Nova Southeastern University
NSUWorks
CEC Theses and Dissertations
College of Engineering and Computing
2015
Seniors with Diabetes-Investigation of the Impact
of Semantic Auditory Distractions on the Usability
of a Blood Glucose Tracking Mobile Application
Jose A. Rivera Rodriguez
Nova Southeastern University, [email protected]
This document is a product of extensive research conducted at the Nova Southeastern University College of
Engineering and Computing. For more information on research and degree programs at the NSU College of
Engineering and Computing, please click here.
Follow this and additional works at: http://nsuworks.nova.edu/gscis_etd
Part of the Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons,
Dietetics and Clinical Nutrition Commons, Endocrinology, Diabetes, and Metabolism Commons,
Graphics and Human Computer Interfaces Commons, and the Health Information Technology
Commons
Share Feedback About This Item
NSUWorks Citation
Jose A. Rivera Rodriguez. 2015. Seniors with Diabetes-Investigation of the Impact of Semantic Auditory Distractions on the Usability of a
Blood Glucose Tracking Mobile Application. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of
Engineering and Computing. (64)
http://nsuworks.nova.edu/gscis_etd/64.
This Dissertation is brought to you by the College of Engineering and Computing at NSUWorks. It has been accepted for inclusion in CEC Theses and
Dissertations by an authorized administrator of NSUWorks. For more information, please contact [email protected].
Seniors with Diabetes-Investigation of the Impact of Semantic
Auditory Distractions on the Usability of a Blood Glucose
Tracking Mobile Application
by
José A. Rivera Rodríguez
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in
Information Systems
Graduate School of Computer and Information Sciences
Nova Southeastern University
2015
We hereby certify that this dissertation, submitted by Jose Rivera-Rodriguez, conforms
to acceptable standards and is fully adequate in scope and quality to fulfill the dissertation
requirements for the degree of Doctor of Philosophy.
_____________________________________________
Maxine S. Cohen, Ph.D.
Chairperson of Dissertation Committee
________________
Date
_____________________________________________
Eric S. Ackerman, Ph.D.
Dissertation Committee Member
________________
Date
_____________________________________________
James Templeton, Ph.D.
Dissertation Committee Member
________________
Date
Approved:
_____________________________________________
Amon B. Seagull, Ph.D.
Interim Dean, College of Engineering and Computing
________________
Date
College of Engineering and Computing
Nova Southeastern University
2015
An Abstract of a Dissertation Submitted to Nova Southeastern University
in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Seniors with Diabetes-Investigation of the Impact of Semantic Auditory
Distractions on the Usability of a Blood Glucose
Tracking Mobile Application
by
José A. Rivera Rodríguez
June 2015
Diabetes is the seventh leading cause of death in the United States. With the population
rapidly aging, it is expected that 1 out of 3 Americans will have diabetes by 2050. Mobile
devices and mobile applications have the potential to contribute to diabetes self-care by
allowing users to manage their diabetes by keeping track of their blood glucose levels.
Usability is important for systems that help people self-manage conditions such as
diabetes. Age and diabetes-related cognitive decline might intensify the impact of
usability issues for the users who need these mobile applications the most. As
highlighted by usability researchers, the context of use (i.e. environment, user, task, and
technology) has a significant impact on usability. The environment (lighting,
temperature, audio and visual distractions, etc.) is of special interest to the mobile
usability arena since in the case of mobile devices, is always changing.
This dissertation aims to support the claim that context and more specifically
environmental distraction such as semantic auditory distractions impact the usability of
mobile applications. In doing so, it attempts to answer the following research questions:
1) Does semantic auditory distractions reduce the effectiveness of a blood glucose
tracking mobile application? 2) Does semantic auditory distractions reduce the efficiency
of a blood glucose tracking mobile application? 3) Does semantic auditory distractions
reduce the user satisfaction of a blood glucose tracking mobile application?
To answer the study research questions, a true experimental design was performed
involving 30 adults with type 2 diabetes. Participants were paired based on their age and
experience with smartphones and randomly assigned to the control (no semantic auditory
distractions) or experimental (semantic auditory distractions) group. Research questions
were tested using the general linear model. The results of this study confirmed that
semantic auditory distractions have a significant effect on efficiency and effectiveness,
and hence they need to be taken into account when evaluating mobile usability. This
study also showed that semantic auditory distractions have no significant effect on user
satisfaction.
This dissertation enhances the current knowledge about the impact of semantic auditory
distractions on the usability of mobile applications within the diabetic senior population.
Acknowledgments
The completion of my dissertation writing process has been a long journey. During
this exciting, but at times exhausting and difficult journey, I was accompanied by a group
of people who supported me in many different ways.
First, I would like to acknowledge my dissertation advisor Dr. Cohen, whose
patience and understanding allowed me to continue this journey that many times seemed
not to have a final destination. I am also grateful to the members of my dissertation
committee, Dr. Templeton, and Dr. Ackerman, for their time, insights, and constructive
feedback.
I am deeply thankful to my family for their love and support during this long
journey. Without them, this dissertation would never have been written. This dissertation
is dedicated to my children, Carolina, and Gerardo, who continually provided the support
and motivation I needed. To my wife, María, who made countless sacrifices to keep our
family together and helped me get to this point. To my mother-in-law, Zulma, for taking
care of my children when I was not available. Last but not least, I want to thank my
parents Jossie and Lydia, who always encouraged me to move forward and provided me
the opportunity to be where I am today.
I also want to acknowledge study participants, staff of the Jackson library, Nancy
Rhodes, RN, BS, MA, CDE, and diabetes organizations in New Jersey, which were
supportive of this project.
“Sometimes life hits you in the head with a brick. Don’t lose faith.”
-Steve Jobs
Table of Contents
Abstract iii
Acknowledgements iv
List of Tables vii
List of Figures viii
Chapters
1. Introduction 1
Background 1
Problem Statement 4
Dissertation Goal 5
Research Questions and/or Hypotheses 6
Relevance and Significance 6
Barriers and Issues 8
Assumptions, Limitations, and Delimitations 9
Definition of Terms 10
Summary 10
2. Review of the Literature
Introduction 11
Diabetes 11
Diabetes and the Role of Technology 12
Diabetes and seniors 14
Diabetes and Cognitive Decline 14
Usability 16
Mobile Usability 18
Usability and Context of Use 20
Physical Environment, Auditory Distractions, Semantic Auditory Distractions, and
Usability 23
Seniors and Usability 27
Usability of Diabetes Self-Management Mobile Applications 28
Summary 31
3. Methodology 32
Research Design 32
Research Methods Employed 35
Participants 39
Data Analysis Techniques 40
Format for Presenting Results 41
Resource Requirements 43
Summary 45
4. Results 46
Data Preparation 46
v
Data Analysis 46
Demographical Data Analysis 49
Task Efficiency Analysis 50
Task Effectiveness Analysis 51
User Satisfaction Analysis 51
Findings 52
Research Question 1 52
Research Question 2 53
Research Question 3 55
Summary of Results 57
5. Conclusions, Implications, Recommendations, and Summary 59
Discussion of Study Findings 59
Conclusions 62
Limitations 63
Implications 64
Recommendations for Further Research 64
Summary 65
Appendices
A. Participant’s Demographic Questionnaire 68
B. Screenshots of the Mobile Application being used in the Study (From Apple Store)
69
C. Usability Test Task List and Scenario 70
D. Summary Usability Study Results 72
E. System Usability Scale (SUS) 73
F. RTLX Rating Sheet 74
G. RTLX-Tally Sheet 75
H. IRB Approval 76
References 77
vi
List of Tables
Tables
1. Definitions of all aspects of the ISO definition of usability (ISO 9241-11 1998) 18
2. List of Independent, Dependent, and Covariate Variables being used in the study 37
3. List of Research Questions, Hypotheses, Null Hypotheses, Variables, Data Type, and
Statistic being used in the Study 42
4. Descriptive Statistics for Age and Experience with Smartphone Variables 47
5. Descriptive Statistics for RTLX and Years with Diagnosis of Diabetes Variables 48
6. Descriptive Statistics for Sample Gender Variable 49
7. Statistics for Efficiency (Time on Task measured in seconds) by group 50
8. Statistics for Effectiveness (Success Rate) by group 51
9. Statistics for User Satisfaction (Measured by SUS scores) by group 52
10. Summary of Study Research Questions Results 58
vii
List of Figures
Figures
1. Coursaris and Kim (2011) Contextual Usability Model 23
2. iPod mini and speakers used to simulate semantic auditory distractions 44
3. Room configuration used to perform usability studies 44
4. Dot plot of Efficiency by Group Controlling for Workload 53
5. Dot plot of Effectiveness by Group Controlling for Workload 55
6. Dot plot of User Satisfaction by Group Controlling for Workload 57
viii
1
Chapter 1
Introduction
Background
Over the next 20 years, it is expected that the number of seniors in the United States
will grow from 40 million in 2010 to 72 million in 2030 (Vincent & Velkoff, 2010). A
growing number of these seniors will potentially need assistance as they age (Steggell,
Hooker, Bowman, Brandt, & Lee, 2007) and most of them will benefit from services that
allow them to maintain their social relationships, health, and ability to live at home
(Mikkonen, Varyrynen, Ikonen, & Heikkila, 2002). Health is a major concern for seniors
(Dinet et al., 2007). One disease that is of particular interest to seniors is diabetes, since
it is estimated that almost 27% of seniors in the United States have diagnosed or
undiagnosed diabetes (Cowie et al., 2009) and it is expected that 1 out of 3 Americans
will have diabetes by 2050 (Boyle, Thompson, Gregg, Barker, & Williamson, 2010).
As part of their health regimen, diabetes patients need to monitor their diet and
blood glucose levels very closely. In fact, there have been studies that have shown
clinically relevant glucose reduction in trials of blood glucose self-monitoring in patients
with type -2 diabetes (Welschen et al., 2005). However, diabetics have described this
blood glucose monitoring procedure as complex (Kouris et al., 2010)
Several technologies have been proposed to assist in the area of diabetes selfmanagement. For example, mobile phones, have shown to be effective in the area of
diabetes self-management (Krishna & Boren, 2008; Patrick, Griswold, Raab, & Intille,
2008; Preuveneers & Berbers, 2008). As a result, a growing number of people are using
2
mobile applications to self-manage diabetes (Holtz & Lauckner, 2012). However, these
applications have several limitations, including but not limited to, lack of personalized
feedback and usability issues (El-Gayar, Timsina, Nawar, & Eid, 2013).
Usability is important for systems that help people self-manage conditions such as
diabetes (Arsand et al., 2012). Improving the usability of diabetes self-management
mobile applications can increase its acceptance and overall health of people with
diabetes. But how can the usability of these diabetes self-management mobile
applications be improved? It is argued that mobile usability studies can help identify
potential usability issues that if addressed, could improve the mobile application’s design,
usability, and ultimately, its acceptance. Mobile applications face key usability
challenges, as users need to share their attention between the task (mobile application)
and the surrounding environment.
Acknowledging these challenges, Hassanein and Head (2003) suggests the need to
consider the following four contextual factors: a) user, b) environment, c) task, and d)
technology when performing mobile usability studies. For example, the environmental
context, is one that exposes mobile device users to constant distractions, such as changes
in visual and auditory stimuli. Distractions increase the user's cognitive load, which
reduces task performance (Smith, Chang, Cohen, Foley, & Glassco, 2005) and, as in the
case of seniors, negatively affect their working memory accuracy (Clapp & Gazzaley,
2012).
Auditory distractions is a type of distraction that includes people talking, phones
ringing, keyboards clicking, background noise, and semantic sounds (sound with
meaning). More specifically, a semantic auditory distraction is the condition in which
3
lexical-semantic information in speech forms the basis of disruption (Marsh, Hughes, &
Jones, 2009). It has been shown that the variance and meaning (semantics) of a sound are
much more powerful distractions (Hughes & Jones, 2001) than the sound intensity or
duration (Jones, 1990). In a situation in which the user’s primary task is to interact with a
mobile application, the simultaneous processing of an irrelevant auditory stimulus (i.e.
semantic auditory distraction) might impact its performance on the primary task
(interacting with the mobile application).
A key element to answer the question about how to improve the usability of
diabetes self-management mobile applications is to better understand how its usability is
affected by the environment and more specifically by typical distractions, such as
semantic auditory distractions. One specific population that would greatly benefit from
the use of diabetes self-management mobile applications is the senior population, more
specifically seniors with diabetes. However, diabetic seniors have some unique
challenges and needs. According to Cukierman, Gerstein, and Williamson (2005), the
diabetic population has a “greater rate of decline in cognitive function and a greater risk
of cognitive decline" (p.2460).
Additionally, in the case of seniors with diabetes, it has been shown that the disease
affects their cognitive performance (Kanaya, Barrett-Connor, Gildengorin, & Yaffe,
2004; Tun, Perlmuter, Russo, & Nathan, 1987). For example, Kanaya et al. (2004)
evaluated changes in cognitive performance over a four-year period and found that older
caucasian women with diabetes were four times more likely to suffer major cognitive
deterioration in verbal fluency compared to caucasian women with normal glucose
tolerance. Similarly, Tun et al. (1987) compared memory self-assessments and
4
performance in diabetics and non-diabetics between 55 and 74 years old and found that
performance on some cognitive tests was negatively affected by age and a diagnosis of
diabetes.
Understanding the unique weaknesses and challenges of an application’s target user
population is important not only for mobile application developers, but also for
researchers in the area of usability. It is well established that seniors with diabetes have
cognitive decline, not only because of their age, but also because of having diabetes. For
a mobile application to be useful to a target population of seniors with diabetes, it needs
to meet their unique needs. Understanding the impact that distractions and more
specifically semantic auditory distractions might have on mobile usability can help
usability researchers and usability professionals develop more comprehensive usability
testing scenarios that could result in better designed mobile applications and ultimately
increased acceptance.
Problem Statement
Mobile devices and mobile applications have shown to help diabetics manage their
disease. However, these mobile applications have several limitations, including but not
limited to, lack of personalized feedback, usability issues, particularly the ease of data
entry, and integration with patients and electronic health records (El-Gayar et al., 2013).
One key usability challenge facing mobile applications is the context of use since it
is constantly changing. As a result, users of mobile applications have to share their
attention between their main task (i.e. mobile application) and the surrounding
environment. Distractions, which are stimuli irrelevant to the subject’s primary task,
5
have been found to negatively affect information processing and performance (Sorqvist,
2010) and senior’s working memory accuracy (Clapp & Gazzaley, 2012). Additionally,
according to Cukierman et al. (2005) “compared to people without diabetes, people with
diabetes have a greater rate of decline in cognitive function and a greater risk of cognitive
decline" (p. 2460).
Understanding the impact of semantic auditory distractions (background speech
with meaning) on the usability of mobile applications specifically when used by diabetic
seniors might help improve the application’s design, its usability, which can ultimately
increase its acceptance and its benefit to the diabetic population. Few studies have
examined the effect of environmental distractions on mobile users’ performance (Crease,
2005; Hoyoung, Jinwoo, Yeonsoo, Minhee, & Youngwan, 2002). However, research has
yet to explore the role of semantic auditory distractions on the usability of mobile
applications, and more specifically within the diabetic senior population.
Dissertation Goal
The goal of this research study was to investigate the impact of context and more
specifically semantic auditory distractions on the usability of a blood glucose tracking
mobile application used by diabetic seniors. The usability of a blood glucose tracking
