Neuropsychology 2014, Vol. 28, No. 2, 229 –237

In the public domain DOI: 10.1037/neu0000045

Instrumental Activities of Daily Living Are Impaired in Parkinson’s Disease Patients With Mild Cognitive Impairment Eva Pirogovsky and Dawn M. Schiehser

Kristalyn M. Obtera and Mathes M. Burke

Veterans Affairs, San Diego Health Care System, San Diego, California; and University of California, San Diego

Veterans Affairs, San Diego Health Care System, San Diego, California

Stephanie L. Lessig, David D. Song, Irene Litvan, and J. Vincent Filoteo Veterans Affairs, San Diego Health Care System, San Diego, California; and University of California, San Diego Objective: Although it is well known that Parkinson’s disease (PD) with dementia results in functional decline, little is known about the impact of mild cognitive impairment in PD (PD-MCI) on day-to-day functioning. Method: Forty-one individuals with PD-MCI, 56 PD patients with normal cognition (PD-NC), and 47 healthy older adults were administered two performance-based measures of instrumental activities of daily living (IADLs) that evaluated medication and financial management. Informants of the PD patients were also administered an IADL questionnaire. Results: There were no significant differences between PD-NC and healthy older adults on the performance-based measures of medication and financial management. However, PD-MCI patients demonstrated significantly lower scores on the performance-based measures of medication and financial management compared with healthy older adults. PD-MCI patients were also impaired compared with PD-NC patients on performance-based medication management, but no difference between these groups was observed for ability to manage finances. Performance-based financial and medication management did not correlate with scores on neuropsychological measures in PD-MCI patients. PD-MCI and PD-NC patients showed comparable scores on the informant-based IADL questionnaire. Conclusions: Performance-based measures of IADLs, particularly medication management ability, are sensitive to subtle functional declines in PD-MCI. Although impairment in performance-based measures is associated with cognitive status in PD, IADLs may be a separate domain of functioning from cognitive functioning in PD-MCI as these measures did not correlate with performance on the neuropsychological measures. Overall, performancebased assessment of IADLs may add to the clinical evaluation of PD-MCI. Keywords: Parkinson’s disease—mild cognitive impairment (PD-MCI), instrumental activities of daily living (IADLs), performance-based functional measures

is common, with approximately 19 –53% of nondemented PD patients meeting criteria for this condition (Litvan et al., 2011). Understanding the earliest clinical and neuropathological changes in PD-MCI is a critically important area of research that may lead to early intervention to help prevent or delay the onset of PDD and improve quality of life for patients and caregivers. However, the study of PD-MCI has been limited by considerable heterogeneity in the definition of PD-MCI. To address this issue, the Movement Disorder Society commissioned a task force to outline diagnostic criteria for PD-MCI (Litvan et al., 2012). These new diagnostic guidelines provide a uniform framework that researchers can now use to study PD-MCI. One critical area of future research outlined by the Movement Disorders Society task force is a better understanding of the effects of PD-MCI on day-to-day functioning and identifying measures that are sensitive to initial changes in everyday functioning (Litvan et al., 2011, 2012). Investigating the clinical impact of PD-MCI on everyday functioning has implications for the development of interventions to improve day-to-day functioning. There is growing evidence that nondemented PD patients demonstrate functional impairment, particularly in higher order instrumental activities of daily living (IADLs) such as medication management, financial

In recent years, mild cognitive impairment in Parkinson’s disease (PD-MCI), defined as cognitive decline that is not normal for age but with essentially normal functional activities, has been recognized as a distinct clinical entity and a potential prodromal stage of PD dementia (PDD; Goldman & Litvan, 2011). PD-MCI

This article was published Online First January 13, 2014. Eva Pirogovsky and Dawn M. Schiehser, Veterans Affairs, San Diego Health Care System, San Diego, California and Department of Psychiatry, University of California, San Diego; Kristalyn M. Obtera and Mathes M. Burke, Veterans Affairs, San Diego Health Care System, San Diego, California; Stephanie L. Lessig, David D. Song, and Irene Litvan, Veterans Affairs, San Diego Health Care System, San Diego, California; Department of Neurosciences, University of California, San Diego; J. Vincent Filoteo, Veterans Affairs, San Diego Health Care System, San Diego, California; Department of Psychiatry, University of California, San Diego. We thank all of the participants and their caregivers for their contributions to this study. This study was supported by Veterans Affairs Merit Award CX12004 to J. Vincent Filoteo. Correspondence concerning this article should be addressed to J. Vincent Filoteo, UC San Diego/VA, 3350 La Jolla Village Drive, San Diego, CA 92161. E-mail: [email protected] 229

