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. Author manuscript; available in PMC: 2019 Apr 9.
Published in final edited form as: Nephrol Nurs J. 2018 Mar-Apr;45(2):171–223.

Improvement of Immunosuppressive Medication Adherence Using a SystemCHANGE Intervention: Case Study of an Older Adult Kidney Transplant Recipient

Cynthia L Russell 1, Courtney Miller 2, Laura M Remy 3, Jennifer L Wessol 4, Angela M Andrews 5, Dana Aholt 6, Debra Clark 7, Karen Hardinger 8, Tara O’Brien 9, Donna Hathaway 10, Kathy Goggin 11,12,13,14
PMCID: PMC6456040  NIHMSID: NIHMS1020755  PMID: 30303638

Abstract

This article reports a case study of an older adult kidney transplant recipient with poor medication adherence enrolled in an innovative six-month SystemCHANGE intervention that seeks to systematically improve medication adherence by identifying and shaping routines, involving others in routines, and using medication-taking feedback through small, patient-led experiments. Medication adherence increased immediately and was sustained throughout the intervention and maintenance phases. This is the first case study to demonstrate effectiveness of the SystemCHANGE intervention for promoting medication adherence in a kidney transplant recipient. The intervention improved the timing of doses by linking them to a regularly occurring behavior and providing feedback. The SystemCHANGE intervention represents a systems-thinking approach for both provider and patients, and gives healthcare providers the tools needed to assist patients in using habits and routines, and feedback to improve medication taking and timing.

Keywords: Medication adherence, transplant, system change, kidney, outcomes

Kidney transplantation is a superior alternative to dialysis, im- proving outcomes and preserving precious financial re sources. Kidney transplant in older adults is increasing. For patients aged 65 years and older, the percentage of kidney transplantation has increased from 7% in 1999 to over 18% in 2017 (United Network of Organ Sharing, 2017). Immunosuppressive medication non-adherence is the leading predictor of kidney transplant rejection, kidney loss, and death, and their associated costs (Williams, Manias, Gaskin, & Crawford, 2014). Nearly one-third of older kidney transplant recipients experience this preventable problem (Russell et al., 2010). Medication non-adherence is expensive, with estimated costs of over $30,000 in the first three years after transplant (Pinsky et al., 2009). In a meta-analysis by Dew and colleagues (2007), predictors of medication non-adherence were older age, non-White ethnicity, low social sup port, and poorer perceived health. One study found that patients’ most frequent barrier to adhering to immunosuppressive medication taking is forgetting (Gordon, Gallant, Sehgal, Conti, & Siminoff, 2009). Even minor deviations from adherence have resulted in negative effects, with non-adherence of only 6% resulting in undesirable outcomes (DeGeest et al., 1998). Traditionally, intervention studies aimed at boosting adherence target cognition (e.g., knowledge), attitudes and beliefs, and behavior (Christensen, Osterberg, & Hansen, 2009; Conn et al., 2009, 2015; Conn, Ruppar, Enriquez, & Cooper, 2016; Haynes, Ackloo, Sahota, McDonald, & Yao, 2008; Kripalani, Yao, & Haynes 2007; McDonald, Garg, & Haynes, 2002; Peterson, Takiya, & Finley, 2003; Roter et al., 1998). However, these have resulted in marginal improvements for individuals with acute and chronic illnesses (Christensen et al., 2009; Conn et al., 2009, 2015, 2016; Haynes et al. 2008; Kripalani et al., 2007; McDonald et al., 2002; Peterson et al., 2003; Roter et al., 1998) and have proven ineffective for adult kidney transplant recipients (Chisholm et al., 2000; Chisholm, Mulloy, Jagadeesan, & DiPiro, 2001; De Bleser, Matteson, Dobbels, Russell, & De Geest, 2009; De Geest et al., 2006; Dejean, Rontaing, Lapeyre-Mestre, Roge, & Durrand, 2004; Dew et al., 2004; Hardstaff, Green, & Talbot, 2003; Klein, Otto, & Krämer, 2006).

In this case study, we reported experiences of an older adult kidney transplant recipient who was non-adherent with her immunosuppressive medication upon enrolling in an innovative six-month SystemCHANGE intervention. SystemCHANGE seeks to systematically improve medication adherence behaviors by identifying and shaping routines, involving others in routines, and using medication-taking feedback through small patient-led experiments. Grounded in Bronfenbrenner’s Socio-Ecological Model (SEM) and Deming’s Plan-Do-Check-Act (PDCA) model (Deming, 2013), SystemCHANGE focuses on environmental factors that have an impact on behavior (Bronfen -brenner, 1977). A brief review of these two models will be provided to explain this innovative approach for personal behavior change.

A person’s ecological environment consists of individual-, micro-, meso-, exo-, and macro-system levels. The individual system includes characteristics of the person (Bronfenbrenner, 1977). The microsystem is represented by the relationship between the person and his or her immediate environ mental setting, including family, peers, health services, or workplace (Bronfenbrenner, 1977). Mesosystem factors refer to interrelations among settings, such as family, healthcare provider, and employer (Bronfen brenner, 1977). Exosystem factors are outside the person’s immediate setting but impact the setting in which the person functions. Finally, the macro -system refers to the culture (e.g., economic, educational, legal, political) that the previously described systems function within (the “milieu”) (Bron -fenbrenner, 1977). In this case study, the SystemCHANGE intervention was implemented at the patient-, micro-, meso-, and exo-level; not at the macro-level.

