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

Non-Randomized Studies (funded, eHealth) (125) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (637) mHealth for Treatment Adherence (467) mHealth for Wellness, Behavior Change and Prevention (3311) Behavior Change (861) Hypertension Prevention and Treatment (296)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65105, first published .
The Use of Mobile Health Technology and Behavioral Economics to Encourage Adherence to Statins and Blood Pressure–Lowering Medication in Adolescents with Familial Hypercholesterolemia or Hypertension: Protocol for a Pre-Post Cohort Study

The Use of Mobile Health Technology and Behavioral Economics to Encourage Adherence to Statins and Blood Pressure–Lowering Medication in Adolescents with Familial Hypercholesterolemia or Hypertension: Protocol for a Pre-Post Cohort Study

The Use of Mobile Health Technology and Behavioral Economics to Encourage Adherence to Statins and Blood Pressure–Lowering Medication in Adolescents with Familial Hypercholesterolemia or Hypertension: Protocol for a Pre-Post Cohort Study

Authors of this article:

Jacob Hartz1, 2 Author Orcid Image ;   Hannah Chiert1 Author Orcid Image ;   Sarah de Ferranti1, 2 Author Orcid Image ;   Tiffany Powell-Wiley3, 4 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

    Protocol

    1Department of Cardiology, Boston Children's Hospital, Boston, MA, United States

    2Department of Pediatrics, Harvard Medical School, Boston, MA, United States

    3Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States

    4Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States

    Corresponding Author:

    Jacob Hartz, MD, MPH

    Department of Cardiology

    Boston Children's Hospital

    300 Longwood Ave

    Boston, MA, 02115

    United States

    Phone: 1 6173550955

    Fax:1 7300600

    Email: jacob.hartz@icloud.com


    Background: Cardiovascular disease (CVD) is a leading cause of mortality and morbidity in the United States, with risk factors such as hypertension and elevated low-density lipoprotein (LDL) cholesterol originating in childhood. While statins and blood pressure–lowering medications can mitigate these risks, adherence is often poor, particularly among youth. Innovative solutions, such as monetary incentives via smartphone apps, may enhance adherence, but evidence in youth is lacking.

    Objective: This study aims to evaluate the efficacy of a smartphone app (Wellth) offering financial incentives to improve adherence to statins and blood pressure–lowering medications among youth aged 12 to 19 years at risk for cardiovascular disease.

    Methods: We initially designed a randomized controlled trial to compare the efficacy of 2 different incentive structures in youth treated with a statin for familial hypercholesterolemia with inadequate adherence. After facing recruitment challenges, the study protocol was changed to evaluating a single incentive in a pre-post design. The primary outcome was the change in adherence rate over the 60-day incentive period compared to the adherence rate during the 14-day run-in period. The secondary outcome was a change in LDL cholesterol level. Adjustments to the protocol were made in response to recruitment challenges during the COVID-19 pandemic, simplifying the incentive structure and expanding eligibility criteria.

    Results: The study is currently undergoing recruitment and collection of data from the first participants. The study has faced recruitment challenges exacerbated by the COVID-19 pandemic, necessitating protocol modifications. Detailed analysis of adherence rates and LDL cholesterol changes is ongoing.

    Conclusions: This study explores the efficacy of monetary incentives delivered through a smartphone app to improve medication adherence in youth at risk for CVD. The findings will be used to build upon the existing literature in an effort to improve medication adherence throughout the life course and ultimately reduce CVD.

    Trial Registration: ClinicalTrials.gov NCT04458766; https://clinicaltrials.gov/study/NCT04458766

    International Registered Report Identifier (IRRID): DERR1-10.2196/65105

    JMIR Res Protoc 2025;14:e65105

    doi:10.2196/65105

    Keywords

    dyslipidemia; hypertension; adherence; mobile health; incentives; behavioral health; youth 


    Cardiovascular disease (CVD) is the leading cause of death and disability in the United States. It leads to US $216 billion in direct costs and an additional US $147 billion in indirect costs each year [1]. While the presentation of CVD events primarily occurs in adulthood, the significant risk factors for CVD, including hypertension and elevated low-density lipoprotein (LDL) cholesterol, have their antecedents in childhood [2,3]. With the use of statins [4-17] and blood pressure–lowering medications [18], substantial reductions in this risk are possible.

    However, adherence to statins and blood pressure–lowering medications is needed to accrue these potential benefits. Unfortunately, medication adherence is frequently poor, with up to 50% of adults not taking all their prescribed medications [19-23]. In adults, nonadherence is associated with an increased risk for CVD events and all-cause mortality [20,24], as well as almost US $44 billion annually in additional health care costs [25,26]. Although studies are limited, adherence to statins and blood pressure–lowering medications also has been shown to be inadequate in youth [27-30]. Youth seem to be particularly vulnerable to nonadherence [31], even in conditions in which the consequences of nonadherence can lead to rapid disease progression or severe, acute symptoms, such as bipolar disorder [32], HIV infection [33], or asthma [34].

