Background: In solid organ transplant patients, non-participation in all aspects of the medical regimen is a prevalent problem associated with adverse consequences particularly in the adolescent and young adult (AYA) age group. This study is the first to evaluate the feasibility, utility and impact of a text messaging (TM) intervention to improve participation in laboratory testing in adolescent liver transplant patients.

Methods: AYA patients, aged 12 to 21 years, were recruited for a prospective pilot trial evaluating a TM intervention delivered over a 1-year period. The intervention involved automated TM reminders with feedback administered according to a prescribed laboratory testing frequency. Participation rate in laboratory testing after the intervention was compared to the year prior. Patient responses and feedback by text and survey were used to assess feasibility, acceptability and use of the intervention.

Results: Forty-two patients were recruited and 33 patients remained enrolled for the study duration. Recipients of the TM intervention demonstrated a significant improvement in participation rate in laboratory testing from 58% to 78% (P<.001). This rate was also significantly higher than in non-intervention controls (P=.003). There was a high acceptability, response rate and a significant correlation with reported versus actual completion of laboratory tests by TM. Conclusions: TM reminders significantly improved participation in laboratory testing in AYA liver transplant patients. The intervention demonstrated feasibility, acceptability, and use with a high proportion of patients who engaged in and perceived a benefit from using this technology.

Keywords: Adolescents, mobile health, mHealth, HIT, self-management, participation, self-care, text messaging, liver transplant.
Citation: 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 Participat Med. 2015 May 22; 7:e7.
Published: May 22, 2015.
Competing Interests: The authors have declared that no competing interests exist.


Facilitating active participation in self-care remains an important goal in the current health care and patient community. [1] In the pediatric literature, non-participation, commonly referred to as “non-adherence” is described as a prevalent problem that has led to poor outcomes, unreliable treatment efficacy assessments, unnecessary clinical interventions and enormous costs to American taxpayers. [2] Organ transplantation represents a life-saving procedure that requires long-term immunosuppressive medication and regular laboratory testing to ensure excellent outcomes. Frequent laboratory testing represents an important mechanism to monitor disease activity as in many patients with chronic illness. However, in adolescent transplant patients, non-participation in all aspects of the medical regimen including laboratory testing is a prevalent problem associated with adverse consequences. [3],[4],[5],[6],[7] The lack of proven interventions requires further studies to address this problem in this high-risk population.

Technology-based approaches represent a promising way to address non-participation in AYA patients. Cellphones, TM, and internet-based tools are widely used in the adolescent population among all socio-economic groups. [8] For many American teens, TM is the preferred method of communication and also represents a low-cost, accessible and convenient way to communicate reminders. [9] Studies in pediatric liver transplant cite forgetfulness as the primary reason for non-participation; therefore, reminder-based TM interventions could address the most common etiology for non-participation. [10] Yet, to date, there is only one study using TM reminders in pediatric liver transplant patients that targeted medication participation in patients of all ages. [11]

To our knowledge, our study is the first to investigate the impact of a TM intervention on participation in laboratory testing in the high-risk population of AYA liver transplant recipients. In a prospective pilot study, we evaluated the impact of TM reminders to improve participation in laboratory testing in adolescent liver transplant patients. Our secondary aim was to determine the feasibility, acceptability, and use of an automated TM program involving feedback for test completion. We hypothesized that the TM intervention would improve participation in laboratory testing in AYA liver transplant patients.


Study Population and Design

This study was a non-randomized pilot feasibility and impact trial of automated TM reminders for laboratory tests. All patients 12-21 years of age and greater than 6 months post-liver transplantation were screened by chart review for eligibility. Participants with access to a mobile device with TM capability were recruited (August 2012 to December 2013) by phone or in person from the outpatient and associated outreach clinics of a quaternary care academic children’s hospital. Participants were not reimbursed for any charges associated with TM. A historical control group of patients without direct contact for consent was included in the post-hoc analysis for comparison. The institutional review board approved the study protocol; consent and assent were obtained as appropriate by age before the study procedures.


