The Multiple Object Test as a Performance Based Tool to Assess Cognitive Driven Activity of Daily Living Function in Parkinson’s Disease

Abstract

Background: There is need for multidimensional quantitative assessment of cognitive driven activities of daily living (ADL) functions in Parkinson’s disease (PD).

Objective: To determine whether there is an ADL profile related to cognitive impairment in PD assessed by the Multiple Object Test (MOT). We assumed MOT performance to be lower in PD patients versus controls and in PD patients with more severe cognitive impairment.

Methods: 50 PD patients with no cognitive impairment (PD-NC), 54 patients with PD-mild cognitive impairment (PD-MCI), 29 with Parkinson’s disease dementia (PDD), and 40 healthy controls (HC) were investigated. Besides comprehensive cognitive testing, the MOT, a performance based test consisting of five routine tasks (e.g., preparing a cup of coffee), was applied. Quantitative (total errors and time) and qualitative (error type) MOT parameters were analyzed.

Results: Total time and number of MOT errors was increased in PD patients compared to controls (p < 0.001). These parameters also differentiated PDD patients from other cognitive groups (p < 0.05). No control subject had ≥ 4 errors in the MOT, but 30% of PD patients, especially PDD, scored above this cut-off. Omission (p < 0.001) and mislocation (p < 0.03) errors were more prominent in PDD than other cognitive groups. Perplexity errors did not differ between PD-MCI and PDD but between PD-NC and PDD (p = 0.01). MOT parameters discriminating between cognitive groups correlated mainly with lower test performance in psychomotor speed and executive function.

Conclusion: Performance based testing is promising to identify quantitative and qualitative ADL aspects differentiating between different cognitive groups which might be helpful for an early detection of PDD.

Keywords

Activities of daily living cognition dementia Parkinson’s disease performance-based assessment

INTRODUCTION

Cognitive deterioration in Parkinson’s disease (PD) is frequent and can have a high negative impact on patients’ quality of life [1]. Point prevalence of Parkinson’s disease dementia (PDD) is close to 30% among PD patients [2], while annually approximately 7% of all PD patients progress to PDD [3]. One of the risk factors for PDD, the stage of mild cognitive impairment (PD-MCI), can support an early diagnosis of cognitive decline [4]; however, a specific activity of daily living (ADL) dysfunction profile associated with a high risk for PDD has not yet been identified. Nevertheless, this is crucial, as the rate of cognitive decline can be reduced by therapeutic intervention [5].

According to the consensus guidelines, the main difference between PD-MCI and PDD are severely impaired ADL functions among PDD patients [6, 7]. However, some patients with PD-MCI also show at least minor ADL impairment, which does not interfere with their daily life [8 –11]. This supports the hypothesis that ADL deteriorate in parallel to cognitive impairment. Therefore worsening in ADL might have the potential to serve as a prodromal marker indicating development of PDD. It is important to mention that ADL can be differentiated into basic ADL (washing, eating, getting dressed, etc.) and the more complex instrumental ADL (using the telephone, managing finances and medication, etc.). Of these, instrumental ADL function is impaired in the earlier stages whereas basic ADL can be preserved for a long time in the course of PDD [12].

It has been speculated that instrumental ADL functions may not be represented by a single construct suggesting a need for a multidimensional approach to assess this function independently from motor performance [13]. Therefore future research for the identification of relevant predictors that indicate the development of PDD is essential.

One way to assess ADL function is a performance-based approach where test participants are asked to perform a certain task that is being instructed verbally. It has been shown that performance based testing might be of great help in differentiating between different cognitive groups [12 –14]. Besides quantitative aspects, performance based testing provides the advantage of observing qualitative characteristics of ADL-dysfunction, e.g., the type of error committed. We thus evaluated the quantitative and qualitative ADL profile in different cognitive PD groups using the Multiple Object Test (MOT). The aim of the following study was to determine whether there is a different ADL profile related to cognitive impairment in PD. MOT performance was compared between (i) PD patients and controls and (ii) different cognitive subgroups of PD patients: patients with no cognitive impairment (PD-NC), with mild cognitive impairment (PD-MCI), and Parkinson’s disease dementia (PDD). We hypothesized the ADL function to be more severely impaired in PD patients than in healthy individuals related to their cognitive decline. Additionally we evaluated the diagnostic ability of the MOT to differentiate between PD patients with and without dementia.

