Rehabilitation Medicine: Diagnosis and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 719

Special Issue Editor


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Guest Editor
Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, 73100 Lecce, Italy
Interests: gait analysis; physical medicine; gait; hip; knee; osteoarthritis; posture; rehabilitation medicine; sports injuries
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Special Issue Information

Dear Colleagues,

Physical and rehabilitation medicine represents a critical pillar of medicine focused on restoring function, enhancing independence, and improving quality of life for individuals with various impairments or disabilities. This Special Issue aims to explore a wide spectrum of subjects, including innovative diagnostic techniques involving tools, imaging technologies, biomarker discoveries, and electrotherapeutical agents that are reshaping the diagnosis of musculoskeletal, neurological, and cognitive impairments. The comprehensive collection of articles within this Special Issue encompasses a wide spectrum of topics, including, but not limited to, the following:

  • Tailored rehabilitation approaches: Addressing personalized rehabilitation plans and interventions that cater to individual patient needs, considering the diverse spectrum of conditions and disabilities.
  • Technology integration in rehabilitation: Assessing the role of artificial intelligence, robotics, virtual reality, and wearable devices in optimizing rehabilitation outcomes, fostering patient engagement, and tracking progress.
  • Innovations in pharmacological interventions: Reviewing emerging pharmaceutical treatments and their implications for managing symptoms and enhancing rehabilitation efficacy.

This Special Issue aims to provide a comprehensive overview of the current state and future directions of rehabilitation medicine, serving as a valuable resource for healthcare professionals, researchers, and policymakers invested in enhancing the lives of individuals undergoing rehabilitation processes. Through the dissemination of cutting-edge research and clinical insights, this collection aspires to foster advancements in diagnosis, management, and patient-centered care within the field of rehabilitation medicine.

Prof. Dr. Andrea Bernetti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • innovative diagnostic techniques
  • imaging technologies
  • biomarker
  • electrotherapeutic agents
  • personalized rehabilitation
  • technology integration in rehabilitation

Published Papers (1 paper)

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Research

9 pages, 1019 KiB  
Article
Determination of Five Sit-to-Stand Test Performance at Discharge of Stroke Patients
by Maria Piedad Sánchez-Martínez, María José Crisostomo, Rodrigo Martín-San Agustín, Joaquina Montilla-Herrador, María Pilar Escolar-Reina, Elisa Valera-Novella and Francesc Medina-Mirapeix
Diagnostics 2024, 14(5), 521; https://doi.org/10.3390/diagnostics14050521 - 29 Feb 2024
Viewed by 518
Abstract
The early identification of performance in the five-repetition sit-to-stand test (5-STS) at discharge in stroke patients could be of interest because it can determine independence for community-based activities. This study aimed to determine whether the initial measurement of the 5-STS test can be [...] Read more.
The early identification of performance in the five-repetition sit-to-stand test (5-STS) at discharge in stroke patients could be of interest because it can determine independence for community-based activities. This study aimed to determine whether the initial measurement of the 5-STS test can be a determinant of the performance level prediction and amount of change in the 5-STS test at discharge in stroke patients. A prospective cohort study was conducted with a sample of 56 patients aged ≤60 d post-stroke. The 5-STS test results, as well as changes in patient condition, were measured at admission (T0) to an outpatient rehabilitation program, after the first month (T1), and at discharge (T2). The mean age was 62.7 (SD = 13.0), 58.9% of the subjects were male, and 75% had suffered an ischemic stroke. A multivariate linear regression model using the 5-STS test at T0 explained 57.7% of the variance in the performance at discharge. Using the 5-STS at T1 increased the variance to 75.5% (p < 0.001). Only the time from stroke onset at T0 significantly contributed to the two models. The measurement of the 5-STS at T0 and the amount of change in its performance at T2 explained 60.2% (p < 0.001) of the variance, while reassessment at T1 explained only 19.3% (p < 0.001). The level of patient performance on the 5-STS test at discharge, as well as any potential change, can be predicted by the admission measure of 5-STS in stroke patients. Full article
(This article belongs to the Special Issue Rehabilitation Medicine: Diagnosis and Management)
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