Correlation of Vertical Perception with Functional Mobility and Risk of Fall in individuals with Parkinsons disease

Parkinson’s disease, a progressive neurodegenerative condition, is known for motor impairments like tremors, bradykinesia, postural instability, and rigidity. Beyond motor symptoms, it involves less-explored alterations in vertical perception, posing challenges in integrating visual, vestibular, and somatosensory information. This can affect the perception of gravitational verticality, impairing postural responses and increasing fall risk. This study focuses on the overlooked aspect of altered vertical perception in Parkinson’s disease and its impact on functional mobility and fall risk. The study aims to provide insights for improved interventions and assessments. Vertical perception will be assessed using subjective visual vertical tests (SVVT) and subjective haptic tests (SHV), alongside functional tests like the timed up and go test and fall efficacy scale - international. The research aims to reveal correlations among these factors. After obtaining research permission, participants meeting predefined criteria will be recruited. Computer-generated randomization tables will divide participants into three groups with distinct assessment sequences. 1/3rd population undergoes SVVT, SHV, timed up and go test, and fall efficacy scale - international; 1/3rd population follows timed up and go test, fall efficacy scale- international, SVVT, SHV, and 1/3rd population has its order as fall efficacy scale - international, SVVT, SHV, and timed up and go test. This approach ensures a comprehensive understanding of the interplay between vertical perception, functional mobility, and fall risk. Data collected will be analyzed to establish correlations of subjective visual vertical and subjective haptic test with functional mobility and fall risk in individuals with Parkinson’s disease. Pearson’s correlation test will be applied if the data follows a normal distribution, while Spearman’s correlation test will be used if the data is not normally distributed.