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management, and food preparation (Manning et al., 2012; Pirogovsky, Woods, Filoteo, & Gilbert, 2012; Young, Granic, Yu Chen, Haley, & Edwards, 2010). These studies compared samples of nondemented PD patients with normal participants, but did not characterize PD patients based on PD-MCI versus PD patients with normal cognition (PD-NC). To date, only one study to our knowledge has examined everyday functioning in PD-MCI and found impairment on a reportbased global disability measure in PD-MCI compared with PD-NC (Leroi, McDonald, Pantula, & Harbishettar, 2012). Although this study provides an important first step in better understanding the impact of PD-MCI on everyday function, there were important limitations. For example, the study used a global disability measure that assesses functioning with one score (ranging from 0% to 100%, completely dependent to completely independent), which does not allow for an assessment of different aspects of everyday functioning, such as IADLs. Because IADLs are more complex functional abilities that rely on higher level cognitive processes compared with basic ADLs that primarily emphasize physical demands (Cahn et al., 1998; Goldberg et al., 2010), measures examining IADLs may be more sensitive to mild cognitive changes in PD. Another important limitation of the Leroi et al. (2012) study, along with most studies examining functional impairment in the literature, is the use of self-report and informant-report measures. These methods of assessment can have various forms of bias, such as emotional factors and lack of insight, which can lead to underestimation or overestimation of a patient’s actual functional abilities (Loewenstein et al., 2001). In contrast, performance-based measures, which assess functional skills directly by having a participant complete a real-world task analogue in the laboratory, provide an attractive alternative. These measures have several advantages including increased ecological validity, minimal reliance on patient or caregiver insight, focus on real-life skills that may be targets for intervention, and standardized assessment and scoring (Bangen et al., 2010; Moore, Palmer, Patterson, & Jeste, 2007). Performance-based measures of IADLs may increase sensitivity in detecting subtle functional impairment in PD-MCI. The current study examined everyday functioning using performance-based IADL measures assessing medication management and financial management in patients diagnosed with PDMCI and PD-NC and healthy older adults. These specific IADLs were chosen because they are often highly relevant in the lives of PD patients. Medication management refers to the ability to carry out a prescribed medication regimen. In PD, medication regimens are often complex and burdensome. Many anti-Parkinsonian medications are taken multiple times a day, with more than half of PD patients taking two to four medications three or four times daily (Leoni et al., 2002). Impairment in medication management may be related to medication nonadherence, which has been linked with an increased risk of worsening PD symptoms (Kulkarni et al., 2008). Financial management involves the ability to count change, balance a checkbook, and perform other tasks necessary to adequately manage finances. Impaired financial management may lead to difficulty paying bills and resulting financial problems such as poor credit or losing financial assets (e.g., car, home, etc.). There is also evidence from non-PD populations that impaired financial management can result in significant caregiver burden (Razani et al., 2007). Overall, medication and financial manage-

ment can have significant implications for functional independence, health, and caregiver burden. The present study also compared scores on the performance-based IADL measures to a widely used informant-based measure IADL questionnaire (Lawton & Brody, 1969). It was hypothesized that PD-MCI patients would show worse scores on all functional measures compared with PD-NC patients and healthy older adults, and owing to their greater sensitivity, that performance-based measures would show the greatest differences between groups.

Method Participants The Department of Veteran Affairs Institutional Review Board approved the current study, and all participants gave verbal consent and signed the Institutional Review Board-approved consent document. The study sample consisted of 97 PD patients and 47 healthy older adults. PD patients were recruited from the Parkinson’s Disease Research Subject Database of the San Diego Veterans Affairs Health Care System/University of California, San Diego, and were diagnosed by a board-certified neurologist who specializes in movement disorders using United Kingdom Brain Bank criteria (Hughes, Daniel, Kilford, & Lees, 1992). Healthy older adult participants were either recruited via an advertisement in local newspapers or were spouses of PD patients. Most of the PD patients were prescribed anti-Parkinsonian medications and were on their normal regimen of dopaminergic agents at the time of testing (medications listed in Table 1). Only one patient was taking an anticholinergic (Procyclidine hydrochloride). Exclusion criteria for PD participants in the study included history of neurologic conditions other than PD, diagnosis of dementia based on formal Movement Disorders Task Force criteria (Emre et al., 2007), major depressive disorder prior to the diagnosis of PD, severe psychiatric disorders (e.g., schizophrenia), substance use disorders, or deep brain stimulation (DBS) surgery. Exclusion criteria for the healthy older adult participants in the study included history of neurologic condition, history of psychosis or major depression, or history of treatment for substance abuse. Some of the data used in this study were also included in a previous manuscript (currently submitted) that focused on predictors of performance-based IADLs in a mixed sample of PD patients.

