Medication Non-Adherence and Tolerance Breaks Among Adolescents and Young Adults With ADHD

Abstract

Objective:

We examine what variables were associated with increased medication non-adherence for adolescents and young adults with ADHD.

Method:

These variables included sociodemographic characteristics such as gender, age, race, and ethnicity but also included self-reported reasons for medication non-adherence as well as the type of and degree of self-reported side effects or adverse events. Results: The following variables were statistically significant predictors of medication non-adherence: being White; forgetting to take the medication; not liking the feeling; and desiring a tolerance break from the medication.

Conclucion:

Tolerance breaks appear to be a novel, self-reported reason for medication non-adherence that emerged among adolescents and young adults with ADHD. Tolerance breaks appear to be relatively common, with one in five adolescents and young adults with ADHD reporting this reason for non-adherence. Future research should further investigate tolerance breaks as a reason for medication non-adherence among adolescents and young adults with ADHD.

Keywords

medication adherence medication status medication trends

Over time, medication can produce two types of changes in effect: tolerance and sensitization (Castells et al., 2021). Tolerance to medications, especially psychostimulants used for the treatment of Attention Deficit Hyperactivity Disorder (ADHD) is not uncommon and even expected (Taylor, 2019). For many medications used to treat individuals with ADHD, increasing tolerance to the medication across time has been repeatedly observed (e.g., Karim et al., 2018; Kharas et al., 2017; Turner et al., 2018). Yet, Krakowski et al. (2018) notes that for children and adolescents with ADHD, tolerance to medication is a topic of much discussion and debate. The ability of ADHD medications to produce tolerance has been considered controversial despite guidance that this is not atypical (Ibrahim & Donyai, 2015; Yanofski, 2011). For example, the most up-to-date guidelines of the American Academy of Child and Adolescent Psychiatry state that, “most children will need dose adjustment upwards as treatment progresses” (Pliszka et al., 2007, p. 906).

Yanofski (2011) notes that tolerance and the decreasing effectiveness of medications for ADHD develops for a variety of reasons. First, changes in pharmacokinetics can occur with development such that smaller doses become no longer appropriate as a child grows and matures (van den Anker et al., 2018; Yanofski, 2011). Second, the progression of the disorder itself may require an increased dose of medication such that the severity of symptoms of ADHD have worsened (Yanofski, 2011). Third, environmental changes (e.g., changing schools, parents’ divorce) may result in new or enhanced stressors that may unmask symptoms, thus requiring an increased dose of a medication to treat new or more severe symptoms of ADHD. Fourth, there is the issue of paradoxical decompensation indicating that tolerance to a medication will inhibit the ability of the individual, either physiologically or psychologically, to respond to symptoms without medication (Weiss et al., 2018). For instance, individuals who have taken ADHD medication across several years may not develop coping mechanisms for their symptoms while medicated than the same children with ADHD who were not medicated (Yanofski, 2011). In this way, paradoxical decompensation views medication for ADHD as a “borrowed benefit” (Yanofski, 2011, p. 50) in that like, “borrowing money from a bank, these symptoms will be paid back” (p. 50) in the future without appropriate psychological/behavioral treatment to complement the medication.

According to Krakowski et al. (2018), tolerance can result in the increasing need for an increasing dose of medication over time. This increasing dosage of medication may be required in order to maintain the same level of response to the medication (Turton & Lingford-Hughes, 2016). Instead of increasing medication dosage, Taylor (2019) indicated that another approach to addressing possible tolerance to medication treatment would be to include breaks from the treatment, “to allow the brain to recover its responsiveness” (p. 544). Lohr (2021) found that families of children with ADHD often use medication breaks or holidays to reduce the tolerance of medications but the value of these breaks or holidays is mixed. Lohr (2021) suggest more evidence is needed for the value of breaks, especially given the range of families who engage in these breaks with anywhere from 25 to 70% of families in their review of the literature. Ibrahim and Donyai (2015) note that any medication break may implicitly be a tolerance break, but medication breaks can occur for a variety of reasons ranging from testing whether the medication was still needed to addressing side effects or adverse events.

