Blended Therapy-Concept as Medical Treatment with Integrated Forms of Therapy for Post-COVID-19-Patients
Year: 2024
Type: article
Study design: Clinical Trial
Abstract: Background: For the medical treatment of post-COVID-19-syndrome, no medical treatment or causal therapy based on evidence- based criteria is currently recommended.By linking digital therapy modules and classic on-site therapy, central data storage and structured information exchange between the actors, a cross-sector, doctor-led blended therapy was developed.This is aimed at the main syndromes of fatigue/immunometabolic depression and sensorimotor instability (easily manageable real-time cognition parameters).The aim is to reduce the key post-COVID-19 symptoms as well as motor and cognitive fatigue parameters.Methodology: The improvement of the leading syndromes of fatigue and sensorimotor instability through stress-controlled standardized training therapy and intensified cognitive behavioral therapy were the overarching goals of the intervention and observation study over a 3-year period (Q1-2021 -Q2-2024).From a population of 407 vaccinated nucleocapsid-positive patients, 78 patients took part in the study.The outcome assessment was carried out on the one hand based on the post-COVID-19-key symptoms and the motor fatigue parameters.On the other hand, secondary psycho-somatic syndromes that emerged during the course of treatment were examined using cognitive fatigue parameters.Results: With blended therapy, the Oost-COVID-19-key symptoms "fatigue", "neuropsychiatric symptoms" and "cardiopulmonary symptoms" were improved by over 70% as well as "pain" by around 14%. Overall (N=78), a therapeutic effect was ob-served for all motor fatigue parameters.The intensification of cognitive behavioral therapy, in which 19 patients took part, also had positive effects and an increasing development of the patients' own activity and their self-
Authors: Thomas Urban, Heinz Reichmann, Peter Lohse, Stefan G. Spitzer, Erich Schneider, Fritjof Reinhardt
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4405328197