PD-MCI Classification PD-MCI was diagnosed based on Movement Disorders Society task force criteria using Level 2 criteria for cognitive impairment, which involves a comprehensive neuropsychological assessment. The PD-MCI criteria include (a) cognitive decline reported by the patient or caregiver, (b) cognitive deficits not severe enough to significantly interfere with functional independence, and (c) cognitive deficits on formal neuropsychological testing (Litvan et al., 2012). Report of cognitive decline was assessed using the Executive Dysfunction subscale of the informant version of the Frontal Systems Behavior Scale (Grace & Malloy, 2001) and the Informant Questionnaire on Cognitive Decline in the Elderly (Jorm & Jacomb, 1989), with the exception of two items that assess functional decline as opposed to cognitive decline (Items 23 and 24).

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Table 1 Demographic and Clinical Characteristics of Study Participants Variable Mean (SD) age (years) Mean (SD) education (years)a Gender (male/female), %b Ethnicity, % Caucasian Mean (SD) Geriatric Depression Scale scoreb,c Mean (SD) Mattis Dementia Rating Scale scorea,b Mean (SD) duration of PD (years) UPDRS Modified Hoehn and Yahr stage, % Stage 0 Stage 1 Stage 1.5 Stage 2 Stage 2.5 Stage 3 Stage 4 Stage 5 Medication type, % L-dopa Dopamine agonist MAO-B inhibitor (Selegiline or Rasagaline) Amantadine Procyclidine hydrochloride

PD-MCI (n ⫽ 41)

PD-NC (n ⫽ 56)

HA (n ⫽ 47)

69.2 (7.1) 15.8 (2.8) 30/11 93 6.2 (5.7) 136.7 (4.0) 5.1 (4.3) 23.1 (12.3)

66.7 (7.5) 17.1 (2.4) 33/23 98 6.2 (5.1) 140.1 (2.9) 6.0 (6.0) 25.6 (12.3)

67.1 (8.7) 16.4 (2.5) 22/25 91 3.1 (4.1) 140.4 (3.6) — —

0 21.4 0 57.1 4.8 9.5 4.8 2.4

1.8 12.7 3.6 54.5 7.3 18.2 1.8 0

— — — — — — — —

66 70 27 07 02

68 62 57 13 00

— — — — —

Note. PD-MCI ⫽ Parkinson’s disease with mild cognitive impairment; PD-NC ⫽ Parkinson’s disease patients without cognitive impairment; HA ⫽ healthy adult comparison group; UPDRS ⫽ Unified Parkinson’s Disease Rating Scale. Post hoc for the following comparisons at p ⬍.05: a PD-MCI versus PD-NC. b PD-MCI versus HA. c PD-NC versus HA.

For a subset of participants that did not identify an informant (n ⫽ 18), self-reported cognitive decline was assessed with the cognitive impairment items (confusion, indecision, reduced concentration, memory; Adams, Matto, & Sanders, 2004) from the Geriatric Depression Scale (GDS; Yesavage et al., 1982–1983) and the Cognition subscale of the PD Questionnaire 39-item version (Peto, Jenkinson, & Fitzpatrick, 1998). To exclude patients with PDD, functional independence was assessed with the IADL items (Items 1–9) on the Lawton and Brody Activities of Daily Living scale (IADL questionnaire; Lawton & Brody, 1969) and Items 23 and 24 on the Informant Questionnaire on Cognitive Decline in the Elderly (items representing functional decline). For the subset of participants that did not identify an informant, functional independence was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS; Goetz et al., 2007) Part 1, Item 1.1, which assesses the effect of cognitive impairment on daily functioning. The comprehensive neuropsychological battery used to diagnose PD-MCI included tests from five cognitive domains including attention and working memory, language, memory, visuospatial functioning, and executive functioning (see Table 2 for the specific measures included in each domain). As per Movement Disorder Society criteria, PD-MCI was diagnosed if there was impairment on at least two neuropsychological tests, represented by either two impaired tests in one cognitive domain or one impaired test in two different cognitive domains. Movement Disorder Society criteria suggest a cutoff for impairment on neuropsychological tests of 1 to 2 standard deviations below appropriate norms. The cutoff for impairment in the current study was set at z ⱕ ⫺1.33. This cutoff

score was chosen because the published standard scores available for neuropsychological measures in the battery are on varying scales (e.g., scaled score, T score, z-score). A standard score that is equivalent to a z-score of ⫺1.33 can be computed for each of the other scales (scaled score ⫽ 6, T score ⫽ 37); therefore, this z-score cutoff allowed for consistency among the different standard scores. PD patients were classified by Drs. Pirogovsky, Schiehser, and Filoteo as PD-MCI or PD-NC by three neuropsychologists. Two of the three neuropsychologists were randomized to classify each patient, so that each patient was given a diagnosis by two of the three neuropsychologists. After each rater independently classified the PD patients, all three raters convened at a consensus conference to discuss any discrepancies in diagnoses. For each discrepancy, the rater who did not evaluate the particular patient came up with the final diagnosis. Interreliability for the three raters was excellent (kappas for each pair of raters ⫽ 0.87, 0.85, and 1.0, ps ⬍ .001; average kappa between the three raters ⫽ 0.91).