There has been limited evidence examining when, how often, and with who these medication breaks may occur for individuals with ADHD. In general, the period of adolescence through young adulthood has been found to be a period where there is a higher risk of the non-adherence and permanent discontinuation of the medication for ADHD (e.g., Barnard-Brak et al., 2020; Biederman et al., 2019; Khan & Aslani, 2019). Adolescents typically exercise more autonomy as compared to younger children, given that many adolescents may even be given responsibility to take their medications independently (Barnard-Brak et al., 2020). Additionally, Biederman et al. (2019) examined medical records from a large health care organization, which indicated that only about half of adolescents with ADHD were adherent to their medication. This result was consistent with Barnard-Brak et al. (2020), indicating that approximately half of the parents of children with ADHD aged 13 years and older reporting medication adherence. Thus, this age group would appear to need the most efforts focused on improving their medication adherence (Brikell et al., 2021). Interestingly, Brikell et al. (2021) also found that with older age at ADHD diagnosis, this was associated with a discontinuation of treatment, which may be due to a variety of reasons.

In addition to age, Khan and Aslani (2019) reviewed the literature that examined factors affecting the discontinuation of medication among individuals with ADHD. In their review of the literature, patients who were female with ADHD were generally more likely to discontinue their medication for ADHD (e.g., Hodgkins et al., 2011; van den Ban et al., 2010; Wang et al., 2016). Reasons can range for these sex differences such as females tend to have less severe symptoms of ADHD thus may be more likely to non-adherent to medication or that girls undergo puberty at earlier ages than boys and thus may develop out of symptoms faster (e.g., Hodgkins et al., 2011; van den Ban et al., 2010; Wang et al., 2016). Patients with comorbid conditions were also more likely to discontinue treatment via medication (e.g., Hodgkins et al., 2011; Wang et al., 2016). Biederman et al. (2019) also found that medication adherence was worse among patients who were older or female as indicated by prescription refills. Barnard-Brak et al. (2013) found gender differences as well but that medication adherence was worse with males rather than females as reported by parents of children with ADHD. Differences in the source of information (i.e., parents versus patients) and age may explain differences in reported adherence such.

The purpose of the current study was to examine the variables associated with increased medication non-adherence for adolescents and young adults with ADHD. These variables included sociodemographic characteristics such as gender, age, race, and ethnicity in addition to self-reported reasons for medication non-adherence, as well as the type of and degree of self-reported side effects or adverse events. To achieve this purpose, we addressed two research questions. We first examined the reasons for medication non-adherence through a combination of close-ended reasons as derived from the literature, as well as open-ended responses that were examined and coded qualitatively. The first research question was, what self-reported reasons for medication non-adherence were reported among adolescents with ADHD? Second, we examined those self-reported responses for non-adherence as predicting the degree of medication adherence in combination with sociodemographic characteristics, as well as self-reported adverse events or side effects. The second research question was, what variables best predicted medication non-adherence among adolescents with ADHD?

Method

Sample

The sample consisted of 196 adolescents and young adults with self-reported ADHD. Approximately 75% (n = 148) identified as female, 15% (n = 29) identified as male, 7% (n = 14) identified as neither female nor male, and 3% (n = 5) chose not to identify a gender. With respect to race, approximately 80% (n = 156) identified as White, 2% (n = 4) identified as African American, 7% (n = 13) identified as Asian, and finally 12% (n = 23) chose not to identify any race. As for ethnicity, approximately 11% (n = 22) identified as Hispanic or Latinx. The average age was 20.47 years (SD = 2.44) with ages ranging from 13 to 24 years. The average age of diagnosis was 13.07 years (SD = 5.33). The average number of years receiving medication for ADHD was 4.62 years (SD = 4.99). The most frequently co-occurring disorder was anxiety at 41% (n = 81) followed by depression at 37% (n = 72), specific learning disability at 7% (n = 13), post-traumatic stress disorder at 5% (n = 9), obsessive compulsive disorder at 5% (n = 9), autism spectrum disorder at 4% (n = 7), Tourette’s disorder at 2% (n = 4), bipolar disorder at 2% (n = 4), oppositional defiant disorder at 2% (n = 3), and mood disorder at 1% (n = 2). These percentages exceed 100% as participants could have more than one co-occurring disorder.