    Multiple factors impact adherence (Figure 1), including patient-related factors (eg, beliefs and expectations), socioeconomic factors, medication-related factors (eg, complexity of the regimen and adverse effects), the disease being treated, and the health care system [35]. Unfortunately, successful interventions to improve medication adherence overall are rare in both adults [36] and youth [36,37]. Further, there are no formally tested interventions to improve adherence to statins or blood pressure–lowering medications in youth. One potential solution to improve adherence is to provide monetary incentives. Traditional economic theory suggests that providing individuals with incentives to complete an action will lead to increased frequency of the occurrence of the desired action. Studies in adults have shown monetary incentives to be modestly successful in certain diseases [38-40], but not all studies have shown positive results [41]. Further, it is unclear if these findings translate to youth.

    ‎
    Figure 1. Five components of medication adherence as proposed by the World Health Organization [35]. The green curved arrows indicate components expected to respond to incentives, while the dashed black arrows are components that are less likely to respond.

    To address this gap, we planned a study using a smartphone app (Wellth) to provide financial incentives to improve medication adherence in youth at risk of CVD. Smartphone apps are promising interventional tools as they are familiar to most youth [42,43]. Data suggests using smartphone apps in a health care setting is of interest to youth [44] across a diverse set of socioeconomic and cultural backgrounds [42]. The Wellth app is designed to improve medication adherence through reminders and by providing monetary rewards for taking a medication as prescribed.

    The objective of this paper is to describe the original study design, recruitment methods, outcomes, analysis plan, strengths, and limitations. In addition, we highlight modifications to the design and recruitment strategies because of social distancing requirements related to the COVID-19 pandemic and the need to improve recruitment. The principal aim of our study is to determine if financial incentives are effective at improving adherence to medications in youth who are at an increased risk of CVD.


    Study Design

    The study, originally titled “Use of Mobile Health Technology and Behavioral Economics to Encourage Adherence to Blood Pressure–Lowering Medications and Statins in Youth at Risk for Cardiovascular Disease,” was designed as a randomized control trial to compare the efficacy of 2 interventions on improving medication adherence to statins in youth aged 12 to 19 years using a smartphone app (Figure 2). The 104-day study consisted of a 14-day run-in period, 2 periods of 30 days in which participants received incentives, and a 30-day follow-up period in which participants were asked to record adherence but did not receive any incentives. During the run-in and follow-up periods, all features of the Wellth app were available, but the participants did not receive any monetary incentives. This approach was used to isolate the effect of the monetary incentive from the other features of the app and to provide an objective baseline measure of adherence. After the 14-day run-in, the participants were eligible to receive incentives during two 30-day incentive periods. Incentives were disbursed at the end of period 1 (day 44) and period 2 (day 74). In addition, we also measured the patients’ adherence in the 30-day period after the incentive periods to determine if the effect of the intervention was sustained. The study was approved by the research ethics board at the Boston Children’s Hospital with a waiver of individual participant consent as this research used data that had previously been obtained through a departmental quality improvement project. Patients who were older than 18 years at any time while participating in the study signed an informed consent form. For patients who were younger than 18 years, informed consent was obtained from their legal guardian, and assent was obtained from the participant.

    ‎
    Figure 2. Final study timeline. The fasting lipid profile included total cholesterol, triglyceride level, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol.

    Eligibility Criteria

    The original eligibility criteria are described in Textbox 1. Patients were eligible to participate if they were aged 12 to 19 years, had a diagnosis of familial hypercholesterolemia (FH), were prescribed a statin, and reported less-than-ideal statin adherence. The diagnosis of FH was based on genetic testing or if the patient met the National Lipid Association recommendations, which are an LDL cholesterol level greater than or equal to 160 mg/dL or a non–high density lipoprotein cholesterol level greater than or equal to 190 mg/dL in patients younger than 20 years [11,19,20,45].

    Textbox 1. Eligibility and exclusion criteria.

    Eligibility criteria

    • Less-than-ideal adherence by self-report
    • Age 12 to 19 years
    • Diagnosis of familial hypercholesterolemia (FH) based on National Lipid Association criteria and/or genetic testing or a diagnosis of hypertension
    • Prescribed a statin or blood pressure–lowering medication
    • Ability to provide written informed consent or have a parent or guardian provide written informed consent

    Exclusion criteria

    • Homozygous FH
    • Residence in a long-term care facility where medications are administered
    • Being pregnant/possibility of becoming pregnant
    • History of adverse effects or allergies to a statin or blood–pressuring lowering medication or any ingredient in one of these medications

    Recruitment

    We recruited patients from a tertiary care hospital and its satellite outpatient clinics. Recruitment took place at outpatient clinic visits in a preventive cardiology clinic. The study clinicians approached eligible patients and provided a brief overview of the study. If the participant was interested, the clinician determined the patient’s eligibility based on self-reported adherence [46,47]. If the patient was eligible and agreed to participate, the clinician conducted the informed consent and enrollment procedures.

