Medication Management Performance in Parkinson's Disease: Examination of Process Errors
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Archives of Clinical Neuropsychology 36 (2021) 1307–1315
Medication Management Performance in Parkinson’s Disease:
Examination of Process Errors
Catherine A. Sumida1 , Francesca V. Lopez2 , Emily J. Van Etten3 , Nicole Whiteley4 , Raeanne C. Moore5 ,
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Irene Litvan6 , Stephanie Lessig6,7 , Paul E. Gilbert8 , Maureen Schmitter-Edgecombe1 ,
J. Vincent Filoteo5,6,9 , Dawn M. Schiehser4,5, *
1
Department of Psychology, Washington State University, Pullman, WA 99164-4820, USA
2
Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA
3
Department of Psychology, University of Arizona, Tucson, AZ, 85721, USA
4
Research Service, Veterans Administration San Diego Healthcare System, San Diego, CA, 92161, USA
5
Department of Psychiatry, University of California San Diego, San Diego, CA 92093, USA
6
Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
7
Neurology Service, Veterans Administration San Diego Healthcare System, San Diego, CA, 92161, USA
8
Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, Department of Psychology, San
Diego State University, San Diego, CA 92182-4611, USA
9
Psychology Service, Veterans Administration San Diego Healthcare System, San Diego, CA, 92161, USA
*Corresponding author at: Research Service, Veterans Administration San Diego Healthcare System; Department of Psychiatry, University of California San
Diego, San Diego, CA 92093, USA. Tel.: +1-858-552-8585x2664; Fax: +1-858-404-8389.E-mail address: dschiehser@ucsd.edu (D. M. Schiehser)
Accepted 19 January 2021
Abstract
Objective: Individuals with Parkinson’s disease (PD) are at risk for increased medication mismanagement, which can lead
to worse clinical outcomes. However, the nature of the errors (i.e., undertaking or overtaking medications) contributing to
mismanagement and their relationship to cognition in PD is unknown. Therefore, this study sought to examine errors committed
on the Medication Management Ability Assessment (MMAA) between PD participants with normal cognition (PD-NC) or mild
cognitive impairment (PD-MCI) relative to healthy adults (HA).
Method: HA (n = 74), PD-NC (n = 102), and PD-MCI (n = 45) participants were administered the MMAA to assess
undertaking, overtaking, and overall errors as well as overall performance (total score). Additionally, participants were
administered a comprehensive neuropsychological battery from which cognitive composites of Attention, Learning, Memory,
Language, Visuospatial, and Executive Functioning were derived.
Results: Separate negative binomial regression analyses indicated the PD-MCI group performed significantly worse overall on
the MMAA (total score) and committed more undertaking and overall errors relative to HA and PD-NC. In the PD-MCI group,
poorer MMAA performance was associated with worse delayed memory performance, whereas cognitive performance was not
related to MMAA in HA or PC-NC.
Conclusion: Compared to PD and healthy adults with normal cognition, PD-MCI patients exhibited greater difficulty with
medication management, particularly with undertaking medications. Poorer medication management in PD-MCI was associated
with worse delayed recall. Thus, PD-MCI patients experiencing memory problems may require additional assistance with their
medications. Findings have clinical relevance suggesting that objective measures of medication errors may assist clinicians in
identifying PD patients needing adherence strategies.
Keywords: Everyday functioning; Parkinson’s disease
Published by Oxford University Press 2021.
This work is written by US Government employees and is in the public domain in the US.
https://doi.org/10.1093/arclin/acab004 Advance Access publication 24 February 20211308 C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315
Introduction
Pharmacotherapy is the primary treatment for Parkinson’s disease (PD). As the disease progresses, medication adherence
becomes increasingly critical for functional independence (Grosset, 2010). However, medication adherence is often suboptimal
in PD, which may be due to several risk factors, such as medication regime complexity, depression, polypharmacy, and cognitive
impairment (Grosset, 2010).