Measures

We administered the online survey via Qualtrics. This survey was administered via the organization, CHADD (Children and Adults with Attention-Deficit/Hyperactivity Disorder, 2020) website with a recruitment message posted to its website for research studies. Recruitment for the survey online took approximately 1 year to complete. Approximately 76% (n = 148) of participants reported taking medication for their ADHD. Approximately 33% (n = 65) of participants reported having received behavioral treatment. Approximately 26% (n = 50) of participants reported having received both medication and behavioral treatment. The degree of medication adherence was measured along a 7-point scale responding to the question, “do you take your medication as prescribed?” Table 1 provides the frequency and percent for each response. The majority of respondents ranged from taking medication almost all the time to sometimes taking medication. Participants also responded as to side effects or adverse events, as well as severity of those symptoms (see Table 2).

Table 1.

Frequency and Percentages for Medication Adherence.

Do you take your medication as prescribed?	f	%
Yes, never miss it	15	7.7
Yes, almost all the time	36	18.4
Yes, most of the time	35	17.9
Sometimes, I forget or don’t feel like taking it	49	25.0
No, most of the time I don’t take it	10	5.1
No, almost never	2	1.0
No, never	1	.5
Missing system	48	24.5

Table 2.

Frequencies and Percentages for Side Effects or Adverse Events.

Symptom	Severity
	None		Mild		Moderate		Severe
	f	%	f	%	f	%	f	%
Decreased appetite	24	12.2	30	15.3	79	40.3	63	32.1
Sore throat	181	92.3	14	6.5	1	0.5	0	0.0
Skin rashes	181	92.3	13	6.6	0	0.0	2	0.9
Dry skin	166	84.7	17	8.7	8	4.1	5	2.6
Dry eyes	158	80.6	24	12.2	11	5.6	3	1.5
Taste/mouth	145	74.0	35	17.9	12	6.1	4	2.0
Nasal congestion	187	95.4	5	2.6	3	1.5	1	0.5
Blood pressure	144	73.5	30	15.3	21	10.7	1	0.5
Excessive talking	123	62.8	28	14.3	32	16.3	13	6.6
Picking skin	136	69.4	18	9.2	22	11.2	20	10.2
Biting nails	135	68.9	21	10.7	24	12.2	16	8.2
Mood swings	112	57.1	33	16.8	37	18.9	14	7.1
Depression	134	68.4	19	9.7	31	15.8	12	6.1
Worry/anxiety	90	45.9	40	20.4	47	24.0	19	9.7
Socially withdrawn	118	60.2	26	13.3	30	15.3	22	11.2
Anger	137	69.9	32	16.3	19	9.7	8	4.1
Weight loss	80	40.8	63	32.1	41	20.9	12	6.1
Weight gain	175	89.3	11	5.6	6	3.1	4	2.0
Upset stomach	134	68.4	41	20.9	15	7.7	6	3.1
Vomiting	186	94.9	9	4.6	1	0.5	0	0.0
Nausea	140	71.4	39	19.9	14	7.1	3	1.5
Thirsty	101	51.5	47	24.0	34	17.3	14	7.1
Constipation	168	85.7	20	10.2	5	2.6	3	1.5
Waking up early	153	78.1	32	16.3	8	4.1	3	1.5
Dizziness	125	63.8	44	22.4	24	12.2	3	1.5
Palpitations	135	68.9	39	19.9	17	8.7	5	2.6
Chest pains	173	88.3	14	7.1	8	4.1	1	0.5
Tremors	160	81.6	28	14.3	7	3.6	1	0.5
Easily agitated	115	58.7	33	16.8	32	16.3	16	8.2
Difficulty urinating	182	92.9	7	3.6	6	3.1	1	0.5
Sleepiness	136	69.4	30	15.3	20	10.2	10	5.1
Difficulty sleeping	81	41.3	34	17.3	49	25.0	32	16.3
Tiredness	117	59.7	39	19.9	28	14.3	12	6.1
Headaches	123	62.8	39	19.9	29	14.8	5	2.6
Diarrhea	166	84.7	17	8.7	8	4.1	5	2.6

