Abstract
Objective
While medication non-adherence is common in bipolar disorder (BD), few studies have specifically assessed non-adherent BD adolescents and young adults (AYAs). This analysis, using screening and baseline data from an ongoing randomized controlled trial, examined the relationship between BD symptoms and adherence in poorly adherent AYAs.
Methods
AYAs ages 13–21 had sub-optimal adherence defined as missing ⩾ 20% of prescribed BD medication. Mean sample (N = 36) age was 19.1 years (SD = 2.0), 66.7 % (N = 24) female, 25.0 % (n = 9) non-white. Adherence was measured via: 1) self-reported Tablets Routine Questionnaire (TRQ) and 2) electronic monitoring (SimpleMed pillbox). Symptoms were measured with the Hamilton Depression Rating Scale (HAM-D), the Young Mania Rating Scale (YMRS), and the Clinical Global Impression Scale (CGI).
Results
Mean percentage of missed BD medications using TRQ was 34.9 (SD = 28.9) at screening and 30.6 (SD = 33.0) at baseline. Mean percentage of missed medication using SimpleMed at baseline was 42.1 (SD = 37.0). The correlation between TRQ and SimpleMed was r = 0.36 (p = 0. 13). Neither CGI nor age were correlated with adherence. Neither TRQ nor SimpleMed were significantly related to HAM-D. YMRS was positively associated with worse adherence for TRQ (r = 0.36, p = 0.03), but not significantly associated with SimpleMed. Adherence did not differ by other demographic attributes.
Conclusion
Adherence levels varied widely in AYA with BD. Adherence monitoring increased adherence by approximately 4.5%, and use of electronic pill monitoring identified a greater proportion of missed medication vs. self-report. BD symptoms may not consistently identify AYA with adherence challenges.
Keywords: adherence, compliance, bipolar disorder, adolescent, youth
Introduction
The onset of bipolar disorder (BD) is common during late adolescence and early adulthood. During this developmentally vulnerable period, adolescents and young adults (AYA) may experience symptomatic and functional worsening due to reduced oversight by caregivers, higher risk-taking behaviors (e.g., drug/alcohol use), and increased family conflict.1
Pharmacotherapy is the primary treatment for AYAs with BD. However, poor medication adherence is a significant problem, with non-adherence rates up to 65%.2,3 Poor adherence is associated with greater morbidity, higher care use, reduced quality of life and early mortality.3–7 Studies in BD find that younger age, fewer mood episodes, and comorbid substance use are predictors of poor adherence.8–12 Many of these characteristics are common in AYAs with BD, who are typically early in their illness course.13
Given the higher likelihood of poor health outcomes among AYAs with BD who are sub-optimally adherent to prescribed medication, getting a better understanding of whether and how BD symptoms and other clinical correlates might help to identify those individuals who may most benefit from approaches that can improve adherence. There have been only a limited number of studies that have specifically evaluated AYA with known poor adherence.14 While database or registry studies using pharmacy refill data may readily identify patients with poor adherence, these research samples often do not provide clinical information on symptom presentation. In contrast, clinical trials provide a wealth of patient-level data, but typically only recruit patients who are known to be reliable and adherent.
This analysis examined the relationship between BD symptom severity and adherence using screen and baseline data collected from an ongoing National Institute of Mental Health (NIMH)-funded randomized controlled trial (RCT) testing a novel behavioral intervention to promote medication adherence in AYAs with BD. This well-characterized, poorly adherent BD sample was assessed on both self-reported and objectively measured medication adherence measures as well as standardized ratings of depression, mania and global psychopathology. Given the wide prevalence of poor adherence among AYA with BD, clarification of correlations between BD symptoms and adherence patterns has the potential to inform approaches to care.
Methods
Overall Study Description
This 2-site prospective 6-month NIMH-funded RCT, from which these analyses are derived, is testing a novel customized adherence enhancement (CAE-AYA) intervention intended to promote BD medication adherence versus an enhanced treatment as usual (ETAU) in up to 40 poorly adherent AYA with BD (MH117206 (PIs Delbello & Sajatovic). Participants were consented/assented, screened for study inclusion/exclusion criteria, and then if deemed eligible for study participation, received an electronic pill-monitoring device (SimpleMed) to assess evidence-based BD medication adherence. Approximately one-week post-screening, those who completed baseline evaluation were randomized on a 1:1 basis to CAE-AYA (n = 20) or ETAU (n = 20). Only screening and baseline data, pooling all participants in both the CAE-AYA and ETAU interventions arms, were used in this RCT interim analysis to examine correlates of 2 methods of adherence assessment (Tablets Routine Questionnaire (TRQ) and objective pill-monitoring (SimpleMed) and BD symptom severity. It must be noted that the COVID pandemic began shortly after the beginning of study implementation and given social contact/travel restrictions associated with the pandemic, all procedures, including research assessments were migrated from an in-person to a virtual format.
