Open access peer-reviewed chapter
Abstract
This chapter will provide a state of the art of digital health interventions for people with sickle cell disease. It will use WHO classification of digital health interventions to elaborate on existing intervention, the gaps, and how technology could be useful to support people with sickle cell disease. A description of the existing possibilities, the current trends, and the future opportunities will be provided. As well, methodologies to increase patient adherence to digital health interventions, the importance of participatory approaches, open innovation, and patient-led approaches to designing such interventions will be discussed. Importantly, a holistic/planetary health approach will be chosen to introduce the subject and ensure to keep a broad eye on the domain and to include sustainability challenges.
Keywords
- digital health interventions
- artificial intelligence
- self-management
- integrated care
- patient empowerment
- participatory design
1. Introduction
Sickle cell disease (SCD) is the world’s most common monogenic pathology. SCD is a complex multisystem red blood cell disorder, which leads many patients to experience acute life-threatening dysfunctions and chronic complications. The hallmarks of the disease are vaso-occlusive pain crises (VOCs), avascular necrosis, hemolytic anemia, endothelial dysfunction, transient ischemic attacks, acute chest syndrome (ACS), bacterial infections, and chronic inflammation. These complications cause reduced Health-Related Quality of Life (HRQoL) and increased mortality [1].
To limit complications and reduce early mortality, integrated care, also known as comprehensive and coordinated care (CPC), is paramount. Indeed, CPC has demonstrated efficacy in improving health outcomes of people affected by multisystemic diseases such as diabetes [2]. In the case of SCD, CPC shall cover different forms of care provision, including acute care (e.g., treatment of VOCs), usual care, defined as routine evaluations and treatments (e.g., transcranial Doppler testing), chronic transfusion therapies, or disease-modifying treatments (e.g., hydroxyurea) [3]. The latter treatments remain underutilized and limited [4]. Access to curative options (e.g., hematopoietic stem-cell transplantations, gene therapy) remains restrained [5]. As well, specific comprehensive and preventative care (CPC) programs are inconsistently available for most people with SCD [6]. Consequently, many patients do not receive adequate treatment, as outlined by evidence-based guidelines, and may suffer from mistrust, stigmatization, or neglect [7].
As a result, patients must rely heavily on themselves and their community to manage symptoms, maintain control over the course of the disease, and preserve an acceptable level of Health-Related Quality of Life (HRQoL) [4, 8]. Because of its complexity, SCD self-management can be particularly challenging to master [9]. Indeed, self-management covers various aspects, ranging from self-care in hospitalization, post-hospitalization care, hospital-at-home care, preventive care, health maintenance, or self-care aspects such as self-monitoring, self-diagnosis, self-treatment [10, 11].
For instance, every day, patients should take prescribed drugs, follow a healthy diet, hydrate frequently, avoid strenuous exercise, check indicators of anemia, which could manifest by increased pallor, dark urine color, or jaundice. As well, patients should observe warning signs of complications, monitor and treat their chronic or acute pain with prescribed medication and other nondrug therapies (e.g., breathing exercise, physical therapy, phytotherapy), or pay attention to numerous and omnipresent potential precipitants of VOCs [9, 10].
Such triggers include certain food, stress, infections, acidosis, dehydration, fatigue, hypoxia, alcohol intoxication, daytime exertion, exercise, airline travel, altitude, pregnancy, nocturnal hypoxemia, or environmental factors including pollution, exposure to the elements, change of temperatures, wind, or humidity [12, 13, 14, 15]. The quantity and complexity of these factors demands autonomy, resilience, high self-efficacy, and empowerment levels, as well as adequate psychosocial support [16, 17].
Due to their socioeconomic positions, most patients lack such skills and consequently adherence to recommended treatments, self-care recommendations, or attendance to routine clinic appointments (RCA) remains low [18]. However, some patients have proven to be exceptions. As long-term users of healthcare services, some acquired knowledge that made them efficient navigators of healthcare systems, while gaining singular expertise in self-management, succeeding to learn how they could improve their HRQoL [19, 20].
As Ballas et al. [20] pointed out, these strongly equipped patients succeeded to identify their own best self-care practices or became able to notice and react to warning signs from their body. Some would journal their symptoms, manage their pain successfully, follow adequate dietary habits, practice moderate physical activity, or attend RCA [21, 22].
The complex nature of SCD such as a high individual and population variability, or a rising number of people affected due to lack of systematic screening, poor awareness about the disease or migrations of populations [23], its scientific and social history, its high economic burden, and the diversity of the endogenous and exogenous factors combining to influence health outcomes (e.g., multifaceted health disparities, stigma, racism, underfunding) require researchers and policymakers to take care of SCD through various lenses [24].
For all these reasons, it appears particularly relevant and urgent to investigate cost-effective and easily scalable interventional strategies to prevent serious complications, avoid hospitalizations, and more generally, empower and improve the quality of life of people with SCD.
2. Strategies to empower people with SCD
Given SCD global burden, the complexity, and number of challenges to be overcome (e.g., neglect, continuous rise, stigma, underfunding) [25, 26, 27, 28, 29], it is important to prioritize effective interventions and shorten the timeframe for action. Fortunately, after the great advances in the 1970s (e.g., American civil rights organizations promised to vote for the future President Nixon if policies and funding were put in place to counter SCD), FDA approval of Hydroxyurea in 1995 [30], repeated calls to action during the last couple of years and recent advances in genomics [26, 27, 28, 31], SCD has been increasingly regarded as an interesting model to study and address from different angles in particular since 2017 [32, 33, 34, 35, 36, 37, 38, 39]. This renewed interest opens hope that research advances could enable possibilities of generalization, reutilization, and transfer to other NCDs, while subsequently empowering more populations and enhancing life opportunities.
To move forward in a coordinated manner, and to effectively shape health and social policies aiming at empowering underserved populations, global health agents need a clear strategy, a unified agenda, and a strong commitment with the adequate resources. To proceed, the American National Academy of Sciences [40], the WHO, and various scholars have proposed priority targets for SCD [4, 26, 41, 42, 43, 44].