To evaluate the impact of cognition on medication adherence in PD, several studies have examined performance-based
measures of medication management (i.e., lab-based tests of organizing, refilling, and memory for past and future medication
taking; Foster, 2014; Manning et al., 2012; Pirogovsky et al., 2013, 2014). In studies of non-demented PD samples, PD
patients with normal cognition (PD-NC) or mild cognitive impairment (PD-MCI) performed significantly worse on medication
management measures compared to healthy adults (HA; Foster, 2014; Manning et al., 2012). In our prior work (Pirogovsky
et al., 2014), we found that PD-MCI participants performed significantly worse than PD-NC and HA on the Medication
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Management Ability Assessment (MMAA; i.e., a role-play measure requiring participants to organize and follow a new
medication routine). However, PD-NC participants’ MMAA performance was equivalent to HA (Pirogovsky et al., 2014). Thus,
medication mismanagement may be differentially affected in cognitively impaired PD patients, even in milder forms like PD-
MCI. This could be relevant in regard to diagnostic criteria for PD-MCI, given that criteria excludes those with cognitive deficits
that significantly interfere with functional independence (Litvan et al., 2012).
To identify the specific medication-taking behaviors contributing to poor medication management, which are not identified by
the MMAA overall score, a supplemental MMAA scoring system was recently developed (Sumida et al., 2019b). This approach
calculates specific error types, including undertaking (i.e., taking too few MMAA medications), overtaking (i.e., taking too
many MMAA medications), and overall errors (i.e., sum of undertaking and overtaking errors; Sumida et al., 2019a). Depending
on the medication, overtaking and undertaking behaviors may have different, but important clinical implications. For example,
overtaking may result in toxicity or increased adverse effects, whereas undertaking PD medications could result in the appearance
of poor response and subsequent prescribing of higher dosages (Grosset, 2010). This approach has been validated in other
neurocognitive disorders. Individuals with amnestic mild cognitive impairment (aMCI) were found to commit more overtaking
and undertaking errors, whereas individuals with Huntington’s disease (HD; Sumida et al., 2019a, 2019b) only committed
more undertaking errors, compared to healthy older adult matched controls (mean age = 70.80) and HA (mean age = 56.45),
respectively. Notably, in an epilepsy sample, more undertaking errors (as well as poorer overall MMAA performance) related
to more real-world missed doses (Margolis et al., 2018). These findings suggest the MMAA process error approach could assist
in identifying specific real-world medication management errors in neurologic samples and aid with clinical decision-making
and interventions. As medication management is an essential part of treating PD symptoms, examining the utility of the MMAA
process error approach in this population, particularly in relation to cognition, has potentially important clinical implications.
Cognitive deficits are common in PD, and neurocognitive changes are a well-known risk factor for medication management
errors and nonadherence (Insel et al., 2006; Stoehr et al., 2008). However, the relationship between cognition and medication
management error types has not been investigated in PD. In non-PD samples, this relationship has recently been examined. For
example, in older adults with non-PD aMCI, more MMAA overtaking and overall errors related to worse processing speed, but
not verbal fluency, cognitive flexibility, working or delayed memory (Sumida et al., 2019b). In a cognitively mixed HD sample,
poorer prospective memory, but not delayed memory or executive functioning, was associated with increased overall errors on
the MMAA (Sumida et al., 2019a). In PD, only two studies have examined cognitive correlates of medication management
performance-based tasks, both of which examined overall performance (but not errors), in non-demented samples. One study
reported that learning, delayed memory, executive functioning and processing speed, but not working memory, were associated
with overall medication management performance (Manning et al., 2012), whereas the other study, which was conducted by
our group, found no relationships between cognition and the MMAA total (i.e., “original”) score in PD (Pirogovsky et al.,
2014). Expanding upon this prior work to examine specific error types could potentially identify treatment targets for tailored
interventions to improve medication adherence in PD.
