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Limb motor learning in individuals with Parkinson's disease as a function of practice--A follow-up pilot experiment to a speech motor learning study.


Individuals with Parkinson's disease (PD) frequently demonstrate problems in learning sequential and complex motor movements [3]. Application of principles of motor learning (PMLs) is known to facilitate motor learning in this population [8]. A previous study that investigated the role of constant, variable, random, and blocked practice conditions in individuals with PD in learning a speech task found no significant differences between these practice conditions [6]. However, the role of these practice conditions in learning complex limb-motor tasks in individuals with PD remains to be systematically investigated. Considering this limitation, this follow-up pilot study examined the role of above-mentioned four practice conditions in spatial and temporal learning of a musical keyboard task in individuals with PD.


The same 16 individuals with PD recruited by Kaipa et al. [6] served as participants. All the participants were administered MDS-UPDRS and Hoehn and Yahr staging scale. Participants' demographic details are presented in Table 1. All the participants were on dopamine replacement therapy but information on Levodopa Equivalent Dosage was unavailable. Data were collected during participants' self-reported "on" state. The participants' cognitive status could not be evaluated through a formal neuropsychological assessment. However, the participants' recent memory, language skills, executive function, and visual spatial functions were informally assessed by engaging them in conversations about their recent events, involving them in a serial naming task, asking them how they would prepare to go on a vacation, and the geographical location of their house, respectively. This ruled out significant cognitive deficits. Additionally, the cognitive subsection of MDS-UPDRS did not reveal cognitive deficits. An informal audiological screening ruled out speech perception deficits among participants. Participants were randomly and equally assigned to one of four practice conditions: (1) constant practice, (2) variable practice, (3) blocked practice, and (4) random practice. An appropriate Human Ethics Committee approved the current experiment and all participants signed an informed consent form prior to their participation.


Participants in all the four practice conditions learned a sequential "target" musical keyboard tune "FBG#A# FG#AA# FG#A#B". Playing a musical keyboard is considered to be a complex motor skill that is orchestrated by sequential fine motor movements, so it has been frequently used to evaluate limb motor learning [2]. The target tune was organized into three musical segments. Each segment had 3 musical notes, with a total of 12 notes across the tune. The duration of the target tune was 13.29 s. Along with the target tune, two "alternate" tunes were created. The first alternate tune contained the same keystrokes as the target tune but differed in temporal duration and was used for training participants involved in variable practice. The second alternate tune was "FA#G#G A#G#FF GAFA#"', and was used for random and blocked practice conditions that required learning two or more tasks differing in motor plans. The second alternate tune differed from the target tune in terms of musical notes and temporal duration. The overall duration of second alternate tune was 11.09 s. Musical notes in each segment were visually depicted by dots of increasing size on the keys. The size of the dots indicated the order of keys to be pressed on the keyboard (e.g., the smallest dot on the key would indicate the first key to be pressed, and so on). For task training purposes, the target as well as alternate musical tunes were prerecorded. The target and alternate tunes are illustrated in Figure 1.


The experiment spanned across three consecutive days. The first two days was the acquisition phase and the final day being the retention phase. During each practice session that lasted for about 90 min, participants were involved in a practice regime of 50 trials per task. The schematic representation of the experimental sequence is depicted in Figure 2. During practice, each participant was instructed to match their productions to the target musical tune as accurately as possible in terms of spatial and temporal characteristics. A total of 50 PowerPoint slides were created to generate 50 practice trials. Each slide had the visual as well as the auditory rendition of the tune. The complete production of the tune following the visual and auditory representations comprised one practice trial. After completion of 10 practice trials, participants received feedback on their spatial and temporal accuracy of the musical tune. The researcher provided summary feedback on their performance on the 10th trial. The researcher measured the overall duration of the musical tune, as well as the individual duration of each segment and pause duration between the segments. The researcher also assessed the spatial accuracy of the keys that were pressed. This allowed researcher to provide feedback on participant's spatial as well as temporal accuracy. Verbal feedback was provided to the participants by displaying the target tune on a sheet of white paper. The researcher indicated whether the participant's production matched the target tune in terms of spatial and temporal accuracy. When providing verbal feedback to the participant on each temporal feature (overall duration, segment duration and pause duration), the researcher used terms like "accurate", "too long" and "too short" in reference to the target musical tune. Thus, the nature of feedback was delayed, low-frequency, knowledge of performance, and knowledge of results.

Across the two-day practice period, each participant received 10 instances of feedback. Constant group participants practiced just the target tune for 50 times on each day of the acquisition phrase and received feedback after every 10 consecutive productions. Participants in variable practice condition practiced 25 trials of target tune and another 25 trials of the first alternate tune during each day of practice. The participants received 5 instances of feedback for target as well as first alternate tunes. Participants involved in random practice condition practiced 25 trials of the target tune as well as another 25 trials of the second alternate tune in a random fashion on each day of practice.

