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Factors Influencing Motivation and Engagement in mHealth Amongst People with Sickle-Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements (Preprint)

Authors:
  • University of Tromsø - The Arctic University of Norway

Abstract and Figures

BACKGROUND Sickle-Cell Disease (SCD) is a hematological genetic disease affecting over 25 million people worldwide. The clinical manifestations of SCD are related to hemolytic anemia and vaso-occlusion, which lead to acute and chronic pain symptoms and organ infarction. With recent advances in childhood care, high-income countries have seen SCD drift from a disease of early childhood mortality to a neglected chronic disease of adulthood. In particular, coordinated, preventive and comprehensive care for adults with SCD are largely under-resourced. Consequently, patients are left to self-manage. Mobile health (mHealth) apps for chronic diseases’ self-management are now flooding app stores. However, evidence remains unclear about the effectiveness of mHealth apps and literature indicates low user engagement and poor adoption rates. Finally, few apps have been developed for SCD patients and none encompass their numerous and complex self-management needs. OBJECTIVE This study aims at identifying factors that may influence the long-term engagement and user adoption of mHealth among the particularly isolated community of adults SCD patients living in low-prevalence, high-income countries. METHODS Semi-structured interviews were conducted. Interviews were audiotaped, transcribed verbatim and analyzed using thematic analysis. Analysis was informed by Braun and Clarke framework and mapped to the COM-B model (capability, opportunity, motivation, and behavior). Interview results were translated into high level functional requirements (FR) and non-functional requirements (NFR) to guide the development of future mHealth interventions. RESULTS 6 males and 4 females were interviewed. Patients were aged between 21 and 55 years old. 30 FR and 31 NFR were extracted from the thematic analysis. The majority of participants (8/10) was concerned about increasing their physical capabilities through the automated regulation of their blood parameters and by becoming able to stop pain symptoms quickly. Regarding the psychological capability aspects, all interviewees desired to receive trustworthy feedback on their self-care management practices. About their physical opportunities, most of respondents (7/10) reflected a strong desire to receive alerts when they would reach their own physiological limitations (i.e. during physical activity). Concerning social opportunity, most of respondents (9/10) reported wanting to learn about the self-care practices of other patients. Relating to motivational aspects, many interviewees (6/10) stressed their need to learn how to avoid the symptoms and underlined their desire to live a normal life. Finally, NFRs included inconspicuousness and customizability of user experience, automatic data collection, data shareability and data privacy. CONCLUSIONS Our findings suggest that motivation and engagement with mHealth technologies among SCD patients living in low-prevalence, high-income countries could be increased by providing features that clearly benefits them. Self-management support and self-care decision aid are patients’ major demands. Since SCD self-management requires a high cognitive load due to complex and multiple components self-care practices, natural user interfaces such as mixed reality or speech recognition should be explored. Some of the required technologies already exist but must be integrated, adapted or improved to meet SCD patients’ specific needs.
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JMIR Preprints Issom et al
Factors Influencing Motivation and Engagement in
mHealth Amongst People with Sickle-Cell Disease in
Low-Prevalence, High-Income Countries: Qualitative
Exploration of Patient Requirements
David-Zacharie Issom, André Henriksen, Ashenafi Zebene Woldaregay, Jessica
Rochat, Christian Lovis, Gunnar Hartvigsen
Submitted to: JMIR Human Factors
on: May 04, 2019
Disclaimer: © The authors. All rights reserved. This is a privileged document currently under peer-review/community
review. Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for
review purposes only. While the final peer-reviewed paper may be licensed under a CC BY license on publication, at this
stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
Table of Contents
Original Manuscript ....................................................................................................................................................................... 5
Supplementary Files ..................................................................................................................................................................... 39
Multimedia Appendixes ................................................................................................................................................................. 40
Multimedia Appendix 1
.................................................................................................................................................................. 40
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
Factors Influencing Motivation and Engagement in mHealth Amongst
People with Sickle-Cell Disease in Low-Prevalence, High-Income
Countries: Qualitative Exploration of Patient Requirements
David-Zacharie IssomBSc, MSc, ; André HenriksenBSc, MSc, ; Ashenafi Zebene WoldaregayBSc, MSc, ; Jessica
RochatBSc, MSc, ; Christian LovisMD, MPH, ; Gunnar HartvigsenBSc, MSc, PhD,
Corresponding Author:
David-Zacharie IssomBSc, MSc,
Phone: +41223722601
Email: david.issom@unige.ch
Abstract
Background: Sickle-Cell Disease (SCD) is a hematological genetic disease affecting over 25 million people worldwide. The
clinical manifestations of SCD are related to hemolytic anemia and vaso-occlusion, which lead to acute and chronic pain
symptoms and organ infarction. With recent advances in childhood care, high-income countries have seen SCD drift from a
disease of early childhood mortality to a neglected chronic disease of adulthood. In particular, coordinated, preventive and
comprehensive care for adults with SCD are largely under-resourced. Consequently, patients are left to self-manage. Mobile
health (mHealth) apps for chronic diseases’ self-management are now flooding app stores. However, evidence remains unclear
about the effectiveness of mHealth apps and literature indicates low user engagement and poor adoption rates. Finally, few apps
have been developed for SCD patients and none encompass their numerous and complex self-management needs.
Objective: This study aims at identifying factors that may influence the long-term engagement and user adoption of mHealth
among the particularly isolated community of adults SCD patients living in low-prevalence, high-income countries.
Methods: Semi-structured interviews were conducted. Interviews were audiotaped, transcribed verbatim and analyzed using
thematic analysis. Analysis was informed by Braun and Clarke framework and mapped to the COM-B model (capability,
opportunity, motivation, and behavior). Interview results were translated into high level functional requirements (FR) and non-
functional requirements (NFR) to guide the development of future mHealth interventions.
Results: 6 males and 4 females were interviewed. Patients were aged between 21 and 55 years old. 30 FR and 31 NFR were
extracted from the thematic analysis. The majority of participants (8/10) was concerned about increasing their physical
capabilities through the automated regulation of their blood parameters and by becoming able to stop pain symptoms quickly.
Regarding the psychological capability aspects, all interviewees desired to receive trustworthy feedback on their self-care
management practices. About their physical opportunities, most of respondents (7/10) reflected a strong desire to receive alerts
when they would reach their own physiological limitations (i.e. during physical activity). Concerning social opportunity, most of
respondents (9/10) reported wanting to learn about the self-care practices of other patients. Relating to motivational aspects,
many interviewees (6/10) stressed their need to learn how to avoid the symptoms and underlined their desire to live a normal life.
Finally, NFRs included inconspicuousness and customizability of user experience, automatic data collection, data shareability
and data privacy.
Conclusions: Our findings suggest that motivation and engagement with mHealth technologies among SCD patients living in
low-prevalence, high-income countries could be increased by providing features that clearly benefits them. Self-management
support and self-care decision aid are patients’ major demands. Since SCD self-management requires a high cognitive load due
to complex and multiple components self-care practices, natural user interfaces such as mixed reality or speech recognition
should be explored. Some of the required technologies already exist but must be integrated, adapted or improved to meet SCD
patients’ specific needs.
(JMIR Preprints 04/05/2019:14599)
DOI: https://doi.org/10.2196/preprints.14599
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JMIR Preprints Issom et al
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Original Manuscript
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JMIR Preprints Issom et al
Original Paper
Factors Influencing Motivation and Engagement in mHealth
Amongst People with Sickle-Cell Disease in Low-Prevalence,
High-Income Countries: Qualitative Exploration of Patient
Requirements
David-Zacharie ISSOM, MSc1,2, André HENRIKSEN, MSc3, Ashenafi Zebene WOLDAREGAY,
MSc4, Jessica ROCHAT, MSc1,2, Christian LOVIS, MD, MPH1,2, Gunnar HARTVIGSEN, MSc,
PhD4


!"#"$%&
'!"#"$%&&
Corresponding Author:
David-Zacharie Issom, BSc, MSc
Division of Medical Information Sciences
Geneva University Hospitals, Switzerland
Rue Gabrielle-Perret-Gentil 4
1205 Geneva, Switzerland
Phone: +41 22 379 08 16
E-mail: david.issom@unige.ch
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
Abstract
Background: Sickle-Cell Disease (SCD) is a hematological genetic disease affecting over 25 million
people worldwide. The clinical manifestations of SCD are related to hemolytic anemia and vaso-
occlusion, which leads to acute and chronic pain symptoms and organ infarction. With recent
advances in childhood care, high-income countries have seen SCD drift from a disease of early
childhood mortality to a neglected chronic disease of adulthood. In particular, coordinated,
preventive, and comprehensive care for adults with SCD are largely under-resourced. Consequently,
patients are left to self-manage. Mobile health (mHealth) apps for chronic disease self-management
are now flooding app stores. However, evidence remains unclear about the effectiveness of mHealth
apps, and the literature indicates low user engagement and poor adoption rates. Finally, few apps
have been developed for SCD patients and none encompasses their numerous and complex self-care
management needs.
Objective: This study aims at identifying factors that may influence the long-term engagement and
user adoption of mHealth among the particularly isolated community of adult SCD patients living in
low-prevalence, high-income countries.
Methods: Semi-structured interviews were conducted. Interviews were audiotaped, transcribed
verbatim, and analyzed using thematic analysis. Analysis was informed by Braun and Clarke
framework and mapped to the COM-B model (capability, opportunity, motivation, and behavior).
Interview results were classified into high level functional requirements (FR) and non-functional
requirements (NFR) to guide the development of future mHealth interventions.
Results: Six males and four females were interviewed. Patients were aged between 21 and 55 years
old. We extracted 30 FR and 31 NFR from the thematic analysis. The majority of participants (8/10)
were concerned about increasing their physical capabilities through the automated regulation of their
blood parameters and by being able to stop pain symptoms quickly. Regarding the psychological
capability aspects, all interviewees desired to receive trustworthy feedback on their self-care
management practices. About their physical opportunities, most of respondents (7/10) reflected a
strong desire to receive alerts when they would reach their own physiological limitations (i.e. during
physical activity). Concerning social opportunity, most of respondents (9/10) reported wanting to
learn about the self-care practices of other patients. Relating to motivational aspects, many
interviewees (6/10) stressed their need to learn how to avoid the symptoms and underlined their
desire to live a normal life. Finally, NFRs included inconspicuousness and customizability of user
experience, automatic data collection, data shareability, and data privacy.
Conclusions: Our findings suggest that motivation and engagement with mHealth technologies
among SCD patients living in low-prevalence high-income countries could be increased by providing
features that clearly benefits them. Self-management support and self-care decision aid are patients’
major demands. Since SCD self-management requires a high cognitive load due to complex and
multiple component daily practices, pervasive health technologies such as wearable sensors,
implantable devices or inconspicuous conversational user interfaces should be explored to ease their
load. Some of the required technologies already exist but must be integrated, bundled, adapted or
improved to meet SCD patients’ specific needs.
Keywords: mHealth; wearables; self-management; sickle-cell disease; user engagement; adoption;
motivation; natural user interfaces; persuasive technologies
Introduction
A tsunami of mHealth apps for chronic disease self-management
Mobile health (mHealth) apps are flooding the app stores, with 200 new apps each day [1]. Many
can significantly improving health outcomes [2,3] by supporting people with diverse medical
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conditions [4]. However, little is known about app usage frequency and long-term engagement
amongst chronic patients [5,6]. Indeed, Robbins et al. [7] underlined that people who would most
benefit from such apps under-use them. To promote mHealth usage, app developers need to
comprehend what could motivate patients engagement [8]. The Theory of Reasoned Action [9,10]
demonstrates that the likelihood to engage in a specific behavior is a function of the motivation to
perform it. Authors such as Coa et al. [11] confirmed that calculating baseline motivation levels
could predict app retention rates. To influence people’s motivation through persuasion rather than
coercion, the field of persuasive technology offers novel user-centered approaches (i.e. co-creation)
[12–18].
The case of Sickle-Cell Disease, one of the world’s most neglected
chronic diseases
In this paper, we focus on Sickle-Cell disease (SCD). SCD is the most common monogenic blood
disorder in the world. Studies approximate 400’000 neonates per year [19,20] and between 25 and
100 million patients worldwide living with the disease [21,22]. In this study, we specifically target
populations from low-prevalence areas of high-income countries. In these settings, the disease is
under-resourced, research-derived evidence is lacking, and patients are particularly isolated [23].
Yet, SCD remains a serious illness. Hemolytic anemia and vaso-occlusive pain crises (VOCs) are the
hallmarks [24]. Patients may suffer severe and potentially lethal complications [25]. Hydroxyurea,
the preferred disease modifying treatment, is under-utilized and not effective for every patient [26].
Furthermore, the only curative option, bone marrow transplant, is largely inaccessible [27].
However, public health interventions such as regional screening programs, preventive care,
coordinated care and comprehensive care plans have been introduced in the major regions of high-
prevalence, high-income countries [28]. These interventions drastically reduced early childhood
mortality and made SCD shift to a chronic disease of adulthood [29].
Nevertheless, such programs have not been implemented widely and remain virtually absent in most
low-prevalence, high-income countries [20,30]. In addition, trained physicians are lacking and
access to specialized healthcare is suboptimal [31–33]. Indeed, SCD patients are particularly prone
to be confronted to stigmatization, to suffer unequal treatment, and to experience healthcare injustice
(i.e. perception as drug seekers) [33–35]. This often leads to mistrust between patients and
healthcare providers [36,37]. As several studies demonstrated [38–44], when patients arrive in
emergency departments, the lack of objective hematological findings and little awareness of them
makes healthcare providers suspicious of the veracity of a VOC. This distrust makes the SCD
community infamously difficult to recruit in research initiatives, hard to engage in interactions with
medical providers and lowers adherence to medical recommendations [36,45–48].