Bronfenbrenner’s SEM integrates the PDCA cycle to form System CHANGE. The quality improvement movement, which Deming originally introduced to the Japanese automobile industry in the 1950s, used the PDCA cycle for improving processes and provides a framework for a change to occur within an organizational system, not a personal system, as with System CHANGE (Deming, 2013). Deming’s model includes the following PDCA steps. First, in the ‘Plan’ step, a problem is defined, and possible causes and solutions are hypothesized (Deming, 2013). Second, in the ‘Do’ step, solutions are developed and conducted. Third, the ‘Check’ step evaluates the results of the plan carried out. As part of evaluating results, the individual determines if the goal was achieved (Deming, 2013). Finally, the ‘Act’ step identifies what was learned in the ‘Check’ step. The person takes action based on what was learned in the ‘Check’ step (Deming, 2013). If the change was successful, the solution is standardized. If the change was not successful, this information informs a new PDCA cycle (Deming, 2013).

Both the SEM and PDCA model form the foundation for the System CHANGE approach (Russell et al., 2011). This innovative approach is a paradigm shift in traditional medication adherence (MA) interventions because it focuses on redesigning the interpersonal environmental system and daily health behavior routines, rather than on increasing patient motivation and intention to improve adherence (Alemi et al., 2000; Alemi, Moore, & Baghi, 2008). In a pilot study using the SystemCHANGE intervention to improve immunosuppressive MA, we found a nearly fourfold greater effect size when compared to previous adherence interventions when using this approach (Russell, 2010). Using this patient-centered approach, we:

  • Assessed the individual’s systems (including other people important for medication taking), how the systems influence medication taking, and possible solutions for improving MA.

  • Implemented the proposed individual systems solutions for improving MA.

  • Tracked MA data.

  • Evaluated MA data through small experiments.

Many systematic reviews and meta-analyses have explored the effective ness of MA interventions in the acute and chronically ill general populations (Christensen et al., 2009; Conn et al., 2009, 2015, 2016; Haynes et al. 2008; Kripalani et al., 2007; McDonald et al., 2002; Peterson et al., 2003; Roter et al., 1998; Russell, Conn, & Jantarakupt, 2006). Historically, interventions based on psychological theories have been tested to enhance knowledge through education, attitude through counseling, and behavior through skills training. Intervention effect sizes in meta-analyses of studies using these approaches have been very small. Motivation- and intention-based interventions have limited benefits for improving and maintaining MA with only half of the studies including chronically ill adults and older adults indicating statistically significant short-term improvements. Studies with transplant recipients have had equally disappointing results with motivation- and intention-based interventions (De Bleser et al., 2009; De Geest et al., 2006; Dejean et al., 2004; Dew et al., 2004; Chisholm et al., 2000, 2001; Hardstaff et al., 2003; Klein et al., 2009). The SystemCHANGE intervention moves beyond motivation and intention to focus on redesigning the interpersonal environmental system and daily health behavior routines to improve execution and persistence in MA.

Methods

A Midwest university Institutional Review Board (IRB) approved the protocol for this study. Methods of the Medication Adherence Given Individual Change (MAGIC) study, which is the parent study with adult kidney transplant recipients, have been previously published (Russell et al., 2016). Three phases were included in this study: screening, intervention, and maintenance (Russell et al., 2016). This case study was a sub-study of the MAGIC study with slightly different inclusion criteria: the participant was to be 65 years of age or older, and recruited from a hospital located in the Midwest. We reported this case study because this participant was at high risk for poor MA.

Case Report

The participant, Ms. B, was an African-American female in her 70s who received her kidney transplant from a deceased donor approximately 10 years before this study was conducted. The participant completed some high school, and was divorced and retired. Details of the screening, intervention, and maintenance phases were described by Russell and colleagues (2016), and are briefly summarized here.

Screening Phase

After consent was obtained, Ms. B was trained on the use of the Medication Event Monitoring System (MEMS) (WestRock Company, 2016) cap and MEMS diary, both of which were used during this phase of the study. MEMS includes a SmartCap and bottle that work together as one unit. An electronic chip in the SmartCap recorded the date and time the bottle was opened (Westrock Company, 2016). In addition, the SmartCap display indicated how many times the bottle was opened (e.g., one, two) and the number of hours passed since the previous opening (Westrock Company, 2016). The diary was used in conjunction with the MEMS cap throughout the screening phase to document anytime the MEMS cap was opened, but a medication was not taken (for example to replenish the supply of medications or to pocket a dose to be taken later). The research assistant (RA) used the information from the diary to correct data at the end of screening to increase data validity.

