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Published on 19.07.18 in Vol 20, No 7 (2018): July

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/10940, first published May 02, 2018.

This paper is in the following e-collection/theme issue:

    Review

    Patient-Centered eHealth Interventions for Children, Adolescents, and Adults With Sickle Cell Disease: Systematic Review

    1Division of Hematology, Oncology and Stem Cell Transplant, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States

    2Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States

    3Department of Pediatrics, Division of Hematology and Oncology, Faculty of Medicine, Zagazig University, Zagazig, Egypt

    4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States

    5Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, United States

    6Department of Hematology, St Jude Children’s Research Hospital, Memphis, TN, United States

    7Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States

    8Department of Psychology, University of Cincinnati, Cincinnati, OH, United States

    9Division of Hematology and Oncology, Department of Pediatrics, Vanderbilt-Meharry Center for Excellence in Sickle Cell Disease, Vanderbilt University Medical Center, Nashville, TN, United States

    10Division of Hematology, Duke University School of Medicine, Durham, NC, United States

    *these authors contributed equally

    Corresponding Author:

    Sherif M Badawy, MS, MD, MBBCh

    Division of Hematology, Oncology and Stem Cell Transplant

    Ann & Robert H Lurie Children's Hospital of Chicago

    225 E Chicago Ave, Box #30

    Chicago, IL, 60611

    United States

    Phone: 1 3122274836

    Fax:1 3122279373

    Email:


    ABSTRACT

    Background: Sickle cell disease is an inherited blood disorder that affects over 100,000 Americans. Sickle cell disease–related complications lead to significant morbidity and early death. Evidence supporting the feasibility, acceptability, and efficacy of self-management electronic health (eHealth) interventions in chronic diseases is growing; however, the evidence is unclear in sickle cell disease.

    Objective: We systematically evaluated the most recent evidence in the literature to (1) review the different types of technological tools used for self-management of sickle cell disease, (2) discover and describe what self-management activities these tools were used for, and (3) assess the efficacy of these technologies in self-management.

    Methods: We reviewed literature published between 1995 and 2016 with no language limits. We searched MEDLINE, EMBASE, CINAHL, PsycINFO, and other sources. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Two independent reviewers screened titles and abstracts, assessed full-text articles, and extracted data from articles that met inclusion criteria. Eligible studies were original research articles that included texting, mobile phone–based apps, or other eHealth interventions designed to improve self-management in pediatric and adult patients with sickle cell disease.

    Results: Of 1680 citations, 16 articles met all predefined criteria with a total of 747 study participants. Interventions were text messaging (4/16, 25%), native mobile apps (3/16, 19%), Web-based apps (5/16, 31%), mobile directly observed therapy (2/16, 13%), internet-delivered cognitive behavioral therapy (2/16, 13%), electronic pill bottle (1/16, 6%), or interactive gamification (2/16, 13%). Interventions targeted monitoring or improvement of medication adherence (5/16, 31%); self-management, pain reporting, and symptom reporting (7/16, 44%); stress, coping, sleep, and daily activities reporting (4/16, 25%); cognitive training for memory (1/16, 6%); sickle cell disease and reproductive health knowledge (5/16, 31%); cognitive behavioral therapy (2/16, 13%); and guided relaxation interventions (1/16, 6%). Most studies (11/16, 69%) included older children or adolescents (mean or median age 10-17 years; 11/16, 69%) and 5 included young adults (≥18 years old) (5/16, 31%). Sample size ranged from 11 to 236, with a median of 21 per study: <20 in 6 (38%), ≥20 to <50 in 6 (38%), and >50 participants in 4 studies (25%). Most reported improvement in self-management–related outcomes (15/16, 94%), as well as high satisfaction and acceptability of different study interventions (10/16, 63%).

    Conclusions: Our systematic review identified eHealth interventions measuring a variety of outcomes, which showed improvement in multiple components of self-management of sickle cell disease. Despite the promising feasibility and acceptability of eHealth interventions in improving self-management of sickle cell disease, the evidence overall is modest. Future eHealth intervention studies are needed to evaluate their efficacy, effectiveness, and cost effectiveness in promoting self-management in patients with sickle cell disease using rigorous methods and theoretical frameworks with clearly defined clinical outcomes.

    J Med Internet Res 2018;20(7):e10940

    doi:10.2196/10940

    KEYWORDS



    Introduction

    Background

    Sickle cell disease (SCD) is an inherited blood disorder that affects more than 5 million individuals worldwide, and about 250,000 babies with SCD are born every year, mainly in Africa [1]. SCD affects over 100,000 Americans, mainly African Americans, many of whom are of lower socioeconomic status [2-5]. Advancements in treatment over the past few decades have changed the course of SCD from a condition of childhood to a chronic disease of adulthood [6]. Individuals with SCD are subject to acute and chronic complications, including vaso-occlusive pain crisis, acute chest syndrome, stroke, cognitive dysfunction, and end-organ damage to the liver, spleen, and kidneys, substantially reducing health-related quality of life and leading to early death [7,8]. Management of these SCD complications has a significant impact on health care utilization in the United States, with over 230,000 emergency room visits per year with an annual health expenditures of US $1.5 billion [9,10].

    As part of the chronic care model [11], creating the informed, activated patient, along with the proactive care team, can lead to better health outcomes. One essential component of the informed, activated patient is the concept of self-management. Self-management has been referred to as the individual’s ability to manage the symptoms, treatment, physical and psychological consequences, and lifestyle changes inherent in living with a chronic condition [12]. SCD patients with more self-management skills can better manage their illness and potentially improve their health outcomes. Self-management skills are key for SCD patients as they encounter challenges related to managing their illness, such as pain management, adequate hydration, balanced nutrition, clinic attendance, and adherence to medication regimens, especially after they transition from pediatric to adult care settings. In particular, medication adherence is an important component of self-management. SCD patients with more reported adherence barriers and negative perceptions of hydroxyurea or SCD reported lower adherence rates and worse health-related quality of life scores [13-15]. In addition, many SCD patients were interested in having mobile apps with up-to-date clinical care guidelines that can improve understanding of the importance of self-management [16] and apps with features to improve their disease self-management [17]. Different techniques have been used to foster greater self-management and involve nontechnological solutions (eg, face-to-face or paper-based interventions). Over the last two decades, however, technological solutions, especially using the internet and mobile phones, to improve self-management have gained momentum with the wide access to mobile devices. These solutions, particularly electronic health (eHealth) interventions, potentially provide the benefit of greater acceptance and dissemination. eHealth has been defined as “an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the internet and related technologies” [18].