These recommendations range from installing national surveillance programs, promoting better use of disease-modifying treatments, increasing the funding of disease-specific research, raising awareness, improving pain relief management, tackling systemic violence (e.g., stigma, racism), improving training for healthcare providers, increasing the numbers of specialists, promoting access to comprehensive and preventative care, empowering communities, creating therapeutic education programs, supporting self-care management, and encouraging the use of Information and Communication Technologies (ICTs) [4, 26, 41, 42].
3. Digitalization as a lever to reduce health inequalities
As research suggests (e.g., health impact pyramid), interventions that could empower as many individuals as possible should be prioritized [45, 46]. Today, as the International Telecommunication Union (ITU) illustrates [47], Information and Communications Technologies (ICTs), thanks to their increasingly important presence in people’s daily lives (e.g., smartphones, smart sensors), could play major roles in driving rapid social transformation, empowering populations from their social determinisms, and accelerating the achievement of challenges of the centuries such as the Sustainable Development Goals (SDGs) [48].
Recent years have seen the rise of digital technologies in the healthcare sector (e.g., computerized drug prescription systems reducing risks of medical errors, remote surgery, early detection of seizures or heart failures) [49]. To achieve most vital SDGs such as
As the WHO acknowledged, thanks to their potential to be scaled up rapidly to reach large numbers of people, digital technologies hold the potential to accelerate a decline in health inequalities through disease-specific actions [50].
Notably, stressing the importance of ensuring that such tools provide an evidence-based improvement in health outcomes, the WHO emphasizes that
The following sections describe WHO’s classification of digital health interventions (DHIs) and illustrate what specific DHI characteristics might support SCD-important challenges [51].
4. Digital health interventions
In the context of low availability of specialized healthcare service and thanks to potential wide reach and relatively low cost, digital health interventions (DHIs) could offer a potential route to help patients become experts in self-management [52, 53, 54, 55].
As per WHO definition, digital health encompasses various concepts including eHealth (i.e., effective use of information and communication for health-related purposes), mHealth (i.e., provision of information and services through mobile technologies), or telemedicine (i.e., remote practice of medical interventions or examination) [56, 57, 58]. Additionally, digital health includes computing techniques (e.g., artificial intelligence, natural language processing, interoperability), which assist in extracting and making sense of a large volume of health-related data (e.g., genomic sequencing, medical imaging, health records, medical devices, wearables, pharmaceutical research, search engines, online patient communities, healthcare payor records) [59, 60]. Health interventions using digital technologies can be classified in four categories, based on the targeted primary user.
Overall, DHIs are increasingly used to provide effective, safe, and scalable interventions improving chronic patient’s health outcomes [61]. However, prior studies found that patients who stand to benefit most from DHIs were least likely to download or use them [62, 63]. With discontinued or inconsistent use, it is less likely that the intended effectiveness of DHIs can be realized.
Studies have shown that DHIs responding effectively to patients’ specific health problems while being easy to use had better long-term engagement [64, 65]. Interestingly, Stenft et al. [66] demonstrated that engagement in DHIs was higher among patients who were dissatisfied with healthcare service delivery (HSD). Additionally, Lee et al. [67] suggested that these patients particularly desired to get access to novel technologies and would request specific digital health services keeping them away from hospitals. As prior studies suggest [68, 69, 70], including patients’ input from the start to the end of design, development and evaluation phases could help creating DHIs that are desired, used, and engaging in the long run.
The potential of digital health technologies for SCD is far from being fully exploited, and patients’ experiential knowledge is largely untapped. Indeed, most DHIs focus on symptom monitoring or medication adherence [71]. However, given the multisystemic nature of SCD, its multiple vulnerability factors, its clinical variability, and severe comorbidities, it is paramount to encompass every components of self-management and to comprehensively support the day-to-day and long-term self-care needs of patients.
5. Categories of digital health interventions
5.1 Category 1: interventions for clients
The first category defines interventions for clients, i.e., individuals such as patients, citizens, or informal caregivers. The aim of such DHIs ranges from improving access to care for remote populations, disseminating targeted education to providers and patients through eHealth, and supporting patient empowerment with mHealth self-management apps targeting smoking cessation, medication dosage calculators, support for medication adherence, symptoms self-monitoring, or remote medical consultations, see Figure 1. For instance, Jacob et al.recently demonstrated how telemedicine could help deliver CPC to remote children with SCD in underserved areas [72].
Figure 1.
Digital health interventions for clients—WHO [
Today, mHealth apps are flooding app stores, with more than 200 new apps each day, and a sharp rise during the COVID-19 outbreak [73, 74, 75]. Many of these apps can significantly improve health outcomes and support people with diverse medical conditions [54, 76].
In the case of SCD, some of the existing DHIs could be those that play a role in targeting individual behavioral factors, which are known to cause more than 35% of premature death and are responsible for a large proportion of disease burden [77]. For instance, DHIs could be greatly adapted to help reduce knowledge-based inequalities among individuals with SCD (e.g., support therapeutic education and self-management, disseminate disease-specific knowledge), or those that could improve awareness about the disease. Several authors have demonstrated tools to support mental health among people with SCD. Some systems utilize text-based technology to conduct psychological interventions [78], while others propose effective ways to conduct cognitive behavioral therapy remotely using mobile apps [79].
As Figure 2 illustrates [8], self-care elements of disease management are particularly important when designing mHealth apps targeting patients. Indeed, because good self-care practices lead to positive health outcomes, DHIs, thanks to their relatively low cost and wide reach, could be a potential route to support people with SCD’s numerous self-care management tasks, for instance, by improving their health literacy or increasing their self-efficacy levels [80, 81, 82].
Figure 2.
Role of self-care in sickle cell disease—Matthie et al. [
Findings from multiple studies have identified an increasing number of digital health interventions aiming to support people with SCD [39, 71, 83]. However, the potential of digital health technologies for people with SCD is far from being fully exploited but has started to accelerate in the recent years. Indeed, most DHIs focus on symptom monitoring or medication adherence, but promising studies described how self-management mHealth apps could enhance patient engagement in disease management [71, 84, 85]. Although not comprehensive, this study provides useful evidence on the needs and wants of adults with SCD.
