Mobile Health Intervention to Promote Medication Adherence Among Adolescents and Young Adults With Chronic Illness
Phase: NA
Conditions: Medication Adherence
Sponsor: Children's Hospital Los Angeles

  
 
Cell Phone Support to Promote Medication Adherence Among Adolescents and Young Adults With 
Chronic Illness  
NCT04241627  
8/8/2022 
 
 

  
 
Protocol 
Abstract 
Objective: Adolescents and young adults (AYA) living with chronic illnesses often struggle to 
adhere to daily oral medication regimens. Mobile health (mHealth) interventions are a promising 
avenue to support AYA in improving adherence, such as through delivering human coaching 
following the Supportive Accountability Model via mobile devices. AYA have reported 
increased use of and preference for text message communication over phone calls, so more 
investigation is needed to understand how cell phone support could be efficaciously delivered. 
Methods: A randomized, controlled, 3-arm pilot trial will evaluate the impact of Cell Phone 
Support (CPS) on medication adherence. Conditions will be CPS delivered by phone calls (CPS-
C), CPS delivered by text messages (CPS-T), or automated text message reminders (ATR). 
Participants will include AYA with various chronic illnesses (i.e., solid organ transplant, 
epilepsy, type 2 diabetes, sickle cell disease), aged 15-20 years (N = 60). Randomization will be 
done via stratified blocks based on sex assigned at birth and focal diagnosis. Outcomes will 
include intervention engagement, participant satisfaction, and medication adherence, assessed 
using multi-method measurement. Additionally, focus groups will be used to assess the impact of 
CPS and understand how patients and healthcare providers experience the intervention. 
Research Background: 
In the US, there are 2 million AYA living with chronic illness, many of whom struggle to 
develop vital illness self-management skills. Over half of AYA are nonadherent to their 
medication, resulting in detrimental health outcomes, increased healthcare expenditures, and 
poorer quality of life (Rapoff, 2010). Since over 95% of AYA use cell phones (Lenhart, 2015), 

  
 
mobile health (mHealth) technology is a promising method for delivering self-management 
interventions. To date, several mHealth interventions have demonstrated efficacy for improving 
medication adherence in a cost-effective manner. Unfortunately, of the existing adherence-
promoting mHealth interventions, nearly all suffer from the same significant limitation: they are 
disease-specific, which significantly limits the pace of implementation. A general, flexible 
mHealth intervention for AYA living with diverse chronic illnesses could meaningfully 
accelerate the timeline for implementation of adherence-promoting interventions in widespread 
practice. 
CPS is an adherence-promoting mHealth intervention that has been piloted separately 
with AYA with HIV (Belzer et al., 2014) and solid organ transplants (Sayegh et al., 2018), 
showing evidence of efficacy for increasing medication adherence. The current version of CPS 
includes short phone calls (<5 minutes) made each weekday by a human coach to provide social 
support, medication reminders, problem-solving coaching, incentives for answering calls, and 
referrals to other services. CPS calls focus on assisting AYA in identifying and accessing 
resources and support from their natural environments, such as finding ways they can receive 
needed help from their families, peers, medical teams, and communities. Pilot results indicate 
that CPS may promote medication adherence in two very different medical conditions; however, 
participant feedback and study enrollment rates suggest adaptations are needed to improve the 
acceptability of CPS. Many pilot participants indicated they would prefer to receive CPS through 
text message conversations with the AF, instead of traditional phone calls. This finding is 
consistent with national trends in technology use and preference; in the past decade, AYA have 
reported believing text messaging is easier, faster, less socially awkward, and more confidential 
than talking on the phone (e.g., Evans, Davidson, & Sicafuse, 2013). Although some promising 