Functional Measures Performance-based measures. Medication management. The Medication Management Ability Assessment (MMAA) is a standardized performance-based measure (Patterson et al., 2002). The MMAA is a role-play task in which the examiner presents a standardized description of the medication regimen for four mock medications. Participants are shown four plastic pill bottles with labels stating the name of the

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Table 2 Neuropsychological Performance of Study Participants and Correlations Between Scores on Neuropsychological Measures and Performance-Based Measures of Instrumental Activities of Daily Living

Measure Attention D-KEFS CWIT Color Naming D-KEFS CWIT Word Reading DOTA Memory CVLT–II 1–5 Total CVLT–II Long Delayed Free Recall WMS–III Logical Memory I WMS–III Logical Memory II WMS–III Visual Reproduction I WMS–III Visual Reproduction II Executive function WCST Perseverative errors WCST Categories D-KEFS CWIT Inhibition D-KEFS CWIT Inhibition/Switching Language MDRS Similarities D-KEFS Letter Fluency D-KEFS Category Fluency Visuospatial JLOT WMS–III Visual Reproduction Copy

PD-MCI (n ⫽ 41)

PD-NC (n ⫽ 56)

HA (n ⫽ 47)

Overall F p value

Correlation (r) with performance-based medication management (MMAA)

Correlation (r) with performance-based financial management (UPSA Finances)

35.9 (6.4) 24.8 (4.6) 5.0 (1.8)

30.8 (5.4) 22.5 (3.3) 6.7 (1.8)

29.3 (6.6) 21.4 (3.4) 7.1 (2.1)

⬍.001a,b ⬍.001a,b ⬍.001a,b

⫺.19 ⫺.23 .04

⫺.19 .04 .02

39.1 (10.0) 7.7 (3.6) 34.7 (9.1) 20.0 (8.1) 67.6 (15.3) 40.5 (17.8)

48.9 (8.7) 10.6 (3.4) 42.2 (8.5) 25.6 (7.2) 79.9 (14.5) 53.8 (19.1)

50.6 (12.1) 11.1 (3.5) 45.1 (9.7) 28.8 (8.4) 76.8 (14.5) 53.6 (21.8)

⬍.001a,b ⬍.001a,b ⬍.001a,b ⬍.001a,b ⬍.001a,b .002a,b

.20 .16 .29 .27 .01 ⫺.08

.17 .24 .19 .22 ⫺.21 ⫺.21

26.5 (16.8) 3.6 (2.3) 77.5 (24.2) 85.1 (30.0)

10.1 (7.6) 5.7 (1.0) 60.1 (13.0) 66.8 (14.2)

17.6 (15.4) 5.0 (1.5) 59.4 (14.4) 65.4 (20.8)

⬍.001a,b,c ⬍.001a,b ⬍.001a,b ⬍.001a,b

.04 .08 ⫺.08 ⫺.04

⫺.08 ⫺.00 ⫺.16 ⫺.06

6.3 (1.3) 33.9 (13.7) 33.8 (9.4)

7.2 (1.0) 43.6 (9.8) 41.4 (8.4)

7.1 (1.2) 42.2 (11.7) 42.2 (9.1)

.001a,b ⬍.001a,b ⬍.001a,b

⫺.07 .17 .17

.28 ⫺.09 ⫺.08

24.3 (5.1) 98.5 (3.7)

25.5 (3.3) 100.0 (3.0)

25.7 (4.2) 98.8 (4.3)

.15 .20

.09 ⫺.04

.16 ⫺.21

Note. Data represent mean raw scores and standard deviations. PD-MCI ⫽ Parkinson’s disease with mild cognitive impairment; PD-NC ⫽ Parkinson’s disease patients without cognitive impairment; HA ⫽ healthy adult comparison group. Correlations between neuropsychological measures and the functional measures are within the PD-MCI group. D-KEFS ⫽ Delis–Kaplan Executive Function System (Delis, Kaplan, & Kramer, 2001); CWIT ⫽ Color Word Interference Test; DOTA ⫽ Adaptive Digit Ordering Test (Werheid et al., 2002); CVLT–II ⫽ California Verbal Learning Test—Second Edition (Delis, Kramer, Kaplan, & Ober, 2000); WMS–III ⫽ Wechsler Memory Scale—Third Edition (Tulsky, Zhu, & Ledbetter, 1997); WCST ⫽ Wisconsin Card Sorting Test (Heaton, Chelune, Talley, Kay, & Curtiss, 1993); MDRS ⫽ Mattis Dementia Rating Scale (Mattis, 1988); JLOT ⫽ Judgment of Line Orientation Test (Benton, Sivan, Hamsher, Vamey, & Spreen, 1983); MMAA ⫽ Medication Management Ability Assessment; UPSA Finances ⫽ UCSD Performance-Based Skills Assessment Financial Skills subscale. Post hoc for the following between group comparisons at p ⬍.05: a PD-MCI ⬍ PD-NC. b PD-MCI ⬍ HA. c PD-NC ⬎ HA.