However, similar to other chronic diseases, the frequency of downloads and long-term adoption remains in its infancy [84]. In an attempt to tackle such an important issue, Philips et al. [86] and Issom et al. [87] proposed to put more efforts in the direction of patient-centered and patient-led approaches when designing digital health interventions, so we can better understand factors encouraging adoption [88]. Authors suggested methodologies such as the Behavior Change Wheel to understand human and societal factors important to take into account to reach higher rates of appreciation and increased motivation in using DHIs by patients [89]. As well, authors suggested the use of systems using lower energetical resources and easy to learn (e.g., chatbots) to foster adherence and reduce costs. Still, more research is needed and important patient-important needs (e.g., reduce the incidence of pains, self-care support, improve self-efficacy, increase disease-specific knowledge, support transition to adulthood) are yet to be tackled effectively [90, 91], but existing research studies are encouraging, showing potential in increasing important these important outcomes [92, 93, 94]. Johnson et al. [95] demonstrated the feasibility of an innovative way to mitigate pain crises by using wearable devices signals to monitor pain and attempt at predicting symptoms using machine learning approaches. Similarly, Ajayi et al.showed how such systems could be used to collect a wide range of biophysical measurements [96]. Yet, today, most apps for people with SCD reported in the scientific literature focus on medication adherence or the monitoring of symptoms [71, 84]. Hankins and Shah analyzed the matter and the importance of adherence and proposed a framework to tackle medication adherence using mobile apps [97].
5.2 Category 2: interventions for healthcare providers
Research is scarce regarding DHIs for healthcare providers specialized in SCD [98]. Nevertheless, such digital health interventions could be helpful in supporting the scarcity of healthcare professionals specialized in SCD care. As Figure 3 illustrates, the WHO promotes the development of tools such as micro-learning apps for healthcare provider training, decision support systems, or infrastructure for remote consultations. Such DHIs hold the potential to increase the pool of specialized healthcare providers, improve patient-provider communication, or coordinate care. Researchers have investigated tools to improve medical decision-making, for instance, by providing guidance on pain management and curative treatment [98, 99] or by supporting diagnostic of sickle cell disease using digital PCR or mobile microscopy [100, 101].
Figure 3.
Digital health interventions for healthcare providers—WHO [
5.3 Category 3: interventions for health systems or resource managers
In the case of SCD, DHIs for health systems managers (Figure 4) could be very useful to support the collection of populational epidemiological data (i.e., civil registration, mortality and morbidity data, geographical prevalence) and public health policies to provide patients with targeted support. As well, such DHIs could be helpful in monitoring the quality of SCD care. Today, advances in specific DHIs to help manage disease-specific logistics (e.g., blood products, oxygen tanks, epidemiological data) or to provide targeted information to clinicians susceptible to encounter people with SCD (e.g., clinical guidelines, emergency protocols, screening equipment) are lacking.
Figure 4.
Digital health interventions for health system managers—WHO [
5.4 Category 4: interventions for data services
Data management is a crucial but challenging aspect of DHIs. As Figure 5 illustrates, DHIs for data services could benefit global health by (1) allowing patients to own their health data; (2) developing robust governance processes that ensure respect of values and principles in the use of data and risk minimization; (3) creating systems that allow for automated collection and aggregation of data; (4) implementing data interoperability standards; (5) allowing anonymized data sharing in real time; and, (6) formatting and representing data so that they can be easily used by patients, healthcare providers, entrepreneurs, or policymakers [102].
Figure 5.
Digital health interventions for data services—WHO [
In the case of SCD, DHIs could be crucial to foster the establishment of national data collection of burden of SCD. DHIs focusing on information management could involve supporting national disease surveillance programs, collecting of mortality and morbidity incidences, synthesizing PROMS, analyzing forecasting (e.g., health outcomes, prevalence), and mapping of socioeconomic assistance to the SCD population or promote the creation of health data cooperatives (e.g., databases owned, partly financed, and controlled by the people who use it) [103, 104, 105].
6. Structural barriers to the effectiveness of digital health interventions
Setting up effective, sustained, and globally scalable digital health interventions that can contribute to the reduction of disparities is challenging. DHI projects often struggle to scale up sufficiently and are often unsustainable for targeted communities once donor funding ceases [106]. As a result, some projects succeed in transforming the lives of those who have been able to access the technologies, while also disadvantaging those without access to them [107]. Consequently, the number of successfully implemented digital interventions that move beyond the pilot or feasibility stage remains limited. Equally, if and when they pass these initial stages [108], scholars have identified that few users use the technologies for a prolonged period of time, despite offering high potential to improve health outcomes and empowering patients [109, 110, 111, 112]. As a result, those who would most benefit from such apps often underuse them [63, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]. The main reasons for low adoption include:
lack of personalization;
lack of perceived added value;
deficient or inadequate infrastructures (e.g., access to Internet connectivity);
lack of equipment, low literacy (e.g., digital, health);
technology gap issues;
maladaptation to local context;
hidden costs;
unwanted data sharing;
insufficiently useful features;
inability to sustain required attention for longer periods of time;
financial unsustainability;
poor usability (e.g., suboptimal design, manual data entry) [113, 125, 126, 127, 128].
These observations may partially be explained using such top-down approaches to design interventions and decision-making. Similarly, in more user-centered design paradigms [120], end users are generally included as partners, from the beginning of the project, during the design process, or when the development of the intervention is complete, in order to test usability or safety, but not as decision-makers. With these processes, i.e., when end users are not contributing to decision-making, it is likely that their important interests are not put as central, prioritized, nor fully acknowledged. Subsequently, this results in technologies that may be disempowering, alienating, or irrelevant to the end users, therefore increasing the risk they will abandon them.