  
 
two-way, interactive text message adherence interventions exist (e.g., Dowshen et al., 2012), all 
have been automated interventions, not live, real-time conversations between human Coaches 
and AYA patients. 
Although AYA prefer texting, it is unknown how delivering CPS through this 
communication mode could impact its effect on adherence. What most distinguishes CPS from 
other mHealth interventions is that human Coaches deliver the intervention in real-time, live 
phone calls, in contrast to the automated reminders which have proliferated in the adherence 
literature. The Supportive Accountability Model provides a framework for understanding how 
technological aspects of mHealth could impact the provision of adherence-focused support [7]. 
This model describes how a positive bond with a credible, caring person creates a sense of 
accountability, which in turn increases engagement in health behaviors. Counterintuitively, text 
message interaction could enhance, rather than dilute, supportive accountability because 
individuals tend to make more positive attributions of their communication partners and engage 
in more self-disclosure when using “lean media” that does not include the voice of the 
communication partner (Mohr, Cuijpers, & Lehman, 2011). These findings, along with current 
AYA technology preferences and practices, suggest CPS may be more acceptable, feasible, and 
effective if delivered by live, real-time, interactive text messaging with human Coaches instead 
of traditional phone calls. 
Research Aims: 
The aim of this randomized pilot trial is to evaluate the acceptability, feasibility, and 
efficacy of CPS delivered by phone calls or text messages to AYAs with several distinct chronic 
illnesses, in preparation for a fully-powered multisite, effectiveness-implementation hybrid trial. 
We propose 1) assessing the feasibility, acceptability, and efficacy of CPS for improving 

  
 
medication adherence, 2) investigating the impact of delivering CPS through live text messages 
versus phone calls using quantitative and qualitative methods, and 3) evaluating patient and 
provider views of CPS to guide future implementation work. The proposed research follows the 
conceptual model (Figure 1), positing that CPS will promote adherence through engaging AYAs 
in calls or texts with a human coach providing social support. We predict that delivering CPS by 
text message will increase feasibility and acceptability, perceived social support, and medication 
compared to voice delivery. This study extends the literature on mHealth to promote AYA 
adherence in several ways. First, although increasingly interactive and personalized automated 
text message interventions are proliferating in the adherence literature, there have been no 
randomized trials published evaluating the impact of live text message conversations on 
medication adherence among AYA. Second, rather than focusing on a single focal diagnoses, 
this study will enroll AYA with diverse chronic illnesses. This approach could greatly increase 
the number of patients having access to self-management support, therefore leading to a greater 
reduction in negative healthcare outcomes, costs, and burden. Third, despite comprising over 
20% of the US AYA population, Latinx patients make up only 5% of mHealth study samples 
(e.g., DeKoekkoek et al., 2015). The proposed study will enroll >50% Latinx participants, based 
on pilot data, increasing national representativeness of the mHealth literation [4].  
Methods: 
Setting and Sample 
This study took place at Children’s Hospital Los Angeles (CHLA), a large, urban, 
quaternary care, free-standing children’s hospital that services approximately 36,000 unique 
AYA patients and cares for nearly one-third of LA County’s Title V beneficiaries. As a safety 
net hospital, CHLA serves the most vulnerable children in the community, many of whom are at 

  
 
high risk for gaps in care and adverse outcomes as they transition to adulthood.  Participants in 
this study including patients who met the following inclusion criteria: 1) 15-20 years old at 
enrollment, 2) a patient at CHLA, 3) taking at least one oral medication per day, 4) provider and 
patient agreement that medication adherence is currently <80%, 5) Access to a cell phone, and 6) 
ability to speak and understand English. Participants were excluded if they had a cognitive 
impairment that precluded them from engaging in the consent/assent process or study protocol. 
Participants with diagnoses of autism spectrum disorder or intellectual disability were eligible as 
long as they were able to describe the purpose of the study, risks and benefits of enrolling, and 
summarize the research procedures in their own words.  Non-English speaking patients were 
excluded because interventions were only developed in English at this stage. In addition, health 
care providers were enrolled to participate in focus groups and interviews. For healthcare 
providers, inclusion criteria was: 1) Status as a healthcare provider at CHLA providing care to 
adolescent and young adult patients taking daily oral medication, and 2) working in a CHLA 
subspecialty clinics which had agreed to refer patients to this study. 
Design 
After passing eligibility screening and completing pre-treatment questionnaires, 
participants will be randomly assigned (1:1:1) to one of three 12-week groups: CPS delivered 
through voice-based phone calls (CPS-C), CPS delivered through text messaging (CPS-T), or 
automated text message reminders (Automated Reminders). A research colleague outside of the 
team used rand( ) function in Excel to create randomization blocks of 6 by sex assigned at birth 
and focal diagnosis. I hope to prevent substantial confounding by using stratified block 
randomization. For example, among the first 6 female participants with organ transplants, 2 
would be assigned to CPS-C, 2 would be assigned to CPS-T, and 2 would be assigned to ATR. 