    Access to personal technology is ubiquitous, and technological solutions are becoming a part of the way health care is delivered. People are more frequently using technology for their health [19,20], and there are government mandates, including meaningful use in the United States [21], that require health care providers to use technology in the care of their patients. Moreover, enhanced patient activation, as well as engagement in medical care and shared decision making, has been associated with improved health outcomes [22-24]. eHealth interventions have been shown to improve patient activation and engagement [25-29], making them a possible solution to improve outcomes. In addition, individuals with SCD and their families want to use technologies for their health [17,30,31]. While some eHealth technological interventions are being created and tested in SCD, these interventions have not been sufficiently evaluated in the few existing studies. Furthermore, a discussion about what interventions exist, how efficacious they are, and how they are being used to improve disease self-management is missing in this population.

    In other chronic diseases, such as diabetes and asthma, a growing body of evidence has described improvements in self-care through the use of technological interventions [32-34]. Additionally, recent systematic reviews showed promising data to support the overall feasibility, acceptability, and efficacy of mobile interventions in improving health outcomes [35-40], although cost effectiveness remains unclear [41]. However, to the best of our knowledge, there has not been a systematic review of technological interventions used to improve self-management in the care of SCD. Further, evidence is growing to support the benefits of using mobile interventions to improve self-management in patients with chronic health conditions living in low- and middle-income countries [42-49]. Given the broad access to personal technology, as well as the high prevalence of SCD in many African countries, developing and implementing evidence-based mobile interventions could provide an opportunity to improve self-management skills and health outcomes in this population.

    Objectives

    To understand how eHealth technology has been used to increase self-management of SCD and to guide future research, we performed a systematic review of the literature with the following objectives: (1) to review the different types of technological tools used for self-management of SCD, (2) to discover and describe what self-management activities these tools were used for, and (3) to assess the efficacy of these technologies in self-management. We conclude with gaps that will need to be addressed in future research.


    Methods

    Literature Search

    This systematic review covered literature published between 1995 and 2016 with no language limits. The search strategy looked for all articles on texting, phones, mobile phone apps, portable software, and other eHealth interventions combined with sickle cell search terms. We intentionally used the Boolean operator OR instead of AND to capture the most comprehensive set of articles possible to which we applied our eligibility criteria. In brief, a medical librarian conducted the literature search in the following sources from inception to August 30, 2016: MEDLINE (through PubMed), EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, CINAHL, PsycINFO, Engineering Village, and ClinicalTrials.gov databases. After the initial search, the results of the search were limited to articles published from 1995 to the date of the search on November 22, 2016. Our search strategy began with the MEDLINE search and was translated to the appropriate syntax for each of the other databases. In addition, we hand searched related themes. Multimedia Appendix 1 shows the detailed search strategies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in the reporting of evidence across the studies reviewed herein (Multimedia Appendix 2) [50]. Two independent reviewers (SMB and RMC) assessed abstracts and articles against the eligibility criteria and critically appraised the methodological quality using established criteria from the Centre for Evidence-based Medicine [51]. Disagreements were resolved by discussion or consultation with a colleague, if needed.

    Eligibility Criteria

    Eligible studies were original research articles reporting randomized controlled trials (RCTs), quasi-experimental studies, or pilot pre-post studies of texting, mobile phone–based apps, or other eHealth interventions designed to improve self-management in pediatric and adult patients with SCD. To be included in this review, the studies had to report at least one primary or secondary outcome related to self-management behavior, such as medication adherence, pain management, or education. We excluded studies focused on physicians or providers, or other aspects of SCD care other than self-management.

    Data Synthesis

    We used a standardized form for data extraction. Data items in the extraction form were the following: first author’s name; publication year; country; aim of the study; participants’ age and sex; study design and setting; sample size; selection criteria; duration of intervention and follow-up; retention rate; components of the study intervention (texting, mobile phone apps, or other eHealth interventions) and comparator (if applicable); self-management measures and outcomes; other related outcome; and theoretical framework.


    Results

    Literature Search

    We retrieved a total of 1680 citations: 1612 identified through searching different databases and 68 through other resources. After we removed duplicates, 1349 original articles remained (Figure 1). Two authors (SMB and RMC) independently screened the article titles and abstracts of the 1349 records against the inclusion criteria, and a total of 59 met all predefined inclusion criteria. The same 2 authors (SMB and RMC) then independently reviewed the full text of these articles in detail against the exclusion criteria and excluded 43 articles. Finally, 16 articles met all predefined criteria to be included in this review with a total of 747 study participants [52-67]. We did not identify any non-English articles that met our inclusion criteria. Figure 1 shows the study PRISMA flowchart and documents the reasons for exclusion of full-text articles.

    Description of Included Studies

    Multimedia Appendix 3 [52-67] summarizes the studies’ characteristics. The aims of the interventions were (1) monitoring or improvement, or both, of medication adherence, including hydroxyurea [53,54,58,63], iron chelation [61], or asthma medications [63]; (2) self-management [52,59,60,62, 64,66]; (3) pain and symptom reporting [52,55,59,60,62,64]; (4) stress, coping, sleep, and daily activities reporting [55,59,62,64]; (5) cognitive training for memory [57]; (6) disease education to improve SCD and [56,61,65-67] reproductive health knowledge [56,65]; and (7) cognitive behavioral therapy [62,64] and guided relaxation interventions [55]. All studies were performed in the United States [52-67]. Enrollment was mainly from clinics [52-58,60-66], as well as inpatients [67], the public [65], online networks [65], home [56,65], or community-based organizations [56,59,65]. All studies were conducted in the outpatient setting [52-66], except for 1 study conducted in the inpatient setting [67]. Most studies included older children or adolescents (mean or median age 10-17 years) [52-54,57-59,61,62,64,66,67], 5 studies included young adults (≥18 years old) [55,56,60,63,65], and 2 studies allowed parents to participate [54,58]. None of the included studies involved potential users, patients, or parents in the development of the intervention before it was tested.

    Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.
    View this figure

    Sample size ranged from 11 to 236, with a median of 21 per study; 6 enrolled fewer than 20 participants [53,57,58,60-62], 6 had 20 to 50 participants [52,55,56,63,64,66], and 4 enrolled more than 50 participants [54,59,65,67].