Consequently, most patients are left to self-manage, rely on poor quality healthcare, and report low
levels of Quality of Life [49,50].
Persuasive mHealth interventions to support an under-supported
population
To make matters worse, as several studies demonstrated [51,52], self-care management is
challenging for people with SCD. Indeed, managing the numerous potential precipitating factors of
VOCs requires high levels of self-efficacy [52–55]. Well-known triggers [56] include inadequate
diet, stress (i.e. exertional, oxidative, and psychological), infections, inflammations, acidosis,
dehydration, fatigue, chronic hemolysis, hypoxia, smoke inhalation, alcohol intoxication, pregnancy,
and environmental factors (i.e. altitude, pollution, extremes of temperature, climate, and wind speed).
In consequence, SCD self-care management practices requires patients to pay special attention to
everything. In an usual day, extreme temperatures changes, bad weather, high altitudes should be
avoided, eating behaviors should be optimized and hydration regular, exercise should be moderate
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and not exhausting, rest should be sufficient, prescribed drugs taken, and stress managed and
avoided as much as possible [57–60]. Lastly, as in all diseases, socio-economic factors such as
education levels, occupation, or income play an important role in empowering patients [61,62].
Mobile health apps, because of their relatively low cost and wide reach, could offer a potential route
to support patients’ numerous self-management tasks [63]. Little work has been done to design
tailored mHealth interventions for the comprehensive self-management needs of people with SCD
patients [64,65]. Today, most existing apps and research focuses on medication adherence [66].
Consequently, solutions encompassing the multiple-component of SCD self-management are absent.
However, Shah et al. [67] suggested that people with SCD could be interested in such tools.
However, as with other chronic diseases, little is known about people with SCD’s mHealth apps
adoption and long-term engagement.
This paper is the last component of a study from which preliminary results have already been
published [68]. This prior publication was the first to elaborate on mHealth long-term engagement
among SCD patients. The authors explored common motivational patterns for mHealth use between
people with SCD, diabetes, and “healthy” people.
This current paper focuses on adults with SCD living in low-prevalence areas of high-income
countries. It ambitions to assess patients’ requirements in terms of value adding digital health tools
and aims at guiding the development of future mHealth interventions that people with SCD would
want to use.
Methods
Inclusion criteria
To be part of the study, applicants had to be diagnosed with SCD or be the caregiver of a person with
SCD . Participants had to be at least 18 years old and able to understand French or English. People
who had been cured (i.e. bone marrow transplantation) were excluded.
Recruitment
The sample for this study was a convenience sample from Switzerland and
Norway, two very low-prevalence, high-income countries. Indeed, compared to an
average in the European Union of 2.5 cases in 10’000 people [69], these two
countries have less than around 1-4 cases per 100’000 people [70–72] and
totalize approximately 100 adult SCD patients. We recruited participants through
the national patient associations’ online support groups. 64 individuals with SCD
were invited to participate. One week after the initial invitation, non-responders
were sent a reminder.
Instrument
The first author (DZI), an expert-patient, conducted most semi-structured interviews using the guide
presented in Table 1. The choice of an “insider” interviewer had been done in order to build a trusted,
warm, and open rapport with the interviewees and to maximize the reception of honest and open
responses. Additionally, the interviewer was already familiar with some participants. JR conducted
the test interviews. Both interviewers have years-long interview experiences. Interviews were
conducted in locations convenient and comfortable for the participants (i.e. university, private
address).
The interview guide was developed previously as a joint effort by all co-authors [68]. It was divided
into five themes: 1) Preliminary questions, 2) Goals, expectations and attitudes, 3) eHealth Literacy
and Data Integration 4) Wearables and sensors, and 5) Data sharing. Data saturation was reached and
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determined by no new information emerging after conducting interviews with all participants.
Interviews were audiotaped and lasted around 60 minutes. Questions were open-ended and
discussions conducted flexibly. Questions were ignored or adapted relatively to the context.
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"(#)$*+,$
Preliminary questions
What is most important for you in your life?
If you had access to a new health technology, which purpose or features should it have?
Goals, expectations and attitudes
What motivates/demotivates you to access online health information?
What are the most difficult things about your self-care?
What could help you become more autonomous with your self-care?
eHealth Literacy and Data Integration
Have you ever used an app that collects health data?
What factors would demotivate you from using such an app?
Wearables and sensors
What indication would you expect from wearable sensors for health self-monitoring?
What is the most valuable indication you would want from devices collecting your data?
Data Sharing
What would you share with other patients, caregivers and doctors and why?
What feedback should be provided by the system?
Data Analysis
Firstly, transcriptions of the resulting interviews were de-identified. Secondly, an inductive thematic
analysis was conducted using the guidelines and checklist from Braun et al. [73]. Codes were
extracted by reading the interviews recursively. Thirdly, emerging patterns were clustered together
and checked for variability and consistency. Themes were interpreted by reading the codes back-and-
forth. Once saturation reached, themes were mapped across the Capability, Opportunity, Motivation
and Behavior model (COM-B), hub of the Behavior Change Wheel (BCW) framework [74]. The
BCW is a fairly recent theory-driven approach that helps to design health interventions for
preventive care [74]. With the specific reading grid it provides, it allows us to identify barriers and
enablers of engagement in any intervention and in our particular case, to identify factors that, if
implemented together, may elicit the long-term engagement and user adoption of mHealth apps [75–
77]. Transcripts were organized and coded using ATLAS.ti software version 8.3.20.0 (ATLAS.ti
Scientific Software Development GmBH).
Finally, in order to make it easier for software developers to comprehend, themes were categorized
into functional requirements (FRs) and non-functional requirements (NFR) [78]. In software
engineering, FRs are descriptions of the specific behaviors and functions of an information system.
They make explicit the features a software should offer to the end-user. In other words it describes
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what the system should do [79]. NFR are descriptions of how the system should operate, which is
not linked to the functionalities. To put simply, it defines how the system should be (e.g. responsive,
intuitive, fast, secure).
Ethics and study approvals
The Norwegian Regional Committees for Medical and Health Research Ethics (REC) and the
Swiss Regional Research Ethics Committee (CCER) approved the study protocol and interview
questions. As required by committees, all participants gave informed consent before the
interview and responses were anonymized.
Results
Participant characteristics
A total of 11 participants (7 males and 4 females) agreed to join the study. Eight patients and three
caregivers took part in the study, but one patient withdrew because of a VOC. This led to a total of 7
patients participating and 3 caregivers. Patients were between 21 and 55 years old. Lastly, 7
participants were residing in Switzerland, 3 in Norway and 1 was partly residing in Congo and in
Switzerland. Table 2 presents the demographics of the population studied.
"(-($$
%
Respondents
Male 50% (n=5)
Age, years
Mean (SD)
Median
35.6 (9.41)
37
Country of residence
Switzerland
DRC
100%
10%
Interview results
This section shows extracted themes from the interview data. The themes were classified into 31 FR
and 30 NFR. We organized them with the COM-B framework and illustrated them with quotations
from interviewees. Table 3 presents some of the most frequent themes that appeared during
interviews. Each theme is a FR or an NFR and belongs to a COM-B system category. In the table,
themes are sorted by COM-B system attribute, type of requirement and then by number of quotes.
The complete list is available in Multimedia Appendix 1.
"( -)$,$$!)./
Requirement Quotes COM-B system Type
Prevent crises by avoiding symptoms 12 Automatic motivation Functional
Family / Social Community Support
(Shareability)
15 Automatic motivation Non-functional
Gain more control on disease through
daily self-care support
28 Physical Capability Functional
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Limit management 9 Physical Capability Functional
Importance of information
trustworthiness
17 Physical Opportunity Non-functional
Invisibility / inconspicuousness 9 Physical Opportunity Non-functional
Similarity with messaging apps 8 Physical Opportunity Non-Functional
Automatic reminders (Automatability) 5 Physical Opportunity Non-Functional
Simplicity 3 Physical Opportunity Non-Functional
Learn trigger factors 14 Psychological Capability Functional
Predict health outcomes 11 Psychological Capability Functional
Receive threshold alerts 10 Psychological Capability Functional
Feedback on self-care practices 17 Psychological Capability Non-Functional
Customizable 8 Reflective Motivation Non-Functional
Privacy 4 Reflective Motivation Non-Functional
Learn what other patients do 14 Social Opportunity Functional
Physical capability
All participants were concerned about not being able to better predict the onset of VOCs or avoid
chronic complications. Another point to consider is that many differentiated general daily self-
management skills (i.e. pain management) from preventive care (i.e. symptoms prevention). As one
patient said:
01$$$,$(,$
,-2$$,-34
567'89
In addition, a couple of interviewees said they would wish novel technologies that automatically
regulates their hematologic parameters (i.e. hemoglobin concentration, leucocyte adherence to
vascular endothelium) [80]. Accordingly, a participant proposed a very innovative solution:
0+($$(.,-4
568 :9
Psychological capability
The majority of participants desired to receive feedback about SCD self-care tasks. Most emphasized
the difficulty of managing their own limits. Some wished to be warned before exceeding their
physical limitations. Namely, an interviewee proposed:
03%$,$;$$
+$$$$$<-4
569
Importantly, most of participants suggested that such warnings should be detected with wearable
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
sensors, releasing their cognitive load. This can be illustrated by the following quote:
03S$,$$-<$,$=
,+$=,,$=-/$,,(-/+
$,-<$1+,---$1+,(
+$-4
568 :9
Social opportunity
For most participants, learning about other patients’ self-care practices was a very important concern.
A majority of participants said they would like to share their own experiences on digital platforms,
like one participant said:
03%,$$-%$$$-!$
,4-
5!, >9
Many participants stressed the importance of social support. Some stated that mHealth could help
them communicate their needs. As one participant suggested:
03(($(($
$,$-34
5!, '89
A majority of participants reported using social media for information sharing, communication or
entertainment. Only a minority used social media to get health information. Many participants
deplored the absence of mechanisms to easily access and control the quality of information. As one
participant said:
03$+$-$
-2$+$-=
-34
56 >9
Physical opportunity
For several interviewees, it was crucial to receive trustworthy information. Many suggested that
wearable sensors could support this. As one participant highlighted:
03$(-%
($?@(($,$
$,(!A6$,$<,$$($13 4
568 :9
Several participants stated that information overload would demotivate their long-term engagement,
precising that mHealth apps should be as discreet as possible. Furthermore, most participants
recognized the potential usefulness of notifications, but only if discrete and not disturbing. As one
participant stressed:
BC(*$,$,@=+D=+D1B
56>779
Notwithstanding, majority of participants preferred user interfaces that are simple to use and do not
require a high learning curve. Some participants believed messaging software were the best
inspiration because of their intuitive interface. As one caregiver said:
03%$,$.$,-%,
$,-%$$,-6$,+,,
$,$-34
5!, >9
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
Motivation
Reecve movaon
Importantly, a majority of participants were not using mHealth apps. One (1/10) participant reported
using Apple Health for menstruation management, hydration, and physical activity management.
Two (2/10) participants reported using Samsung Health for blood oxygen recording and physical
activity management. Another point to consider is that all participants expressed a lack of specificity
and adaptability to SCD particularities in the usual health apps (i.e. normal values for people with
SCD are diseased values for healthy people). As one participant stressed:
0"$,$(<,$!-$(
-=-34
56E 79
Privacy issues were also a fundamental concern for most interviewees. Many feared to lose control
on their data. As one participant said:
03,!;F(,(-34
56 >9
Finally, most participants preferred customizable information systems. As one interviewee said:
03($+$$-$-3 4
568G9
Automac movaon
The three main motivators identified were: 1) strengthen social support, 2) prevent VOC, and 3)
reduce the limitations to functioning and independent living. Regarding the first motivator,
participants wished to be able to enjoy their families and give back to their communities. As one
participant said:
H,$$$,($,$$-
1$$1$-H
56>77-9
Regarding the second motivator, all participants stated their desire to prevent the excruciating pain
crises. As one participant said:
03((,$$(($$$
--34
56'89
More importantly, most participants stressed the importance of living as normal a life as possible and
stay in good health. This can be illustrated by the following quote:
03($(+-.
$$($$,(34
568G9
Discussion
Principal findings
To the best of our knowledge, this is the first study to explore factors that could influence the long-
term engagement with mHealth interventions of adults with SCD living in low-prevalence, high-
income countries. As a result, interviewees described how mHealth could benefit their life and
detailed what could increase their long-term engagement and motivation towards mHealth apps
usage.
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Functional requirements in the prism of the COM-B framework
In order to maximize the chances of eliciting behavior change and engagement, as many as possible
patient requirements should be implemented. In other words, providing patients with information or
alerts is not sufficient to lead to behavior change. However, implementing simultaneously other
features such as therapeutic education (e.g. lessons, accompaniment), specifically designed wearable
devices could maximize engagement. In the following sections, we propose potential solutions and
summarize what needs to be done before being able to develop them.
Increasing Physical Capability: Regulate hematological parameters
Patients suggested how pioneering technologies such as blood regulating nanorobots could inspire
future pharmaceutical research or stimulate patient-led research initiatives [81]. By altering
hematological parameters, some innovative pharmaceutical compounds are already aligned with
patients’ suggestions [56,82]. For instance, recent clinical trials of crizanlizumab, an antibody acting
against endothelial adhesion, have shown to significantly reduce the frequency of VOCs [83].