  
 
The research colleague placed cards in sealed envelopes indicating the group assignment, and 
after the participant signed the consent form, the PI opened the envelope and informed the 
participant of the group they had been assigned to.  
Procedures  
This study was approved by the Children’s Hospital Los Angeles Institutional Review 
Board. Adult patients provided informed consent, and parents provided permission and minor 
participants providing assent. Adult participants or parents of minor participants signed a waiver 
of HIPAA to participate in the study since text-messaging is not a HIPAA compliant form of 
communication. Surveys were administered via REDCap, with up to 3 invitations sent by text 
message over the course of one week, at pre-treatment (0 weeks), mid-treatment (6 weeks), post-
treatment (12 weeks), and follow-up (18 weeks). Participants were compensated with a $50 gift 
card for the pre-treatment surveys, and $25 gift cards at each of the three followed assessment 
periods. Focus groups with participants were held in same-study-condition cohorts via WebEX 
after at least 2 parcipants had completed the 12-week intervention. Participants were 
compensated with a $20 gift card for joining the focus group. Participants received a $15 gift 
card for mailing back their MEMS Cap at 18 weeks. A community advisory board (CAB) 
consisted of N = 8 18-24 year old current and former patients was recruited in June 2020. The 
CAB meetings bimonthly via WebEx to provide guidance and feedback on each stage of the 
research.  
Interventions 
Cell Phone Support.  A human coach delivered CPS either by phone calls (CPS-C) or live, real-
time, two-way text messaging (CPS-T), between 3-5 days per week for 12 weeks. Participants 

  
 
were given the choice of the frequency of their calls/texts based on CAB guidance. Coaches were 
undergraduate research assistants earning course credit at the University of Southern California. 
They will participate in a single, 2-hour training on the promotion of medication adherence, with 
1 hour of phone-based group supervision every 2 weeks throughout the trial, similar to the prior 
pilot studies of CPS [3,4]. Coaches were trained in delivering CPS through phone calls (CPS-C) 
and text messages (CPS-T), and delivered both CPS conditions. Each CPS interaction will last no 
more than 5 minutes, following the outline below. Participants received monthly incentives 
worth $40 contingent upon answering over 75% of the Coaches calls or text messages. Examples 
of CPS call content from the organ transplant pilot study [4] include: 1) coaching patients to 
reach out to their social worker for assistance navigating insurance changes, 2) teaching patients 
how to use cell phone alarms to remind them to take medications, 3) supporting patients to 
identify ways a parent could help them wake up on time to take scheduled doses on weekend 
mornings, 4) assisting patients to devise strategies to link medication-taking with daily routines, 
e.g., brushing teeth, and 5) making referrals to family therapy when family conflict interferes 
with medication-taking. CPS is based on theories of social support, in that the COACHprovides 
support for adherence directly to the AYA patient while optimizing the support available from 
their natural networks, such as parents, peers, romantic partners, healthcare providers, or others. 
Coaches were trained to refer patients to their providers during daytime hours or the 
physician-on-call during night hours if they make medical complaints, ask about their health 
status, or express confusion about their prescribed medications. Healthcare provider and CAB 
feedback indicated that it is important for physicians to have accurate knowledge of patients’ 
medication adherence in order to monitor health and safety, and adjust medication dosages 
effectively. To ensure that health care providers can monitor and follow-up on adherence issues 

