Cite this article: Kalichira A, Briars L, Simon J, Czech K, John E, et al. (2015) Pilot Investigation into the Impact of Mobile Health (mHealth) Applications on Medication Adherence in Adolescent Renal Transplant Patients. J Clin Nephrol Res 2(2): 1024.

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*Corresponding authorAsha Kalichira, Department of Pharmacy, University of Illinois at Chicago, 164 PHARM MC 886833 S. Wood St., Chicago, IL 60612, Tel: 312-775-2203; Email:

Submitted: 06 November 2015

Accepted: 25 November 2015

Published: 27 November 2015

ISSN: 2379-0652

Copyrighta© 2015 Kalichira et al.

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Research Article

Pilot Investigation into the Impact of Mobile Health (mHealth) Applications on Medication Adherence in Adolescent Renal Transplant PatientsAsha Kalichira1, Leslie Briars2, Joseph Simon2, Kimberly Czech3, Eunice John3 and Lawrence Pawola4

1Department of Pharmacy, University of Illinois at Chicago, USA2Department of Pharmacy Practice, University of Illinois at Chicago, USA3Department of Pediatrics, University of Illinois at Chicago, USA4Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, USA

INTRODUCTIONThe advent of superior immunosuppressive (IMS) therapies

and surgical techniques has increased 1-year renal graft survival to as much as 98.7% in kidney transplant patients [1]. Graft survival in adolescents aged 12 to 17 years is comparable at 96.1%. However, 5-year graft survival in this younger population is lower when compared to other age groups, with the exception of recipients older than 65 years [1,2]. The 1-year time point is significant, as it marks a time in which there are fewer clinic visits for monitoring [3]. While factors such as co-morbid diseases, extent of donor match, and donor type are involved in graft loss in all populations, the adolescent age group in particular is notorious for high rates of medication non-adherence resulting in graft loss [4].

Medication adherence is defined by the World Health Organization as “the extent to which a person’s behavior– taking

medication, following a diet, and/or executing lifestyle changes – corresponds with agreed recommendations from a health care provider” [5]. Published rates of non-adherence in pediatric renal transplant patients range from 3 to 71%, with the wide range often attributed to differing definitions of non-adherence [6]. However, as much as 12% of graft failures in adolescents can be attributed to non-adherence, a rate that has been found to be 4 times higher than adults [7]. In fact, adolescents make up 60.6% of transplant graft losses due to non-adherence [8]. This is significant since 45% of all pediatric transplants occur in this age group [6].

These statistics are troublesome when coupled with the consequences associated with non-adherence such as increased hospitalizations, additional medication, poorer health outcomes, graft failure, and even death. Therefore, there is considerable importance tied to the development of strategies that can be

Abstract

Immunosuppressive (IMS) therapy has significantly improved renal graft survival in recipients of all ages. However, IMS therapy non-adherence among adolescents is significantly higher than other age groups, resulting in higher rates of graft loss. Interventions and methods to improve adherence in this demographic are also lacking. The use of technology and mobile smart phone applications, in particular, are popular among this age group. This pilot study sought to explore the impact of a mobile reminder application on medication adherence in the adolescent renal transplant population. Adolescents aged 12 to 22 years old, at least 1 year post renal transplantation were enrolled and asked to use a smart phone medication adherence application for 6 months. Changes in adherence were assessed primarily through standard deviation of IMS blood levels; secondary measures included self-assessment surveys, pharmacist perceived adherence surveys, and medication possession ratio (MPR). Eight patients were initially enrolled in the study. Based on SD of IMS, there was no significant change post intervention. Secondary measures show an increase in the number of individuals considered to have “high adherence” by pharmacist assessment, and a decrease in the number with “low adherence” through patient self-survey. MPR was unable to be calculated due to lack of access to information. A larger study is needed to determine whether using a smart phone adherence application could be a useful tool in improving adherence in adolescent renal transplant patients.

