combines availability, cooperation andharmonization 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 MarkkuKallio. 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-
  15. 15. xvlaborate as well as learn and share their experiences within their user community. The chapter presentsa case study of the users of Duodecim mobile healthcare information system. The second section presents research on Patient Monitoring and Wearable Devices. Chapter VII, thefirst chapter in this section, is titled “Patient Monitoring in Diverse Environments” and is authored byYousef 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 chronicdisorders such as hypertension, diabetes, anorexia nervosa, chronic pain, or severe obesity. The authorexplored 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 suggestingapproaches for enhancements. The following chapter, Chapter VIII, is titled “Monitoring Hospital Patientsusing Ambient Displays” authored by Monica Tentori, Daniela Segura, and Jesus Favela. This chapterexplores the use of ambient displays to promptly notify hospital workers of relevant events related totheir patients. To highlight the feasibility and applicability of ambient displays in hospitals, this chapterpresents two ambient displays aimed at creating a wearable connection between patients and healthcareproviders. The authors also discuss issues and opportunities for the deployment of ambient displaysfor patient monitoring. Chapter IX is titled “Towards Easy-to-uUse, Safe, and Secure Wireless MedicalBody 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), whichare an integral part of any pervasive healthcare system. It discusses suitable wireless technologies andstandardization 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 ofBSNs and for user identification and presented a lightweight mechanism that would enable a transparentsecurity setup for BSNs used in pervasive healthcare systems. Chapter X is titled “Sensing of Vital Signs and Transmission Using Wireless Networks” and is authoredby 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 indicatedthat the system functioned with a clinically acceptable performance, and transferred medical data witha reasonable quality, even though the system was tested under totally uncontrolled circumstances duringthe patients’ daily activities. Both the patients and the healthcare personnel who participated expressedtheir 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 mobilephones as a platform for real-time physiological monitoring and analysis. The authors presented twomobile phone-based prototypes that explore the impact of real-time physiological monitoring in the dailylife of users. The first prototype is called HealthGear; this is a system to monitor users while they aresleeping and automatically detect sleep apnea events; the second is TripleBeat, a prototype that assistsrunners in achieving predefined exercise goals via musical feedback and two persuasive techniques: aglanceable 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 thehealthcare arena. Chapter XII, the first chapter in this section, is titled “A Framework for CapturingPatient Consent in Pervasive Healthcare Applications.” It is authored by Giovanni Russello, ChangyuDong, and Naranker Dualy and describes a new framework for pervasive healthcare applications wherethe patient’s consent plays a pivotal role. In the framework presented, patients are able to control the
  16. 16. xvidisclosure of their medical data. The patient’s consent is implicitly captured by the context in whichhis or her medical data is being accessed. Context is expressed in terms of workflows. The executionof 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 byFilipe Meneses and Adriano Moreira. This chapter discusses how context and location can be used ininnovative applications and how to use a set of solutions and technologies that enable the developmentof innovative context and location-aware solutions for healthcare area. The chapter highlights how amobile 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 availabilityof mobile devices and wireless networks, and the tendency for them to become ubiquitous in our dallylives, creates a favourable technological environment for the emergence of new, simple, and added-valueapplications for healthcare. Chapter XIV is titled “Modeling Spatiotemporal Developments in SpatialHealth Systems” is authored by Bjorn Gottfried and discusses Spatial health systems and the supportthese 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. Thework focuses on how spatial health systems monitor the physical activity of people in order to determinehow 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 importanceof 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 patientscontext aware healthcare systems is clearly needed . The authors propose an adaptation middleware thatconsists 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 optimalassignment given the latest context information. The research presented in this book is important due to the emergence of pervasive computing andhealth 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 becomethe norm in the future.