  
 
impacting health, we will alert them to patients’ nonadherence, whether disclosed in a CPS 
contact or in response to the automated text reminder, after fully explaining this in the informed 
consent process. Despite the challenge to internal validity, providers will be informed as to 
which condition their patients are assigned, to allow them to integrate the mHealth intervention 
into their treatment plan. The PI informed healthcare teams of participants’ engagement in the 
CPS interventions, barriers to adherence, and progress on improving adherence in a summary 
email at least every two weeks. 
Automated Text Reminders (ATR). The comparison condition will include automated text 
message reminders (ATR) modeled after an intervention used with a sample of AYA liver 
transplant patients, which was associated with increased adherence based on medication blood 
levels [14]. Specifically, text messages stated: “It’s time to take your medicine! Please reply with 
any text.” Texts were sent through automated survey invitations feature in REDCap, using 
Twilio. When participants enrolled, they selected the time(s) of day they preferred to receive 
their text reminders, and the PI checked their expressed preference against the electronic health 
record to confirm the frequency (i.e., once daily, twice daily) was consistent with their 
medication list. If participants confirmed receipt of at least 75% of their automated text 
reminders each month during the 12-week intervention, they received a $40 gift card as an 
incentive. The PI informed healthcare teams of the frequency with which participants were 
confirming their automated texts in a summary email at least every two weeks. 
Intervention Training and Fidelity 
CPS interventions were delivered by 6 “adherence facilitators" or "coaches" who were 
undergraduate research students enrolled in experiential research course at University of 

  
 
Southern California (either in Department of Psychology or Department of Preventive 
Medicine). Coaches underwent a background check and completed the CITI Human Subjects 
Protection course, TrojanLearn Mandated Reporter course, and Praesidium Social Media Safety 
course, as recommended by the USC Department of Risk Management and Insurance. Coaches 
participated in 2 4-hour training sessions to learn the intervention, involving a combination of 
lecture and role-plays with feedback. Coaches participated in weekly group supervision with the 
PI, and all calls and texts were recorded, uploaded via HIPAA-compliant REDCap file upload 
link, and reviewed by PI using a fidelity checklist. Coaches completed a short survey after each 
contact to record whether the participant answered their call or text, whether they reported taking 
their medication since the last call, topics covered in the call, and any referrals given. Additional 
details about the CPS intervention can be found in prior publications [3,4]. 
Measures 
Demographic Variables and Covariates. At pre-treatment, participants reported their age, sex 
assigned at birth, gender identity, racial identity, and ethnicity. They also reported their highest 
level of education, whether they had a job, and with whom they resided. In addition, they 
described their parents’ highest level of education and completed the Family Affluence Scale II 
(FAS II; Boyce, Torsheim, Currie, and Zambon, 2006). The FAS II is a 4-item scale that can be 
summed, and used to categorize participants as low socioeconomic status (0-2), medium 
socioeconomic status (3-5), or high socioeconomic status (6-9). Previous studies have concluded 
that the FAS II is a valid and reliable measured of socioeconomic status (Liu et al., 2012).  At 
follow-up, participants were asked to report on how COVID-19 had impacted their lives, using 
the COVID-19 Exposure and Family Impact Scales (Kazak et al., 2021). Finally, medication 
complexity will be calculated based on data in the electronic health record. We will follow the 

  
 
procedures described by Lilly et al. (2013) in calculate a Medication Regimen Complexity Index 
(MRCI) calculated a score for the focal diagnosis and the non-focal diagnosis. 
Primary Outcomes: Adherence Dependent Variables 
 
Self-Reported Adherence. In this study, medication adherence over the past 3 months 
was measured by a 3-item visual analogue scale (VAS) developed by Walsh, Dalton, and 
Gazzard (1998). The VAS  uses percentage scales that contains the percent (%) value from 0 to 
100 in which each point on the scale suggests the percent of time of medication adherence within 
a specified duration of time: 0% would mean “none of the time” or “never”; 50% would mean 
half of the time; 100% would mean “all of the time” or “always”, and numbers in between would 
mean amounts between “never” and “always” (Walsh, Dalton, & Gazzard, 1998). For each time 
frame, three visual analogue scales are provided, assessing 1) the percentage of the time patients 
took their medicine, 2) the percentage of the time patients took all the doses for the day, and 3) 
the percentage of the time patients took their medicine according to the directions (Walsh, 
Dalton, & Gazzard, 1998). Administering self-report adherence surveys by computer may 
decrease social desirability bias (Stirratt et al., 2015). 
 
