Highlights
Multiple sclerosis (MS) is a chronic condition that affects the central nervous system, disrupting communication between the brain and the body.
MS is often called the 'disease of a thousand faces' because its symptoms vary widely not only from person to person but even on a day-to-day basis. Common symptoms include fatigue, blurred vision, muscle stiffness, loss of balance, and cognitive challenges. These symptoms are difficult to identify externally, unpredictable, change rapidly, and can be attributed to many other causes besides MS. All these make MS especially difficult to manage.
Such transient and subjective symptoms can occur multiple times in a single day, making it difficult to track and report accurately. Often:
This creates a clinical blind spot. Without continuous data, treatment decisions rely on incomplete information, increasing the risk of relapses or progression.
Symptoms of MS are highly subjective as it is governed by the respective immune systems of affected individuals.
Even when two patients have lesions in the same area of the brain or spinal cord, their symptoms can still differ dramatically. For example, one patient may experience vision problems, while another struggles with balance or speech. The difference lies in individual brain connectivity, immune response, and overall health profile.
This means treatment cannot be one size fits all. It must be personalised, considering both the patients’ medical history and their unique symptom patterns.
Modern healthcare is moving towards integrated monitoring systems that combine the depth of past medical records with the immediacy of real-world data.
Historical information from magnetic resonance imaging (MRI) scans, lab results, and neurological tests, provides a strong foundation for understanding disease history and progression. But MS is dynamic, and what happens between clinic visits often goes unseen. That’s where digital biomarkers come in.
Using wearables, smartphones, and non-invasive sensors, a holistic view of the patient’s functional reality can be captured through specific, quantitative indicators:
MS patients can comfortably track key indicators like gait, speech patterns, heart rate variability (HRV), and sleep passively through a smartwatch and smartphone, which integrate seamlessly into daily life without adding strain. More specialised sensors such as EEG headbands, smart glasses, and sEMG patches will be reserved for short, scheduled assessments triggered by specific symptoms or clinical needs. This approach minimises patient burden while ensuring comprehensive monitoring and timely interventions when necessary.
To understand what is normal for a person, the system passively monitors for a period by tracking gait and mobility, speech and cognitive function, heart and sleep metrics, among other parameters, and builds personalised baseline.
This baseline is personalised, not generic. It reflects each patient’s unique lifestyle and health patterns.
Intelligent alerts: After establishing the baseline, the system continuously tracks and compares real-time data against this personalised profile. If it detects persistent deviations—such as slower walking speed, increased speech pauses, or reduced sleep quality, it compares these signals against the patient’s baseline and clinical history.
When patterns suggest disease activity or treatment drift, the system generates smart alerts. These alerts can:
Smart alerts with reason codes include context such as: ‘Mobility decline beyond normal fatigue pattern’; ‘speech slowdown persists despite adequate sleep, possible MS activity’, or ‘sleep disruption likely driving fatigue, consider lifestyle adjustment’.
Not every symptom is MS-related; to avoid false alarms, the system uses multi-layered intelligence validated by research.
Symptoms such as fatigue after a long day or even slower typing caused by stress can mimic MS signs. The following multi-layered approach can help diagnose MS:
Systematic reviews and prospective studies support this layered approach as both feasible and clinically meaningful for motor, cognitive, mood, sleep, and fatigue monitoring.
Collecting data is only the first step. What truly matters is how it helps doctors make better decisions.
Conclusion
This approach is a big step forward in managing multiple sclerosis. It makes life easier for patients by removing the need to track symptoms manually, gives clinicians a clear, real-time picture of health, and helps improve outcomes through timely, personalised care.
By integrating historical medical records, personalised baselines, and real-world digital biomarkers, healthcare can transition from reactive treatment to proactive, precision-driven care. This digital AI-powered approach makes invisible symptoms visible and actionable, creating a smarter, connected, and patient-centric ecosystem that not only transforms MS management but also sets the stage for addressing other complex chronic conditions in the future.