Going beyond PA: Assessing sensorimotor capacity with wearables in multiple sclerosis—a cross-sectional study

Journal of NeuroEngineering and Rehabilitation

Research

Heike Vojta¹  Peter Rieckmann²  Stephanie Schmidle³  Joachim Hermsdörfer³  Philipp Gulde⁴ 

[1] Centre for Clinical Neuroplasticity, Medical Park Loipl, Medical Park SE, Bischofswiesen, Germany;Centre for Clinical Neuroplasticity, Medical Park Loipl, Medical Park SE, Bischofswiesen, Germany;Friedrich-Alexander University Erlangen-Nurnberg, Erlangen, Germany;Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany;Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany;Centre for Clinical Neuroplasticity, Medical Park Loipl, Medical Park SE, Bischofswiesen, Germany;

关键词: Multiple sclerosis;    Physical activity;    Sensorimotor capacity;    Accelerometer;    Gyroscope;    Wrist-worn actigraphy;    Smartwatch;   

DOI  :  10.1186/s12984-023-01247-z

 received in 2022-06-03, accepted in 2023-09-13,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundWearable technologies are currently clinically used to assess energy expenditure in a variety of populations, e.g., persons with multiple sclerosis or frail elderly. To date, going beyond physical activity, deriving sensorimotor capacity instead of energy expenditure, is still lacking proof of feasibility.MethodsIn this study, we read out sensors (accelerometer and gyroscope) of smartwatches in a sample of 90 persons with multiple sclerosis over the course of one day of everyday life in an inpatient setting. We derived a variety of different kinematic parameters, in addition to lab-based tests of sensorimotor performance, to examine their interrelation by principal component, cluster, and regression analyses.ResultsThese analyses revealed three components of behavior and sensorimotor capacity, namely clinical characteristics with an emphasis on gait, gait-related physical activity, and upper-limb related physical activity. Further, we were able to derive four clusters with different behavioral/capacity patterns in these dimensions. In a last step, regression analyses revealed that three selected smartwatch derived kinematic parameters were able to partially predict sensorimotor capacity, e.g., grip strength and upper-limb tapping.ConclusionsOur analyses revealed that physical activity can significantly differ between persons with comparable clinical characteristics and that assessments of physical activity solely relying on gait can be misleading. Further, we were able to extract parameters that partially go beyond physical activity, with the potential to be used to monitor the course of disease progression and rehabilitation, or to early identify persons at risk or a sub-clinical threshold of disease severity.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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