mobile application was measured on two different environmental contexts (with semantic
auditory distractions and without semantic auditory distractions). Results from this study
provide insights as to the impact of semantic auditory distractions on the usability of
mobile applications used by diabetic seniors.
6
Research Questions and/or Hypotheses
The hypothesis (H) of this research is that measures of usability attributes will be
significantly lower for the diabetic seniors who were exposed to semantic
auditory distractions. The null hypothesis (H0) is that there is no significant
difference between the means of the two matched groups (environment with and
without semantic auditory distractions) on any of the three usability attributes
(efficiency, effectiveness, and user satisfaction). This hypothesis (H) was
selected to answer the following research questions:
Research Question 1. Do semantic auditory distractions reduce the efficiency of
a blood glucose tracking mobile application used by diabetic seniors?
Research Question 2. Do semantic auditory distractions reduce the effectiveness
of a blood glucose tracking mobile application used by diabetic seniors?
Research Question 3. Do semantic auditory distractions reduce the user
satisfaction of a blood glucose tracking mobile application used by diabetic
seniors?
Relevance and Significance
It is estimated that by 2030, there will be about 72.1 million older persons, more
than twice their number in 2000. A growing number of seniors will potentially need
assistance as they age (Steggell et al., 2007) and it has been documented that the most
beneficial services for seniors in the future will be those that allow them to maintain their
social relationships, health, and ability to live at home (Mikkonen et al., 2002).
7
Based on estimates, almost 27% of seniors in the United States have diagnosed or
undiagnosed diabetes (Cowie et al., 2009). Additionally, it is expected that 1 out of 3
Americans will have diabetes by 2050 (Boyle et al., 2010). One proposed method to help
individuals with diabetes better manage their disorder is through the use of mobile
devices. For example, Krishna and Boren (2008) evaluated the impact of cell phone
interventions for people with diabetes and concluded that cell phone and text message
interventions can improve clinically relevant diabetes-related health outcomes.
Increasing numbers of people are using mobile applications to self-manage diabetes
(Holtz & Lauckner, 2012). El-Gayar et al. (2013) reviewed diabetes self-management
commercial applications available on the Apple App Store, and articles published
between January 1995 and August 2012 to determine whether mobile diabetes
applications had been helping patients with type 1 or type 2 diabetes self-manage their
condition. Their review established the potential for mobile applications to have a
positive impact on diabetes self-management; however, the authors also indicated that
research was needed to improve usability, perceived usefulness, and ultimately, adoption
of the technology.
Few studies have examined the effect of environmental distractions on mobile
users’ performance (Crease, 2005; Hoyoung et al., 2002). However, the role of auditory
distractions, specifically semantic auditory distractions on the usability of mobile
applications used by diabetic seniors, is unknown. Acknowledging this gap in the
literature, this study investigated the impact of semantic auditory distractions on the
usability of a mobile application used to track blood glucose levels with diabetic senior
users. This research study adds to the body of knowledge in the area of mobile usability
8
by investigating the impact of semantic auditory distractions on the usability of mobile
applications used by diabetic seniors.
Barriers and Issues
Mobile users in particular needed to share their attention between the task at hand
(interacting with the mobile application) and their surrounding environment. The concept
of the context of use as it relates to usability suggests that many variables beyond the
interface impact usability. As a result, usability experiments need to consider the various
contextual factors. According to Hassanein and Head (2003), when assessing the
usability of mobile devices and services, the following factors should be considered:
1) User (i.e. age, education, culture).
2) Environment (i.e. lighting, noise, visual distractions).
3) Task (i.e. complexity, interactivity).
4) Technology (interface design, device size, and weight).
Performing mobile usability studies is challenging because not all factors that
could impact usability could be considered. In fact, most of the time, usability studies
that follow a rigorous research design only include one factor.
To minimize variables that could impact the outcome of this study, the researcher
attempted to control various aspects of the usability study. For example, all study
participants were required to use the same iOS device, which was provided by the
researcher. It is interesting to note that some participants inquired about the possibility to
use their own iOS devices during the experiment. Since the characteristics of the
hardware being used could impact the results of the experiment, only the iOS device
9
provided by the researcher was used. In addition, to control for the type of semantic
auditory distractions during the study, the researcher made sure that participants who
were required to be exposed to semantic auditory distractions used the same TED Talk at
exactly 70 decibels.
However, one variable that could not be well controlled during the study was
where the usability studies took place. While most of the usability studies were done in
local libraries and senior centers in New Jersey, the environment (lighting conditions,
room temperature and room configuration) could not be controlled due to differences in
the setup of the rooms used during the study.
Assumptions, Limitations, and Delimitations
This research study used several questionnaires (Demographic, SUS, RTLX) to
capture the study participant’s demographic information, workload and their satisfaction
with the mobile application being used. It is assumed that participants were honest in
answering questionnaire questions. It is very important to note that answers to
participant’s age and experience with smartphone were used to match participants and
randomly assign them to either the experimental or control groups. Also, the researcher
did not verify the diagnosis of diabetes of any of the participants. No medical
information showing that the participant had been diagnosed with diabetes was required.
This study was conducted exclusively with residents of the state of New Jersey.
Although there may be regional differences, the sample is representative of seniors, but
generalizations should be made with caution.
10
Definitions of Terms
Cognitive Load Theory: A theory that holds that the resources allocated for
cognitive processing are limited and that learning is impaired when a task exceeds the
capacity of the limited resources (de Jong, 2009).
Semantic Auditory Distraction: The condition where lexical-semantic information
in speech forms the basis of disruption (Marsh, Hughes, & Jones, 2008).
Seniors: The American Association of Retired Persons (AARP) considers an older
adult to be those over 50 years old. For the purposes of this study, a senior or older adult
refers to adults who are at least 50 years old.
Summary
In this chapter, an introduction to the research related to this proposed study was
described. Chapter 1 began providing background information followed by a description
of the problem statement. Next, the dissertation goal and hypotheses proposed to answer
the research questions were identified and discussed. The chapter continued with a
discussion about the relevance and significance of the research study followed by the
study barriers and issues. It concluded with a section on the assumptions, limitations, and
delimitations and the definitions of terms.
11
Chapter 2
Review of the Literature
Introduction
The purpose of this chapter is to present a review of literature that covers the main
topics applicable to diabetes, the role of technology and mobile devices in diabetes selfmanagement, diabetes and seniors and cognitive decline, usability, mobile usability,
contextual usability, distractions and usability, seniors and usability and the usability of
diabetes self-management mobile applications. These topics provided background and
support to the proposed research study, which focused on investigating the impact of
semantic auditory distractions on the usability of a blood glucose tracking mobile
application among diabetic seniors.
Diabetes
Diabetes is a metabolic disease that is characterized by high glucose levels in the
body that occur because of inadequate insulin supplies and according to the Center for
Disease Control and Prevention (CDC), is the seventh leading cause of death in the
United States. Most of the food eaten is digested into glucose, which enters the
bloodstream and is used by the cells for growth and energy. Insulin, which is a hormone
produced by the pancreas is needed for the glucose to be absorbed into cells. The
pancreas produces the proper amount of insulin necessary for the glucose to be
transferred into the cells. In people with diabetes, the pancreas produces either
insufficient or no insulin whatsoever. As a result of this, the body loses its source of fuel
12
even though the bloodstream might contain sufficient glucose. Diabetes is categorized
into three different types: type 1, formerly called juvenile diabetes, type 2, formerly
called adult onset diabetes, and gestational diabetes.
About 90-95% of diabetics have type 2 diabetes, which is most often associated
with older age, obesity, and family history of diabetes, previous history of gestational
diabetes, physical inactivity, and certain ethnicities. The prevalence of type 2 diabetes in
all age, gender and ethnic groups in the U.S. is expected to more than double (from 5.6%
to 12%) between 2005 and 2050 (Narayan, Boyle, Geiss, Saaddine, & Thompson, 2006).
Regardless of the type, diabetes is associated with several long-term
complications affecting virtually all parts of the body. According to the American
Diabetes Association, some of the potential complications of diabetes include: heart and
blood vessel disease, stroke, kidney failure, amputation, and nerve damage. Basic
therapies for type 1 diabetes include healthy eating, physical activity, and taking insulin;
basic treatment for type 2 diabetes are the usually the same as for type 1 diabetes, with
the exception of using oral medication, instead of insulin.
It has been shown that self-monitoring blood glucose, in combination with
lifestyle changes, is associated with improvement in blood glucose control (Allemann,
Houriet, Diem, & Stettler, 2009). However, many diabetics have characterized this
monitoring procedure as complex (Kouris et al., 2010).
Diabetes and the Role of Technology
Self-management in diabetes is strongly connected with the patient’s quality of
life (Glasgow et al., 1999). The aim of telemedicine in the field of diabetes and diabetes
13
self-management is different according to the type of diabetes. In type 1 diabetes, which
is associated with relatively complex insulin regimens, the goal is to assist patients in
achieving better blood glucose control via more accurate adjustment of insulin doses. In
type 2 diabetes, there are two basic goals: 1) accurate and timely adjustment of insulin
dosage, and 2) improvement in blood glucose control through dietary and/or physical
activity changes. According to Franc et al. (2011), there appeared to be two promising
approaches:
1. The use of hand-held communication devices, especially smartphones. These
systems can provide immediate patient assistance (e.g., insulin dose calculation for
specific meals and/or food choices), and all data stored in the device can be transmitted to
authorized caregivers, enabling remote monitoring and even tele-consultation.
2. The use of systems combining an Internet system (or a mobile phone with
access to a remote server) with a system that allows communication between the patient
and the healthcare provider (e.g., by e-mail, texting or telephone calls).
Several studies have showed the effectiveness of mobile phones for assisting with
diabetes self-management (Baldus & Patterson, 2008; Kirwan, Vandelanotte, Fenning, &
Duncan, 2013; Krishna & Boren, 2008). For example, Patrick et al. (2008) discussed the
effectiveness of mobile devices for uploading clinical information for health monitoring.
Similarly, Krishna and Boren (2008) evaluated the impact of cell phone interventions for
persons with diabetes by reviewing 20 studies published in Medline between 1966 and
2007. This study found that cell phone and text message interventions could improve
clinically relevant diabetes-related health outcomes. However, as concluded by Franc et
al. (2011), the most common patient expectation regarding the use of technological
14
interventions was that the system "should be easy to use, with readily available, pocketsized electronic devices" (p. 474).
Diabetes and seniors
The CDC (2007) reports that as of 2007, type 1diabetes and type 2 diabetes
combined, were most prevalent in adults age 60 and over, accounting for nearly onequarter of the diagnosis rate (23.1%). It is estimated that almost 27% of seniors in the
United States have diagnosed or undiagnosed diabetes (Cowie et al., 2009). These
numbers are expected to grow. In fact, it is expected that 1 out of 3 Americans will have
diabetes by 2050 (Boyle et al., 2010).
It is known that the ability of pancreatic beta cells to react to glucose is reduced
with age (Thearle & Brillantes, 2005). This decline in beta-cell insulin secretion is a
major factor in the development of diabetes. Elderly diabetics are at higher risk for
developing geriatric syndromes, such as depression, cognitive dysfunction, and urinary
incontinence, than non-diabetic older persons
Diabetes and Cognitive Decline
It is known that physical and cognitive changes associated with the natural aging
process become more noticeable at age 45 and that these cognitive changes create a need
for interfaces that have fewer distractions, provide memory cues, and are simple to learn
and understand (Hawthorn, 2000). Several investigations have shown that cognitive
performance in older adults is adversely affected by whether they are diabetics or not
(Kanaya et al., 2004; Raji et al., 2005; Sinclair, Girling, & Bayer, 2000).
15
For example, Raji et al. (2005) concluded that having diabetes predicted increased
cognitive impairment, as measured by the St. Louis Mental Status Examination
(SLUMD) scale. Similarly, Kanaya et al. (2004) evaluated change in cognitive
performance over a four-year period using a sample of 999 caucasian women and men,
aged 42 to 89 years, who were part of the Rancho Bernardo Study. The results of this
investigation showed that after four years, older caucasian women with diabetes were
four times more likely to suffer major cognitive deterioration in verbal fluency compared
to caucasian women with normal glucose tolerance or impaired glucose tolerance. In
another study, Sinclair et al. (2000) evaluated cognitive dysfunction in older diabetics.
Based on a case-control study that involved 396 older diabetics and 393 non-diabetics,
the researchers found that older persons with type 2 diabetes demonstrated worse
cognitive functioning than older non-diabetics.
To better understand if there is a relationship between cognitive decline and
diabetes, Allen, Frier, and Strachan (2004) performed a literature search and identified 10
studies (nine population-based and one of case-controlled design) that included a diabetic
population and assessments of cognitive functions at baseline and follow-up. While no
association between type 2 diabetes and cognitive decline was demonstrated in the casecontrolled study, these studies provide compelling evidence to support the view that
people with type 2 diabetes are at increased risk of developing cognitive impairment in
comparison with the general population. Additionally, Cukierman et al. (2005) reviewed
and summarized prospective data relating diabetes status to changes in cognitive function
over time and concluded that “compared to people without diabetes, people with diabetes
have a greater rate of decline in cognitive function and a greater risk of cognitive decline"
16
(p. 2460). In summary, it has been established that cognitive decline is not only
associated with the natural aging process, but it is also negatively affected by diabetes.
Usability
Usability has been defined and given different interpretations across time; some
definitions are based on measurable features and others provide a generic context.
Different researchers have provided definitions based on usability features (Bevan &
Macleod, 1994; Shackel, 2009). Basically, all definitions use similar attributes focused
on the versatility of the system and the way in which it provides users with an overall
experience of usability, based on the success of the attributes in relation to the results
obtained by them.
Shackel (2009) defined four measurable attributes of usability: 1) effectiveness,
referring to completion of tasks with some level of performance by a certain percentage,
2) learnability, completion of tasks in a specified time since the start of using the system
with some specified support and training and with some relearning time for returning or
intermittent users, 3) flexibility, completion of tasks in different ways or with variations
in tasks and/or environments and 4) attitude, completion of tasks with acceptable levels
of human effort (tiredness, discomfort, frustration, and personal effort), in order to obtain
satisfaction in using the system.
Another researcher in the area of Human-Computer Interaction (HCI), Nielsen
(2012), defines usability as a quality attribute that assesses the ease of use of user
interfaces. According to Nielsen (2012), usability has the following five attributes:
17