  17. 17. Section IMobile Health Applications and Technologies
  18. 18. 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
  19. 19. 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
  20. 20. Evaluation of Two Mobile Nutrition Tracking Applicationsbooks (Bernheim, Combs, Smith, Gupta, 2005), dinner. The nutritional analysis is given on aand obtain information about an environment separate screen. Researchers at Indiana Universityfrom embedded barcodes (Fitzmaurice, Khan, studied how three people with CKD used Diet-Buxton, Kurtenback, Balakrishnan, 2003). MatePro to monitor nutritional consumption overMobile phones used for IVRSs have been used a three-month period. They found participants hadfor 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 CKDheart failure, and diabetes (Piette, 2000). In this participants used BalanceLog over a four-monthsection, we discuss specifically how PDAs and period. They found that participants improvedmobile phones have been used for interventions their dietary intake using the electronic nutritionand nutritional monitoring. monitoring system (Sevick et al., 2005). Both applications evaluated in these studies requiredPDA 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 toCurrently, there are many PDA applications that scan food items and obtain nutritional informationcan 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 applicationculture (USDA) has a PDA nutrient database that did not have an extensive UPC/nutrition databaseprovides people with a mechanism for looking up because none are freely available. Although thethe nutritional information of foods. Users must application does not allow users to save intakecorrectly type the first few letters of a food item information, the application shows that integrationthey are looking for into a search box and then click of scanners and nutrition information is possiblethrough 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 informationand BalanceLog (http://www.healthetech.com/) available online about specific food items. Duringuse the USDA database along with other fast food their five-week study with twenty participantsnutritional 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 intion information for a set of specific nutrients. terms of enjoyment and usefulness. Similar to aCalorieKing (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), participantsthe ability to save consumption information. In in the Microsoft study did not always bring theaddition, it has a nutritional tracking application PDA with them despite being enthusiastic PDAspecific 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 phoneitems. Unlike the USDA database, users must nutrition monitoring applications. Those whotype in portion size. Food items are also broken use the commercial application myFoodPhoneup into three subsections - breakfast, lunch, and take pictures of foods they are consuming with
  21. 21. 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
  22. 22. 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
  23. 23. Evaluation of Two Mobile Nutrition Tracking Applicationsner because it was small, easy to use, and gave PDA beeps and shows appropriate feedback whenvisual 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 patientsup the scanning light perpendicular to the bar- can use the Tungsten T3 and Socket Scannercode, 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
  24. 24. Evaluation of Two Mobile Nutrition Tracking ApplicationsApplication Design If the food item was not successfully scanned, a red “X” would appear on the Barcode scanningWe created a simple application, Barcode Ed, unsuccessful page and participants could decidebecause we wanted to isolate participants’ abil- whether to scan again or return to the home screenity 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 thebarcodes. However, once they were prompted, time the recording was saved. We also recordedwe found they primarily ate frozen, canned, and how many times participants played back theirprepared foods. Thus, for participants to use voice recordings. We did not record how manyan easy input mechanism like scanning, they failed barcode scans were attempted because itwould have to learn how to identify barcodes was difficult to differentiate when a participantand use the scanner. We only used scanning and was scanning the same object or gave up andvoice recording in this study because we did attempted to scan a new object during the samenot want to overburden novice computer users period of time. Also, participants sometimes didwith a complex interface because they may have not use the scan button on the Barcode scanningdecreased 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 determiningin 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 usedcould view icons 10mm or larger (Moor et al., the application.2004). When participants turned on the PDA,they would view the Home screen. Participants Participantscould choose to voice record by pressing theVoice button or scan a barcode by pressing the Participants were asked to participate in the studyScan 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 ownvoice recording and show participants how many food or have the ability to go out and purchaseminutes and seconds they recorded on the Voice food, (3) willing to meet with researchers duringrecording 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 themplay back their recording on the Voice recording and input food items consumed. Ten participantsplay 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 anymorethe Home screen. When participants pressed the because of a medical emergency and anotherScan button, participants could see a red laser participant dropped out because he did not wantline emitted by the scanner. Participants lined the to record what he was eating (n = 8). We lost twoscanner line perpendicularly across the barcode participants during phase two for similar reasonsthey 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 yearsappear on the Barcode scanning success screen. old (s.d. = 16.28). Half of the participants were