Therefore, this study aimed to expand upon our prior work (Pirogovsky et al., 2014) by re-examining PD-MCI patient’s
medication management abilities using the MMAA process error score approach; and secondarily, exploring relationships
between these errors and specific domains of cognition. Given the similar neurocognitive frontal deficits observed in individuals
with HD and PD-MCI (Meireles & Massano, 2012; Paulsen, 2011), we hypothesized that the PD-MCI group would make
significantly more MMAA overall and undertaking errors compared to PD-NC and HA groups. Considering PD-MCI patients
frequently experience changes or impairments in executive functioning, attention, and memory (Meireles & Massano, 2012),
we also hypothesized that these abilities would be associated with MMAA performance.C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315 1309
Table 1. Demographic characteristics of study participants
HA (n = 74) PD-NC (n = 103) PD-MCI (n = 45)
M (SD) Range M (SD) Range M (SD) Range F p
Age (Years) 67.09 (8.76) 51–82 67.34 (8.15) 45–90 66.80 (8.98) 40–80 .065 .937
Education (Years) 15.97 (2.39) 11–20 16.77 (2.22) 12–20 16.21 (2.53) 12–20 2.67 .072
Sex (M/F)a 33/41 - 64/39c - 38/7c - HA;
Demographic, disease characteristics, questionnaires, and neuropsychological test performances are presented as the mean (standard deviation) of raw scores.
Frequency counts are presented for sex, single/multi-domain, Hoehn and Yahr stages. MDRS = Mattis Dementia Rating Scale; FTT = Finger Tapping Test
(t-Score); LED = Levodopa Equivalency Dosage
Method
A convenience sample of PD-NC (n = 102), PD-MCI (n = 45), and HA (n = 74) participants were retrospectively derived
from a parent study of cognition in PD. This dataset included participants from our 2013 (non-demented nPD = 98, nHA = 47;
61% overlap) and 2014 (nPD-MCI = 41, nPD-NC = 56, nHA = 47; 61% overlap) papers that assessed the MMAA (Pirogovsky
et al., 2013, 2014). The numbers of new subjects included were: PD-MCI = 19, PD-NC = 41, HA = 27. PD participants were
diagnosed using the UK Brain Bank Criteria (Hughes et al., 1993) by a board-certified neurologist specializing in movement
disorders and were recruited from the Movement Disorders Clinic at the the University of California San Diego and/or the
Veterans Affairs San Diego Healthcare System. The Department of Veterans Affairs Institutional Review Board approved the
study, and all participants provided verbal and written informed consent.
The Movement Disorder Society Task Force Guidelines criteria (Litvan et al., 2012) was used to classify non-demented PD
participants into PD-NC and PD-MCI, and PD-MCI into single-domain (n = 3) and multi-domain (n = 42). PD-MCI (level II)
was defined as self-reported or observed cognitive decline or complaints, informant-reported functional independence via the
Instrumental Activities of Daily Living items (Lawton & Brody, 1969) and impairment (i.e., ≤1.33 SD) on at least two tests
within one or more domains; each domain was comprised of two tests (see Table 2 notes for tests; Litvan et al., 2012), which was
a modification from prior diagnostic criteria (Pirogovsky et al., 2014). The frequency of participants whose performance reached
the impaired range on at least one test per domain is as follows: attention (n = 25), memory (n = 13), executive functioning
(n = 19), language (n = 21), and visuospatial (n = 23).