Participants in blocked practice condition practiced the target tune from trials 1 through 25 and second alternate tune from trials 26 through 50 on day 1. This practice order was reversed on the second day. Participants in random as well as blocked practice conditions received 5 instances of feedback on target as well as second target tunes. Participants in all the four practice conditions returned on third day for the retention phase and reproduced 5 trials of the target tune without further practice or feedback. These productions were audio recorded for acoustic analysis.


The five retention phase trials were included for data analysis. Spatial analysis focused on production accuracy of the target tune by determining the Percentage of Keystrokes Correct (PKC). This measure was obtained by dividing the number of correct keystrokes produced by the total number of keystrokes produced and multiplying by 100. A mean PKC from the 5 retention trials of each participant as well as a group PKC mean for the 4 participants in each group were obtained. The production accuracy of alternate tunes was not evaluated as the study focused on learning outcomes of target tune only. Temporal analysis determined the temporal synchrony of the participants' retention trials with the target tune. Participants' each retention trial as well as the original rendition of the target tune was fed into the Audacity acoustic analysis software. The software displayed the target tune on the top panel of the computer screen and the bottom panel displayed the participants' retention trial. The participants' productions were acoustically aligned to the target tune. The onset point was same for the participants' productions and the target tune. The offset point of production waveforms was based on offset of target tune waveform. The temporal synchrony between the target tune and the participants' production was calculated through phi correlation using a MATLAB-based program. Similar to PKC, a mean phi correlation was obtained for each participant's 5 retention trials and a grand mean phi correlation value was calculated for the 4 participants in each of the 4 practice conditions. The PKC and phi correlation values were subjected to a Kruskal-Wallis H test to compare spatial and temporal learning between participants in the four practice conditions. Pearson product moment correlation was used to calculate intra-rater reliability by randomly choosing 20% of the data. Intra-rater reliability of spatial and temporal analyses was r = 0.99 and r = 1.00, respectively. All correlations were significant (p < 0.05).


Spatial learning

PKC mean values from all the four practice conditions are shown in Table 2. Results indicated no significant differences in spatial learning between participants in four practice conditions, %2(3) = 5.93 p = 0.11. Spatial learning outcomes for the speech learning task from Kaipa et al. [3] are included in Table 2 for comparison. Similar to the current results, there were no significant differences between participants in four practice conditions in learning the speech task. Interestingly, the grand mean of Percentage of Phonemes Correct (PPC) and PKC values are 75% and 66% suggesting similar retention outcomes for both the tasks.

Temporal learning

Mean phi correlation values are presented in Table 2. The temporal learning outcomes of the speech task from Kaipa et al. [6] are also included in Table 2. Similar to the findings of Kaipa et al. the current study's results also revealed no significant differences between participants of four practice conditions, %2(3) = 3.04, p = 0.38. The participants demonstrated slightly higher correlation during the keyboard learning task (0.16) in comparison to the speech learning task (0.09).


There are limited studies that have evaluated motor learning in PD within the context of PMLs [7, 9]. A drawback of these studies has been that they had not considered the spatial and temporal aspects of motor learning. To our knowledge this is the first study to investigate the spatial and temporal learning of a complex keyboard learning task in individuals with PD. The findings of this study suggest a lack of preponderance of a specific practice condition in spatial learning of a complex limb-based task, which is in line with the findings of Kaipa et al. [6]. Prior findings related to the role of practice conditions in spatial limb-motor learning in individuals with PD are equivocal. Sidaway et al. [9] compared random versus blocked practice conditions in learning 5-key press patterns in four participants with PD. Retention findings revealed an advantage of random practice. On the contrary, Lin et al. [7] compared random versus blocked practice condition in 20 adults with PD who learned practicing a three-lever movement task. Retention tests revealed participants benefited from blocked practice. As per the concept of contextual interference, practice conditions that require increased cognitive load such as random practice in which a learner is required to practice two or more tasks with differing motor plans facilitate motor learning. Interestingly, the beneficial effects of random practice were not observed in the current study as well in the study by Lin et al. This discrepancy in findings can be attributed to the "challenge point framework" (CPF) [5]. According to CPF, practice conditions that influence motor learning interact with the task difficulty and learner's skill. Differences in difficulty of the to-be learned task could have contributed to discrepancy in findings between the current study, Lin et al., and Sidaway et al. Additionally, people with PD are known to present with task switching difficulties [4]. When participants with PD in the current study were required to learn a complex sequential task as a keyboard tune, practice conditions that required participants to switch between two tasks (as in case of variable, random, and blocked practice conditions) could have been detrimental to participants' learning. Similar to spatial learning, participants did not differ on temporal learning of the keyboard task based on the practice condition. This is comparable to the findings of Kaipa et al. [6] who also found that patients with PD did not differ in temporal learning of a speech task based on the structure of practice. Similar to spatial learning, it is possible that as the complexity of the to-be learned task increases the preponderance of a specific practice condition disappears. Additionally, the timing deficits exhibited by people with PD could have contributed to diminished temporal learning.


