ArticleSensors (Basel, Switzerland)2021
Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
25 citing papers in PubMed, 38 citations in OpenAlex.
- Joint torque estimation from daily living motion for passive sarcopenia monitoring in older adults.Journal of neuroengineering and rehabilitation · 2026Article
- Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.PloS one · 2026Article
- Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors.Scientific reports · 2025Article
- Article
- Investigation into Application of AI and Telemedicine in Rural Communities: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Implementing an inclusive digital health ecosystem for healthy aging: a case study on project SingaporeWALK.JAMIA open · 2025Article
- Advancing fall risk prediction in older adults with cognitive frailty: A machine learning approach using 2-year clinical data.PloS one · 2025Article
- Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Quantitative biomechanical analysis in validating a video-based model to remotely assess physical frailty: a potential solution to telehealth and globalized remote-patient monitoring.Biomedical engineering letters · 2024Article
- Development of a Frailty Prediction Model Among Older Adults in China: A Cross-Sectional Analysis Using the Chinese Longitudinal Healthy Longevity Survey.Nursing open · 2024Article
- Physical Frailty Prediction Using Cane Usage Characteristics during Walking.Sensors (Basel, Switzerland) · 2024Article
- Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.JMIR formative research · 2024Article
- The Application of Digital Frailty Screening to Triage Nonhealing and Complex Wounds.Journal of diabetes science and technology · 2024Article
- CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living.Sensors (Basel, Switzerland) · 2024Article
- Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement.Sensors (Basel, Switzerland) · 2024Article
- Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer.Scientific reports · 2024Article
- Entropy in Electroencephalographic Signals Modulates with Force Magnitude During Grasping - A Preliminary Report.Journal of motor behavior · 2024Article
- Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.Journal of medical Internet research · 2023Article
- Cellular Senescence and Frailty in Transplantation.Current transplantation reports · 2023Article
- Measurement of Trunk Movement during Sit-to-Stand Motion Using Laser Range Finders: A Preliminary Study.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.