All PD participants were tested on their normal medication dosages. Daily levodopa equivalent dosage (LED) was calculated
using Tomlinson et al.’s (2010) criteria. Disease stage was determined using the modified Hoehn and Yahr Scale (H&Y; Goetz
et al., 2004). Objective motor function was assessed with the Finger Tapping Test (FTT; Reitan & Wolfson, 1985). All participants
completed the Geriatric Depression Scale (GDS; total scores range from 0–30; Yesavage et al., 1983) with higher scores
indicating greater depressive symptomatology. PD exclusion criteria included history of neurologic conditions other than PD,
secondary causes of PD, or history of psychosis or substance use disorder treatment. HA participants were recruited with local
newspaper advertisements or were PD participants’ spouses. HA exclusion criteria included history of neurologic conditions,
cognitive decline, major depression, psychosis, or substance abuse treatment. PD and HA participants with Mattis Dementia
Rating Scale (MDRS) scores ≤123 were excluded (Mattis, 1988). Participants who discontinued performance on the MMAA
were excluded (HA = 0, PD-NC = 1, PD-MCI = 2; n = 221 after exclusion). Participants whose performance was discontinued
due to reaching the allotted 15-min MMAA task time were included after their inclusion was substantiated via outlier analysis
(see Data Analysis). See Table 1 for study sample characteristics.1310 C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315
Participants completed the MMAA according to the MMAA manual version 4.1 (This manual is available upon email request
of the author; Patterson et al., 2002). Participants were presented with dosages and instructions for four mock medications. After
a 45 to 60-min delay, participants role-played a mock day by stating the dosage time and whether they would consume food prior
to physically handing the administrator pills (beans). The task ended when the participant stated their last dosage or 15 min had
passed. Participants are not allowed to organize the pills using pen or paper, or arrange the medications on the table (MMAA
manual version 4.1). Standard scoring procedures were used to calculate the MMAA original total score (i.e., binary scoring
ranging between 0 and 33 points with higher scores indicating better performance; MMAA manual version 4.1). However, to
assist in statistical model fit (see Data Analysis) the binary scoring was reversed (0 = a correct behavior; higher scores indicating
worse performance). Process error analysis (see Sumida et al., 2019a for further explanation) included calculating the number of
overtaking (i.e., count of pills over the target dose within an attempt and summed across attempts and medications), undertaking
(i.e., count of pills under the target dose within an attempt and summed across attempts and medications), and overall (overtaking
and undertaking sum) errors. Following correspondence with the MMAA author (T. Patterson, personal communication, n.d.),
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if a participant’s verbal and behavioral performance did not match (e.g., “I will take 1 BRB” but hands 2 BRB), scores reflected
only the behavior and disregarded the verbal statement. This represented a scoring alteration from our prior studies (Pirogovsky
et al., 2013, 2014). Discrepancies between verbal response and behavior occurred in four different instances (one instance per
participant) across the three groups (HA = 1, PD-NC = 1, PD-MCI = 2) and included a mixture of correct verbal and correct
behavior responses with no discernable pattern of undertaking or overtaking.
SPSS version 26 was used to analyze the data. MMAA variables were checked separately within diagnostic groups for outliers
using studentized deleted residual and Cook’s D (absolute values >3 and Cook’s D > 4/n were considered indicative of outliers;
Judd, McClelland, & Ryan, 2009). Results indicated three PD-NC participants overall errors (ranging from 12 to 16 errors) were
inconsistent to the overall PD-NC sample (n = 105) and dropped from the sample (final n = 102). Outliers were not observed
in the PD-MCI (n = 45) and HA samples (n = 74).
A nonparametric statistical approach was selected to analyze MMAA performance due to Shapiro–Wilks tests indicating
that MMAA variables were significantly non-normally distributed. Model specification began with analyzing differences in
demographic (age, sex, education), disease-related characteristics (MDRS, FTT, LED, H&Y, disease duration), and GDS
between the groups and separate chi square tests or one-way analysis of variance tests (ANOVA) indicated that sex, depression
level, and MDRS performance significantly differed between groups (see Table 1). Spearman correlations between MMAA
variables, the group variable (PD-NC, PD-MCI and HA), demographic, disease-related characteristics, and GDS were then
used to identify any relevant control variables for model specification. Sex significantly related to groups (rs = .29; p < .001),
overtaking (rs = −.18; p = .009), and overall variables (rs = −.15; p = .028). In the PD groups, disease duration significantly
related to undertaking (rs = −.17; p = .042), overall error (rs = −.20; p = .015), and MMAA original total (reverse coded;
rs = −.24; p < .001), but not the PD group (PD-NC and PD-MCI combined; p = .208). FTT, H&Y, LED, and GDS did not
relate to MMAA performance (all ps > .103). Together, these results indicated that only sex needed to be controlled in group
comparisons of MMAA performance.
