INNOVATIVE ADVANCED PHYSIOTHERAPY ALGORITHM FOR LOW BACK PAIN

Introduction: Low back pain (LBP) is a prevalent issue with a wide array of causes necessitating varied therapeutic strategies. Given its significant impact on the global population and quality of life, innovative approaches in physiotherapy are essential. Materials and Methods: This study was conducted at the Medica Expert Medical Center, where 29,786 primary examinations of individuals over 18 years old were reviewed. The study focuses on the development and application of an advanced physiotherapy algorithm designed to enhance classification and treatment modalities for LBP. Results: In 2022, nearly half (47%) of the examinations were related to LBP, highlighting the need for specialized therapeutic approaches. The innovative algorithm implemented demonstrated substantial improvements in patient outcomes, including better pain management and enhanced mobility. Discussion: The treatment-based classification system updated in 2015 was expanded to include a wider range of physiotherapy tasks, significantly aiding in the personalization and effectiveness of treatment protocols. This has implications for both the efficiency of clinical practice and patient quality of life. Conclusion: The innovative advanced physiotherapy algorithm for LBP offers a detailed and adaptable approach that improves upon traditional classification systems. Its application has shown promising results in clinical practice, suggesting a substantial advancement in the management of LBP. References: A comprehensive list of references from recent studies and significant literature supports the findings and methodologies applied in the study, including contributions from major health organizations and previous empirical research.

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Publication Details

Journal
Journal of IMAB - Annual Proceeding (Scientific Papers)
Published
2024-10-10
DOI
https://doi.org/10.5272/jimab.2024304.5789
Primary Topic
Medical Imaging and Analysis
Type
article
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article

INNOVATIVE ADVANCED PHYSIOTHERAPY ALGORITHM FOR LOW BACK PAIN

Aleksandar Andreev
Journal of IMAB - Annual Proceeding (Scientific Papers)
Medical Imaging and Analysis
article

INNOVATIVE ADVANCED PHYSIOTHERAPY ALGORITHM FOR LOW BACK PAIN

Aleksandar Andreev
article en

Abstract

Introduction: Low back pain (LBP) is a prevalent issue with a wide array of causes necessitating varied therapeutic strategies. Given its significant impact on the global population and quality of life, innovative approaches in physiotherapy are essential. Materials and Methods: This study was conducted at the Medica Expert Medical Center, where 29,786 primary examinations of individuals over 18 years old were reviewed. The study focuses on the development and application of an advanced physiotherapy algorithm designed to enhance classification and treatment modalities for LBP. Results: In 2022, nearly half (47%) of the examinations were related to LBP, highlighting the need for specialized therapeutic approaches. The innovative algorithm implemented demonstrated substantial improvements in patient outcomes, including better pain management and enhanced mobility. Discussion: The treatment-based classification system updated in 2015 was expanded to include a wider range of physiotherapy tasks, significantly aiding in the personalization and effectiveness of treatment protocols. This has implications for both the efficiency of clinical practice and patient quality of life. Conclusion: The innovative advanced physiotherapy algorithm for LBP offers a detailed and adaptable approach that improves upon traditional classification systems. Its application has shown promising results in clinical practice, suggesting a substantial advancement in the management of LBP. References: A comprehensive list of references from recent studies and significant literature supports the findings and methodologies applied in the study, including contributions from major health organizations and previous empirical research.

Journal of IMAB - Annual Proceeding (Scientific Papers)Vol. 30(4)
Openalex Percentile: Top 84%
Medical Imaging and Analysis
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