medication and the frequency and amount of medication to be taken daily, as well as whether the medication should be taken with food or on an empty stomach. Following a 1-hr delay, participants are given four medication bottles and prompted to walk through their day, saying when they would wake up, eat their meals, and take their medications, handing over the correct number of mock pills (beans) to the examiner. Points are given for taking the correct number of pills for each medication, correctly taking it with or without food, and taking medication the correct number of times a day (range ⫽ 0 –33). The MMAA has shown excellent test–retest reliability and evidence of predictive validity and discriminative validity in non-PD samples (Patterson et al., 2002). Managing finances. The Financial Skills subscale of the UCSD Performance-Based Skills Assessment (UPSA Finances; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001) is a performance-based measure in which participants are asked to complete two tasks: (1) They are provided with coins and bills and are asked to count out certain amounts and make change, and (2) they are provided with a mock bill from the local utility company and are required to make out a check. Points are given for each correct element of this process (e.g., count of money is correct, change given is correct, check is made out to the utility company, the written amount corresponds to the bill, check is signed, etc.;

range ⫽ 0 –11). The UPSA summary score (a score representing the sum of all subscales) has excellent interrater reliability (Patterson et al., 2001), discriminative validity (Goldberg et al., 2010; Patterson et al., 2001), and concurrent validity (Patterson et al., 2001). The scoring system used for each of the performance-based measures did not penalize for motor factors (e.g., poor penmanship on the check writing task). Informant-based measure. The IADL questionnaire (Lawton & Brody, 1969) is a widely used measure that is completed by a patient’s informant. The IADL questionnaire includes items assessing eight domains of functioning: (1) responsibility for medications, (2) ability to handle financial matters, (3) mode of transportation, (4) telephone use, (5) shopping, (6) housekeeping, (7) food preparation, and (8) laundry. For each item, the caregiver selects a statement that best represents the patient’s current functioning. Each item includes responses that reflect a spectrum of functioning, including no functional impairment, mild levels of functional impairment, and more severe functional impairment (scores ranging from 0 to 2, with lower scores indicating greater severity of impairment). For example, for the item representing responsibility for own medications, the responses include (1) “Is responsible for taking medication in correct dosages at correct times,” (2) “Takes responsibility if medication is prepared in

EVERYDAY FUNCTION AND PARKINSON’S DISEASE

advance in separate dosages,” and (3) “Is not capable of dispensing own medication.” The total score was used for analyses (range ⫽ 0 –16, with lower scores representing more severe functional impairment). The IADL questionnaire was administered to PD participants only given that healthy control participants did not have an informant.

Statistical Analyses Group differences on neuropsychological, informant-based functional measures, and performance-based functional measures were analyzed using one-way analyses of variance (ANOVAs) followed by Tukey post hoc comparison tests. Pearson r correlational analyses (or point biserial correlations for dichotomous variables) were used to examine relationships between demographic and disease variables, performance on neuropsychological measures, and functional measures in the PD-MCI group. Pearson r correlational analyses were used to examine relationships between the performance-based and informant-based functional measures in the PD-MCI group. Given that the focus of the current study was the nature and correlates of IADLs in PD-MCI, all correlational analyses were conducted only in the PD-MCI group. Follow-up analyses were conducted to examine relationships between specific items on the informant-based IADL questionnaire (i.e., items about medication management and ability to manage finances) and the respective performance-based measures of these aspects of IADLs. Analyses of the distribution of scores on the finance and medication management items on the IADL measure showed that informants’ ratings ranged from 1 to 2 on medication management and financial management items (no informant rated a patient at 0). Thus, the relationship between specific IADL items and the performance-based measures were examined with Mann–Whitney U tests, with the rating on the IADL item as the independent variable and scores on the performance-based measure as the dependent variable. The critical alpha level for all analyses was set at .05.

Results Group Characteristics Forty-one patients met the Movement Disorders Society Task Force criteria for PD-MCI and 56 met criteria for PD-NC. In the

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PD-MCI group, 98% were classified as multidomain MCI and 2% were classified as single-domain MCI. Demographic and clinical characteristics of the three groups (PD-MCI, PD-NC, healthy older adults) are shown in Table 1. The study groups were comparable in age and ethnicity (see Table 1). PD-MCI patients had a lower level of education compared with PD-NC patients, and PD-NC patients had a higher proportion of men than the healthy older adult group (see Table 1). Both PD-MCI and PD-NC groups had a higher level of self-reported depressive symptoms compared with healthy older adults (GDS; see Table 1). Patients’ motor disease severity was tested off medication using the UPDRS Part 3 and there were no significant differences in motor symptom severity between PD-MCI and PD-NC patients (see Table 1). There was also no significant difference in disease duration between the two groups. PD-MCI patients demonstrated lower scores in global cognitive functioning (Mattis Dementia Rating Scale; Mattis, 1988) than PD-NC patients and healthy older adults (see Table 1). In addition, PD-MCI patients had significantly lower scores than PD-NC patients and healthy older adults on neuropsychological measures assessing attention and working memory, executive function, memory, and language, but no significant group differences were found in measures of visuospatial function (see Table 2).

