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Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder that affects >1% of individuals worldwide and is manifested by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor symptoms such as cognitive impairment and depression. Non-pharmacological interventions such as dance therapy are becoming increasingly popular as complementary therapies for PD, in addition to pharmacological treatments that are currently widely available. Dance as a sensorimotor activity stimulates multiple layers of the neural system, including those involved in motor planning and execution, sensory integration, and cognitive processing. Dance interventions in healthy older people have been associated with increased activation of the prefrontal cortex, as well as enhanced functional connectivity between the basal ganglia, cerebellum, and prefrontal cortex. Overall, the evidence suggests that dance interventions can induce neuroplastic changes in healthy older participants, leading to improvements in both motor and cognitive functions. Dance interventions involving patients with PD show better quality of life and improved mobility, whereas the literature on dance-induced neuroplasticity in PD is sparse. Nevertheless, this review argues that similar neuroplastic mechanisms may be at work in patients with PD, provides insight into the potential mechanisms underlying dance efficacy, and highlights the potential of dance therapy as a non-pharmacological intervention in PD. Further research is warranted to determine the optimal dance style, intensity, and duration for maximum therapeutic benefit and to determine the long-term effects of dance intervention on PD progression.
Keywords: dance, neurodegeneration, tremor, rhythm, sensorimotor integration
Published in DiRROS: 29.06.2023; Views: 360; Downloads: 183
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Abstract: Background Sarcopenia is a common skeletal muscle syndrome that is common in older adults but can be mitigated by adequate and regular physical activity. The development and severity of sarcopenia is favored by several factors, the most influential of which are a sedentary lifestyle and physical inactivity. The aim of this observational longitudinal cohort study was to evaluate changes in sarcopenia parameters, based on the EWGSOP2 definition in a population of active older adults after eight years. It was hypothesized that selected active older adults would perform better on sarcopenia tests than the average population. Methods The 52 active older adults (22 men and 30 women, mean age: 68.4±5.6 years at the time of their first evaluation) participated in the study at two time points eight-years apart. Three sarcopenia parameters were assessed at both time points: Muscle strength (handgrip test), skeletal muscle mass index, and physical performance (gait speed), these parameters were used to diagnose sarcop0enia according to the EWGSOP2 definition. Additional motor tests were also performed at follow-up measurements to assess participants’ overall fitness. Participants self-reported physical activity and sedentary behavior using General Physical Activity Questionnaire at baseline and at follow-up measurements. Results In the first measurements we did not detect signs of sarcopenia in any individual, but after 8 years, we detected signs of sarcopenia in 7 participants. After eight years, we detected decline in ; muscle strength (-10.2%; p<.001), muscle mass index (-5.4%; p<.001), and physical performance measured with gait speed (-28.6%; p<.001). Similarly, self-reported physical activity and sedentary behavior declined, too (-25.0%; p=.030 and −48.5%; p<.001, respectively). Conclusions Despite expected lower scores on tests of sarcopenia parameters due to age-related decline, participants performed better on motor tests than reported in similar studies. Nevertheless, the prevalence of sarcopenia was consistent with most of the published literature. Trial registration The clinical trial protocol was registered on ClinicalTrials.gov, identifier: NCT04899531
Keywords: elderly, physical activities, sedentary behavior, skeletal muscle disorder, sarcopenia
Published in DiRROS: 29.05.2023; Views: 332; Downloads: 173
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Abstract: There is conflicting evidence about how interference control in healthy adults is affected by walking as compared to standing or sitting. Although the Stroop paradigm is one of the best-studied paradigms to investigate interference control, the neurodynamics associated with the Stroop task during walking have never been studied. We investigated three Stroop tasks using variants with increasing interference levels – word-reading, ink-naming, and the switching of the two tasks, combined in a systematic dual-tasking fashion with three motor conditions – sitting, standing, and treadmill walking. Neurodynamics underlying interference control were recorded using the electroencephalogram. Worsened performance was observed for the incongruent compared to congruent trials and for the switching Stroop compared to the other two variants. The early frontocentral event-related potentials (ERPs) associated with executive functions (P2, N2) differentially signaled posture-related workloads, while the later stages of information processing indexed faster interference suppression and response selection in walking compared to static conditions. The early P2 and N2 components as well as frontocentral Theta and parietal Alpha power were sensitive to increasing workloads on the motor and cognitive systems. The distinction between the type of load (motor and cognitive) became evident only in the later posterior ERP components in which the amplitude non-uniformly reflected the relative attentional demand of a task. Our data suggest that walking might facilitate selective attention and interference control in healthy adults. Existing interpretations of ERP components recorded in stationary settings should be considered with care as they might not be directly transferable to mobile settings.
Keywords: Stroop task, mobile brain imaging, mobile body imaging, event-related potential, dual tasking
Published in DiRROS: 29.03.2023; Views: 405; Downloads: 255
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Abstract: The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain–computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals’ performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.
Keywords: aging, elderly, machine learning, visual oddball study, brain-computer interface
Published in DiRROS: 01.02.2023; Views: 399; Downloads: 211
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Abstract: Efficient movements require intact motor and cognitive function. There is a growing literature on motor-cognitive interventions to improve the overall quality of life of healthy or diseased older people. For such interventions, novel technological advances are crucial not only in terms of motivation but also to improve the user experience in a multi-stimuli world, usually offered as a mixture of real and virtual environments. This article provides a classification system for movement-related research dealing with motor-cognitive interventions performed in different extents of a virtual environment. The classification is divided into three categories: (a) type of digital device with the associated degree of immersiveness provided; (b) presence or absence of a human-computer interaction; and (c) activity engagement during training, defined by activity >1.5 Metabolic Equivalent of task. Since virtual reality (VR) often categorizes different technologies under the same term, we propose a taxonomy of digital devices ranging from computer monitors and projectors to head-mounted VR technology. All immersive technologies that have developed rapidly in recent years are grouped under the umbrella term Extended Reality (XR). These include augmented reality (AR), mixed reality (MR), and VR, as well as all technologies that have yet to be developed. This technology has potential not only for gaming and entertainment, but also for research, motor-cognitive training programs, rehabilitation, telemedicine, etc. This position paper provides definitions, recommendations, and guidelines for future movement-related interventions based on digital devices, human-computer interactions, and physical engagement to use terms more consistently and contribute to a clearer understanding of their implications.
Keywords: extended reality, virtual reality, augmented reality, mixed reality, exergaming, taxonomy, classification
Published in DiRROS: 21.12.2022; Views: 401; Downloads: 202
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