    We supplemented clinic recruitment by recruiting patients with FH treated with a statin by phone using a database maintained by the preventive cardiology program, which includes approximately 400 patients. We estimated that 20 patients per month who met the eligibility criteria would be seen. If 50 percent of those patients agreed, we expected to complete the recruitment of 30 patients in 6 months (power calculation shown below). Patients could be removed from the study if there was concern for their safety or there was a change in the medication that they were prescribed. Patients could voluntarily stop participating in the study at any time.

    Outcomes

    The primary outcome was medication adherence as measured by the Wellth app and defined as the proportion of doses taken per doses prescribed. The adherence rate used in the analysis was measured over one 14-day period and three 30-day periods (days 1-14, days 15-44, days 45-74, and days 75-104). Secondary outcomes included change in the LDL cholesterol level from baseline until the end of the intervention period (day 74). LDL cholesterol was considered a baseline measurement if it was obtained within 60 days from the day of consenting to the study. The covariates we included in the analysis were gender, age, and race/ethnicity.

    Description of Wellth App

    The Wellth app was the primary tool used in the study to promote medication adherence. Patients used the app to receive reminders, make check-ins (ie, document adherence), check their reward balance, and view their adherence history. Participants downloaded the app to their smartphone without any additional software or encryption needed. In order to prevent patients from being excluded based on the costs of the necessary hardware, we provided a smartphone and data plan free of charge to patients. Wellth monitored app use and uploaded photos through a secure, customized analytics dashboard for data monitoring and customer support.

    Structure of Incentives

    Initially, participants were to be randomized to 1 of 2 incentive structures. The first structure of the incentive was based on the principles of present bias and loss aversion. Present bias refers to the tendency of individuals to prefer more immediate rewards and outcomes and to discount future risks [48]. For instance, studies suggest that even small rewards, if provided frequently and immediately after a participant completes a desired task, can improve adherence in adults and may be more effective at promoting behavior change than larger rewards provided in the distant future [36,49,50]. The principle of loss aversion was included to help strengthen the incentive and is based on findings from studies that suggest individuals place more value on money or objects that they have than on new objects of the same value [51].

    Participants received US $30 at the beginning of each of the two 30-day periods but were only able to access the money at the end of the 30-day periods. However, participants could view their balance within the app at any time. For each missed check-in, the participant lost US $2 from the amount to be paid out at the end of the 30-day period. Participants who missed more than 15 check-ins did not receive any money at the end of the 30-day period.

    In the second incentive approach, we planned for participants to not receive rewards for each check-in but instead receive the chance to receive a reward of varying amounts. This incentive structure is similar to that of a slot machine. As in the first incentive structure, the total amount a participant could receive over the two 30-day periods was US $60. However, participants would not lose money if a check-in was missed; rather, they would lose an opportunity to receive a reward. The random distribution of the reward was structured to ensure that a fully adherent participant would receive a full US $60 over the study period. However, in this arm of the study, it was also possible that a participant could receive US $60 even if they were not fully adherent.

    Power

    Power calculations were planned to be performed for the primary outcome variable of adherence rate at the end of the 60-day period. The mean difference in adherence rates at 60 days after the start of the intervention (day 74) would be compared to 0%, which was the value that would be expected if there was no difference between the two interventions using a 2-sided paired t test conducted at the .05 level of significance. The mean adherence rate was hypothesized to be 80% when patients received small frequent awards and 74% when receiving a large, randomly delivered reward, suggesting a mean within-patient difference of 6%. The SD of the adherence rate was assumed to be 10% at each time point [27], and the correlation between the two measures of adherence was assumed to be 0.50, resulting in an SD of differences in adherence rate of 10%. If the correlation between measurements of adherence was conservatively assumed to be 0.30, the SD of differences in adherence rate was estimated to be 11.8%. We expected to enroll 30 patients.

    Statistical Analysis

    Data were planned to be analyzed on an intention-to-treat basis. Baseline patient characteristics measured prior to randomization would be compared between the two intervention groups to look for imbalances. Primary and secondary outcomes would be compared between groups on day 44, day 74, and day 104. Continuous variables would be compared using the 2-sample t test or Wilcoxon rank sum test. Differences in categorical variables would be assessed using the Fisher exact test. Analyses of the primary outcome variables (ie, percentage change in LDL cholesterol and proportion of days adherent) would use a significance level of .025 at each follow-up time point; all other comparisons would be performed at the .05 level of significance. If differences in baseline characteristics were detected between the two groups, linear regression would be used to perform additional comparisons of the primary and secondary outcomes, controlling for these potential confounders. Transformations would be applied to continuous outcomes that were not normally distributed.