Sample
Study inclusion criteria were purposely broad in order to generalize to real-world AYAs with BD, and required individuals to be between the ages of 13 and 21 years old, have a DSM-5 diagnosis of BD type I or II confirmed by the Structured Clinical Interview for DSM-5 (SCID-5), poor adherence defined as missing ⩾ 20% of prescribed evidence-based BD medications, i.e., mood stabilizer (e.g., lithium, valproic acid, or carbamazepine) or second generation antipsychotics, for the past week or past 30 days. Individuals less than 18 years old provided written informed assent and a legal guardian provided written informed consent. Individuals 18 years and older provided their own written informed consent. The only individuals excluded were those unable to participate in study procedures, individuals with a DSM-5 diagnosis of an autism spectrum or primary psychotic disorder, documented or suspected IQ < 70, those who have recently (in the past month) started a new psychotherapy/behavioral intervention, or those at imminent risk of suicide or homicide. The study was approved by the local Institutional Review Board (IRB).
Measures
The analysis used research assessments conducted at screening and baseline. BD diagnosis was confirmed using the Structured Clinical Interview for DSM-5 (SCID-5) Demographic variables assessed included age, sex, race/ethnicity, living situation, parental education level, educational/school, and socioeconomic status.15 Adherence was assessed in 2 ways, via the self-reported Tablets Routine Questionnaire (TRQ),16 and electronic monitoring via the SimpleMed pillbox.17 BD symptoms were measured with the Hamilton Depression Rating Scale (HAM-D), the Young Mania Rating Scale (YMRS), and the Clinical Global Impression Scale (CGI).
Medication Adherence
Tablets Routine Questionnaire (TRQ)
TRQ is a self-report measure which identifies the proportion of days with missed medication in the past 7 and past 30 days.16,17 Higher scores represent worse adherence. TRQ has demonstrated a statistically significant association with past non-adherence, non-adherence in the past month, and non-adherence in the past week and has been shown to correlate highly with lithium levels.18 In this RCT, TRQ was assessed for each evidence-based BD maintenance medication (lithium, anticonvulsant, antipsychotic) prescribed. For individuals on more than one medication, an average TRQ was calculated. For this analysis, we only used past 7 day TRQ as it could be readily compared to past week adherence using the SimpleMed assessment measure.
Electronic Pill Monitoring
Study participants used SimpleMed boxes,19 which record the time and date of opening and can assess adherence with multiple medications. Participants were sent the box after meeting inclusion/exclusion at the screening visit and were then instructed on its use during a virtual training session. Participants were asked to load a week’s worth of their BD medications into the box each week. When it was time to take their medication, the participant removed the dose for that day/time. SimpleMed sends the data about whether or not the compartment was opened via an internal battery-operated cellular modem. The data are linked to a SimpleMed ID and kept in the SimpleMed secure cloud portal that study staff can access. A medication dose was counted as “taken” if the cell on the pillbox was opened within two hours of the prescribed time. For this analysis, a day (within the past 7 days) was counted as “missed” if one or more daily doses was not taken. As with the TRQ, an average SimpleMed score was calculated for individuals on more than one BD medication. A limitation of the SimpleMed technology is that they had to be kept near a window, are very large and thus not easily portable, are vulnerable to being lost and only 19 individuals returned usable SimpleMed data at baseline.
BD Symptoms
Symptoms were measured with the Hamilton Depression Rating Scale (HAM-D),20 the Young Mania Rating Scale (YMRS),21 and the Clinical Global Impression Scale (CGI).22
Data Analysis
This analysis used screening (TRQ) and baseline (TRQ, SimpleMed, demographic and clinical information) data from all individuals who completed the baseline evaluation and were randomized to the RCT (N = 36). First, demographic and clinical information was summarized using descriptive statistics, including the distribution of TRQ scores at screening and baseline and SimpleMed at baseline. The SimpleMed data were not available at screening as individuals received the pillboxes only after passing screen. The non-parametric Spearman’s rank correlation assessed the association between baseline SimpleMed and TRQ adherence scores, and between the adherence scores and clinical attributes (YMRS, HAMD, CGI, and age) for individuals who were prescribed more than 1 BD medication. We also examined associations between TRQ and SimpleMed scores in relation to demographic attributes (gender, race/ethnicity, BD type, and family history of BD) using the non-parametric Krushal-Wallis equality-of-populations rank test. A two-sided alpha of 5% was considered statistically significant.