However, this substance requires frequent intra-venous injections. Therefore, knowing the current
under-utilization of orally administrated therapies such as hydroxyurea, intra-venous therapies bring
a supplementary barrier.
In the meantime, non-pharmaceutical strategies based on information technologies could inform
patients on how to adapt their behaviors (i.e. dietary change) to alter blood parameters (i.e.
hemoglobin levels, oxygen levels, and inflammation) [84–87] while novel devices, inspired from
other clinical populations, could be created. This can be illustrated by what is happening in the
Diabetes Do It Yourself (DIY) community [88] (i.e. #WeAreNotWaiting and #DIYPS). Here,
impatient patients self-organized to hacked blood glucose monitoring system and insulin pump. They
proceed to create a system that can, after clever calculation, automatically inject the needed dose of
insulin. In comparison, one could foresee similar initiatives of DIY solutions supporting SCD self-
care practices. In particular, As a reminder, anemia and oxygen desaturation are common
complications amongst people with SCD. Since oxygen delivery by hemoglobin increases when the
amount of red blood cell and hemoglobin increases, one could imagine to create a closed-loop
system using a wearable hemoglobin meter or a blood oxygen meter. This could subsequently be
combined with an auto-injector filled with ultra-short term anemia reducing treatments [89] (e.g.
erythropoietin alpha, vitamin B12, oral folic acid, and Voxelotor [90] ).
Nonetheless, it is important to consider that a significant amount of work is required before such a
system could be created and made accurate or safe. Even if bypassing any approval from a health
authority (e.g. Federal Drugs Administration), implantable auto-injectors and specific bio-monitoring
devices would have to be engineered. Then adequate software would have to be created and be able
to analyze significant quantity of patient-generated data. Indeed, in order to be accurate and effective,
such algorithm should be trained on a high quantity of data. Afterwards, the data-driven algorithm
should be able to actuate injection of individualized doses of the adequate substances. All of this
requires a tremendous amount of skilled bio-engineering work. Another point to consider is the lower
socio-economic status of members of the SCD Community [91] and the low awareness of SCD
amongst the general population, especially in high-income, low-prevalence countries. This
mechanically leads to a scarcity of patient innovators and interested independent researchers. In
consequence, few people would be skilled to build such a specialized system.
Therefore, the SCD community could start with less invasive, less complex, but also less cognitively
unloading solutions. A simpler system could propose patients to ingest relevant drugs or dietary
supplements (e.g. anti-inflammatory, anti-oxidants), after a bio-sensor [92] (e.g. blood oxygen,
inflammation) detects a threshold. In addition, a mHealth app could send an alert. On one hand,
medication adherence would then become an issue to overcome. Additionally, if taken, the effect of
the substance would be delayed compared to a direct subcutaneous injection.
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To summarize, the most effective solution using today’s technology would be a closed loop system
with an auto-injector and smart algorithm, functioning without any patient intervention, but, there is
still a long and challenging way to go before such a system could be built.
Increasing Physical Capability: Stop pain fast
Quick pain relief was a very important concern for most interviewees. In current VOCs self-
management, pain crises are treated at home with oral painkillers [93,94]. When oral drugs are no
longer sufficient or when complications surge, patients need to visit emergency departments to
receive acute care [38]. However, aware of the several challenges they would face in emergency
departments (i.e. long waiting times, recurrent stigma, unrelieved pain, prolonged hospitalizations),
many patients choose to postpone admission until pain becomes totally unbearable [95,96].
These challenges could partly explain why interviewees were highly motivated by mHealth
interventions that could help them relieve their pain as fast as possible. To this end, novel sensors and
software measuring pain levels through physiological signs or electrical signals [97] could be
combined with implantable pumps for intrathecal opioid therapy or subcutaneous injections [98].
However, in order to release cognitive load and avoid patients to calculate themselves how much
they should inject, smart, accurate, safe and individualized algorithms would have to be developed.
In the USA, one mHealth app helps children and adolescents with SCD to inform family, physicians
or friends about their health status [99]. Recently, innovative digital health interventions have been
deployed to facilitate emergency care process [100]. We could also imagine apps that allow sufferers
to support and motivate each other or to come together to share their experience of treatment
efficacies [101,102]. Existing mHealth apps for pain management could be adapted to SCD [103].
Finally, Virtual Reality could be used has a new option for pain relief through patient distraction
[104].
Increasing Psychological Capability: Quality feedback on self-care
practices
The complexity of SCD self-care tasks demands various skills (e.g. high cognitive capabilities, good
disease-specific knowledge) [105]. Literature has shown that only one percent of SCD patients were
able to master them [106]. Therefore, it is easy to understand why most interviewees desired to
receive feedback on their self-care practices.
For instance, a release on the cognitive load could be partially solved by using natural user interface
(e.g. text, voice, mixed reality, augmented reality), gamification items [107] and simple data
visualization [108–111] when providing patients with targeted information. The technology exists
but needs to be integrated, adapted, bundled and improved [112–114]. Inspiration could already be
taken from existing systems for people with diabetes [115,116].
Furthermore, studies have shown that behaviors change techniques for self-management such as
health coaching could improve patient-important outcomes like self-efficacy, self-management or
medication adherence by 80% [117–120]. For instance, collecting physiological data and behavioral
data such as eating behaviors and oxygen levels then automatically reporting them to a SCD expert,
could allow the latter to provide advices directly inside the mHealth app. This would not require to
overcome many barriers since data could already be collected through fitness and wellness apps and
smartwatches. However, the biggest challenge reside in the development of smart algorithms and
methods to allow the automated interpretation of such individualized and heterogenous data[121]. In
the meantime, coaching and high quality electronic patient decision aids could be created to structure
information and help make patients make informed decisions [122–124].
Regarding disease-specific knowledge, studies have reported that many patients did not follow
medical recommendations [45], but may still search for health information online. When the quality
of information is poor, disparities in health information accessibility are created [125,126]. Frost et
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al. [127] have already discussed the promises of online health information for people with SCD, and
Breakey et al. [128] argued that information was not always adequate nor of good quality.
Consequently, the use of artificial intelligence [129] with evaluation criteria such as Health On the
Net code [130] or the DISCERN [131] framework could facilitate provision of quality material to
patients. Finally, content constructed with the help of SCD patients and using consumer health
vocabulary, simple patient language and thesauri could improve the communication of health
information and improve adequacy [132,133].
Increasing Physical Opportunity: Receive alerts when reaching own
physical limits
Already struggling with normal life challenges, several interviewees strongly desired help to manage
the potential triggers of VOC. However, objective laboratory, clinical, hematological, biochemical,
and rheological data are not easy to self-monitor [41–43,134–136]. However, existing non-invasive
sport-related wearable devices technologies could help to monitor some markers of hematological
parameters [97]. For instance, sport watches with pulse oximeters [137], connected bottles,
oxidative stress monitor, and pH meters [138,139] could be used. To this end, smart algorithms
could be developed to provide patients with individualized feedback.
Studies have shown that poor physical functioning was frequent amongst people with SCD, making
their participation in sports difficult [140,141]. mHealth interventions specifically tailored for
physical activity support could be developed to assist people with SCD. Accurate sensors could be
integrated during the practice of physical activity (i.e. exhaustion) or after exercise (i.e. recovery)
[142,143].
Increase Social Opportunity: Learn what other patients do
One of the most reported motivational factors was the desire to learn from other patients. This
consideration is coherent with the stigma and isolation faced by SCD patients living in low-endemic
areas [144]. This concern is also consistent with the general lack of educational interventions [145]
and the scarcity of specialized health care providers [146]. It is well known that chronic patients
hold non-negligible experiential knowledge (e.g. effective dietary supplements and where to find
them, tips) and often share it on social networks [147–149]. However, the knowledge available on
these platforms is difficult to extract, the quality is difficult to guarantee, and the information often
unstructured, hence difficult to mine [150].
Well-funded and organized social networks such as Patients Like Me or Diabetes online communities
(e.g. TuDiabetes, glu, and Diabetes Daily) could serve as inspiration to structure patients’
experiential knowledge [151]. However, in comparison to the total SCD population, few SCD
patients use online networks. Studies from Ragnedda et al. [152] have demonstrated that socially
disadvantaged groups (e.g. gender, ethnicity, and disability) tended to use the internet less than more
advantaged groups. Conversely, a study from Issom et al. [153] suggests that SCD patients would be
willing to use such online platforms if the quality of information is ensured and if it is specifically
tailored for people with SCD.
Consequently, SCD-specific online communities such as OneSCDVoice [154] could be turned into
persuasive Q&A Social Networks ensuring the medical accuracy of patients’ shared experiences
[155]. In addition, artificial intelligence techniques could be used to help detect bad quality
information [156]. Another issue is the multitude of SCD online communities (e.g. hundreds of
Facebook groups, various websites). This spreads the information, fragments messages and
complicates access to experiential knowledge. Additionally, since people with SCD and their
caregivers have lower educational levels [62], their organizational skills and digital literacy are
reduced, subsequently hindering their access to online health information.
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Main motivations: Live a normal life and learn to avoid symptoms
A majority of participants stated that their highest motivation would be to be asymptomatic. When
bone marrow transplant is not possible, or when hydroxyurea does not significantly reduce
symptoms frequency, alternative treatment options such as self-management interventions, chronic
transfusions or red blood cells exchange could be proposed [157]. Transfusions have shown to
significantly reduce the frequency of VOCs but require high quantities of rare phenotype blood. In
addition, phenotype matching is difficult in high-income, low-prevalence areas. Self-management
interventions require high level of self-efficacy. Electronic patient decisions aids (ePtDAs) could be
helpful. Indeed, ePtDAs have proven effective in engaging patients in self-care processes and helping
patients choose alternative treatment options [158]. To date, there are no such tools for SCD.
However, a study form Kulandaivelu et al. [159] showed that people with SCD asked for such help.
In order to push for the development of such systems, patients would require to be more aware of
novel treatment options, innovative solutions or self-management support possibilities. As a result,
they could better organize to raise awareness amongst potential payers (e.g. philanthropists and
pharmaceutical companies) who could help finance the development of such advanced solutions.
However, this remains a challenge for the socio-economically disadvantaged majority of people with
SCD [62].
Non-functional requirements
This section discusses the most frequently NFRs reported by the interviewees. Patient-important
NFRs are crucial when developing software [160]. A mismatch between them and the final product
could lead to low adoption rates and discourage app usage.
Automatability
Participants clearly preferred automatic health data acquisition rather than manual data entry. To
date, there is no SCD specific mHealth intervention using automated data capture. However, data
from Electronic Health Records (EHRs), smartphone sensors, or wearable devices [161] could be
automatically collected in future mHealth interventions for people with SCD [162].
Invisibility
Invisibility or inconspicuousness is the ability of a system not to attract attention. As many
interviewees reported and consistently with existing literature [163], mHealth interventions are more
likely to be adopted if they clearly reduce the inconvenience and burden of self-management tasks,
while being discreet. Indeed, bulky and inelegant wearable devices could indicate to other people
that wearer has a disease. Implantable devices, discreet patches, wearables with subtle design or
integrable into daily life objects (e.g.. contact lenses, implants, and bottles) could be preferred [164–
167].
Similarity with messaging apps
More than half of respondents called for a mHealth intervention that provides a similar user
experience than the apps they are using the most (i.e. messaging apps). Only few studies have
explored conversational designs for mHealth interventions. However, some conversational user
interfaces have been successfully used to reduce obesity or as support for mental health interventions
[168,169]. Such systems have already proven to encourage behavior changes and have high levels
of acceptability. This could encourage similar designs in future studies for SCD patients.
Shareability
In the context of online health information, shareability [170] is the capacity of a patient-generated
health data to be shared with third parties. By allowing patients to share their knowledge, mHealth
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apps could highlight valuable information that clinicians cannot offer [171] and ease social support
[172]. As for people with rare diseases, Q&A social networks [173] could be leveraged [174].
Privacy
In the mHealth context, privacy is the ability of a patient to seclude information about their medical
condition [175]. Being aware of the rarity of SCD in their countries, interviewees were particularly
attentive to this issue. Participants emphasized that control should be given on what they share and to
whom. Studies for other chronic diseases showed that privacy is a very important aspect [176].
Answering this concern by taking into account regulations (i.e. GDPR and HIPAA) when designing
mHealth interventions would be key to reduce poor adoption [177]. Here, existing compliant data
management platforms with dynamic consent management [178] or personal data cooperatives
[179] could be used. As well, novel de-identification approaches could help implement this patient-
important NFR [180].
Customizability
In order to maximize acceptability and inconspicuousness, most participants proposed to customize
the timing of delivery of push notifications. This finding is aligned with a study from Morrison et al.
[181] suggesting that notifications with tailored timing could enhance exposure to mHealth
interventions. However, several patients stressed their dislike of recurrent notifications (i.e. water
intake), saying it would remind them of their disease. This is aligned with a study from Bidargaddi et
al. [182] suggesting that notifications should be sent at mid-day or on week-ends. The truth probably
lies in between, where a system could propose patients to choose from a range of predefined settings.
Finally, as the interviews demonstrated, patients did not think that existing health apps where
adapted to their needs. As a result, any new digital health solution for people with SCD should
include patients in every phase of the development and focus on SCD particularities. Lastly, the
developed system should be strongly marketed as a patient-centered solution.