Initially a Poisson regression was selected to analyze group differences on the MMAA variables, but goodness-of-fit tests
indicated over-dispersion in the overtaking errors (X 2 value/df = 2.0), undertaking errors (X 2 value/df = 2.8), and overall errors
(X 2 value/df = 2.5) and under-dispersion in the MMAA original total (X 2 value/df = .28). To improve model fit, a negative
binomial regression was selected and the MMAA original total was reverse coded (see Measures). Goodness-of-fit tests indicated
a good fit using the negative binomial distribution (overtaking X 2 value/df = .79, undertaking X 2 value/df = .89, overall error
X 2 value/df = .77, MMAA original total reverse coded X 2 value/df = 1.01). Therefore, negative binomial regressions were used
to examine the group effect on all MMAA outcome variables, while controlling for sex. The MMAA outcome measures were
examined with planned comparisons of HA versus each of the PD groups and PD-NC versus PD-MCI. Standardized mean
difference (SMD), an effect size, was calculated using the Coxe method (Coxe, West, & Aiken, 2009).
Composite scores were created to assess learning, delayed free recall memory (denoted as delayed memory here), attention,
executive functioning, visuospatial, and language and were derived from the neuropsychological measures outlined in Pirogov-
sky et al., (2014) and are displayed in Table 2. Measures, where higher scores indicated worse performance, were reverse coded
first for consistency. Internal z-scores were created using the entire sample (both PD and HA) for the tests within each domain
and then averaged to create respective composites. Exploratory Spearman correlations with standardized neuropsychological
composites were performed only for MMAA variables demonstrating significant differences between groups. A p-value of .05
was used.C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315 1311
Table 2. Neuropsychological composites of study participants
HA (n = 74) PD-NC (n = 102)c PD-MCI (n = 45)
M (SD) Range M (SD) Range M (SD) Range F p
Learning ab
.26 (.76) −1.85–1.87 .09 (.68) −2.04–1.76 −.63 (.70) −2.59–.60 23.161312 C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315
Table 3. Negative binomial regression results
Statistics
B IRR SE Wald X2 p SMD
Overtaking Errors Omnibus Test - - - 9.32 .025 -
Group - - - 3.50 .173 -
Sex - - - 7.73 .005 -
Undertaking Errors Omnibus Test - - - 8.43 .038 -
Group - - - 8.91 .012
PD-MCI vs. HA .83 2.29 .30 7.77 .005 .82
PD-NC vs. HA .02 1.02 .27 .01 .943 .01
PD-MCI vs. PD-NC −.81 .45 .32 6.45 .011 .43
Sex - - - .23 .630
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Overall Error Score Omnibus Test - - - 13.47 .004 -
Group Effect - - - 9.61 .008 -
PD-MCI vs. HA .49 1.64 .22 5.24 .022 .50
PD-NC vs. HA −.14 .87 .19 .54 .460 .10
PD-MCI vs. PD-NC −.63 .53 .21 9.31 .002 .40
Sex - - - 4.88 .027
MMAA Total Score Omnibus Test - - - 15.02 .002 -
Group - - - 12.20 .002 -
PD-MCI vs. HA .70 2.00 .22 10.33 .001 .81
PD-NC vs. HA .04 1.05 .19 .05 .821 .04
PD-NC vs. PD-MCI −.65 .52 .22 9.05 .003 .42
Sex - - - 1.52 .218 -
Table 4. Spearman correlations between undertaking, overall error and MMAA original scores and cognitive composites
Executive
Learning Delayed Memory Attention Functioning Visuospatial Language
rs p rs p rs p rs p rs p rs p
PD-NC (n = 102) a
Undertaking Errors .04 .70 .05 .62 .02 .84 −.01 .90 .06 .53 .16 .10
Overall Error −.09 .37 −.08 .42 −.10 .31 −.09 .38 .04 .69 .02 .89
MMAA Original −.10 .31 −.06 .53 −.03 .78 −.00 .99 −.01 .91
PD-MCI (n = 45)b
Undertaking Errors −.11 .46 −.22 .15 −.20 .21 .17 .29 −.11 .49 −.11 .49
Overall Errors −.21 .16 −.36 .02 −.28 .07 −.08 .62 −.21 .18 −.28 .07
MMAA Original −.26 .08 −.37 .01 −.16 .31 .01 .97 −.08 .62 −.19 .20
HA (n = 74)