During the screening phase, the RA telephoned Ms. B at weeks 1 and 8 to ask her to describe how she used the MEMS cap and diary, and to retrain as needed. She was also contacted at the end of screening to complete a short MEMS use survey. The survey consisted of four questions:

  • Tell me what you think of the MEMS cap.

  • Do you think the MEMS cap had a negative, neutral, or positive overall effect on your medication taking routine? Explain.

  • How practical do you think using the MEMS cap on a daily basis was for you? Explain.

  • Describe any instances when you think using the MEMS cap as directed was difficult.

At the end of the screening phase, Ms. B returned the MEMS cap to the RA, who downloaded data from the MEMS cap to the medAmigo™ database. MedAmigo uses predefined algorithms to process data obtained from the MEMS cap and create a graphical display of the participant’s medication taking, a numerically-based MEMS report, and the participant’s MA score. The MA score range is 0 to 100 (100%). Calculation methods have been published, but a brief explanation will be provided (Russell et al., 2006). A score of either 0 (not taken), 0.25 (taken early/late), or 050 (taken on time) is calculated for both morning and evening doses. The first 30 days of data were eliminated to account for the Hawthorne Effect, as in our previous study (Russell et al., 2011). The RA then reviewed Ms. B’s MEMS report and assessed for both medication taking (e.g., twice a day dosing) and timing (e.g., administered 12 hours apart). Ms. B’s MA score was 74% for the screening phase. Based upon our prior studies, an MA score of less than 85% indicated medication nonadherence (Frazier, Davis-Ali, & Dahl, 1994). Therefore, Ms. B was eligible to move on to the intervention phase. She was contacted to discuss her screening MA score, and she agreed to continue into the intervention phase of the study.

Baseline Home Visit and Surveys

The baseline home visit involved the transition from the screening phase to the intervention phase. During the visit, Ms. B completed five surveys using an electronic format with a computer supplied by the RA. These surveys included the Social Support Appraisal (SSA), Long-Term Medi -cation Behavior Self-Efficacy Scale (LTMBSES), one question from the Rand 36-item Short Form (SF) Health Survey, the Medication Adherence Barriers Scale (MABS), and the Systems Thinking Scale (STS). The following briefly describes each survey in the order of completion by Ms. B.

The SSA measures the degree to which one feels respected, involved, and cared for within the support system of family and friends (Vaux et al., 1986). The SSA consists of 23 questions with the Likert scale anchors of (1) strongly agree, (2) agree, (3) disagree, and (4) strongly disagree (total score range of 23 to 92), with a low score indicating high social support. The SSA scale has been shown to have good reliability and validity (Vaux et al., 1986). The LTMBSES assesses confidence in performing medication taking behaviors (Denhaerynck et al., 2003). The 27-question LTMBSES used a Likert scale of 1 = very little confidence to 5 = quite a lot of confidence (total score range of 27 to 135), and has good reliability (Cronbach’s alpha 0.86) and validity (De Geest et al., 1995; Wierdsma, van Zuilen, & van der Bijl, 2011).

One question from the Rand 36-Item SF Health Survey was used to assess perceived health status which was, “In general, would you say your health is…” Choices included (a) excellent, (b) very good, (c) good, (d) fair, or (e) poor. This question has good reliability and validity (Bowling, 2005). The 28-item MABS evaluated obstacles impacting successful medication taking (Chisholm, Lance, Williamson, & Mulloy, 2005). A Likert-type scale was utilized with the following choices: (1) never, (2) rarely, (3) sometimes, (4) often, or (5) always (total score range of 28 to 140). A low score on this survey indicates fewer medication barriers.

The STS evaluates “systems thinking,” which is defined as an individual’s ability to identify patterns and comprehend how these recurrences either create or hinder change (Dolansky & Moore, 2013). The STS included 20 questions and five answer choices: (1) never, (2), seldom, (3) some of the time, (4) often, or (5) most of the time. The instrument utilized a Likert-type scale rating of 0 to 4 (0 = never, and 4 = most of the time). The score range is 0 to 80, with higher scores indicating strong systems-thinking skills. The STS has good reliability and construct and discriminate validity (Dolansky & Moore, 2013).

Intervention Phase – Delivery of SystemCHANGE Intervention

Full details of the System CHANGE intervention delivery methods are previously published, but patient-specific information and process will be summarized here (Russell et al., 2016). The intervention phase was six months in length. At the baseline visit, the RA began reviewing key concepts of the SystemCHANGE with the participant using the PowerPoint delivery method. Key points of the SystemCHANGE intervention discussed with the participant are outlined in Table 1.

Table 1.

SystemCHANGE Overview – Participant Information Provided at Baseline Home Visit

The Four Steps of SystemCHANGE SystemCHANGE Statements Reviewed with Participant SystemCHANGE Questions for Participant to Evaluate Solution SystemCHANGE Implementation Tools
1. Explore habits around medication taking.

  • SystemCHANGE focuses on changing medication-taking routines.

  • Medication taking should not depend on trying harder or on a person’s memory.

  • Does this habit help or hinder medication taking?

  • Is the medication arranged within habits so that medications are in the right place at the right time?