METHODS

Patients

In this prospective study, 133 patients with idiopathic PD [15] were consecutively recruited from our outpatient clinic, the Parkinson Clinic Leun-Biskirchen. Of those, 60 patients were primarily assessed in a former study using the same cognitive assessment within the frame of a longitudinal study [16]. Inclusion criteria were: age above 46 years (to minimize the likelihood for a genetic variant of PD and to present data of a representative control group with varying age to consider age effects on the performance of activity of daily living function), onset of dementia at least one year after PD diagnosis, normal or corrected hearing/visual abilities, and German as first language. All patients with other neurological diseases affecting the central nervous system, deep brain stimulation, history of drug or alcohol addiction, or a Mini-Mental State Examination (MMSE) score below 18 (testing not feasible) were excluded from study participation. Patients were tested while taking their optimized medication.

To compare the number and quality of errors committed in the performance based test, a healthy control group of 40 participants with no neurological diseases, no history of drug or alcohol abuse, no medication affecting central nervous system and no clinical diagnosis of dementia was included.

The study was approved by the local ethical committee (approval number: 121/2009 BO 2). All subjects gave written informed consent.

Diagnosis of cognitive groups

Diagnosis of cognitive groups was based on the guidelines of the Movement Disorder Society (MDS) Task Force [6, 7].

PDD was classified according to the results of (i) an extensive neuropsychological examination (classification of cognitive domains see below) demonstrating cognitive impairment in at least two domains (test result <1.5 SD of norm population) as well as (ii) the clinically rated impact on ADL function, with dementia at least one year after PD diagnosis. Patients were diagnosed with PD-MCI if their performance was impaired in at least two tests (test result <1.5 SD of norm population) of the neuropsychological testing battery, but impairment was not yet potent enough to interfere severely with ADL function. A neuropsychologist and a physician performed the clinical ADL rating in a personalized interview on patients’ and caregivers’ reports of significant ADL dysfunction primarily caused by cognitive worsening in the domestic environment or on the interviewers’ impression of the patients’ behavior.

Patients who did not meet criteria for PD-MCI or PDD were classified as PD patients with no cognitive impairment (PD-NC). PD patients with major depression were only excluded if depression was rated as primary source for cognitive worsening and dementia according to the examiners judgment.

Assessments

Clinical scales

Patient demographics, medical history, and medication (specified as levodopa equivalent dose LEDD, neuroleptics, and antidepressive drugs) were recorded. The Unified Parkinson’s Disease Rating Scale part III (UPDRS-III) and the Hoehn & Yahr (H&Y) scale were applied to evaluate motor function. The Beck Depression Inventory (BDI) was used to account for depression with a cut-off of 18 points for a high likelihood for major depression [17].

Neuropsychological assessment

Neuropsychological examination included a comprehensive test-battery subdivided into five different cognitive domains according to the results of a previous explorative factor analysis [18]. “Executive function” was assessed using the Trail Making Test (TMT) part B, the figure test as a part of the NAI (Nuernberger Altersinventar), the digit span backwards of the WMS-R (Wechsler-Memory-Scale-Revised), the Tower of London (TL-D), and the Berliner Apraxie Test (BAXT). The “Attention” domain was recorded using the Test for Attentional Performance (TAP), specifically the subtests “Alertness” and “Go-Nogo”. The domain “Praxis and visual function” was analyzed according to performance in two praxis subtests of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), first copying of simple line drawings and second recall of those drawings. Additionally, the object decision part of the Visual Object and Space Perception Battery (VOSP) was used to test this domain. TMT part A, the Boston Naming test, and the verbal fluency test, all deducted from the CERAD test-battery, were applied to assess the domain of “psychomotor speed and naming ability”. Finally the memory domain was tested using three subtests from the CERAD (word-list memory, word-list recall after delay, word-list recognition and word-list intrusion, meaning incorrect responses in the word-list recall) and the Logical Memory l and ll of the WMS-R.

ADL questionnaires

For validation purpose, we applied two additional instrumental ADL-Scales. The instrumental ADL questionnaire of Lawton and Brody (Lawtons IADL) examines eight instrumental ADL actions (e.g., shopping, driving a car) [19]. The NAA (Nuernberger-Alters-Aktivitäten-Scale) is a self-questionnaire consisting of 20 items to assess instrumental ADL function, which has been validated for PD patients [14].

The Multiple Object Test

The Multiple Object Test (MOT) is a performance based test [20] including five verbal commands that the patient is supposed to perform using the items presented for each task (presented utensils in brackets): (1) light a candle (candle, matches, candlestand), (2) open a padlock (padlock, key), (3) drink a glass of water (glass, closed bottle of water), (4) prepare a letter ready for mailing (paper with standard phrases, envelope, stamp), and (5) prepare a cup of coffee (cup, coffee, filter, filter device, kettle).