7. People with neglected diseases: from technology enthusiasts to patient innovators
Individuals with orphan diseases and affected by diabetes have been leaders in fostering these approaches using crowdsourcing (i.e., individuals of varying knowledge and skills voluntarily undertaking a task for mutual benefit) or crowdsensing techniques (i.e. using smartphones to sense, collect, and analyze data) to accelerate knowledge discovery and promote patient empowerment [129, 130].
The most famous patient-led innovations are PatientsLikeMe (i.e., the first online community for people with Amyotrophic lateral sclerosis), the #WeAreNotWaiting and #OpenAPS movements (i.e., do-it-yourself methods for creating an artificial pancreas system) [131], the Nightscout project (i.e., a parent-developed solution for remote blood glucose monitoring), or the Crohnology project (e.g., an online platform for patient-to-patient information sharing).
These projects have rapidly reached high number of users, some of them have been acquired by companies in order to ensure financial sustainability or scale-up [132]. These early success stories highlight the importance of user-driven initiatives in research and development and show high potential for patient empowerment [133].
8. Disparate evidence
In June 2019, the WHO and the Organization for Economic Co-operation and Development (OECD) from the European Commission (CE) supported PLR approaches, concluding that digital health interventions should be designed to meet the needs of people and health systems and suit local contexts [134]. Aligned with these conclusions, bottom-up models such as PLR or the free innovation paradigm are becoming increasingly popular [135, 136], exemplifying how researchers in academia, industry, and patient communities can create patient-centric solutions and reduce the disease burden together. Only a few PLR initiatives are mentioned in the scientific literature about SCD, suggesting that most DHIs have been driven by healthcare professionals of software implementers [39, 71]. With their potential of being implemented and scaled up rapidly globally when adequately implemented [137, 138, 139], DHIs targeting people with SCD may hold the capacity to address various health inequalities faced by historically disadvantaged populations.
9. Conclusions
Digital health interventions to empower with sickle cell disease remain relatively scarce if we look at their number compared with diseases with higher prevalence, but current research shows a strong potential for improving health outcomes. The important aspects to work on are the human factors allowing a strong adhesion in its interventions and to seek to better understand how to use artificial intelligence to lead to the prediction of symptoms and then to prevent them.
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References
- 1.
Piel FB, Steinberg MH, Rees DC. Sickle cell disease. New England Journal of Medicine. 2017; 376 :1561-1573 - 2.
Grosse SD, Schechter MS, Kulkarni R, et al. Models of comprehensive multidisciplinary care for individuals in the United States with genetic disorders. Pediatrics. 2009; 123 :407-412 - 3.
Sandhu MK, Cohen A. Aging in sickle cell disease: Co-morbidities and new issues in management. Hemoglobin. 2015; 39 :221-224 - 4.
Kanter J, Kruse-Jarres R. Management of sickle cell disease from childhood through adulthood. Blood Reviews. 2013; 27 :279-287 - 5.
Leonard A, Tisdale J, Abraham A. Curative options for sickle cell disease: Haploidentical stem cell transplantation or gene therapy? British Journal of Haematology. 2020; 189 (3):408-423. DOI: 10.1111/bjh.16437 - 6.
de Montalembert M. Current strategies for the management of children with sickle cell disease. Expert Review of Hematology. 2009; 2 :455-463 - 7.
Haywood C, Lanzkron S, Bediako S, et al. Perceived discrimination, patient trust, and adherence to medical recommendations among persons with sickle cell disease. Journal of General Internal Medicine. 2014; 29 :1657-1662 - 8.
Matthie N, Jenerette C, McMillan S. Role of self-care in sickle cell disease. Pain Management Nursing: Official Journal of the American Society of Pain Management Nurses. 2015; 16 :257-266 - 9.
Blakemore S. Self-care is key in sickle-cell disease. Emergency Nurse: The Journal of the RCN Accident and Emergency Nursing Association. 2016; 24 :9 - 10.
Jenerette CM, Brewer C, Leak AN. Self-Care recommendations of middle-aged and older adults with sickle cell disease. Nursing Research and Practice. 2011 Aug 15; 2011 :e270594 - 11.
Brown S-E, Weisberg DF, Balf-Soran G, et al. Sickle cell disease patients with and without extremely high hospital use: Pain, opioids, and coping. Journal of Pain and Symptom Management. 2015; 49 :539-547 - 12.
Ahmadi M, Jahani S, Poormansouri S, et al. 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 :18-26 - 13.
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; 24 :561-587 - 14.
Jenerette CM, Lauderdale G. Successful aging with sickle cell disease: Using qualitative methods to inform theory. Journal of Theory Construction & Testing. 2008; 12 :16-24 - 15.
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 :58-63 - 16.
Mora MA, Luyckx K, Sparud-Lundin C, et al. Patient empowerment in young persons with chronic conditions: Psychometric properties of the Gothenburg Young Persons Empowerment Scale (GYPES). PLoS One. 2018; 13 :e0201007 - 17.
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; 16 :418-424 - 18.
Cecilio SG, Pereira SA d S, Pinto V d S, et al. Barriers experienced in self-care practice by young people with sickle cell disease. Hematology, Transfusion and Cell Therapy. 2018; 40 :207-212 - 19.
Kanter J, Issom D-Z. Mind Over Matter: One Person’s Journey From Patient to Change-Maker. 2018. Available from: Thehematologist/Profiles/8892.aspx [Accessed: October 17, 2018] - 20.
Ballas SK. Self-management of sickle cell disease: A new frontier. Journal of the National Medical Association. 2010; 102 :1042-1043 - 21.
Amertil NP. Self-management in Adult Clients with Sickle Cell Disease (SCD). 1997. Available from: http://books.google.se/books/about/Self_management_in_Adult_Clients_with_Si.html?id=H-HdtgAACAAJ&pgis=1 [Accessed: September 24, 2014] - 22.
Cordeiro RC, Ferreira SL, Santos AC d C, et al. Experiences of illness among individuals with sickle cell anemia and self-care strategies. Acta Paulista de Enfermagem. 2014; 27 :499-504 - 23.
