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Published on 15.05.17 in Vol 5, No 5 (2017): May

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

    Original Paper

    Text Messaging and Mobile Phone Apps as Interventions to Improve Adherence in Adolescents With Chronic Health Conditions: A Systematic Review

    1Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Division of Hematology, Oncology and Stem Cell Transplant, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

    2Zagazig University Faculty of Medicine, Department of Pediatrics, Division of Hematology and Oncology, Zagazig, Egypt

    3Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Division of Hematology, Oncology and Stem Cell Transplant, Chicago, IL, United States

    4University of Kansas School of Medicine, Department of Pediatrics, Wichita, KS, United States

    5Advocate Children's Hospital, Department of Pediatrics, Oak Lawn, IL, United States

    6Northwestern University Feinberg School of Medicine, Galter Health Sciences Library, Chicago, IL, United States

    7Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Division of Adolescent Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

    Corresponding Author:

    Sherif M Badawy, MD, MS, MBBCh

    Ann & Robert H. Lurie Children's Hospital of Chicago

    Department of Pediatrics, Division of Hematology, Oncology and Stem Cell Transplant

    Northwestern University Feinberg School of Medicine

    225 E Chicago Ave, Box 30

    Chicago, IL, 60611

    United States

    Phone: 1 3122274836

    Fax:1 3122279376



    Background: The number of adolescents with chronic health conditions (CHCs) continues to increase. Medication nonadherence is a global challenge among adolescents across chronic conditions and is associated with poor health outcomes. While there has been growing interest in the use of mHealth technology to improve medication adherence among adolescents with CHCs, particularly text messaging and mobile phone apps, there has been no prior systematic review of their efficacy.

    Objective: The purpose of this review was to systematically evaluate the most recent evidence for the efficacy of text messaging and mobile phone apps as interventions to promote medication adherence among adolescents with CHCs.

    Methods: PubMed, Embase, CENTRAL, PsycINFO, Web of Science, Google Scholar, and additional databases were searched from 1995 until November 2015. An additional hand search of related themes in the Journal of Medical Internet Research was also conducted. The Preferred Reporting Results of Systematic Reviews and Meta-Analyses guidelines were followed. Two reviewers independently screened titles/abstracts, assessed full-text articles, extracted data from included articles, and assessed their quality using Grades of Recommendation, Assessment, Development, and Evaluation criteria. Included studies were described in original research articles that targeted adherence in adolescents with CHCs (12-24 years-old).

    Results: Of the 1423 records examined, 15 met predefined criteria: text messaging (n=12) and mobile phone apps (n=3). Most studies were performed in the United States (11/15, 73%), were randomized-controlled trials (8/15, 53%), had a sample size <50 (11/15, 73%), and included adherence self-report and/or biomarkers (9/15, 60%). Only four studies were designed based on a theoretical framework. Approaches for text messaging and mobile phone app interventions varied across studies. Seven articles (7/15, 47%) reported significant improvement in adherence with moderate to large standardized mean differences. Most of the included studies were of low or moderate quality. Studies varied in sample size, methods of adherence assessment, and definition of adherence, which prohibited performing a meta-analysis.

    Conclusions: The use of text messaging and mobile phone app interventions to improve medication adherence among adolescents with CHCs has shown promising feasibility and acceptability, and there is modest evidence to support the efficacy of these interventions. Further evaluation of short- and long-term efficacy and cost-effectiveness of these interventions is warranted given the early and evolving state of the science.

    JMIR Mhealth Uhealth 2017;5(5):e66




    In the United States, about 15-20% of children and adolescents have chronic health conditions (CHCs) (eg, asthma, diabetes), a number that has doubled in the past 20 years accompanied by increased health care expenses [1,2]. The increased rate of children and adolescents with CHCs is mainly driven by the rising prevalence of asthma and obesity, and the advances in medical care that have led to improved survival over time [3,4]. Adolescents with CHCs have specific health needs and contend with daily challenges involving their illness-related routine, including administration of daily or weekly medications, diet restrictions, lifestyle changes, laboratory monitoring, and outpatient follow-up with medical teams [5].

