Abstract
African American emerging adults (age 18–29 years) tend to have poor asthma outcomes, possibly due to poor adherence to medication. Few studies have explored barriers to controller adherence in this population. This study utilized electronic daily diaries to assess barriers to adherence and asthma symptoms among 141 African American emerging adults with uncontrolled persistent asthma and poor adherence. Participants reported symptoms M = 3.43 days (of 7 days). They reported unintentional (e.g., forgetting) and intentional (e.g., choosing not to take) barriers to adherence, but forgetting, being too busy, and sleeping through a dose were the most common. Significant correlations were found between symptoms and barriers, as well as asthma control and medication adherence in the expected directions. Asthma symptoms and number of barriers were significant predictors of asthma control. Existing intervention strategies such as text-messaging may prove effective to address these barriers, but measuring and addressing adherence remains complex.
African Americans have poorer asthma outcomes than most other racial/ethnic groups (Zahran et al., 2018), even after controlling for socioeconomic variables (Lieu et al., 2002). Poor adherence to medications may be one reason underlying these outcomes (Bruzzese et al., 2012). Barriers to adherence among African Americans have been found to include lack of knowledge, misconceptions about medication, and external factors influencing controller usage (e.g., schedule, peers; Norful et al., 2020), but this has not been fully explored in emerging adults.
This project focused on 18- to 29-year-old African Americans in emerging adulthood, the developmental period beyond adolescence but before adulthood (Arnett, 2004) largely neglected in research. In formative work with this population, our team conducted interviews about reasons for poor medication adherence (MacDonell et al., 2014). Reasons were categorized into unintentional (e.g., forgetting) and intentional (e.g., choosing not to take medications; Horne, 2006), and aligned with developmental tasks of emerging adulthood. “Forgetting” was frequently cited as a reason to miss medications, but participants also described choosing not to take medications because of side effects, concerns about efficacy, and needs to assert control over health and move on from a “childish” disease.
This study expands research on barriers to adherence in African American emerging adults with asthma by exploring their daily experiences via electronic daily diaries, which may be more sensitive than retrospective recall methods (Okupa et al., 2013). Diaries were utilized to collect data on barriers from a list derived from our formative research, and frequency of asthma symptoms. We hypothesized that “forgetting” would be the most common barrier, but intentional reasons/barriers would occur frequently. We expected that participants who reported more barriers would report more symptoms, and both barriers and symptoms would predict asthma control.
Method
Participants and Procedures
Data were part of the baseline assessment for a clinical trial of an intervention targeting medication adherence (MacDonell et al., 2018). Participants were recruited from an urban university and affiliated medical center and community settings. Participants were 18 to 29 years old, African American, diagnosed with persistent asthma, and prescribed a controller medication, with poor self-reported adherence (defined as <80%) in the past 30 days and uncontrolled asthma as defined by the Asthma Control Test (ACT; Nathan et al., 2004). Exclusion criteria included pregnancy, inability to understand English, other serious medical conditions, and/or an active psychiatric disorder. Of 1,577 screened, 1,314 were not eligible, 122 refused, and 141 were enrolled. The Human Investigation Committees at the university and medical center approved the project.
Following consent and orientation, participants were prompted via text message to complete a diary for 8 consecutive days (Day 1 was practice). Participants then completed an in-person data collection and were compensated $50.
Measures
Asthma Control
Asthma control was assessed using the ACT (Nathan et al., 2004), a five-item self-report questionnaire with scores from 5 (poor control) to 25 (complete control). An ACT score >19 indicates well-controlled asthma.
Lung Functioning
Pulmonary functioning was assessed using forced expiratory maneuvers obtained with a portable calibrated recording spirometer (KoKo). FEV1/FVC ratio percentage predicted, defined as FEV1 (a person’s vital capacity that they are able to expire in the first second of forced expiration) divided by the average FVC (the full, forced vital capacity) in the population for any person of similar age, sex, and body composition, was used. Research assistants were trained to administer the KoKo using American Thoracic Society standards.