Royal CDM, Babyak M, Shah N, et al. Sickle cell disease is a global prototype for integrative research and healthcare. Advanced Genetics. 2021; 2 :e10037 - 24.
Berghs M, Ola B, Chavez ACD, et al. Time to apply a social determinants of health lens to addressing sickle cell disorders in sub-Saharan Africa. BMJ Global Health. 2020; 5 :e002601 - 25.
Grosse SD, Odame I, Atrash HK, et al. Sickle cell disease in Africa: A neglected cause of early childhood mortality. American Journal of Preventive Medicine. 2011; 41 :S398-S405 - 26.
Lopez AD, Williams TN, Levin A, et al. Remembering the forgotten non-communicable diseases. BMC Medicine. 2014; 12 :200 - 27.
Piel FB, Weatherall DJ. Sickle-cell disease: A call to action. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2015; 109 :355-356 - 28.
Carroll CP. Sickle cell disease: Time to act. Pain Management. 2011; 1 :291-293 - 29.
Chakravorty S, Williams TN. Sickle cell disease: A neglected chronic disease of increasing global health importance. Archives of Disease in Childhood. 2015; 100 :48-53 - 30.
Siddiqi A-E-A, Jordan LB, Parker CS. Sickle cell disease—The American Saga. Ethnicity & Disease. 2013; 23 :245-248 - 31.
Executive Board 117. Executive Board 117th session, Geneva, 23-27 January 2006: Resolutions and decisions, annexes [Internet]. Geneva: World Health Organization; 2006. Available from: https://apps.who.int/iris/handle/10665/20686 - 32.
Leonard A, Tisdale JF. A pause in gene therapy: Reflecting on the unique challenges of sickle cell disease. Molecular Therapy. 2021; 29 :1355-1356 - 33.
Salinas Cisneros G, Thein SL. Recent advances in the treatment of sickle cell disease. Frontiers in Physiology. 2020; 11 . DOI: 10.3389/fphys.2020.00435/full - 34.
Brodsky RA, DeBaun MR. Are genetic approaches still needed to cure sickle cell disease? The Journal of Clinical Investigation. 2020; 130 :7-9 - 35.
Carden MA, Little J. Emerging disease-modifying therapies for sickle cell disease. Haematologica. 2019; 104 :1710-1719 - 36.
Oniyangi O, Cohall DH. Phytomedicines (medicines derived from plants) for sickle cell disease. Cochrane Database of Systematic Reviews (Online). 2015 Jan; 4 :CD004448 - 37.
Imaga NA. Phytomedicines and nutraceuticals: Alternative therapeutics for sickle cell anemia. The Scientific World Journal. 2013; 2013 :e269659 - 38.
Williams H, Tanabe P. Sickle cell disease: A review of nonpharmacological approaches for pain. Journal of Pain and Symptom Management. 2016; 51 :163-177 - 39.
Lobitz S, Curtis K, Sostmann K. Digital Health Interventions (DHIs) to Support the Management of Children and Adolescents with Sickle-Cell Disease. In: Curtis K, editor. Sickle Cell Disease [Internet]. Rijeka: IntechOpen; 2016 [cited 2022 Aug 17]. p. Ch. 14. Available from: https://doi.org/10.5772/64315 - 40.
Martinez RM, Osei-Anto HA, McCormick M. Summary Table of Strategic Plan and Blueprint for Sickle Cell Disease Action. Addressing Sickle Cell Disease: A Strategic Plan and Blueprint for Action. National Academies Press (US); 2020 Available from: https://www.ncbi.nlm.nih.gov/books/NBK566458/ - 41.
Aygun B, Odame I. A global perspective on sickle cell disease. Pediatric blood & cancer. 2012; 59 :386-390 - 42.
Kanter J, Smith WR, Desai PC, et al. Building access to care in adult sickle cell disease: Defining models of care, essential components, and economic aspects. Blood Advances. 2020; 4 :3804-3813 - 43.
Regional Committee for Africa 60. Sickle-Cell Disease: A strategy for the WHO African Region. 2011. Available from: https://apps.who.int/iris/handle/10665/1682 [Accessed: June 2, 2021] - 44.
National Academies of Sciences E. Addressing Sickle Cell Disease: A Strategic Plan and Blueprint for Action. 2020. Available from: https://www.nap.edu/catalog/25632/addressing-sickle-cell-disease-a-strategic-plan-and-blueprint-for [Accessed: June 2, 2021] - 45.
Naik A, Bagga S, Sénécal M. PBI15 economic burden of sickle cell disease: A targeted global literature review. Value in Health. 2019; 22 :S420 - 46.
Lee S, Vania DK, Bhor M, et al. Patient-reported outcomes and economic burden of adults with sickle cell disease in the United States: A systematic review. International Journal of General Medicine. 2020; 13 :361-377 - 47.
ITU. ICTs to achieve the United Nations Sustainable Development Goals. ITU News. 2018. Available from: https://news.itu.int/icts-united-nations-sustainable-development-goals/ [Accessed: June 30, 2021] - 48.
Postcapitalism | Paul Mason | Macmillan. US Macmillan. Available from: https://us.macmillan.com/postcapitalism/paulmason/9780374536732 [Accessed: May 27, 2021] - 49.
FDA. What is Digital Health?. FDA. FDA; 2020. Available from: https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health [Accessed: June 17, 2021] - 50.
WHO | WHO Guideline: Recommendations on digital interventions for health system strengthening [Internet]. WHO. World Health Organization; 2019 [cited 2020 Jun 17]. Available from: http://www.who.int/reproductivehealth/publications/digital-interventions-health-system-strengthening/en/ - 51.
WHO. Classification of digital health interventions v1.0 / A shared language to describe the uses of digital technology for health. World Health Organization; 2018. Available from: http://www.who.int/reproductivehealth/publications/mhealth/classification-digital-health-interventions/en/ - 52.
Anderson K, Burford O, Emmerton LL, et al.Mobile health apps to facilitate self-care: A qualitative study of user experiences. van Ooijen PMA, editor. PLoS One 2016; 11 :e0156164 - 53.