    Medication adherence in particular is an important component of the treatment regimen as it often drives long-term outcomes. It is also a global public health problem, and it represents a barrier to achieving optimal health as a primary cause of treatment failure and avoidable complications [5]. Medication nonadherence rates are estimated to be 50-75% among pediatric patients with CHCs, with some evidence of lower adherence among adolescents [5,6]. Medication nonadherence has been associated with poor health-related quality of life (HRQOL) scores, increased morbidity and mortality, and increased health care utilization with an estimated US $100-300 billion of annual avoidable health care costs [6-13]. Engaging adolescents with CHCs in self-management skill building, including medication adherence, has long-term benefits [14-16]. Although treatment regimen and monitoring requirements vary across pediatric CHCs, most adolescents with CHCs perceived adherence barriers as multifaceted, but with common attributes across conditions [17].

    Earlier systematic reviews and meta-analyses of pediatric patients with CHCs have shown evidence of a positive impact of interventions on medication adherence, HRQOL, and family functioning as well as reduction in health care utilization [18-24], although with primarily small effect sizes and methodological limitations. There has been growing interest in the use of technology to improve medication adherence and self-management skills in the last few years. Adolescents have ubiquitous access to mobile technology, in particular text messaging and mobile phone apps, across levels of social position and status [25-27]. The adoption of these technologies by adolescents has opened up new opportunities to link patients with their providers and to improve self-management and medication adherence. A recent review examined the efficacy of mobile apps in improving self-management skills, not specifically medication adherence, among adolescents with a physical CHC or long-term condition. However, they were not able to draw concrete conclusions because of the lack of evidence-based studies and the heterogeneity of the included studies [28].

    The purpose of this review is to systematically evaluate the most recent evidence for the efficacy of text messaging and mobile phone app interventions in promoting medication adherence among adolescents with CHCs. We focused on text messaging and mobile phone app interventions in particular because these technologies are the most widely and frequently used by adolescents and are thus most likely to serve as the basis for future intervention development.


    We followed the guidelines for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in the reporting of evidence across the studies we reviewed (Multimedia Appendix 1) [29]. This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42015025907) [30].

    Article Retrieval

    A librarian (LO) collaboratively developed the highly sensitive medical subject headings (MeSH) term‒based search strategies with other review authors (SB, LK) and from July to September 2015 ran searches in the following databases: PubMed MEDLINE; Embase; Cochrane Central Register of Controlled Trials (CENTRAL) on the Wiley platform; the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (EBSCO); PsycINFO (EBSCO); Web of Science; Center for Review and Dissemination; and Inspec (EBSCO). Additional searches were run in November 2015 using the following sources: Proquest Dissertations; Scopus;; World Health Organization Clinical Trials;; Institute of Electrical and Electronics Engineers Explore; and Google Scholar. Search strategies for all databases except MEDLINE were adapted from the PubMed MEDLINE strategy. All databases were searched back to 1995, which is a point in time when access to mobile phones began to increase rapidly. No language limits were applied. The search strategy specified keywords, including text messaging, phones, mobile apps, and portable software combined with adherence or compliance, and search terms related to child, pediatric, adolescents, and youth. We also reviewed the search strategies of previous studies to include additional terms. See Multimedia Appendix 2 for complete search strategies in each database. An additional hand search of related themes in the Journal of Medical Internet Research was also conducted. We also attempted to identify additional studies by searching the reference lists of key studies and relevant systematic reviews. We contacted authors of included publications to obtain additional studies meeting the inclusion criteria.

    Study Selection

    The inclusion criteria were as follows: (1) adolescents (12-24 years old) [31] with a chronic illness requiring long-term daily or weekly medications for ≥12 months, (2) original research articles, (3) studies that were either randomized controlled trials, quasi-experimental studies, or pilot/feasibility studies (including single arm, pre-posttest), (4) text messaging or mobile phone‒based interventions (app or mobile intervention), and (5) medication adherence as the primary or secondary outcome. The exclusion criteria included (1) mean or median age of entire study cohort in the study was either <12 years old, >24 years old, or not specified, (2) adolescent participants were not the target of the intervention (eg, intervention targets babies born to adolescent mothers with human immunodeficiency virus or targets parents of adolescents), (3) text messaging and mobile phone apps as interventions focused on disease monitoring or ecological momentary assessment, but not meant to improve medication adherence, or (4) technology-based interventions other than text messaging and mobile phone apps, such as Web- or Internet-based interventions, personal digital assistant, etc.