Learnability - The level of ease for a person to complete basic tasks the first
time they interact with the system.

Efficiency - Referring to the speed in which users complete their tasks.

Memorability - For users who have not used the system for some time,
quickness in regaining proficiency.

Errors - Number of errors committed; their severity and ease of recovering
from them.

Satisfaction - The feeling of pleasure in using the system.
However, perhaps the most widely used definition of usability was provided by
the International Organization for Standardization, in Standard ISO 9241-11, which
defines usability as “the extent to which a product can be used by specified users to
achieve specified goals with effectiveness, efficiency, and satisfaction in a specified
context of use” (Sec. 3). A definition of every aspect of the usability definition is
presented in Table 1.
According to Jokela, Iivari, Matero, and Karukka (2003), the ISO 9241-11
definition of usability shows that usability is a function of users of a product or system,
and in addition, for each user, usability is a function of achieving goals in terms of the
attributes of effectiveness, efficiency, satisfaction, and the context of use. For the
purposes of this research, usability is defined as defined by the ISO 9241-11 standard,
which includes the usability attributes of effectiveness, efficiency, and satisfaction.
18
Table 1
Definitions of all aspects of the ISO definition of Usability (ISO 9241-11 1998)
Aspect
Definition
the accuracy and completeness with which users achieve specified
Effectiveness goals
Efficiency
the resources expended in relation to the accuracy and completeness
with which users achieve goals
Satisfaction
the freedom from discomfort and positive attitudes toward the use of
the product
Context of use the users, tasks, equipment (hardware, software, and materials), and
the physical and social environments in which a product is used
Work System the system, consisting of users, equipment, tasks, and a physical and
social environment, for the purpose of achieving particular goals;
also the context of use consists of those components of the work
system that are treated as given when specifying or measuring
usability
User
the person who interacts with the product
Goal
the intended outcome
Task
the activities required to achieve a goal; these activities can be
physical or cognitive, and job responsibilities can determine goals
and tasks
Product
the part of the equipment (hardware, software, and materials) for
which usability is to be specified or evaluated
Measure
the value resulting from measurement and the process used to obtain
that value
Mobile Usability
Usability testing is used at different stages of the development process. It can be
done at the exploratory stage when designers are trying to conceive the correct design or
it can be done to ensure that the system, device, or software is meeting certain
requirements. There are several types of usability studies. According to Shneiderman,
Plaisant, Cohen, and Jacobs (2009) usability testing can be conducted using one of the
following methods or a combination of methods:

Paper mockups or prototyping.

Discount usability testing.
19

Competitive usability testing.

Universal usability testing.

Field tests and portable labs.

Remote usability testing.

Can-you-break-this tests.
Due to the advent of mobile devices, the usability evaluation for mobile devices is
becoming an emerging research area in Human Computer Interaction. Pascoe, Ryan, and
Morse (2000) noted that portable computing devices are very different because of their
inherent mobility, which allow users to work while on the move. As a result, researchers
in the area of Human-Computer Interaction (HCI) are concerned with the way usability
studies of mobile devices are performed. For example, Johnson (1998) pointed out that
laboratory evaluations do not simulate the real context and lack the desired ecological
validity. As a result, more attention should be paid to the context of use.
According to Ryan and Gonsalves (2005), “While it appears possible to use
existing usability techniques and guidelines for the development of mobile applications,
some unique characteristics are needed to create usable mobile applications” (p.115).
Performing usability studies on mobile devices is a difficult task since mobile usability
includes challenges related to mobile context, connectivity, small screen size, different
display resolutions, limited processing capability, power, and data entry methods.
Nayebi, Desharnais, and Abran (2012) described three types of evaluation
methodologies being used in mobile usability studies. The first one is laboratory
experiments, in which usability studies take place in a usability lab where the testing
takes place. While this setting can be optimal for certain studies, it can also affect the
20
results, since it isolates the users from their “normal environments”, possibly causing
differences in the usability results. The second evaluation methodology described is field
studies, which involves observations and interviews. The third methodology is described
as hands-on measurements, which are designed to quantitatively measure the usability of
mobile applications, as defined by ISO 15939.
Usability and Context of Use
There are many different definitions for context. For example, Brown, Bovey,
and Xian (1997) define context as location, identities of the people around the user, the
time of the day, the season of the year, and temperature. On the other hand, Ryan,
Pascoe, and Morse (1998) defines context as the user's location, the environment,
identity, and time. The ISO 9241-11, in its definition of usability, defined the context of
use as: the users, tasks, equipment (hardware, software, and materials), and the physical
and social environments in which a product is used. The standard also states the relevant
characteristics of the physical and social environment need to be described. These
characteristics can be further described as follows:

The wider technical environment, for example, the local area networks.

The physical environment, for example, noise level, privacy, ambient
qualities, potential health hazards, and safety issues.

The ambient environment, for example, the temperature and humidity.

The social and cultural environment, for example, the work practices,
organizational structure, and attitudes.
21
The context of use is a very important aspect of the usability evaluation,
especially in the area of mobile usability. The characteristics of the context (the users,
tasks, equipment, and environment) may be as important in determining usability as the
characteristics of the product itself (Bevan & Macleod, 1994). The factors, which may
affect the user behavior, need to be considered during usability evaluations since the user
may exhibit varying behaviors depending on the context of use. Although there are many
definitions of context, it is important to understand that usability experiments need to
consider various contextual factors (Liu, Zhu, Holroyd, & Seng, 2011).
Focused on mobile contextual usability, Coursaris and Kim (2011) used a
contextual usability framework for a mobile computing environment proposed by
Coursaris and Kim (2006) to examine 100 empirical mobile usability studies published
between 2000 and 2010. The framework (see Figure 1) contains three main elements.
The outer circle represents the four contextual factors (i.e., User, Technology,
Task/Activity, and Environment) that impact usability. The inner circle represents the
key usability dimensions (i.e., effectiveness, efficiency, satisfaction, etc.). The box on
the top of contextual factors provides a list of consequences of usability (i.e., improving
systems integration, increasing adoption, etc.).
Some of the results of the analysis performed by Coursaris and Kim (2011) are as
follows:

Empirical mobile usability studies focused on investigating task
characteristics (47%), technology (46%), and environment (14%), and user
characteristics (14%).
22

In contrast with earlier data set of 45 empirical studies published by 2006
Coursaris and Kim (2006), the distribution of research emphasis included
research on task (56%), user (26%), technology (22%), and environmental
characteristics (7%).