Wu Y, Yao X, Vespasiani G, et al. 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; 5 :e35 - 54.
Kitsiou S, Paré G, Jaana M, et al. Effectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews. Li D, editor. PLOS ONE. 2017; 12 :e0173160 - 55.
Free C, Phillips G, Felix L, et al. The effectiveness of M-health technologies for improving health and health services: A systematic review protocol. BMC Research Notes. 2010; 3 :250 - 56.
Woods KF, Johnson JA, Kutlar A, et al. Sickle cell disease telemedicine network for rural outreach. Journal of Telemedicine and Telecare. 2000; 6 :285-290 - 57.
Betjeman TJ, Soghoian SE, Foran MP. mHealth in Sub-Saharan Africa. International Journal of Telemedicine and Applications. 2013; 2013 :482324 - 58.
Bediang G, Perrin C, Ruiz de Castañeda R, et al. The RAFT telemedicine network: Lessons learnt and perspectives from a decade of educational and clinical services in low- and middle-incomes countries. Frontiers in Public Health. 2014; 2 :180 - 59.
Kruse CS, Goswamy R, Raval Y, et al. Challenges and opportunities of big data in health care: A systematic review. JMIR Medical Informatics. 2016; 4 :e38 - 60.
Suciu G, Suciu V, Martian A, et al. Big data, internet of things and cloud convergence—An architecture for secure E-health applications. Journal of Medical Systems. 2015; 39 :141 - 61.
Murray E, Hekler EB, Andersson G, et al. Evaluating digital health interventions: Key questions and approaches. American Journal of Preventive Medicine. 2016; 51 :843-851 - 62.
Vaghefi I, Tulu B. The continued use of mobile health apps: Insights from a longitudinal study. JMIR Mhealth Uhealth. 2019:7 - 63.
Robbins R, Krebs P, Jagannathan R, et al. Health app use among US mobile phone users: Analysis of trends by chronic disease status. JMIR mHealth and uHealth. 2017; 5 (12):e197 - 64.
Klein SK, McCarthy DM, Hostetter MH. Taking Digital Health to the Next Level: Promoting Technologies That Empower Consumers and Drive Health System Transformation. New York, NY United States: Commonwealth Fund; 2014 - 65.
Brahmbhatt R, Niakan S, Saha N, et al. Diabetes mHealth apps: Designing for greater uptake. Studies in Health Technology and Informatics. 2017; 234 :49-53 - 66.
Senft N, Everson J. eHealth engagement as a response to negative healthcare experiences: Cross-sectional survey analysis. Journal of Medical Internet Research. 2018; 20 :e11034 - 67.
Lee AV, Moriarty JP, Borgstrom C, et al. What can we learn from patient dissatisfaction? Analysis of dissatisfying events at an academic medical center. Journal of Hospital Medicine. 2010; 5 :514-520 - 68.
Ward R, Taha KM. Patient Involvement as Experts in the Development and Assessment of a Smartphone App as a Patient Education Tool for the Management of Thalassemia and Iron Overload Syndromes. 2016; Available from: http://www.tandfonline.com/action/journalInformation?journalCode=ihem20 . 10.1080/03630269.2016.1217875 - 69.
Burton C, Rycroft-Malone J. An untapped resource: Patient and public involvement in implementation. International Journal of Health Policy and Management. 2015; 4 :845-847 - 70.
Tatara N, Arsand E, Bratteteig T, et al. Usage and perceptions of a mobile self-management application for people with type 2 diabetes: Qualitative study of a five-month trial. Studies in Health Technology and Informatics. 2013; 192 :127-131 - 71.
Badawy SM, Cronin RM, Hankins J, et al. Patient-centered ehealth interventions for children, adolescents, and adults with sickle cell disease: Systematic review. Journal of Medical Internet Research. 2018; 20 :e10940 - 72.
Jacob SA, Carroll AE, Bennett WE. A feasibility study of telemedicine for paediatric sickle cell patients living in a rural medically underserved area. Journal of Telemedicine and Telecare. 2021; 27 :431-435 - 73.
Mgbako O, Miller EH, Santoro AF, et al. COVID-19, Telemedicine, and Patient Empowerment in HIV Care and Research. AIDS and Behavior. 2020; 24 :1990-1993 - 74.
Faezipour M, Abuzneid A. Smartphone-based self-testing of COVID- 19 using breathing sounds. Telemedicine and e-Health. 2020 Oct 1; 26 (10):1202-1205. DOI: 10.1089/TMJ.2020.0114 - 75.
DigitalHealth.net . Mobile health apps news in brief. Digital Health. 2021. Available from:https://www.digitalhealth.net/2021/01/mobile-health-apps-news-in-brief-7/ [Accessed: June 8, 2021] - 76.
Faiz Aiman Bin Azhar1 JSD. A Systematic Review of Factors Influencing the Effective Use of mHealth Apps for Self-care. 2016 3rd International Conference On Computer And Information Sciences (ICCOINS). 2016. Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7783213 . - 77.
Krokstad S, Ding D, Grunseit AC, et al. Multiple lifestyle behaviours and mortality, findings from a large population-based Norwegian cohort study—The HUNT Study. BMC Public Health. 2017; 17 :58 - 78.
Cheng C, Brown RC, Cohen LL, et al. iACT—An interactive mHealth monitoring system to enhance psychotherapy for adolescents with sickle cell disease. Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. 2013; 2013 :2279-2282 - 79.
Badawy SM, Abebe KZ, Reichman CA, et al. Comparing the effectiveness of education versus digital cognitive behavioral therapy for adults with sickle cell disease: Protocol for the cognitive behavioral therapy and real-time pain management intervention for sickle cell via mobile applications (CaRISMA) Study. JMIR Reserach Protocol. 2021; 10 :e29014 - 80.
Kumar D, Arya M. mHealth is an innovative approach to address health literacy and improve patient-physician communication—An HIV testing exemplar. Journal of Mobile Technology in Medicine. 2015; 4 :25-30 - 81.
