ArticleSensors (Basel, Switzerland)2020
A Microservices e-Health System for Ecological Frailty Assessment Using Wearables.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technological applications to enhance independence in daily activities for older adults: a systematic review.Frontiers in public health · 2024Pooled it
- AI-Driven Activity of Daily Living Monitoring: A Comprehensive Review of Ambient, Wearable, and Fusion-Based Sensing Technologies.Annals of biomedical engineering · 2026Review
- Novel Frailty Assessment Based on Multidimensional Physical Frailty Parameters Using Unsupervised Clustering in Respiratory Diseases: A Pilot Study.Journal of clinical medicine · 2026Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA).Frontiers in aging · 2025Article
- GPS Suitability for Physical Frailty Assessment.Sensors (Basel, Switzerland) · 2024Article
- Systematic design of health monitoring systems centered on older adults and ADLs.BMC medical informatics and decision making · 2024Article
- Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.Journal of medical Internet research · 2023Article
- Enhancing the Longevity and Functionality of Ti-Ag Dry Electrodes for Remote Biomedical Applications: A Comprehensive Study.Sensors (Basel, Switzerland) · 2023Article
- A psycho-educational intervention programme for parents with SGA foetuses supported by an adaptive mHealth system: design, proof of concept and usability assessment.BMC medical informatics and decision making · 2022Article
- Effects of Remotely Supervised Physical Activity on Health Profile in Frail Older Adults: A Randomized Controlled Trial Protocol.Frontiers in aging neuroscience · 2022Article
- An Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) for Individuals With Coronary Heart Disease: Development and Usability Testing Analysis.JMIR mHealth and uHealth · 2021Article
- Unobtrusive Sensors for the Assessment of Older Adult's Frailty: A Scoping Review.Sensors (Basel, Switzerland) · 2021Article
- Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.
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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.