    Data Extraction

    We used a standardized form for data extraction. Data items in the extraction form included the following: first author’s name; publication year; country; condition or disease focus of the study; participants’ age; study design; duration of intervention and follow-up; components of text messaging or mobile phone app interventions; control group (if applicable); adherence measures; adherence rates; other outcome measures such as disease-related outcomes of morbidity and mortality, HRQOL, and self-efficacy or self-management skills; and theoretical framework. Two authors coded all included articles individually, and then the lead author (SB) independently reviewed all codes. Disagreements were resolved by discussion or by consultation with a third author, if needed.

    Quality Assessment and Strength of the Evidence

    Studies described in each article were evaluated for the quality of evidence using the GRADE approach (Grades of Recommendation, Assessment, Development, and Evaluation) [32]. This method evaluates four different key domains including consistency, directness, risk for bias, and precision of the evidence. Two authors graded all included articles individually, and then the senior author (LK) independently reviewed all grades. Disagreements were resolved by discussion or by consultation with a third author, if needed.

    Data Analysis

    Data were analyzed quantitatively and qualitatively. Our primary outcome measure was mean change in medication adherence rate, and data were analyzed for those who had baseline and follow-up values. We also analyzed mean change in adherence-related laboratory markers. Standardized mean differences (SMD) with 95% confidence intervals were calculated—using means and standard deviations—to evaluate the efficacy of text messaging and mobile phone‒based interventions in improving subjective and objective measures of adherence, including adherence rates and adherence-related laboratory markers [33]. Data were analyzed using Stata 13.


    Literature Search

    A total of 1423 citations were retrieved; 1137 in the July-August 2015 search and 286 in the search in November 2015. After removing duplicates, 1118 original articles remained (see Figure 1). Two authors (SB and LK) independently screened the article titles and abstracts of 1118 records against inclusion criteria, and a total of 156 records met all predefined inclusion criteria. Two authors (SB and LK) then independently reviewed the full text of these articles in detail against the exclusion criteria, and 141 articles were excluded. A total of 15 articles met all predefined criteria to be included in this review [34-48]. We did not identify any non-English articles that met our inclusion criteria. The study flowchart and reasons for exclusion of full text papers were documented in an adapted PRISMA study flowchart (see Figure 1) [49].

    Figure 1. Flow of studies through the review according to PRISMA guidelines.
    View this figure

    Study Characteristics

    Although our search included studies published since 1995, no eligible studies were identified before 2005, with most studies (12/15, 80%) published since 2010 (Table 1) [34-39,41-44,46,48]. Most studies were performed in the United States (in whole or in part; 11/15, 73%) [34,36-38,41,42,44-48]. Studies targeted eight different CHCs, including those of adolescents with acne (n=2) [34,39], asthma (n=1) [47], depression (n=1) [42], diabetes mellitus (n=4) [35,40,43,46], human immunodeficiency virus (n=2) [37,41], liver transplant (n=2) [44,45], sickle cell disease (n=2) [36,38], and systematic lupus erythematosus (n=1) [48]. Most studies were small in size (ie, ˂50 participants; 11/15, 73%) [34-37,42-48], and just over half (8/15, 53%) included samples of young adults (with a mean or median age ≥18 and ≤24 years old) [34,37,39,41-43,47,48]. In terms of study design, more than half were RCTs (8/15, 53%) [34,39-43,47,48], and the remainder were primarily single-arm nonrandomized trials (6/15, 40%) [35-37,44-46] or retrospective chart reviews (1/15, 7%) [38]. The duration of the studies varied: 2-4 weeks (2/15, 13%) [42,43], 12-16 weeks (5/15, 33%) [34,35,39,46,47], 24 weeks (3/15, 20%) [36,37,41], and 12 months or more (5/15, 33%) [38,40,44,45,48]. Only one study evaluated the durability of intervention effects in a crossover design with 6-month follow-up in one of the study arms after the intervention was discontinued [41]. Measures of medication adherence included self-report (9/15, 60%) or biomarkers (9/15, 60%), as well as other forms of monitoring (7/15, 47%), such as Medication Event Monitoring System caps, directly observed therapy, pill count, and pharmacy records abstraction. Three studies (20%) measured additional adherence behaviors, including completion of laboratory visits [44], clinic visits [48], and monitoring of peak expiratory flow values in patients with asthma [47]. Only four studies (27%) incorporated explicit theoretical approaches or frameworks into the model of intervention effects, including Theory of Planned Behavior [43], Gamification theory [35], and Social Cognitive Theory [40,41]. Most studies (12/15, 80%) were rated “low” or “moderate” according to the GRADE criteria (Table 1) [32], with low ratings primarily due to limitations in design as well as imprecision of results.