Group Differences on Functional Measures A one-way ANOVA revealed a significant main effect of group (PD-MCI, PD-NC, healthy older adults) on the performance-based measure of medication management, F(2, 141) ⫽ 5.62, p ⫽ .004. Tukey post hoc comparison tests revealed that PD-MCI patients had significantly lower scores on performance-based medication management compared with both PD-NC patients and healthy older adults (see Table 3). There were no significant differences observed between PD-NC patients and healthy older adults on this measure. A one-way ANOVA revealed a significant main effect of group (PD-MCI, PD-NC, healthy older adults) on the performance-based measure of financial management, F(2, 141) ⫽ 5.04, p ⫽ .008. Tukey post hoc comparison tests showed that the PD-MCI group had significantly lower scores on performancebased financial management compared with the healthy older adult group, but the difference between PD-MCI and PD-NC groups did not reach statistical significance (see Table 3). No significant differences were observed between PD-NC patients and healthy

Table 3 Scores on Instrumental Activities of Daily Living Measures in Study Participants Measure

PD-MCI (n ⫽ 41)

PD-NC (n ⫽ 56)

HA (n ⫽ 47)

d1

d2

MMAAa,b UPSA Financesb IADL questionnaire

29.4 (3.9) 9.9 (0.9) 13.9 (2.3)

31.2 (3.3) 10.1 (0.8) 14.2 (2.1)

31.5 (2.1) 10.4 (0.5) —

0.49 0.23 0.13

0.68 0.67 —

Note. Data represent mean raw scores and standard deviations. PD-MCI ⫽ Parkinson’s disease with mild cognitive impairment; PD-NC ⫽ Parkinson’s disease patients without cognitive impairment; HA ⫽ healthy adult comparison group; d1 ⫽ Cohen’s d effect size for mean differences between PD-MCI and PD-NC groups on the functional measures; d2 ⫽ Cohen’s d effect size for mean differences between PD-MCI and HA groups on the functional measures; MMAA ⫽ Medication Management Ability Assessment; UPSA Finances ⫽ UCSD Performance-Based Skills Assessment Financial Skills subscale; IADL questionnaire ⫽ Instrumental Activities of Daily Living Scale. For the IADL questionnaire, n ⫽ 33 for the PD-MCI group and n ⫽ 46 for the PD-NC group. Post hoc for the following comparisons at p ⬍.05: a PD-MCI ⬍ PD-NC. b PD-MCI ⬍ HA.

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older adults on this measure. In addition, there was no significant correlation between the two performance-based functional measures in the PD-MCI group (ps ⬎ .05). There were no significant differences between the PD-MCI and PD-NC groups on the informant-based IADL questionnaire, F(1, 77) ⫽ 0.36, p ⫽ .55 (see Table 3). Follow-up analyses of the specific items on the informant-based IADL measures showed that only three informants on the medication management item and six informants on the financial management item endorsed any functional decline on these items in the PD-MCI patients (i.e., a score less than 2). There were no significant correlations between the total score on the informant-based IADL questionnaire and either of the performance-based functional tasks in PD-MCI patients (ps ⬎ .05). In addition, analyses showed no association between the specific informant-based IADL item about medication management and the performance-based measure of medication management, nor was there an association between the IADL item about financial management and the performance-based measure of financial management in PD-MCI patients (ps ⬎ .05). Because there was a significant difference between groups in education level, we conducted analyses of covariance with group as the independent variable, education level as the covariate, and scores on the functional measures as the dependent variables to examine whether group differences in education level accounted for differences on the functional measures. The addition of education as a covariate did not change the main effect of group or the results of the post hoc comparison tests for the MMAA and UPSA Finances task.

Correlations Between Functional Measures and Demographic and Disease Variables in PD-MCI There were no significant correlations between the MMAA or UPSA Finances and age, years of education, depression level (GDS), gender, duration of PD, or UPDRS Part 3 (rs ranging from .001 to .29, ps ⬎ .05). In addition, there were no significant correlations between MMAA or UPSA Finances and global cognition (Mattis Dementia Rating Scale) or any of the specific neuropsychological measures (see Table 2; ps ⬎ .05).

