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Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review

INTRODUCTION: Type 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive method...

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Autores principales: Moura da Silva, Patrícia Mayara, Oliveira Bezerra, Ana Beatriz, Araújo Farias, Luanna Barbara, Ribeiro, Tatiana Souza, Morya, Edgard, Cavalcanti, Fabrícia Azevêdo da Costa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862448/
https://www.ncbi.nlm.nih.gov/pubmed/35190422
http://dx.doi.org/10.1136/bmjopen-2021-051981
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author Moura da Silva, Patrícia Mayara
Oliveira Bezerra, Ana Beatriz
Araújo Farias, Luanna Barbara
Ribeiro, Tatiana Souza
Morya, Edgard
Cavalcanti, Fabrícia Azevêdo da Costa
author_facet Moura da Silva, Patrícia Mayara
Oliveira Bezerra, Ana Beatriz
Araújo Farias, Luanna Barbara
Ribeiro, Tatiana Souza
Morya, Edgard
Cavalcanti, Fabrícia Azevêdo da Costa
author_sort Moura da Silva, Patrícia Mayara
collection PubMed
description INTRODUCTION: Type 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive methods to forecast complications and develop early strategies to guide treatments. To date, no research has systematically summarised the predictive methods used to assess type 2 diabetic gait. Therefore, this protocol aims to identify which predictive methods have been employed to assess the diabetic gait. METHODS AND ANALYSIS: This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) statement. Electronic searches of articles from inception to January 2022 will be performed, from May 2021 to 31 January 2022, in the Web of Science, MEDLINE, Embase, IEEE Xplore Digital Library, Scopus, CINAHL, Google Scholar, APA PsycInfo, the Cochrane Library and in references of key articles and grey literature without language restrictions. We will include studies that examined the development and/or validation of predictive methods to assess type 2 diabetic gait in adults aged >18 years without amputations, use of assistive devices, ulcers or neuropathic pain. Two independent reviewers will screen the included studies and extract the data using a customised charting form. A third reviewer will resolve any disagreements. A narrative synthesis will be performed for the included studies. Risk of bias and quality of evidence will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. ETHICS AND DISSEMINATION: Ethical approval is not required because only available secondary published data will be analysed. The findings will be disseminated through peer-reviewed journals and/or presentations at relevant conferences and other media platforms. PROSPERO REGISTRATION NUMBER: CDR42020199495.
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spelling pubmed-88624482022-03-15 Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review Moura da Silva, Patrícia Mayara Oliveira Bezerra, Ana Beatriz Araújo Farias, Luanna Barbara Ribeiro, Tatiana Souza Morya, Edgard Cavalcanti, Fabrícia Azevêdo da Costa BMJ Open Diabetes and Endocrinology INTRODUCTION: Type 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive methods to forecast complications and develop early strategies to guide treatments. To date, no research has systematically summarised the predictive methods used to assess type 2 diabetic gait. Therefore, this protocol aims to identify which predictive methods have been employed to assess the diabetic gait. METHODS AND ANALYSIS: This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) statement. Electronic searches of articles from inception to January 2022 will be performed, from May 2021 to 31 January 2022, in the Web of Science, MEDLINE, Embase, IEEE Xplore Digital Library, Scopus, CINAHL, Google Scholar, APA PsycInfo, the Cochrane Library and in references of key articles and grey literature without language restrictions. We will include studies that examined the development and/or validation of predictive methods to assess type 2 diabetic gait in adults aged >18 years without amputations, use of assistive devices, ulcers or neuropathic pain. Two independent reviewers will screen the included studies and extract the data using a customised charting form. A third reviewer will resolve any disagreements. A narrative synthesis will be performed for the included studies. Risk of bias and quality of evidence will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. ETHICS AND DISSEMINATION: Ethical approval is not required because only available secondary published data will be analysed. The findings will be disseminated through peer-reviewed journals and/or presentations at relevant conferences and other media platforms. PROSPERO REGISTRATION NUMBER: CDR42020199495. BMJ Publishing Group 2022-02-21 /pmc/articles/PMC8862448/ /pubmed/35190422 http://dx.doi.org/10.1136/bmjopen-2021-051981 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Diabetes and Endocrinology
Moura da Silva, Patrícia Mayara
Oliveira Bezerra, Ana Beatriz
Araújo Farias, Luanna Barbara
Ribeiro, Tatiana Souza
Morya, Edgard
Cavalcanti, Fabrícia Azevêdo da Costa
Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title_full Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title_fullStr Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title_full_unstemmed Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title_short Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
title_sort existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review
topic Diabetes and Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862448/
https://www.ncbi.nlm.nih.gov/pubmed/35190422
http://dx.doi.org/10.1136/bmjopen-2021-051981
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