    Table 1. Summary of included studies that focused on improving adherence in adolescents with CHCs using text message or mobile phone app interventions.
    View this table

    Intervention Type

    The majority of interventions used text messaging to promote medication adherence via reminders or motivational messages (12/15, 80%) [34,37-41,43-46,48]. Of these 12 studies, only one combined text messages with other in-person intervention components (educational sessions) [47]. Additional interventions used other mobile phone‒based approaches or apps (3/15, 20%), including multifunction mobile phone apps [35,42] and mobile phone‒based directly observed therapy [36].

    Intervention Characteristics

    Text Messaging Interventions

    Text messaging interventions varied by frequency of messaging, message content, and added functionality. Most of text messaging interventions included one [36-38,40-43,45], two [34,39,48], or three [47,48] daily text reminders, or even more frequently in relation to meals [46]. Other studies provided reminders at different frequencies including weekly [40,41,46,47]; monthly, bi-monthly, or quarterly for laboratory monitoring [44]; and 1, 3, and 7 days before scheduled clinic appointments [48]. Most studies sent text message reminders that were customized to the patient’s medication regimen or personal preferences in terms of both scheduling (ie, time/day) and message content [34,37-41,43-48]. In addition, four studies had patients and/or parents create the content of the text messages themselves [38,40,45,46], and two of these studies included the use of a text message pool or bank [40,46]. The most sophisticated intervention designs also included messages related to dosing, side effects, adherence barriers, goal setting, positive reinforcement [39,40,43,46], or targeted to theoretical constructs [43]. Additional functionalities included patient reporting of physiological information via text (eg, peak expiratory flow in asthma patients) [47]; prompting of text-back responses from patients (two-way text messaging approach) [34,37,41,44,45]; sending text messages to parents if patients did not respond to scheduled reminders [45]; and the ability to request messages from individuals within their social network via the intervention platform [35,46] or from a motivational support network [40]. None of the text message intervention studies included a reward system or scheduled incentives for participants. Table 2 describes the approach and the components of different text messaging interventions.

    Table 2. Description of text message interventions.
    View this table
    Mobile Phone App Interventions

    Mobile phone‒based interventions included native apps for delivery of medication reminders [42]; a multifunction app that includes integration of a wireless device for physiological measurement and visualization (ie, glucometer); self-monitoring alerts and prompts for gamification features to incentivize engagement with the app, with a secure network for peer communication [35]; and a multifunction app that includes daily alert messages, creation and sharing of patient videos to directly observe adherence to therapy with feedback/follow-up, positive feedback/reinforcement messages, and incentives for adherence [36]. Table 3 describes the approach and the components of different mobile phone app interventions.

    Study Outcomes

    Of the 15 studies reviewed, 7 (47%) demonstrated statistically significant differences in medication adherence or related health outcomes [36,37,41,43-45,47]. The majority of the studies included in this review provided enough information to calculate standardized outcomes, such as SMDs. Most studies reported overall moderate to large SMDs of subjective and objective markers of adherence; however, most SMDs had wide 95% confidence intervals (Table 4). Several studies combined data related to the assessment of the efficacy or the usability/feasibility of different interventions reporting high levels of satisfaction and few technical or feasibility problems [35-37,40,41,44-46]. Additional results of each individual study are summarized in Multimedia Appendix 3 for text message interventions, and Multimedia Appendix 4 for mobile phone app interventions.

    Table 3. Description of mobile phone app interventions.
    View this table


    Principal Findings

    Medication nonadherence is a widespread problem in pediatric CHCs, and among adolescents in particular. In this systematic review, we identified 15 studies that met all our inclusion criteria. Our results suggest that there is modest evidence to support the efficacy of text messaging and mobile phone apps as interventions to improve medication adherence in adolescents with CHCs. Most of the included studies were of low to moderate quality because of methodological limitations, imprecision of results, or both. The included studies showed evidence of the acceptability and feasibility of text messaging and mobile phone app interventions, suggesting a potentially promising area of intervention development and further investigations in the near future to better understand their efficacy and cost-effectiveness.

    Our findings suggest moderate SMDs for most included interventions, which is consistent with earlier reports of adherence-enhancing interventions (ie, non-technology specific). However, given the heterogeneity of the included studies, the observed moderate effect size should be interpreted with caution [18,23]. In contrast, Pai and McGrady reported small effect sizes in a systematic review of adherence-promoting interventions [20]. In our review, the quality of the included studies was low to moderate, which was similar to a recent review of findings for technology-mediated interventions for treatment adherence across all age groups [19], and more specifically for adolescents with chronic physical conditions [28].

