ArticleJournal of healthcare engineering2011
Evolution of an Early Illness Warning System to Monitor Frail Elders in Independent Living.
Article in Journal of healthcare engineering, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Benefits and barriers associated with the use of smart home health technologies in the care of older persons: a systematic review.BMC geriatrics · 2024Pooled it
- Linguistic summarization of in-home sensor data.Journal of biomedical informatics · 2019Trial
- Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study.JMIR research protocols · 2025Article
- Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study.JMIR aging · 2023Article
- Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study.Journal of medical systems · 2018Article
- Older Adults' Perceptions of and Preferences for a Fall Risk Assessment System: Exploring Stages of Acceptance Model.Computers, informatics, nursing : CIN · 2017Article
- Technology-based measurements for screening, monitoring and preventing frailty.Zeitschrift fur Gerontologie und Geriatrie · 2016Review
- A Semantic Approach with Decision Support for Safety Service in Smart Home Management.Sensors (Basel, Switzerland) · 2016Article
- A New Paradigm of Technology-Enabled ‘Vital Signs’ for Early Detection of Health Change for Older Adults.Gerontology · 2015Article
- Generating sensor data summaries to communicate change in elders' health status.Applied clinical informatics · 2014Article
- Evaluation of health alerts from an early illness warning system in independent living.Computers, informatics, nursing : CIN · 2013Article
- Automated technology to speed recognition of signs of illness in older adults.Journal of gerontological nursing · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
This paper describes the evolution of an early illness warning system used by an interdisciplinary team composed of clinicians and engineers in an independent living facility. The early illness warning system consists of algorithms which analyze resident activity patterns obtained from sensors embedded in residents' apartments. The engineers designed an automated reasoning system to generate clinically relevant alerts which are sent to clinicians when significant changes occur in the sensor data, for example declining activity levels. During January 2010 through July 2010, clinicians and engineers conducted weekly iterative review cycles of the early illness warning system to discuss concerns about the functionality of the warning system, to recommend solutions for the concerns, and to evaluate the implementation of the solutions. A total of 45 concerns were reviewed during this period. Iterative reviews resulted in greater efficiencies and satisfaction for clinician users who were monitoring elder activity patterns.
Identifiers
What OpenQuestion holds
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.