  25. 25. Evaluation of Two Mobile Nutrition Tracking Applicationsmale; 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 eachparticipants graduated from high school, and one phase of the study, talked to researchers aboutparticipant completed 10th grade. Participants had their experience, and verbally completed a modi-been receiving dialysis treatments on average of fied Questionnaire for User Interface Satisfactionfive 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 everycomputer. Usage frequency ranged from every time they met with researchers for a total of thirtycouple of months to once a week for a half hour. dollars during phase 1. For phase 2, participantsParticipants primarily played games and surfed received five dollars each time they met with thethe 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 atonly. 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 ofhow many food items they consumed had bar- the second phase to test basic Barcode Ed skillscodes - 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 physicalthey did not have to monitor any nutrients or qualities; voice recording with play back; and dofluid. 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 scanwith the participants and found that all of them ranged from a cardboard soup mix box that is easyhad to monitor fluid and nutrients such as sodium, to scan because of the material; a can of chips thatpotassium, phosphorus, and protein. None of the is somewhat difficult to scan because of materialpatients recorded their fluid or nutrient consump- and barcode orientation; and a bag of candy thattion prior to the study. is difficult to scan because it is amorphous and made of shiny material. Researchers measuredDesign and Procedure how many times it took participants to success- fully complete each task. We measured the timeWe met with participants during dialysis sessions it took to complete each competency skill withfour 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 voicewe collected background information and taught record food items when they consumed theparticipants how to turn the PDA on, insert the items. Participants were instructed to scan thescanner, and use the application. Participants barcodes on food items first and voice recordingpracticed scanning various food items and voice items only if they could not scan the barcode orrecording 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 viaPDA, 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 givenand barcodes from the PDAs via Bluetooth. The a phone number of a researcher to contact if theyresearchers played back the voice recordings to had any questions during the study. Participantsensure 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
  26. 26. Evaluation of Two Mobile Nutrition Tracking Applicationsuse of the application that had images similar to Barcode Scanning and Voicethose shown in Figure 1. Recording FrequencyFindings One of the motivating factors for the first phase of the Barcode Education study was to teachThe 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 activelydetail. participating in the study without meeting withFigure 2. Graph of the number of voice recordings and barcode scans participants input over the twobarcode education study phases (dotted line denotes study break). Faces underneath each day denotewhen researchers met with participants
  27. 27. Evaluation of Two Mobile Nutrition Tracking Applicationsresearchers every other day. The first two days of recordings. Since the participants were unable tothe second study phase were promising because read the name on the food item, they were not ableparticipants were scanning everything they con- to say what they were eating (e.g., Lucky Charmssumed and only voice recorded items without cereal). Instead, participants said, “I had cereal forbarcodes (e.g., fresh produce). However, after the breakfast.” When we met with participants andsecond day, participants realized everything had played the recordings for transcription, we werebarcodes 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 theThus, during the third and fourth day of the study, food items. After two to three sessions, the lowparticipants began voice recording food items they literacy participants recorded more descriptivehad 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 identifyshown 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 threeat the end of the week. Indeed, three participants of the items mentioned in the 195 recordings.acknowledged that they had forgotten to inputfoods on more than one occasion because they had Barcode Ed vs. Self Reported Foodnot been visited by a researcher. Participants were Itemsmore 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 muchphase, participants rarely scanned barcodes and they consumed and discussed how many mealstypically 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 dayunstructured manner. For example, one partici- fairly recently and could easily describe to uspant recorded, “I ate a small apple, a lunch meat the exact number of items they consumed. Wesandwich, and a boost for lunch. I ate … eggs, asked participants if they had a good or bad dayand 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 theymore on the 13th day of the study, they told us would consume, including the type of day theythat they had remembered they would see a re- were having in the calculation. Participants atesearcher on the following day to finish the study. more than they estimated for an average of threeOf 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 theyof the study. estimated, they typically consumed on average 3.5 more items than estimated – nearly doublingVoice Recording Food Items their normally recorded intake of 4.4 items (s.d. = 3.27)1.We thought voice recording food items was aneasy alternative input method when participants Participant Compliancecould not scan. However, participants with lowliteracy skills were initially unable to give suf- For this study, we loosely defined compliance asficient identifying information in their voice inputting at least one food item a day. Similar10
