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Age and environment-related differences in gait in healthy adults using wearables
- Source :
- NPJ Digital Medicine, npj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
- Publication Year :
- 2020
-
Abstract
- Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.
- Subjects :
- Quality of life
medicine.medical_specialty
Population
Computer applications to medicine. Medical informatics
R858-859.7
Medicine (miscellaneous)
Wearable computer
Health Informatics
Accelerometer
lcsh:Computer applications to medicine. Medical informatics
Predictive markers
Article
03 medical and health sciences
0302 clinical medicine
Gait (human)
Quality of life (healthcare)
Physical medicine and rehabilitation
Health Information Management
medicine
030212 general & internal medicine
Functional ability
education
Wearable technology
education.field_of_study
business.industry
Computer Science Applications
Gait analysis
lcsh:R858-859.7
business
Biomedical engineering
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 23986352
- Volume :
- 3
- Database :
- OpenAIRE
- Journal :
- NPJ digital medicine
- Accession number :
- edsair.doi.dedup.....99924eafa2e72f57d7b7605871cd0ea2