Since the 1980s, it’s been known that neurological soft signs (NSS) can distinguish schizophrenia from healthy individuals. NSS are subtle neurological impairments, mainly affecting sensory integration, balance, rapid movements, and orientation. Identifying them can improve diagnosis and treatment, yet assessing them has been hindered for over 40 years due to subjectivity in rating systems, making measurement and comparison challenging.
In 2016, Dr. Torous and his colleagues were the first to define “digital phenotyping,” the use of data from smart devices to identify behavior patterns that can detect disease. In the case of NSS, physicians can collect information from a smartphone’s motion sensors, like those that adjust the screen display from portrait to landscape and react to gaming-related gestures, to detect behavior patterns as possible NSS markers. For example:
A smartphone makes objective measurements so it can quantitatively evaluate how well a patient does on a test rather than have the examiner decide whether the patient is impaired. In addition, using smartphones for NSS measurement could reduce the time necessary for neurological office visits since patients could complete aspects of the examination before or after a visit.
“Incorporating digital phenotyping into NSS assessment offers the potential to make measurement more scalable, accessible, and directly comparable across locations, cultures, and demographics,” Dr. Torous and his colleagues note. Some tests may require use of a camera, but with proper security and privacy features in place the data could be safely employed.
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“The rapid adoption of telehealth measures due to the COVID-19 pandemic increased patient buy-in and enhanced privacy considerations, making it an advantageous time to introduce smartphone sensing technology and tools to measure NSS,” the authors conclude.
Reference :
- Potential Role of Smartphone Technology in Advancing Work on Neurological Soft Signs with a Focus on Schizophrenia – (https://pubmed.ncbi.nlm.nih.gov/37699066/)
Source: Eurekalert
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