Results
Overall Sample Description
Table 1 shows demographic and clinical characteristics in this sample of 36 poorly adherent AYA with BD. Mean age was 19.1 years (SD = 2.0) and mean age of onset was 16.0 years (SD = 4.1). Two thirds of the sample identified as female, 77.8% were age 18 years or older, 8.3% identified as Black with a quarter of the sample identifying as non-White. The majority of participants had Type I BD (86%). At baseline, the mean HAM-D (mean = 7.1, SD = 4.7) and YMRS scores (mean = 6.0, SD = 7.3) were consistent with relatively mild BD manic and depressive symptom severity. Table 1B shows selected characteristics of the sample of individuals (n = 19) who had both SimpleMed and TRQ data at the baseline time point.
Table 1. Demographic and Clinical Characteristics of 36 Poorly Adherent Adolescents and Young Adults (AYA) Enrolled in an Adherence Intervention.
| Variable | Mean (SD) | N (%) | ||
| Age N = 36 | 19.1 (2.0) | |||
| Sample ⩾18 years | 28 (77.8%) | |||
| Gender | ||||
| Male | 8 (22.2%) | |||
| Female | 24 (66.7%) | |||
| Other | 4 (11.1%) | |||
| Race N = 36 | ||||
| Black | 3 (8.3%) | |||
| White | 27 (75.0%) | |||
| Asian | 1 (2.8%) | |||
| Other | 5 (13.9%) | |||
| Hispanic N = 36 | 3 (8.3%) | |||
| Living situation N = 36 | ||||
| Alone | 4 (11.1%) | |||
| Parents/Guardian/Caregiver | 18 (50.0%) | |||
| Others (e.g., roommate) | 14 (38.9%) | |||
| Education (in years) N = 36 | 12.7 (1.9) | |||
| Socioeconomic status N = 36 | ||||
| $US 0–35,000 yr | 12 (33.3%) | |||
| $US 36–75,000 yr | 7 (19.4%) | |||
| $US 76–150,000 yr | 5 (13.9%) | |||
| $US 150,000+yr | 4 (11.1%) | |||
| Prefer not to answer/don’t know | 8 (22.2%) | |||
| BD diagnosis type N = 36 | ||||
| BD I | 29 (80.6%) | |||
| BD II | 6 (16.7%) | |||
| Other | 1 (2.8%) | |||
| Mean age of BD onset N = 36 | 16.0 (4.1) | |||
| Positive family history of BD N = 36 | 23 (65.7%) | |||
| History of hospitalization for BD N = 36 | 20 (55.6%) | |||
| Mean number of previous BD hospitalizations N = 20 | 2.4 (3.1) | |||
| TRQ* at study screen N = 36 | 34.9 (28.8) | |||
| TRQ at baseline N = 36 | 30.6 (33.0) | |||
| SimpleMed at baseline N = 19 | 42.1 (37.0) | |||
| CGI at baseline N = 36 | 2.3 (1.0) | |||
| HAM-D at baseline N = 36 | 7.1 (4.7) | |||
| YMRS at baseline N = 36 | 6.0 (7.3) |
* TRQ = Tablet Routines Questionnaire (value reflects the proportion of missed medication and higher scores indicate worse adherence higher scores indicate worse adherence). TRQ average across all medications for the past 7 days. CGI= Clinical Global Impression, Overall Bipolar Illness score.
HAM-D= Hamilton Depression Rating Scale, YMRS = Young Mania Rating Scale.
Table 1B. Adherence (proportion of Missed Medication) and BD Symptoms in Those Who Had SimpleMed at Baseline (N=19).
| Variable | Mean (SD) | |
| TRQ at study screen | 42.1 (27.1) | |
| TRQ at baseline | 33.1 (35.0) | |
| SimpleMed at baseline | 42.1 (37.0) | |
| CGI at baseline | 2.5 (1.0) | |
| HAM-D at baseline | 6.4 (7.2) | |
| YMRS at baseline | 6.4 (7.2) |
Treatment Adherence
As illustrated in Figure 1A, the mean percentage of days with missed doses of prescribed BD medications by TRQ at the screening time point was 34.9 (SD = 28.9). As noted in Figure 1B, the percentage of missed medication as assessed by the TRQ improved from the time of screening evaluation to the baseline time point with a mean baseline TRQ of 30.6 (SD = 33.0). It must be noted that the adherence promotion component of the RCT for use of electronic pill monitoring did not occur prior to study baseline. As noted in Figure 1C, the mean percentage of missed medication as assessed by SimpleMed at baseline was 42.1 (SD = 37.0). Figure 1D shows histograms that illustrate baseline adherence measured by both TRQ and SimpleMed approaches. As noted in the histograms, there was substantial variability in the proportions of missed medication within this sample of AYA with BD, with the SimpleMed approach capturing a higher percentage (>10%) of missed medications compared to TRQ (Table 1).