Lessons learned
Our findings highlighted participants’ very clear expectations towards mHealth apps. Respondents
seemed undeniably motivated to use “an invisible technology” that would accompany their self-care
practices (i.e. personalized feedback). Participants were very critical towards privacy issues and
information quality. Given the numerous and complex day-to-day self-care management tasks that
SCD patients face, and taking into account the isolation of living in low-prevalence, high-income
countries, it appears that, in order to get long-term engagement and adoption, mHealth apps must add
clear value and be particularly tailored to patients’ needs.
From requirements to successful implementation
For such a mHealth solution to have an impact once implemented, quality and perceived value must
be distinguished. However, the Digital Health world still lacks a standardized mechanism for health
app quality evaluation and certification [183]. There is no consensus for guidelines, nor clear criteria
to help recognize what a quality mHealth app is. For instance, people with Diabetes, with more than
300 apps on the Google Play Store, can easily struggle to find what apps they should download.
Although, some app curation websites [184] help end-users to find health apps ranked by quality.
However, the analytic methods vary for each website. For instance in the UK, the NHS Digital and
National Institute for Health and Care Excellence have chosen clinical effectiveness, regulatory
approval, clinical safety, privacy and confidentiality, security, usability and accessibility,
interoperability, technical stability, and change management as criteria. Other websites could use
other criteria, for instance apps which there is published medical evidence.
As a result, app developers should spend energy to meet patient-important requirements and thrive to
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meet as many quality indicators as possible. This can be summarize by the following steps:
-Take a patient-centered approach:
oMake sure the app improve patients outcomes;
oBeing validated in terms of clinical outcomes.
-Offer a real solution to self-management problems faced by patients:
oMeet patients requirements;
oDo better than any alternative and cheaper;
-Be compatible with the existing healthcare information systems infrastructure:
oImplement interoperability standards by design;
oAnswer privacy and safety requirements;
Key points
Nevertheless, it is clear that there is no “invisible technology” yet to support SCD patients’ self-
care practices. However, the various technological pieces needed to build such an ubiquitous
system are largely available today. Nonetheless, they are scattered, not bundled nor adapted to the
specificity of SCD. The following steps summarize what needs to be done prior implementing the
key requirements:
Disease-modifying functionalities (i.e. pain relief, regulation of hematological variables)
to reduce poor adoption rates by providing patients with:
ocontinuous blood oxygen meters;
ospecific auto-injectors;
osmart software that includes algorithms able to manage and make sense of the big
datasets generated.
Targeted information (e.g. alerts when approaching limits, access to other patients’ self-
care practices) could influence long-term engagement by providing patients with:
oknowledge adapted to their health literacy levels;
ouser interface matching their digital literacy levels;
oinformation adapted to their disease-specific knowledge and if necessary offer
assistance to interpret the information.
Controlling data flows (e.g. shareability, privacy, quality information) could influence
patients’ motivation to start using apps by:
ostoring data on personal data cooperatives;
oimplementing existing interoperable standards;
ousing algorithms and hire content moderators (e.g. physicians, expert patients) to
monitor content creation and the quality of information.
Future plans
Given the complexity of SCD self-management, supporting patient-important self-care needs with
mHealth interventions will be challenging. However, such systems will be key to fill the gaps in
healthcare delivery service. Further work is needed to implement patient requirements. Prioritization
could be done using participatory approaches. Tools such as the APEASE criteria, a set of
benchmarks from the BCW framework, can be helpful to decide what content should be included and
what intervention delivery strategies should be used. In addition to proven added value with the
effective implementation of patient-important requirements, other non-functional requirements
include affordability, practicability, effectiveness and cost-effectiveness, acceptability, side-effects,
safety and equity.
In addition, DELPHI surveys [185], a technique consisting of seeking experts’ views to obtain a
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level of agreement by transforming opinion into group consensus, could be sent to expert-patients
[123,186].
Finally, motivational factors will have to be assessed in the long run to maintain high user
engagement levels. This could be done using tailored frameworks for mHealth engagement analysis
such as the AMUsED framework [187].
Strengths and limitations
The study has a number of strengths and limitations. Using the COM-B model to identify
motivational factors is a relatively recent approach in the field of mHealth. However, in this
example, the lens offered by the model was helpful to gain a full picture of patients’ motivations.
Additionally, this model allowed us to classify patient requirements into explicit categories and
helped us to discuss potentially useful technologies to meet patients’ unmet needs.
The age range of the sample size was large, consequently, younger patients may feel more
comfortable using digital health interventions than older ones. Additionally, our sample size
approximates 10% of the adults with SCD living in the selected low-prevalence, high-income
countries. Also, because of the notorious difficulty to enroll SCD patients in studies, we were
surprised by this relatively high response rate. The fact that the main interviewer was an expert-
patient may have eased enrollment and facilitated trust building but also added a bias.
Finally, the study results could be affected by the recruitment criteria. Since participants were
selected inside an active SCD community, it is possible that those who volunteered to be interviewed
were more in search for new coping solutions and had more positive views about the disease than
those who declined or did not replied.
Conclusions
Since interviewees were particularly explicit in what could benefit them, this study provides initial
insights on how to build mHealth apps that could engage particularly isolated SCD populations. The
use of qualitative methods enabled in-depth exploration of interviewees responses. As well, the BCW
and its hub, the COM-B model could be used as a robust framework to inform the development of
future persuasive technologies for people with SCD. As patients highlighted, future research should
focus on supporting their self-care decisions. Exploring the integration, adaption or improvement of
highly adopted mHealth interventions for other chronic diseases could be helpful.
Acknowledgements
This study would not have been possible without the support of the Swiss Sickle-Cell Association
and the Norwegian Sickle-Cell Association. The authors thanks Henna Martilla, Gerit Pfühl, Martin
Mikalsen and Keiichi Sato for their input in the earlier phases.
Authors contributions
DZI, AH, AZW, CHL and GH conceived the qualitative study. DZI conducted the interviews with the
support from JR. DZI wrote the manuscript and subsequent revisions were undertaken with the
support and input from all authors. DZI, AH, and AZW created the interview guide, with support
from all authors. DZI conducted the interviews. DZI coded the data and co-analyzed them with the
support of AZW and AH. All authors approved the final manuscript.
Conflicts of Interest
None declared.
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Abbreviations
BCW: Behavior Change Wheel
COM-B: Capability, Opportunity, Motivation, Behavior
ePtDA: Electronic Patient Decision Aid
SCD: Sickle-Cell Disease
VOC: Vaso-occlusive crisis
References
1. research2guidance - 325,000 mobile health apps available in 2017 – Android now the leading
mHealth platform [Internet]. [cited 2018 Jul 6]. Available from:
http://www.webcitation.org/76VKcYo9e
2. Wu Y, Yao X, Vespasiani G, Nicolucci A, Dong Y, Kwong J, Li L, Sun X, Tian H, Li S. Mobile
App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of
Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR
mHealth and uHealth 2017 Mar;5(3):e35. PMID:28292740
3. Kitsiou S, Paré G, Jaana M, Gerber B. Effectiveness of mHealth interventions for patients with
diabetes: An overview of systematic reviews. Li D, editor. PLOS ONE 2017
Mar;12(3):e0173160. [doi: 10.1371/journal.pone.0173160]
4. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, Patel V, Haines A. The Effectiveness
of Mobile-Health Technology-Based Health Behaviour Change or Disease Management
Interventions for Health Care Consumers: A Systematic Review. PLOS Medicine 2013 Jan
15;10(1):e1001362. [doi: 10.1371/journal.pmed.1001362]
5. Asimakopoulos S, Asimakopoulos G, Spillers F. Motivation and User Engagement in Fitness
Tracking: Heuristics for Mobile Healthcare Wearables. Informatics 2017 Jan;4(4):5. [doi:
10.3390/informatics4010005]
6. Thies K, Anderson D, Cramer B. Lack of Adoption of a Mobile App to Support Patient Self-
Management of Diabetes and Hypertension in a Federally Qualified Health Center: Interview
Analysis of Staff and Patients in a Failed Randomized Trial. JMIR Hum Factors [Internet] 2017
Oct 3 [cited 2019 Jan 31];4(4). PMID:28974481
7. Robbins R, Krebs P, Jagannathan R, Jean-Louis G, Duncan DT. Health App Use Among US
Mobile Phone Users: Analysis of Trends by Chronic Disease Status. JMIR Mhealth Uhealth
[Internet] 2017 Dec 19 [cited 2019 Jan 30];5(12). PMID:29258981
8. Lewis C. Irresistible Apps: Motivational Design Patterns for Apps, Games, and Web-based
Communities. Apress; 2014. ISBN:978-1-4302-6422-4
9. Madden TJ, Ellen PS, Ajzen I. A Comparison of the Theory of Planned Behavior and the
Theory of Reasoned Action. Pers Soc Psychol Bull 1992 Feb 1;18(1):3–9. [doi:
10.1177/0146167292181001]
10. Glanz K, Rimer BK, Viswanath K. Health Behavior and Health Education: Theory, Research,
and Practice. 4th Edition. San Francisco, CA: John Wiley & Sons; 2008. ISBN:978-0-7879-
9614-7
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
11. Coa K, Patrick H. Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text
Messaging Program. JMIR mHealth and uHealth 2016;4(3):e114. [doi: 10.2196/mhealth.5992]
12. Fogg BJ. Persuasive Technology: Using Computers to Change What We Think and Do.
Ubiquity [Internet] 2002 Dec [cited 2019 Feb 26];2002(December). [doi:
10.1145/764008.763957]
13. Hamari J, Koivisto J, Pakkanen T. Do Persuasive Technologies Persuade? - A Review of
Empirical Studies. In: Spagnolli A, Chittaro L, Gamberini L, editors. Persuasive Technology
Springer International Publishing; 2014. p. 118–136.
14. Orji R, Moffatt K. Persuasive technology for health and wellness: State-of-the-art and emerging
trends. Health Informatics J 2018 Mar 1;24(1):66–91. [doi: 10.1177/1460458216650979]
15. Eyles H, Jull A, Dobson R, Firestone R, Whittaker R, Te Morenga L, Goodwin D, Mhurchu
CN. Co-design of mHealth Delivered Interventions: A Systematic Review to Assess Key
Methods and Processes. Current Nutrition Reports 2016 Sep;5(3):160–167. [doi:
10.1007/s13668-016-0165-7]
16. Sanders EB-N, Stappers PJ. Co-creation and the new landscapes of design. CoDesign 2008 Mar
1;4(1):5–18. [doi: 10.1080/15710880701875068]
17. Schnall R, Bakken S, Rojas M, Travers J, Carballo-Dieguez A. mHealth Technology as a
Persuasive Tool for Treatment, Care and Management of Persons Living with HIV. AIDS Behav
2015 Jun;19(0 2):81–89. PMID:25572830
18. Schnall R, Rojas M, Bakken S, Brown W, Carballo-Dieguez A, Carry M, Gelaude D, Mosley
JP, Travers J. A user-centered model for designing consumer mobile health (mHealth)
applications (apps). Journal of Biomedical Informatics 2016 Apr 1;60:243–251. [doi:
10.1016/j.jbi.2016.02.002]
19. Aygun B, Odame I. A global perspective on sickle cell disease. Pediatric blood & cancer 2012
Aug;59(2):386–90. PMID:22535620
20. Lobitz S, Telfer P, Cela E, Allaf B, Angastiniotis M, Johansson CB, Badens C, Bento C, Bouva
MJ, Canatan D, Charlton M, Coppinger C, Daniel Y, Montalembert M de, Ducoroy P, Dulin E,
Fingerhut R, Frömmel C, García Morin M, Gulbis B, Holtkamp U, Inusa B, James J,
Kleanthous M, Klein J, Kunz JB, Langabeer L, Lapouméroulie C, Marcao A, Soria JLM,
McMahon C, Ohene Frempong K, Périni J-M, Piel FB, Russo G, Sainati L, Schmugge M,
Streetly A, Tshilolo L, Turner C, Venturelli D, Vilarinho L, Yahyaoui R, Elion J, Colombatti R.
Newborn screening for sickle cell disease in Europe: recommendations from a Pan-European
Consensus Conference. British Journal of Haematology 2018;183(4):648–660. [doi:
10.1111/bjh.15600]
21. Saraf SL, Molokie RE, Nouraie M, Sable CA, Luchtman-Jones L, Ensing GJ, Campbell AD,
Rana SR, Niu XM, Machado RF, Gladwin MT, Gordeuk VR. Differences in the clinical and
genotypic presentation of sickle cell disease around the world. Paediatric Respiratory Reviews
2014 Mar 1;15(1):4–12. [doi: 10.1016/j.prrv.2013.11.003]
22. ADOPTING CONSENSUS TEXT, GENERAL ASSEMBLY URGES MEMBER STATES,
UNITED NATIONS SYSTEM TO RAISE AWARENESS OF SICKLE–CELL ANAEMIA ON
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
19 JUNE EACH YEAR | Meetings Coverage and Press Releases [Internet]. [cited 2019 Feb
26]. Available from: http://www.webcitation.org/76VKrj7nL
23. Brousse V, Makani J, Rees DC. Management of sickle cell disease in the community. BMJ
(Clinical research ed) 2014 Mar;348(mar10 11):g1765. PMID:24613806
24. Vichinksy EP, DeBaun MR. Vaso-occlusive pain management in sickle cell disease - UpToDate
[Internet]. 2019 [cited 2019 Feb 19]. Available from: http://www.webcitation.org/76Iqk3Tpt
25. Rice L, Teruya M. Sickle cell patients face death in the ICU*. Critical care medicine 2014
Jul;42(7):1730–1. PMID:24933050
26. Kanter J, Kruse-Jarres R. Management of sickle cell disease from childhood through adulthood.
Blood Reviews 2013 Nov 1;27(6):279–287. [doi: 10.1016/j.blre.2013.09.001]
27. Meier ER, Johnson T, Pinkney K, Velez MC, Kamani N, Odame I. Access to hematopoietic
stem cell transplant for patients with sickle cell anemia. Pediatric Blood & Cancer
2018;65(9):e27105. [doi: 10/ggcm3t]
28. Lanzkron S, Carroll CP, Haywood C. Mortality rates and age at death from sickle cell disease:
U.S., 1979-2005. Public health reports (Washington, DC : 1974) 128(2):110–6.