MEMS Caps Adherence. Participants (and caregivers, in the case of minor participants) 
will be oriented to using MEMS caps for one of the medications they take daily of their focal 
diagnosis (i.e., immunosuppressant for transplant, antiepileptic drug for epilepsy, hydroxyurea 
for sickle cell disease, metformin for type 2 diabetes). Microelectronic circuitry records the dates 
and times the caps are opened. We will calculate the percentage of prescribed doses that appear 
to have been taken through week 6, 12 and 18. MEMS caps are considered the “gold standard” in 

  
 
adherence research and show moderate validity with both patient populations (Gerson et al., 
2004; Cramer et al., 1995). 
 
Electronic Health Record Adherence Indicators. Many of the focal diagnoses have 
regular labs collected as part of usual clinical care. For example, medication trough levels are 
usually collected periodically for the purpose of therapeutic drug monitoring for AYA living 
with solid organ transplants (i.e., immunosuppressant medications) and epilepsy (i.e., anti-
epileptic drugs.). For other diagnoses, regularly collected labs are considered partially indicative 
of adherence, such as the hemoglobic A1C test for type 2 diabetes and mean corpuscular 
volume/fetal hemoglobin for sickle cell disease. 
Secondary Outcomes: Psychosocial Dependent Variables 
Participants were invited via text message to complete the following surveys at all four 
assessment periods., via RED Cap. 
Social Support. Participants responded to the MOS Social Support survey to report on 
their perceived social support (Sherbourne & Stewart, 1991). There are three subscales that 
comprise the MOS: emotional/informational support, tangible support, and affectionate support. 
Self-Efficacy. Three items assessed self-efficacy for taking medications as prescribed. 
The items included the following: “How sure are you that you can take the right amounts of your 
medicine at the right times?”; “How sure are you that you can do better with taking the right 
amounts of your medicine at the right times?”; and “How sure are you that you can take the right 
amounts of your medicine at the right times even if you were very tempted not to?” These items 
were scored using a 5-point Likert scale ranging from 1 (very sure I can) to 5 (very sure I 
cannot). For the current study, responses were reverse-coded and items summed to form a 

  
 
composite self-efficacy score (max possible score = 15.0) as in (Kolmodin MacDonell et al., 
2016).  
 
Depression. We assessed depressive symptoms using the PHQ-9 (Kroenke et al., 2001). 
Analytic Plan 
We will begin by examining the distribution and missingness of our outcome of interest, 
VAS. To take advantage of every available data point and account for repeated measures 
clustered within individual participants, we will run mixed-effect models in {STATA/SAS/R}, 
using a nested model-building process. If VAS is normally-distributed, we will specify linear 
mixed-effects models; otherwise, we may need to transform or dichotomize the outcome to 
ensure model assumptions are met. We will first run a simple model with only time (0, 6, 12, 18 
weeks) as a Level I fixed effect predictor and VAS as a dependent variable, with the participant 
identification number included as a Level II random effect due to intra-individual correlation of 
VAS over time. Then, we will run increasingly complex models with intervention assignment 
(Cell Phone Support calls or texts n = 40 versus automated text reminders n = 20) includes as a 
Level II fixed effect predictor, and then sex assigned at birth, focal medical diagnosis,  
medication complexity, age, and racial/ethnic identity as Level II fixed effect predictors, as well, 
to adjust for known or theoretical covariates. I will examine the impact of including increasing 
number of hypothesized predictors on model fit by examining AIC and R2, and pare down the 
model if the model fit does not improve with all the hypothesized predictors including. This will 
lead to presenting parsimonious models that estimate the independent effect of the intervention 
on outcomes of interest, adjusted for all other predictors in the models.  Finally, I will also assess 
whether model assumptions were met by plotting residuals versus fitted values. When I am 