    Ethical Considerations

    This study was approved by the Boston Children's Hospital Institutional Review Board (IRB-A00032841-2).

    A majority of participants in the proposal are younger than 21 years and thus meet the National Institutes of Health’s definition of children. The study will not exclude participants based on race/ethnicity, gender, or sexual preference. The final study sample will be representative of the patient population of the Boston Children’s Hospital Preventive Cardiology Program. Our sampling plan was developed in consultation with a statistician to ensure appropriate representation of all groups.

    Privacy is a fundamental concern any time that personal information is transmitted electronically, especially if a third party is involved. Wellth does not share patient data with any third party. Wellth uses deidentified datasets wherever possible, including internal reports and communications. Wellth will share data with members of the Boston Children’s Hospital study team needed for data analysis and patient safety monitoring. Wellth uses HITRUST (Health Information Trust Alliance)-certified, HIPAA (Health Insurance Portability and Accountability Act)-compliant application hosting services provided by HealthcareBlocks. All computers used by Wellth are encrypted, including hosting computers. Every member of the company performs annual HIPAA compliance training. All software development and company activities are performed inside the United States. We specifically instructed participants not to upload photos that include any personally identifying information.


    The intervention was developed to encourage patients to take their medication every day, and the pre-post study was used to determine the efficacy of this intervention. The findings of the study will help determine if monetary incentives can lead to increased adherence and help determine if this is a viable strategy that can be expanded. Recruitment started in August 2019 and concluded in August 2024 (n=9 participants were recruited). Data collection was ongoing throughout the trial and will be analyzed after study completion. The study’s findings are to be published after study completion, which is expected in December 2025 or January 2026.


    Anticipated Findings

    We expect to find that providing small, frequent financial incentives will improve medication adherence among youth taking medications to reduce their risk for cardiovascular disease. We expect that this improved adherence will be demonstrated by an increase in adherence as measured by the Wellth app.

    Unfortunately, only a few studies with adolescents have used incentives to promote behavioral change. In a study with youth with type 1 diabetes, incentives had a positive impact on glucose monitoring frequency and adherence to the prescribed insulin regimen [50]. However, patients with diabetes mellitus fundamentally differ from those with hypertension or dyslipidemia, as the latter conditions are typically asymptomatic [7]. While studies in adults have had mixed results [52], adolescents differ substantially regarding attitudes toward health, and they have less mature cognitive function and capacities than adults [49].

    COVID-19 Pandemic

    Our initial protocol and recruitment strategy had to be modified in response to the challenges that arose during the COVID-19 pandemic. In the original proposal, we planned to recruit patients seen during routine outpatient visits. However, in the early months of the pandemic, outpatient clinic appointments were limited to those with urgent concerns, which rarely included patients with dyslipidemia. Enrolling participants remotely, though, posed several challenges. The first was connecting the patient to our research assistant. Unlike an in-person clinic visit in which the research assistant is readily available, connecting the research assistant with the patient during a remote encounter required arranging additional contact with the patient. We found that if there were any delays in connecting to the research assistant, eligible participants seemed to quickly lose interest.

    A second problem arose in obtaining written consent from participants seen via telehealth visits. Unfortunately, the infrastructure for obtaining an electronic signature was initially unavailable and written consent was required by our institution. The written consent was rarely completed during the course of a virtual visit. Instead, families typically planned to send the signed consent forms that evening or the next day, which often did not occur despite repeated attempts to contact eligible patients. In addition, errors in the consent, such as missing signatures or illegible handwriting, often led to patients not participating, as obtaining corrected consent forms was challenging.

    The third barrier that arose was related to technical issues signing patients up for the Wellth app. While configuring the app for the patient was not cumbersome, and there were no complaints by participants enrolling in the study, adding study-related information (eg, participant identification number and study codes) was sometimes technically challenging. We could not have the participant download the smartphone app until the consent was finalized, which often meant arranging a separate time to ensure the app was operational, and some participants dropped out after signing the consent form because of the additional time commitment.

    Low Recruitment

    Recruitment for the trial was more difficult than expected, in part related to the COVID-19 pandemic. We were concerned that the complexity and duration of the trial were deterrents. Over the first year, we made a series of changes to simplify the intervention.

    First, we decided to use only the first intervention, which involved small, frequent rewards, while removing the second intervention, which entailed random, variable rewards. This was based on our perception that the first intervention was conceptually easier for pediatric participants to understand. Since we would no longer be comparing two types of incentive structures, we also removed randomization and created a pre-post study design (Figure 2). Adherence measures collected via the app during the 14-day run-in period would be used as the baseline adherence measure to be compared to adherence recorded on day 44, day 74, and day 104.

    The second modification was to shorten the trial duration by removing the 30-day follow-up period. We received feedback from potential participants during recruitment that the length of the trial prevented them from participating. As this portion of the trial was included mainly to assess the sustainability of the incentive, it made more sense to remove it rather than shorten the incentive period.