PMID:23450875
29. Rahimy MC, Gangbo A, Ahouignan G, Adjou R, Deguenon C, Goussanou S, Alihonou E.
Effect of a comprehensive clinical care program on disease course in severely ill children with
sickle cell anemia in a sub-Saharan African setting. Blood 2003 Aug 1;102(3):834–838.
PMID:12702514
30. Kuznik A, Habib AG, Munube D, Lamorde M. Newborn screening and prophylactic
interventions for sickle cell disease in 47 countries in sub-Saharan Africa: a cost-effectiveness
analysis. BMC Health Serv Res [Internet] 2016 Jul 26 [cited 2019 Mar 2];16. PMID:27461265
31. Ware RE. Is sickle cell anemia a neglected tropical disease? PLoS neglected tropical diseases
2013 Jan;7(5):e2120. PMID:23750287
32. Scott RB. Health Care Priority and Sickle Cell Anemia. JAMA 1970 Oct 26;214(4):731–734.
[doi: 10.1001/jama.1970.03180040039008]
33. Evensen CT, Treadwell MJ, Keller S, Levine R, Hassell KL, Werner EM, Smith WR. Quality of
care in sickle cell disease: Cross-sectional study and development of a measure for adults
reporting on ambulatory and emergency department care. Medicine 2016 Aug;95(35):e4528.
PMID:27583862
34. Keane B, Defoe L. Supported or stigmatised? The impact of sickle cell disease on families.
Community practitioner : the journal of the Community Practitioners’ & Health Visitors’
Association 2016 Jun;89(6):44–7. PMID:27443031
35. Smith LA. Sickle Cell Disease: A Question of Equity and Quality. PEDIATRICS 2006
May;117(5):1763–1770. PMID:10617723
36. Stevens EM, Patterson CA, Li YB, Smith-Whitley K, Barakat LP. Mistrust of Pediatric Sickle
Cell Disease Clinical Trials Research. Am J Prev Med 2016;51(1 Suppl 1):S78-86.
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
PMID:27320470
37. Tsyvkin E, Riessman C, Mathew P. Distrust and Conflict in Sickle Cell Disease: Intersecting
Narratives of Patients and Physicians. Blood 2015 Dec 3;126(23):4472–4472.
38. Campos J, Lobo C, Queiroz AMM, do Nascimento EM, Lima CB, Cardoso G, Ballas SK.
Treatment of the acute sickle cell vaso-occlusive crisis in the Emergency Department: a
Brazilian method of switching from intravenous to oral morphine. European journal of
haematology 2014 Jul;93(1):34–40. PMID:24571671
39. Almuqamam M, Diaz – Frias J, Malik M, Mohamed AA, Sedrak A. Emergency management of
SCD pain crises: Current practices and playing variables. Pediatric Hematology Oncology
Journal 2018 Aug 1;3(2):37–41. [doi: 10.1016/j.phoj.2018.06.002]
40. Matthie N, Jenerette C. Sickle cell disease in adults: developing an appropriate care plan. Clin J
Oncol Nurs 2015 Oct;19(5):562–567. PMID:26688919
41. Ballas SK. The sickle cell painful crisis in adults: phases and objective signs. Hemoglobin
1995;19(6):323–333. PMID:8718691
42. Ballas SK, Kesen MR, Goldberg MF, Lutty GA, Dampier C, Osunkwo I, Wang WC, Hoppe C,
Hagar W, Darbari DS, Malik P. Beyond the Definitions of the Phenotypic Complications of
Sickle Cell Disease: An Update on Management. The Scientific World Journal 2012;2012:1–55.
[doi: 10.1100/2012/949535]
43. Ballas SK. More definitions in sickle cell disease: Steady state v base line data. American
Journal of Hematology 2012;87(3):338–338. [doi: 10.1002/ajh.22259]
44. Sant’Ana PG dos S, Araujo AM, Pimenta CT, Bezerra MLPK, Junior SPB, Neto VM, Dias JS,
Lopes A de F, Rios DRA, Pinheiro M de B. Clinical and laboratory profile of patients with
sickle cell anemia. Rev Bras Hematol Hemoter 2017;39(1):40–45. PMID:28270345
45. Haywood C, Lanzkron S, Bediako S, Strouse JJ, Haythornthwaite J, Carroll CP, Diener-West
M, Onojobi G, Beach MC, IMPORT Investigators. Perceived discrimination, patient trust, and
adherence to medical recommendations among persons with sickle cell disease. Journal of
general internal medicine 2014 Dec;29(12):1657–62. PMID:25205621
46. Haywood C, Lanzkron S, Diener-West M, Haythornthwaite J, Strouse JJ, Bediako S, Onojobi
G, Beach MC. Attitudes Towards Clinical Trials Among Patients with Sickle Cell Disease. Clin
Trials 2014 Jun;11(3):275–283. PMID:24532686
47. Inoue S, Kodjebacheva G, Scherrer T, Rice G, Grigorian M, Blankenship J, Onwuzurike N.
Adherence to hydroxyurea medication by children with sickle cell disease (SCD) using an
electronic device: a feasibility study. International journal of hematology 2016 Aug;104(2):200–
7. PMID:27225236
48. Haywood C, Bediako S, Lanzkron S, Diener-West M, Strouse J, Haythornthwaite J, Onojobi G,
Beach MC, IMPORT Investigators. An unequal burden: poor patient-provider communication
and sickle cell disease. Patient education and counseling 2014 Aug;96(2):159–64.
PMID:24935607
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
49. Adeyemo TA, Ojewunmi OO, Diaku-Akinwumi IN, Ayinde OC, Akanmu AS. Health related
quality of life and perception of stigmatisation in adolescents living with sickle cell disease in
Nigeria: A cross sectional study. Pediatric blood & cancer 2015 Jul;62(7):1245–51.
PMID:25810358
50. Ahmadi M, Jahani S, Poormansouri S, Shariati A, Tabesh H. The Effectiveness of self
management program on quality of life in patients with sickle cell disease. Iranian journal of
pediatric hematology and oncology 2015;5(1):18–26. PMID:25914799
51. Blakemore S. Self-care is key in sickle-cell disease. Emergency nurse : the journal of the RCN
Accident and Emergency Nursing Association 2016 May;24(2):9. PMID:27165379
52. Ahmadi M, Shariati A, Jahani S, Tabesh H, Keikhaei B. The Effectiveness of Self-Management
Programs on Self-Efficacy in Patients With Sickle Cell Disease. Jundishapur Journal of Chronic
Disease Care [Internet] 2014 [cited 2019 Jan 23];3(3). [doi: 10.17795/jjcdc-21702]
53. Adegbola M. Spirituality, Self-Efficacy, and Quality of Life among Adults with Sickle Cell
Disease. Southern online journal of nursing research [Internet] 2011 Apr;11(1).
PMID:21769284
54. Molter BL, Abrahamson K. Self-efficacy, transition, and patient outcomes in the sickle cell
disease population. Pain management nursing : official journal of the American Society of Pain
Management Nurses 2015 Jun;16(3):418–24. PMID:25047808
55. Treadwell M, Johnson S, Sisler I, Bitsko M, Gildengorin G, Medina R, Barreda F, Major K,
Telfair J, Smith WR. Self-efficacy and readiness for transition from pediatric to adult care in
sickle cell disease. International journal of adolescent medicine and health 2016
Nov;28(4):381–388. PMID:26226116
56. Khan SASA, Damanhouri G, Ali A, Khan SASA, Khan A, Bakillah A, Marouf S, Al Harbi G,
Halawani SH, Makki A. Precipitating factors and targeted therapies in combating the perils of
sickle cell disease— A special nutritional consideration. Nutrition & metabolism 2016
Jan;13:50. PMID:27508000
57. Norris SL, Engelgau MM, Venkat Narayan KM. Effectiveness of Self-Management Training in
Type 2 Diabetes: A systematic review of randomized controlled trials. Diabetes Care 2001 Mar
1;24(3):561–587. [doi: 10.2337/diacare.24.3.561]
58. Jenerette CM, Leak AN, Sandelowski M. Life stories of older adults with sickle cell disease.
The ABNF journal : official journal of the Association of Black Nursing Faculty in Higher
Education, Inc 2011;22(3):58–63. PMID:21901994
59. Jenerette CM, Lauderdale G. Successful Aging with Sickle Cell Disease: Using Qualitative
Methods to Inform Theory. Journal of theory construction & testing NIH Public Access; 2008
Apr 1;12(1):16–24. PMID:19838320
60. Ahmadi M, Jahani S, Poormansouri S, Shariati A, Tabesh H. The Effectiveness of self
management program on quality of life in patients with sickle cell disease. Iranian journal of
pediatric hematology and oncology Shahid Sadoughi University of Medical Sciences and
Health Services; 2015;5(1):18–26. PMID:25914799
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
61. Schroeder SA. We Can Do Better Improving the Health of the American People. New
England Journal of Medicine 2007 Sep 20;357(12):1221–1228. PMID:17881753
62. de Jesus AC da S, Konstantyner T, Lôbo IKV, Braga JAP. SOCIOECONOMIC AND
NUTRITIONAL CHARACTERISTICS OF CHILDREN AND ADOLESCENTS WITH
SICKLE CELL ANEMIA: A SYSTEMATIC REVIEW. Rev Paul Pediatr 2018;36(4):491–499.
PMID:30540112
63. Anderson K, Burford O, Emmerton LL, Wiederhold B, Riva G, Graffigna G, Lorig K, Sobel D,
Stewart A, Jr BB, Bandura A, Ritter P, Holman H, Lorig K, Chodosh J, Morton S, Mojica W,
Maglione M, Suttorp M, Hilton L, Williams M, Baker D, Honig E, Lee T, Nowlan A, Baker D,
Gazmararian J, Williams M, Scott T, Parker R, Green D, Baker D, Parker R, Williams M, Clark
W, Williams M, Parker R, Baker D, Parikh N, Pitkin K, Coates W, Gill P, Kamath A, Gill T,
Finkelstein E, Chay J, Bajpai S, Miller K, Beck R, Bergenstal R, Goland R, Haller M, McGill J,
Blödt S, Pach D, Roll S, Witt C, Nilges P, Köster B, Schmidt C, Morrison L, Hargood C, Lin
SS, Dennison L, Joseph J, Hughes S, Cooper S, Foster K, Naughton F, Leonardi-Bee J, Sutton
S, Ussher M, Haug S, Castro R, Filler A, Kowatsch T, Fleisch E, Schaub M, Proudfoot J, Clarke
J, Birch M, Whitton A, Parker G, Manicavasagar V, Eyles H, McLean R, Neal B, Doughty R,
Jiang Y, Mhurchu C, Hasford J, Uricher J, Tauscher M, Bramlage P, Virchow J, Zichermann G,
Cunningham C, Pandey A, Hasan S, Dubey D, Sarangi S, Krebs P, D D, Licskai C, Sands T,
Ferrone M, Ryan D, Price D, Musgrave S, Malhotra S, Lee A, Ayansina D, Liu W, Huang C,
Wang C, Lee K, Lin SS, Kuo H, Kirwan M, Vandelanotte C, Fenning A, Duncan M, Jeon E,
Park H-A, McCarroll M, Armbruster S, Pohle-Krauza R, Lyzen A, Min S, Nash D, Pan D, Dhall
R, Lieberman A, Petitti D, Davis F, Madden T, Ellen P, Ajzen I, Fishbein M, Ajzen I, Albarracin
D, Hornik R, Yarbrough A, Smith T, Briz-Ponce L, García-Peñalvo F, Cho J, Quinlan M, Park
D, Noh G, Kim J, Park H-A, Janz N, Becker M, Becker M, Radius S, Rosenstock I, Drachman
R, Schuberth K, Teets K, Cormier D, Ferreira D, Vise K, Cahalin L, Stoyanov S, Hides L,
Kavanagh D, Zelenko O, Tjondronegoro D, Mani M, Antezana G, Bidargaddi N, Blake V,
Schrader G, Kaambwa B, Quinn S, Kenny R, Dooley B, Fitzgerald A, Chiang L, Huang J, Yeh
K, Lu C, Velsor-Friedrich B, Pigott T, Srof B, Hesse-Biber S, Leavy P, Holden R, Karsh B-T,
Scheibe M, Reichelt J, Bellmann M, Kirch W, Doherty G, Coyle D, Matthews M, Jin B, Ji Y,
Charmaz K, Radcliffe C, Lester H, Green J, Thorogood N, Braun V, Clarke V, Fereday J, Muir-
Cochrane E, Underwood B, Birdsall J, Kay E, Elias P, Rajan N, McArthur K, Dacso C, Hebly P,
Lister C, West J, Cannon B, Sax T, Brodegard D, Phillips D, Burbules N, Anderson K,
Emmerton LL, Korhonen I, Parkka J, Gils MV. Mobile Health Apps to Facilitate Self-Care: A
Qualitative Study of User Experiences. van Ooijen PMA, editor. PLOS ONE 2016
May;11(5):e0156164. [doi: 10.1371/journal.pone.0156164]
64. Crosby LE, Ware RE, Goldstein A, Walton A, Joffe NE, Vogel C, Britto MT. Development and
evaluation of iManage: A self-management app co-designed by adolescents with sickle cell
disease. Pediatric blood & cancer 2017 Jan;64(1):139–145. PMID:27574031
65. Issom D-Z, Zosso A, Wipfli R, Ehrler F, Lovis C. Meeting Sickle Cell patientsunmet needs
with eHealth tools : a preliminary study. Proceedings from The 13th Scandinavien Conference
on Health Informatics (SHI 2015), June 15–17, 2015, Tromsø, Norway 2015;(115):12.