  28. 28. 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 11CSSTBRCR
  29. 29. Evaluation of Two Mobile Nutrition Tracking Applicationsbarcode monitoring application or the mobile We provided participants with a Nokia 6682phone IVRS over a two week period. Participants mobile phone to provide participants the abilitywere 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 withMethodology belt clips to the participants. We programmed the phone so that pressing any button would dial theIn this section, we discuss the hardware selected number for recording their food items.for the study and design of the applications usedfor capturing participant input. Application DesignHardware 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 thatan attached barcode scanner to test participants’ participants did not have the ability to recordability to scan barcodes of food items. For the unstructured voice recordings. If the food itemPDA, we chose an off-the-shelf Pocket PC from did not have a barcode, the participant could notHewlett 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’ abilitybetter 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 calllarge, 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 webFigure 4. Integrated voice response system overview12
  30. 30. Evaluation of Two Mobile Nutrition Tracking Applicationsserver where a CGI script selected participant before completion. Two people dropped out aftergrammar files (Nuance GSL Grammar Format), the second day due to lack of interest and onereturning 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 emergencyand 2 command operators, ‘done’ and ‘wrong.’ surgery and remained in the hospital during theThe 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 acall. ‘Wrong’ incremented a counter, such that if result of working with this type of chronically illsaid twice without an intervening positive rec- population. Here, we report on the six participantsognition, 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. The12 were compound words (e.g., fish sticks), 27 youngest participant was 36 and the oldest wasused optional phrase operators where a portion 65. Four of the participants were female. Fiveneed not be uttered (e.g., French fries; French is participants identified themselves as Black orconditional) and 50 optional phrase operators African American, and one as White. Oneinitially existed. There were 4 subset uses of the participant had a ninth grade education, twodisjunction operator [] (e.g., ([green baked] beans) had completed high school and three had someis valid for ‘green beans’ or ‘baked beans’). community college. We updated the grammar throughout the study One participant had undergone dialysis for 23based on participant interviews and the items voice years. The remaining participants ranged fromrecorded through IVRS interaction. The Voxeo 2-5 years of dialysis treatment. Two participantsplatform also provided detailed logs of each call, said they did not try to keep track of their nutrientidentifying the caller and the interaction sequence or fluid consumption. Two participants did notbetween the participant and VoiceXML prompts. keep track of nutrients, but attempted to limitThe interaction sequence logs included timeouts, their fluid intake by either not drinking liquidsgrammar recognition errors labeled No Match, over the weekend or “staying conscious” of howprompts, 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 usedcounter variables were submitted to a MySQL a journal and was conscious of portion sizes; theDatabase—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 sodiumparticipant and a list of identified food items. and potassium intake. We have found in previousWhen a participant recorded an item for addition studies that participants in this population oftento their grammar, the WAV file was submitted to tell researchers what they think they want to hearour 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, whileParticipants the other used his laptop daily, including bringing it to the dialysis sessions. One participant hadWe 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 itvolunteered for the study, but three dropped out very often. The final three participants said they 13
  31. 31. Evaluation of Two Mobile Nutrition Tracking Applicationswere not familiar with computers, although one Participants were paid ten dollars (US) at thehad three years of typing experience and said she end of each week of the study, for a total of twentycould use a keyboard. Three participants owned dollars. Payment did not depend on the numbermobile phones. of times they recorded food itemsDesign and Procedure FindingsFor most participants, the study lasted a total of The key findings of our second case studytwo weeks. However some participants had extra were:time with one of the applications because badweather caused them to miss the dialysis session • Participants spent less time recording inputin which they were supposed to change technol- with the IVRSogy. 