    Table 4. Effect size d for the main outcomes of included studies.
    View this table

    While text messaging and mobile phone app interventions offer a straightforward approach to address adherence to medications among adolescents, with broad application across CHCs, some challenges exist. None of the included studies measured the long-term durability of intervention effects across randomized conditions; thus, there is a need to establish the exposure or dosage needed to sustain long-term effects. In addition, the characteristics of the included studies also suggest that there is a need for improvement in intervention design; only four included theoretical models or approaches to target specific mechanisms of action to optimize efficacy [35,40,41,43]. McGrady and colleagues have recommended that articulation of the underlying mechanism of action for adolescent-specific adherence interventions is an advancement much needed to bring developmental and behavioral specificity to this growing area of research [50]. Furthermore, only two studies measured potential moderators of the intervention effect [41,43].

    Several of the studies measured feasibility and acceptability outcomes and found high levels of satisfaction and few feasibility issues, which are promising for advancements in these technologies and consistent with earlier reports [19,21,28]. The high level of satisfaction is noteworthy given the frequency of messaging, which for most studies included at least daily messages, reflecting a relatively high tolerance in this group for intervention. The evidence of feasibility in these studies suggests that adolescent-specific text messaging and mobile phone‒based approaches may be an important and promising area of future intervention development. Given methodological limitations in the studies reviewed, larger studies with long-term outcomes are warranted, particularly with sufficient power for clinically important outcomes. Recent evidence supports the efficacy of text message and mobile phone app interventions to promote preventive behavior among adolescents [51]. Furthermore, in addition to efficacy data, cost-effectiveness is another important aspect of intervention evaluation [9,22,52]. The cost to develop and maintain each intervention can be a barrier to widespread use of these interventions. Cost also impacts the variability in patients’ access to technologies. Formal economic evaluation of different interventions will inform health care authorities to decide whether adoption of such interventions would be economically efficient [22,52]. In a related systematic review, we found insufficient evidence regarding the cost-effectiveness of text messaging and mobile phone apps to promote adherence in adolescents with CHCs [52]. Our findings highlight the need for further investigation of cost-effectiveness to inform the scalability, sustainability, and future cost savings of such approaches [52].

    Strengths and Limitations

    Our systematic review has a number of strengths. First, we conducted our review following the recommendations for rigorous systematic reviews methodology [32,49,53]. Second, we used a highly sensitive search strategy guided by a librarian information specialist and had no language restrictions in order to minimize publication bias by identifying as many relevant studies as possible. Additional resources were also searched including published systematic reviews, clinical trial registries, and different electronic databases. Third, although we limited our search to studies published since 1995, there were no eligible studies identified before 2005. Therefore, the possibility that we have missed earlier studies is very small. Finally, 2 authors completed the review process independently at all stages.

    Our systematic review of the literature should be considered within the context of some potential methodological limitations. First, similar to any other systematic literature review, although our search criteria were designed to be comprehensive, it is possible that we missed relevant articles. Second, we included only articles published in peer-reviewed journals, and publication bias with the tendency to report positive study results cannot be excluded [54]. Third, a number of studies reported insufficient information, the definition of adherence varied, and the study sample size and ages as well as methods of adherence assessment used in the included studies were heterogeneous. These limitations prohibited a meta-analysis from being performed [55]. Finally, many of the included studies had relatively small sample sizes.


    The number of adolescents with CHCs continues to increase and medication nonadherence is a clear challenge. The use of text messaging and mobile phone app interventions to improve medication adherence among adolescents with CHCs has shown promising feasibility and acceptability; however, there is modest evidence to support their efficacy. Further evaluation of short- and long-term efficacy and cost-effectiveness of these interventions is warranted. In addition, better understanding of barriers to medication adherence would inform further development of text message and/or mobile phone app interventions to improve adherence and health outcomes in adolescents with CHCs, such as sickle cell disease [56,57]. Adolescents are frequent users of text messaging and mobile phone apps, and engaging adolescents with CHCs in their self-management is critical for improving long-term outcomes. Seeking adolescents’ perspectives could enhance uptake and long-term engagement, while minimizing patient fatigue. The currently available data from medication adherence studies using text messaging and mobile phone app interventions are heterogeneous, particularly in relation to process and outcomes measures, which limit the evidence generated and conclusions that can be drawn from those studies. Nevertheless, consistent use of Web-based and mobile health interventions reporting guidelines [58] would enhance comparative research across studies [59]. The functionalities of different mobile technologies continue to improve with gradually decreasing cost suggesting potential economies of scale where interventions could be delivered to large populations.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA checklist.

