“suMus,” a novel digital system for arm movement metrics and muscle energy expenditure

Abstract

Objective: In the field of non-treatable muscular dystrophies, promising new gene and cell therapies are being developed and are entering clinical trials. Objective assessment of therapeutic effects on motor function is mandatory for economical and ethical reasons. Main shortcomings of existing measurements are discontinuous data collection in artificial settings as well as a major focus on walking, neglecting the importance of hand and arm movements for patients’ independence. We aimed to create a digital tool to measure muscle function with an emphasis on upper limb motility.Methods: suMus provides a custom-made App running on smartwatches. Movement data are sent to the backend of a suMus web-based platform, from which they can be extracted as CSV data. Fifty patients with neuromuscular diseases assessed the pool of suMus activities in a first orientation phase. suMus performance was hence validated in four upper extremity exercises based on the feedback of the orientation phase. We monitored the arm metrics in a cohort of healthy volunteers using the suMus application, while completing each exercise at low frequency in a metabolic chamber. Collected movement data encompassed average acceleration, rotation rate as well as activity counts. Spearman rank tests correlated movement data with energy expenditure from the metabolic chamber.Results: Our novel application “suMus,” sum of muscle activity, collects muscle movement data plus Patient-Related-Outcome-Measures, sends real-time feedback to patients and caregivers and provides, while ensuring data protection, a long-term follow-up of disease course. The application was well received from the patients during the orientation phase. In our pilot study, energy expenditure did not differ between overnight fasted and non-fasted participants. Acceleration ranged from 1.7 ± 0.7 to 3.2 ± 0.5 m/sec2 with rotation rates between 0.9 ± 0.5 and 2.0 ± 3.4 rad/sec. Acceleration and rotation rate as well as derived activity counts correlated with energy expenditure values measured in the metabolic chamber for one exercise (r = 0.58, p < 0.03).Conclusion: In the analysis of slow frequency movements of upper extremities, the integration of the suMus application with smartwatch sensors characterized motion parameters, thus supporting a use in clinical trial outcome measures. Alternative methodologies need to complement indirect calorimetry in validating accelerometer-derived energy expenditure data.

Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases

“…Since gene therapies aim to improve symptoms after only one injection, developing DTx as an adjunct digital intervention or/and "biologic + digital" combination product may support a patient's journey before and after the correction of a mutated gene. In the case of the treatments for amyotrophic lateral sclerosis with tofersen [332] and spinal muscular atrophy with onasemnogege abeparvovec [333], these patients could use digital technologies for monitoring therapy outcomes and delivering neurorehabilitation exercises [334][335][336][337]. "One-time treatment" gene therapies for indications where self-management and self-efficacy can improve therapy outcomes can be developed together with DTx that provide clinically meaningful benefits beyond the injection of DNA vectors [338].…”

The use of digital tools in rare neurological diseases towards a new care model: a narrative review

“…In this clinical trial, patients were recruited based on the genetic diagnosis of FSHD and a certain range of disability, without considering the well-known phenotypic variability in this disease, so that also the use of a digital outcome measure as RWS can become difficult to interpret. Gerhalter et al [86] developed suMus, a smartwatch app equipped with motor exercises videos. Performances are recorder by the inertial sensors of the smartwatch and after each session information feedback from the patient is asked.…”