    Description of Study Methodologies

    Study design varied in the included studies: 7 were pre-post pilot or feasibility trials [53,56-58,60,61,66], 5 were RCTs [55,62-65], 2 were single-arm observational studies [52,59], 1 was a quasi-experimental study [67], and 1 was a retrospective study [54]. Of the RCTs, 3 were nonblinded [62-64], 1 was single-blinded (participants) [65], and 1 was double-blinded [55]. Details of allocation concealment and study blinding were not adequately reported. None of the RCTs used intention-to-treat analysis. Retention rates differed across studies: less than 80% in 4 studies [52,60,64,67], from 80% to less than 100% in 6 studies [55,58,61-63,65], 100% in 1 study [53], and not reported in 5 studies [54,56,59,66,67]. The duration of the intervention ranged from 3 days to 12 months as follows: 3 months or less [55-57,60,62,63,67], more than 3 to 6 months or less [53,58,59,61,64], or more than 6 up to 12 months [52,54]. A total of 3 studies implemented a reward system to enhance participant engagement during the study intervention [52,53,67], and 1 study assessed the sustainability of the intervention effects with 3-month follow-up after the completion of the active intervention [61]. Additionally, 6 studies were informed by a clear theoretical framework for their intervention effects, as follows: health belief model [63]; theory-based game design [66]; gate control theory [55]; transactional stress model [67]; coping theoretical model [67]; theory of reasoned action [56,65]; and Kolb experiential learning theory [56,65].

    Description of eHealth Interventions

    Interventions included text messaging [52-54,59,63], native mobile apps [57,60,61], Web-based apps [52,55,56,59,65], mobile directly observed therapy [53,61], internet-delivered cognitive behavioral therapy [62,64], electronic pill bottle [58], or interactive gamification [66,67]. A total of 4 studies indicated regular or mobile phone ownership or access as a requirement for study participation [52-54,63], while loaner phones [59,62,64], loaner tablets [55-57,65], or both [60,61] were available in other studies. Multimedia Appendix 4 [52-67] summarizes the various intervention components for all included studies.

    Intervention Effects on Outcomes

    Study outcomes varied across studies, including medication adherence [53,54,58,61,63], disease knowledge [56,61,65-67], reproductive health knowledge [56,65], pain or symptom reporting completion rates [52,55,59,60,62,64], health care utilization [54,59], total opioid use [55], self-management skills [52,59,62,64,66], and coping and social support [67]. Almost all studies (15/16, 94%) reported improvement in self-management outcomes [52-57,59-67]. Most (10/16, 63%) reported high satisfaction and acceptability of different study interventions [52,53,55,56,59-62,65,67], while 6 studies did not report on these outcomes. Table 1 summarizes the main outcomes for all included studies.

    Table 1. Summary of the main outcomes for all included studies with eHealth interventions.
    View this table

    Discussion

    Principal Findings

    eHealth is increasingly being used for self-management of a variety of chronic diseases, including asthma, diabetes, and hypertension [46], as well as SCD. Despite systematic reviews describing eHealth use in other chronic diseases, to our knowledge, this is the first systematic review of eHealth for self-management of SCD. Our review demonstrates a range of eHealth interventions measuring a variety of outcomes, which showed improvement in multiple components of self-management of SCD. We also showed that few eHealth studies in SCD used rigorous methods, were grounded in theoretical frameworks, or measured clinical outcomes. This review describes the promise of eHealth to improve the care of individuals with SCD; however, many areas of future research can help demonstrate the usefulness of eHealth in this population.

    Most studies were in children and adolescents with SCD. Many of these studies focused on adherence to medications such as hydroxyurea or iron chelation. Other systematic reviews looked at medication adherence using eHealth [68-73], with 1 of them looking specifically at the adolescent population [39]. Most of the studies in pediatric SCD had small sample sizes, and 1 was an RCT. These studies confirmed improvement in medication adherence in the participants receiving eHealth interventions. There is significant promise for improving medication adherence using eHealth in SCD, but larger, more methodologically rigorous studies demonstrating an improved effect are needed. Most of the other studies in our review focused on pain management and coping strategies in children. These studies also demonstrated good adherence to pain diaries and improved coping. One systematic review looked at the use of eHealth in pain [74], but the studies in this review were primarily in middle-aged participants. Another review described that studies in eHealth for pain in children are lacking [75]. The studies in this review exhibited the potential for eHealth interventions to improve self-management of pain in children with painful chronic diseases. Further, in our review, only 2 studies allowed caregivers to participate. Caregivers are an essential component of the care of the child, and more studies are needed to evaluate the use of eHealth in the parent-and-child dynamic to better understand optimal use of eHealth for both parts of this dynamic.

    Intervention design did not vary according to patient characteristics, such as age, educational level, or other SCD-specific factors, which would be important for future intervention studies to consider as a strategy to optimize behavior change and long-term engagement. Additionally, 1 study was conducted in an inpatient setting, where management is more controlled by the health care system, whereas the goal of self-management interventions is to engage and empower patients in the outpatient setting with their day-to-day activities. In the outpatient setting, the health care system has less control, and the patient has more responsibility for disease management. More research is needed to evaluate the value of starting behavior change in the inpatient setting that could help to maintain intervention effects in the outpatient setting.

    Relatively few studies evaluated eHealth in adults with SCD. This is in contrast to the number of systematic reviews of the use of eHealth in adults with other chronic diseases [33,46,76]. Most of the studies in adults with SCD focused on pain or knowledge about reproductive health in this age range, with only 1 study focused on medication adherence. However, the overall number of studies was fewer, and they were less concentrated on medication adherence, than the studies focused on eHealth use in children with SCD. More studies are needed in adults with SCD to demonstrate improvement in other components of care, including medication adherence for other medications, coping strategies, and clinic appointment adherence. Interestingly, half of the studies in this group were RCTs, which was more than those conducted in children and adolescents.

