2024 American Academy of Neurology Abstract Website

Objective:

Assessing the feasibility of using mobile phone data to identify features of parkinsonism in a rural populations in Gujarat, India.

Background:

In many underserved areas of the world, there is a scarcity of neurologists. In low-income countries, there are around 0.03 per 100K neurologists, whereas there are 3 100K in high-income countries. However, for conditions such as Parkinson’s Disease (PD) or Essential Tremor (ET), proper and early diagnosis is important for long term management. Our project aims to address this gap by using mobile phone video to diagnose PD and ET.

Design/Methods:

In a previous study, we collected data from a random sampling of patients from a rural area of Gujarat, India. Patients were examined using the Unified Parkinson’s Disease Rating Scale (UPRDS). Using a survey, clinicians indicated if features of parkinsonism (gait abnormality, slowness, rigidity, resting tremor) were seen for each patient and whether they could diagnose parkinsonism. The raters consisted of 2 neurology resident PGY-4's, 1 internal medicine attending, 2 neurology attendings, and 1 movement disorders specialist. Agreement and kappa statistics were calculated between different rater pairs.

Results:

Notably, the kappa value for diagnosing parkinsonism between the internal medicine attending and the neurology attending was 0.7 and between the internal medicine attending and movement disorder attending was 0.8, showing substantial agreement. Furthermore, the levels of agreement between internal medicine attendings and neurology attendings for presence of gait abnormalities and tremor also was within the range of kappa values showing substantial agreement.

Conclusions:

In underserved areas, primary care physicians often provide specialty care for patients. When using videos of patients examined through the UPDRS, internal medicine attendings diagnose parkonsinism with comparable reliability as movement disorder specialists. This suggests that, in areas with limited access to specialty care, mobile technology can be leveraged to diagnose patients early and provide expedited and efficient treatment.