Parmanto B, Pramana G, Yu DX, et al. iMHere: A novel mHealth system for supporting self-care in management of complex and chronic conditions. JMIR mhealth and uhealth. 2013; 1 :e10 - 82.
Hou C, Xu Q , Diao S, et al. Mobile phone applications and self-management of diabetes: A systematic review with meta-analysis, meta-regression of 21 randomized trials and GRADE. Diabetes, Obesity and Metabolism. 2018. DOI: 10.1111/dom.13307 - 83.
Badawy SM, Cronin RM, Liem RI, et al. Digital behavioural interventions for people with sickle cell disease. The Cochrane Library. 2021; 2021 (4) - 84.
Alberts NM, Badawy SM, Hodges J, et al. Development of the incharge health mobile app to improve adherence to hydroxyurea in patients with sickle cell disease: User-centered design approach. JMIR mHealth and uHealth. 2020; 8 (5):e14884 - 85.
Badawy SM. iManage: A novel self-management app for sickle cell disease. Pediatric Blood & Cancer. 2017; 64 :e26289 - 86.
Phillips S, Kanter J, Mueller M, et al. Feasibility of an mHealth self-management intervention for children and adolescents with sickle cell disease and their families. Translational Behavioral Medicine. 2021; 11 :724-732 - 87.
Issom D-Z, Hardy-Dessources M-D, Romana M, et al. Toward a conversational agent to support the self-management of adults and young adults with sickle cell disease: Usability and usefulness study. Front Digit Health. 2021 [cited 2021 May 22]; 3 . DOI: 10.3389/fdgth.2021.600333/full - 88.
Issom D-Z, Henriksen A, Woldaregay AZ, et al. Factors influencing motivation and engagement in mobile health among patients with sickle cell disease in low-prevalence, high-income countries: Qualitative exploration of patient requirements. JMIR Human Factors. 2020; 7 :e14599 - 89.
Michie S, van Stralen MM, West R. The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science. 2011; 6 :42 - 90.
Carey PJ. Addressing the global health burden of sickle cell disease. International Health. 2014 - 91.
Kulandaivelu Y, Lalloo C, Ward R, et al. 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 :e11058 - 92.
Kumar R, Das A. The potential of mHealth as a game changer for the management of sickle cell disease in india. JMIR mHealth and uHealth. 2021; 9 :e25496 - 93.
Palermo TM, Zempsky WT, Dampier CD, et al. iCanCope with sickle cell pain: Design of a randomized controlled trial of a smartphone and web-based pain self-management program for youth with sickle cell disease. Contemporary Clinical Trials. 2018; 74 :88-96 - 94.
Frost JR, Cherry RK, Faro EZ, et al. Improving sickle cell transitions of care through health information technology. American Journal of Preventive Medicine. 2016; 51 :S17-S23 - 95.
Johnson A, Yang F, Gollarahalli S, et al. Use of mobile health apps and wearable technology to assess changes and predict pain during treatment of acute pain in sickle cell disease: Feasibility study. JMIR mHealth and uHealth. 2019; 7 :e13671 - 96.
Ajayi TA, Salongo L, Zang Y, et al. Mobile health-collected biophysical markers in children with serious illness-related pain. Journal of Palliative Medicine. 2021; 24 :580-588 - 97.
Hankins JS, Shah N. Tackling adherence in sickle cell disease with mHealth. The Lancet Haematology. 2020; 7 :e713-e714 - 98.
Luo L, King AA, Carroll Y, et al. Electronic Health Record-Embedded Individualized pain plans for emergency department treatment of vaso-occlusive episodes in adults with sickle cell disease: Protocol for a preimplementation and postimplementation study. JMIR Res Protoc. 2021; 10 :e24818 - 99.
Veludhandi A, Ross D, Sinha CB, et al. A decision support tool for allogeneic hematopoietic stem cell transplantation for children with sickle cell disease: Acceptability and usability study. JMIR Formative Research. 2021; 5 :e30093 - 100.
Breslauer DN, Maamari RN, Switz NA, et al. Mobile phone based clinical microscopy for global health applications. PLoS One. 2009; 4 :e6320 - 101.
Barrett AN, McDonnell TCR, Chan KCA, et al. Digital PCR analysis of maternal plasma for noninvasive detection of sickle cell anemia. Clinical Chemistry. 2012; 58 :1026-1032 - 102.
WHO. WHO | Big data in global health: Improving health in low- and middle-income countries[Internet]. WHO. World Health Organization; [cited 2021 Jun 4]. Available from: https://www.who.int/bulletin/volumes/93/3/14-139022/en/ - 103.
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. Cham: Springer International Publishing; 2019. pp. 141-149. DOI: 10.1007/978-3-030-04363-6_9 - 104.
Odame I. Perspective: We need a global solution. Nature. 2014; 515 :S10 - 105.
Albers DJ, Levine M, Gluckman B, et al.Personalized glucose forecasting for type 2 diabetes using data assimilation. Mac Gabhann F, editor. PLOS Computational Biology. 2017; 13 :e1005232 - 106.
Information and Communication Technology for Development (ICT4D) [Internet]. Routledge and CRC Press. 2021. Available from: https://www.routledge.com/Information-and-Communication-Technology-for-Development-ICT4D/Heeks/p/book/9781138101814 - 107.
Azzopardi-Muscat N, Sørensen K. Towards an equitable digital public health era: Promoting equity through a health literacy perspective. European Journal of Public Health. 2019; 29 :13-17 - 108.
Kruse C, Betancourt J, Ortiz S, et al. Barriers to the use of mobile health in improving health outcomes in developing countries: Systematic review. Journal of Medical Internet Research. 2019 Oct 9 [cited 2021 May 24]; 21 (10). Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6811771/ - 109.
Peng W, Kanthawala S, Yuan S, et al. A qualitative study of user perceptions of mobile health apps. BMC Public Health. 2016; 16 :1158 - 110.