    Studies in other chronic diseases measured outcomes unexplored in SCD. Multiple systematic reviews of eHealth in other chronic diseases saw improvements in clinical outcomes such as glycemic, blood pressure, and lipid level control [46,77]. None of the studies in our review evaluated the effect of eHealth interventions on outcomes for SCD, such as episodes of acute chest syndrome, strokes, or vaso-occlusive episodes of pain requiring emergency room visits or hospitalizations. Some of the pain studies in our review evaluated days and severity of pain, but these studies did not measure those pain episodes that resulted in health care utilization. Other reviews demonstrated improvement in clinic appointment adherence with eHealth interventions [76]. While there were preliminary studies in SCD describing an eHealth app to help with clinic appointments [78], there were no formal evaluation studies to demonstrate improvement in clinic attendance. Other studies looked at improving patient activation using eHealth in other diseases [79]; Risling and colleagues’ review was primarily about patient portals that improve activation. Our review did not identify any studies that demonstrated improvement in patient activation in SCD, and we found no studies of patient portals as the eHealth intervention. Expanding the range of outcomes measured in the use of eHealth for self-management of SCD is a potential area for future research.

    While we included articles that reported RCTs, quasi-experimental studies, or pilot pre-post studies, many preliminary studies and clinical trials are underway to develop and evaluate the next set of eHealth tools. These studies include preliminary needs assessments [17,80-83], processes for development of a tool [83,84], prototypes [84-87], pilot feasibility studies [31,83,88,89], and ongoing clinical trials [90,91] for eHealth interventions. A reason there may be fewer interventions published about SCD could be related to health information technology disparities with other diseases such as cystic fibrosis [92]. There is promise that mobile health technologies can help bridge this digital divide. With the increased uptake of mobile technology use and the number of preliminary studies in SCD, this is a prime area for future research. In addition, many studies have discussed improvement in self-management using eHealth in low- and middle-income countries [42-49]. While our review of the literature saw a paucity of studies from these countries, there is significant potential for eHealth to improve self-management of SCD in Africa, where the burden of SCD is much greater than in higher-income countries and the improvement in self-management with eHealth has been demonstrated [42-49]. Despite the promise of bridging the digital divide, lack of access to the mobile data plans required to deliver eHealth interventions could be a potential barrier to the wide dissemination of such interventions in middle- and low-income countries.

    Despite the potential of eHealth to improve self-management of SCD, the SCD community and their health care providers need to exercise caution when using eHealth interventions. Many eHealth apps are available in online stores, but their evaluation is lacking. As seen in a systematic review of pain apps, little research of the many apps available has been published [93]. Most apps have not been studied and are not regulated by governmental bodies. Use of these apps can come with significant risk to the accuracy of information delivered, as well as data privacy and security risks. The accuracy of the information included in different health apps is another major risk for users, and the associated costs with purchasing these apps could be a burden for many patients and a potential barrier to uptake. Evaluation of eHealth interventions as they are made available will be crucial in aiding providers and patients to choose eHealth tools that will be safe and effective in improving the care of people with chronic diseases.

    Strengths and Limitations

    Our systematic review has a number of strengths. First, in our review, we followed the recommendations for rigorous systematic review methodology [50,51,94-96]. Second, we conducted a review with a highly sensitive search strategy, guided by a medical information specialist, with no language restrictions so as to minimize publication bias and identify the largest possible number of relevant studies. Our search also included published systematic reviews, clinical trial registries, and various electronic databases. Third, although our search was limited to studies published since 1995, we identified no eligible studies before 2005, and therefore we believe that the possibility of missing earlier studies is very small. Fourth, 2 authors completed the review process independently at all stages of the systematic review.

    Some potential methodological limitations of our systematic review warrant discussion. First, similar to any other systematic literature review, although we planned our search criteria to be as comprehensive as possible, the possibility of missing a few relevant articles cannot be excluded. Second, to identify the strongest available evidence, we included articles that were published in peer-reviewed journals, and therefore there could be a publication bias with the tendency to report positive study results [97]. Third, the study sample sizes and ages, and the definition of adherence to preventive behaviors and other related outcomes varied. These limitations prohibited a meta-analysis from being performed [98]. Fourth, some of the included studies had relatively small sample sizes.

    Conclusions

    Our systematic review is, to the best of our knowledge, the first to evaluate eHealth for self-management in pediatric and adult patients with SCD. We identified several eHealth interventions measuring a variety of outcomes, which showed improvement in multiple components of self-management of SCD. Despite the promising feasibility and acceptability of eHealth interventions in improving self-management of SCD, the evidence overall is modest. However, with the increased access to mobile technology, eHealth interventions have great potential to improve health outcomes in patients with SCD, as well as other chronic diseases. Future eHealth intervention studies are needed to evaluate their efficacy, effectiveness, and cost effectiveness in promoting self-management in patients with SCD using rigorous methods and theoretical frameworks with clearly defined clinical outcomes. This review describes the promise of eHealth to improve self-management in individuals with SCD; however, there are many areas of future research that can help demonstrate their usefulness in this population.

    Acknowledgments

    SMB was supported by grant number K12HS023011 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. RMC was supported by the National Heart, Lung, And Blood Institute of the US National Institutes of Health under award number K23HL141447. The authors also thank Ms Linda O’Dwyer (Galter Health Sciences Library, Northwestern University Feinberg School of Medicine, Chicago, IL, United States) for her support with the literature search.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Search strategies.

    PDF File (Adobe PDF File), 41KB

    Multimedia Appendix 2

    Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

    PDF File (Adobe PDF File), 66KB

    Multimedia Appendix 3

    Summary of included studies focused on eHealth interventions.

    PDF File (Adobe PDF File), 37KB

    Multimedia Appendix 4

    Summary of the components of eHealth interventions for all included studies.