66. Badawy SM, Cronin RM, Hankins J, Crosby L, DeBaun M, Thompson AA, Shah N. Patient-
Centered eHealth Interventions for Children, Adolescents, and Adults With Sickle Cell Disease:
Systematic Review. Journal of Medical Internet Research 2018;20(7):e10940. [doi:
10.2196/10940]
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
67. Shah N, Jonassaint J, De Castro L, Castro LD. Patients welcome the Sickle Cell Disease Mobile
Application to Record Symptoms via Technology (SMART). Hemoglobin 2014 Apr;38(2):99–
103. PMID:24512633
68. Woldaregay AZ, Issom D-Z, Henriksen A, Marttila H, Mikalsen M, Pfuhl G, Sato K, Lovis C,
Hartvigsen G. Motivational Factors for User Engagement with mHealth Apps. Studies in health
technology and informatics 2018;249:151–157. PMID:29866972
69. European Medicines Agency. EU/3/10/832 [Internet]. European Medicines Agency. 2018 [cited
2019 Nov 5]. Available from: https://perma.cc/F9DE-WL8Q
70. Graesdal JS, Gundersen K, Holm B, Waage A. [Thalassemia and sickle-cell disease in Norway].
Tidsskr Nor Laegeforen 2001 Feb 28;121(6):678–680. PMID:11293347
71. Schmugge M, Speer O, Ozsahin A, Martin G. La drépanocytose en Suisse.2e partie: Mesures
thérapeutiques et prophylactiques. 2008 Aug 20;8. [doi: 10.4414/fms.2008.06563]
72. Schmugge M, Speer O, Hulya Ozsahin A, Martin G. La drépanocytose en Suisse. 1re partie:
Physiopathologie, Clinique. Forum Médical Suisse 2008 Aug 13;8(33):582–586. [doi: 10.4414/
fms.2008.06560]
73. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology
2006 Jan 1;3(2):77–101. [doi: 10.1191/1478088706qp063oa]
74. Michie S, van Stralen MM, West R. The behaviour change wheel: A new method for
characterising and designing behaviour change interventions. Implement Sci 2011 Apr 23;6:42.
PMID:21513547
75. Curtis KE, Lahiri S, Brown KE. Targeting Parents for Childhood Weight Management:
Development of a Theory-Driven and User-Centered Healthy Eating App. JMIR mHealth and
uHealth 2015;3(2):e69. [doi: 10.2196/mhealth.3857]
76. Litterbach E-K, Russell CG, Taki S, Denney-Wilson E, Campbell KJ, Laws RA. Factors
Influencing Engagement and Behavioral Determinants of Infant Feeding in an mHealth
Program: Qualitative Evaluation of the Growing Healthy Program. JMIR Mhealth Uhealth
[Internet] 2017 Dec 18 [cited 2019 Dec 27];5(12). PMID:29254908
77. Korpershoek YJG, Vervoort SCJM, Trappenburg JCA, Schuurmans MJ. Perceptions of patients
with chronic obstructive pulmonary disease and their health care providers towards using
mHealth for self-management of exacerbations: a qualitative study. BMC Health Serv Res
[Internet] 2018 Oct 4 [cited 2019 Dec 27];18. PMID:30286761
78. Volere Requirements Specification Template | Request PDF [Internet]. [cited 2019 Feb 4].
Available from: http://www.webcitation.org/76VLGpcyD
79. Chen L, Ali Babar M, Nuseibeh B. Characterizing Architecturally Significant Requirements.
IEEE Software 2013 Mar;30(2):38–45. [doi: 10/ggfttp]
80. Akinbami A, Dosunmu A, Adediran A, Oshinaike O, Adebola P, Arogundade O.
Haematological values in homozygous sickle cell disease in steady state and haemoglobin
phenotypes AA controls in Lagos, Nigeria. BMC Res Notes 2012 Aug 1;5:396.
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
PMID:22849350
81. Mader LB, Harris T, Kläger S, Wilkinson IB, Hiemstra TF. Inverting the patient involvement
paradigm: defining patient led research. Research Involvement and Engagement 2018 Jul
10;4(1):21. [doi: 10.1186/s40900-018-0104-4]
82. Manwani D, Frenette PS. Vaso-occlusion in sickle cell disease: pathophysiology and novel
targeted therapies. Blood 2013 Dec;122(24):3892–8. PMID:24052549
83. Ataga KI, Kutlar A, Kanter J, Liles D, Cancado R, Friedrisch J, Guthrie TH, Knight-Madden J,
Alvarez OA, Gordeuk VR, Gualandro S, Colella MP, Smith WR, Rollins SA, Stocker JW,
Rother RP. Crizanlizumab for the Prevention of Pain Crises in Sickle Cell Disease. New
England Journal of Medicine 2017 Feb 2;376(5):429–439. PMID:27959701
84. Skrøvseth SO, \AArsand E, Godtliebsen F, Joakimsen RM. Data-Driven Personalized Feedback
to Patients with Type 1 Diabetes: A Randomized Trial. Diabetes Technology & Therapeutics
2015;17(6):150309084240009. [doi: 10.1089/dia.2014.0276]
85. Fontana JM, Farooq M, Sazonov E. Automatic Ingestion Monitor: A Novel Wearable Device for
Monitoring of Ingestive Behavior. IEEE Trans Biomed Eng 2014 Jun;61(6):1772–1779.
PMID:24845288
86. Andreu Perez J, Leff D, Ip H, Yang G-Z. From Wearable Sensors to Smart Implants - Towards
Pervasive and Personalised Healthcare. IEEE transactions on bio-medical engineering [Internet]
2015 Apr; PMID:25879838
87. Kim J, Kim M, Lee M-S, Kim K, Ji S, Kim Y-T, Park J, Na K, Bae K-H, Kyun Kim H, Bien F,
Young Lee C, Park J-U. Wearable smart sensor systems integrated on soft contact lenses for
wireless ocular diagnostics. Nat Commun [Internet] 2017 Apr 27 [cited 2019 Feb 20];8.
PMID:28447604
88. DIYPS.org [Internet]. DIYPS.org. 2018 [cited 2019 Dec 26]. Available from:
https://perma.cc/CUD4-9H2F
89. Dimković N. [Erythropoietin-beta in the treatment of anemia in patients with chronic renal
insufficiency]. Med Pregl 2001 Jun;54(5–6):235–240. PMID:11759218
90. Vichinsky E, Hoppe CC, Ataga KI, Ware RE, Nduba V, El-Beshlawy A, Hassab H, Achebe
MM, Alkindi S, Brown RC, Diuguid DL, Telfer P, Tsitsikas DA, Elghandour A, Gordeuk VR,
Kanter J, Abboud MR, Lehrer-Graiwer J, Tonda M, Intondi A, Tong B, Howard J. A Phase 3
Randomized Trial of Voxelotor in Sickle Cell Disease. New England Journal of Medicine 2019
Aug 8;381(6):509–519. PMID:31199090
91. Bird Y, Lemstra M, Rogers M, Moraros J. The relationship between socioeconomic
status/income and prevalence of diabetes and associated conditions: A cross-sectional
population-based study in Saskatchewan, Canada. Int J Equity Health [Internet] 2015 Oct 12
[cited 2019 Dec 26];14. PMID:26458543
92. Salvo P, Dini V, Kirchhain A, Janowska A, Oranges T, Chiricozzi A, Lomonaco T, Di Francesco
F, Romanelli M. Sensors and Biosensors for C-Reactive Protein, Temperature and pH, and
Their Applications for Monitoring Wound Healing: A Review. Sensors (Basel) 2017 Dec
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
19;17(12). PMID:29257113
93. Adams-Graves P, Bronte-Jordan L. Recent treatment guidelines for managing adult patients
with sickle cell disease: challenges in access to care, social issues, and adherence. Expert Rev
Hematol 2016 Jun;9(6):541–552. PMID:27098013
94. Amid A, Odame I. Improving outcomes in children with sickle cell disease: treatment
considerations and strategies. Paediatric drugs 2014 Aug;16(4):255–66. PMID:24797542
95. Benjamin LJ, Swinson GI, Nagel RL. Sickle cell anemia day hospital: an approach for the
management of uncomplicated painful crises. Blood 2000 Feb;95(4):1130–6. PMID:10666181
96. Haywood C, Naik R, Beach MC, Lanzkron S. Do Sickle Cell Patients Wait Longer to See
Physicians in the Emergency Department? Blood 2011 Nov 18;118(21):2070–2070.
97. Chu Y, Zhao X, Han J, Su Y. Physiological Signal-Based Method for Measurement of Pain
Intensity. Front Neurosci [Internet] 2017 May 26 [cited 2019 Dec 26];11. PMID:28603478
98. Martin Paiz JA. Intrathecal Morphine Therapy for Chronic Non-malignant Pain Using a
Constant Flow Infusion System. J Pain Relief [Internet] 2015 [cited 2019 Dec 26];04(01). [doi:
10.4172/2167-0846.1000168]
99. Voice Crisis Alert V2 [Internet]. Apple App Store. [cited 2019 Feb 18]. Available from:
http://www.webcitation.org/76HKd49mM
100. Ehrler F, Lovis C, Rochat J, Schneider F, Gervaix A, Galetto-Lacour A, Siebert JN. [InfoKids:
changing the patients’ journey paradigm in an Emergency Department]. Rev Med Suisse 2018
Sep 5;14(617):1538–1542. PMID:30226668
101. Markotic F, Vrdoljak D, Puljiz M, Puljak L. Risk perception about medication sharing among
patients: a focus group qualitative study on borrowing and lending of prescription analgesics. J
Pain Res 2017 Feb 10;10:365–374. PMID:28243140
102. Beyene KA, Sheridan J, Aspden T. Prescription Medication Sharing: A Systematic Review of
the Literature. Am J Public Health 2014 Apr;104(4):e15–e26. PMID:24524496
103. Thurnheer SE, Gravestock I, Pichierri G, Steurer J, Burgstaller JM. Benefits of Mobile Apps in
Pain Management: Systematic Review. JMIR mHealth and uHealth 2018;6(10):e11231. [doi:
10.2196/11231]
104. Pourmand A, Davis S, Marchak A, Whiteside T, Sikka N. Virtual Reality as a Clinical Tool for
Pain Management. Curr Pain Headache Rep 2018 Jun 15;22(8):53. [doi: 10/gdr7hs]
105. Jenerette CM, Lauderdale G. Successful Aging with Sickle Cell Disease: Using Qualitative
Methods to Inform Theory. Journal of theory construction & testing 2008 Apr;12(1):16–24.
PMID:19838320
106. Ballas SK. Self-management of sickle cell disease: a new frontier. Journal of the National
Medical Association 2010 Nov;102(11):1042–3. PMID:21141292
107. Miller AS, Cafazzo JA, Seto E. A game plan: Gamification design principles in mHealth
applications for chronic disease management. Health informatics journal [Internet] 2014 Jul;
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
PMID:24986104
108. Medhi I, Patnaik S, Brunskill E, Gautama SNN, Thies W, Toyama K. Designing mobile
interfaces for novice and low-literacy users. ACM Transactions on Computer-Human
Interaction 2011;18(1):1–28. [doi: 10.1145/1959022.1959024]
109. Fu LP, Landay J, Nebeling M, Xu Y, Zhao C. Redefining Natural User Interface. Extended
Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems [Internet]
New York, NY, USA: ACM; 2018 [cited 2019 Feb 20]. p. SIG19:1–SIG19:3. [doi:
10.1145/3170427.3190649]
110. McLaughlin AC, Matalenas LA, Coleman MG. 11 - Design of human centered augmented
reality for managing chronic health conditions. In: Pak R, McLaughlin AC, editors. Aging,
Technology and Health [Internet] San Diego: Academic Press; 2018 [cited 2019 Feb 20]. p.
261–296. [doi: 10.1016/B978-0-12-811272-4.00011-7]
111. Gutiérrez F, Verbert K, Htun NN. PHARA: An Augmented Reality Grocery Store Assistant.
Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile
Devices and Services Adjunct [Internet] New York, NY, USA: ACM; 2018 [cited 2019 Feb 20].
p. 339–345. [doi: 10.1145/3236112.3236161]
112. Talbot MT “Brett.” Virtual Reality and Interactive Gaming Technology for Obese and Diabetic
Children: Is Military Medical Technology Applicable? J Diabetes Sci Technol 2011 Mar
1;5(2):234–238. PMID:21527087
113. Elsweiler D, Hors-Fraile S, Ludwig B, Said A, Schäfer H, Trattner C, Torkamaan H, Calero
Valdez A. Second Workshop on Health Recommender Systems: (HealthRecSys 2017).