    Third, we expanded the protocol to include patients with hypertension who had been prescribed a blood pressure–lowering medication (eg, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, or calcium-channel blockers) to increase the pool of eligible participants. We did not change the incentive structure, even for those patients who were prescribed regimens with twice-daily dosing. In these patients, we required both doses to be taken to earn the reward. The diagnosis of hypertension was based on chart review (ie, hypertension-associated diagnosis codes in the International Classification of Diseases, ninth or tenth revisions) and was not based on blood pressure–specific thresholds.

    Limitations

    As has been acknowledged, recruiting for pediatric studies can be difficult. To overcome this obstacle, we changed the protocol and recruitment strategies. In the future, we plan to increase the pool of recruits by expanding to other clinics outside of Boston Children’s Hospital. Another potential limitation is the inability to ensure that the increase in adherence is specifically from the incentives and not another component of the Wellth app. We have tried to mitigate this potential problem by not offering the incentive for the first two weeks. We will compare the adherence during the first two weeks with adherence during the periods when incentives are provided to isolate the effect of the incentive.

    Future Directions

    This research protocol will test whether providing incentives that leverage the principles of behavioral economics can improve medication adherence in youth with FH and hypertension. Understanding the effectiveness of different types of incentives is crucial to improve medication adherence. The incentive structure in regard to the type of reward, frequency of receiving the award, and timing of delivery of the reward can lead to different responses. In fact, there is evidence that inappropriate reward structures can serve as a negative enforcer.

    In addition, this proposal will harness the power of digital health technology, taking advantage of the fully developed Wellth app to reduce costs and simplify the intervention. Digital health apps allow for automating patient–health care provider interactions, improving monitoring using cameras with embedded metadata (eg, date and time), and reducing the complexity of interventions, allowing for more widespread distribution of interventions.

    Patients with FH and hypertension provide an ideal cohort to explore the role of different incentive packages. FH is rarely symptomatic in youth, and treatment is typically well tolerated. One critical factor is that missed doses do not generally lead to acute complications in either disease. Therefore, health care providers and caretakers can safely distance themselves from interfering, which prevents adding biases introduced by frequent health care provider engagement.

    It should be acknowledged that there are important differences between those prescribed lipid-lowering therapy for an inherited condition and those treated for hypertension, which is typically an acquired condition. Further, blood pressure–lowering medications have a different side effect profile than statins and may be prescribed more than once daily, which may impact adherence.

    Conclusion

    This proposal will elicit information critical for creating developmentally appropriate incentive structures to improve medication adherence in youth with risk factors for CVD. However, the efficacy of monetary incentives is extrapolated from adult studies, and it remains to be seen if they are ideal for youth.

    Acknowledgments

    JH acknowledges that research reported in this publication was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health (award K23HL145109).

    TP-W is funded by the Division of Intramural Research in the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Institute on Minority Health and Health Disparities. The contributions of the NIH author(s) were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. Boston Children’s Hospital and the authors personally have been named as defendants in a lawsuit brought by MMAS Research, LLC, which is ongoing, that concerns the attribution of the Morisky Medication Adherence Scale (MMAS). We have received competing demands from MMAS Research, LLC, and another entity, Morisky Medication Adherence Research, LLC, concerning the proper attribution for the MMAS. Because we are uncertain how the MMAS should be attributed and who owns the intellectual property rights, we are providing the attribution that both MMAS Research, LLC, and Morisky Medication Adherence Research, LLC, have demanded that we use.

    Data Availability

    The datasets used and analyzed in this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    All authors contributed to the study conception and design. JH, SdF, and TP-W were responsible for the primary design of the study. Material preparation and data was collected by JH and SdF. The first draft of the manuscript was written by JH, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Peer review report form the HLBI Mentored Patient-Oriented Research Review Committee - Heart, Lung, and Blood Initial Review Group MPOR (MA) - National Heart, Lung, and Blood Institute (National Institutes of Health, USA).