  • Life Routines Form

  • Life Cycles Form
2. Try small experiments that change medication taking habits.

  • SystemCHANGE makes medication taking an effortless habit by trying “small experiments.”

  • Challenges with medication taking are no one’s fault. It is because habits and routines are not helping with medication taking.

  • Is this suggested possible solution based on habits and routines, or personal motivation/effort?

  • Possible Solutions Form and 22-Item Questionnaire
3. Track medication taking and timing with the MEMS cap.

  • Medications should be in the right place at the right time to improve medication taking and timing.

  • Are there notes on the MEMS Diary that the RA needs to be informed of to accurately reflect medication taking?

  • MEMS and MEMS Diary
4. Evaluate how the change is working.

  • Habits and routines are key to medication taking success.

  • Are the original possible solutions working, or is it necessary to discuss new solutions to improve medication taking?

  • Monthly MEMS Report
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MEMS Report

Following completion of the above four steps, the MEMS report from the screening period was reviewed with the participant (see Figure 1). In brief, the MEMS report includes the medication the partici pant is taking (e.g., mycophenolate mofetil), how often the medication is taken (e.g., two times per day), and the medication taking times (e.g., 9:00 a.m. and 9:00 p.m.). In addition to the medication-taking times, the “medication taking window” is established. The medication taking window is +/− 1.5 hours of the medication taking times, which supports maintenance of a therapeutic drug level (Claxton, Cramer, & Pierce, 2001). For example, if a participant took his or her medications at 9:00 a.m. and 9:00 p.m., the medication taking window would be from 7:30 a.m. to 10:30 a.m. and 7:30 p.m. to 10:30 p.m. These components of the MEMS report are used by medAmigo to calculate the MA score.

Figure 1.

Figure 1

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Ms. B’s MEMS Reports

The MEMS report also displays a calendar, which is used to visualize the days included in the report. The calendar will either have blue boxes that indicate the participant took his or her medications twice a day, or gray boxes that show the medication was not taken or taken only once a day. In addition, a scatter graph is shown to help the RA and participant visualize medication taking events. Blue dots appear on the chart, which represents every time the MEMS cap is opened and the participant presumably took his or her medication. The X-axis shows the date range of the report, which matches the dates on the calendar. The Y-axis indicates a 24-hour period (e.g., 3:00 a.m. 6:00 a.m., 9:00 a.m. … 3:00 a.m.). A bar chart on the report indicates the days of the week (e.g., Monday-Sunday) and the percentage of missed medication doses on specific days. Non-monitored periods (such as hospitalization) are indicated by light grey shading. This report provides objective data for the participant and RA to accurately visualize medication-taking habits to gain insights into where efforts for improvement in medication taking should be focused. Ms. B’s MEMS reports for screening are shown in Figure 1.

Following review of the MEMS report, Ms. B was asked the following questions to determine if she had other people who were important to her medication taking and/or involved in her routines. The questions included:

  • Do you have anyone who helps you with your medication taking?

  • Do you physically take the pills yourself?

  • Who refills your pill box, if you use one?

  • How do you get your prescriptions refilled?

Ms. B answered “no” to all questions, thereby indicating she received no assistance with her medication taking and was independent in her medication taking regimen. If a participant answered “yes” to any of the questions, the RA would have completed the Important People to Medication Taking Form and conduct the two-week follow-up call. This call is meant to verify that the participant has talked to his or her Important Person about the medication taking routine, and to confirm the decided-upon solution and intervention start date.

The next two steps of the intervention included the RA completing the Life Routines Form (see Table 2) and completing the Life Cycles Form (see Figure 2) in cooperation with Ms. B. The purpose of these forms is to explore habits and routines within the environment (e.g., home, family) (Alemi & Neuhauswer, 2005). Specific -ally, the Life Routines Form allows Ms. B to provide details of her daily routines starting from when she wakes up until she goes to bed. Emphasis was placed on activities in the morning and evening around medication taking times. In addition, her weekly routines (e.g., religious services) and monthly routines (e.g., medication refills) were discussed. The discussion indicated that Ms. B had a predictable schedule throughout the week, waking up around 8:00 a.m., showering, making a cup of coffee, taking her medications around 9:00 a.m., and reading the paper while eating breakfast. Ms. B’s evening routine was likewise predictable. She would have dinner around 7:00 p.m., watch television or read, take her medications, and go to bed. Ms. B’s other activities from 9:00 a.m. to 6:30 p.m. were also reviewed, but were not emphasized because they were not closely associated with medication taking times. The RA also asked Ms. B to describe where she kept her medications because medication location is crucial to successful medication taking. For example, Ms. B kept her medications by the coffee pot in the kitchen. This location was not changed due to its central location. Medication taking was supported because medications were in the right place for morning and evening medication taking times.

Table 2.

