Andy
Senior Member (Voting rights)
Highlights
• Disability level (assessed by six-minute walk test) in people with multiple sclerosis was estimated using the data collected from wearable devices in free-living conditions.
• Fitbit minute-level step counts were analysed from which features were extracted.
• Elastic net, gradient boosted trees and random forest were employed to build machine learning models.
• Promising estimation and classification performances were obtained.
• Features capturing the upper end of the step count distribution showed greater importance.
Abstract
Background and objectives: Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.
Methods: In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).
Results: The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.
Conclusions: This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.
Open access, https://www.sciencedirect.com/science/article/pii/S0169260722005855
• Disability level (assessed by six-minute walk test) in people with multiple sclerosis was estimated using the data collected from wearable devices in free-living conditions.
• Fitbit minute-level step counts were analysed from which features were extracted.
• Elastic net, gradient boosted trees and random forest were employed to build machine learning models.
• Promising estimation and classification performances were obtained.
• Features capturing the upper end of the step count distribution showed greater importance.
Abstract
Background and objectives: Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.
Methods: In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).
Results: The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.
Conclusions: This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.
Open access, https://www.sciencedirect.com/science/article/pii/S0169260722005855
