Activity Monitors as a Measure of Physical Function in Degenerative Cervical Myelopathy

Degenerative cervical myelopathy (DCM) is the most common cause of spinal dysfunction across the world and a major cause of disability. DCM describes a group of conditions that progressively impinge on the spinal cord resulting in functional impairment and a reduced quality of life. Degenerative diseases of the spine increase in prevalence with increases in age. A cadaver study of 469 adults found cervical stenosis of at least one level in 19.2% of adults, 24.5% in those older than 50 and 27% of those older than 70 years. By 2050 the global population over 60 years of age is expected to approach 2 billion. DCM is certainly a prevalent disease in the elderly and will only become more common. For these reasons DCM must be addressed thoroughly in research to improve outcomes.

Symptoms of afflicted patients often include neck pain, numbness/clumsiness in the hands, weakness, upper and lower extremity motor deficits, balance and gait impairment, among others. Non-surgical treatment has limited efficacy, while early surgical treatment is recommended to halt progression, initiate recovery and improve symptoms. Surgical treatment is widely agreed upon, yet measurement of surgical outcomes can be challenging.

Traditionally, assessment of outcomes has been performed subjectively using questionnaires/functional assessments. These question-based outcome measures are popular due to practicality and ease of administration. Examples that have been used in Degenerative Cervical Myelopathy (DCM) include Neck Disability Index (NDI), Myelopathy Disability Index (MDI), Japanese Orthopedic Association (JOA), European Myelopathy Score (EMS), Nurick Score, Ranawat Score, Odom's criteria and Short Form-36 survey (SF-36). This introduces incompatibility when comparing studies using different questionnaires. In addition to the lack of consensus in a standard questionnaire, there are inherent biases in the use of self-reported measures. Self-reported questionnaires and objective disability measures do not always agree, particularly when recent setbacks distort a patients' perception of total improvement. Personal evaluation is subject to inherent bias with a disconnect between perception of disability and actual objective performance.

Gait disturbance may be the most be the most prominent physical finding in DCM, and walking tests have been established as effective, objective measures in DCM pre and post-operatively. A patient's activity level at home provides valuable data on recovery and surgical efficacy, yet prior to wearable technology required self-reported data. Accelerometers have been used to remedy this problem by measuring activity trends in a quantifiable manner, without relying on the subjective response of the patient. They have recently been implemented in orthopedic research. Investigators have compared self-rated disability with objective accelerometer data in patients suffering from lumbar spinal stenosis (LSS) and found self-rated disability did not always reflect objective data. Some investigators used these methods to objectively measure function after surgical intervention, lumbar spinal stenosis decompression. Results are promising and require further investigation into the application of this new tool.

In summary accelerometers offer longitudinal objective measurement of walking and the ability to look at trends in activity levels. Eventually walking measures could be used to monitor patients with mild myelopathy for progression and possibly even be part of a remote screening tool (i.e. a phone tracks walking distance, speed, number of falls per month and prompts for monthly or quarterly handwriting samples). We aim to apply similar concepts of prior investigations to compare accelerometer and question-based outcome data on DCM patients before and after undergoing surgical intervention, to determine the most practical and accurate method for outcome measurement. By incorporating activity monitor data and looking at relationships between the different measures, we will be able to better understand the disease and limit the effort required to diagnose and monitor it.