Technology for home-based frailty assessment and prediction: A systematic review
Chao Bian MS*, Bing Ye MS, Charlene H. Chu RN GNC PhD, Katherine S. McGilton PhD RN FAAN, Alex Mihailidis PhD PEng
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AbstractBackground: The current clinical frailty assessments are time-consuming and subjective which can lead to inaccurate results and delayed medical attention. Sensor technology and artificial intelligence enable home-based frailty assessment; however, there are no systematic reviews of existing technological methods for home-based frailty assessment and prediction.
Objective: To analyze and synthesize the frailty criteria, sensor technology, and the statistical or artificial intelligence methods used in home-based frailty assessment and prediction.
Methods: An exhaustive database search was performed. Three reviewers screened all studies by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The sensors and AI used for assessing frailty were synthesized with a particular focus on home-based technology. The Sackett’s Level of Evidence Scale was also used to evaluate clinical evidence for the included studies.
Results: Body-worn sensors were the most commonly used (72%) technology in home-based frailty assessment. All of the body-worn sensors were accelerometer-based. 88% of the included studies measured physical activity for assessing frailty commonly defined by Fried’s Frailty Index (75%). Heterogenous machine learning algorithms have been applied for classifying frailty. However, none of the AI methods were tested for the predictability of frailty. Only one longitudinal study followed up older participants for 10 years and revealed a high odds ratio for the development of frailty using physical activity.
Conclusion: The database search was limited to definitions of physical frailty and the English language. Various types of sensor technology with good accuracy are used to measure specific frailty criteria and functional tests. However, there is a lack of longitudinal studies for predicting frailty progression. To date, there is limited testing of the sensors using older populations with functional and cognitive comorbidities and because they are at higher risk of frailty they should be a priority moving forward.Keywords: Frailty, assessment, technology, home, sensor, Artificial Intelligence
Chao Bian MS*, Bing Ye MS, Charlene H. Chu RN GNC PhD, Katherine S. McGilton PhD RN FAAN, Alex Mihailidis PhD PEng (2020). Technology for home-based frailty assessment and prediction: A systematic review. Gerontechnology, 19(3), 1-13
https://doi.org/10.4017/gt.2020.19.003.06