Ms. B’s Life Routines Form

Daily Routines Morning Routines
8:15 a.m. – Take shower
8:30 a.m. – Drink coffee
9:00 a.m. – Take medications and eat breakfast
9:15 a.m. – Read paper while eating breakfast
9:30 a.m. to 6:30 p.m. – Conduct house work and run errands
Evening Routines
6:30 p.m.to 7:00 p.m. – Come home to apartment
7:00 p.m. – Eat dinner and watch television
9:00 p.m. – Take medications and continue to watch television
9:30 p.m. – Brush teeth and get ready for bed
10:00 p.m. to 10:30 p.m. – Go to bed
Weekly Routines Tuesdays: 5:45 p.m. to 8:30 p.m. – Attend choir practice
Wednesday: 10:30 a.m. to 12:30 p.m. – Attend Bible study
Wednesday: 6:30 p.m. to 8:30 p.m. – Attend church
Sundays: 7:00 a.m. to 1:00 p.m. – Attend Sunday school and church
Monthly Routines Medications mailed to Ms. B on the first day of the month
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Figure 2.

Figure 2

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Life Cycles Form

The next step was to complete the Life Cycles Form, which focuses on recurring routines around medication taking times (Alemi & Neuhauswer, 2005) for Ms. B’s morning and evening routines. This form illustrates the organization and structure of Ms. B’s routines. Typically, habits are random events that do not allow understanding routines as an interconnected system (Alemi & Neuhauswer, 2005). By focusing on recurring events in Ms. B’s day, the Life Cycles Form emphasizes these routines as a system and how each habit impacts the sequence of habits of her daily life (Alemi & Neuhauswer, 2005). This form is intended to help Ms. B and the RA visualize Ms. B’s routines and provide insight on ways in which her medication taking could be made more effortless by connecting it to some of these daily routines.

The final step in the intervention was completion of the Possible Solutions Form, which assists in prior-itizing implementation of System CHANGE solutions (see Table 3). These possible solutions focus on changes that impact Ms. B’s system (e.g., habits, routines), not ones that require motivation or effort (Alemi & Neuhauswer, 2005). Initially, with the support of the RA, the participant identified possible solution ideas. The RA guided Ms. B in reviewing the MEMS report and Life Cycles Form throughout this process to help visualize where medication taking could be improved. For example, Ms. B’s MEMS report revealed that her timing was consistent with her morning medication taking but inconsistent in the evening. The RA guided Ms. B to implement the prioritized System CHANGE solution (experiment) of setting her cell phone alarm at 9:00 a.m. and 9:00 p.m. to help her use resources within her personal environment to improve medication taking times.

Table 3.

Ms. B’s Possible Solutions Form

Idea 1: To obtain and use a travel pillbox.
Patient asked herself these questions about the stated idea. Yes No
Focuses on events that happen before taking my medications on time. X
Does not rely on my motivation or commitment. X
Changes my environment. X
Once done, stays done. No need to make the change again. X
If it fails to improve medication taking, it is no one’s fault. X
If the solution doesn’t work to improve medication taking, there is no reason to try harder to make it work. X
Will increase the time between medication taking failures. X
Does not rely on my memory to take medications. X
Indirectly improves medication taking and timing. X
Is a change in a recurring life routine. X
Requires more than one person to bring it about. X
If solution is done today, it will improve medication taking in the future, maybe not today. X
Leads to timely medication taking as part of another task. X
Involves a physical change. X
Provides resources (time, equipment) for timely medication taking. X
Changes who I spend time with. X
Affects others who live with me. X
Changes what I do for fun and social gatherings. X
Leaves no choice but to take medication on time. X
Changes a group activity. X
If it fails to work, it gives me new insights about what to do next. X
Rearranges the sequence of my daily living activities. X
Idea 2: Set a cellphone alarm at 9:00 a.m. and 9:00 p.m.
Patient asked herself these questions about the stated idea. Yes No
Focuses on events that happen before taking my medications on time. X
Does not rely on my motivation or commitment. X
Changes my environment. X
Once done, stays done. No need to make the change again. X
If it fails to improve medication taking, it is no one’s fault. X
If the solution doesn’t work to improve medication taking, there is no reason to try harder to make it work. X
Will increase the time between medication taking failures. X
Does not rely on my memory to take medications. X
Indirectly improves medication taking and timing. X
Is a change in a recurring life routine. X
Requires more than one person to bring it about. X
If solution is done today, it will improve medication taking in the future, maybe not today. X
Leads to timely medication taking as part of another task. X
Involves a physical change. X
Provides resources (time, equipment) for timely medication taking. X
Changes who I spend time with. X
Affects others who live with me. X
Changes what I do for fun and social gatherings. X
Leaves no choice but to take medication on time. X
Changes a group activity. X
If it fails to work, it gives me new insights about what to do next. X
Rearranges the sequence of my daily living activities. X
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In addition to timing, Ms. B also had several missed doses on her MEMS Report, due to her attendance at religious services on Wednesdays and Sundays. She would wait to take her medications until she returned home, which would often make her medication taking greater than 12 hours apart. The RA suggested the possibility of carrying an extra dose of her medication in her purse for when she is out of her home and her cell phone alarm goes off. She was receptive to this idea and later obtained a travel pill case to keep with her. These two SystemCHANGE solutions (e.g., cell phone alarm, travel pill case) focus on both medication availability (e.g., taking), and timing. To help prioritize which possible solutions were more personal system-oriented (versus motivation- or intention-oriented), Ms. B completed the Possible Solutions Form for each of the possible solutions she identified.