    PDF File (Adobe PDF File), 67KB

    Multimedia Appendix 2

    Search strategies.

    PDF File (Adobe PDF File), 151KB

    Multimedia Appendix 3

    Text message intervention outcomes.

    PDF File (Adobe PDF File), 58KB

    Multimedia Appendix 4

    Mobile phone app intervention outcomes.

    PDF File (Adobe PDF File), 32KB


    1. Van CJ, Gortmaker SL, Perrin JM. Dynamics of obesity and chronic health conditions among children and youth. JAMA 2010 Feb 17;303(7):623-630. [CrossRef] [Medline]
    2. Berry JG, Hall M, Hall DE, Kuo DZ, Cohen E, Agrawal R, et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi-institutional study. JAMA Pediatr 2013 Feb;167(2):170-177 [FREE Full text] [CrossRef] [Medline]
    3. Sawyer SM, Afifi RA, Bearinger LH, Blakemore S, Dick B, Ezeh AC, et al. Adolescence: a foundation for future health. Lancet 2012 Apr 28;379(9826):1630-1640. [CrossRef] [Medline]
    4. Smith JM, Martz K, Blydt-Hansen TD. Pediatric kidney transplant practice patterns and outcome benchmarks, 1987-2010: a report of the North American Pediatric Renal Trials and Collaborative Studies. Pediatr Transplant 2013 Mar;17(2):149-157. [CrossRef] [Medline]
    5. Sabate E. Adherence to Long-term Therapies: Evidence for Action. 2003. World Health Organization   URL: [accessed 2016-10-15] [WebCite Cache]
    6. Rapoff M. Adherence to pediatric medical regimens. In: Issues in Clinical Child Psychology. 2nd ed. New York: Springer Science+Business Media; 2010.
    7. DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research. Med Care 2004 Mar;42(3):200-209. [Medline]
    8. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care 2002 Sep;40(9):794-811. [CrossRef] [Medline]
    9. McGrady ME, Hommel KA. Medication adherence and health care utilization in pediatric chronic illness: a systematic review. Pediatrics 2013 Oct;132(4):730-740 [FREE Full text] [CrossRef] [Medline]
    10. Davis K. Agency for Healthcare Research and Quality. 2011. Statistical brief #343: Health care expenses and utilization for children with special health care needs, 2008. Estimates for the U.S. civilian noninstitutionalized population   URL: [accessed 2017-03-28] [WebCite Cache]
    11. Fredericks EM, Magee JC, Opipari-Arrigan L, Shieck V, Well A, Lopez MJ. Adherence and health-related quality of life in adolescent liver transplant recipients. Pediatr Transplant 2008 May;12(3):289-299. [CrossRef] [Medline]
    12. New England Health Institute. NEHI research shows patient medication nonadherence costs health care system $290 billion annually. 2009.   URL: http:/​/www.​​news/​344-nehi-research-shows-patient-medication-nonadherence-costs-health-care-system-290-billion-annually/​view [accessed 2017-03-27] [WebCite Cache]
    13. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care 2005 Jun;43(6):521-530. [Medline]
    14. Staples B, Bravender T. Drug compliance in adolescents: assessing and managing modifiable risk factors. Paediatr Drugs 2002;4(8):503-513. [Medline]
    15. Smith BA, Shuchman M. Problem of nonadherence in chronically ill adolescents: strategies for assessment and intervention. Curr Opin Pediatr 2005 Oct;17(5):613-618. [Medline]
    16. KyngAs HA, Kroll T, Duffy ME. Compliance in adolescents with chronic diseases: a review. J Adolesc Health 2000 Jun;26(6):379-388. [Medline]
    17. Hanghøj S, Boisen KA. Self-reported barriers to medication adherence among chronically ill adolescents: a systematic review. J Adolesc Health 2014 Feb;54(2):121-138. [CrossRef] [Medline]
    18. Kahana S, Drotar D, Frazier T. Meta-analysis of psychological interventions to promote adherence to treatment in pediatric chronic health conditions. J Pediatr Psychol 2008 Jul;33(6):590-611 [FREE Full text] [CrossRef] [Medline]
    19. Mistry N, Keepanasseril A, Wilczynski NL, Nieuwlaat R, Ravall M, Haynes RB, Patient Adherence Review Team. Technology-mediated interventions for enhancing medication adherence. J Am Med Inform Assoc 2015 Apr;22(e1):e177-e193. [CrossRef] [Medline]