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 andof one week with each technology. better with the IVRS on dialysis days We primarily used the same methods described • Participants can record more items consumedin the first study. In this section, we describe ad- with the IVRS, but the scanner applicationditions we made to the methods. For the phone is more usable for a larger audienceapplication, we taught participants how to turn the • Input mechanism preference is not alwaysphone on and off, how to dial the number to record linked with the participants’ performancetheir meals and how to record food items with with the technologythe voice recognition application, making sure tospeak one food item at a time very clearly. Barcode Scanning and IVRS During each session, the researcher asked Frequency of Useparticipants about any problems they were hav-ing with the application, if there were any food Despite participants using each technology for atitems 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 onlyapplication and their general opinions about its five days (s.d. = 1.4 days) and the mobile phoneusefulness. In addition, we asked participants to to input items with the IVRS on average of 4.5list the foods they had eaten in the last 24 hours days (s.d. = 2.95 days). We found that participantsso that we could compare their recall with what who used the technologies on most of the studythey 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 thewere 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 advancepants were asked to record their last meal, which medical research to help themselves and theirrequired 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 notmeal. We recorded the number of times partici- dependent on frequency of use. Participants whopants attempted to complete each task and noted did not use the technologies regularly in the studyany difficulties they were having. If necessary, sometimes forgot the PDA in their homes andwe retrained and retested the participant. expressed a reluctance to integrate technologies14
  32. 32. Evaluation of Two Mobile Nutrition Tracking ApplicationsTable 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. Ifbetween personal computer and mobile phone us- technology is going to take too much time, thenage outside of the study and their willingness to individuals will not be willing to use it. We see inincorporate 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 tolooking at participant input sessions. We defined the PDA scanning application. Scanning tookan input session for the PDA scanner application more time because (1) occasionally the scanneras events that occurred within 10 minutes of each popped out of the SDIO card holder and had toother because we found participants took longer be replaced multiple times and (2) participantsto scan items in realistic situations (e.g., cooking were multitasking during scanning sessions andmeals). We defined an input session for the IVRS input food items as they were doing an activityas 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’ whoon a per input session basis, we found participants multi-tasked with the PDA application showed thatoverall had more input sessions with the IVRS they are willing to integrate the technology intothan with the PDA (13.67 input sessions versus their lives. However, it also shows that raw input11.67 input sessions), but they had similar amount times may not be the best measure of efficientof 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- Performanceber of sessions each participant had with eachdevice, and the total and average time spent in Besides the actual usage of the technologies ineach session. Participants 1-3 had the PDA the this study, we wanted to study the participantfirst week of the study, while participants 4-6 had performance with each input mechanism. Forthe mobile phone. this study, we defined performance as the ratio of Looking at the time participants spent on unsuccessful to successful attempts at recordinginput gives us insight into how realistic it would food items. We observed that performance was 15
  33. 33. Evaluation of Two Mobile Nutrition Tracking Applicationsnot consistent on all days. The ratio of unsuccess- Electronic Input vs. Self Reported Foodful to successful barcode scans on dialysis days Itemswas 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 foodperformed better with voice recording on dialysis they ate in the last 24 hours each time we metdays – they had better performance on three out of with them. We then compared their 24 hourthe four non-dialysis days. Thus, on non-dialysis recall to the foods they electronically input intodays participants performed better with the scan- either the scanning program or IVRS with Vennner application and on dialysis days, participants diagrams shown in Figures 5 and 6 . The relativeperformed better using the IVRS. ratios between these three numbers provide us We also studied how participants interacted insight into how participants used the electronicwith 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 scanningcommand operators to record food items. We show that participants did not record everythingfound on average that 53% of the time participants they ate. Indeed, participants were somewhatdid not use command operators correctly during limited with their ability to electronically recordIVRS sessions. Participants did not say, “Wrong,” because the scanning application required allwhen items were not recognized by the IVRS for recorded items to have barcodes and the IVRS27% 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 participantsthe 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-foodparticipants feedback on their food consumption items. Overall, it appears that participants canin future implementations. capture more items they consume with the IVRS.Figure 5. Venn diagram of food items in 24 hour recall and items scanned16

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