A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease

Abstract

Amyotrophic lateral sclerosis (ALS) is the most common subtype of motor neuron disease (MND). The current gold-standard measure of progression is the ALS Functional Rating Scale—Revised (ALS-FRS(R)), a clinician-administered questionnaire providing a composite score on physical functioning. Technology offers a potential alternative for assessing motor progression in both a clinical and research capacity that is more sensitive to detecting smaller changes in function. We reviewed studies evaluating the utility and suitability of these devices to evaluate motor function and disease progression in people with MND (pwMND). We systematically searched Google Scholar, PubMed and EMBASE applying no language or date restrictions. We extracted information on devices used and additional assessments undertaken. Twenty studies, involving 1275 (median 28 and ranging 6–584) pwMND, were included. Sensor type included accelerometers (n = 9), activity monitors (n = 4), smartphone apps (n = 4), gait (n = 3), kinetic sensors (n = 3), electrical impedance myography (n = 1) and dynamometers (n = 2). Seventeen (85%) of studies used the ALS-FRS(R) to evaluate concurrent validity. Participant feedback on device utility was generally positive, where evaluated in 25% of studies. All studies showed initial feasibility, warranting larger longitudinal studies to compare device sensitivity and validity beyond ALS-FRS(R). Risk of bias in the included studies was high, with a large amount of information to determine study quality unclear. Measurement of motor pathology and progression using technology is an emerging, and promising, area of MND research. Further well-powered longitudinal validation studies are needed.

Remote monitoring of amyotrophic lateral sclerosis using wearable sensors detects differences in disease progression and survival: a prospective cohort study

“…Although mostly studied as a proof-of-concept in ALS, 14 previous research has shown the feasibility of accelerometery, demonstrating significant correlations with the ALSFRS-R. 19 , 20 , 21 , 22 , 23 , 24 This study additionally delineated the relationship between the longitudinal trajectory of VMI and overall survival, and demonstrated its ability to differentiate between fast- and slow-progressing patients. This holds significance as it aids in interpreting effect sizes in terms of clinical relevance, thereby contributing to the translation of accelerometer-derived markers to meaningful outcomes.…”

“…Contrarily to survey-based methods, outcomes based on digital health technology purposely assess only one disease domain (e.g., speech, motor, or respiratory function) and allow for an objective and detailed assessment, potentially improving their quantification of domain-specific disease progression. 11 , 12 , 13 For the assessment of motor function, various devices have been developed, 14 collecting data during specific exercises or obtaining data passively throughout the day. Advantages of outcomes derived from passive monitoring are that they are not bound to the patient's ability to complete a specific exercise, may have higher compliance, 15 and may better reflect the patient's real-world functioning.…”

“…Additionally, while already more closely examined in other neuromuscular diseases, 28 , 29 operational aspects of accelerometery, such as the required monitoring scheme, have not been fully elucidated for ALS and often remain arbitrary. 14 …”

Developing a digital mind body medicine supportive care intervention for people with amyotrophic lateral sclerosis using stakeholder engagement and design thinking

“… 12 , 19 , 41 In addition, there are evolving technical solutions for different impairments, such as high-tech augmentative and alternative communication technologies. 17 , 42 Furthermore, remote settings showed to be supportive of involving family members and assistant nurses in both ALS care and participatory research. 12 , 19 , 41 …”

Consumer-grade wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

“…Quantitative motor outcome measures may be task-based (i.e., measuring behavior during performance of a specific task) or task-free, where an individual's natural behavior is measured passively and continuously at home. There has been recent development of several task-based approaches to quantify speech and limb function in ALS using scalable technologies at home 9,[16][17][18] and only a single report of a task-free approach in ALS using a waist-worn accelerometer 19 . Task-based measures, however, have some of the same limitations as rating scales in that they are based on a relatively small number of data samples and cannot easily account for diurnal and day-to-day variability, they rely on the participant's ability and motivation to perform the task, and they are susceptible to learning and placebo effects.…”