Improving the Recovery of Patients with Subacromial Pain Syndrome with the DAid Smart Textile Shirt

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ID: 267863
2020
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Abstract
Wearable technologies provide many possibilities for applications in medicine, and especially in physiotherapy, where tracking and evaluation of body motion are of utmost importance. Despite the existence of multiple smart garments produced for applications in physiotherapy, there is limited information available on the actual impact of these technologies on the clinical outcomes. The objective of this paper is to evaluate the impact of the Double Aid (DAid) smart shirt, a purely textile-based system, on the training process of patients with subacromial pain syndrome. A randomized controlled trial was performed where patients with subacromial pain syndrome had to perform the assigned training exercises while employing the DAid smart shirt system. The core point of each exercise was to perform a movement while holding the shoulders stationary. The smart shirt was designed to sense even slight shoulder motion thus providing the patient with feedback on the accuracy of the motion, and allowing the patient to adjust the movement. The appropriate muscles should be strengthened through an increased effort to control the shoulder motion. The recovery of patients using the feedback system at the end of the treatment was compared to that of a reference group through standardized tests—the Disabilities of the Arm, Shoulder, and Hand score (DASH score), Closed Kinetic Chain Upper Extremity Stability test (CKCUES test), and internal/external rotation ratio. The test group that used the DAid system demonstrated significantly better results of the performed tests for all applied outcome measures compared to the reference group (p < 0.001). An overall positive impact on the patient recovery was observed from the DAid smart shirt system when applied for rehabilitation training of patients with subacromial pain syndrome.
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semjonova2020sensorsimproving Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guna Semjonova;Janis Vetra;Vinita Cauce;Alexander Oks;Alexei Katashev;Peteris Eizentals;Semjonova, Guna;Vetra, Janis;Cauce, Vinita;Oks, Alexander;Katashev, Alexei;Eizentals, Peteris;
Journal sensors
Year 2020
DOI
10.3390/s20185277
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