Finally, the RA evaluated the Possible Solution scores, noting that setting the alarm scored higher than using a travel pill case. However, in Ms. B’s situation, both possible solutions were implemented because the medication availability (e.g., medication taking influenced by the travel pill case) and medication timing (e.g., setting the cell phone alarm) were crucial to her medication taking success.

At the conclusion of the hour-anda-half home visit, Ms. B was provided with a MEMS reader and instructed on its use. The MEMS reader was used to transmit data wirelessly from the MEMS cap to the medAmigo database. The RA remotely retrieved the participant’s medication taking information from the medAmigo database and generate the monthly MEMS report. Ms. B was also given a new MEMS diary, which was identical to the form used in screening described above. In addition, she was provided a magnetic clip to hang her screening and monthly MEMS reports on the refrigerator. This “storyboard” approach was intended to be a stimulus cue to increase Ms. B’s awareness of her medication taking and engage her family and friends in the process.

Following the baseline home visit, Ms. B was encouraged to use the MEMS cap for the next six months of the intervention phase. Throughout the six-month intervention phase, the RA called Ms. B monthly and asked her to download her data using the MEMS reader. The RA would then mail Ms. B a copy of the MEMS report, along with a monthly gift card. The following week, the RA called the participant at a previously scheduled time to review the MEMS report, address any MEMS diary entries, and assess if any additional changes needed to be made with Ms. B’s routine. In addition, the following questions were asked during each of the intervention phone calls:

  • Tell me what you are learning about your medication taking.

  • Do you think changes to your routines that you have made are changing your medication taking?

  • Do you need to make any other changes to your medication taking routines?

Ms. B did not make any additional changes to her routine throughout the intervention because her two original Possible Solutions were successful.

Maintenance Phase

The SystemCHANGE intervention was concluded after the first six months, and the maintenance phase was initiated to assess whether gains in medication taking could be sustained. Throughout months 7 to 12, the RA mailed the participant a gift card and a letter reminding her to continue to use the MEMS cap and MEMS diary, and how many months remained until study completion. At the end of the six-month maintenance phase, the MEMS reader, MEMS cap, and MEMS diary were returned to the RA. The RA also asked Ms. B the same MEMS use survey ques tions that were asked at the end of the screening phase.

Results

Ms. B’s screening MA score was 74%, which was less than the medication non-adherence cut point of 85%, and therefore, allowed her to enter the intervention phase. Ms. B’s monthly MA scores for the screening, intervention, and maintenance phases are shown in Figure 3. The two possible solutions (e.g., setting cellphone alarm, carrying a travel pill case) remained successful throughout the intervention and maintenance phases. From the screening phase to the first month of the intervention phase, Ms. B increased her MA score by 22%, from 74% to 90%. She maintained MA scores from 90% to 100% throughout the intervention and maintenance phases. The overall increase in MA was 32.4%. Thus, both the taking and timing of Ms. B’s medication taking were markedly improved.

Figure 3.

Figure 3

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Ms. B’s Adherence Scores

Survey Findings

In addition to Ms. B’s MA scores, results of her baseline surveys will be discussed. First, Ms. B scored 23 on the SSA instrument, with a mean score of 1 “strongly agree” on the total scale, and on the family and friends sub-scales. This indicated she appraised her social support, including support from family and friends, as very high. Second, Ms. B scored 116 on the LTMBSES, with a mean score of 4.3, indicating she had “quite a lot” of self-confidence in taking her immunosuppressive medications. Third, Ms. B stated she perceived her current health as “very good” on the Rand 36-Item SF Health Survey question, the second highest ranking. Next, Ms. B scored 35 on the MABS, with a mean of 1.25, denoting she almost “never” perceives any medication taking barriers. Last, Ms. B scored 50 on the STS, with a mean score of 2.5, signifying she uses systems thinking “some of the time” to “often” when making an improvement.

At the six-month assessment, Ms. B’s score on the SSA instrument increased (from 23 to 39); her score on the LTMBSES decreased (from 116 to 112); she rated her health as “good” on the Rand 36-Item SF Health Survey question (down from “very good” at baseline); her score decreased on the MABS instrument (from 35 to 30); her score on the STS decreased (from 50 to 37); and her MA score increased.

When responding to the MEMS cap use survey after screening, Ms. B stated: “Using the MEMS cap was a good experience, and the time went by fast. The MEMS was very practical for medications that I take every day, like my transplant medications. It was not difficult to use at all.”

At the end of maintenance, when responding to the same survey, she commented:

Even when I’m busy, it’s important to take my meds at the right time. The alarm helped me take my pills on time. The pills in my purse really helped me take my meds when I’m not at home. I really enjoyed using it because when I originally started using the MEMS bottle, I didn’t take it on time. Now I’m ‘programmed’ to take my medicine on time. I thought it was practical because of the positive impact it had on my medication taking. I became more aware of my medication taking and timing with the cap, and it wasn’t a problem to use the bottle. Originally, I thought it was difficult going out with the cap, but I got used to it after a while and took the bottle with me.