Pratap A, Neto EC, Snyder P, et al. Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants. NPJ Digital Medicine. 2020; 3 :1-10 - 111.
Owen JE, Jaworski BK, Kuhn E, et al. mHealth in the Wild: Using Novel Data to Examine the Reach, Use, and Impact of PTSD Coach. JMIR Mental Health. 2015; 2 :e3935 - 112.
Baumel A, Muench F, Edan S, et al. Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. Journal of Medical Internet Research. 2019; 21 :e14567 - 113.
Niazkhani Z, Toni E, Cheshmekaboodi M, et al. Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: A systematic review. BMC Medical Informatics and Decision Making. 2020; 20 :153 - 114.
Huang H-Y, Bashir M. Users’ adoption of mental health apps: Examining the impact of information cues. JMIR mHealth and uHealth. 2017; 5 :e83 - 115.
Maiga G, Namagembe F. Predicting adoption of mHealth technology in resource constrained environments. In: IST-Africa Conference Proceedings. Vol. 2014. IEEE; 2014. pp. 1-12. Available from: http://ieeexplore.ieee.org/document/6880628/ - 116.
Vishwanath A, Scamurra SD. Barriers to the adoption of electronic health records: Using concept mapping to develop a comprehensive empirical model. Health Informatics Journal. 2007; 13 :119-134 - 117.
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics. 2017; 101 :75-84 - 118.
Gagnon M-P, Ngangue P, Payne-Gagnon J, et al. m-Health adoption by healthcare professionals: A systematic review. Journal of the American Medical Informatics Association. 2016; 23 :212-220 - 119.
Adu MD, Malabu UH, Malau-Aduli AEO, et al. Users’ preferences and design recommendations to promote engagements with mobile apps for diabetes self-management: Multi-national perspectives. PLoS One. [Internet]. 2018; 13 (12). [cited 2020 Jul 29]. Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287843/ - 120.
Birnbaum F, Lewis DM, Rosen R, et al. Patient engagement and the design of digital health. Academic Emergency Medicine. 2015; 22 :754-756 - 121.
Irizarry T, DeVito DA, Curran CR. Patient portals and patient engagement: A state of the science review. Journal of Medical Internet Research. 2015; 17 :e148 - 122.
Klasnja P, Kendall L, Pratt W, et al. Long-term engagement with health-management technology: A dynamic process in diabetes. AMIA. Annual Symposium proceedings AMIA Symposium. 2015; 2015 :756-765 - 123.
Scherer EA, Ben-Zeev D, Li Z, et al. Analyzing mHealth engagement: Joint models for intensively collected user engagement data. JMIR mHealth and uHealth. 2017; 5 :e1 - 124.
Miller S, Ainsworth B, Yardley L, et al. A framework for analyzing and measuring usage and engagement data (AMUsED) in digital interventions: Viewpoint. Journal of Medical Internet Research. 2019; 21 :e10966 - 125.
Rossmann C, Riesmeyer C, Brew-Sam N, et al. Appropriation of mobile health for diabetes self-management: Lessons from two qualitative studies. JMIR Diabetes. [Internet]. 2019 Mar 29 [cited 2021 May 24]; 4 (1). Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460309/ - 126.
Klonoff DC. Benefits and limitations of self-monitoring of blood glucose. Journal of Diabetes Science and Technology. 2007; 1 :130-132 - 127.
Ancker JS, Witteman HO, Hafeez B, et al. “You Get Reminded You’re a Sick Person”: Personal data tracking and patients with multiple chronic conditions. Journal of Medical Internet Research. 2015; 17 :e202 - 128.
Krebs P, Duncan DT. Health app use among US mobile phone owners: A national survey. JMIR mHealth and uHealth. 2015; 3 :e101 - 129.
Yang D, Xue G, Fang X, et al. Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Transactions on Networking. 2016; 24 :1732-1744 - 130.
Estellés-Arolas E, González-Ladrón-de-Guevara F. Towards an integrated crowdsourcing definition. Journal of Information Science. 2012; 38 :189-200 - 131.
Litchman ML, Walker HR, Fitzgerald C, et al. Patient-driven diabetes technologies: Sentiment and personas of the #WeAreNotWaiting and #OpenAPS movements. Journal of Diabetes Science and Technology. 2020; 14 :990-999 - 132.
UnitedHealth group acquires PatientsLikeMe. MobiHealthNews. 2019 [cited 2021 Jun 11]. Available from: https://www.mobihealthnews.com/content/unitedhealth-group-acquires-patientslikeme - 133.
Li Y, Liu C, Du N, et al. Extracting medical knowledge from crowdsourced question answering website. IEEE Transactions on Big Data. 2020; 6 :309-321 - 134.
European Commission. Communication on enabling the digital transformation of health and care in the Digital Single Market; empowering citizens and building a healthier society | Shaping Europe’s digital future. 2021. Available from: https://digital-strategy.ec.europa.eu/en/library/communication-enabling-digital-transformation-health-and-care-digital-single-market-empowering - 135.
McCorkell L, Assaf GS, Davis EH, et al. Patient-led research collaborative: Embedding patients in the Long COVID narrative. Pain Reports. [Internet]. 2021; 6 (1). [cited 2021 Jun 6]. Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112577/ - 136.
Demonaco H, Oliveira P, Torrance A, et al. When patients become innovators. In: Tiwari R, Buse S, editors. Managing Innovation in a Global and Digital World: Meeting Societal Challenges and Enhancing Competitiveness. Wiesbaden: Springer Fachmedien; 2020. pp. 121-129. DOI: 10.1007/978-3-658-27241-8_9 - 137.
Labrique AB, Wadhwani C, Williams KA, et al. Best practices in scaling digital health in low and middle income countries. Globalization and Health. 2018; 14 :103 - 138.
Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health. 2011; 38 :65-76 - 139.
Proctor EK, Landsverk J, Aarons G, et al. Implementation research in mental health services: An emerging science with conceptual, methodological, and training challenges. Administration and Policy in Mental Health. 2009; 36 . DOI: 10.1007/s10488-008-0197-4