Cell phone numbers were collected and the data were entered into a secure website with password protection. The TM intervention was fully automated and administered from January 2013 to January 2014. Patients agreed to receive messages on a predetermined schedule according to the frequency of lab tests required either monthly, bi-monthly (every 2 months) or quarterly, which was confirmed by chart review and based on clinical stability and time from transplantation.

Patients received a TM reminder the first Monday of each month in which testing was required. They also received a message the last Monday of the month to inquire about completion of laboratory testing with associated feedback responses. The messages were designed as follows: “Hi, this is a reminder to get your lab tests done this month!” End of the month reminders were structured as: “Did you get your lab tests done this month? Reply 1 for YES and 2 for NO.” If “YES,” the system responded with “Great! Please call our office at [office number] to review the results,” and if NO, they received “Please get your lab tests. It’s important to check the health of your liver!” If a non-coded response was received, the system sent the following message: “Unrecognized message. For questions or to opt out of the program, please contact [study investigator at office number].”

All responses were recorded by the system and stored on a 128-bit SSL secure server. The cost to maintain the intervention included an average of $31 per month for the website domain and phone number and $0.01 per TM. TM was not reimbursed but all patients had unlimited TM plans.


Laboratory Participation

The primary outcome of participation rate in laboratory testing was calculated as the percentage of completed versus expected laboratory tests. The number of expected laboratory tests ranged from an average of four to 12 depending on medical complications and time from transplant. Expected frequency was defined by health care provider documentation in the chart. Completion of laboratory tests was confirmed by documented results in the electronic medical record the year prior to (2012) and during the study (2013).

Feasibility, Acceptability, and Use

Feasibility considerations included the number of eligible and willing participants with continuous coverage for the study duration. Patients were asked to complete an online or paper survey regarding text messaging utilization, phone characteristics, and communication preferences prior to the study. The web interface tracked errors and disconnected numbers. Rate of change in frequency of laboratory testing owing to health status was also calculated. Acceptability evaluation included survey feedback about the utility of the TM intervention. Use data included the rate of participant response as well as the overall and specific number of responses sent during the intervention. We also evaluated the correlation between reported and actual events regarding completion of laboratory tests.

Statistical Analysis

Statistical analyses were performed using JMP Pro 10 statistical software (SAS Institute, Cary, NC) with significance set at P<.05. Demographic characteristics were analyzed for descriptive purposes. Paired-sample t-tests were used to compare participation rate in laboratory testing before and after the intervention. Matched paired or unpaired t tests were used as appropriate to compare secondary continuous outcomes. The Cochran-Mantel-Haenszel test was used to analyze stratified categorical data. The Shapiro-Wilk test was conducted to evaluate participation data for normality and on this basis non-parametric analyses were subsequently used. The one-way ANOVA test was conducted to determine any effect of TM frequency on laboratory participation rate. Feasibility, acceptability, and use of the intervention were also assessed using descriptive statistics. An agreement test was performed to compare the reported versus actual outcome regarding completion of laboratory testing.


Forty-two patients (age 12-21) consented to participation and 33 (79%) completed the study. None of the patients dropped out of the study; nine were later excluded due to various reasons (Fig. 1). Demographic data for the enrolled patients are presented in Table 1. In the intervention group, 11 patients received monthly TM (Group 1), nine patients received bi-monthly TM (Group 2), and 13 patients received quarterly TM (Group 3). There were no adverse events reported or confidentiality breaches during the study.

Figure 1. Study diagram including patient enrollment and data analysis for the prospective TM study.

Figure 1. Study diagram including patient enrollment and data analysis for the prospective TM study.

Table 1. TM Participant Characteristics.

Table 1. TM Participant Characteristics.