Discussion

This is the first case study to detail the process and provide individual evidence of the effectiveness of SystemCHANGEas a method for promoting MA in the kidney transplant population. The SystemCHANGE intervention was immediately effective in improving MA for this individual. During the first month, the participant experienced a 22% increase in MA, and over the course of the six-month intervention, there was an overall increase in her MA score of 32.4%. These results are clinically meaningful because an increase in the MA score of 32.4% indicates Ms. B’s consistently late morning doses were regularly taken on time. Additionally, this improvement was sustained during the entire year of the study.

Similar to this study’s findings, SystemCHANGE interventions have produced promising improvements in behaviors, such as physical exercise and healthy eating habits for weight loss (Moore & Charvat, 2012; Webel et al., 2013). Other obdurate problems have also been reduced, including stress, hypertension, sleep disorders, and asthma attacks (Alemi et al., 2000; Alemi & Neuhauswer, 2005; Webel et al., 2013). In our recent pilot randomized controlled trial, we tested the SystemCHANGE intervention in a small group of adults with a kidney transplant and found a statistically significant improvement in immunosuppressive MA (p = 0.03) with a large effect size of 1.4 (Russell et al., 2010). This case study is also similar to our previous MA study results because it was immediately effective, sustained over a long period of time, and showed a clinically effective change (Matteson & Russell, 2013; Russell et al., 2010).

Results of this case study are in contrast to a large body of MA behavior change literature published over the past 30 to 40 years in which cognitive (e.g., education) and affective (e.g., attitudes and beliefs) interventions have been minimally effective (Conn et al., 2015; Peterson et al., 2003; Roter et al., 1998). The SystemCHANGE intervention employed in this study targets an environmental, personal systems approach rather than cognitive and/or affective approaches. A focus on personal systems helps move beyond personal intention and motivation, and instead, uses small experiments linked to routines to improve medication taking and timing behavior.

The improvement in MA through the SystemCHANGE intervention validated the theoretical foundations of SEM and PDCA cycle. During the SystemCHANGE home visit, the ‘Plan’ step of the PDCA cycle was initiated by evaluating Ms. B’s environment using the levels of the SEM, including the home and outside environment, relationships, personal routines, and connections between routines. Two solutions were identified during the ‘Plan’ step to incorporate medication taking into existing morning and bedtime routines. These solutions were implemented during the ‘Do’ step, in which Ms. B utilized possible solutions of the cell phone alarm and travel pill case. For the ‘Check’ step, MA was tracked utilizing MEMS caps. Finally, in the ‘Act’ step, Ms. B’s success was evaluated by reviewing the MEMS report data during monthly phone calls to determine if improvements in medication taking occurred. Since the change was successful, solutions were implemented and maintained. If solutions were not successful, this information would inform a new PDCA cycle. One assumption of the SEM is if changes in the environment occur, the behavior of the person will change (Riekert, Ockene, & Pbert, 2013). The de -scribed case study exemplifies how changes in Ms. B’s environment and routines lead to a change in MA behavior. By placing medications strategically in the environment and using cues, MA improved.

Long-standing beliefs by health-care providers indicated that marital and educational status would predict medication non-adherence in this case; additionally, recent meta-analyses have documented non-White ethnicity, advanced age, poor perceived health, and poor perceived social support as risk factors (Dew et al., 2007; Russell et al., 2006). We hypothesized Ms. B would struggle with medication non-adherence through the intervention and maintenance phases of the study. She was of non-White ethnicity, older age, divorced, and did not graduate from high school, which led us to put her in a high-risk category. Instead, Ms. B had strong social support, high self-efficacy, perceived her health as “very good,” and had a low number of barriers to MA. The previous body of literature shows patients on immunosuppressant medications following transplant are more likely to be medication non-adherent when they perceive their health as poor and/or have low social support (Dew et al., 2007). Results from the MEMS cap data through the intervention and maintenance phases demonstrated Ms. B’s solutions of setting an alarm and carrying a travel pill case improved her medication taking and timing. SystemCHANGE was an effective intervention for this participant. SystemCHANGE moved beyond demographic and psychosocial variables, which were previously thought and reported to be correlated with medication non-adherence, by using small solutions (experiments) carried out by the participant.

Previous research has discussed the importance of systems thinking in the practice of continuous quality improvement (Dolansky & Moore, 2013). This current study used the STS to assess the participant’s thought processes when making changes in her life. Systems thinking was defined by Dolansky and Moore (2013) as “the ability to recognize, understand, and synthesize the interactions, and interdependencies in a set of components designed for a specific purpose” (p. 5). Based on Ms. B’s STS score, we expected her to think regularly about how her environment, culture, family, and routines played a role in her medication taking behaviors. With the assistance of the RA and through the SystemCHANGE process, Ms. B assessed her daily and weekly routines, which led to the identification of the two possible solutions (e.g., setting an alarm, travel pill case). Ms. B’s skill in the systems thinking process allowed her to be successful in the SystemCHANGE intervention.

