"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.

Keywords: accelerometers; apple watch; energy expenditure; inertial sensors; muscular dystrophies; neuromuscular diseases; outcome measures; smartwatch.

FIGURE 1

Study design for calorimetry and movement measuements. Subjects performed four different exercises in the metabolic chamber with defined resting periods. Movement measurement were recorded during exercises indicated in orange, while the metabolic chamber recorded also during the resting periods. ΔEE: difference of energy expenditure between exercise and pause phase.

FIGURE 2

Programming of the suMus App: (A) Tailored exercise plans designed by the therapists are visualized in the App; (B) During the training, sensors register acceleration and rotation rate; (C) After the exercise, the patient is asked to provide feedbacks; (D) After completing the whole exercise plan, patients are informed through a visual input that rest is suggested.

FIGURE 3

Monthly feedbacks on difficulty, pain, and fun levels by the cohort of patients in the seven-month period of orientation phase.

FIGURE 4

(A) Acceleration (m/sec²) and (B) rotation rate (grad/sec) during the four exercises in 14 healthy controls. Exercise 1: cherry picking, exercise 2: little flyer, exercise 3: shadow boxing, exercise 4: hand biking.

FIGURE 5

(A) ΔEE (KJ/min) derived from indirect calorimetry using the metabolic chamber during the four different exercises in 14 healthy controls. Exercise 1: cherry picking, exercise 2: little flyer, exercise 3: boxing, exercise 4: hand biking; (B) Differentiation of ΔEE (kJ/min) during the four different exercises in 5 (blue dots) and 10 (black dots) healthy controls with and without overnight fasting, respectively.

FIGURE 6

(A) Total activity counts, (B) medium activity counts, and (C) low activity counts derived from the accelerometry data for free arm movement exercises 1–3 in 14 healthy controls. (D) total activity counts for exercise 4 in 14 healthy controls. Exercise 1—cherry picking, exercise 2—little flyer, exercise 3—shadow boxing, exercise 4—hand biking.

FIGURE 7

Linear regression analysis of ΔEE values with (A) average acceleration values, (B) average rotation levels and (C) acceleration-derived average total activity counts for exercise 2. Coefficients of determination (r2) and the statistical significance p-value are indicated.

FIGURE 8

suMus Roadmap. Critical steps of suMus from concept to validation in patients.