Participation in Laboratory Testing

The average participation rate in laboratory testing among TM patients significantly increased from 58% (M=0.58, SD=0.31) to 78% (M=0.78, SD=0.30) after the reminder intervention by the Wilcoxon matched pairs test (p<.001). We then conducted a secondary post-hoc analysis using a comparison group of 51 patients age 12-20 not enrolled in the study (Figure 2). The average participation rate changed from 57% (M=0.57, SD=0.29) to 61% (M=.61, SD=0.33) after the intervention period, which was not a significant change (p=.38). Comparing the two groups, there was no significant difference in the mean laboratory participation rate at baseline (P=.80) but a significant difference was noted after the study period (P=.03). We also calculated a significant mean increase in laboratory participation in the intervention group compared to the non-intervention group after the study period (P=.003). We then compared subject change in laboratory participation (improved/no change/worse) by starting participation quartile between the intervention and non-intervention groups (Figure 3). Patients were grouped accordingly: Level I=0-25%, Level II=25-50%, Level III=50-75%, Level IV=75-100%. There was a significant association between starting quartile and change in participation rate by group (text vs. no-text) using the stratified analysis (p=.002). [caption id="attachment_3606" align="alignleft" width="550"]Figure 2: Comparison of change in mean laboratory participation within the TM group (P<0.001) and a comparison non-TM group (P=0.38), 1 year before and 1 year after the TM study period. There was a significant increase in mean participation in the TM group compared to the non-TM group (P=0.03). Figure 2: Comparison of change in mean laboratory participation within the TM group (P<0.001) and a comparison non-TM group (P=0.38), 1 year before and 1 year after the TM study period. There was a significant increase in mean participation in the TM group compared to the non-TM group (P=0.03).[/caption] [caption id="attachment_3602" align="alignleft" width="550"]Figure 3: Analysis comparing the proportional response (Improved, No Change or Worse) in participation by starting quartile of laboratory completion in the TM group (A) compared to the Non-TM (ΦTM) group (B). There was a significant association between starting quartile and change in participation by group (p=.002). Figure 3: Analysis comparing the proportional response (Improved, No Change or Worse) in participation by starting quartile of laboratory completion in the TM group (A) compared to the Non-TM (ΦTM) group (B). There was a significant association between starting quartile and change in participation by group (p=.002).[/caption]

In the TM group, 22 patients (67%) improved and three (1%) had worse laboratory participation. Eight patients did not change but six had 100% participation prior to TM and all maintained perfect participation post-TM. In the non-TM group, 20 patients (39%) improved and 17 (33%) had worse participation. Fourteen patients did not change participation; 11 had 100% perfect participation at baseline but only six of those patients maintained perfect participation in this group.

A subgroup analysis was conducted in the intervention group to determine whether there was an effect of testing frequency on participation. The one-way ANOVA test did not detect a significant effect of TM frequency on laboratory participation (p=.64).

Feasibility, Acceptability, and Use

Of the 44 patients screened, only two patients (5%) did not enroll; one was excluded owing to lack of a mobile phone and the other declined. Twenty-five of the 42 enrolled patients (60%) completed pre-study questionnaires. All of the patients reported having unlimited text messaging and therefore no additional cost to participate. Thirteen patients were already using their phones for either medication or clinic reminders but none for laboratory tests. All survey participants preferred secure email or text messaging as the primary method of contact about their medical care; the majority preferred text messaging (68%) to email (32%).

Of the 42 patients initially enrolled, nine patients (21%) were later excluded owing to medical or technical reasons (Figure 1). None of the patients discontinued the intervention. No phone numbers were disconnected according to the study log; however, it was discovered that one cellphone number changed and 2 patients did not receive messages for an unclear reason. These patients were excluded from statistical analyses. During the study, 11 patients had a change in laboratory frequency based on clinical stability; nine had a decreased frequency and two required more frequent testing.

Thirty patients, (77%) of those with a working cellphone number, responded with at least one text message during the study. Twelve patients (29%) tried to communicate additional messages using the program; the majority represented clarifications regarding a revised laboratory schedule. An average of 5.7 text messages were sent per patient (SD=3.6, range 1-13). The majority of responses represented “YES” replies with a total of 95 (M=3.2, SD 2.8, range 0-11) compared to 70 “NO” replies (M=2.4, SD 2.4, range 0-11). The majority of patients who responded (83%) sent at least one “NO” message when laboratory tests were not completed.

