Wearable Sensors Show Promise in Tracking Multiple Sclerosis Progression

New research indicates that wearable sensors, commonly found in wrist-worn devices, may help identify individuals with multiple sclerosis (MS) who are at a higher risk of worsening disability and brain volume loss. These devices track daily activity patterns and can reveal changes that may precede those detected by traditional clinical tests.

How Wearable Sensors Are Being Used in MS Research

A study involving 238 people with MS, with an average age of 55 and an average disease duration of 13 years, utilized wrist-worn sensors (Fitbit Inspire 3) to continuously monitor physical activity for two weeks every three months. The sensors recorded data on light, moderate, and intensive exercise, sedentary time, and sleep-wake rhythms. Neurological tests were conducted every six months to assess disability levels, and brain MRI scans were performed at the beginning of the study and again after two years to measure changes in brain volume. Source

Key Findings: Activity Levels and Disease Progression

Researchers found that 120 participants exhibited signs of disease progression during the study. A decrease in daily physical activity was notably associated with a greater risk of deterioration. Specifically, individuals with reduced activity in the first half of the day were approximately 20% more likely to experience disease progression compared to those with stable activity levels. Source

a decline in morning activity, particularly between 8:00 and 10:00 am, correlated with a loss of brain volume. Each standard deviation decrease in activity was linked to a 0.18% reduction in total brain volume, a 0.34% loss in deep gray matter, and a 0.35% reduction in thalamus volume. Source

The Potential for Early Detection

According to Kathryn C. Fitzgerald of Johns Hopkins University, wearable technology offers a potentially simple and accessible method for detecting subtle changes in MS earlier than current methods allow. “Identifying patients at risk of disease progression in a timely manner is essential to reducing long-term disability, but current tests for measuring MS disability are not designed to detect small changes,” Fitzgerald stated. Source

AI and MS Progression

In related research, Swedish scientists have developed an artificial intelligence (AI) model capable of identifying the type of multiple sclerosis (MS) a patient has with approximately 90% accuracy. This model aids in recognizing the transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS), a shift that often goes undiagnosed for an average of three years. Accurate identification is crucial as the two forms require different treatment approaches. The AI model analyzes clinical data from over 22,000 patients in the Swedish MS registry, including neurological tests, MRI scans, and treatment information. Source

Limitations and Future Research

Researchers emphasize that the current findings demonstrate a correlation between changing activity patterns and disease progression, but do not establish a causal relationship. Additional studies are needed to confirm these results and address limitations, such as the absence of a control group without MS, which makes it difficult to determine the extent to which observed activity changes are related to normal aging. The study population was also relatively older and already exhibited some degree of disability, suggesting that the findings may not be fully generalizable to younger patients or those with milder forms of MS. Source

Despite these limitations, the research suggests that wearable sensors could play a significant role in the future of MS monitoring, potentially enabling earlier detection of disease progression and informing the development of new treatments.