    PDF File (Adobe PDF File), 261KB

    References

    1. Piel FB, Patil AP, Howes RE, Nyangiri OA, Gething PW, Dewi M, et al. Global epidemiology of sickle haemoglobin in neonates: a contemporary geostatistical model-based map and population estimates. Lancet 2013 Jan 12;381(9861):142-151 [FREE Full text] [CrossRef] [Medline]
    2. Brousseau DC, Panepinto JA, Nimmer M, Hoffmann RG. The number of people with sickle-cell disease in the United States: national and state estimates. Am J Hematol 2010 Jan;85(1):77-78 [FREE Full text] [CrossRef] [Medline]
    3. Mvundura M, Amendah D, Kavanagh PL, Sprinz PG, Grosse SD. Health care utilization and expenditures for privately and publicly insured children with sickle cell disease in the United States. Pediatr Blood Cancer 2009 Oct;53(4):642-646. [CrossRef] [Medline]
    4. Steiner CA, Miller JL. Sickle cell disease patients in U.S. hospitals, 2004: Statistical Brief #21. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD: Agency for Healthcare Research and Quality; 2006.
    5. Yusuf HR, Atrash HK, Grosse SD, Parker CS, Grant AM. Emergency department visits made by patients with sickle cell disease: a descriptive study, 1999-2007. Am J Prev Med 2010 Apr;38(4 Suppl):S536-S541 [FREE Full text] [CrossRef] [Medline]
    6. Quinn CT, Rogers ZR, McCavit TL, Buchanan GR. Improved survival of children and adolescents with sickle cell disease. Blood 2010 Apr 29;115(17):3447-3452 [FREE Full text] [CrossRef] [Medline]
    7. Panepinto JA, Bonner M. Health-related quality of life in sickle cell disease: past, present, and future. Pediatr Blood Cancer 2012 Aug;59(2):377-385. [CrossRef] [Medline]
    8. Badawy SM, Thompson AA, Lai J, Penedo FJ, Rychlik K, Liem RI. Health-related quality of life and adherence to hydroxyurea in adolescents and young adults with sickle cell disease. Pediatr Blood Cancer 2017 Jun;64(6):e26369. [CrossRef] [Medline]
    9. Lanzkron S, Carroll CP, Haywood C. The burden of emergency department use for sickle-cell disease: an analysis of the national emergency department sample database. Am J Hematol 2010 Oct;85(10):797-799 [FREE Full text] [CrossRef] [Medline]
    10. Centers for Disease Control and Prevention. Sickle cell disease: data & statistics. Atlanta, GA: CDC; 2016.   URL: https://www.cdc.gov/ncbddd/sicklecell/data.html [accessed 2018-07-11] [WebCite Cache]
    11. Wagner JL, Alberts SR, Sloan JA, Cha S, Killian J, O'Connell MJ, et al. Incremental costs of enrolling cancer patients in clinical trials: a population-based study. J Natl Cancer Inst 1999 May 19;91(10):847-853. [Medline]
    12. Barlow J, Wright C, Sheasby J, Turner A, Hainsworth J. Self-management approaches for people with chronic conditions: a review. Patient Educ Couns 2002;48(2):177-187. [Medline]
    13. Badawy SM, Thompson AA, Lai J, Penedo FJ, Rychlik K, Liem RI. Adherence to hydroxyurea, health-related quality of life domains, and patients' perceptions of sickle cell disease and hydroxyurea: a cross-sectional study in adolescents and young adults. Health Qual Life Outcomes 2017 Jul 05;15(1):136 [FREE Full text] [CrossRef] [Medline]
    14. Badawy SM, Thompson AA, Penedo FJ, Lai J, Rychlik K, Liem RI. Barriers to hydroxyurea adherence and health-related quality of life in adolescents and young adults with sickle cell disease. Eur J Haematol 2017 Mar 17;98(6):608-614. [CrossRef] [Medline]
    15. Badawy SM, Thompson AA, Liem RI. Beliefs about hydroxyurea in youth with sickle cell disease. Hematol Oncol Stem Cell Ther 2018 Jan 31 [FREE Full text] [CrossRef] [Medline]
    16. Utrankar A, Mayo-Gamble TL, Allen W, Novak L, Kassim AA, Bonnet K, et al. Technology use and preferences to support clinical practice guideline awareness and adherence in individuals with sickle cell disease. J Am Med Inform Assoc 2018 May 07:doi: 10.1093/jamia/ocy036-doi:[Epub ahead of print]. [CrossRef] [Medline]
    17. Badawy SM, Thompson AA, Liem RI. Technology access and smartphone app preferences for medication adherence in adolescents and young adults with sickle cell disease. Pediatr Blood Cancer 2016 May;63(5):848-852. [CrossRef] [Medline]
    18. Eysenbach G. What is e-health? J Med Internet Res 2001 Jun;3(2):E20. [CrossRef] [Medline]
    19. Fox S. Health information online. Washington, DC: Pew Research Center; 2014.   URL: http://www.pewinternet.org/2014/02/13/health-information-online-2/ [accessed 2018-07-11] [WebCite Cache]
    20. Mobile fact sheet. Washington, DC: Pew Research Center; 2018 Feb 05.   URL: http://www.pewinternet.org/fact-sheet/mobile/ [accessed 2018-07-11] [WebCite Cache]
    21. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010 Aug 5;363(6):501-504. [CrossRef] [Medline]
    22. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood) 2013 Feb;32(2):207-214. [CrossRef] [Medline]
    23. Peters AE, Keeley EC. Patient engagement following acute myocardial infarction and its influence on outcomes. Am J Cardiol 2017 Nov 01;120(9):1467-1471. [CrossRef] [Medline]
    24. Sawesi S, Rashrash M, Phalakornkule K, Carpenter JS, Jones JF. The impact of information technology on patient engagement and health behavior change: a systematic review of the literature. JMIR Med Inform 2016;4(1):e1 [FREE Full text] [CrossRef] [Medline]
    25. Haas K, Martin A, Park KT. Text message intervention (TEACH) improves quality of life and patient activation in celiac disease: a randomized clinical trial. J Pediatr 2017 Jun;185:62-67.e2 [FREE Full text] [CrossRef] [Medline]
    26. John ME, Samson-Akpan PE, Etowa JB, Akpabio II, John EE. Enhancing self-care, adjustment and engagement through mobile phones in youth with HIV. Int Nurs Rev 2016 Dec;63(4):555-561. [CrossRef] [Medline]
    27. Knoerl R, Lee D, Yang J, Bridges C, Kanzawa-Lee G, Lita SG, et al. Examining the impact of a web-based intervention to promote patient activation in chemotherapy-induced peripheral neuropathy assessment and management. J Cancer Educ 2017 Mar 06:doi: 10.1007/s13187-017-1200-0-doi: 10.1[Epub ahead of print]. [CrossRef] [Medline]
