RT Journal Article
SR Electronic
A1 Martinikova, Martina
A1 Ruzinak, Robert
A1 Hnilicova, Petra
A1 Bittsansky, Michal
A1 Grendar, Marian
A1 Babalova, Lucia
A1 Skacik, Pavol
A1 Kantorova, Ema
A1 Nosal, Vladimir
A1 Turcanova Koprusakova, Monika
A1 Sivak, Jozef
A1 Sivakova, Jana
A1 Biringerova, Zuzana
A1 Kolarovszki, Branislav
A1 Zelenak, Kamil
A1 Kurca, Egon
A1 Sivak, Stefan
T1 Safety and efficacy of simple training protocol in patients after mild traumatic brain injury
JF Biomedical papers
YR 2024
VO 168
IS 4
SP 295
OP 303
DO 10.5507/bp.2023.013
UL https://biomed.papers.upol.cz/artkey/bio-202404-0004.php
AB Aims. Mild Traumatic Brain Injury (mTBI) is the most common type of craniocerebral injury. Proper management appears to be a key factor in preventing post-concussion syndrome. The aim of this prospective study was to evaluate the effect and safety of selected training protocol in patients after mTBI. Methods. This was a prospective study that included 25 patients with mTBI and 25 matched healthy controls. Assessments were performed in two sessions and included a post-concussion symptoms questionnaire, battery of neurocognitive tests, and magnetic resonance with tractography. Participants were divided into two groups: a passive subgroup with no specific recommendations and an active subgroup with simple physical and cognitive training. Results. The training program with slightly higher initial physical and cognitive loads was well tolerated and was harmless according to the noninferiority test. The tractography showed overall temporal posttraumatic changes in the brain. The predictive model was able to distinguish between patients and controls in the first (AUC=0.807) and second (AUC=0.652) sessions. In general, tractography had an overall predictive dominance of measures. Conclusion. The results from our study objectively point to the safety of our chosen training protocol, simultaneously with the signs of slight benefits in specific cognitive domains. The study also showed the capability of machine learning and predictive models in mTBI patient recognition.