Previous research suggests that using Fitts' law; attentional focus or challenge point framework (CPF) is beneficial in balance control studies. A scoping review was conducted to examine studies that utilized these motor behavior concepts during balance control tasks. An extensive literature search was performed up to January 2018. Two independent reviewers conducted a study selection process followed by data extraction of the search results. Forty-six studies were identified, with 2 studies related to CPF, 12 studies related to Fitts' law and 32 studies related to focus of attention. The CPF appears to be a useful method for designing a progressive therapeutic program. Fitts' law can be used as a tool for controlling the difficulty of motor tasks. Focus of attention studies indicate that adopting an external focus of attention improves task performance. Overall, studies included in this review report benefit when using the selected motor behavior concepts. However, the majority (>80%) of studies included in the review involved healthy populations, with only three clinical trials. In order to ascertain the benefits of the selected motor behavior concepts in clinical settings, future research should focus on using these concepts for clinical trials to examine balance control among people with balance impairments.
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http://dx.doi.org/10.1080/00222895.2019.1582472 | DOI Listing |
Science
January 2025
Department of Medicine and Surgery, University of Parma, Parma, Italy.
The current understanding of primate natural action organization derives from laboratory experiments in restrained contexts (RCs) under the assumption that this knowledge generalizes to freely moving contexts (FMCs). In this work, we developed a neurobehavioral platform to enable wireless recording of the same premotor neurons in both RCs and FMCs. Neurons often encoded the same hand and mouth actions differently in RCs and FMCs.
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January 2025
Psychology, University of British Columbia, Vancouver, British Columbia, Canada.
Our cognitive capacities like working memory and attention are known to systematically vary over time with our physical activity levels, dietary choices, and sleep patterns. However, whether our metacognitive capacities--such as our strategic use and optimization of cognitive resources--show a similar relationship with these key lifestyle factors remains unknown. Here we addressed this question in healthy young adults by examining if physical activity, diet, and sleep patterns were predictive of self-reported metacognitive status.
View Article and Find Full Text PDFPLoS One
January 2025
School of Sports Science, Harbin Normal University, Harbin, China.
Objective: To explore the impact of aerobic and resistance training on walking and balance abilities (UPDRS-III, Gait Velocity, Mini-BESTest, and TUG) in individuals with Parkinson's disease (PD).
Method: All articles published between the year of inception and July 2024 were obtained from PubMed, Embase, and Web of Science. Meta-analysis was conducted with RevMan 5.
PLoS One
January 2025
Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: The impact of moderate-to-vigorous physical activity (MVPA) on all-cause mortality in type 2 diabetes (T2D) patients with severe abdominal aortic calcification (SAAC) remains unclear.
Methods: We analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2013-2014, including T2D patients aged 40 years and older. AAC was assessed using the Kauppila scoring system, with SAAC defined as a score >6.
PLoS One
January 2025
Department of Orthopedics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Ratchathewi, Bangkok, Thailand.
Among control methods for robotic exoskeletons, biologically inspired control based on central pattern generators (CPGs) offer a promising approach to generate natural and robust walking patterns. Compared to other approaches, like model-based and machine learning-based control, the biologically inspired control provides robustness to perturbations, requires less computational power, and does not need system models or large learning datasets. While it has shown effectiveness, a comprehensive evaluation of its user experience is lacking.
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