Contextual Interference Can Facilitate Motor Learning in Older Adults and in Individuals With Parkinson's Disease

2016 ◽  
Vol 48 (6) ◽  
pp. 509-518 ◽  
Author(s):  
Ben Sidaway ◽  
Bradley Ala ◽  
Katherine Baughman ◽  
Joshua Glidden ◽  
Stephanie Cowie ◽  
...  
2021 ◽  
Author(s):  
Soraya Lahlou ◽  
Ella Gabitov ◽  
Lucy L. W. Owen ◽  
Daphna Shohamy ◽  
Madeleine Sharp

Patients with Parkinson's disease, who lose the dopaminergic projections to the striatum, are impaired in certain aspects of motor learning. Recent evidence suggests that, in addition to its role in motor performance, the striatum plays a key role in the memory of motor learning. Whether Parkinson's patients have impaired motor memory and whether motor memory is modulated by dopamine at the time of initial learning is unknown. To address these questions, we measured memory of a learned motor sequence in Parkinson's patients who were either On or Off their dopaminergic medications. We compared them to a group of older and younger controls. Contrary to our predictions, motor memory was not impaired in patients compared to older controls, and was not influenced by dopamine state at the time of initial learning. To probe post-learning consolidation processes, we also tested whether learning a new sequence shortly after learning the initial sequence would interfere with later memory. We found that, in contrast to younger adults, neither older adults nor patients were susceptible to this interference. These findings suggest that motor memory is preserved in Parkinson's patients and raise the possibility that motor memory in patients is supported by compensatory non-dopamine sensitive mechanisms. Furthermore, given the similar performance characteristics observed in the patients and older adults and the absence of an effect of dopamine, these results raise the possibility that aging and Parkinson's disease affect motor memory in similar ways.


Author(s):  
Robbin Romijnders ◽  
Elke Warmerdam ◽  
Clint Hansen ◽  
Julius Welzel ◽  
Gerhard Schmidt ◽  
...  

Abstract Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.


Gerontology ◽  
2021 ◽  
pp. 1-7
Author(s):  
Ram kinker Mishra ◽  
Catherine Park ◽  
He Zhou ◽  
Bijan Najafi ◽  
T. Adam Thrasher

<b><i>Introduction:</i></b> Parkinson’s disease (PD) progressively impairs motor and cognitive performance. The current tools to detect decline in motor and cognitive functioning are often impractical for busy clinics and home settings. To address the gap, we designed an instrumented trail-making task (iTMT) based on a wearable sensor (worn on the shin) with interactive game-based software installed on a tablet. The iTMT test includes reaching to 5 indexed circles, a combination of numbers (1–3) and letters (A&amp;B) randomly positioned inside target circles, in a sequential order, which virtually appears on a screen kept in front of the participants, by rotating one’s ankle joint while standing and holding a chair for safety. By measuring time to complete iTMT task (iTMT time), iTMT enables quantifying cognitive-motor performance. <b><i>Purpose:</i></b> This study’s objective is to examine the feasibility of iTMT to detect early cognitive-motor decline in PDs. <b><i>Method:</i></b> Three groups of volunteers, including 14 cognitively normal (CN) older adults, 14 PDs, and 11 mild cognitive impaireds (MCI), were recruited. Participants completed MoCA, 20 m walking test, and 3 trials of iTMT. <b><i>Results:</i></b> All participants enabled to complete iTMT with &#x3c;3 min, indicating high feasibility. The average iTMT time for CN-Older, PD, and MCI participants were 20.9 ± 0.9 s, 32.3 ± 2.4 s, and 40.9 ± 4.5 s, respectively. After adjusting for age and education level, pairwise comparison suggested large effect sizes for iTMT between CN-older versus PD (Cohen’s <i>d</i> = 1.7, <i>p</i> = 0.024) and CN-older versus MCI (<i>d</i> = 1.57, <i>p</i> &#x3c; 0.01). Significant correlations were observed when comparing iTMT time with the gait speed (<i>r</i> = −0.4, <i>p</i> = 0.011) and MoCA score (<i>r</i> = −0.56, <i>p</i> &#x3c; 0.01). <b><i>Conclusion:</i></b> This study demonstrated the feasibility and early results supporting the potential application of iTMT to determine cognitive-motor and distinguishing individuals with MCI and PD from CN-older adults. Future studies are warranted to test the ability of iTMT to track its subtle changes over time.


2017 ◽  
Vol 32 (12) ◽  
pp. 1729-1737 ◽  
Author(s):  
Shahmir Sohail ◽  
Lei Yu ◽  
Julie A. Schneider ◽  
David A. Bennett ◽  
Aron S. Buchman ◽  
...  

2021 ◽  
Vol 8 ◽  
pp. 237437352199722
Author(s):  
Wissam Deeb ◽  
Christopher W Hess ◽  
Noheli Gamez ◽  
Bhavana Patel ◽  
Kathryn Moore ◽  
...  

Parkinson’s disease and parkinsonism are common chronic neurodegenerative disorders that tend to affect older adults and cause physical and sometimes cognitive limitations. Given that these limitations could impact successful telemedicine use, we aimed to investigate the experiences of patients with parkinsonism using telemedicine during the COVID-19 pandemic. A 19-item survey was emailed to patients with parkinsonism following telemedicine visits at a single US tertiary care parkinsonism specialty clinic. Seventy-four individuals responded, out of 270 invitations sent. Almost two-thirds (61.6%) of the respondents were comfortable with using technology in general, and almost all were very satisfied with their telemedicine experience. The most commonly reported benefits included cost and travel savings, ease of access to a specialist, and time savings. Issues with technology and previsit instructions were the most commonly identified challenges (28%). Urgent implementation, due to the pandemic, of telemedicine care for patients with parkinsonism was feasible and well received. The challenges most commonly reported by patients could be potentially alleviated by better education and support.


Neuroreport ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yu-Chen Chung ◽  
Beth E. Fisher ◽  
James M. Finley ◽  
Aram Kim ◽  
Andrew J. Petkus ◽  
...  

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