Eighty-nine (94%) of the “YES” messages correctly corresponded to a completed laboratory test. Seventy-three (96%) of the “NO” replies corresponded to not completing a laboratory test by chart review. This excellent agreement was supported by the kappa statistic (k=0.89, p<0.001) (95% CI=0.82, 0.96). McNemar’s test failed to reject the null hypothesis (p=0.32), which supported agreement between reported and actual completion of laboratory tests by chart review. Thirty patients completed a post-study survey. The majority of patients (80%) positively responded that the program was helpful. The remaining patients all thought it could be helpful for other patients or if better coordinated to her/his schedule. The majority of patients (70%) responded that the TM reminders definitely or possibly “made them more likely to get laboratory tests;” one patient was not sure and 26% did not think it impacted their care. Three patients reported technical issues (two who never received messages) and one reported a change in phone number after the intervention. Most of the patients (71%) wanted the reminders to be re-activated, even those who did not report a definite benefit. A larger proportion (97%) felt that TM reminders could be helpful for at least one aspect of self-management.


Our study demonstrated a significant improvement in laboratory participation using a low cost, automated TM reminder intervention in AYA liver transplant recipients. This effect was also significant compared to the remainder of our AYA population followed during the same time period. We also showed the high rate of acceptability and communication using a TM intervention in our AYA population. This is the first study to evaluate and show a positive impact of a TM intervention specifically in a high-risk transplant population.

Active participation in medical care represents a prevalent, challenging, and costly problem for pediatric and adult health care providers. [12] Adolescent transplant patients are known to represent a particularly vulnerable group, with worse long term outcomes that include rejection and death attributed to non-participation. [13] As such, there is an interest in the development and evaluation of effective solutions. Despite knowledge of the problem and its associated outcomes, few intervention studies exist in the literature specific to this population. The majority of TM studies have addressed medication and clinic appointment participation with mixed results. [14],[15]

Our study is the first report to address participation in laboratory testing, which is an important element of self-management. For liver transplant patients, it represents the primary means to screen and monitor organ health. In addition, regular laboratory testing tracks the immunosuppressant drug levels, which provide as assessment of medication participation. [16] Our study did not evaluate an impact on medical outcomes. However, we hypothesize that increasing the number of recorded laboratory values may improve earlier detection of non-participation and medical complications; this could improve outcomes and potentially lower health costs through earlier intervention if studied over a longer period in a larger population. [17]

An important feature of TM interventions includes the capability for efficient implementation, increased patient engagement and an ability to address disparities in existing care. [14],[17] As a primary form of communication among adolescents, mobile interventions can be used effectively across geographic and socioeconomic boundaries. [14] Our study included patients of all ages in the AYA spectrum that was demographically representative of our area. [18] Cost concerns and language barriers did not preclude participation. These are important considerations as non-participation is associated with subgroup risk factors such as low socioeconomic class. As both an effective and inclusive intervention, this TM approach represents a promising means to reach, engage, and impact care among those with the greatest need. [8],[10]

We noted a high rate of patients who attempted communication through the program. Our AYA patients were reliable with strong correlation between reported and actual completion of laboratory tests. Minor discrepancies may have resulted from reporting errors or information bias related to documentation in the medical record. Feedback from our patients confirmed an interest in incorporating TM for other aspects of care; this interest and engagement of AYA patients in technology-based interventions is supported by other studies. [14],[19],[20],[21],[22] The ability of TM to offer beneficial reminders and increased communication should also promote self-management skills, which are an important element for the successful transition to adult care. [23]

This low cost, low resource-intensive intervention significantly improved rates of laboratory testing. Of note, only 3 patients had a decreased rate of testing after the intervention. On further analysis, lack of clear communication regarding expected laboratory frequency contributed to worse participation. There was also a high rate of change in required lab frequency based on clinical stability in our study. This suggests the presence of additional system factors as potential barriers to optimal participation and an opportunity to improve care. [24]

Limitations to our study include the single center recruitment, a specific disease population, and a small sample size. The study was not randomized or controlled and we used the patient as her/his own historical control. Anticipating the possible impact of selection bias (non-participatory patients are less likely to enroll in studies) and regression to the mean, or the effect of time, we did utilize a comparison group. Only the TM group significantly improved participation in laboratory testing at the individual and group level. In patients with perfect participation at baseline, all who received TM maintained perfect participation compared to only 55% in the non-TM group.