Based on the MOT performance, the following error types were coded (see Supplementary Table 1 for details): perplexity: “trial and error” behavior when performing the task, omission: skipping a sequence of the task, mislocation: the action is performed at an inappropriate place, misuse: inappropriate use of an object, sequence error: performance of task in wrong sequence of action. Deviating from the original version of the MOT, the error type “clumsiness” was not coded, as differentiation between motor impairment and other reasons for clumsiness in PD-patients is delicate.

Our data analysis included (i) the total number of errors with a maximum of 25 total errors and 5 per item, (ii) the total time needed for completion (no speed instruction was given; time was measured in seconds starting from the time the instruction was given until the time the test person completed the task or had to give up when being unable to finish), and (iii) the type of error committed. Qualitative and quantitative classification of errors was based on an anonymized video, where only the hands, upper body part without face, and utensils were shown. The rating was performed by an investigator blinded to both cognitive performance and other clinical test results.

Statistical analyses

Data were analyzed using SPSS 20.0 for Windows (IBM SPSS Corporation, New York USA). Non-parametric statistical tests were used. Data were therefore reported as frequency, median and range. Demographic and clinical parameters of healthy controls and the total PD group (PD total) were compared using either Mann-Whitney-U Test, Chi-square or Fishers-Exact Test. Logistic regression including age and education as covariates (p < 0.05 between PD and HC), was performed on the MOT parameters. A value of p < 0.05 was considered as significant.

Demographic characteristics as seen in Table 1 of PD subgroups (PDNC, PD-MCI, PDD) were analyzed using the Kruskal-Wallis Test, Chi-square test, and the Mann-Whitney-U for post-hoc testing (p < 0.017 interpreted as significant between group differences).

For identification of diagnostic accuracy of the MOT, a ROC-curve statistic was applied. The statistic used the PDD group as reference variable among all PD patients and the total number of errors in the MOT as test variable. We report the classification accuracy, sensitivity and specificity, and positive and negative predictive value for diagnosis of PDD.

A linear regression model with the MOT parameter as dependent variable and group membership coded as dummy variable (PD-NC as reference group) also including confounding variables (age, BDI, UPDRS-III score, intake of antidementiva and neuroleptics) was applied. A Bonferroni corrected alpha level of p < 0.01 for the error analysis and a value of p < 0.005 for the item analysis was interpreted as statistical significant. Same procedure of linear regression using dummy variables was used for the comparison of neuropsychological testing (Supplementary Table 2). For post-hoc analysis of PD-MCI with and without ≥ 4 errors in the MOT a logistic regression was performed using age as confounding variable when comparing the test results. Correlation between neuropsychological test results, IADL questionnaires, UPDRS-III and the MOT parameters as seen in Supplementary Table 3 was performed using the Pearson’s correlation coefficient including the total group of PD patients.

RESULTS

Characteristics of study groups

Of the 133 patients, 50 PD patients were classified as PD-NC, 54 patients as PD-MCI, and 29 had PDD (Table 1). Neuropsychological data are reported in Supplementary Table 2. As expected, PDD patients scored significantly lower than PD-NC in almost all tasks assessed (p < 0.05). Values of PD-MCI patients were located between those of PD-NC and PDD for nearly all tests (Supplementary Table 2).

PDD patients were older (p < 0.001), had a higher BDI-score (p = 0.02), higher UPDRS-III and H&Y stage scores (p < 0.001) as well as a higher number of patients with intake of antidementiva (p < 0.001) and neuroleptics (p < 0.001) compared to both the PD-MCI and the PD-NC group. Except for the H&Y, which was supposed to highly correlate with the UPDRS-III score, these variables were included as covariates for analysis of ADL performance.

Controls were younger (p = 0.02) and had more years of education (p = 0.02) than PD patients; both variables were included as confounder in further group analysis.

MOT performance of healthy controls versus all PD patients

Our logistic regression model showed that the total number of errors (median = 2, 0 to 21; Table 2) as well as the total time needed for completion was higher in PD patients (p < 0.001) than in healthy controls (see Fig. 1A, B). In particular, the number of perplexity (p < 0.001), mislocation (p = 0.02), and sequence errors (p = 0.02) was increased in PD patients compared to controls (Fig. 2). No control individual scored four or more errors in the MOT (median = 1, 0 to 3; Table 2), but 40 (30%) of PD patients(8% PD-NC, 30% PD-MCI, 69% PDD) scored above this value.