Analyses

To examine the reasons for medication non-adherence, we utilized a combination of close-ended reasons across five categories as derived from the literature as well as open-ended responses when selecting “other” for a reason were coded. We first open coded responses for broad categories then we combined categories as revealed to be conceptually associated using a grounded theory approach (Blair, 2015). After open-coding, responses were subsequently chunked and grouped as appropriate. To predict the degree of medication adherence, we utilized multiple linear regression techniques. Values of R-squared were used to assess model fit, where values of 0.03, 0.09, and 0.25 or larger may be viewed as small, medium, and large respectively (Gravetter et al., 2020). Variance inflation factor (VIF) values less than 10 and tolerance greater than 0.10 would indicate minimal multi-collinearity. The statistical significance of individual predictors (α ≤ .05) was then assessed.

Results

The most frequently reported reason for medication non-adherence was wanting to feel normal or independent of medication but without specific negative consequences at 38.3% of respondents. The second most frequently reported reason for non-adherence was not liking the feeling generally at 37.8% of the sample. The third most frequently reported reason for non-adherence was participants forgetting to take medication at 36.2%. Approximately 20%, or one in five respondents, reported not taking medication as the result of a tolerance break, as the fourth most frequently reported reason. This reason of tolerance breaks was one that emerged in coding open-ended responses. Tolerance break responses could be disaggregated in two categories: effectiveness and dependency or addiction concerns. Below is an example for each category:

Effectiveness Concern: If I take it too regularly, I feel I build up a tolerance to it.

Dependency/Addiction Concern: I was worried about becoming dependent or addicted to my medication so I would skip it on weekends or school breaks.

The fifth most reported reason for non-adherence was the logistics of refilling at 15.3%. Finally, cost or price of medication was the sixth, and least most reported, reason for non-adherence at 8.2%. Table 3 provides the frequency and percentage for each reason.

Table 3.

Frequency and Percentage by Reason.

Reason	F	%
Tolerance	39	19.9
Forgot	71	36.2
Didn’t like feel	74	37.8
Logistics of refilling	30	15.3
Feel normal/independent	75	38.3
Cost	16	8.2

We then evaluated what variables predicted the degree of medication adherence. The model appeared to fit the data well with an r² value of 0.29. The regression line was statistically significant also indicating acceptable model fit, F(13, 195) = 3.51, p < .001. Table 4 provides the regression coefficients values. Four predictors were statistically significant at the 0.05 level or less. Participants who were White were significantly more likely to have a higher degree of non-adherence, β = .19, p = .03. Participants who reported forgetting were significantly more likely to have a higher degree of non-adherence, β = .29, p < .001. Participants who reported not feeling like taking medication were significantly more likely to have a higher degree of non-adherence, β = .38, p < .001. Participants who reported taking tolerance breaks were significantly more likely to have a higher of medication non-adherence, β = .17, p = .045. As for multi-collinearity, tolerance and VIF values across the covariates were all within bounds.

Table 4.

Regression Coefficient Values.

Model	Unstandardized coefficients		β	t	p	Tolerance	VIF
Model	B	SE	β	t	p	Tolerance	VIF
(Constant)	0.81	0.86		0.93	.35
Years medicated	0.00	0.03	.01	0.05	.96	0.54	1.86
Age diagnosed	0.00	0.02	−.02	−0.19	.85	0.52	1.91
Comorbid disorder	0.03	0.24	.01	0.14	.89	0.80	1.25
Behavioral treatment	0.30	0.26	.11	1.13	.26	0.68	1.48
Female	0.13	0.22	.05	0.61	.55	0.89	1.12
White†	0.62	0.29	.19	2.17	.03	0.81	1.23
Forgot	0.75	0.23	.29	3.25	<.001	0.79	1.27
Didn’t like feel	0.96	0.24	.38	4.03	<.001	0.72	1.38
Logistics of refilling	−0.01	0.27	.00	−0.03	.97	0.87	1.15
Feel normal/independent	0.33	0.21	.13	1.53	.13	0.90	1.11
Tolerance	0.50	0.25	.17	2.03	.045	0.89	1.13
Cost	0.45	0.37	.11	1.22	.22	0.86	1.16
Severity of side effects	0.00	0.01	−.01	−0.15	.88	0.91	1.10

†

Statistically significant covariates have been shaded.