The current study is not without limitations. First, as this was a pilot study it employed a small sample size, which makes it difficult to extrapolate the findings to a large population. Second, it is important to recognize that the practice period was limited to 50 trials per day for a total of two days. It is possible that additional training of the participants may have resulted in improved/increased temporal as well as spatial learning of the target task. Similarly, assessing retention after an interval of more than 24 hours may also have influenced the learning outcomes. Finally, participants did not receive formal audiological and cognitive evaluations. It is likely that the informal cognitive evaluations in this study were not sensitive to identify mild cognitive impairment that is common in PD [1].


The preliminary findings interestingly suggest that limb motor learning is not influenced by practice condition in people with PD. This could be primarily due to a small sample size. The descriptive results indicate that participants involved in blocked and random practice demonstrated poorer spatial learning in comparison to participants in constant and variable practice conditions. It is imperative for future studies to employ a higher sample size, increase the length of the practice sessions and evaluate retention on a long-term basis to better understand the theoretical underpinnings of limb motor learning. Another aspect that is worthy of emphasizing is that the current study employed practice conditions that offered high contextual interference to participants. It is well known that participants with PD have inherent task-switching difficulties. It would also be ideal for future studies to incorporate a serial practice condition that offer low contextual interference initially and then switch to higher contextual interference. This would allow us to be aware of the effects of contextual interference on motor learning in individuals with PD. This line of research can have ramifications on developing evidence-based rehabilitation of motor deficits in individuals with PD.


[1.] Aarsland, D., K. Brennick, and T. Fladby. Mild cognitive impairment in Parkinson's disease. Curr Neurol Neurosci Rep 11:371-378, 2011.

[2.] Bangert, M., A. Wiedemann, and H. C. Jabusch. Effects of variability of practice in music: a pilot study on fast goal-directed movements in pianists. Fron Hum Neurosci 8:1-11, 2014

[3.] Behrman, A. L., J. H. Cauraugh, and K. E. Light. Practice as an intervention to improve speeded motor performance and motor learning in Parkinson's disease. J Neurol Sci 174: 127-136, 2000.

[4.] Benecke, R., J. Rothwell, J. Dick, B. Day, and C. Marsden. Disturbance of sequential movements in patients with Parkinson's disease. Brain 110:361-379, 1987.

[5.] Guadagnoli, M. A., and T. D. Lee. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav 36:212-224, 2004

[6.] Kaipa, R., R. D. Jones, and M. P. Robb. Are individuals with Parkinson's disease capable of speech-motor learning?-A preliminary evaluation. Parkinsonism Relat Disord 28:141-45, 2016.

[7.] Lin, C. H., K. J. Sullivan, A. D. Wu, S. Kantak, and C. J. Winstein. Effect of task practice order on motor skill learning in adults with Parkinson disease: a pilot study. Phys Ther 87:1120-1131, 2007.

[8.] Nieuwboer, A., L. Rochester, L. Muncks, and S. P. Swinnen. Motor learning in Parkinson's disease: limitations and potential for rehabilitation. Parkinsonism Relat Disord 15:S53-S58, 2009.

[9.] Sidaway, B., R. M. Gordon, M. Hopkins, M. Kershaw, C. Marean, and N. Wilkins. Random and blocked practice schedule effects on motor skill learning in individuals with Parkinson's disease. J Neurol Phys Ther 30:204-205, 2006.

Ramesh Kaipa

Department of Communication Sciences and Disorders, Oklahoma State University, Stillwater, Oklahoma


Ramesh Kaipa, Ph.D. Assistant Professor and Program Director Department of Communication Sciences & Disorders 042 Murray Hall Oklahoma State University Stillwater, OK 74078 Phone: 405-744-7956 Fax: 405-744-8070 ramesh. kaipa@okstate. edu
Table 1. Participants' demographic details. The standard deviations
are indicated within parenthesis

Parameter                           Data

Number of participants              16
Mean age                            70 years (7.58)
Age range                           57-78 years
Onset range of Parkinson's disease   4-12 years
Motor UPDRS mean score              45.37 (12.2)
Hoehn & Yahr mean staging            1.81 (0.54)

Table 2. Mean PPC, PKC and Phi correlation values for the participants
across the four practice conditions. Data from Kaipa et al. [3] are
reported for comparison purposes. Standard deviations are reported
within parenthesis.

                     Constant      Variable      Random

Spatial learning
Keyboard task (PKC)  82.2 (21.5)   77.8 (17.0)   54.25 (28.6)
Speech task (PPC)    78.15 (25.7)  72.55 (9.1)   74.85 (21.3)
Temporal learning
(Phi correlations)
Keyboard task         0.16 (0.14)   0.24 (0.17)   0.10 (0.07)
Speech task           0.13 (0.06)   0.10 (0.17)   0.05 (0.06)


Spatial learning
Keyboard task (PKC)  49.55(20.19)
Speech task (PPC)    77.55 (17.7)
Temporal learning
(Phi correlations)
Keyboard task         0.15 (0.15)
Speech task           0.09 (0.20)

PKC - Percentage of Keystrokes Correct; PPC-Percentage of Phonemes
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Author:Kaipa, Ramesh
Publication:Clinical Kinesiology: Journal of the American Kinesiotherapy Association
Date:Mar 22, 2018
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