Keywords•Adherence•mHealth•Mobile applications•Adolescent adherence•Transplant

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utilized towards increasing adherence in this population. Current interventional techniques that have been employed include patient education, clinician interventions, and counseling. The most promising interventions use behavioral, motivational, and educational approaches to improve adherence, however it has been noted that technology utilization may be the future for improving adherence [8]. Unfortunately, the majority of interventions related to medication adherence were conducted in other chronic disease states; only 4 studies out of 122 published on this topic involved pediatric transplant populations [6,8]. This indicates a present need for continued investigation of possible interventions within this population [9].

Of growing interest is the use of technology to augment or change patient behaviors. As a relatively new medium in which providers can improve access to care, technology’s potential has yet to be fully realized in the medical community [10]. Mobile technology has come to the forefront of society, and has a 73% global adoption rate [9]. Mobile devices include laptops, mobile phones, tablets, portable media devices, and personal digital assistants. Interest in the expansion of mobile technologies into healthcare has led to the development of the term “mobile-health” (mHealth) defined as “the delivery of healthcare services via mobile communication device” by the Foundation for the National Institutes of Health [11]. An advantageous feature of this subset of technology is its ability to be used almost anywhere, at any time. Mobile phone use has seen widespread adoption, with rates of ownership near 91% in American adults [12]. mHealth interventions using mobile phones typically operate through two features: Short Message Service (SMS) or applications, also known as “apps.” SMS operates through sending and receiving short, text-based messages, while applications function much like software programs; both features are found on many mobile phones.

Adoption of this type of technology into medical care has been slow, which is unfortunate, especially considering the extent in which adolescents utilize these devices. Considered to be “tech-savvy”, as many as 95% of 12 to 17 year olds use the internet, 77% own a cell phone, and 76% use social media [13]. It is logical to conclude that methods utilizing social networking, mobile phones, and the internet all have potential as a medium for delivering healthcare, including enhancement of medication adherence. However, there are limited studies examining the potential influence these technologies may have on medication adherence in this population. Thus, the objective of this pilot study was to see whether the use of a mobile application would encourage improved adherence in the adolescent population in the time period when monitoring decreases, approximately one year after transplantation.

MATERIAL AND METHODSThis study was approved by the University of Illinois at

Chicago (UIC) Institutional Review Board (IRB) and data was collected from December 2013 to October 2014. All renal transplant recipients monitored in the Pediatric Nephrology Clinic at The University of Illinois Hospital and Health Sciences System’s Children and Adolescent centers were screened.

Individuals fitting the inclusion criteria included those patients 12 to 22 years old, greater than 1 year post renal transplant, on therapy requiring blood drug level monitoring, and owning a smart phone or tablet. Exclusion criteria included non-English speaking patients or minors with non-English speaking parents, and participants unable to understand the study. Informed consent, or parental consent if a minor, was obtained for all enrollees.

The application selected for the study was the free edition of Dosecast© which is developed by Montuno Software and publically available for Android and Apple operating systems. This particular program was chosen after the principle investigator evaluated various free applications available in 2013. Based on user reviews, testing of reliability, ease of use, flexibility in scheduling, and privacy, this particular application was chosen. Education on the features of the phone application was provided and participants were shown how to enter their medication regimen and adjust settings. They were instructed to enter all of their medications and use the application for 6 months. During this period, the primary measurement of adherence was determined by the standard deviation (SD) of tacrolimus drug levels and compared to the SD of levels 1 year prior to intervention [14]. Secondary outcomes included adherence measures using a validated patient survey developed by Morisky and colleagues [16], and a pharmacist survey completed by a single clinical pharmacist involved in medication management in this clinic. Both of these surveys were administered at enrollment and exit of the study. In addition, the medication possession ratio (MPR) during the 6-month study period, and 1 year prior to intervention was also to be recorded. A brief exit survey on acceptance of the application was given at the end of the study. Wilcoxon’s matched pairs and Chi squared tests were used to test significance.

RESULTS AND DISCUSSIONDemographics: Thirteen subjects were enrolled in the study.