    20. Pai Ahna LH, McGrady M. Systematic review and meta-analysis of psychological interventions to promote treatment adherence in children, adolescents, and young adults with chronic illness. J Pediatr Psychol 2014 Sep;39(8):918-931. [CrossRef] [Medline]
    21. McGrady ME, Ryan JL, Gutiérrez-Colina AM, Fredericks EM, Towner EK, Pai ALH. The impact of effective paediatric adherence promotion interventions: systematic review and meta-analysis. Child Care Health Dev 2015 Nov;41(6):789-802. [CrossRef] [Medline]
    22. McGrady ME, Hommel KA. Targeting Health Behaviors to Reduce Health Care Costs in Pediatric Psychology: Descriptive Review and Recommendations. J Pediatr Psychol 2016 Sep;41(8):835-848. [CrossRef] [Medline]
    23. Dean AJ, Walters J, Hall A. A systematic review of interventions to enhance medication adherence in children and adolescents with chronic illness. Arch Dis Child 2010 Sep;95(9):717-723. [CrossRef] [Medline]
    24. Graves MM, Roberts MC, Rapoff M, Boyer A. The efficacy of adherence interventions for chronically ill children: a meta-analytic review. J Pediatr Psychol 2010 May;35(4):368-382 [FREE Full text] [CrossRef] [Medline]
    25. Lenhart A. Teens, Social Media & Technology Overview 2015. Washington, DC; 2015. Pew Research Center   URL: [accessed 2016-11-04] [WebCite Cache]
    26. Smith A. U.S. Smartphone Use in 2015. Washington, DC; 2015. Pew Research Center   URL: [accessed 2016-11-05] [WebCite Cache]
    27. 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]
    28. 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]
    29. 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]
    30. Badawy S, Sino M, Kaviany S. Text messaging and smartphone app interventions to improve adherence in adolescents and young adults: a systematic review and meta-analysis.: University of York PROSPERO   URL: [accessed 2017-03-28] [WebCite Cache]
    31. World Health Organization. Department of Child and Adolescent Health and Development. Geneva, Switzerland; 2001. The Second Decade: Improving Adolescent Health and Development   URL: [accessed 2016-10-25] [WebCite Cache]
    32. Higgins JPT, Green S. The Cochrane Collaboration. 2011. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0   URL: [accessed 2016-08-05] [WebCite Cache]
    33. Lipsey M, Wilson D. Practical meta-analysis (Applied Social Research Methods). Thousand Oaks, CA: Sage; 2001.
    34. Boker A, Feetham HJ, Armstrong A, Purcell P, Jacobe H. Do automated text messages increase adherence to acne therapy? Results of a randomized, controlled trial. J Am Acad Dermatol 2012 Dec;67(6):1136-1142. [CrossRef] [Medline]
    35. Cafazzo JA, Casselman M, Hamming N, Katzman DK, Palmert MR. Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res 2012;14(3):e70 [FREE Full text] [CrossRef] [Medline]
    36. 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]
    37. Dowshen N, Kuhns LM, Johnson A, Holoyda BJ, Garofalo R. Improving adherence to antiretroviral therapy for youth living with HIV/AIDS: a pilot study using personalized, interactive, daily text message reminders. J Med Internet Res 2012;14(2):e51 [FREE Full text] [CrossRef] [Medline]
    38. 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]
    39. Fabbrocini G, Izzo R, Donnarumma M, Marasca C, Monfrecola G. Acne smart club: an educational program for patients with acne. Dermatology 2014;229(2):136-140. [CrossRef] [Medline]
    40. Franklin VL, Waller A, Pagliari C, Greene SA. A randomized controlled trial of Sweet Talk, a text-messaging system to support young people with diabetes. Diabet Med 2006 Dec;23(12):1332-1338. [CrossRef] [Medline]
    41. Garofalo R, Kuhns LM, Hotton A, Johnson A, Muldoon A, Rice D. A Randomized Controlled Trial of Personalized Text Message Reminders to Promote Medication Adherence Among HIV-Positive Adolescents and Young Adults. AIDS Behav 2016 May;20(5):1049-1059. [CrossRef] [Medline]
    42. Hammonds T, Rickert K, Goldstein C, Gathright E, Gilmore S, Derflinger B, et al. Adherence to antidepressant medications: a randomized controlled trial of medication reminding in college students. J Am Coll Health 2015;63(3):204-208. [CrossRef] [Medline]
