We should monitor our patients with wearable technology instead of neurological examination – Yes

At most, patients with multiple sclerosis (MS) may spend 2 hours with their clinician annually. During this time, the neurological examination will form a large basis of their doctor’s assessment of their MS type and MS stability, and may even dictate the landscape of disease-modifying treatments offered to them. However, this examination is largely unchanged from 150 years ago,^1,2 at a time when objective modalities to query the nervous system were virtually non-existent – as were treatments to preserve neurological ability. ³ For MS purposes, the examination is not sensitive enough to capture interval development of inflammatory lesions (most of which are asymptomatic). ⁴ Even the standardized MS-specific Expanded Disability Status Scale (EDSS) is famously weighted towards observable ambulatory impairment, at the expense of measures dependent on patient reports or subjective response to a stimulus. Furthermore, the EDSS hardly considers the synergistic effects of each functional system on overall function. For example, a patient’s physical and social activity may be more impacted by fatigue, anxiety and wearing a pad for urinary incontinence (EDSS 2.5) than by reliance on a cane (EDSS 6). Despite our patients’ reports of the accumulating and synergistic toll that these ‘invisible’ symptoms take on their quality of life, year after year many neurological evaluations conclude that ‘the patient is clinically stable’.

If our goal as clinicians is to get ahead of the curve in monitoring and even thwarting progression before it becomes clinically observable (i.e. visible to the doctor), then we need measures that provide accurate, objective and specific information. We have arrived at a point where wearables can solve these challenges. Wearable technology, consisting of devices made from a variety of materials attached to different limbs or body parts, can record real-time information about an individual’s ‘physiological condition and motion activities’. ⁵ Wearables with these characteristics would include the commercial accelerometers and gyroscopes most commonly evaluated ⁶ and an expanding array of other biosensors. ⁷ We further include in our consideration the sensors included in handheld devices (e.g. in smartphones) as well as ambient biosensors that are being clinically validated. Studies have shown that wearable devices are well received in the MS community and adopted by patients of similar ages and disability levels to the general MS population. Furthermore, adherence is fairly high, at least in the short term.^8,9 These findings put to rest prior concerns about the ability of many people with MS to adopt wearable or other digital technologies to navigate their economic, social or medical worlds.

Wearables present distinct advantages over the clinic-based neurological examination. First, by enabling discrete and passive acquisition of objective data, they can overcome a clinician’s inherently biased lens, the limitations of patient-reported function, as well as any ‘observer effects’ arising in the clinical visit (e.g. a patient’s anxiety-induced poor performance during testing). Tracking sleep patterns or episodic falls in real-time, for example, will be more dependable than relying on the inevitably biased self-report by patients with unevenly perceived deficits and possibly cognitive issues. Second, they provide a more sustained snapshot of a patient’s function, as their durability allows the collection of patient-generated data over long periods of time with minimal patient burden. This sustained image reduces noise created by the neurological visit itself (e.g. fatigue due to travel to clinic, parking stressors), and by other temporally related factors (e.g. a recent cold), that can impact function on a clinic day. This in turn can uncover stronger relationships between functional domains. Third, this protracted timeframe also provides granularity about any periods of variability between clinical visits that could signal relapses, improved attempts at exercise or fluctuations in functional domains at risk of irreversible decline. For example, trends in activity data might identify opportunities to promote increased activity (e.g. by noting that activity is generally increased in the morning or on grocery shopping days) or during certain seasons. ⁹ Fourth, features extracted from wearables can be more sensitive to change than in-clinic examination. For example, a study of activity levels over 1 year showed that an MS patient’s average daily step count (STEPS) reveals important variability in real-world ambulation, even within disability levels (e.g. in patients with an EDSS of 6.0, STEPS ranged from 1097 to 7150). ¹⁰ Clearly, these patients have different abilities and function, yet both are classified by the EDSS with the same rating. Finally, in addition to passive data collection, wearables can also collect data during specific tasks, such as performing a 6-minute walking test or responding to a survey. This enables collection of repeated measures which over time better approximate a patient’s true function. Using apps to deliver reminders to patients to complete these in real-time reduces the risk of ‘forgetting’ or amplifying the responses. Altogether then, wearables provide a more holistic, ecologically valid picture of the patient in their lived world, including early warnings of loss of function, and highlight how function across various domains interacts to impact overall patient well-being.

For wearables to realize their full potential in the MS clinic, clear challenges must be overcome. First, during this time of rapid proliferation of wearable technology, clinical validation will always lag several steps behind technological innovation and the designed obsolescence of specific devices. Therefore, attention should be placed to validating the clinical utility of features extractable from various brands (and generations) of devices, such as daily step count. Second, attention must be paid to disparities in access to these technologies, whether educational, socioeconomic, geographic or due to patients’ specific functional impairments. Third, when patient action is required to obtain a performance measure through the wearable device (such as a timed tapping test or balance test), the ideal sampling rate should be determined to balance patient burden and data granularity. Fourth, algorithms are needed to interpret trends in observed measures and to both uncover predictive patterns for disability progression and alert to changes in function. Fifth, ideal protections on patient privacy should be determined. Most importantly, workflows are required to ensure the efficient delivery of patient-generated wearable data to the clinician and to the point of care, and their subsequent visualization as digestible and actionable information. Ideally, these data must be delivered right into the electronic medical record to ensure that they are not relegated, as are many wearable data currently, to the digital graveyard.

Clinically, patients tell us they have deteriorated, and their qualities of life have deteriorated, long before we ascertain this on an objective neurological examination or scale. Wearables provide a sustained, naturalistic way of making the invisible visible to the clinician, of rendering eloquent patterns of silent progression. The neurological examination will of course remain relevant to establish an initial diagnosis and to evaluate new complaints. But to monitor progression, wearables will provide the opportunity to detect, observe and promptly address any changes in function in people with MS. This will free up some time during the episodic clinical encounter to discuss actionable ways of preventing decline and improving function. Much of the work now depends on building seamless ways of placing digested, actionable patient-generated data from wearables into the hands of clinicians to enable them to become clinically transformative.