  
 
confident I have developed the most parsimonious, best-fitting model, I will calculate effect size 
estimates for the average difference in mean VAS between the Cell Phone Support call and text 
groups (n = 40) and the automated text reminder group (n = 20).   
Then, we will follow the sample approach to predicting MEMS Caps adherence 
outcomes, and depression, social support, and self-efficacy. We will also run exploratory 
analyses using electronic-health record abstracted values (e.g., immunosuppressant levels, anti-
epileptic drug levels, hemoglobin A1C, mean corpuscular volume/fetal hemoglobin) within focal 
diagnosis groups.  
Finally, for exploratory purposes, we will calculate pre-post effect sizes for subgroups of 
interest to explore whether interventions are comparably efficacious by focal diagnosis, gender, 
and racial/ethnic identity. Stratification was used to reduce the variation in potential confounders 
to be spread similarly between conditions, but that is not always entirely successful, especially in 
a small pilot trial. Therefore, we will conduct exploratory analyses including measured 
covariates as potential confounders or effect modifiers in the model-building process. 
Missing Data Plan 
I will evaluate the impact of missing data on my results by re-running these analyses 
using inverse probably weighting. It is possible that missing data will not be completely at 
random (i.e., participants with lower medication adherence may also be less likely to adhere to 
the online survey schedule).  
Power Analysis 
Using STATA SE Version 15 software, we estimated the statistical power achieved with 
our proposed sample, assuming the Cohen’s d effect size of 0.85 between groups seen in our 
preliminary data, within-subject intra-class correlation of 0.5 for repeated measures. We used the 

  
 
design effect for clustered data (3 post-baseline measures, intraclass correlation=0.5) to compute 
the effective sample size for each group.  The table below shows estimated power to detect 
differences in CPS (combined) versus automated reminders, and for the comparison of CPS 
strategies (original vs live text) in the total sample and by medical condition.  To be more 
conservative, we also provide the statistical power for the CPS comparisons with a smaller effect 
size of 0.75 and 0.50.  We also computed power to detect simple pre/post (e.g., baseline-12 
week) changes between groups.  There is approximately 87% statistical power for group 
comparisons using the combined sample with the effect size of 0.85; power is reduced for 
subgroup comparisons and the smaller effect size. However, as this study is a pilot randomized 
trial, the sample is more than sufficient to estimate effect sizes and assess feasibility for use in 
future proposals. See Table 1 for details. 
 
 

  
 
References 
Austin, J. K., MacLeod, J., Dunn, D. W., Shen, J., & Perkins, S. M. (2004). Measuring stigma in 
children with epilepsy and their parents: instrument development and testing. Epilepsy & 
Behavior, 5(4), 472-482. 
Belzer, M. E., Naar-King, S., Olson, J., Sarr, M., Thornton, S., Kahana, S. Y., ... & Adolescent 
Medicine Trials Network for HIV/AIDS Interventions. (2014). The use of cell phone 
support for non-adherent HIV-infected youth and young adults: An initial randomized 
and controlled intervention trial. AIDS and Behavior, 18(4), 686-696. 
Cramer, J., Vachon, L., Desforges, C., & Sussman, N. M. (1995). Dose frequency and dose 
interval compliance with multiple antiepileptic medications during a controlled clinical 
trial. Epilepsia, 36(11), 1111-1117. 
DeKoekkoek, T., Given, B., Given, C. W., Ridenour, K., Schueller, M., & Spoelstra, S. L. 
(2015). mHealth SMS text messaging interventions and to promote medication 
adherence: an integrative review. Journal of Clinical Nursing, 24(19-20), 2722-2735. 
Dowshen, N., Kuhns, L. M., Johnson, A., Holoyda, B. J., & Garofalo, R. (2012). Improving 
adherence to antiretroviral therapy for youth living with HIV/AIDS: A pilot study using 
personalized, interactive, daily text message reminders. Journal of Medical Internet 
Research, 14(2), e51. 
Evans, W. P., Davidson, L., & Sicafuse, L. (2013). Someone to listen: Increasing youth help‐
seeking behavior through a text‐based crisis line for youth. Journal of Community 
Psychology, 41(4), 471-487. 