Of these, 4 withdrew each for different reasons, including having no interest in the study, the investigator was unable to contact the patient for installation of the application, IMS levels were not regularly monitored, and a patient was no longer followed in this clinic during the study period. One participant was excluded from analysis due to initiation of medications known to cause significant interference with IMS levels. Median age of participants was 17 years old, (range: 13-21). Six of the eight study participants were female, owned an Android phone, had private insurance, and 5 received their graft from a living donor with a median time since transplantation at 3.5 years (range: 2-8 years). Complete demographic information is described in Table 1.

Primary Outcome: Median standard deviation (SD) of IMS levels in the year prior to the study was 1.38 ng/mL, with only one participant with SD greater than 2.0 ng/mL, the threshold that has been predictive of late rejection [14]. After the intervention, median SD of IMS levels was found to be 1.07 ng/mL and one participant had a measured SD greater than the threshold. The sample size was not large enough to test for statistical significance. A summary of the primary outcome can

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Table 1: Demographics and Baseline Characteristics.

N = 8

Age – Median (Range) 17 (13-21)

Male – % (n) 25 (2)

Race – % (n)

White 25 (2)

AA 25 (2)

Hispanic 37.5 (3)

Asian 12.5 (1)

Type of Insurance – % (n)

Private 75 (6)

Government 25 (2)

Smartphone OS – % (n)

Android 75 (6)

Apple 25 (2)Living – Donor Transplant

– % (n) 62.5 (5)

Years since Transplant – Median (Range) 3.5 (2-8)

Renal Transplants >1 – % (n) 25 (2)

Past Rejection – % (n) 37.5 (3)Number of medications –

Median (Range) 8.5(3-12)

Responsibility of medications* – % (n)

Patient 62.5 (5)

Parent 50 (4)*Question allowed for more than one response, therefore n may be greater than total number of participants in the study

Table 2: Primary Outcome – IMS Levels.

Baseline Intervention

Median SD (ng/mL) 1.38 1.07

SD > 2.0 – % (n) 11.1 (1) 11.1 (1)

Table 3: Pharm D Assessment of Adherence.

Baseline - % (n) Intervention - % (n)

Low 12.5 (1) 12.5 (1)

Medium 12.5 (1) 0 (0)

High 75 (6) 87.5 (7)

Table 4: Adherence Self-Assessment.

Baseline Baseline* Intervention

(n = 8) (n = 3) (n = 3)

Low - % (n) 25 (2) 66.7 (2) 33.3 (1)

Medium - % (n) 62.5 (5) 33.3 (1) 66.7 (2)

High - % (n) 12.5 (1) – –

*Data from those that completed pre and post surveys

be found in Table 2.

Secondary Outcomes: The pharmacist assessment of ad-herence indicated that 75% of participants were considered to have “high” adherence at the start of the study. After the inter-vention, 87.5% were considered to have high adherence. Patient self-survey prior to the study showed that the majority (62.5%) perceived themselves to have “medium” adherence. Participa-tion in the self-survey at the end of the study was limited, with only 37.5% of subjects responding. Of these, 66.7% considered themselves to have “medium” adherence. Comparison of the pre and post-intervention results of individuals who completed both surveys showed that 66.7% considered themselves to have “low” adherence and 33.3% had “medium adherence prior to use of the application. Afterwards, 33.3% scored “low” and 66.6% scored “medium” adherence. A third measure of adherence, the MPR, was to be examined as well. However, due to resource limitations this was not successful; see discussion for further information. Finally, of the four respondents to the exit survey, 75% found the application helpful in reminding them to take their medications and 75% would consider using it in the future. Tables 3 and 4 summarize the secondary outcomes.

Lack of adherence to immunosuppressive therapy is one of the leading causes of graft rejection in adolescent transplant patients. In addition, few studies have examined methods in which to improve adherence in the pediatric population. This is one of the first to examine the use of a smart phone application for adherence in adolescents, however due to study limitations (see below) it is difficult to make concrete conclusions.

The primary outcome of this study shows that use of the mHealth adherence application did not correlate with increasing the number of individual’s adherent to IMS therapy by measure of the SD of tacrolimus concentrations. However, the impact on the conclusion of this study highlights the limitations of using the SD of IMS levels as a surrogate measure of adherence, and the overall difficulty in studying adherence.