Medication Adherence
Adherence was measured using a six-item, yes/no questionnaire about adherence over the past 4 weeks (e.g., During the past 4 weeks, have you ever forgotten to use your controller medication?; Brooks et al., 1994). Higher scores (maximum = 6) indicated poor adherence.
Daily Diary
Participants were texted to complete a diary via Qualtrics at the end of each day for 7 days. Participants were asked to select any symptoms they had experienced that day from a list provided. They were also asked to select any barriers to taking medication from a list of 12 barriers (Table 1) plus options for “other” and “none.”
Frequency of Participants Reporting Barriers to Medication Adherence.
Statistical Analysis
Descriptive statistics were calculated for all variables. Correlation coefficients examined the strength of associations among number of days of symptoms, FEV1/FVC (percentage predicted), asthma control, number of barriers, adherence, and lung functioning. Multiple linear regression was used to develop a model for predicting asthma control from these variables. Missing data were subject to listwise deletion. All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC).
Results
Participants (N = 141) ranged from 18 to 29 years with M = 23.2 (SD = 3.44). There were 114 females and 27 males, and 133 (94.3%) identified as Black/African American and eight as African American/mixed race. Forty-nine (34.8%) had biological children. More than 47% (47.5%) had a high school degree/GED, 30.5% had some college/tech school, and 10.0% were college/tech school graduates. Sixty-one percent were currently employed full- or part-time. ACT scores (Nathan et al., 2004) ranged from 5 to 25 with M = 15.83 (3.98), indicating most participants (73.0%) had poorly controlled asthma at baseline. Lung functioning, as FEV1/FVC, was M = 88.18 (SD = 12.23) percentage predicted, with over 24% of the sample below 80% at baseline, indicating suboptimal lung functioning. Participants took medication as prescribed M = 62.60% of the time (SD = 33.51) in the month prior.
Medication Adherence
Participants scored M = 2.07 (SD = 1.47) on the measure of adherence. The most frequent reason for nonadherence was forgetting (71.6%). This was followed by carelessness taking medication (45.4%) and stopping medication because feeling better (30.5%).
Electronic Daily Diary
Thirty-four (24.1%) had missing data due to technological misfunction or nonresponse resulting in N = 105 for analyses of diary data.
Asthma Symptoms
Participants ranged from 0 to 7 days of days with symptoms, M = 3.43 days (SD = 2.21). Twelve (8.5%) reported 0 days, 44 (31.2%) 1 to 3 days, and 51 (36.2%) the majority of days (4 or more). Thirteen (9.2%) experienced symptoms every day.
Barriers to Adherence
Table 1 details the number of participants (of 105) reporting each category of barrier and the mean number of times each barrier was reported across participants over 7 days. Participants reported 0 to 12 categories of barriers over 7 days, M = 1.49 (SD = 1.86). Fifty-two (36.9%) reported zero barriers, 69 (48.9%) reported one to three, 16 (11.3%) reported four to six, and two (1.4%) reported eight to 12. After “no barriers to report today,” the most frequent barrier was “I forgot,” followed by “too busy,” “slept through a dose,” and “away from home.”
Associations Between Variables
Symptoms and barriers were significantly correlated with one another, as well as asthma control (Nathan et al., 2004), and adherence (Brooks et al., 1994) in the expected directions. Days of symptoms was negatively correlated with asthma control, r = −.46, p < .01, and positively correlated with barriers, r = .22, p < .05, and adherence (r = .24, p < .05). Number of barriers was positively correlated with asthma control, r = .19, p < .05, and adherence, r = .17, p ≤ .05. Lung functioning was significantly correlated with asthma control (r = .17, p < .05) but not with barriers, adherence, or symptoms.
Linear Regression
Regression (Table 2) was conducted to investigate whether asthma symptoms, number of barriers reported, lung functioning, adherence, and age would significantly predict asthma control. The model explained 25.2% of the variance and was a significant predictor of asthma control, F(5, 96) = 6.47, p = .001. Asthma symptoms (β = −.478, p < .001) and number of barriers (β = .245, p = .010) contributed significantly to the model while lung functioning (β = .071, p = .432), adherence (β = −.093, p = .322), and age (β = −.001, p = .994) did not.