Proceedings of the Eleventh ACM Conference on Recommender Systems [Internet] New York,
NY, USA: ACM; 2017 [cited 2019 Feb 20]. p. 374–375. [doi: 10.1145/3109859.3109955]
114. Oing T, Prescott J. Implementations of Virtual Reality for Anxiety-Related Disorders:
Systematic Review. JMIR Serious Games 2018;6(4):e10965. [doi: 10.2196/10965]
115. Izahar S, Lean QY, Hameed MA, Murugiah MK, Patel RP, Al-Worafi YM, Wong TW, Ming
LC. Content Analysis of Mobile Health Applications on Diabetes Mellitus. Front Endocrinol
(Lausanne) [Internet] 2017 Nov 27 [cited 2019 Mar 2];8. PMID:29230195
116. Rossi MG, Bigi S. mHealth for diabetes support: a systematic review of apps available on the
Italian market. Mhealth [Internet] 2017 May 4 [cited 2019 Mar 2];3. PMID:28567412
117. Lagger G, Pataky Z. Efficacy of therapeutic patient education in chronic diseases and obesity.
Patient Education and Counseling Elsevier; 2010 Jun 1;79(3):283–286. [doi:
10.1016/J.PEC.2010.03.015]
118. Assal J-P, Golay A. Patient education in Switzerland: from diabetes to chronic diseases. Patient
Education and Counseling Elsevier; 2001 Jul 1;44(1):65–69. [doi: 10.1016/S0738-
3991(01)00105-7]
119. Boehmer KR, Barakat S, Ahn S, Prokop LJ, Erwin PJ, Murad MH. Health coaching
interventions for persons with chronic conditions: a systematic review and meta-analysis
protocol. Systematic reviews BioMed Central; 2016;5(1):146. PMID:27585627
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
120. Ghorob A. Health coaching: teaching patients how to fish. Family practice management 2013
Jan;20(3):40–2. PMID:23939739
121. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J
2019 Jun;6(2):94–98. PMID:31363513
122. Gheondea-Eladi A. Patient decision aids: a content analysis based on a decision tree structure.
BMC Med Inform Decis Mak 2019 Jul 19;19(1):137. [doi: 10.1186/s12911-019-0840-x]
123. Elwyn G, O’Connor A, Stacey D, Volk R, Edwards A, Coulter A, Thomson R, Barratt A, Barry
M, Bernstein S, Butow P, Clarke A, Entwistle V, Feldman-Stewart D, Holmes-Rovner M,
Llewellyn-Thomas H, Moumjid N, Mulley A, Ruland C, Sepucha K, Sykes A, Whelan T.
Developing a quality criteria framework for patient decision aids: online international Delphi
consensus process. BMJ 2006 Aug;333(7565):417–417. [doi: 10.1136/bmj.38926.629329.AE]
124. Stacey D, Kryworuchko J, Belkora J, Davison BJ, Durand M-A, Eden KB, Hoffman AS,
Koerner M, Légaré F, Loiselle M-C, Street RL. Coaching and guidance with patient decision
aids: A review of theoretical and empirical evidence. BMC Medical Informatics and Decision
Making 2013 Nov 29;13(2):S11. [doi: 10.1186/1472-6947-13-S2-S11]
125. Jacobs W, Amuta AO, Jeon KC. Health information seeking in the digital age: An analysis of
health information seeking behavior among US adults. Alvares C, editor. Cogent Social
Sciences 2017 Jan 1;3(1):1302785. [doi: 10.1080/23311886.2017.1302785]
126. Tan SS-L, Goonawardene N. Internet Health Information Seeking and the Patient-Physician
Relationship: A Systematic Review. Journal of medical Internet research 2017 Jan;19(1):e9.
PMID:28104579
127. Frost JR, Cherry RK, Faro EZ, Crosby LE, Britto M, Tuchman LK, Horn IB, Homer CJ, Jain A.
Improving Sickle Cell Transitions of Care Through Health Information Technology. American
Journal of Preventive Medicine 2016 Jul;51(1):S17–S23. [doi:
10.1016/J.AMEPRE.2016.02.004]
128. Breakey VR, Harris L, Davis O, Agarwal A, Ouellette C, Akinnawo E, Stinson J. The quality of
information about sickle cell disease on the Internet for youth. Pediatric blood & cancer 2017
Apr;64(4):e26309. PMID:27786409
129. Zhang Y, Cui H, Burkell J, Mercer RE. A Machine Learning Approach for Rating the Quality of
Depression Treatment Web Pages. 2014 Mar 1 [cited 2019 Feb 21]; [doi:
https://doi.org/10.9776/14065]
130. Boyer C, Dolamic L. Automated Detection of HONcode Website Conformity Compared to
Manual Detection: An Evaluation. J Med Internet Res [Internet] 2015 Jun 2 [cited 2019 Feb
18];17(6). PMID:26036669
131. Charnock D, Shepperd S, Needham G, Gann R. DISCERN: an instrument for judging the
quality of written consumer health information on treatment choices. J Epidemiol Community
Health 1999 Feb;53(2):105–111. PMID:10396471
132. Qenam B, Kim TY, Carroll MJ, Hogarth M. Text Simplification Using Consumer Health
Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation. J
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
Med Internet Res [Internet] 2017 Dec 18 [cited 2019 Mar 2];19(12). PMID:29254915
133. Smith CA. Consumer language, patient language, and thesauri: a review of the literature. J Med
Libr Assoc 2011 Apr;99(2):135–144. PMID:21464851
134. Shape oscillations of single blood drops: applications to human blood and sickle cell disease |
Scientific Reports [Internet]. [cited 2019 Feb 20]. Available from: perma.cc/GYM9-RU8Y
135. Li X, Du E, Lei H, Tang Y-H, Dao M, Suresh S, Karniadakis GE. Patient-specific blood
rheology in sickle-cell anaemia. Interface Focus [Internet] 2016 Feb 6 [cited 2019 Feb 20];6(1).
PMID:26855752
136. Jacob E, Beyer JE, Miaskowski C, Savedra M, Treadwell M, Styles L. Are There Phases to the
Vaso-Occlusive Painful Episode in Sickle Cell Disease? Journal of Pain and Symptom
Management 2005 Apr 1;29(4):392–400. [doi: 10.1016/j.jpainsymman.2004.07.006]
137. Wang G, Zhang S, Dong S, Lou D, Ma L, Pei X, Xu H, Umar F, Guo W, Luo J. Stretchable
Optical Sensing Patch System Integrated Heart Rate, Pulse Oxygen Saturation and Sweat pH
Detection. IEEE Trans Biomed Eng 2018 Aug 20; PMID:30130170
138. Jin W, Wu L, Song Y, Jiang J, Zhu X, Yang D, Bai C. Continuous intra-arterial blood pH
monitoring by a fiber-optic fluorosensor. IEEE Transactions on Biomedical Engineering
2011;58(5):1232–1238.
139. Anastasova S, Crewther B, Bembnowicz P, Curto V, Ip HM, Rosa B, Yang G-Z. A wearable
multisensing patch for continuous sweat monitoring. Biosensors and Bioelectronics 2017 Jul
15;93:139–145. [doi: 10.1016/j.bios.2016.09.038]
140. Diaw M, Samb A, Diop S, Sall ND, Ba A, Cissé F, Connes P. Effects of hydration and water
deprivation on blood viscosity during a soccer game in sickle cell trait carriers. British journal
of sports medicine 2014 Feb;48(4):326–31. PMID:22685122
141. Omwanghe OA, Muntz DS, Kwon S, Montgomery S, Kemiki O, Hsu LL, Thompson AA, Liem
RI. Self-Reported Physical Activity and Exercise Patterns in Children With Sickle Cell Disease.
Pediatr Exerc Sci 2017;29(3):388–395. PMID:28530510
142. Morita AA, Silva LKO, Bisca GW, Oliveira JM, Hernandes NA, Pitta F, Furlanetto KC. Heart
Rate Recovery, Physical Activity Level, and Functional Status in Subjects With COPD. Respir
Care 2018;63(8):1002–1008. PMID:29765005
143. Thompson D, Batterham AM, Peacock OJ, Western MJ, Booso R. Feedback from physical
activity monitors is not compatible with current recommendations: A recalibration study.
Preventive Medicine 2016 Oct 1;91:389–394. [doi: 10.1016/j.ypmed.2016.06.017]
144. Zempsky WT, Loiselle KA, McKay K, Lee BH, Hagstrom JN, Schechter NL. Do children with
sickle cell disease receive disparate care for pain in the emergency department? The Journal of
emergency medicine 2010 Nov;39(5):691–5. PMID:19703740
145. Ballas SK. Current issues in sickle cell pain and its management. Hematology / the Education
Program of the American Society of Hematology American Society of Hematology Education
Program 2007 Jan;97–105. PMID:18024616
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
146. Ogu UO, Billett HH. Comorbidities in sickle cell disease: Adult providers needed! Indian J Med
Res 2018 Jun;147(6):527–529. PMID:30168482
147. Bender JL, Jimenez-Marroquin M-C, Jadad AR. Seeking Support on Facebook: A Content
Analysis of Breast Cancer Groups. Journal of Medical Internet Research 2011;13(1):e16. [doi:
10.2196/jmir.1560]
148. Rosa SD, Sen F. Health Topics on Facebook Groups: Content Analysis of Posts in Multiple
Sclerosis Communities. Interactive Journal of Medical Research 2019;8(1):e10146. [doi:
10.2196/ijmr.10146]
149. Harpel T. Pregnant Women Sharing Pregnancy-Related Information on Facebook: Web-Based
Survey Study. Journal of Medical Internet Research 2018;20(3):e115. [doi: 10.2196/jmir.7753]
150. Troncone A, Cascella C, Chianese A, Iafusco D. Using computerized text analysis to assess
communication within an Italian type 1 diabetes Facebook group. Health Psychology Open
2015 Nov;2(2):205510291561533. [doi: 10.1177/2055102915615338]
151. Okun S, Goodwin K. Building a learning health community: By the people, for the people.
Learning Health Systems 2017;1(3):e10028. [doi: 10.1002/lrh2.10028]
152. The Digital Divide [Internet]. [cited 2019 Dec 29]. Available from:
https://dl.acm.org/citation.cfm?id=2886349
153. Issom D-Z, Hartvigsen G, Bonacina S, Koch S, Lovis C. User-Centric eHealth Tool to Address
the Psychosocial Effects of Sickle Cell Disease. Studies in health technology and informatics
2016;225:627–8. PMID:27332283
154. oneSCDvoice | join our Sickle Cell Community [Internet]. [cited 2019 Feb 18]. Available from:
http://www.webcitation.org/76VMO9bsi
155. Adaji I, Vassileva J. Persuasive Patterns in Q&A Social Networks. Proceedings of the 11th
International Conference on Persuasive Technology - Volume 9638 [Internet] Berlin,
Heidelberg: Springer-Verlag; 2016 [cited 2019 Feb 22]. p. 189–196. [doi: 10.1007/978-3-319-
31510-2_16]
156. Hu Z, Zhang Z, Yang H, Chen Q, Zuo D. A deep learning approach for predicting the quality of
online health expert question-answering services. Journal of Biomedical Informatics 2017 Jul
1;71:241–253. [doi: 10.1016/j.jbi.2017.06.012]
157. Mize L, Burgett S, Xu J, Rothman J, Shah N. The Use of Chronic Transfusions in Sickle Cell
Disease for Non-Stroke Related Indications. Blood 2014 Dec 6;124(21):4934–4934.