    PDF File (Adobe PDF File), 143 KB
    1. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2021 Update: a report from the American Heart Association. Circulation. Feb 23, 2021;143(8):e254-e743. [FREE Full text] [CrossRef] [Medline]
    2. Juonala M, Järvisalo MJ, Mäki-Torkko N, Kähönen M, Viikari JS, Raitakari OT. Risk factors identified in childhood and decreased carotid artery elasticity in adulthood: the Cardiovascular Risk in Young Finns Study. Circulation. Sep 06, 2005;112(10):1486-1493. [CrossRef] [Medline]
    3. Nicklas TA, von Duvillard SP, Berenson GS. Tracking of serum lipids and lipoproteins from childhood to dyslipidemia in adults: the Bogalusa Heart Study. Int J Sports Med. May 2002;23 Suppl 1(S1):S39-S43. [CrossRef] [Medline]
    4. Statins for the prevention of cardiovascular events. National Institute for Health and Clinical Excellence. 2006. URL: https://www.nice.org.uk/guidance/ta94 [accessed 2025-07-24]
    5. Scientific Steering Committee on behalf of the Simon Broome Register Group. Mortality in treated heterozygous familial hypercholesterolaemia: implications for clinical management. Scientific Steering Committee on behalf of the Simon Broome Register Group. Atherosclerosis. Jan 1999;142(1):105-112. [Medline]
    6. Baigent C, Keech A, Kearney PM, Blackwell L, Buck G, Pollicino C, et al. Cholesterol Treatment Trialists' (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet. Oct 08, 2005;366(9493):1267-1278. [CrossRef] [Medline]
    7. Braamskamp MJ, Kastelein JJ, Kusters DM, Hutten BA, Wiegman A. Statin Initiation During Childhood in Patients With Familial Hypercholesterolemia: Consequences for Cardiovascular Risk. J Am Coll Cardiol. Feb 02, 2016;67(4):455-456. [FREE Full text] [CrossRef] [Medline]
    8. Braamskamp MJ, Langslet G, McCrindle BW, Cassiman D, Francis GA, Gagné C, et al. Efficacy and safety of rosuvastatin therapy in children and adolescents with familial hypercholesterolemia: results from the CHARON study. J Clin Lipidol. 2015;9(6):741-750. [CrossRef] [Medline]
    9. de Jongh S, Ose L, Szamosi T, Gagné C, Lambert M, Scott R, et al. Simvastatin in Children Study Group. Efficacy and safety of statin therapy in children with familial hypercholesterolemia: a randomized, double-blind, placebo-controlled trial with simvastatin. Circulation. Oct 22, 2002;106(17):2231-2237. [CrossRef] [Medline]
    10. Gidding SS, Champagne MA, de Ferranti SD, Defesche J, Ito MK, Knowles JW, et al. American Heart Association Atherosclerosis‚ Hypertension‚Obesity in Young Committee of Council on Cardiovascular Disease in Young‚ Council on CardiovascularStroke Nursing‚ Council on Functional GenomicsTranslational Biology‚Council on LifestyleCardiometabolic Health. The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association. Circulation. Dec 01, 2015;132(22):2167-2192. [CrossRef] [Medline]
    11. Jacobson TA, Maki KC, Orringer CE, Jones PH, Kris-Etherton P, Sikand G, et al. National Lipid Association recommendations for patient-centered management of dyslipidemia: part 2. J Clin Lipidol. 2015;9(6 Suppl):S1-122.e1. [FREE Full text] [CrossRef] [Medline]
    12. Kavey RW, Allada V, Daniels SR, Hayman LL, McCrindle BW, Newburger JW, American Heart Association Expert Panel on PopulationPrevention Science, American Heart Association Council on Cardiovascular Disease in the Young, American Heart Association Council on EpidemiologyPrevention, American Heart Association Council on Nutrition‚ Physical ActivityMetabolism, American Heart Association Council on High Blood Pressure Research, American Heart Association Council on Cardiovascular Nursing, American Heart Association Council on the Kidney in Heart Disease, et al. Interdisciplinary Working Group on Quality of CareOutcomes Research. Cardiovascular risk reduction in high-risk pediatric patients: a scientific statement from the American Heart Association Expert Panel on Population and Prevention Science; the Councils on Cardiovascular Disease in the Young, Epidemiology and Prevention, Nutrition, Physical Activity and Metabolism, High Blood Pressure Research, Cardiovascular Nursing, and the Kidney in Heart Disease; and the Interdisciplinary Working Group on Quality of Care and Outcomes Research: endorsed by the American Academy of Pediatrics. Circulation. Dec 12, 2006;114(24):2710-2738. [CrossRef] [Medline]
    13. Nawrocki JW, Weiss SR, Davidson MH, Sprecher DL, Schwartz SL, Lupien P, et al. Reduction of LDL cholesterol by 25% to 60% in patients with primary hypercholesterolemia by atorvastatin, a new HMG-CoA reductase inhibitor. Arterioscler Thromb Vasc Biol. May 1995;15(5):678-682. [CrossRef] [Medline]