While the authors noted the proportion of empirical usability studies that
considered the environment doubled, they concluded the environmental
context is the area with the greatest potential for future mobile usability
research.
Since the objective of this research study is focused on the impact of semantic
auditory distractions on the usability of a mobile blood glucose tracking mobile
application used by diabetic seniors, the next subsection will provide an overview of the
physical environment, auditory distractions, and semantic auditory distractions, as they
relate to usability.
23
Figure 1. Coursaris and Kim (2011) Contextual Usability Model
Physical Environment, Auditory Distractions, Semantic Auditory Distractions, and
Usability
The portable nature of mobile devices means the context of use may change while
performing the same task. As users find themselves in different physical environments,
auditory and/or visual distractions may arise. As a result, it is important to understand
the factors that influence mobile usability by taking into consideration environmental
distractions.
In the area of mobile usability evaluation, one key factor to consider is the fact
that mobile users need to share their attention between the primary task (i.e. the mobile
24
application) and the surrounding environment. Visual and audio distractions in the
surrounding environment may impact mobile users. A distraction can be defined as a
stimulus that is irrelevant and intended to be ignored (e.g., people talking while a student
is attempting to complete math homework). It has been documented that distractions
negatively affect information processing and performance (Sorqvist, 2010).
According to Sweller (1988), Cognitive Load Theory (CLT) explains that only a
certain amount of information can be used and stored in working memory without
exceeding the natural processing capacity. Distractions add to the total cognitive stimuli
(i.e. load), which reduce one’s capacity to process information efficiently and effectively.
Increased cognitive load may affect performance negatively by reducing the individual’s
attention control, accuracy, working memory, and retrieval efficiency (Lavie, 2010).
Cognitive load can be assessed by measuring mental load, mental effort, and
performance using an empirical approach that includes primary and secondary task
measurements, as well as subjective rating scales (Paas, Tuovinen, Tabbers, & Van
Gerven, 2003). The NASA-Task Load Index (Cao, Chintamani, Pandya, & Ellis, 2009)
for example, uses six dimensions to assess mental workload: mental demands, physical
demands, temporal demands, performance, effort, and frustration level. A score from 0
to 100 (assigned to the nearest point 5) is obtained on each of the tool scales. The
NASA-TLX uses a weighting process that requires a paired comparison task. The task
requires the participant to choose which dimension is more relevant to the workload for a
particular task across all pairs of the six dimensions. The workload scale is obtained for
each task by multiplying the weight by the individual dimension scale score, summing
across scales, and dividing by the total weights (Hill et al., 1992)
25
The administration of the NASA-TLX can be cumbersome and time consuming
especially since it is administered after each task or scenario. As a result, a different
type of TLX scale was developed called the NASA Raw Task Load Index (RTLX). The
RTLX computes a score by taking the sum of the TLX test and dividing it by the number
of dimensions (6). This new way to score the NASA-TLX was found to be almost
equivalent to the original TLX scale R= .977 (p<10−6 ) (Byers, Bittner, & Hill,
1989),with far less time involved for analysis.
The role of both auditory and visual distractions on performance is well
documented in the literature. For example, Hughes and Jones (2003) concluded that a
quiet environment results in higher efficiency while the presence of irrelevant sound
lowers mental efficiency and performance. They also concluded that increased
variability of background noise results in lower performance.
Several usability studies have found that the environmental context does affect
mobile usability (Coursaris, Hassanein, Head, & Bontis, 2012; Hummel, Hess, & Grill,
2008; Tsiaousis & Giaglis, 2008, 2010). For example, Tsiaousis and Giaglis (2008)
evaluated the effects of environmental distractions on mobile website usability. A
preliminary test on 20 users was conducted which confirmed that environmental
distractions have a direct effect on mobile website usability.
Tsiaousis and Giaglis (2010) performed a pilot study with 30 participants and an
experiment with 64 participants, ages 15-40 years, who were exposed to different types
of distractions and found that environmental distractions (level of lighting, proximity of
nearby people, the motion of nearby objects/people) had a significant effect on mobile
website usability. Regarding auditory distractions, Tsiaousis and Giaglis (2010) found
26
that semantic auditory distractions (meaningfulness of sounds) had a negative effect on
usability while the variance of auditory distraction (varying sound levels) had no effect
on the usability of a mobile website.
Interested in evaluating the effect of environmental conditions on usability,
Hummel et al. (2008) monitored environmental conditions such as light, acceleration,
sound, temperature, and humidity during usability experiments. Usability studies were
performed with seven users by changing their environmental conditions. The study
showed that on average, the performance of the users decreased in terms of higher error
rates and delays.
Similarly, Coursaris et al. (2012) investigated the impact of distractions and
confirmation of pre-trial expectations on usability and its subsequent effect on
consumers’ behavioral intention toward using a mobile device for wireless data services.
Their empirical study, which included 93 participants ranging from under 21 years up to
55 years old, confirmed the impact of distractions on efficiency, effectiveness, users’
satisfaction, and behavioral intention to use a mobile device for wireless data services.
Participants were assigned to 1 of 4 groups (Quite/Alone and static, Quite/Alone and
mobile, auditory and visual stimuli/static, and auditory and visual stimuli/static) and
asked to complete the same tasks during the usability test. This empirical study found
that age had a considerable impact on distractions, based on the finding that older
subjects were more negatively impacted compared to younger ones.
27
Seniors and Usability
Another contextual factor of importance when evaluating usability is the users. In
this research study, the targeted population is comprised of seniors with diabetes. The
reason is that according to Dinet et al. (2007), health is a major concern for seniors.
Increasing acceptability of technology that can help them manage their health should be
based not only on a good understanding of their requirements, but also on their
misgivings. As noted by Holzinger, Searle, and Nischelwitzer (2007), “the design and
development of mobile applications for elderly must support the users to overcome their
fears and enable them to accept technological aids and mobile devices without
reservations” (p. 924).
In general, problems of seniors can be categorized as cognition, motivation,
physical, and perception (Holzinger et al., 2007). It is known that physical and cognitive
changes associated with the natural aging process become more noticeable at age 45 and
that these cognitive changes create a need for interfaces that have fewer distractions,
provide memory cues, and are simple to learn and understand (Hawthorn, 2000).
Additionally, according to Cukierman et al. (2005), seniors with diabetes have been
shown to have a “greater rate of decline in cognitive function and a greater risk of
cognitive decline" (p.2460). More specifically, several studies (Kanaya et al., 2004; Tun
et al., 1987) have shown that cognitive performance in older adults is adversely affected
by diabetes.
For example, Kanaya et al. (2004) evaluated changes in cognitive performance
over a four-year period and found that older caucasian women with diabetes were four
times more likely to suffer major cognitive deterioration in verbal fluency compared to
28
caucasian women with normal glucose tolerance. Similarly, Tun et al. (1987) compared
memory self-assessments and performance in diabetics and non-diabetics between 55 and
74 years old and found that performance on some cognitive tests was negatively affected
by age and a diagnosis of diabetes.
In another study with seniors, Tsaprounis, Touliou, Kalogirou, Agnantis, and
Bekiaris (2012) measured task completion (user success rates) and user acceptance
ratings on a platform with four mobile applications. The authors concluded that all four
applications were regarded as usable, easy to learn, and desired by most users, but also
noted that further improvements could be made resulting into an even more usable
system by decreasing complexity and the amount of information being displayed.
Usability of Diabetes Self-Management Mobile Applications
Usability is important for systems that help people self-manage conditions such as
diabetes (Arsand et al., 2012). Research in the area of usability of diabetes selfmanagement mobile applications is limited. In one of the few usability studies focused
on mobile diabetes self-management applications, Whitlock and McLaughlin (2012)
examined the usability of three iOS blood glucose tracking applications via hierarchical
task analysis of the log entry process and heuristic evaluation of graph displays. The
study uncovered usability problems with the interface and display of graph-based
information that might limit their accessibility to older users.
In another study, Waite, Martin, Curtis, and Nugrahani (2013) measured the
usability of four mobile applications (Diabetes Diary, Diabetes Personal Manager,
GluCoMo, and Rapid Calc) for blood glucose tracking with eight research people (4
29
adults and 4 children) with type 1 diabetes. The goal of this research was to uncover
common usability issues with blood glucose tracking applications developed for people
with type 1 diabetes. The study participants were asked to perform the following six
tasks in all four of the glucose-tracking applications:
1. Set measurement units to mmol/L.
2. Log blood glucose level of 6.7 mmol/L.
3. Log carbohydrate intake of 50 g.
4. Log insulin dose of 5.5 units.
5. Display data graphically.
6. Export data via e-mail.
The study measured effectiveness, efficiency, simplicity, satisfaction, and
cognitive load and found that accuracy and the ability to enter personalized data quickly,
efficiently and conveniently were key issues. The study also found data entry errors,
which could have been exacerbated due to complications of diabetes.
With the goal of understanding menu navigation performance in respect to age
and domain knowledge with diabetes patients, Calero Valdez, Ziefle, Horstmann,
Herding, and Schroeder (2009) performed an experimental study with 23 participants and
found that user’s age was the best predictor of navigational performance on a small
screen device. It was noted that users older than 61 years showed drastically inferior
navigational performance. Additionally, the authors concluded that domain knowledge
and health status showed no significant influence on the measured performance criteria.
Focusing on patients with type 2 diabetes, Lyles et al. (2011) enrolled eight
diabetes patients and conducted a qualitative thematic analysis of semi-structured
30
interviews and found that some individuals are receptive to using web-based and mobile
communications service. However, the authors noted that technology could also add
frustrations to diabetes self-management.
Using a different approach, Garcia, Martin, Garcia, Harrison, and Flood (2011)
reviewed a total of 400 mobile applications and presented a heuristic evaluation method
for mobile applications for self-management of type 1 diabetes. The authors used
Keystroke Level Modeling (KLM) and heuristic evaluations (done by expert evaluators)
to measure the usability of the mobile applications. The KLM highlighted some
differences in the usability and efficiency of the applications. For example, the number
of keystrokes for finishing the assigned tasks on average was 29 on the iPhone, 34 on the
Android and 119 on the BlackBerry. The study also concluded that the best applications
for each of the platforms were the Diabetes Diary (iOS), Glucool (Android) and iRecordIt
(Blackberry).
In another study, Preuveneers and Berbers (2008) assessed the usefulness and
overall satisfaction of a mobile phone application prototype that helped individuals with
type 1 insulin dependent diabetes manage their disease. The authors noted, “From a
usability perspective, the participants were quite happy to use a mobile phone as
handwriting issues were no longer an issue and in some respects the blood glucose
measurements were more accurately logged than before” (p.186).
After reviewing commercial diabetes self-management applications available on
the Apple App Store and articles published from January 1995 to August 2012, El-Gayar
et al. (2013) concluded that research was needed to improve usability, perceived
usefulness, and, ultimately, adoption of the technology.
31
Summary
In Chapter 2, an overview of the relevant literature was presented. The literature
review began with an overview of diabetes and how technology can help diabetics
manage their disease. This chapter also provided a review of literature related to the
effect of diabetes on cognitive abilities and seniors. In the area of usability, this chapter
provided a general overview of usability, mobile usability, and the importance of the
context of use, physical environment, and distractions. The chapter concluded with a
review of studies related to seniors and usability and the usability of diabetes selfmanagement mobile applications.
32
Chapter 3
Methodology
This chapter begins with an overview of the study research questions and related
hypotheses. This is followed by a discussion about research design, experiment task
instructions, and the demographic and field study questionnaires. An examination of the
procedures that were followed in conducting this research, including the recruiting and
selection of the participants, the pre-testing of the instruments, the field study, and the
statistical analysis of the research data, is then presented. The chapter concludes with a
review of the steps that were taken to ensure both the reliability and the validity of the
research results.
Research Design
The research was conducted using a popular true experimental design referred as
posttest-only control group design where no pretest for either the control group or the
experimental group was administered. A matched subjects design, which relied upon
matching every participant in one group with an equivalent in another group, was used to
answer the following research questions:
1. Do semantic auditory distractions reduce the efficiency of blood glucose
tracking mobile applications used by diabetic seniors?
2. Do semantic auditory distractions reduce the effectiveness of blood
glucose tracking mobile applications used by diabetic seniors?
3. Do semantic auditory distractions reduce the user satisfaction of blood
glucose tracking mobile applications used by diabetic seniors?
33
A basic outline of how the experiment was conducted is as follows:
1. Prospective study participants were pre-screened via a demographic
questionnaire to ensure that they met the study requirements for age and
diabetes diagnose (See Appendix A).
2. Participants that met study requirements were asked to review the study
consent form. Once all questions and or concerns about the consent form
were discussed, participants were asked to sign the consent form.
3. The researcher explained the experimental procedure to the participant.
4. The researcher provided a general overview of the mobile application
being used. To be consistent, the researcher read the Diabetes Diary
mobile application description presented in the Apple application store.
5. The researcher used matched pairs based on age and experience with
smartphones to randomly assign participants to the experimental treatment
(environmental conditions include semantic auditory distractions) and the
control group (environmental conditions without semantic auditory
distractions).
6. Exposed the experimental group participants to the experimental treatment
(semantic auditory distractions). Playing a TED talk through speakers in
the background created the environment with semantic auditory
distractions. The TED Talk (which played at 70 decibels as measured by
the Decibel 10th iPhone application) continued playing until the
participant completed all the assigned tasks. According to a table of Noise
34
sources and their effects ("Comparative Examples of Noise Levels,"
2000), radio or TV-audio was measured at 70 decibels.
7. Requested participants to read scenarios and complete associated tasks
(See Appendix C) using the Diabetes Diary application (See Attachment
B) on an iPhone 4S.
8. Efficiency and effectiveness were measured on each of the four tasks for
both the experimental and control groups. Data from each participant was
captured and summarized in a “Summary Usability Study Results “ form
(See Appendix D).
9. The researcher requested participants to complete the SUS (See Appendix
E) and the RTLX survey (See Appendix F and G).
10. The researcher thanked the participants for their time and dismissed them.
As usability studies have shown that age (Mayhorn, Lanzolla, Wogalter, &
Watson, 2005), and level of expertise affect performance in terms of task completion
time and number of errors (Mizobuchi, Chignell, & Newton, 2005), study participants
were matched by age and experience with smartphones and then randomly assigned to
either the control group (not exposed to semantic auditory distractions) or the
experimental group (exposed to semantic auditory distractions).
35
Research Methods Employed
A true experimental design referred to as posttest-only control group design
where no pretest for either the control group or the experimental group was used to
determine the impact of semantic auditory distractions on the usability of a blood glucose
tracking mobile application. The hypothesis posited for this study is that measures of
usability attributes will be lower for diabetic seniors exposed to semantic auditory
distractions. This hypothesis (H) was selected to answer the following research
questions:
Research Question 1. Do semantic auditory distractions reduce the efficiency of a
blood glucose tracking mobile application used by diabetic seniors?
Research Question 2. Do semantic auditory distractions reduce the effectiveness
