ReviewJournal of Parkinson's disease2024
Action Observation and Motor Imagery as a Treatment in Patients with Parkinson's Disease.
Review in Journal of Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Motor imagery training promotes motor learning and brain plasticity without fatigability in people with progressive multiple sclerosis.European journal of physical and rehabilitation medicine · 2025Trial
- Bilateral intermittent theta-burst stimulation as a priming strategy to enhance action observation and imitation training in early parkinson's disease: a proof-of-concept study.Journal of neuroengineering and rehabilitation · 2025Trial
- Effects of active action observation on cognitive, emotional, motor, and somatosensory outcomes in adolescents with juvenile idiopathic arthritis: a prospective exploratory case series.Frontiers in human neuroscience · 2026Article
- The Impact of Dance-Based Physical Activity on Sensorimotor and Psychological Function in Parkinson's Disease: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Brain functional reorganization in pediatric patients with spinal cord injury and the impact of motor imagery training on it: a preliminary study.IBRO neuroscience reports · 2025Article
- Article
- Digital dance programs for Parkinson's disease: challenges and opportunities.Frontiers in psychology · 2025Article
- Beyond walking: gait context and demands shape arousal and valence evoked by observation in Parkinson's disease.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Action observation (AO) and motor imagery (MI) has emerged as promising tool for physiotherapy intervention in Parkinson's disease (PD). This narrative review summarizes why, how, and when applying AO and MI training in individual with PD. We report the neural underpinning of AO and MI and their effects on motor learning. We examine the characteristics and the current evidence regarding the effectiveness of physiotherapy interventions and we provide suggestions about their implementation with technologies. Neurophysiological data suggest a substantial correct activation of brain networks underlying AO and MI in people with PD, although the occurrence of compensatory mechanisms has been documented. Regarding the efficacy of training, in general evidence indicates that both these techniques improve mobility and functional activities in PD. However, these findings should be interpreted with caution due to variety of the study designs, training characteristics, and the modalities in which AO and MI were applied. Finally, results on long-term effects are still uncertain. Several elements should be considered to optimize the use of AO and MI in clinical setting, such as the selection of the task, the imagery or the video perspectives, the modalities of training. However, a comprehensive individual assessment, including motor and cognitive abilities, is essential to select which between AO and MI suite the best to each PD patients. Much unrealized potential exists for the use AO and MI training to provide personalized intervention aimed at fostering motor learning in both the clinic and home setting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.