Additional limitations included factors that reduced our total study population, which included many unanticipated medical events. However, the biggest technical challenge resulted from the change in laboratory frequency in a large proportion of patients. As such, the messages did not always correctly correspond to the month of required testing. Many patients felt this was a barrier to the utility and acceptability of the intervention. However, despite these technical difficulties, the majority of patients felt the reminders were beneficial and made attempts to communicate to correct their schedule. Incorporating an improved monitoring system and regularly reviewing patient responses could address these issues.

These limitations to our automated system must be weighed against the advantages. Other studies show benefits in cost, feasibility, and acceptability of an automated approach, especially if supported by theory and user-specific responses. [25],[26],[27] A supplementary strategy could be to introduce more patient control, which has been found to be a well-accepted and utilized means of TM support. [20] Adding a more tailored or personalized content also appears to achieve a significant benefit. [14],[25],[26],[27] The results of our study and participant feedback provide further encouragement that mobile health applications remain important tools for our AYA populations. Continuing to modify and evaluate these approaches may achieve the most effective means to impact participation and significantly improve care. [28]


Our study demonstrated the feasibility, acceptability, and efficacy of a TM reminder intervention to improve participation in laboratory testing in AYA liver transplant recipients. It highlights the impact of using a low-cost, fully automated means of communication that incorporated feedback to address an important aspect of self-management in a high-risk group. A future study might increase personalization, better coordination with management goals and bi-directional patient communication to achieve better results. Further longitudinal studies in larger, controlled populations should evaluate an impact on medical outcomes.


We thank John Tamaresis, PhD for his dedicated and exceptional statistical support for the project. We all thank all of our patients and families for their participation.

All phases of this study were supported by the Elizabeth and Russell Siegelman Fellowship, Child Health Research Institute, Stanford CTSA grant number UL1 RR025744.

This trial has been registered at www.clinicaltrials.gov (identifier NCT01696331).


  1. Finn NB. Addressing the problem of medication non-participation. J Participat Med. 2014 Oct 10; 6:e13.
  2. Cheng J. and Walter E., 2005, Non-adherence in pediatrics. In: Bosworth HB, Oddone EZ, Weinberger M, ed.Patient Treatment Adherence: Concepts, Interventions, and Measurement. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.; 2005, p.239.
  3. Berquist RK, Berquist WE, Esquivel CO, Cox KL, Wayman KI, Litt IF. Adolescent non-adherence: prevalence and consequences in adolescent liver transplant recipients. Pediatr Transplant. 2006;10(3):304-310.
  4. Burra P, Germani G, Gnoato F, et al. Adherence in liver transplant patients. Liver Transpl. 2011;17(7)760-770.
  5. Dobbels F, Van-Damme-Lombaert R, Vanhaecke J, De Geest S. Growing pains: non-adherence with the immunosuppressive regimen in adolescent transplant recipients. Pediatr Transplant. 2005;9(3):381-390.
  6. recipients: an assessment of risk factors and natural history. Pediatr Transplant. 2000;4(3):200-206.
  7. Shemesh E, Shneider BL, Savitzky JK, et al. Medicaiton adherence in pediatric and adolescent liver transplant recipients. Pediatrics. 2004;113(4):825-832.
  8. Lenhart, Amanda. Teens, cell phones and texting: Text messaging becomes centerpiece communication. Pew Internet & American Life Project. Washington, DC: Pew Research Center. Available at: http://pewresearch.org/pubs/1572/teens-cell-phones-text-messages. Accessed July 17, 2014.
  9. Cole-Lewis H, Kershaw T. Text Messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev. 2010;32(1)56-69.
  10. Shemesh E. Non-adherence to medicatios following pediatric liver transplantation. Pediatr Transplant. 2004;8(6):600-605.
  11. Miloh T, Annunziato R, Arnon R, et al. Improved Adherence and Outcomes for Pediatric Liver Transplant Recipients by Using Text Messaging. Pediatrics. 2009;124(5):844-850.
  12. McGrady ME and Hommel KA. Medication adherence and health care utilization in pediatric chronic illness: a systematic review. Pediatrics 2013;132(4):730-740.
  13. Kelly D, Wray J. The Adolescent Liver Transplant Patient. Clin Liver Dis. 2014;18(3):613-632.
  14. Head KJ, Noar SM, Iannarino NT, Harrington NG. Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Soc Sci Med. 2013; 97:41-48.
  15. Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 2013;10(1):e1001363.