Undertaking Errors −.11 .34 −.12 .29 −.11 .34 −.11 .37 −.01 .91 −.11 .36
Overall Errors −.12 .31 −.18 .13 −.08 .48 −.18 .13 −.09 .45 .01 .94
MMAA Original −.12 .30 −.19 .11 −.09 .46 −.16 .17 −.12 .29 −.04 .75
Notes: MMAA original score was reversed coded, such that higher scores indicated worse performance. The cognitive composites were coded, such that higher
scores indicated better performance. a Correlations with attention composite has an n = 103; b correlations with attention and executive functioning composites
have an n = 44
The current study provides preliminary evidence for the utility of the MMAA process error approach in detecting
specific medication mismanagement behaviors in PD-MCI. Findings suggest that despite diagnostic criteria stipulating intact
instrumental functioning, some PD-MCI participants were observed having medication management difficulties resulting in
substantial increases in errors, particularly undertaking. However, the MMAA has not been ecologically validated in PD, and
performance on such lab-based measures may not reflect real-world medication management. That said, our study indicates
cognitive status should be considered when developing a medication management plan for PD-MCI patients, as errors could
result in uncontrolled symptoms, and subsequently affect treatment decisions. Thus, medication performance-based measures
could assist providers in monitoring PD patients on specific medication adherence behaviors.
Our findings of an association between poorer MMAA performance (overall errors and original total) and worse delayed
memory performance, but not with other cognitive domains, indicate that delayed recall memory is the most critical cognitive
ability for medication management in PD-MCI. This is consistent with prior studies that showed worse delayed memory related
to poorer performance on the MMAA (Sumida et al., 2019b) and another medication management task (Manning et al., 2012)C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315 1313
in non-PD aMCI and non-demented PD samples, respectively. Interestingly, our prior work in PD did not identify any MMAA
cognitive correlates (Pirogovsky et al., 2014), although methodological differences (i.e., use of cognitive composites versus
individual test scores, an alternative MMAA scoring system, differences in diagnostic criteria, and larger sample sizes) may
explain this. That said, in our previous studies with another movement disorder populations (i.e., HD; Sumida et al., 2019a),
medication mismanagement was significantly associated with worse prospective memory, which suggests that examining other
cognitive abilities, such as prospective memory in future studies, could help clarify the cognitive processes underlying medication
management. Nevertheless, our current results further our understanding of medication management by demonstrating delayed
memory (without cues) is associated with medication management (void of compensatory devices) in PD-MCI. Thus, PD-MCI
patients, particularly those with memory problems, may require additional support in managing their medications. However, due
to various uncontrolled factors outside of the testing environment, real-world extrapolations from these findings are limited. For
example, increased motor symptoms during the wearing off period have the potential to cue a patient to take their medication,
which may, in turn, ameliorate a tendency toward undertaking medications in real life. Still, results provide guidance for further
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assessment and intervention (i.e., implementation of behavioral strategies such as alarms on cell phones for remembering to take
medications) for PD patients who are cognitively at risk.