    28. Milani RV, Lavie CJ, Bober RM, Milani AR, Ventura HO. Improving hypertension control and patient engagement using digital tools. Am J Med 2017 Jan;130(1):14-20. [CrossRef] [Medline]
    29. Solomon M, Wagner SL, Goes J. Effects of a Web-based intervention for adults with chronic conditions on patient activation: online randomized controlled trial. J Med Internet Res 2012;14(1):e32 [FREE Full text] [CrossRef] [Medline]
    30. Cronin RM, Hankins JS, Adams-Graves P, Thompson AA, Kalinyak K, Byrd J, et al. Barriers and facilitators to research participation among adults, and parents of children with sickle cell disease: a trans-regional survey. Am J Hematol 2016 Dec;91(10):E461-E462 [FREE Full text] [CrossRef] [Medline]
    31. Shah N, Jonassaint J, De Castro L. Patients welcome the Sickle Cell Disease Mobile Application to Record Symptoms via Technology (SMART). Hemoglobin 2014;38(2):99-103. [CrossRef] [Medline]
    32. Al-Durra M, Torio M, Cafazzo JA. The use of behavior change theory in Internet-based asthma self-management interventions: a systematic review. J Med Internet Res 2015;17(4):e89 [FREE Full text] [CrossRef] [Medline]
    33. de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self-management of long-term illnesses. Cochrane Database Syst Rev 2012;12:CD007459. [CrossRef] [Medline]
    34. Holtz B, Lauckner C. Diabetes management via mobile phones: a systematic review. Telemed J E Health 2012 Apr;18(3):175-184. [CrossRef] [Medline]
    35. Thakkar J, Kurup R, Laba T, Santo K, Thiagalingam A, Rodgers A, et al. Mobile telephone text messaging for medication adherence in chronic disease: a meta-analysis. JAMA Intern Med 2016 Mar;176(3):340-349. [CrossRef] [Medline]
    36. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR Mhealth Uhealth 2015;3(1):e20 [FREE Full text] [CrossRef] [Medline]
    37. Majeed-Ariss R, Baildam E, Campbell M, Chieng A, Fallon D, Hall A, et al. Apps and adolescents: a systematic review of adolescents’ use of mobile phone and tablet apps that support personal management of their chronic or long-term physical conditions. J Med Internet Res 2015 Dec 23;17(12):e287 [FREE Full text] [CrossRef] [Medline]
    38. Badawy SM, Kuhns LM. Texting and mobile phone app interventions for improving adherence to preventive behavior in adolescents: a systematic review. JMIR Mhealth Uhealth 2017 Apr 19;5(4):e50 [FREE Full text] [CrossRef] [Medline]
    39. Badawy SM, Barrera L, Sinno MG, Kaviany S, O'Dwyer LC, Kuhns LM. Text messaging and mobile phone apps as interventions to improve adherence in adolescents with chronic health conditions: a systematic review. JMIR Mhealth Uhealth 2017 May 15;5(5):e66 [FREE Full text] [CrossRef] [Medline]
    40. Badawy SM, Thompson AA, Kuhns LM. Medication adherence and technology-based interventions for adolescents with chronic health conditions: a few key considerations. JMIR Mhealth Uhealth 2017 Dec 22;5(12):e202 [FREE Full text] [CrossRef] [Medline]
    41. Badawy SM, Kuhns LM. Economic evaluation of text-messaging and smartphone-based interventions to improve medication adherence in adolescents with chronic health conditions: a systematic review. JMIR Mhealth Uhealth 2016 Oct 25;4(4):e121 [FREE Full text] [CrossRef] [Medline]
    42. Abaza H, Marschollek M. mHealth application areas and technology combinations. a comparison of literature from high and low/middle income countries. Methods Inf Med 2017 Aug 08;56(7):e105-e122. [CrossRef] [Medline]
    43. Cho Y, Lee S, Islam SMS, Kim S. Theories applied to m-health interventions for behavior change in low- and middle-income countries: a systematic review. Telemed J E Health 2018 Feb 13. [CrossRef] [Medline]
    44. Feroz A, Perveen S, Aftab W. Role of mHealth applications for improving antenatal and postnatal care in low and middle income countries: a systematic review. BMC Health Serv Res 2017 Nov 07;17(1):704 [FREE Full text] [CrossRef] [Medline]
    45. Hurt K, Walker RJ, Campbell JA, Egede LE. mHealth interventions in low and middle-income countries: a systematic review. Glob J Health Sci 2016 Sep 01;8(9):54429. [CrossRef] [Medline]
    46. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The impact of mHealth interventions: systematic review of systematic reviews. JMIR Mhealth Uhealth 2018 Jan 17;6(1):e23 [FREE Full text] [CrossRef] [Medline]
    47. Oliver-Williams C, Brown E, Devereux S, Fairhead C, Holeman I. Using mobile phones to improve vaccination uptake in 21 low- and middle-income countries: systematic review. JMIR Mhealth Uhealth 2017 Oct 04;5(10):e148 [FREE Full text] [CrossRef] [Medline]
    48. Peiris D, Praveen D, Johnson C, Mogulluru K. Use of mHealth systems and tools for non-communicable diseases in low- and middle-income countries: a systematic review. J Cardiovasc Transl Res 2014 Nov;7(8):677-691. [CrossRef] [Medline]
    49. Ridgeway K, Dulli LS, Murray KR, Silverstein H, Dal SL, Olsen P, et al. Interventions to improve antiretroviral therapy adherence among adolescents in low- and middle-income countries: a systematic review of the literature. PLoS One 2018;13(1):e0189770 [FREE Full text] [CrossRef] [Medline]
    50. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 2009 Oct;62(10):1006-1012. [CrossRef] [Medline]
    51. Howick J, Chalmers I, Glasziou P, Greenhalgh T, Heneghan C, Liberati A. The Oxford Levels of Evidence 2. Oxford, UK: Centre for Evidence-Based Medicine; 2011.   URL: http://www.cebm.net/index.aspx?o=5653 [accessed 2018-07-11] [WebCite Cache]
    52. Bakshi N, Smith ME, Ross D, Krishnamurti L. Novel metrics in the longitudinal evaluation of pain data in sickle cell disease. Clin J Pain 2017 Jun;33(6):517-527. [CrossRef] [Medline]