MOT performance of cognitive PD groups

PDD patients showed worse performance compared to both PD-NC and PD-MCI in the total number of errors (p ≤ 0.004) (Fig. 1A), total time needed for completion (PD-NC: p = 0.005, PD-MCI p = 0.05) (Fig. 1B), and in the total number of omission (p < 0.001) (Fig. 2). Additionally, PDD patients made more perplexity (p = 0.01) errors than PD-NC, but the rate of misuse and sequence errors did not differ between the cognitive groups. Mislocation errors just showed a tendency toward worse performance among PDD compared to PD-MCI.

After Bonferroni correction the items best discriminating PDD from other cognitive groups were the time to open a padlock (p < 0.001) and to prepare a letter (error: p < 0.001).

The ROC analysis revealed an area under the curve of 0.85 (p < 0.001) for the total number of MOT errors. More than 4 errors in the MOT was identified to best discriminate non demented PD patients from PDD, with a classification accuracy of 78%, sensitivity of 69%, specificity of 81%, and positive and negative predictive value of 50% and 90%.

Correlation analysis

Except for the Logical Memory I and the number of CERAD word list intrusion, lower performance in the neuropsychological (sub)tests correlatedsignificantly with an increased number of total MOT errors (–0.14 ≤ r ≤ –0.50; p < 0.05 see Supplementary Table 3 for details) in all PD patients.

Among the type of errors, highest association was found between the neuropsychological test scores and the number of perplexity errors (–0.18 ≤ r ≤ –0.49; p < 0.05), except for the two memory tasks mentioned above.

Omission errors correlated significantly with executive, memory, attention, and visual spatial performance (–0.18 ≤ r le; –0.37; p ≤ 0.05).

The number of mislocation errors was mainly related to lower memory function (e.g., CERAD word list subtest, Logical memory I and II, – 0.19 ≤ r ≤ – 0.27; p < 0.05), reduced psychomotor speed and set-shifting (TMT part A and B, –0.29 ≤ r ≤ –0.31; p < 0.01), and lower scores in the BAXT (r = –0.25; p < 0.01). The number of errors classified as misuse was significantly associated with lower visual-spatial (CERAD Praxis delay, r = –0.23; p < 0.01) and psychomotor speed ability (TMT part A, r = –0.19, p < 0.05). Lower working memory performance (digit span forward, r = –0.19; p < 0.05) correlated with an increase in sequence errors.

Moreover the total time to MOT completion, the number of total MOT errors, as well as perplexity and omission errors (–0.36 ≤ r ≤ 0.56; p < 0.001), was significant correlated to the Lawtons-IADL and NAA score. However, analysis of the MOT parameters and the UPDRS-III showed significant association between all MOT parameters and motor function (–0.23 ≤ r ≤ 0.50; p < 0.001).

Post-Hoc comparison: PD-MCI patients with and without ≥ 4 MOT errors

As none of the healthy participants committed four errors or more (Fig. 1A, Table 1) and this value was confirmed by the ROC-analysis to be associated to dementia in PD, this cut-off was used to discriminate those PD-MCI patients with (n = 38, 70.4%) and without (n = 16, 29.6%) increased errors in the MOT. PD-MCI patients with MOT performance ≥ 4 errors (median = 4, 4 to 6), were older than PD-MCI <4 MOT errors (p < 0.05). Thus, age was included as a confounder for motor and cognitive analysis. Analyses of neuropsychological test performance showed no difference between PD-MCI patients with and without ≥ 4 MOT errors.

DISCUSSION

The aim of the study was to identify different ADL profiles among cognitive PD subgroups using a performance based approach. Additionally we tested the ability of the MOT to differentiate healthy controls from PD patients and PD patients with and without dementia.

So far, qualitative aspects of ADL dysfunction in PD are poorly understood [21], emphasizing the need for a detailed inspection of quantitative and qualitative errors in different cognitive PD groups. We were able to identify a specific ADL profile associated with cognitive decline and dementia in our PDsample.

Diagnostic ability of the MOT

Initially, a comparison of healthy controls and PD patients revealed an increase in errors and time for item completion in the MOT. Thus, application of the MOT supports previous reports that many PD patients show at least minor impairment of ADL function in performance based testing [22]. In our study the best parameter for estimating ADL impairment in different PD subtypes was the total number or errors and the total time needed for completion, supporting the clinical impression of reduced instrumental ADL function in our PDD group and previous data on quantitative differences in performance-based tests [10, 22]. In our sample, no control subject had more than 3 errors in the MOT compared to 30% PD patients with ≥ 4 errors in this test; most of them diagnosed as expected as PDD (69%) but also quite a high number as PD-MCI (30%). This cut-off was also identified to best discriminate non-demented (PD-NC and PD-MCI) PD patients from PDD, with moderately high diagnostic accuracy (78%), a satisfactory specificity of 81%, but lower sensitivity (69%).