  16. Stuber ML, Shemesh E, Seacord D, et al. Evaluating non-adherence to immunosuppressant medications in pediatric liver transplant patients. Pediatr Transplant. 2008;12(3):284-288.
  17. Huang JS, Terrones L, Tompane T, et al. Preparing adolescents with chronic disease for transition to adult care: a technology program. Pediatrics. 2014; 133(6):e1639-46.
  18. 8-2012 Demographic And Housing 5-Year Estimates, Table DP05 [California]; generated by Rebecca McKenzie; using American FactFinder; http://factfinder2.census.gov; (4 August 2014).
  19. Woolford SJ, Blake N, Clark SJ. Texting, Tweeting, and Talking: E-communicating with adolescents in primary care. Contemporary Pediatrics. http://contemporarypediatrics.modernmedicine.com/contemporary-pediatrics/news/texting-tweeting-and-facebook-e-communicating-adolescents-primary-care. Accessed June 3, 2014.
  20. Britto MT, Munafo JK, Schoettker PJ, et al. Pilot and feasibility test of adolescent-controlled text messaging reminders. Clin Pediatr. 2012; 51(2):114-121.
  21. 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;23(12):1332-1228.
  22. Guse K, Levine D, Martins S, et al. Interventions using new digital media to improve adolescent sexual health: a systematic review. J Adolesc Health. 2012;51(6)535-43.
  23. Callahan ST, Winitzer RF, Keenan P. Transition from pediatric to adult-oriented health care: a challenge for patients with chronic disease. Curr Opin Pediatr. 2001;13(4):310-316.
  24. Dew MA, Dabbs AD, Myaskovsky L, et al. Meta-analysis of medical regimen adherence outcomes in pediatric solid organ transplantation. Transplantation. 209;88(5):736-46.
  25. Ramanathan N, Swendeman D, Comulada WS, Estrin D, Rotheram-Borus MJ. Identifyng preferences for mobile health applications for self-monitoring and self-management:focus group findings from HIV-positive persons and young mothers. Int J Med Inform. 2013;82(4):e38-46.
  26. Ranney ML, Choo EK, Cunningham RM, et al. Acceptability, language and structure of text message-based behavioral interventions for high-risk adolescent females: a qualitative study. J Adolesc Health. 2014; 55(1):33-40.
  27. Ingersoll K, Dillingham R, Reynolds G, et al. Development of a personalized bidirectional text messaging tool for HIV adherence assessment and intervention among substance abusers. J Subst Abuse Treat. 2014; 46(1)66-73.
  28. Wu YP and Hommel KA. Using technology to assess and promote adherence to medical regimens in pediatric chronic illness. J Pediatr. 2014;164(4):922-7.

Copyright: © 2015 Rebecca B. McKenzie, William E. Berquist, Megan A. Foley, K.T. Park, Jered E. Windsheimer, and Iris F. Litt. Published here under license by The Journal of Participatory Medicine. Copyright for this article is retained by the authors, with first publication rights granted to the Journal of Participatory Medicine. All journal content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License. By virtue of their appearance in this open-access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.