of a blood glucose tracking mobile application used by diabetic seniors?
Research Question 3. Do semantic auditory distractions reduce the user
satisfaction of a blood glucose tracking mobile application used by diabetic
seniors?
The null hypothesis (H0) is that there is no statistical difference between the
means of the two matched groups (environment where participants are exposed to
semantic auditory distractions vs. environment where participants are not exposed to
semantic auditory distractions) on any of the three usability measurements (efficiency,
effectiveness and user satisfaction).
As usability studies have shown that age (Mayhorn et al., 2005), and level of
expertise affect performance in terms of task completion time and number of errors
(Mizobuchi et al., 2005), study participants were matched by age and experience with
36
smartphones and then randomly assigned to either the control group (not exposed to
semantic auditory distractions) or the experimental group (exposed to semantic auditory
distractions).
Inclusion criteria were 50 to 85 years of age, and a diagnosis of either type 1 or
type 2 diabetes. Exclusion criteria included vision, hearing, or speech problems that
entirely precluded the use of an iPhone. This research study was approved by the Nova
Southeastern University Institutional Review Board (See Appendix H).
All individual usability studies performed in support of this research study were
done on an iPhone 4S running the Diabetes Diary mobile application. Usability was
measured by the following three usability attributes: effectiveness, efficiency, and user
satisfaction. As task workload has a significant impact on usability (Cowie et al., 2009),
this study measured overall task workload using the RTLX questionnaire and its scores
were used as covariate to control for differences in participant’s perceived task workload.
Table 2, lists the independent variable, dependent variables, and the covariate of
the study. The independent variable was represented by the presence, or not, of semantic
auditory distractions. The dependent variables were represented by measurements of
effectiveness, efficiency, and user satisfaction. The covariate task workload was
measured through the RTLX.
37
Table 2
List of Independent, Dependent, and Covariate Variables Being Used in the Study
Independent
Dependent
Covariate
Variable
Variable
Semantic Auditory
Efficiency
Task Workload
Distractions (Yes/No)
Effectiveness
User Satisfaction
User performance was documented in terms of effectiveness and efficiency. As
indicated by Vuolle, Aula, Kulju, Vainio, and Wigelius (2008), “Effectiveness - or the
accuracy and completeness with which intended goals are achieved - can be measured by
the percent or number of errors, the success-failure ratio, or the percentage of tasks
completed” (p.2). Effectiveness in this study was reported by the percentage of tasks
completed successfully (# of tasks completed successfully/total numbers of tasks
assigned).
Vuolle et al. (2008), defined efficiency as “a measure of the amount of human,
economic, and temporal resources that are expended in attaining a required level of
product effectiveness” (p. 2). According to Vuolle et al. (2008), some of the measures of
efficiency included time taken to complete tasks; time spent using help or documentation,
and estimations of subjective workload. This study captured the time taken to complete
each of the tasks assigned as well as the time taken to complete all tasks assigned as a
measure of efficiency.
Typical examples for questionnaires measuring user satisfaction are the
Questionnaire for User Interaction Satisfaction (QUIS), the Post-Study System Usability
Questionnaire (PSSUQ) or the System Usability Scale (SUS) (Brooke, 1996). Bangor,
38
Kortum, and Miller (2008) justified using the SUS over other survey instruments
because:
1. The questionnaire is made up of 10 items.
2. It is not restricted to any technology. Researchers have used the SUS to evaluate
websites, cellular phones, and television application interfaces among other user
interfaces.
3. The results obtained from the SUS questionnaire are a single score ranging from 0
to 100.
Even though Brooke (1996) did not refer to the SUS as a satisfaction score, several
HCI researchers have used it to measure the satisfaction aspect of usability (Everett,
Byrne, & Greene, 2006). The reliability and validity of the SUS have been investigated
and confirmed in several studies (Lewis & Sauro, 2009). The SUS questionnaire was
selected for this study because it is quick to administer and also for the ease it provides in
scoring the gathered data. For this study, the questionnaire was slightly modified by
replacing the word “system” with the word “mobile application” (See Appendix E).
The NASA-Task Load Index (Cao et al., 2009) uses six dimensions to assess
mental workload: mental demands, physical demands, temporal demands, performance,
effort and frustration level. A score from 0 to 100 (assigned to the nearest point 5) is
obtained on each of the tool scales. The NASA-TLX uses a weighting process that
requires a paired comparison task. The task requires the participant to choose which
dimension is more relevant to the workload for a particular task across all pairs of the six
dimensions. The workload scale is obtained for each task by multiplying the weight by
39
the individual dimension scale score, summing across scales, and dividing by the total
weights (Hill et al., 1992).
The administration of the NASA-TLX can be cumbersome and time consuming
especially since it is administered after each task or scenario. As a result, a different
type of TLX scale was developed called the NASA Raw Task Load Index (RTLX). The
RTLX computes a score by taking the sum of the TLX test and dividing it by the number
of dimensions (6). This new way to score the NASA-TLX was found to be almost
equivalent to the original TLX scale R= .977 (p<10−6 )(Byers et al., 1989), with far less
time involved for analysis. The participant’s cognitive workload was captured via the
RTLX (See Appendix F and G). Mental workload was assessed using RTLX scores, an
un-weighted average of the sub-scale values.
Participants
Regarding the sample size for usability studies, Burnham and Overton (1978);
Nielsen (2012) used the magic number five as a rule of thumb for sample sizes for
formative usability tests, believing this sample size would be enough to reveal about 85%
of the usability problems. However, Perfetti and Landesman (2001) argued that it could
take over 90 participants to discover all the usability problems.
Recruitment of participants was a major challenge in this study. The researcher
consulted several people about potential recruitment strategies for this study. Some
people cautioned the researcher that many seniors might be reluctant to respond to
advertisements because of concerns about sharing medical information.
40
The researcher recruited seniors by sending flyers to senior centers, friends, coworkers, diabetes organization and support groups in New Jersey and by using snowballsampling techniques in which the researcher accessed study participants through contact
information provided by other participants of the study.
According to Noy (2008):
Snowball sampling is arguably the most widely employed method of sampling in
qualitative research in various disciplines across the social sciences. It is
sometimes used as the main vehicle through which informants are accessed, or as
an auxiliary mean, which assists researchers in enriching sampling clusters, and
accessing new participants and social groups when other contact avenues have
dried up. (p. 330)
People interested in participating in the study called the researcher and after a
brief overview of the study, the usability study was scheduled. The sampling strategy
utilized a random, convenience sample as selection would be from participants who had
type 1 or type 2 diabetes and were between 50 and 85 years old. A total of 30 diabetics
between 50 and 85 years old ended up participating in this study.
Data Analysis Techniques
Data captured during the usability study were prepared for analysis by
transferring raw data from source documentation to a statistical analysis tool.
Participant’s ID, age, diabetes type, RTLX results, effectiveness, efficiency, and user
satisfaction results were extracted or calculated from documentation and entered in Stata
41
Release 13 for further analysis. Table 3 provides an overview of the study research
questions, hypotheses, variable data types and statistics that were used in this study.
Descriptive statistics were calculated for all study variables. This included the
mean and standard deviation for all continuous variables; counts and percentages for
categorical data. Research questions were tested using the general linear model with and
without a covariate. Model assumptions, specifically homogeneity of regression slopes,
were examined for research questions. To ascertain if the groups were comparable,
bivariate analyses were made between the groups.
Finally, data and results from the analysis of the data were used to make sense of
the study findings. The data was examined to summarize and explain how the data would
be used to answer the study research questions.
Format for Presenting Results
To provide a better picture of the raw data collected in the usability test and to
facilitate the analysis of data, tables and graphs were developed. Graphical
representation (i.e. charts and graphs) of raw and analyzed data was generated using both
Microsoft Excel and Stata Release 13.
Table 3
List of Research Questions, Hypotheses, Null Hypotheses, Variables, Data Type, and Statistic being used in the Study
Research Question
Hypothesis
Null Hypothesis
Variables
Data type
Statistic
Do semantic auditory
distractions reduce the
efficiency of a blood glucose
tracking mobile application
used by diabetic seniors?
Efficiency is lower
for diabetic seniors
who are exposed
to semantic
auditory
distractions
The mean value
of the
differences in
efficiency equals
zero.
Efficiency
Ratio
General
Linear Model
Do semantic auditory
distractions reduce the
effectiveness of a blood
glucose tracking mobile
application used by diabetic
seniors?
Effectiveness is
lower for diabetic
seniors who are
exposed to
semantic auditory
distractions
The mean value
of the
differences in
effectiveness
equals zero.
Effectiveness
Ratio
General
Linear Model
Do semantic auditory
distractions reduce the user
satisfaction of a blood glucose
tracking mobile application
used by diabetic seniors?
User satisfaction is
lower for diabetic
seniors who are
exposed to
semantic auditory
distractions
The mean value
of the
differences in
user satisfaction
(SUS scores)
equals zero.
User
Satisfactionmeasured by
the SUS
scores.
Interval
General
Linear Model
43
Resource Requirements
Since this research involved human subjects, Institutional Review Board (IRB)
approval was obtained before the study was conducted. The following resources were
also required to perform the study:
Software
1. Mobile iOS Application Diabetes Diary developed by Friday Forward
(http://diabetesdiary.fridayforward.com) (See Appendix B).
2. Stata Statistical Software: Release 13.
3. Decibel 10th iPhone mobile application- used to measure decibels.
Hardware
1. iPhone 4S
2. Portable Speakers
3. iPod mini with small speakers.- See Figure 2
Usability Room
See Figure 3 for a sample room configuration
Study Participants
Adults between 50 and 85 years old with a dioagnosis of diabetes were used to
conduct this experiment.
44
Figure 2. iPod mini and speakers used to simulate semantic auditory distractions
Figure 3. Room configuration used to perform usability studies
45
Summary
In Chapter 3, the methodology employed in this study was identified and
described. The chapter began with a discussion of the selection of research methods;
instruments used proposed sample and data analysis techniques. Finally, the format for
presenting the study results and the resource requirements were discussed.
46
Chapter 4
Results
This chapter outlines the results of the data analysis procedures stated in Chapter
3. This study will attempt to provide answers to the three research questions with the
primary purpose of investigating the impact of semantic auditory distractions on the
usability of a blood glucose tracking mobile application used by diabetic seniors.
Data Preparation
Once data from all 30-study participants was captured, participant’s demographic
data, performance results, SUS questionnaire and RTLX results were keyed into an Excel
spreadsheet for further analysis. The spreadsheet was then reviewed for completeness
before it was imported into Stata Statistical Software: Release 13 for statistical analysis.
Data Analysis
Before the statistical analyses were conducted, the groups were examined to see if
they differed by relevant study variables. This is an important consideration given the
matched-group methodology. Results indicate no differences between the groups for age,
gender, experience with smartphones, workload and years with a diagnosis of diabetesSee Tables 4, 5 and 6. As such, the groups can be considered comparable for statistical
purposes.
47
Table 4.
Descriptive Statistics for Age and Experience with Smartphone Variables
Group
N Mean
SD
Lower 95% CI
Age
Upper 95% CI
With distractions
15
66.67
7.47
62.53
70.80
Without distractions
15
66.53
7.68
62.28
70.79
-5.53
5.80
Difference
0.13
P-Value
p = 0.961
Group
N
Mean
SD
Lower 95% CI
Upper 95% CI
P-Value
Experience with
With distractions
15
1.20
1.25
0.51
1.89
p = 0.422
Smartphone
Without distractions
15
0.87
0.97
0.33
1.41
-0.50
1.17
Difference
0.33
48
Table 5
Descriptive Statistics for RTLX and Years with Diagnosis of Diabetes Variables
Group
N Mean
SD
Lower 95% CI
RTLX
Upper 95% CI
With distractions
15
55.13
7.95
50.73
59.53
Without distractions
15
52.00
9.70
46.63
57.37
-3.50
9.77
Difference
3.13
P-Value
p = 0.341
Group
N
Mean
SD
Lower 95% CI
Upper 95% CI
P-Value
Years with
With distractions
15
5.27
4.67
2.68
7.85
p = 0.113
Diagnosis of
Without distractions
15
8.80
6.95
4.95
12.65
Diabetes
Difference
-7.96
0.89
-3.53
49
Table 6
Descriptive Statistics for Sample Gender variable
Group
Female
Male
With
10 (67%)
5 (33%)
Without
7 (47%)
8 (53%)
Total
17 (57%)
13 (33%)
P-Value
p = 0.269
Demographical Data Analysis
A total of 30 participants agreed to participate in this usability study. The
convenience sample consisted of 30 adults 50 to 85 years old with a diagnosis of diabetes
in the state of New Jersey. Participants in this research were weighted more female (57%,
n=17) than male (33%, n=13). According to the 2010 U.S. census of the adult population
65 and older a similar ratio is seen in the general public, 56.9% female versus 43.1%
male (Werner, 2011), so the ratio seen in this study is representative.
It is important to note that while the intention of this study was to enroll
participants with both type 1 and type 2 diabetes, all seniors who participated in this
study had been diagnosed with type 2 diabetes. Not having study participants with type
1 diabetes is not unexpected since according to the Center for Disease Control and
Prevention (Centers for Disease Control Prevention, 2014) type 2 diabetes accounts for
95% of all the diabetes cases in the United States. Participants of this study had an
average age of 66.6 years with an average of 7 years with a diagnosis of diabetes. In
terms of experience with smartphones, participants had an average of only one year of
experience.
50
Task Efficiency Analysis
Task efficiency was measured by total time on task. Participants were asked to
read four different scenarios, which required them to perform a series of tasks. The time
from the moment the participant indicated that he or she was ready to begin the study
until the participant indicated to the researcher that the task was completed was captured.
The sum of the time taken to complete all tasks was calculated and used as a measure of
efficiency. It is important to note that in the case of total time on task, less time is a
measure of better efficiency. Table 7 provides a summary of the average time taken to
complete all four tasks by the group.
Table 7
Statistics for Efficiency (Time on Task measured in seconds) by group
Group
N
Mean
SD
Min
Max
With Distractions
15
594.40
89.56
431.00
754.00
Without Distractions
15
516.13
103.73
396.00
761.00
51
Task Effectiveness Analysis
Task effectiveness was measured by total task success rate. Each participant was
asked to read four different scenarios. Each scenario required participants to perform a
series of tasks. Once the participant indicated that the particular task had been
completed, the researcher proceeded to review the outcome of the task and determine if
the task had been successfully completed. A zero would be assigned to the result if the
task were not successfully completed; similarly, a one would be assigned to the result if
the task were completed successfully. Table 8 provides a summary of the task success
rate (percentage) for each of the four tasks by a group.
Table 8
Statistics for Effectiveness (Success Rate) by group
Group
N
Mean
SD
Min
Max
With Distractions
15
71.67
22.89
25.00
100.00
Without Distractions
15
88.33
16.00
50.00
100.00
User Satisfaction Analysis
User Satisfaction was measured by the SUS questionnaire. Once participants
completed all four scenarios, they were asked to complete the SUS questionnaire. The
overall SUS score was calculated by multiplying the sum of the scores by 2.5. Table 9
provides a summary of the overall satisfaction SUS scores.
52
Table 9
Statistics for User Satisfaction (Measured by SUS scores) by group
Group
N
Mean
SD
Min
Max
With Distractions
15
56.33
16.34
0.00
70.00
Without Distractions
15
63.67
5.89
57.50
80.00
Findings
Research Question 1
Analysis of data on the first research question was focused on investigating
whether semantic auditory distractions reduce the efficiency of a blood glucose tracking
mobile application used by diabetic seniors. The researcher’s hypothesis was that the
efficiency would be lower for participants, which were exposed to semantic auditory
distractions.
To answer research question one, two statistical approaches were used. First a
general linear model with no covariates was run. Second, the model was run again using
the participant's total workload (RTLX questionnaire results) as a covariate. The
homogeneity of regression slopes was looked at by testing the interaction of group
(Exposed vs. Non-Exposed) by RTLX; where an interaction would indicate that the
regression coefficient for group is significantly different across participant's workload
levels. The group by RTLX interaction was not statistically significant (p=0.335);
therefore, homogeneity of regression slopes was assumed. The results of the model are
as follows (See Figure 4)
53