Contrary to expectations, medication management was not related to executive functioning or attention. It is possible that our
measures did not capture the executive function/attentional abilities that may be necessary for medication management in PD.
As executive functions/attention encompasses a wide range of abilities, future studies may wish to examine executive functions
more broadly (e.g., sustained attention). Also contrary to expectations, although delayed memory was associated with overall
errors and MMAA performance in PD-MCI, undertaking did not statistically relate to any cognitive abilities. This is consistent
with prior studies demonstrating a group difference, but no significant cognitive correlates of undertaking behaviors in clinical
or healthy older adult samples (Sumida et al., 2019a, 2019b). Thus, although the diagnosis of PD-MCI may be associated with
undertaking errors, performances in specific cognitive domains may not independently predict undertaking errors on the MMAA.
As such, studies with a more detailed neurocognitive battery and larger samples are warranted to better understand the nature
of undertaking in PD.
There are a few limitations to consider. Unlike real-world medication adherence, the MMAA does not allow participants to
use common compensatory strategies like a medication box (Boron, Rogers, & Fisk, 2013). Because the relationship between PD
MMAA performance and real-world adherence remains unexamined, this study’s results may not accurately depict real-world
medication adherence. Therefore, ecological validity research is needed to discern the MMAA’s clinical merit. Second, the
sample composition (i.e., primarily White, highly educated and mostly multi-domain PD-MCI) may affect the generalizability
of these findings to other subpopulations of PD patients. Subsequent investigations should consider including PD patients with
varying levels of cognitive impairment and greater ethnic and educational diversity. Although not a primary study question,
we found that across groups, men committed more overtaking, but not undertaking errors. Although it is beyond the scope of
this study, future examination of the relationships among sex, cognitive status, and medication error type (e.g., undertaking vs.
overtaking) in PD is recommended. Future work with larger sample sizes should also include an examination of specific error
types for each MMAA medication, which may identify medication instructions that could be more challenging to manage or
certain clinical group(s) that may be more prone to undertaking versus overtaking.
In summary, this study extends prior research by examining error types in PD-MCI on a performance-based medication
management measure. Our findings indicate that PD-MCI performs significantly worse on the MMAA and commit more
undertaking errors when compared to PD-NC and HA; the latter of whom did not differ. These results may reflect real-
world medication mismanagement (i.e., without external compensatory aids) possibly relating to PD-MCI patients committing
undertaking errors in their real life. However, future ecological validity studies will need to examine whether undertaking errors
translates to real-world nonadherence. Nevertheless, objective measures of medication management like the MMAA may be
a vital clinical tool for detecting PD-MCI patients needing assistance with medication management. These findings add to a
continued line of research demonstrating the sensitivity of the process error approach in capturing more nuanced aspects of
medication management errors.
Contributors
All authors made substantial contributions to the development of the study, analysis and/or interpretation of data, drafting of
the article, and/or providing critical review of the manuscript, and have approved the final version.
Acknowledgements
We thank Dr. Pirogovsky Turk and members of the NeuroCognition and Movement Laboratory for their help in collecting
and scoring data and Maureen O’Donnell for her statistical consultation. We would like to thank all the participants for their
contributions to this study.1314 C.A. Sumida et al. / Archives of Clinical Neuropsychology 36 (2021); 1307–1315
Funding
This work was supported by grants from the U.S. Department of Education: Graduate Assistance in Areas of National
Need [P200A150115 to C.S.] and the Department of Veterans Affairs, Veterans Health Administration, Office of Research
and Development CSR&D Merit Review Award [5I01CX000813-04 to J.V.F.] and RR&D Merit Award [1 I01 RX001691-01A1
to D.M.S.].
Conflicts of Interest
None.
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