    43. Louch G, Dalkin S, Bodansky J, Conner M. An exploratory randomised controlled trial using short messaging service to facilitate insulin administration in young adults with type 1 diabetes. Psychol Health Med 2013;18(2):166-174. [CrossRef] [Medline]
    44. McKenzie RB, Berquist WE, Foley MA, Park KT, Windsheimer JE, Litt IF. Text Messaging Improves Participation in Laboratory Testing in Adolescent Liver Transplant Patients. J Particip Med 2015;7 [FREE Full text] [Medline]
    45. Miloh T, Annunziato R, Arnon R, Warshaw J, Parkar S, Suchy FJ, et al. Improved adherence and outcomes for pediatric liver transplant recipients by using text messaging. Pediatrics 2009 Nov;124(5):e844-e850 [FREE Full text] [CrossRef] [Medline]
    46. Mulvaney SA, Anders S, Smith AK, Pittel EJ, Johnson KB. A pilot test of a tailored mobile and web-based diabetes messaging system for adolescents. J Telemed Telecare 2012 Mar;18(2):115-118 [FREE Full text] [CrossRef] [Medline]
    47. Ostojic V, Cvoriscec B, Ostojic SB, Reznikoff D, Stipic-Markovic A, Tudjman Z. Improving asthma control through telemedicine: a study of short-message service. Telemed J E Health 2005 Feb;11(1):28-35. [CrossRef] [Medline]
    48. Ting TV, Kudalkar D, Nelson S, Cortina S, Pendl J, Budhani S, et al. Usefulness of cellular text messaging for improving adherence among adolescents and young adults with systemic lupus erythematosus. J Rheumatol 2012 Jan;39(1):174-179. [CrossRef] [Medline]
    49. 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]
    50. McGrady ME, Ryan JL, Brown GA, Cushing CC. Topical Review: Theoretical Frameworks in Pediatric Adherence-Promotion Interventions: Research Findings and Methodological Implications. J Pediatr Psychol 2015 Sep;40(8):721-726 [FREE Full text] [CrossRef] [Medline]
    51. 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]
    52. 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]
    53. Centre for Reviews Dissemination. Systematic Reviews: CRD's Guidance for Undertaking Reviews in Health Care (Internet). York, UK; 2009.   URL: [accessed 2016-03-02] [WebCite Cache]
    54. 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]
    55. Bartolucci A, Hillegass W. Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses. In: Chiapelli F, editor. Evidence-Based Practice: Toward Optimizing Clinical Outcomes. Berlin: Springer-Verlag Berlin Heidelberg; 2010:17-33.
    56. 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). [CrossRef] [Medline]
    57. 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. [CrossRef] [Medline]
    58. Eysenbach G, Consort-EHEALTHGroup. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res 2011 Dec 31;13(4):e126 [FREE Full text] [CrossRef] [Medline]
    59. Augestad KM, Berntsen G, Lassen K, Bellika JG, Wootton R, Lindsetmo RO, Study Group of Research Quality in Medical Informatics and Decision Support (SQUID). Standards for reporting randomized controlled trials in medical informatics: a systematic review of CONSORT adherence in RCTs on clinical decision support. J Am Med Inform Assoc 2012;19(1):13-21 [FREE Full text] [CrossRef] [Medline]


    CHCs: chronic health conditions
    GRADE: Grades of Recommendation, Assessment, Development, and Evaluation
    HRQOL: health-related quality of life
    m-DOT: Mobile Direct Observed Therapy
    PEF: peak expiratory flow
    PEFM: peak expiratory flow meter
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    PROSPERO: International Prospective Register of Systematic Reviews
    SMD: standardized mean differences

    Edited by G Eysenbach; submitted 31.03.17; peer-reviewed by W Evans, L Dayer; comments to author 24.04.17; revised version received 28.04.17; accepted 29.04.17; published 15.05.17

    ©Sherif M Badawy, Leonardo Barrera, Mohamad G Sinno, Saara Kaviany, Linda C O’Dwyer, Lisa M Kuhns. Originally published in JMIR Mhealth and Uhealth (, 15.05.2017.

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