There is a significant effect of semantic auditory distractions on efficiency,
F(1,28)=4.89,P=0.035). The group without auditory distraction was more
efficient (Mean difference =78.26[95% CI: 5.78, 150,75], p=0.035).

There is no significant effect of semantic auditory distractions on efficiency
after controlling for participant's workload F(1,27=3.69,P=0.065).
Figure 4. Dot plot of Efficiency by Group Controlling for Workload
Research Question 2
Analysis of data on the second research question was focused on investigating
whether semantic auditory distractions reduce the effectiveness of a blood glucose
tracking mobile application used by diabetic seniors. The researcher’s hypothesis was
that the effectiveness would be lower for participants, which were exposed to semantic
auditory distractions.
54
To answer research question two, two statistical approaches were used. First, an
analysis using a general linear model with no covariates was performed. Second, the
model using the participant's workload (RTLX questionnaire) as a covariate was re-run.
The homogeneity of regression slopes was looked at by testing the interaction of group
(Exposed vs. Non-Exposed) by RTLX; where an interaction would indicate that the
regression coefficient for group is significantly different across participant's workload
levels. The group by RTLX interaction was not statistically significant (p=0.900);
therefore, homogeneity of regression slopes was assumed. The results of the model are
as follows (See Figure 5):

There is a significant effect of semantic auditory distractions on effectiveness,
F(1,30)=5.34,P=0.028). The group without auditory distraction was more
effective (Mean difference=16.67[95% CI: 1.89, 31.43],p=0.035).

There is a significant effect of semantic auditory distractions on effectiveness
after controlling for participant's workload F (1,27=5.08,P=0.032). The group
without auditory distraction was more effective (Mean difference=16.81[95%
CI: 1.50, 3212], p=0.033)
55
Figure 5. Dot plot of Effectiveness by Group Controlling for Workload
Research Question 3
Analysis of data on the third research question was focused on investigating
whether semantic auditory distractions reduce the user satisfaction of a blood glucose
tracking mobile application used by diabetic seniors. The researcher's hypothesis was
that the user satisfaction would be lower for participants who were exposed to
semantic auditory distractions.
To answer research question three, two statistical approaches were used. First, an
analysis using a general linear model with no covariates was performed. Second, the
model using the participant's workload (RTLX questionnaire) as a covariate was rerun. The homogeneity of regression slopes was looked at by testing the interaction of
group (Exposed vs. Non-Exposed) by RTLX; where an interaction would indicate
56
that the regression coefficient for group is significantly different across participant's
workload levels. The group by RTLX interaction was not statistically significant
(p=0.166); therefore, homogeneity of regression slopes was assumed. Additionally,
one subject in the semantic auditory distraction group failed to report a satisfaction
score. This subject was removed from the statistical analysis. The results of the
model are as follows (See Figure 6):

There is no significant effect of semantic auditory distractions on satisfaction,
F(1,27)=2.61,P=0.118).

There is no significant effect of semantic auditory distractions on satisfaction
after controlling for participant's workload F(1,26=2.64,P=0.116).
57
Figure 6. Dot plot of User Satisfaction by Group Controlling for Workload
Summary of Results.
The data collected, analyzed and reported in this chapter shows that semantic
auditory distractions have a significant effect on efficiency and effectiveness and no
significant effect on satisfaction. It is also important to point out that after controlling for
participant’s workload, semantic auditory distractions have no significant effect on
efficiency and satisfaction but a significant effect on effectiveness.
Table 10 summarizes the results of this dissertation study.
58
Table 10
Summary of Study Research Questions Results
Research
Question
RQ1
Description
Results
Do semantic auditory distractions reduce the efficiency of a
There is a significant effect of semantic
blood glucose tracking mobile application used by diabetic
auditory distractions on efficiency.
seniors?
RQ2
Do semantic auditory distractions reduce the effectiveness of a There is a significant effect of semantic
blood glucose tracking mobile application used by diabetic
auditory distractions on effectiveness.
seniors?
RQ3
Do semantic auditory distractions reduce the user satisfaction
There is no significant effect of
of a blood glucose tracking mobile application used by diabetic semantic auditory distractions on
seniors?
satisfaction.
59
Chapter 5
Conclusions, Implications, Recommendations, and Summary
The aim of this chapter is to consolidate the findings made in this dissertation
study; limitations and challenges encountered in the course of achieving the research
goals are discussed. The implications of the findings from this study and how they
contribute to the achievement of the research goals, as defined in Chapter 1 are also
discussed. Lastly, several recommendations for future research are proposed.
Discussion of Study Findings
The first research question of this study focused on investigating whether
semantic auditory distractions reduce the efficiency of a blood glucose tracking mobile
application used by diabetic seniors. The researcher’s hypothesis was that the efficiency
would be lower for participants exposed to semantic auditory distractions. As indicated
in Chapter 4, the results of the model indicate that there is a significant effect of semantic
auditory distractions on efficiency, F (1,28)=4.89,P=0.035). The group that performed
the study tasks without auditory distraction was more efficient (Mean difference
=78.26[95% CI: 5.78, 150,75], p=0.035) than the group that performed the study tasks
with semantic auditory distractions. These results imply that diabetic seniors are more
efficient with mobile applications when they are not exposed to semantic auditory
distractions.
Hence, the researcher’s hypothesis is supported by the results of this study. These
results are similar to the ones reported by Tsiaousis and Giaglis (2010) which found that
the time needed to complete a task when browsing a mobile website is significantly
60
affected by the conditions of use particularly the semantic of environmental sounds.
Coursaris et al. (2012) also found that distractions do have a significant negative impact
on the perceived efficiency of mobile device use. However, it is important to note that
Coursaris et al. measured efficiency using self-reported data instead of performance –
related data as used in this dissertation study.
An interesting finding is that after statistically controlling for cognitive workload,
this study showed that semantic auditory distractions have no significant effect
F(1,27=3.69,P=0.065) on efficiency, indicating a possible mediating role for cognitive
workload in measurements of efficiency.
The second research question of this study focused on investigating whether
semantic auditory distractions reduced the effectiveness of a blood glucose tracking
mobile application used by diabetic seniors. The researcher’s hypothesis was that the
effectiveness would be lower for participants exposed to semantic auditory distractions.
As indicated in Chapter 4, the results of the model indicate that there is a significant
effect of semantic auditory distractions on effectiveness F(1,30)=5.34,P=0.028).
Effectiveness was significantly higher on the group without auditory distractions (Mean
difference=16.67[95% CI: 1.89, 31.43], p=0.035). Hence, the researcher’s hypothesis is
supported by the results of this study.
These results imply that diabetic seniors are more
effective with mobile applications when they are not exposed to semantic auditory
distractions.
These results are similar to the ones reported by Tsiaousis and Giaglis (2010)
which found that the effectiveness when browsing a mobile website is significantly
affected by the conditions of use particularly the semantic of environmental sounds.
61
Coursaris et al. (2012) also found that distractions do have a significant negative impact
on the perceived effectiveness of mobile device use. However, it is important to note that
Coursaris et al. measured effectiveness using self-reported data instead of performance–
related data as used in this dissertation study.
An interesting finding is that even after statistically controlling for cognitive
workload, this study showed that semantic auditory distractions have a significant effect
on effectiveness F (1,27=5.08,P=0.032). The group without auditory distraction was more
effective (Mean difference=16.81[95% CI: 1.50, 3212], p=0.033)
The third research question of this study focused on investigating whether
semantic auditory distractions reduce the user satisfaction of a blood glucose tracking
mobile application used by diabetic seniors. The researcher’s hypothesis was that the
user satisfaction would be lower for participants exposed to semantic auditory
distractions. As indicated in Chapter 4, the results of the model indicate that there is no
significant effect of semantic auditory distractions on user satisfaction
F(1,27)=2.61,P=0.118). These results imply that the satisfaction that diabetic seniors
have when interacting with mobile applications is not affected by the presence of
semantic auditory distractions.
Hence, the researcher’s hypothesis is not supported by the results of this study.
These results are similar to the ones reported by Tsiaousis and Giaglis (2010) which
found that while user satisfaction is influenced by task complexity, it is relatively
unaffected by the context of use.
User satisfaction has been shown to be positively influenced by information
quality Chung, Dong, and Shin (2014); Dwivedi, Kapoor, Williams, and Williams
62
(2013), social presence Park, Oh, and Lee (2011), and enjoyment Lee, Moon, Kim, and
Mun (2015). Semantic auditory distractions tend to reduce cognitive resources but
cannot influence the information quality of the mobile application, whether users feel that
they personally met the other user during the use of the mobile application (social
presence) or the extent to which the activity of using the mobile application is perceived
to be enjoyable. This could explain why semantic auditory distractions had no impact on
user satisfaction.
An interesting finding is that even after statistically controlling for cognitive
workload, this study showed that semantic auditory distractions have no significant effect
on user satisfaction F(1,26=2.64,P=0.116).
Conclusions
The results from this study suggest that semantic auditory distractions have an
impact on a participant's usability performance metrics. Participant's efficiency and
effectiveness were found to suffer when exposed to semantic auditory distractions when
compared to the environment without semantic auditory distractions. Results from this
study also suggest that there is no significant effect of semantic auditory distractions on
user satisfaction. However statistical methods performed and documented in Chapter 4
indicate that after controlling for participant's workload, there is no significant effect of
semantic auditory distractions on efficiency or user satisfaction but still it does have a
significant effect on effectiveness. While participant's workload was found to be slightly
higher in the group that was exposed to semantic auditory distractions (Mean difference=
3.13), this difference was not found to be significant.
63
In conclusion, this dissertation study showed that the percentage of tasks
completed successfully, and the total time needed to complete a task when diabetic
seniors use a mobile application are significantly affected by semantic auditory
distractions, whereas user satisfaction is not.
Limitations
This study has several limitations. As documented in Chapter 3, this study
targeted a population comprised of seniors with a diagnosis of diabetes. More
specifically, this study targeted diabetic seniors between 50 and 85 years old who live in
the state of New Jersey. Originally, this researcher targeted adults between 65 and 85
years old with a diagnosis of diabetes with consideration to the exclusionary criteria
stated on the Institutional Review Board submission. However, recruiting adults between
65 and 85 years old with a diagnosis of diabetes for usability testing was more difficult
than initially predicted.
Recruitment of participants for this study was accomplished by contacting
diabetes organizations in New Jersey such as the New Jersey diabetes association, and
several diabetes support groups. Also, this researcher recruited participants through word
of mouth and by reaching out to friends, family and coworkers. Recruiting signs were
posted in most of the following places: 1) Senior Centers in New Jersey, 2) Public
Libraries, 3) Nursing Homes, 4) Adult Communities, 5) Endocrinologist offices, and 6)
Churches.
Since the participation of seniors 65-85 years old was low, it was decided to
broaden the age study criteria from 65-85 to 50-85 years old. This change increased the
64
population. Thus, this study sample consisted of 30 adults aged 50-85 with a diagnosis of
diabetes (type 1 or 2).
Implications
The findings in this study provide significant contributions to the field of
Information Systems and are of particular interest to human-computer interaction
usability researchers and professionals in the usability and medical area who are
interested in understanding the effect of environmental and user context in usability. This
study provides support to the importance of considering environmental context and more
specifically external audio distractions (as in the case of semantic auditory distractions)
on efficiency and effectiveness, which are two of the three most used measures of mobile
usability. Not to generalize, but researchers and practitioners in the area of mobile
usability could rely on user satisfaction questionnaires such as the SUS, knowing that
their results will not be affected by auditory distractions and more specifically semantic
auditory distractions.
Recommendations for Further Research
Several recommendations can be made for future research based on the findings
of this study. First, it is recommended that the methodology and procedures utilized in
this study be applied to seniors without diabetes to investigate if semantic auditory
distractions have the same impact on usability as the one shown in this study. It is likely
that some of the challenges that diabetic seniors faced in this study while dealing with a
smartphone are challenges that any beginner user, regardless of age, would have to deal
65
with when learning to interact with a smartphone of which they have little previous
experience. Another interesting study would be to perform a similar study with type 1
diabetic seniors. Since type 1 diabetes can become a very serious disease if not managed
appropriately, participants might be more motivated and perform better in the usability
study knowing that this type of mobile application could help them manage their diabetes
and prevent serious health complications.
Summary
Diabetes is the seventh leading cause of death in the United States. With the
population rapidly aging, it is expected that 1 out of 3 Americans will have diabetes by
2050 (Boyle et al., 2010). Diabetics can manage their disease by choosing what, how
much and when to eat, getting physically active, taking medication if needed, and
tracking their blood glucose levels. Self-management in diabetes is strongly connected
with the patient’s quality of life (Glasgow et al., 1999). However, diabetics have
indicated that tracking data to manage their disease is a complex process (Kouris et al.,
2010). For this reason, researchers have been evaluating how technology can be used to
simplify this process.
Several technologies have been proposed to assist in the area of diabetes selfmanagement. Mobile phones, for example, have shown to be effective in the area of
diabetes self-management (Krishna & Boren, 2008; Patrick et al., 2008; Preuveneers &
Berbers, 2008). As a result, a growing number of people are using mobile applications to
self- manage diabetes (Holtz & Lauckner, 2012). However, these mobile applications
have several limitations, including but not limited to, lack of personalized feedback,
66
usability issues, particularly the ease of data entry, and integration with patients and
electronic health records (El-Gayar et al., 2013).
Usability is important for systems that help people self-manage conditions such as
diabetes (Arsand et al., 2012). It is argued that mobile usability studies before mobile
applications are released to the public could identify usability issues that can improve
application's design, usability, and acceptance. Mobile applications face key usability
challenges, as users need to share their attention between the task (mobile application)
and the surrounding environment.
Diabetics have unique challenges and needs. In fact, the diabetic population has a
greater rate of decline in cognitive function and a greater risk of cognitive decline
(Cukierman et al., 2005). In the case of seniors with diabetes, it has been shown that the
disease affects their cognitive performance (Kanaya et al., 2004; Tun et al., 1987).
Understanding factors that affect the senior diabetic population will help develop
applications that will allow people self-manage their diabetes.
As highlighted by usability researchers, the context of use (i.e. environment, user,
task, and technology) has a significant impact on mobile usability (Liu et al., 2011). The
characteristics of the context (the users, tasks, equipment, and environment) may be as
important in determining usability as the characteristics of the product itself (Bevan &
Macleod, 1994). The environment (lighting, temperature, audio and visual distractions,
etc.) is of special interest to the mobile usability arena since in the case of mobile devices,
is always changing. This dissertation aims to support the claim that environmental
context and more specifically environmental distraction such as semantic auditory
distractions impact the usability of mobile applications. In doing so, it attempts to answer
67
the following research questions: 1) Do semantic auditory distractions reduce the
effectiveness of a blood glucose tracking mobile application? 2) Do semantic auditory
distractions reduce the efficiency of a blood glucose tracking mobile application? 3) Do
semantic auditory distractions reduce the user satisfaction of a blood glucose tracking
mobile application?
To answer the study research questions, a posttest-only control group design
experiment was performed involving 30 adults with type 2 diabetes. Adults between 50
and 85 years old were targeted to participate in the study. Participants were paired based
on their age and experience with smartphones and randomly assigned to the control (no
semantic auditory distractions) or experimental (semantic auditory distractions) group.
During the usability study, participants were asked to read four scenarios and complete
the assigned tasks. Efficiency, effectiveness, user satisfaction, and RTLX measurements
were collected from each of the participants. Data collected was analyzed using the
general linear model and used to answer the study research questions.
The results of this study confirmed that semantic auditory distractions have a
significant effect on efficiency and effectiveness, and hence they need to be taken into
account when evaluating mobile usability with diabetic seniors. This study also showed
that semantic auditory distractions have no significant effect on user satisfaction.
Regarding the effect of cognitive workload on usability, this study showed that after
statistically controlling for cognitive workload, semantic auditory distractions have a
significant impact on effectiveness but no impact on efficiency or user satisfaction.
68
Appendix A
Participant’s Demographic Questionnaire
69
Appendix B
Screenshots of the Mobile Application being used in the Study (From Apple
Store)
70
Appendix C
Usability Test Task List and Scenario
Task
Number
1
Description
Record Data
Scenario
1. You want to record your most recent glucose reading (Before
Breakfast) along with other information. Open the "Diabetes
Diary" mobile application and record and save the following
information:

Glucose – "100 mg/dl"

Carbs– "28 grams"

Insulin dose – "4.7 units"

Insulin type – "Novolin"

Insulin site – "Right arm"

Note - "I will enjoy a nice dinner tonight"
2. Go to the Diabetes Diary “Home Screen”
2
1. Your partner just reminded you to log your glucose readings from
last night at 8:30 p.m. Open the "Diabetes Diary" mobile
application and record the following information:

Glucose – "150 mg/dl"

Carbs– "23 grams"

Note - "I had a great day"
2. Go to the Diabetes Diary “Home Screen”
3
Review Recent
Data
1. Your doctor just called and asked you to write down your glucose
readings for the last 3 days. Open the "Diabetes Diary" mobile
application and write down your glucose readings for the last 3
days.
Date: _____
Glucose:________mg/dl
71
Task
Number
Description
Scenario
Date: _____
Glucose:________ mg/dl
Date: _____
Glucose:________ mg/dl
2. Your doctor also wanted you to find and write down your mean
glucose reading from the last seven days. Using the "Diabetes
Diary" mobile application, find and write down your mean
glucose reading for the last seven days.
The Mean glucose reading for the last seven days is:
______________ mg/dl
3. Go to the Diabetes Diary “Home Screen”
4
Share
Information
1. Your doctor called and asked you to e-mail him all your glucose
readings for the last seven days (using CSV format). Open the
"Diabetes Diary" mobile application and email your glucose
readings for the last seven days.
Your doctor’s email address is: [email protected]. Enter “My
glucose readings” in the subject line.
2. Go to the Diabetes Diary “Home Screen”
72
Appendix D
Summary Usability Study Results
73
Appendix E
System Usability Scale (SUS)
74
Appendix F
RTLX Rating Sheet
75
Appendix G
RTLX-Tally Sheet
76
Appendix H
IRB Approval
77
References
Allemann, S., Houriet, C., Diem, P., & Stettler, C. (2009). Self-monitoring of blood
glucose in non-insulin treated patients with type 2 diabetes: a systematic review
and meta-analysis. Curr Med Res Opin, 25(12), 2903-2913. doi:
10.1185/03007990903364665
Allen, K. V., Frier, B. M., & Strachan, M. W. (2004). The relationship between type 2
diabetes and cognitive dysfunction: longitudinal studies and their methodological
limitations. Eur J Pharmacol, 490(1-3), 169-175. doi:
10.1016/j.ejphar.2004.02.054
Arsand, E., Froisland, D. H., Skrovseth, S. O., Chomutare, T., Tatara, N., Hartvigsen, G.,
& Tufano, J. T. (2012). Mobile Health Applications to Assist Patients with
Diabetes: Lessons Learned and Design Implications. Journal of diabetes science
and technology, 6(5), 1197-1206. doi: 10.1177/193229681200600525
Baldus, T., & Patterson, P. (2008). Usability of pointing devices for office applications in
a moving off-road environment. Appl Ergon, 39(6), 671-677. doi:
10.1016/j.apergo.2008.01.004
Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An Empirical Evaluation of the System
Usability Scale. International Journal of Human-Computer Interaction, 24(6),
574-594. doi: 10.1080/10447310802205776
Bevan, N., & Macleod, M. (1994). Usability measurement in context. Behaviour &
Information Technology, 13(1-2), 132-145. doi: 10.1080/01449299408914592
Boyle, J. P., Thompson, T. J., Gregg, E. W., Barker, L. E., & Williamson, D. F. (2010).
Projection of the year 2050 burden of diabetes in the US adult population:
dynamic modeling of incidence, mortality, and prediabetes prevalence.
Population Health Metrics, 8(1), 29.
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in
industry, 189, 194.
78
Brown, P. J., Bovey, J. D., & Xian, C. (1997). Context-aware applications: from the
laboratory to the marketplace. IEEE Personal Communications, 4(5), 58-64. doi:
10.1109/98.626984
Burnham, K. P., & Overton, W. S. (1978). Estimation of the size of a closed population
when capture probabilities vary among animals. Biometrika, 65(3), 625-633. doi:
10.1093/biomet/65.3.625
Byers, J. C., Bittner, A. C., & Hill, S. G. (1989). Traditional and raw task load index
(TLX) correlations: Are paired comparisons necessary? Paper presented at the
International Industrial Ergonomics and Safety Conference, Cincinnati, Ohio.
Calero Valdez, A., Ziefle, M., Horstmann, A., Herding, D., & Schroeder, U. (2009).
Effects of Aging and Domain Knowledge on Usability in Small Screen Devices
for Diabetes Patients HCI and Usability for e-Inclusion (Vol. 5889, pp. 366-386):
Springer Berlin Heidelberg.
Cao, A., Chintamani, K. K., Pandya, A. K., & Ellis, R. D. (2009). NASA TLX: software
for assessing subjective mental workload. Behav Res Methods, 41(1), 113-117.
doi: 10.3758/BRM.41.1.113
Centers for Disease Control Prevention. (2014). National Diabetes Statistics Report:
Estimates of Diabetes and Its Burden in the United States, 2014. Atlanta, GA: US
Department of Health and Human Services.
Chung, K.-H., Dong, Y.-H., & Shin, J.-I. (2014). The Relationship between Micro-blog’s
Characteristics, User Satisfaction, and e-Loyalty: The Moderating Effect of
Gender.
Clapp, W. C., & Gazzaley, A. (2012). Distinct mechanisms for the impact of distraction
and interruption on working memory in aging. Neurobiol Aging, 33(1), 134-148.
doi: 10.1016/j.neurobiolaging.2010.01.012
Comparative Examples of Noise Levels. (2000). from
http://www.industrialnoisecontrol.com/comparative-noise-examples.htm
Coursaris, C., & Kim, D. (2006). A Qualitative Review of Empirical Mobile Usability
Studies. Paper presented at the AMCIS 2006.
79
Coursaris, C., & Kim, D. (2011). A Meta-Analytical Review of Empirical Mobile
Usability Studies. Journal of Usability Studies, 6(3), 117-171.
Coursaris, C. K., Hassanein, K., Head, M. M., & Bontis, N. (2012). The impact of
distractions on the usability and intention to use mobile devices for wireless data
services. Computers in Human Behavior, 28(4), 1439-1449. doi:
10.1016/j.chb.2012.03.006
Cowie, C. C., Rust, K. F., Ford, E. S., Eberhardt, M. S., Byrd-Holt, D. D., Li, C., . . .
Geiss, L. S. (2009). Full accounting of diabetes and pre-diabetes in the U.S.
population in 1988-1994 and 2005-2006. Diabetes Care, 32(2), 287-294. doi:
10.2337/dc08-1296
Crease, M. (2005). Measuring the effect of distractions on mobile users' performance.
Measuring Behavior.
Cukierman, T., Gerstein, H. C., & Williamson, J. D. (2005). Cognitive decline and
dementia in diabetes--systematic overview of prospective observational studies.
Diabetologia, 48(12), 2460-2469. doi: 10.1007/s00125-005-0023-4
de Jong, T. (2009). Cognitive load theory, educational research, and instructional design:
some food for thought. Instructional Science, 38(2), 105-134. doi:
10.1007/s11251-009-9110-0
Dinet, J., Brangier, E., Michel, G., Vivian, R., Battisti, S., & Doller, R. (2007). Older
People as Information Seekers: Exploratory Studies About Their Needs and
Strategies. In C. Stephanidis (Ed.), Universal Acess in Human Computer
Interaction. Coping with Diversity (Vol. 4554, pp. 877-886): Springer Berlin
Heidelberg.
Dwivedi, Y. K., Kapoor, K. K., Williams, M. D., & Williams, J. (2013). RFID systems in
libraries: An empirical examination of factors affecting system use and user
satisfaction. International Journal of Information Management, 33(2), 367-377.
doi: http://dx.doi.org/10.1016/j.ijinfomgt.2012.10.008
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013). Mobile applications for diabetes
self-management: status and potential. J Diabetes Sci Technol, 7(1), 247-262. doi:
10.1177/193229681300700130
80
Everett, S. P., Byrne, M. D., & Greene, K. K. (2006). Measuring the Usability of Paper
Ballots: Efficiency, Effectiveness, and Satisfaction. Proceedings of the Human
Factors and Ergonomics Society Annual Meeting, 50(24), 2547-2551. doi:
10.1177/154193120605002407
Franc, S., Daoudi, A., Mounier, S., Boucherie, B., Dardari, D., Laroye, H., . . .
Charpentier, G. (2011). Telemedicine and diabetes: achievements and prospects.
Diabetes Metab, 37(6), 463-476. doi: 10.1016/j.diabet.2011.06.006
Garcia, E., Martin, C., Garcia, A., Harrison, R., & Flood, D. (2011). Systematic Analysis
of Mobile Diabetes Management Applications on Different Platforms. In A.
Holzinger & K.-M. Simonic (Eds.), Information Quality in e-Health (Vol. 7058,
pp. 379-396): Springer Berlin Heidelberg.
Glasgow, R. E., Fisher, E. B., Anderson, B. J., LaGreca, A., Marrero, D., Johnson, S. B., .
. . Cox, D. J. (1999). Behavioral science in diabetes. Contributions and
opportunities. Diabetes Care, 22(5), 832-843. doi: 10.2337/diacare.22.5.832
Hassanein, K., & Head, M. (2003). Ubiquitous Usability: Exploring Mobile Interfaces
within the Context of a Theoretical Model. Lecture Notes in Computer Science
183.
Hawthorn, D. (2000). Possible implications of aging for interface designers. Interacting
with Computers, 12(5), 507-528. doi: 10.1016/s0953-5438(99)00021-1
Hill, S. G., Iavecchia, H. P., Byers, J. C., Bittner, A. C., Zaklade, A. L., & Christ, R. E.
(1992). Comparison of four subjective workload rating scales. Human Factors:
The Journal of the Human Factors and Ergonomics Society, 34(4), 429-439.
Holtz, B., & Lauckner, C. (2012). Diabetes management via mobile phones: a systematic
review. Telemed J E Health, 18(3), 175-184. doi: 10.1089/tmj.2011.0119
Holzinger, A., Searle, G., & Nischelwitzer, A. (2007). On Some Aspects of Improving
Mobile Applications for the Elderly. In C. Stephanidis (Ed.), Universal Acess in
Human Computer Interaction. Coping with Diversity (Vol. 4554, pp. 923-932):
Springer Berlin Heidelberg.
Hoyoung, K., Jinwoo, K., Yeonsoo, L., Minhee, C., & Youngwan, C. (2002). An
empirical study of the use contexts and usability problems in mobile Internet.
81
Paper presented at the 35th Annual Hawai International Conference on System
Sciences.
Hughes, R., & Jones, D. M. (2001). The intrusiveness of sound: Laboratory findings and
their implications for noise abatement. Noise & Health, 4(13), 51-70.
Hughes, R. W., & Jones, D. M. (2003). Indispensable benefits and unavoidable costs of
unattended sound for cognitive functioning. Noise Health, 6(21), 63-76.
Hummel, K. A., Hess, A., & Grill, T. (2008). Environmental context sensing for usability
evaluation in mobile HCI by means of small wireless sensor networks. Paper
presented at the 6th International Conference on Advances in Mobile Computing