    14. Neil A, Cooper J, Betteridge J, Capps N, McDowell I, Durrington P, et al. Reductions in all-cause, cancer, and coronary mortality in statin-treated patients with heterozygous familial hypercholesterolaemia: a prospective registry study. Eur Heart J. Nov 2008;29(21):2625-2633. [FREE Full text] [CrossRef] [Medline]
    15. Rodenburg J, Vissers M, Wiegman A, van Trotsenburg A, van der Graaf A, de Groot E, et al. Statin treatment in children with familial hypercholesterolemia: the younger, the better. Circulation. Aug 07, 2007;116(6):664-668. [CrossRef] [Medline]
    16. Wiegman A, Gidding SS, Watts GF, Chapman MJ, Ginsberg HN, Cuchel M, et al. European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia in children and adolescents: gaining decades of life by optimizing detection and treatment. Eur Heart J. Sep 21, 2015;36(36):2425-2437. [FREE Full text] [CrossRef] [Medline]
    17. Wiegman A, Hutten BA, de Groot E, Rodenburg J, Bakker HD, Büller HR, et al. Efficacy and safety of statin therapy in children with familial hypercholesterolemia: a randomized controlled trial. JAMA. Jul 21, 2004;292(3):331-337. [CrossRef] [Medline]
    18. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care. Sep 2002;40(9):794-811. [CrossRef] [Medline]
    19. Chi MD, Vansomphone SS, Liu I-LA, Cheetham C, Green KR, Scott RD, et al. Adherence to statins and LDL-cholesterol goal attainment. Am J Manag Care. Apr 01, 2014;20(4):e105-e112. [FREE Full text] [Medline]
    20. De Vera MA, Bhole V, Burns LC, Lacaille D. Impact of statin adherence on cardiovascular disease and mortality outcomes: a systematic review. Br J Clin Pharmacol. Oct 2014;78(4):684-698. [FREE Full text] [CrossRef] [Medline]
    21. Fung V, Graetz I, Reed M, Jaffe MG. Patient-reported adherence to statin therapy, barriers to adherence, and perceptions of cardiovascular risk. PLoS One. 2018;13(2):e0191817. [FREE Full text] [CrossRef] [Medline]
    22. Pittman DG, Chen W, Bowlin SJ, Foody JM. Adherence to statins, subsequent healthcare costs, and cardiovascular hospitalizations. Am J Cardiol. Jun 01, 2011;107(11):1662-1666. [CrossRef] [Medline]
    23. Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ. May 17, 2008;336(7653):1114-1117. [FREE Full text] [CrossRef] [Medline]
    24. Wei L, Wang J, Thompson P, Wong S, Struthers AD, MacDonald TM. Adherence to statin treatment and readmission of patients after myocardial infarction: a six year follow up study. Heart. Sep 2002;88(3):229-233. [FREE Full text] [CrossRef] [Medline]
    25. Gatwood J, Bailey J. Improving medication adherence in hypercholesterolemia: challenges and solutions. Vasc Health Risk Manag. 2014;10:615-625. [FREE Full text] [CrossRef] [Medline]
    26. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. Aug 04, 2005;353(5):487-497. [CrossRef] [Medline]
    27. Braamskamp MJAM, Kusters DM, Avis HJ, Smets EMA, Wijburg FA, Kastelein JJP, et al. Long-term statin treatment in children with familial hypercholesterolemia: more insight into tolerability and adherence. Paediatr Drugs. Apr 2015;17(2):159-166. [FREE Full text] [CrossRef] [Medline]
    28. Joyce NR, Wellenius GA, Eaton CB, Trivedi AN, Zachariah JP. Patterns and predictors of medication adherence to lipid-lowering therapy in children aged 8 to 20 years. J Clin Lipidol. 2016;10(4):824-832.e2. [FREE Full text] [CrossRef] [Medline]
    29. Weinstock RS, Trief PM, Burke BK, Wen H, Liu X, Kalichman S, et al. Antihypertensive and lipid-lowering medication adherence in young adults with youth-onset type 2 diabetes. JAMA Netw Open. Oct 02, 2023;6(10):e2336964. [FREE Full text] [CrossRef] [Medline]
    30. Eakin MN, Brady T, Kandasamy V, Fivush B, Riekert KA. Disparities in antihypertensive medication adherence in adolescents. Pediatr Nephrol. Aug 2013;28(8):1267-1273. [FREE Full text] [CrossRef] [Medline]
    31. Normansell R, Kew KM, Stovold E. Interventions to improve adherence to inhaled steroids for asthma. Cochrane Database Syst Rev. Apr 18, 2017;4(4):CD012226. [FREE Full text] [CrossRef] [Medline]
    32. Goldstein TR, Krantz M, Merranko J, Garcia M, Sobel L, Rodriguez C, et al. Medication adherence among adolescents with bipolar disorder. J Child Adolesc Psychopharmacol. Dec 2016;26(10):864-872. [FREE Full text] [CrossRef] [Medline]
    33. Reisner SL, Mimiaga MJ, Skeer M, Perkovich B, Johnson CV, Safren SA. A review of HIV antiretroviral adherence and intervention studies among HIV-infected youth. Top HIV Med. 2009;17(1):14-25. [FREE Full text] [Medline]