Discussion The present study is the first study to our knowledge to examine IADLs in PD-MCI. We found that individuals diagnosed with PD-MCI demonstrated significantly lower scores on both the performance-based measures of medication management and financial management compared with healthy older adults. PD-MCI patients were also impaired compared with PD-NC patients on the performance-based medication management task, but the difference between groups on the performance-based financial management task did not reach statistical significance. These results suggest that PD-MCI is associated with IADL impairment, specifically in the ability to manage finances and medications. However, given that significant differences between PD-MCI and PD-NC patients were found on the performance-based measure of medication management only, it appears that medication management may be most sensitive to functional impairment in PD-MCI. The Movement Disorders Society task force diagnostic criteria for PD-MCI state that cognitive deficits should not be severe enough

to significantly interfere with functional independence, although the definition allows for “subtle difficulties on complex functional tasks” (Litvan et al., 2011). The current study found small to medium effect sizes for differences between PD-MCI patients and PD-NC patients and healthy older adults on performance-based measures of IADLs. Although there is no operational definition of “subtle difficulties,” it appears that the findings of the current study are consistent with the Movement Disorders Society definition of functional decline, as IADL impairments in our PD-MCI group were relatively mild. The current study also examined informant-based IADLs in PD-MCI and PD-NC participants. No significant differences were detected between groups on the informant-based IADL questionnaire, suggesting that informants did not endorse more IADL decline for PD-MCI participants compared with PD-NC participants. In addition, there was no significant correlation between the IADL questionnaire and scores on the performance-based measures in PD-MCI patients. These results highlight the importance of the method of assessment used to examine functional impairment. Studies in PD and non-PD samples, such as patients with Alzheimer’s disease, have found that informants may overestimate or underestimate a patient’s functional abilities (Loewenstein et al., 2001; Shulman et al., 2006; Wadley, Harrell, & Marson, 2003). Inaccurate reporting by informants may be related to a number of factors, such as caregiver depression, denial, and lack of knowledge about the patient’s day-to-day activities (Loewenstein et al., 2001; Loewenstein & Mogosky, 1990; Schiehser et al., 2013). One study found that informants of Alzheimer’s patients with higher scores in global cognition were more likely to overestimate functional capacity, suggesting that patients with less cognitive impairment may appear to their caregivers to better perform daily functional tasks than may be the actual case (Loewenstein et al., 2001). This issue may be even more relevant in milder cognitive impairment (e.g., PD-MCI), in which functional deficits are likely to be less obvious than in those with more severe cognitive deficits (e.g., PDD). Consistent with this notion, in our sample of PD-MCI patients, very few informants endorsed any decline in ability to manage medications (n ⫽ 3) or finances (n ⫽ 9) on the informantbased IADL measure. Thus, informant-based assessment may not detect subtle IADL declines in PD-MCI. The results of the present study suggest that performance-based measures (particularly measures assessing medication management) may be more sensitive to subtle functional declines compared with informant-report in PDMCI patients. However, it is also possible that the informant-based measure used in the current study did not allow the informant to endorse more subtle functional declines, as each item had a relatively restricted range of levels of functioning to choose from. It is possible that other informant-based measures may be more sensitive to early functional decline in PD-MCI patients. Future studies should examine this possibility. In the current study, there were no significant correlations within the PD-MCI group between the performance-based functional tasks and any of the neuropsychological measures. That is, even though participants were diagnosed with PD-MCI on the basis of neuropsychological impairment and PD-MCI participants showed significantly worse scores on most of the neuropsychological measures relative to the comparison groups (see Table 2), lower scores on cognitive tests were not related to lower scores on functional tasks. This was somewhat surprising, as cognitive def-