Data from secondary measures of adherence showed an increase in pharmacist and patient self-assessed measures of adherence from use of the medication reminder application. However, limitations of the study make it difficult to draw strong conclusions from this data. The pharmacist’s measure of adherence is considered subjective and has not been shown to correlate with outcomes [15]. Also, while the self-assessment is an established tool to assess adherence, the lack of participation at the conclusion of the study limits any interpretation [16]. The MPR of IMS therapy was to be an additional measure of adherence in this study but was not completed due to limited access to data. This was owing to incomplete refill histories documented in medical records and lack of resources available to the investigator in which to collect data directly from participants’ pharmacies.

Study limitations: The culmination of limitations during the course of this study may have contributed to the lack of impact found by using the adherence phone application.

Measurement of adherence: It is known that measurement of adherence is difficult and often requires the use of surrogate

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markers. SD of IMS was chosen as the primary outcome measure in this study due to its objectivity; yet, it is not a perfect measure and subject to a number of other factors unrelated to adherence. This study attempted to prevent this issue by using a number of objective and subjective indicators to build a comprehensive picture of adherence, but was limited by several issues detailed below. In addition, the version of the application used in this study was unable to provide information on use of the application itself during the study period. Obtaining that information objectively, through the application, assessments, or surveying may have provided additional information into the utility of this application.

Small sample size: The small study size and lack of control group contributed to the inability to draw conclusions from these outcomes. However, several of these factors could be ameliorated for future studies. Using multiple centers and broadening inclusion criteria could have allowed for a larger sample size. For example, this study may have had increased enrollment if participants speaking other languages were included or individuals without smart phones were provided a device for use within the study. This study chose not to provide incentives for participation in the study; however, doing so may have increased enrollment and participation in the study.

Lack of participation: Another limitation of this study was meager participation by those enrolled, ranging from lack of use of the application to poor response to surveys. Increased use of the application may have been recorded if the study had been designed to include additional follow up sites in the community or if parents and clinicians had greater involvement. On this last point, the relationship between these individuals and the patient might allow them to more frequently check the status of the patient to ensure medications were entered correctly into the application. This would ensure that the reminders from the application continued to be relevant to the patient, and would not be ignored simply because they were inapplicable. Another method of increasing use of the application may have been modifying inclusion criteria. Instead of targeting patients greater than 1 year post-transplant, a more fitting population may have been those that were newly transplanted or patients/parents seeking to transition responsibility of medication adherence to the adolescent. The author believes this may be a time at which medication administration routines are not established and when individuals/families might be the most willing to try, and continue to use, such a tool to aid in developing that routine. The results of this study may provide evidence to that fact, as most participants were not in this stage and already considered themselves to have medium to high adherence. Thus, the author hypothesizes that since many patients found their current system to be sufficient, they were less likely to adopt newer tools to aid in that process. Finally, survey response rates in the post-intervention adherence survey may have been higher if patients were asked to complete them during regular clinic visits, instead of online, as was done in this study.

Lack of medication history: In addition, the MPR may be better calculated if refill histories were readily accessible, either

by direct access to pharmacy records, resources such as fax machines, or regular documentation through visual checks of medication/receipts during scheduled clinic visits.

CONCLUSIONAlthough the results of this pilot study make it difficult

to develop any definitive conclusion on the impact of using a smart phone application on medication adherence in adolescent renal transplant patients, there were several lessons learned. It suggests that there is still a potential for positive benefit, and that a larger, more resource-intensive study is needed to accurately measure the influence of such an intervention. In addition, this population’s enthusiasm to use mHealth applications, coupled with the increasing integration of technology in their lives, develops a compelling case for providers to investigate methods in which technology could be leveraged to improve patient outcomes.

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Kalichira A, Briars L, Simon J, Czech K, John E, et al. (2015) Pilot Investigation into the Impact of Mobile Health (mHealth) Applications on Medication Adherence in Adolescent Renal Transplant Patients. J Clin Nephrol Res 2(2): 1024.

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