Discussion

The results of the current study indicate that tolerance breaks emerged as a statistically significant reason for medication non-adherence as reported by adolescents and young adults with ADHD. The other following variables were statistically significant predictors of medication non-adherence: being White; forgetting to take the medication; and not liking the feeling. Tolerance breaks appear to be a novel reason for medication non-adherence that emerged among adolescents and young adults with ADHD. Adolescents and young adults are not unfamiliar with the concept of tolerance breaks in other areas. For e-cigarette use among young adults, Kong et al. (2021) found that the most frequently reported reason for quitting vaping was either taking a “tolerance break” (p. 4) or health reasons. For these young adults using e-cigarettes as studied in Kong et al. (2021), a “tolerance break” was described as a short-term break anywhere from a few days up to a few weeks in order, “to ‘reset’ their tolerance so that they could feel the ‘buzz’ of nicotine again” (p. 4). The use of this term as it applies to ADHD medication non-adherence may have transferred over from areas such as this.

In the current study, tolerance breaks appeared to be divided into two distinct categories: tolerance breaks for effectiveness concerns or tolerance breaks for dependency or addiction concerns. Tolerance breaks related to effectiveness concerns often indicated a planned nature to these breaks such that one participant stated:

I would skip taking them for a couple days, when I did not have work or school or otherwise felt like I needed it, to hopefully lower my tolerance and have it work better when I do take it again.

One participant articulated this planned nature of tolerance break as related to effectiveness concerns more explicitly stating:

I used to try to take weekends off to give myself a break, and to keep the medication effective (like a tolerance break).

Planning for re-introduction of medication did not appear to be part of statements regarding tolerance breaks for dependency or addiction concerns, which included simply stating:

I'm worried about becoming dependent.

The current study did not directly inquire about tolerance breaks; so, we relied on participant generation of this reason for medication non-adherence, as well as its explanation. Future research should further delve into the reasons for these tolerance breaks by asking under what conditions do individuals with ADHD consider tolerance breaks and why?

Several limitations should be discussed that were revealed in conducting the current study. Yanofski (2011) notes that individuals who have been nonadherent may have been dosed inadequately or misdiagnosed to begin with, thus medication non-adherence may be a consequence of inaccurate diagnosis or treatment. Previous research has not indicated racial differences in medication non-adherence, but the current sample was overwhelmingly White, which may be considered a limitation of the current study. As a result, this finding should be viewed with caution. Additionally, recruitment for the study was a matter of self-selection of participants who desired to share their perceptions and experiences associated with ADHD treatment online resulting in a convenience or non-probabilistic sample. Self-selected participants may reveal more skewed responses, which would in turn impact results of the study.

In conclusion, the results of the current study suggest tolerance breaks are relatively common, with one in five adolescents and young adults with ADHD reporting tolerance as the reason for medication non-adherence. This reason for medication non-adherence may be considered relatively novel for the research literature among individuals with ADHD. Future research should further discern the understanding of individuals with ADHD in making these decisions regarding medication non-adherence. For instance, future research should discern whether tolerance breaks are more likely the function of concerns about future effectiveness, dependency/addiction, or the combination thereof. The current study did not provide this as a close-ended response option rather only as an open-ended option as provided by participants. Understanding why individuals with ADHD take these tolerance breaks will help healthcare professionals advise patients and their families more effectively. Knowledge about the underlying thought processes associated with non-adherence would appear to benefit healthcare providers in working with patients and their families.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Lucy Barnard-Brak

Author Biographies

Lucy Barnard-Brak, PhD, is a Professor of Special Education at the University of Alabama. She earned her PhD from Texas Tech University in Educational Psychology. Her research examines medication adherence among adolescents and young adults with ADHD.

Allie Marques, EdS, is a doctoral candidate in Special Education at the University of Alabama. Her research examines special education outcomes of students in public school settings.

Carolina Kudesey, MA, is a third-year doctoral student in Special Education at the University of Alabama. Her research examines the self-monitoring skills of students with intellectual and developmental disabilities.

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