and Multimedia, Linz, Austria.
Johnson, P. (1998). Usability and Mobility; Interactions on the move. Paper presented at
the First Workshop on Human-Computer Interaction with Mobile Devices.
Jokela, T., Iivari, N., Matero, J., & Karukka, M. (2003). The standard of user-centered
design and the standard definition of usability: analyzing ISO 13407 against ISO
9241-11. Paper presented at the Latin American conference on Human-computer
interaction, Rio de Janeiro, Brazil.
Jones, D. (1990). Recent advances in the study of human performance in noise.
Environment International, 16(4-6), 447-458. doi: 10.1016/0160-4120(90)90013v
Kanaya, A. M., Barrett-Connor, E., Gildengorin, G., & Yaffe, K. (2004). Change in
cognitive function by glucose tolerance status in older adults: a 4-year prospective
study of the Rancho Bernardo study cohort. Arch Intern Med, 164(12), 13271333. doi: 10.1001/archinte.164.12.1327
Kirwan, M., Vandelanotte, C., Fenning, A., & Duncan, M. J. (2013). Diabetes selfmanagement smartphone application for adults with type 1 diabetes: randomized
controlled trial. J Med Internet Res, 15(11), e235. doi: 10.2196/jmir.2588
Kouris, I., Mougiakakou, S., Scarnato, L., Iliopoulou, D., Diem, P., Vazeou, A., &
Koutsouris, D. (2010). Mobile phone technologies and advanced data analysis
towards the enhancement of diabetes self-management. Int J Electron Healthc,
5(4), 386-402.
82
Krishna, S., & Boren, S. A. (2008). Diabetes self-management care via cell phone: a
systematic review. J Diabetes Sci Technol, 2(3), 509-517. doi:
10.1177/193229680800200324
Lavie, N. (2010). Attention, Distraction, and Cognitive Control Under Load. Current
Directions in Psychological Science, 19(3), 143-148. doi:
10.1177/0963721410370295
Lee, D., Moon, J., Kim, Y. J., & Mun, Y. Y. (2015). Antecedents and consequences of
mobile phone usability: Linking simplicity and interactivity to satisfaction, trust,
and brand loyalty. Information & Management, 52(3), 295-304.
Lewis, J. R., & Sauro, J. (2009). The Factor Structure of the System Usability Scale. In
M. Kurosu (Ed.), Human Centered Design (Vol. 5619, pp. 94-103): Springer
Berlin Heidelberg.
Liu, C., Zhu, Q., Holroyd, K. A., & Seng, E. K. (2011). Status and trends of mobilehealth applications for iOS devices: A developer's perspective. Journal of Systems
and Software, 84(11), 2022-2033. doi: 10.1016/j.jss.2011.06.049
Lyles, C. R., Harris, L. T., Le, T., Flowers, J., Tufano, J., Britt, D., . . . Ralston, J. D.
(2011). Qualitative evaluation of a mobile phone and web-based collaborative
care intervention for patients with type 2 diabetes. Diabetes Technol Ther, 13(5),
563-569. doi: 10.1089/dia.2010.0200
Marsh, J., Hughes, R. W., & Jones, D. M. (2009). Interference by process, not content,
determines semantic auditory distraction. Cognition, 110(1), 23-38. doi:
10.1016/j.cognition.2008.08.003
Marsh, J. E., Hughes, R. W., & Jones, D. M. (2008). Auditory distraction in semantic
memory: A process-based approach☆. Journal of Memory and Language, 58(3),
682-700. doi: 10.1016/j.jml.2007.05.002
Mayhorn, C. B., Lanzolla, V. R., Wogalter, M. S., & Watson, A. M. (2005). Personal
Digital Assistants (PDAs) as Medication Reminding Tools: Exploring Age
Differences in Usability.
83
Mikkonen, M., Varyrynen, S., Ikonen, V., & Heikkila, M. O. (2002). User and Concept
Studies as Tools in Developing Mobile Communication Services for the Elderly.
Personal and Ubiquitous Computing, 6(2), 113-124. doi: 10.1007/s007790200010
Mizobuchi, S., Chignell, M., & Newton, D. (2005). Mobile text entry: relationship
between walking speed and text input task difficulty. Paper presented at the
Proceedings of the 7th international conference on Human computer interaction
with mobile devices & services, Salzburg, Austria.
Narayan, K., Boyle, J. P., Geiss, L. S., Saaddine, J. B., & Thompson, T. J. (2006). Impact
of Recent Increase in Incidence on Future Diabetes Burden: US, 2005-2050.
Diabetes Care.
Nayebi, F., Desharnais, J. M., & Abran, A. (2012, April 29 2012-May 2 2012). The state
of the art of mobile application usability evaluation. Paper presented at the 25th
IEEE Canadian Conference on Electrical & Computer Engineering (CCECE).
Nielsen, J. (2012). Usability 101: Introduction to Usability. 2014, from
http://www.nngroup.com/articles/usability-101-introduction-to-usability/
Noy, C. (2008). Sampling Knowledge: The Hermeneutics of Snowball Sampling in
Qualitative Research. International Journal of Social Research Methodology,
11(4), 327-344. doi: 10.1080/13645570701401305
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load
measurement as a means to advance cognitive load theory. Educational
psychologist, 38(1), 63-71.
Park, S., Oh, D., & Lee, B. (2011). Analyzing User Satisfaction Factors for Instant
Messenger-Based Mobile SNS. In J. Park, L. Yang, & C. Lee (Eds.), Future
Information Technology (Vol. 185, pp. 280-287): Springer Berlin Heidelberg.
Pascoe, J., Ryan, N., & Morse, D. (2000). Using while moving: HCI issues in fieldwork
environments. ACM Transactions on Computer-Human Interaction, 7(3), 417437. doi: 10.1145/355324.355329
Patrick, K., Griswold, W. G., Raab, F., & Intille, S. S. (2008). Health and the mobile
phone. Am J Prev Med, 35(2), 177-181. doi: 10.1016/j.amepre.2008.05.001
84
Perfetti, C., & Landesman, L. (2001). Eight is not enough. User Interface Engineering.
Preuveneers, D., & Berbers, Y. (2008). Mobile phones assisting with health self-care: a
diabetes case study. Paper presented at the 10th international conference on
Human computer interaction with mobile devices and services, Amsterdam.
Raji, M. A., Tang, R. A., Heyn, P. C., Kuo, Y. F., Owen, S. V., Singh, S., & Ottenbacher,
K. J. (2005). Screening for cognitive impairment in older adults attending an eye
clinic. J Natl Med Assoc, 97(6), 808-814.
Ryan, C., & Gonsalves, A. (2005). The effect of context and application type on mobile
usability: an empirical study. Paper presented at the Twenty-eighth Australasian
conference on Computer Science, Australia.
Ryan, N. S., Pascoe, J., & Morse, D. R. (1998). Enhanced Reality Fieldwork: the
Context-aware Archaeological Assistant. Paper presented at the Computer
Applications in Archaeology.
Shackel, B. (2009). Usability – Context, framework, definition, design and evaluation.
Interacting with Computers, 21(5-6), 339-346. doi: 10.1016/j.intcom.2009.04.007
Shneiderman, B., Plaisant, C., Cohen, M., & Jacobs, S. (2009). Designing the User
Interface: Strategies for Effective Human-Computer Interaction (5th ed.):
Prentice Hall.
Sinclair, A. J., Girling, A. J., & Bayer, A. J. (2000). Cognitive dysfunction in older
subjects with diabetes mellitus: impact on diabetes self-management and use of
care services. Diabetes Research and Clinical Practice, 50(3), 203-212. doi:
10.1016/s0168-8227(00)00195-9
Smith, D. L., Chang, J., Cohen, D., Foley, J., & Glassco, R. (2005). A simulation
approach for evaluating the relative safety impact of driver distraction during
secondary tasks. Paper presented at the 12th World Congress on Intelligent
Transport Systems.
Sorqvist, P. (2010). The role of working memory capacity in auditory distraction: a
review. Noise Health, 12(49), 217-224. doi: 10.4103/1463-1741.70500
85
Steggell, C. D., Hooker, K., Bowman, S., Brandt, J., & Lee, M. (2007). Acceptance of
gerotechnologies by older women in rural areas. Paper presented at the
Proceedings of the 2nd International Conference on Technology and Aging.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive
Science, 12(2), 257-285. doi: 10.1016/0364-0213(88)90023-7
Thearle, M., & Brillantes, A. M. B. (2005). Unique characteristics of the geriatric
diabetic population and the role for therapeutic strategies that enhance glucagonlike peptide-1 activity. Current Opinion in Clinical Nutrition & Metabolic Care,
8(1), 9-16.
Tsaprounis, T., Touliou, K., Kalogirou, K., Agnantis, K., & Bekiaris, E. (2012). Mobile
Applications for Independent Living of Isolated Elderly. Paper presented at the
Second International Conference on Mobile Services, Resources, and Users.
Tsiaousis, A. S., & Giaglis, G. M. (2008). Evaluating the Effects of the Environmental
Context-of-Use on Mobile Website Usability. Paper presented at the Proceedings
of the 2008 7th International Conference on Mobile Business.
Tsiaousis, A. S., & Giaglis, G. M. (2010). An Empirical Assessment of Environmental
Factors that Influence the Usability of a Mobile Website. Paper presented at the
9th International Conference on Mobile Business.
Tun, P. A., Perlmuter, L. C., Russo, P., & Nathan, D. M. (1987). Memory self-assessment
and performance in aged diabetics and non-diabetics. Exp Aging Res, 13(3), 151157. doi: 10.1080/03610738708259317
Vincent, G. K., & Velkoff, V. A. (2010). The next four decades: The older population in
the United States: 2010 to 2050: US Department of Commerce, Economics and
Statistics Administration, US Census Bureau.
Vuolle, M., Aula, A., Kulju, M., Vainio, T., & Wigelius, H. (2008). Identifying Usability
and Productivity Dimensions for Measuring the Success of Mobile Business
Services. Advances in Human-Computer Interaction, 2008, 1-9. doi:
10.1155/2008/680159
Waite, M., Martin, C., Curtis, S., & Nugrahani, Y. (2013). Mobile phone applications and
type 1 diabetes: An approach to explore usability issues and the potential for
enhanced self-management. Diabetes & Primary Care, 15(1).
86
Welschen, L. M. C., Bloemendal, E., Nijpels, G., Dekker, J. M., Heine, R. J., Stalman,
W. A. B., & Bouter, L. M. (2005). Self-Monitoring of Blood Glucose in Patients
With Type 2 Diabetes Who Are Not Using Insulin: A systematic review. Diabetes
Care, 28(6), 1510-1517. doi: 10.2337/diacare.28.6.1510
Werner, C. A. (2011). The older population: 2010. US Department of Commerce,
Economics and Statistics Administration, US Census Bureau Retrieved from
https://www.census.gov/prod/cen2010/briefs/c2010br-09.pdf.
Whitlock, L. A., & McLaughlin, A. C. (2012). Identifying Usability Problems of Blood
Glucose Tracking Apps for Older Adult Users. Proceedings of the Human
Factors and Ergonomics Society Annual Meeting, 56(1), 115-119. doi:
10.1177/1071181312561001