Figure 1A.
TRQ at Screening
Figure 1B.
TRQ at Baseline
Figure 1C.
SimpleMed at Baseline
Figure 1D.
Medication Adherence Using Both TRQ and SimpleMed at Baseline
Correlates of Treatment Adherence
The correlation between TRQ and SimpleMed was r = 0.36 (p = 0. 13; Table 2). Neither CGI nor age were correlated with adherence. Neither TRQ nor SimpleMed were significantly related to HAM-D scores. In contrast, YMRS was positively associated with worse adherence for TRQ (r = 0.36, p = 0.03), however, this was not was evident in the subset with SimpleMed. There was no evidence suggesting adherence may differ by other demographic attributes (Table 3). A family history of BD was associated with worse adherence as measured by TRQ (p = 0.04) but it was not significantly associated with SimpleMed adherence (p = 0.15), though it was trending in the same direction of effect.
Table 2. Spearman Rank Correlation Between Adherence Measures at Baseline and Clinical Attributes in Non-Adherent AYA with BD.
| Item | TRQ (N=36) | SimpleMed (N=19) | TRQ (those with SimpleMed; N=19) | |||
| TRQ | – | 0.36, p = 0.13 | – | |||
| SimpleMed | 0.36, p = 0.13 | – | 0.36, p = 0.13 | |||
| (N = 19) | ||||||
| CGI | 0.11, p = 0.51 | 0.39, p = 0.10 | 0.08, p = 0.75 | |||
| HAM-D | –0.28, p = 0.10 | –0.10, p = 0.70 | –0.35, p = 0.14 | |||
| YMRS | 0.36, p = 0.03 | 0.09, p = 0.70 | 0.07, p = 0.79 | |||
| Age | –0.01, p = 0.97 | –0.21, p = 0.39 | –0.05, p = 0.83 |
TRQ = Tablets Routine Questionnaire (higher scores indicate worse adherence). TRQ average across all medications for the past 7 days, CGI= Clinical Global Impression, Overall Bipolar Illness total score HAM-D= Hamilton Depression Rating Scale total score, YMRS = Young Mania Rating Scale total score
Table 3. Comparison of Adherence at Baseline and Key Demographic Variables in Non-Adherent AYA with BD.
| Attribute | TRQ (N=36) Mean (SD) |
SimpleMed (N=19) Mean (SD) |
TRQ (those with SimpleMed; N=19) Mean (SD) |
|||||||||
| Gender | ||||||||||||
| Male | 39.3 (41.6) | p = 0.14 | 45.7 (46.7) | p = 0.90 | 54.3 (44.4) | p = 0.25 | ||||||
| Female | 32.1 (31.0) | 39.3 (34.5) | 28.6 (30.5) | |||||||||
| Other | 3.6 (7.1) | 50 (50.5) | 7.1 (10.1) | |||||||||
| Race | ||||||||||||
| Black | 32.8 (35.6) | p = 0.96 | 44.0 (39.8) | p = 0.70 | 39.6 (39.6) | p = 0.47 | ||||||
| White | 19.0 (21.8) | 57.1 (24.7) | 19.0 (21.8) | |||||||||
| Asian | 23.6 (0.0) | – | – | |||||||||
| Other | 25.7 (29.3) | 19.0 (33.0) | 19.0 (16.5) | |||||||||
| BD diagnosis type | ||||||||||||
| BD I | ||||||||||||
| BD II | 30.5 (33.4) | p = 0.26 | 38.5 (35.7) | p = 0.28 | 31.9 (37.0) | p = 0.33 | ||||||
| Other | 19.0 (14.8) | 40 (38.3) | 22.9 (12.8) | |||||||||
| 100 (0.0) | 100 (0.0) | 100 (0.0) | ||||||||||
| Family history of BD | ||||||||||||
| Yes | 39.8 (35.5) | p = 0.043 | 52.4 (36.2) | p = 0.15 | 41.7 (38.3) | p = 0.32 | ||||||
| No | 15.5 (20.6) | 28.6 (35.0) | 21.4 (25.2) | |||||||||
p-values are from non-parametric Kruskal-Wallis equality-of-populations rank test.