To differentiate PDD from PD-MCI, the severity of instrumental ADL impairment is most crucial. It is discussed that ADL impairment develop in parallel to cognitive worsening [23]. In our sample, nearly 30% of the PD-MCI patients had a higher number of errors than any individual of the control group. Therefore our data are in accordance with studies reporting an increase rate of errors among PD-MCI patients in performance-based tests [24]. In accordance with previous findings, the cognitive profile of those PD-MCI patients with slight ADL impairment did not differ compared to those with MOT scores within the range of controls [24]. This implicates that even at this point there is still doubt as to what extent neuropsychological testing can predict cognitive worsening at the PD-MCI level [25, 26]. In Alzheimer’s disease, the combination of slight ADL impairment and cognitive state of MCI increases the risk for dementia [27, 28].

In PD, the predictive value of slight ADL impairment among PD-MCI has not yet been investigated. The acceptable specificity and negative predictive value of the MOT for the diagnosis of PDD argues for the position that this test might be useful to verify dementia. However, sensitivity of the MOT for PDD is lower.

Therefore, we can only speculate if PD patients with more than 4 errors in the MOT might be at potential risk for progression of cognitive dysfunction and PDD—a hypothesis that needs to be proven by longitudinal data.

Errors associated with cognitive performance in PD

Addressing the question of qualitative aspects in ADL profiles, we were able to identify a higher number of perplexity, mislocation, and sequence errors as more common among PD. Although perplexity and mislocation errors were associated with dysfunction in nearly all cognitive domains, sequence errors were not found to be primarily associated with cognitive worsening.

Our PDD patients had a higher number of perplexity, omission, and mislocation errors than patients with no dementia. Giovannetti and co-workers also found a slight increase in omission errors in PDD compared to PD and Alzheimer’s disease patients, which could not be statistically verified in their sample [21]. Moreover, in this previous study, different categories such as errors in sequence, substitution, and perseveration were combined to one single error category (commission), which might partly explain differences to our study results. Our results indicate that the state of PDD can be characterized by a trial and error ADL behavior, omission of important aspects within the ADL sequence, and an increase in visual-spatial ADL errors. In a study using questionnaires, PD patients judged themselves to be more easily distracted and disoriented, resembling our results of an increase in perplexity errors in the progression of PD [29]. Interestingly, this error type did not differentiate between PD-MCI patients and PDD in our sample, assuming that this behavior might be an early sign within the development of dementia in contrast to omission and mislocation errors which were more prominent in the PDD group. A higher rate of perplexity errors was found to be mainly associated with lower performance in the TMT part A and B assessing psychomotor speed and executive function (set-shifting) in our PD sample. The important role of executive function for instrumental ADL function has also been reported in previous studies [30, 31], which is supported by our qualitative error analysis. Therefore, our results differ from studies which were not able to show associations between performance based ADL and cognitive data [22, 24] by primarily using global scales which do not reflect different cognitive functions. Thus, more studies using unobtrusive ADL assessments differentiating between qualitative aspects are needed to confirm this profile of ADL dysfunction in PDD.

Limitations

The main limitation of our study is the confounding factor of motor function on ADL testing. We minimized the influence of motor function on MOT performance by correcting for this confounder in the group comparisons and excluded clumsiness errors in our analysis, as this error seems most vulnerable to motor impairment. However we cannot rule out that motor performance affected our results, as it can be also seen in the results of the correlation analysis. But even by use of other ADL tools, motor impairment cannot be completely excluded as a source of ADL dysfunction. In contrary, the advantage of performance based testing is that this impairment becomes visible and thus can be partially excluded from the analysis [32]. Also a gender effect might influence performance based ADL testing especially in tasks like preparing a cup of coffee.

In summary, we conclude that performance based testing is promising to identify quantitative and qualitative aspects differentiating between different cognitive groups, especially PDD. The result might be of help to detect an ADL profile predicting progressive cognitive decline in PDD.

Footnotes

ACKNOWLEDGMENTS

We thank our patients for their participation. Special thanks to all physicians who were of great help with the neurological assessments.

Authors’ disclosures available online ().

Appendix

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