Dance as a neurorehabilitation strategy: A systematic review

Aldana-Benítez, Daniel; Caicedo-Pareja, María José; Sánchez, Diana Patricia; Ordoñez-Mora, Leidy Tatiana

doi:10.1016/j.jbmt.2023.04.046

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Consequently, falls can lead to psychological conditions such as anxiety and depression [ 14 , 15 ]. Numerous research studies on the risk of falls are currently underway, with ongoing investigations into various innovations and intervention ideas [ 16 - 19 ]. These studies encompass the identification of fall risk factors [ 20 , 21 ], strategies for prevention [ 22 , 23 ], and the outcomes following rehabilitation [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

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Background Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv

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Section: Introductionmentioning

confidence: 99%

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

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The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review (Preprint)

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2023

Preprint

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BACKGROUND Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence, on the other hand, represents an innovative tool for creating predictive statistical models of fall risk through data analysis. OBJECTIVE The aim of this review was to analyze the available evidence on the applications of artificial intelligence in the analysis of data related to postural control and fall risk. METHODS A literature search was conducted in six databases with the inclusion criteria: (i) published within the last 5 years (from 2018 to the present); (ii) they had to apply some method of artificial intelligence; (iii) artificial intelligence analyses had to be applied to data from samples consisting of humans; (iv) the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. RESULTS A total of 2987 articles were obtained, of which 22 articles were finally selected. Data extraction for subsequent analysis varied in the different studies: 18 of them extracted data through tests or functional assessments, and the remaining four through existing medical records. Different artificial intelligence techniques were used throughout the articles. All the research included in the review obtained accuracy values of over 70% in the predictive models obtained through artificial intelligence. CONCLUSIONS The use of artificial intelligence proves to be a valuable tool for creating predictive models of fall risk. The utilization of this tool could have a significant socio- economic impact as it enables the development of low-cost predictive models with a high level of accuracy. CLINICALTRIAL PROSPERO ID: CRD42023443277.

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Dance as a neurorehabilitation strategy: A systematic review

Cited by 2 publications

References 30 publications

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review (Preprint)

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