158. Scalia P, Durand M-A, Kremer J, Faber M, Elwyn G. Online, Interactive Option Grid Patient
Decision Aids and their Effect on User Preferences. Med Decis Making 2018 Jan 1;38(1):56–
68. [doi: 10.1177/0272989X17734538]
159. Kulandaivelu Y, Lalloo C, Ward R, Zempsky WT, Kirby-Allen M, Breakey VR, Odame I,
Campbell F, Amaria K, Simpson EA, Nguyen C, George T, Stinson JN. Exploring the Needs of
Adolescents With Sickle Cell Disease to Inform a Digital Self-Management and Transitional
Care Program: Qualitative Study. JMIR Pediatrics and Parenting 2018;1(2):e11058. [doi:
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
10.2196/11058]
160. Yusop N, Zowghi D, Lowe D. The impacts of non-functional requirements in web system
projects. International Journal of Value Chain Management - Int J Value Chain Manag 2008 Jan
1;2. [doi: 10.1504/IJVCM.2008.016116]
161. Using Technologies for Data Collection and Management | Epidemic Intelligence Service |
CDC [Internet]. 2019 [cited 2019 Feb 19]. Available from: https://perma.cc/8J62-RPGE
162. Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, Perelman D, Colbert E,
Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP, Kluger M, Kluger M, Perret-
Guillaume C, Joly L, Benetos A, Reule S, Drawz P, Kent B, Mitchell P, McNicholas W,
Fronczek R, Raymann R, Overeem S, Romeijn N, van Dijk J, Lammers G, Raymann R, Swaab
D, Someren EV, Bouchard C, Rankinen T, Lewis D, Kamon E, Hodgson J, Bloss C, Wineinger
N, Peters M, Boeldt D, Ariniello L, Kim J, Greenfield M, Doberne L, Kraemer F, Tobey T,
Reaven G, Pei D, Jones C, Bhargava R, Chen Y, Reaven G, Abbasi F, McLaughlin T,
Lamendola C, Lipinska I, Tofler G, Reaven G, Chen N, Holmes M, Reaven G, Altman D, Bland
J, Bland J, Altman D, Czeisler C, Weitzman E, Moore-Ede M, Zimmerman J, Knauer R,
Javorka M, Zila I, Balharek T, Javorka K, Snyder F, Hobson J, Morrison D, Goldfrank F,
Nakayama T, Ohnuki Y, Niwa K, Torii M, Yamasaki M, Sasaki T, Nakayama H, Ostchega Y,
Porter K, Hughes J, Dillon C, Nwankwo T, Kubo H, Yanase T, Akagi H, Hampson N,
Kregenow D, Mahoney A, Kirtland S, Horan K, Holm J, Peacock A, Cottrell J, Lebovitz B,
Fennell R, Kohn G, Lee A, Yamamoto L, Relles N, Roubinian N, Elliott C, Barnett C, Blanc P,
Chen J, Marco TD, Kelly P, Swanney M, Frampton C, Seccombe L, Peters M, Beckert L,
Kishimoto A, Tochikubo O, Ohshige K, Yanaga A, Humphreys S, Deyermond R, Bali I,
Stevenson M, Fee J, Fonseca V, Magri C, Xuereb R, Fava S, Martins D, Tareen N, Pan D,
Norris K, Grajewski B, Waters M, Yong L, Tseng C, Zivkovich Z, 2nd RC, Lim M, Sigurdson
A, Ron E, Alvarez L, Eastham S, Barrett S, Muhm J, Rock P, McMullin D, Jones S, Lu I, Eilers
K, Coste O, Beers PV, Touitou Y, Peterson D, Martin-Gill C, Guyette F, Tobias A, McCarthy C,
Harrington S, Aeschbacher S, Schoen T, Dorig L, Kreuzmann R, Neuhauser C, Schmidt-
Trucksass A, Aune D, Hartaigh BÓ, Vatten L, Beddhu S, Nigwekar S, Ma X, Greene T, Festa A,
Jr. RD, Hales C, Mykkanen L, Haffner S, Wang L, Cui L, Wang Y, Vaidya A, Chen S, Zhang C,
Zhang D, Shen X, Qi X, Ribeiro M, Sacramento J, Gonzalez C, Guarino M, Monteiro E, Conde
S, Rowe J, Young J, Minaker K, Stevens A, Pallotta J, Landsberg L, Straznicky N, Grima M,
Sari C, Eikelis N, Lambert E, Nestel P, Licht C, de Geus E, Penninx B, Snyder M, Topol E,
Nuhr M, Hoerauf K, Joldzo A, Frickey N, Barker R, Gorove L, Ferraro M, G P, Lowe H, Ferris
T, Hernandez P, Weber S, McLaughlin T, Craig C, Liu L-F, Perelman D, Allister C, Spielman D.
Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful
Health-Related Information. Kirkwood T, editor. PLOS Biology 2017 Jan;15(1):e2001402. [doi:
10.1371/journal.pbio.2001402]
163. Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You Get
Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic
Conditions. J Med Internet Res [Internet] 2015 Aug 19 [cited 2019 Feb 12];17(8).
PMID:26290186
164. Halko S, Kientz JA. Personality and Persuasive Technology: An Exploratory Study on Health-
Promoting Mobile Applications. In: Ploug T, Hasle P, Oinas-Kukkonen H, editors. Persuasive
Technology Springer Berlin Heidelberg; 2010. p. 150–161.
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
165. Chiu M-C, Chang S-P, Chang Y-C, Chu H-H, Chen CC-H, Hsiao F-H, Ko J-C. Playful Bottle: A
Mobile Social Persuasion System to Motivate Healthy Water Intake. Proceedings of the 11th
International Conference on Ubiquitous Computing [Internet] New York, NY, USA: ACM;
2009 [cited 2019 Feb 26]. p. 185–194. [doi: 10.1145/1620545.1620574]
166. Elsherif M, Hassan MU, Yetisen AK, Butt H. Wearable Contact Lens Biosensors for Continuous
Glucose Monitoring Using Smartphones. ACS Nano 2018 Jun 26;12(6):5452–5462. [doi:
10.1021/acsnano.8b00829]
167. Motiv Ring | 24/7 Smart Ring | Fitness + Sleep Tracking | Online Security Motiv Ring
[Internet]. Motiv. [cited 2019 Feb 20]. Available from: https://mymotiv.com/
168. Stein N, Brooks K. A Fully Automated Conversational Artificial Intelligence for Weight Loss:
Longitudinal Observational Study Among Overweight and Obese Adults. JMIR Diabetes 2017
Nov;2(2):e28. [doi: 10.2196/diabetes.8590]
169. Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using Psychological Artificial
Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled
Trial. JMIR Mental Health 2018;5(4):e64. [doi: 10.2196/mental.9782]
170. shareable (adjective) definition and synonyms | Macmillan Dictionary [Internet]. [cited 2019
Feb 27]. Available from: http://www.webcitation.org/76VLwj70x
171. Hartzler A, Pratt W. Managing the personal side of health: how patient expertise differs from
the expertise of clinicians. Journal of medical Internet research 2011 Aug;13(3):e62.
PMID:21846635
172. Elaheebocus SMRA, Weal M, Morrison L, Yardley L. Peer-Based Social Media Features in
Behavior Change Interventions: Systematic Review. Journal of Medical Internet Research
2018;20(2):e20. [doi: 10.2196/jmir.8342]
173. Lochmüller H, Badowska DM, Thompson R, Knoers NV, Aartsma-Rus A, Gut I, Wood L,
Harmuth T, Durudas A, Graessner H, Schaefer F, Riess O. RD-Connect, NeurOmics and
EURenOmics: collaborative European initiative for rare diseases. European Journal of Human
Genetics 2018 Jun;26(6):778. [doi: 10.1038/s41431-018-0115-5]
174. Vasilescu B, Serebrenik A, Devanbu P, Filkov V. How Social Q&A Sites Are Changing
Knowledge Sharing in Open Source Software Communities. Proceedings of the 17th ACM
Conference on Computer Supported Cooperative Work & Social Computing [Internet] New
York, NY, USA: ACM; 2014 [cited 2019 Feb 22]. p. 342–354. [doi: 10.1145/2531602.2531659]
175. Arora S, Yttri J, Nilsen W. Privacy and Security in Mobile Health (mHealth) Research. Alcohol
Res 2014;36(1):143–151. PMID:26259009
176. Zhao J, Freeman B, Li M. Can Mobile Phone Apps Influence People’s Health Behavior
Change? An Evidence Review. J Med Internet Res [Internet] 2016 Nov 2 [cited 2019 Jan
31];18(11). PMID:27806926
177. Cohen IG, Mello MM. HIPAA and Protecting Health Information in the 21st Century. JAMA
2018 Jul 17;320(3):231–232. [doi: 10.1001/jama.2018.5630]
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
178. Pryv Enables Businesses to Manage Privacy & Personal Data. [Internet]. Pryv. [cited 2019 Dec
26]. Available from: https://pryv.com/
179. Hafen E. Personal Data Cooperatives A New Data Governance Framework for Data
Donations and Precision Health. In: Krutzinna J, Floridi L, editors. The Ethics of Medical Data
Donation [Internet] Cham: Springer International Publishing; 2019 [cited 2019 Dec 26]. p. 141–
149. [doi: 10.1007/978-3-030-04363-6_9]
180. Gaudet-Blavignac C, Foufi V, Wehrli E, Lovis C. De-identification of French medical
narratives. Swiss Medical Informatics [Internet] 2018 Sep 8 [cited 2019 Feb 26];34(00). [doi:
10.4414/smi.34.00417]
181. Morrison LG, Hargood C, Pejovic V, Geraghty AWA, Lloyd S, Goodman N, Michaelides DT,
Weston A, Musolesi M, Weal MJ, Yardley L. The Effect of Timing and Frequency of Push
Notifications on Usage of a Smartphone-Based Stress Management Intervention: An
Exploratory Trial. PLOS ONE 2017 Jan 3;12(1):e0169162. [doi:
10.1371/journal.pone.0169162]
182. Bidargaddi N, Almirall D, Murphy S, Nahum-Shani I, Kovalcik M, Pituch T, Maaieh H,
Strecher V. To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push
Notifications to Increase Proximal Engagement With a Mobile Health App. JMIR Mhealth
Uhealth [Internet] 2018 Nov 29 [cited 2019 Jan 31];6(11). PMID:30497999
183. Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a
path to validation. npj Digital Medicine 2019 May 13;2(1):1–9. [doi: 10.1038/s41746-019-
0111-3]
184. Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and
selecting mobile health apps: strategies for healthcare providers and healthcare organizations.
Transl Behav Med 2014 Dec 1;4(4):363–371. [doi: 10.1007/s13142-014-0293-9]
185. Hasson F, Keeney S, McKenna H. Research guidelines for the Delphi survey technique. J Adv
Nurs 2000 Oct;32(4):1008–1015. PMID:11095242
186. Garvelink MM, Ter Kuile MM, Louwé LA, Hilders CGJM, Stiggelbout AM. A Delphi
consensus study among patients and clinicians in the Netherlands on the procedure of informing
young breast cancer patients about Fertility Preservation. Acta Oncol 2012 Nov;51(8):1062–
1069. PMID:23050612
187. Miller S, Ainsworth B, Yardley L, Milton A, Weal M, Smith P, Morrison L. A Framework for
Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions:
Viewpoint. Journal of Medical Internet Research 2019;21(2):e10966. [doi: 10.2196/jmir.10966]
https://preprints.jmir.org/preprint/14599 [unpublished, non-peer-reviewed preprint]
JMIR Preprints Issom et al
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Introduction: Sickle cell disease (SCD) is a chronic disease that can cause significant complications including acute chest syndrome, recurrent pain and stroke. Current guidelines for the use of chronic transfusions include primary and secondary prevention of stroke. Although there is currently limited support for the routine use of transfusions for acute vaso-occlusive crisis (VOC), there has been increasing use of chronic transfusions as an alternative treatment for recurrent VOC. Moreover, there is evidence that patients on chronic transfusions have less VOC. We sought to review the outcomes of patients at our institution placed on chronic transfusions for non-stroke related indications. Methods: We performed a retrospective cohort study to summarize clinical and nonclinical features of sickle cell patients on transfusions for non-stroke related complications. Demographic, clinical, and laboratory information were summarized. Acute care events per month were calculated for both the year prior and up to one year following initiation of chronic transfusions. Acute care events were defined as emergency department visits or hospitalization. Results: Of the 378 patients with SCD treated in the pediatric specialty clinic, there were 21 patients being either chronically transfused or exchange transfused. Six (20%) of these patients were initiated on chronic blood transfusions (CBT) for recurrent pain crisis (median age = 12, range 8 to 17). One of these patients also had suspected hepatic sequestration. All patients were type SS and had been treated with hydroxyurea (HU) for an average length of 6.5 years (range 1 to 12 years) at a mean dose of 25 mg/kg (SD 4) prior to initiation of CBT. All patients continued on HU during chronic blood transfusions. Patients were on chronic transfusions for a median of 11 months (range 3 to 58 months) with mean %S while on transfusions of 39.6% (SD 10). Patients were transfused on average every 5 weeks (range 4 to 6 weeks). Following initiation of transfusions, 50% were started on chelation based on criteria of having a ferritin >1000 ng/mL. Mean peak ferritin was not significantly increased in the year following the start of CBT (513 ng/mL ± 343 vs. 1260 ± 934, p=0.13). There was one new alloantibodies (anti-Jk) reported following initiation of CBT, which developed within 3 months. Acute care visits per month were significantly higher in the year prior as compared to after initiation of chronic blood transfusions (1.04 ± 0.45 vs. 0.28 ± 0.22, respectively; p=0.006) (Figure 1). Discussion: We found that patients started on chronic transfusions for pain crisis had a non-significant increase in peak ferritin and a significant reduction in acute care visits. Prior to CBT, all patients had been initiated on hydroxurea (mean dose of 25mg/kg) in an attempt to treat recurrent VOC. However, following therapy for an average of 6.5 years, patients were placed on CBT to prevent further acute care visits and reduce morbidity. All patients were continued on HU while on CBT with no dose adjustment or effort to titrate to maximum tolerated dose. While on CBT, patients had a mean %S of 40%, which is higher than the recommended goal of 30% for stroke related indications. Importantly, despite the higher mean %S, there was a drastic and significant decrease in acute care visits. It should be noted that although only three patients (50%) of patients were placed on chelation, the remaining three had been on transfusions for less than or equal to 6 months and likely to require chelation with continued therapy. The expected elevated ferritin highlights the difficulty in long-term treatment with chronic transfusion and risk for eventual iron overload. The balance between the clinical benefit and potential long-term complications leads to individual assessment of the risks and benefits prior to initiation of chronic transfusions for VOC. These results advocate for the use of prospective studies to evaluate the role for chronic transfusions for non-stroke related indications in SCD. Figure 1 Figure 1. Disclosures Shah: Novartis: Speakers Bureau.
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The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
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Personalized health research depends on aggregated sets of personal data from millions of people. Given that personal data can be copied, individuals are entitled to copies of their data and individuals are the ultimate aggregators of all their personal data, citizens are elevated to new roles at the center of health research and a novel personal data economy. There, citizens, not some multinational company, control the use of and benefit from the intellectual and economic value of these data. Here, I show that democratically controlled nonprofit personal data cooperatives provide a governance and trust framework for data sharing and data donation. They also provide a means of attaining improved precision health and a digital society in which socio-economic asymmetries can be balanced.