Neuromodulation: Digital Health Technologies and their Applications

MedTech Outlook | Thursday, September 02, 2021

Digital health can spur patient-centric innovation in neuromodulation by integrating existing methods to uncover response predictors and digital biomarkers.

FREMONT, CA:  Digital health innovation is increasingly focused on tackling current concerns. Numerous technologies have demonstrated promise in treating disorders that are frequently treated using neuromodulation therapy. These enabling technologies are discussed in detail below, along with examples of their applications.

Gamification

Gamification enhances the maintenance of neuromodulation therapy by engaging patients and providing objective data about their status. Numerous AndroidTM and iOS applications combine gaming aspects with helping manage motor and cognitive symptoms linked with Parkinson's disease. Patients' motor symptoms are monitored using games that require finger tapping, walking, and speaking. Additionally, gamification applications track mental health and cognitive symptoms to enhance memory, attention, and problem-solving abilities. The Food and Drug Administration (FDA) recently approved the first digital treatment based on a game to treat youngsters with attention deficit and hyperactivity disorder (ADHD). This demonstrates the evolving paradigm and increasing cultural acceptance of digital solutions.

Existing commercial virtual reality gaming systems, such as the Nintendo WiiTM and the X-Box KinectTM, can be used to strengthen gait and balance in people with mobility impairments. Numerous platforms have already been established for exergaming, which requires players to move in response to gamified on-screen duties. Finally, immersive systems create a realistic environment for symptom tracking that may be implemented into a game.

Gamification aims to alleviate some of the stress associated with therapy maintenance while also giving possibilities for rehabilitation. As this field advances, applications will be able to recommend individualized work levels through a combination of machine learning (ML) and artificial intelligence (AI) capabilities. This patient-centered approach has the potential to boost involvement and result in improved therapeutic outcomes.

Computational Medicine

Any healthcare paradigm's ultimate goal should be to improve patients' lives. Frequent consultations and real-world data collecting become possible using the technology indicated above. However, the discipline must still agree on using data and subsequent predictions and personalizing interventions while considering comorbidities.

Computer medicine is the process of improving diagnostic and treatment approaches using insights from computational models. One technique in computational medicine is to employ data analytics and control theory models to elicit the desired reaction from the system. Certain comorbid conditions may also act as predictors of symptoms. For instance, pain patients frequently experience sleep disturbances. Early adoption of wearables that assess sleep quality may aid in symptom prediction. Following spinal cord stimulation (SCS), these parameters can be examined constantly to determine the clinical response objectively. This data can develop a simple phenomenological model to simulate reactions found in a healthy system, where non-noxious stimuli normally do not induce pain. Eventually, this model will deliver programming recommendations based on patient-specific data.

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