Discussion
Medication adherence is critical for optimal outcomes in AYAs with BD.23 Risks associated with multiple relapses are widely recognized and minimizing or eliminating relapse is the primary treatment goal for improving recovery and maintaining wellness in BD.24–27 Promoting early medication adherence and care engagement in AYAs with BD can thus have lasting down-stream benefits that positively change the entire trajectory of health.
This analysis provides new data on adherence behaviors among at-risk AYAs with BD. Among our sample of poorly adherent AYAs, missed drug rates varied substantially, with an average of approximately one-third of medication being missed in the past week. The simple act of adherence monitoring increased adherence behaviors by approximately 4.5 %. Our findings also suggest that the use of electronic pill monitoring identified rates of missed BD medications that were approximately 12% higher /worse than self-report. Our findings have important clinical implications given the prevalence and negative effects of suboptimal medication adherence on health among people with BD across the lifespan.
Adherence behaviors can vary substantially over time. In our sample, focusing on and monitoring adherence led to an apparent Hawthorne effect. It is possible that this effect is only temporary, but it is also possible that sustained monitoring, especially using readily available technologies such as apps or other platforms that can be implemented over personal devices may have potential to impact adherence longer-term. Self-report of adherence behaviors is significantly corrected with electronic pill-cap monitoring, but may fail to document the full scope of missed BD medication. As electronic pill monitoring, such as that used in our study, may be impractical for use in standard clinical care settings, additional sources of adherence information (such as parent report or pharmacy refills) may help to identify a composite and more accurate picture of medication taking among AYA with BD.
Somewhat surprisingly, we did not generally find a significant association between medication adherence and BD symptoms. This is in contrast with a study by Gershon and colleagues that used ecological momentary assessment (EMA) to examine patient characteristics impacting adherence in youth with BD.28 The study by Gershon and colleagues found that higher current mood elevation symptom severity predicted significantly poorer adherence. While our results suggested that manic symptoms may be a clinical indicator of worse perceived medication adherence, manic or depressive symptom severity, especially when measured by objective measures, may not consistently identify AYAs with adherence challenges. An important caveat is that since the AYAs in this sample had fairly low manic, depressive and overall low global psychopathology to begin with, findings may not generalize to AYAs who have more severe BD symptoms. Another possible reason for the lack of correlation between BD symptoms and adherence in this analysis is the virtual assessment method imposed by the COVID pandemic. While a recent literature review found that there were no differences in symptom outcomes between in-person vs. remotely delivered psychotherapy, it is not clear that in-person vs. remote symptom evaluation are interchangeable.29
We generally did not find significant demographic or clinical correlates with adherence in this sample, with the exception of AYAs with a family history of BD having a greater likelihood of self-reporting worse adherence. It is possible that AYAs who have been exposed to family members with BD (and possible lived experience of the effects of sub-optimal adherence in their family) were more likely to acknowledge missing BD medication.
There were a number of potential limitations to our analysis including the modest sample size, generally low levels of manic and depressive symptoms among enrolled BD participants, some missing data on the SimpleMed adherence collection, and the fact that the most poorly adherent patients are unlikely to volunteer to participate in a research study. However, this study explicitly enrolled BD patients with poor adherence, thus allowing for insights that might not be obtained in a typical therapeutic clinical trial.
In conclusion, the proportion of prescribed medication that an individual with BD actually takes can vary widely. Both self-report and automated medication monitoring are reasonable methods of evaluating adherence although automated methods are likely to provide a more sensitive assessment of missed drug. Additional investigation into how clinicians can identify BD patients at high risk for poor medication adherence and what interventions can optimize adherence is greatly needed.
Contributor Information
Martha Sajatovic, Sajatovic, Department of Psychiatry and Neurological & Behavioral Outcomes Center, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Jennifer B Levin, Levin, Department of Psychiatry and Neurological & Behavioral Outcomes Center, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Avani Modi, Modi, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA..
Molly McVoy, McVoy, MD, Department of Psychiatry and Neurological & Behavioral Outcomes Center, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Larry F Forthun, Forthun, Family, Youth and Community Sciences, University of Florida, Gainesville, FL, USA..
Raechel Cooley, Cooley, Department of Psychiatry, University of Cincinnati, College of Medicine, Cincinnati, OH, USA..
Jessica Black, Black, Department of Psychiatry and Neurological & Behavioral Outcomes Center, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Carla Conroy, Conroy, Department of Psychiatry and Mood Disorders Program, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Kaylee Sarna, Sarna, Department of Psychiatry and Neurological & Behavioral Outcomes Center, Case Western Reserve University School of Medicine and University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA..
Farren B Briggs, Briggs, PhD, Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA..
Melissa DelBello, DelBello, Department of Psychiatry, University of Cincinnati, College of Medicine, Cincinnati, OH, USA..
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