  
 
Gamwell, K. L., Baudino, M. N., Bakula, D. M., Sharkey, C. M., Roberts, C. M., Grunow, J. E., 
... & Chaney, J. M. (2018). Perceived illness stigma, thwarted belongingness, and 
depressive symptoms in youth with inflammatory bowel disease (IBD). Inflammatory 
bowel diseases, 24(5), 960-965. 
Gerson, A. C., Furth, S. L., Neu, A. M., & Fivush, B. A. (2004). Assessing associations between 
medication adherence and potentially modifiable psychosocial variables in pediatric 
kidney transplant recipients and their families. Pediatric Transplantation, 8(6), 543-550. 
Kazak, A. E., Alderfer, M., Enlow, P. T., Lewis, A. M., Vega, G., Barakat, L., ... & Phan, T. L. 
(2021). COVID-19 Exposure and Family Impact Scales: Factor Structure and Initial 
Psychometrics. Journal of Pediatric Psychology. 
Knight, J. R., Shrier, L. A., Bravender, T. D., Farrell, M., Vander Bilt, J., & Shaffer, H. J. (1999). 
A new brief screen for adolescent substance abuse. Archives of pediatrics & adolescent 
medicine, 153(6), 591-596. 
Kolmodin MacDonell, K., J. Jacques-Tiura, A., Naar, S., Isabella Fernandez, M., & ATN 
086/106 Protocol Team. (2016). Predictors of self-reported adherence to antiretroviral 
medication in a multisite study of ethnic and racial minority HIV-positive youth. Journal 
of Pediatric Psychology, 41(4), 419-428. 
Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ‐9: validity of a brief depression 
severity measure. Journal of general internal medicine, 16(9), 606-613. 

  
 
Libby, A. M., Fish, D. N., Hosokawa, P. W., Linnebur, S. A., Metz, K. R., Nair, K. V., ... & 
Hirsch, J. D. (2013). Patient-level medication regimen complexity across populations 
with chronic disease. Clinical therapeutics, 35(4), 385-398. 
Lenhart A. (2015). Teens, social media & technology overview 2015. USA: Pew Research 
Center. Retrieved from: http://www.pewinternet.org/2015/04/09/teens-social-media-
technology-2015/ 
Mohr, D. C., Cuijpers, P., & Lehman, K. (2011). Supportive accountability: a model for 
providing human support to enhance adherence to eHealth interventions. Journal of 
Medical Internet Research, 13(1), e30. 
Rapoff M. (2010). Adherence to pediatric medical regimens (2nd ed.). New York: Springer 
Science+Business Media. 
Sayegh, C. S., Szmuszkovicz, J., Menteer, J. D., Sherer, S., Thomas, D., Lestz, R., & Belzer, M. 
(2018a). Cell phone support to improve medical adherence among solid organ transplant 
recipients. Pediatric Transplantation, e13235.  
Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social science & 
medicine, 32(6), 705-714. 
Stirratt MJ, Dunbar-Jacob J, Crane HM, Simoni JM, Czajkowski S, Hilliard ME, Aikens JE, 
Hunter CM, Velligan DI, Huntley K, Ogedegbe G. Self-report measures of medication 
adherence behavior: recommendations on optimal use. Translational behavioral medicine. 
2015 Dec 1;5(4):470-82. 

  
 
Walsh, J. C., Dalton, M., & Gazzard, B. G. (1998). Adherence to combination antiretroviral 
therapy assessed by anonymous patient self-report. AIDS, 12(17), 2361-2363.