Medication non-adherence is a complex issue and continues to be a problem in the kidney transplant population, with one-third experiencing this preventable phenomenon (De Geest et al., 1998; Dew et al., 2007; Gordon et al., 2009). Non-adherence to immunosuppressive medications can lead to rejection, kidney loss, and death (Desmyttere, Dobbels, Cleemput, & De Geest, 2005; Douglas, Blixen, & Bartucci, 1996; Nevins, Kruse, Skeans, & Thomas, 2001; Sabaté, 2003; Shoskes, Avelino, Barba, & Sender, 1997). Previous research founded in psychological theories has shown little sustained effect on these health behaviors (Peterson et al., 2003; Roter et al., 1998). SystemCHANGE improves MA because it directs the participant to redesign his or her environment and routines linked to medication-taking behaviors (Webel et al., 2013). This specific case study is an exceptional exemplar of a kidney transplant recipient who altered her environment and routine, which improved her medication taking and timing. SystemCHANGE is an innovative and effective method to enhance MA. The ability for the participant to assess her environment and routines to create solutions with the assistance of the RA is notewor thy. Teaching participants how to assess their immediate environment and personal routines related to medication taking can be beneficial in improving MA. This role could be expanded to include others, including community health workers.

Limitations and Future Research

There are inherent limitations of the single case study design. While the case study design allows for in-depth understanding and analysis of a situation, findings cannot be broadly generalized. A potential weakness of the case study design is the researcher’s familiarity with the participant, which may make objectivity more challenging. However, the main outcome was objective and could not be impacted by the RA’s relationship with the patient.

This case study has important implications for further research for using the SystemCHANGE intervention to change behaviors in other chronically ill populations. This approach should be tested in patients in other populations who struggle with MA (e.g., people after stroke, people with heart failure). When an individual has multiple behaviors to try to change (e.g., diet, exercise, smoking cessation), behavior change intervention meta-analyses indicate best results occur when the individual focused on improving one behavior at a time (Conn et al., 2009). Once someone becomes a ‘systems thinker’ by using the SystemCHANGE intervention, the individual may have the skills needed to tackle new behavioral challenges. While this is only one case of the successful use of the SystemCHANGE intervention for MA, and practice change cannot be based on one case study, we believe it provides a valuable foundation for future research and insights for those interested in pursuing further work with this intervention. The next step in testing the SystemCHANGE intervention is to conduct a fully powered, randomized control trial testing the SystemCHANGE intervention on MA and outcomes accounting for mediators and moderators for MA.

Conclusions

This is the first case study to provide evidence of the effectiveness of SystemCHANGE as a method for promoting MA in a kidney transplant recipient. The SystemCHANGE inter vention resulted in immediate and sustained improvements in MA. The patient experienced a clinically meaningful increase in MA (32.4%) by improving the timing of doses by linking them to a regularly occurring behavior.

Implications for Practice.

The SystemCHANGE intervention offers healthcare providers the tools needed to support patients on how to improve medication taking and timing. By using the SystemCHANGE intervention, providers can assist patients specifically in using habits and routines, feedback, and important others who “touch” the medication taking process to increase MA. For providers, this is a new way of thinking about changing patient behaviors. This approach moves away from the traditional focus of trying to change knowledge, motivation, and intention to improve medication taking. Patient education, motivation, and intention are necessary, but not sufficient, for behavior change. The SystemCHANGE intervention represents a ‘systems thinking’ approach for both providers and patients. Systems thinking involves using the individual’s personal systems to support behavior change. By placing medications in the right place in the patient’s environment and using the patient’s personal systems, medication can be taken at the right time.

Acknowledgement:

The authors thank the following people for their assistance in participant recruitment: Dr. Daniel Murillo, Marilee Clites, and Cady Pembroke.

Support for this project was provided by Frontiers: The Heartland Institute for Clinical and Translational Research and Kansas City Area Life Science Institute (Russell).

Footnotes

Statement of Disclosure: The authors reported no actual or potential conflict of interest in relation to this continuing nursing education activity.

Contributor Information

Cynthia L. Russell, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO..

Courtney Miller, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO..

Laura M. Remy, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO..

Jennifer L. Wessol, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO..

Angela M. Andrews, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO..

Dana Aholt, University of Missouri – Kansas City, School of Nursing and Health Studies, Kansas City, MO, and a member of ANNA’s Central Missouri Chapter..

Debra Clark, University of Tennessee Health Science Center, College of Nursing, Memphis, TN..

Karen Hardinger, Division of Pharmacy Practice and Administration, University of Missouri-Kansas City, Kansas City, MO..

Tara O’Brien, Ohio State University, Columbus, OH, and a member of ANNA’s Bluff city Chapter..

Donna Hathaway, University of Tennessee Health Science Center, College of Nursing, Memphis, TN, and a member of ANNA’s Bluff City Chapter..

Kathy Goggin, Department of Pediatrics,; Children’s Research Institute, Health Services and Outcomes Research, Children’s Mercy Hospital; University of Missouri – Kansas City, School of Medicine Pharmacy, Kansas City, MO.

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