Summary of Multiple Regression Analysis for Asthma Control.
Discussion
This study was among the first to explore barriers to adherence among African American emerging adults with uncontrolled asthma. This population faces significant asthma disparities, so it is critical that they are included in research. Moreover, this study utilized electronic daily diaries rather than relying on recall-reliant methods. Participants reported asthma symptoms on nearly 50% of days. Over one third reported symptoms the majority of days. Participants who reported more barriers also had more days of symptoms.
“Forgetting” was the most prevalent barrier to adherence. This is consistent with formative qualitative work (MacDonell et al., 2014). After forgetting, the most prevalent barriers were “too busy,” “slept through a dose,” and “away from home.” Like forgetting, these barriers are unintentional reasons for missing medications (Horne, 2006) and related to changes or shifts in schedule. To address unintentional reasons, existing strategies may prove effective (Haynes et al., 2008; McDonald et al., 2002). In particular, text-messaged reminders to take or refill medications may be a promising approach (MacDonell et al., 2012).
Unintentional reasons for nonadherence were common. However, our team has found that a significant number of emerging adults who state they “forgot,” when asked additional questions (e.g., describe forgetting), describe deciding not to take medications as prescribed (MacDonell et al., 2014). In this study, nearly one third participants indicated they had stopped taking medications because they “felt better.” For people with persistent asthma, controller medications should typically be taken each day. However, emerging adulthood is a time of increasing independence and assertation of personal control (Arnett, 2004), which may extend to day-to-day health care decisions. This must be recognized when assessing adherence and designing interventions for this age group.
“Did not have any medication” was a barrier for over 18% participants. This may be complex and involve intentional and/or unintentional reasons. People may face systemic barriers such as cost of prescriptions, insurance, and/or transportation. This study was conducted in an urban area hard-hit by economic inequities. Programs that provide free or low-cost prescriptions, prescription by mail, and telehealth may help overcome these barriers. However, it cannot be determined if participants did not have medications because they chose not to fill/refill the prescription. Approximately one third of adult asthma patients have concerns about adverse effects from medication (Horne, 2006). These concerns typically center on side effects and dependence (Horne, 2006). In formative work, we found that many emerging adults perceived asthma as a childhood disease, and medications as fostering dependency (MacDonell et al., 2014). Interventions that address underlying motivations, beliefs, and perceptions about asthma, and include education might be more effective in addressing intentional barriers. Any successful approach must consider the unique aspects of emerging adulthood, such as increasing independence, changing social supports, and perceptions of risk.
In this study, emerging adults who reported more asthma symptoms and poor adherence also reported more barriers to adherence. However, this association did not hold for asthma control. The measurement of asthma is complex. Self-report of asthma symptoms, even via daily diary, is subject to errors in recall and perception. Participants in this study have lived with asthma their entire lives, with an average age of diagnosis at 5.1 years. Symptoms like coughing and wheezing are commonplace and may only be considered problematic when they become severe. Intervention programs might include education, but medical providers might also discuss patient expectations for asthma control.
This research adds to a very small body of literature, but there are limitations. The study included participants from one urban area. It also had a high representation of females, even considering that women are disproportionally affected by asthma (Schiller et al., 2012). This makes results difficult to generalize to the broader population. Another limitation is that some participants did not report poor asthma control at baseline, despite doing so on the eligibility screener, perhaps due to the time elapsed from enrollment. This study also found an inconsistent association between lung functioning, symptoms, and asthma control, suggesting that additional measures may be needed to better capture these constructs. With the exception of lung functioning, the study relied on self-report measures, and there was a large amount of missing diary data. Researchers might consider strategies to increase response rate (e.g., incentives), and utilize mHealth approaches to capture more detailed data on daily experiences.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the National Institutes of Health (NHLBI, 1R01HL133506; MacDonell).