    34. Desager K, Vermeulen F, Bodart E. Adherence to asthma treatment in childhood and adolescence - a narrative literature review. Acta Clin Belg. Oct 2018;73(5):348-355. [CrossRef] [Medline]
    35. De Geest S, Sabaté E. Adherence to long-term therapies: evidence for action. Eur J Cardiovasc Nurs. Dec 2003;2(4):323. [CrossRef] [Medline]
    36. van Driel ML, Morledge MD, Ulep R, Shaffer JP, Davies P, Deichmann R. Interventions to improve adherence to lipid-lowering medication. Cochrane Database Syst Rev. Dec 21, 2016;12(12):CD004371. [FREE Full text] [CrossRef] [Medline]
    37. Jones M, Moffatt F, Harvey A, Ryan JM. Interventions for improving adherence to airway clearance treatment and exercise in people with cystic fibrosis. Cochrane Database Syst Rev. Jul 18, 2023;7(7):CD013610. [CrossRef] [Medline]
    38. Linnemayr S, Stecher C, Mukasa B. Behavioral economic incentives to improve adherence to antiretroviral medication. AIDS. Mar 13, 2017;31(5):719-726. [FREE Full text] [CrossRef] [Medline]
    39. Raiff BR, Jarvis BP, Dallery J. Text-message reminders plus incentives increase adherence to antidiabetic medication in adults with type 2 diabetes. J Appl Behav Anal. Dec 2016;49(4):947-953. [FREE Full text] [CrossRef] [Medline]
    40. Strang S, Park SQ, Strombach T, Kenning P. Applied economics: The use of monetary incentives to modulate behavior. Prog Brain Res. 2016;229:285-301. [CrossRef] [Medline]
    41. Thirumurthy H, Asch DA, Volpp KG. The uncertain effect of financial incentives to improve health behaviors. JAMA. Apr 16, 2019;321(15):1451-1452. [CrossRef] [Medline]
    42. Molfenter T, Bhattacharya, Gustafson. The roles of past behavior and health beliefs in predicting medication adherence to a statin regimen. Patient Prefer Adherence. 2012;6:643-651. [FREE Full text] [CrossRef] [Medline]
    43. Riekert K. Promoting adherence and increasing life span. Johns Hopkins Adv Stud Med. 2009;9(1):14-19. [FREE Full text]
    44. Cell phone and smartphone ownership demographics. Pew Research Internet Project. 2014. URL: https://www.pewresearch.org/internet/2015/04/01/chapter-one-a-portrait-of-smartphone-ownership/ [accessed 2018-01-22]
    45. Ademi Z, Watts GF, Juniper A, Liew D. A systematic review of economic evaluations of the detection and treatment of familial hypercholesterolemia. Int J Cardiol. Sep 10, 2013;167(6):2391-2396. [CrossRef] [Medline]
    46. Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). May 2008;10(5):348-354. Berlowitz DR, Foy CG, Kazis LE, Bolin L, Morisky Widget MMAS, Steven Trubow, MMAS Research LLC, ed. [FREE Full text] [CrossRef] [Medline]
    47. Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). May 2008;10(5):348-354. Retracted in: J Clin Hypertens (Greenwich). 2023 Sep;25(9):889. [CrossRef] [Medline]
    48. Benabou R, Tirole J. Intrinsic and extrinsic motivation. Rev Econ Studies. Jul 2003;70(3):489-520. [CrossRef]
    49. Blakemore S, Robbins TW. Decision-making in the adolescent brain. Nat Neurosci. Sep 2012;15(9):1184-1191. [CrossRef] [Medline]
    50. Mulvaney S, Lee JM. Motivating health behaviors in adolescents through behavioral economics. JAMA Pediatr. Dec 01, 2017;171(12):1145-1146. [FREE Full text] [CrossRef] [Medline]
    51. Sen AP, Sewell TB, Riley EB, Stearman B, Bellamy SL, Hu MF, et al. Financial incentives for home-based health monitoring: a randomized controlled trial. J Gen Intern Med. May 2014;29(5):770-777. [FREE Full text] [CrossRef] [Medline]
    52. Hoskins K, Ulrich CM, Shinnick J, Buttenheim AM. Acceptability of financial incentives for health-related behavior change: An updated systematic review. Prev Med. Sep 2019;126:105762. [CrossRef] [Medline]

    ‎
    CVD: cardiovascular disease
    FH: familial hypercholesterolemia
    HIPAA: Health Insurance Portability and Accountability Act
    HITRUST: Health Information Trust Alliance
    LDL: low density lipoprotein

    Edited by T Leung; The proposal for this study was peer-reviewed by: NHLBI Mentored Patient-Oriented Research Review Committee - Heart, Lung, and Blood Initial Review Group MPOR (MA) - National Heart, Lung, and Blood Institute (National Institutes of Health, USA). See the Multimedia Appendix for the peer-review report; submitted 05.08.24; accepted 15.03.25; published 14.08.25.

    Copyright

    ©Jacob Hartz, Hannah Chiert, Sarah de Ferranti, Tiffany Powell-Wiley. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.08.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.