    53. Creary SE, Gladwin MT, Byrne M, Hildesheim M, Krishnamurti L. A pilot study of electronic directly observed therapy to improve hydroxyurea adherence in pediatric patients with sickle-cell disease. Pediatr Blood Cancer 2014 Jun;61(6):1068-1073. [CrossRef] [Medline]
    54. Estepp JH, Winter B, Johnson M, Smeltzer MP, Howard SC, Hankins JS. Improved hydroxyurea effect with the use of text messaging in children with sickle cell anemia. Pediatr Blood Cancer 2014 Nov;61(11):2031-2036. [CrossRef] [Medline]
    55. Ezenwa MO, Yao Y, Engeland CG, Molokie RE, Wang ZJ, Suarez ML, et al. A randomized controlled pilot study feasibility of a tablet-based guided audio-visual relaxation intervention for reducing stress and pain in adults with sickle cell disease. J Adv Nurs 2016 Jun;72(6):1452-1463. [CrossRef] [Medline]
    56. Gallo AM, Wilkie DJ, Wang E, Labotka RJ, Molokie RE, Stahl C, et al. Evaluation of the SCKnowIQ Tool and Reproductive CHOICES Intervention Among Young Adults With Sickle Cell Disease or Sickle Cell Trait. Clin Nurs Res 2014 Aug;23(4):421-441. [CrossRef] [Medline]
    57. Hardy SJ, Hardy KK, Schatz JC, Thompson AL, Meier ER. Feasibility of home-based computerized working memory training with children and adolescents with sickle cell disease. Pediatr Blood Cancer 2016 Sep;63(9):1578-1585. [CrossRef] [Medline]
    58. Inoue S, Kodjebacheva G, Scherrer T, Rice G, Grigorian M, Blankenship J, et al. Adherence to hydroxyurea medication by children with sickle cell disease (SCD) using an electronic device: a feasibility study. Int J Hematol 2016 Aug;104(2):200-207. [CrossRef] [Medline]
    59. Jacob E, Duran J, Stinson J, Lewis MA, Zeltzer L. Remote monitoring of pain and symptoms using wireless technology in children and adolescents with sickle cell disease. J Am Assoc Nurse Pract 2013 Jan;25(1):42-54 [FREE Full text] [CrossRef] [Medline]
    60. Jonassaint CR, Shah N, Jonassaint J, De Castro L. Usability and feasibility of an mHealth intervention for monitoring and managing pain symptoms in sickle cell disease: the Sickle Cell Disease Mobile Application to Record Symptoms via Technology (SMART). Hemoglobin 2015;39(3):162-168. [CrossRef] [Medline]
    61. Leonard S, Anderson LM, Jonassaint J, Jonassaint C, Shah N. Utilizing a novel mobile health “selfie” application to improve compliance to iron chelation in pediatric patients receiving chronic transfusions. J Pediatr Hematol Oncol 2017 Apr;39(3):223-229. [CrossRef] [Medline]
    62. McClellan CB, Schatz JC, Puffer E, Sanchez CE, Stancil MT, Roberts CW. Use of handheld wireless technology for a home-based sickle cell pain management protocol. J Pediatr Psychol 2009 Jun;34(5):564-573 [FREE Full text] [CrossRef] [Medline]
    63. Pernell BM, DeBaun MR, Becker K, Rodeghier M, Bryant V, Cronin RM. Improving medication adherence with two-way short message service reminders in sickle cell disease and asthma. A feasibility randomized controlled trial. Appl Clin Inform 2017 May 24;8(2):541-559. [CrossRef] [Medline]
    64. Schatz J, Schlenz AM, McClellan CB, Puffer ES, Hardy S, Pfeiffer M, et al. Changes in coping, pain, and activity after cognitive-behavioral training: a randomized clinical trial for pediatric sickle cell disease using smartphones. Clin J Pain 2015 Jun;31(6):536-547. [CrossRef] [Medline]
    65. Wilkie DJ, Gallo AM, Yao Y, Molokie RE, Stahl C, Hershberger PE, et al. Reproductive health choices for young adults with sickle cell disease or trait: randomized controlled trial immediate posttest effects. Nurs Res 2013 Oct;62(5):352-361 [FREE Full text] [CrossRef] [Medline]
    66. Yoon SL, Godwin A. Enhancing self-management in children with sickle cell disease through playing a CD-ROM educational game: a pilot study. Pediatr Nurs 2007;33(1):60-3, 72. [Medline]
    67. Hazzard A, Celano M, Collins M, Markov Y. Effects of STARBRIGHT World on knowledge, social support, and coping in hospitalized children with sickle cell disease and asthma. Child Health Care 2002;31(1):69-86. [CrossRef]
    68. Anglada-Martinez H, Riu-Viladoms G, Martin-Conde M, Rovira-Illamola M, Sotoca-Momblona JM, Codina-Jane C. Does mHealth increase adherence to medication? Results of a systematic review. Int J Clin Pract 2015 Jan;69(1):9-32. [CrossRef] [Medline]
    69. DeKoekkoek T, Given B, Given CW, Ridenour K, Schueller M, Spoelstra SL. mHealth SMS text messaging interventions and to promote medication adherence: an integrative review. J Clin Nurs 2015 Oct;24(19-20):2722-2735. [CrossRef] [Medline]
    70. Linn AJ, Vervloet M, van Dijk L, Smit EG, Van Weert JCM. Effects of eHealth interventions on medication adherence: a systematic review of the literature. J Med Internet Res 2011;13(4):e103 [FREE Full text] [CrossRef] [Medline]
    71. Rootes-Murdy K, Glazer KL, Van wert MJ, Mondimore FM, Zandi PP. Mobile technology for medication adherence in people with mood disorders: a systematic review. J Affect Disord 2018 Dec;227:613-617. [CrossRef] [Medline]
    72. Tran N, Coffman JM, Sumino K, Cabana MD. Patient reminder systems and asthma medication adherence: a systematic review. J Asthma 2014 Jun;51(5):536-543. [CrossRef] [Medline]
    73. Vervloet M, Linn AJ, van Weert JCM, de Bakker DH, Bouvy ML, van Dijk L. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc 2012;19(5):696-704 [FREE Full text] [CrossRef] [Medline]
    74. Nicholl BI, Sandal LF, Stochkendahl MJ, McCallum M, Suresh N, Vasseljen O, et al. Digital support interventions for the self-management of low back pain: a systematic review. J Med Internet Res 2017 May 21;19(5):e179 [FREE Full text] [CrossRef] [Medline]
    75. Slater H, Campbell JM, Stinson JN, Burley MM, Briggs AM. End user and implementer experiences of mHealth technologies for noncommunicable chronic disease management in young adults: systematic review. J Med Internet Res 2017 Dec 12;19(12):e406 [FREE Full text] [CrossRef] [Medline]
