ArticleBiomedical engineering online2020
Postural control learning dynamics in Parkinson's disease: early improvement with plateau in stability, and continuous progression in flexibility and mobility.
Article in Biomedical engineering online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Instrumental balance assessment in Parkinson's disease and parkinsonism. A systematic review with critical appraisal of clinical applications and quality of reporting.Frontiers in neurology · 2025Pooled it
- Improvements in clinical signs of Parkinson's disease using photobiomodulation: a prospective proof-of-concept study.BMC neurology · 2021Trial
- Improvements in clinical signs and symptoms of Parkinson's disease using photobiomodulation: a five-year follow-up.BMC neurology · 2024Article
- Performing Dynamic Weight-Shifting Balance Exercises With a Smartphone-Based Wearable Telerehabilitation System for Home Use by Individuals With Parkinson's Disease: A Proof-of-Concept Study.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Margins of postural stability in Parkinson's disease: an application of control theory.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundBalance training improves postural control in Parkinson's disease (PD). However, a systematic approach for the development of individualized, optimal training programs is still lacking, as the learning dynamics of the postural control in PD, over a training program, are poorly understood.
objectivesWe investigated the learning dynamics of the postural control in PD, during a balance-training program, in terms of the clinical, posturographic, and novel model-based measures.
methodsTwenty patients with PD participated in a balance-training program, 3 days a week, for 6 weeks. Clinical tests assessed functional balance and mobility pre-training, mid-training, and post-training. Center-of-pressure (COP) was recorded at four time-points during the training (pre-, week 2, week 4, and post-training). COP was used to calculate the sway measures and to identify the parameters of a patient-specific postural control model, at each time-point. The posturographic and model-based measures constituted the two sets of stability- and flexibility-related measures.
resultsMobility- and flexibility-related measures showed a continuous improvement during the balance-training program. In particular, mobility improved at mid-training and continued to improve to the end of the training, whereas flexibility-related measures reached significance only at the end. The progression in the balance- and stability-related measures was characterized by early improvements over the first 3 to 4 weeks of training, and reached a plateau for the rest of the training.
conclusionsThe progression in balance and postural stability is achieved earlier and susceptible to plateau out, while mobility and flexibility continue to improve during the balance training.
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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.