EVERYDAY FUNCTION AND PARKINSON’S DISEASE

icits are generally thought to underlie functional deficits in non-PD cognitively impaired populations (e.g., Alzheimer’s disease, MCI). Studies in various populations (e.g., healthy older adults, MCI, HIV) show significant relationships between neuropsychological functioning and IADLs (Heaton et al., 2004; Mariani et al., 2008; Tuokko, Morris, & Ebert, 2005). Furthermore, the current results are inconsistent with studies in nondemented PD that have found significant relationships between cognitive functioning and reportbased global functional measures (Rosenthal et al., 2010) and performance-based medication management (Manning et al., 2012). However, these studies did not characterize PD-MCI and did not separate PD-MCI from PD-NC participants. In the one study to date that has examined functional impairment in PD-MCI patients, correlational analyses were conducted in a combined group of PD-MCI, PD-NC, and PDD participants, and correlations were observed between global disability and global cognition (Leroi et al., 2012). However, this study did not address whether there are significant relationships between cognition and everyday functioning solely in PD-MCI patients, and inclusion of the PDD participants in analyses may have driven the correlations between cognition and disability. The current study was the first to study relationships between cognition and IADLs specifically in PD-MCI. One potential explanation for the lack of correlations between cognition and functional measures in the current study is that IADLs, at least as measured by performance-based methods, may be a separate domain of functioning from neuropsychological abilities in PD-MCI patients. In other words, because PD-MCI patients showed impairment in performance-based IADLs but this deficit did not correlate with scores on neuropsychological tests, the ability to perform certain IADLs and various cognitive abilities could have different underpinnings in PD-MCI patients. This suggests that measuring IADLs with performance-based measures may add information to the assessment of PD-MCI patients that is not available with standard neuropsychological measures. An alternative explanation for these results is that there are indeed relationships between cognition and function in PD-MCI patients, but that the neuropsychological measures used in the current study were not sensitive to this relationship, and that measures assessing other aspects of cognition (e.g., planning, sequencing, prospective memory) may be associated with functional decline. It is also possible that the functional tasks used in the current study did not actually measure functional capacity or real-world functioning in PD-MCI patients. Future studies are needed to examine the ecological validity of these functional measures in PD-MCI patients. Another possibility is that functional impairment on these tasks is more related to other symptoms in PD-MCI, such as motor symptoms or depression. However, this is less likely because there were no significant correlations among motor symptoms (UPDRS Part 3) or depression (GDS) and performance on either of the performance-based IADL tasks. In addition, the current study did not find a correlation between scores on the two performance-based functional tasks in PD-MCI patients. These results suggest that these two tasks are measuring different aspects of IADLs and that evaluation of PD-MCI may require multiple types of tests to assess a patient’s ability to perform day-to-day tasks. Moreover, these results suggest that impairment in one IADL does not necessarily imply impaired performance in other IADLs in PD-MCI patients. Future investi-

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gations may wish to examine other forms of IADLs (e.g., meal preparation, telephone use, transportation) in PD-MCI patients and their relation with each other and with cognition. Another important finding that emerged from the current study was that PD-NC participants did not significantly differ from healthy older adults on the performance-based measures of medication and financial management. Thus, one would expect a PD patient without cognitive impairment to have intact medication and financial management abilities. Given that PD-MCI patients were impaired relative to healthy older adults on both of the performance-based measures, these results suggest that impairment in medication and financial management ability is associated with cognitive status in PD rather than PD per se. In addition, because PD-MCI and PD-NC patients were comparable in disease variables (disease duration, severity of motor symptoms, severity of depressive symptoms), and these factors did not correlate with scores on the performance-based IADL measures in PD-MCI, noncognitive aspects of PD (motor symptoms, depressive symptoms) do not appear to be associated with these IADL impairments. There are limitations of this study that should be considered. First, although performance-based measures are considered to be more representative of actual day-to-day functioning compared with patient and caregiver report, they are not without limitations. Individuals with subtle functional declines may use compensatory strategies (e.g., pill boxes, automatic online bill paying) in their home environment. Performance-based measures, which are administered in an artificial laboratory environment, do not take into account potential compensatory strategies that may be used by patients in daily life. Nonetheless, performance-based measures are one of the best available objective methods for assessing everyday function. In addition, the external validity of the present study was limited by the demographic characteristics of the PD and healthy older adult participants, as most individuals were highly educated and Caucasian. Future longitudinal studies should examine whether early IADL decline (and which specific aspects of IADL impairment) in PDMCI predicts more rapid cognitive decline (PDD), as early IADL decline is associated with more rapid cognitive decline in non– PD-MCI samples (Artero et al., 2008; Purser, Fillenbaum, Pieper, & Wallace, 2005). In addition, studies are needed to identify what mode of functional assessment (e.g., performance-based, informant-report, etc.) is most valid in predicting progression to PDD. This would allow researchers to use functional measures to identify those individuals with greatest risk for cognitive decline. Future studies should also examine whether different PD-MCI subtypes show differential impairment in IADLs. The present study was not able to examine differences in subtypes given the small sample size in the PD-MCI single-domain group (n ⫽ 1). Although the performance-based measures used in the current study were not overly time-consuming for a laboratory setting (10 –30 min), future studies may also wish to examine the validity of briefer versions of performance-based measures that will be more useful for clinical settings. Future research of everyday functioning in PD-MCI patients is likely to aid in the clinical management of this group, and has implications for the development of interventions targeted at improving IADLs and quality of life.

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Received July 16, 2013 Revision received September 19, 2013 Accepted October 4, 2013 䡲

Correction to Reckess, Varvaris, Gordon, and Schretlen (2013) In the article “Within-Person Distributions of Neuropsychological Test Scores as a Function of Dementia Severity” by Gila Z. Reckess, Mark Varvaris, Barry Gordon, and David J. Schretlen (Neuropsychology, Advance online publication. November 11, 2013. doi: 10.1037/neu0000017), Figures 2 and 3 published Online First in black and white. These figures should have appeared in color. All versions of this article have been corrected. DOI: 10.1037/neu0000081

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