    76. Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R, Car J. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev 2013;12:CD007458. [CrossRef] [Medline]
    77. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015;17(2):e52 [FREE Full text] [CrossRef] [Medline]
    78. Modi AC, Crosby LE, Hines J, Drotar D, Mitchell MJ. Feasibility of web-based technology to assess adherence to clinic appointments in youth with sickle cell disease. J Pediatr Hematol Oncol 2012 Apr;34(3):e93-e96 [FREE Full text] [CrossRef] [Medline]
    79. Risling T, Martinez J, Young J, Thorp-Froslie N. Evaluating patient empowerment in association with eHealth technology: scoping review. J Med Internet Res 2017 Sep 29;19(9):e329 [FREE Full text] [CrossRef] [Medline]
    80. Anderson C, Torres S, Gokuli S, Brandow AM, Panepinto JA. Assessment of communication modes in patients and families with sickle cell disease. Pediatr Blood Cancer 2013 May;60(5):887. [CrossRef] [Medline]
    81. Issom D, Hartvigsen G, Bonacina S, Koch S, Lovis C. User-centric eHealth tool to address the psychosocial effects of sickle cell disease. Stud Health Technol Inform 2016;225:627-628. [Medline]
    82. Vaughn LM, McLinden D, Jacquez F, Crosby L, Slater S, Mitchell M. Understanding the social networks of parents of children with sickle cell disease. J Health Care Poor Underserved 2011 Aug;22(3):1014-1029 [FREE Full text] [CrossRef] [Medline]
    83. Crosby LE, Ware RE, Goldstein A, Walton A, Joffe NE, Vogel C, et al. Development and evaluation of iManage: a self-management app co-designed by adolescents with sickle cell disease. Pediatr Blood Cancer 2017 Jan;64(1):139-145. [CrossRef] [Medline]
    84. Craven M, Lang A, Martin J. Developing mHealth apps with researchers: multi-stakeholder design considerations. In: Marcus 1, editor. Design, User Experience, and Usability: User Experience Design for Everyday Life Applications and Services: Third international Conference, DUXU 2014. Cham, Switzerland: Springer; 2014:15-24.
    85. Cheng C, Brown C, New T, Stokes TH, Dampier C, Wang MD. SickleREMOTE: a two-way text messaging system for pediatric sickle cell disease patients. 2012 Presented at: IEEE-EMBS International Conference on Biomedical and Health Informatics; Jan 2-7, 2012; Hong Kong and Shenzhen, China p. 408-411.
    86. Washington G, Ward J, Kameka M. Spare me: towards an empathetic tool for helping adolescents & teenagers cope with sickle cell. 2015 Presented at: ICHI 2015: IEEE International Conference on Healthcare Informatics; Oct 21-23, 2015; Dallas, TX, USA p. 555-561.
    87. Durfee R, Venugopalan J, Ren J, Wang M. Med-vest: a wearable sensory platform. 2014 Presented at: IEEE Healthcare Innovation Conference (HIC); Oct 8-10, 2014; Seattle, WA, USA.
    88. Cheng C, Brown RC, Cohen LL, Venugopalan J, Stokes TH, Wang MD. iACT--an interactive mHealth monitoring system to enhance psychotherapy for adolescents with sickle cell disease. Conf Proc IEEE Eng Med Biol Soc 2013;2013:2279-2282. [CrossRef] [Medline]
    89. Yang S, Jacob E, Gerla M. Web-based mobile e-diary for youth with sickle cell disease. 2012 Presented at: IEEE Consumer Communications and Networking Conference (CCNC); Jan 14-17, 2012; Las Vegas, NV, USA p. 385-389.
    90. Creary S, Chisolm DJ, O'Brien SH. ENHANCE-(Electronic Hydroxyurea Adherence): a protocol to increase hydroxyurea adherence in patients with sickle cell disease. JMIR Res Protoc 2016 Oct 03;5(4):e193 [FREE Full text] [CrossRef] [Medline]
    91. Makubi A, Sasi P, Ngaeje M, Novelli EM, Mmbando BP, Gladwin MT, et al. Rationale and design of mDOT-HuA study: a randomized trial to assess the effect of mobile-directly observed therapy on adherence to hydroxyurea in adults with sickle cell anemia in Tanzania. BMC Med Res Methodol 2016 Dec 18;16(1):140 [FREE Full text] [CrossRef] [Medline]
    92. Barriteau CM, Thompson AL, Meier ER, Pecker LH. Sickle cell disease related internet activity is three times less frequent than cystic fibrosis related internet activity. Pediatr Blood Cancer 2016 Nov;63(11):2061-2062. [CrossRef] [Medline]
    93. de la Vega R, Miró J. mHealth: a strategic field without a solid scientific soul. a systematic review of pain-related apps. PLoS One 2014;9(7):e101312 [FREE Full text] [CrossRef] [Medline]
    94. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. Oxford, UK: The Cochrane Collaboration; 2011.   URL: http://handbook-5-1.cochrane.org/ [accessed 2018-07-11] [WebCite Cache]
    95. Centre for Reviews and Dissemination. Systematic Reviews: CRD's Guidance for Undertaking Reviews in Health Care. York, UK: University of York; 2009.   URL: https://www.york.ac.uk/media/crd/Systematic_Reviews.pdf [accessed 2018-07-11] [WebCite Cache]
    96. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015 Jan;4:1 [FREE Full text] [CrossRef] [Medline]
    97. Dubben H, Beck-Bornholdt H. Systematic review of publication bias in studies on publication bias. BMJ 2005 Aug 20;331(7514):433-434 [FREE Full text] [CrossRef] [Medline]
    98. Bartolucci AA, Hillegass WB. Overview, strengths, and limitations of systematic reviews and meta-analyses. In: Chiappelli F, Caldeira XM, Neagos N, Oluwadara OO, Ramchandani MH, editors. Evidence-Based Practice: Toward Optimizing Clinical Outcomes. Berlin, Germany: Springer-Verlag; 2010:17-34.


    Abbreviations

    eHealth: electronic health
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    RCT: randomized controlled trial
    SCD: sickle cell disease


    Edited by G Eysenbach; submitted 02.05.18; peer-reviewed by SE Brown, S Creary; comments to author 24.05.18; revised version received 06.06.18; accepted 21.06.18; published 19.07.18

    ©Sherif M Badawy, Robert M Cronin, Jane Hankins, Lori Crosby, Michael DeBaun, Alexis A Thompson, Nirmish Shah. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.07.2018.

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