Literature DB >> 29915807

Validating a Commercial Device for Continuous Activity Measurement in the Older Adult Population for Dementia Management.

Tanvi Banerjee1, Matthew Peterson2, Quintin Oliver1, Andrew Froehle2,3, Larry Lawhorne2.   

Abstract

With the introduction of the large number of fitness devices on the market, there are numerous possibilities for their use in managing chronic diseases in older adults. For example, monitoring people with dementia using commercially available devices that measure heart rate, breathing rate, lung volume, step count, and activity level could be used to predict episodic behavioral and psychological symptoms before they become distressing or disruptive. However, since these devices are designed primarily for fitness assessment, validation of the sensors in a controlled environment with the target cohort population is needed. In this study, we present validation results using a commercial fitness tracker, the Hexoskin sensor vest, with thirty-one participants aged 65 and older. Estimated physiological measures investigated in this study are heart rate, breathing rate, lung volume, step count, and activity level of the participants. Findings indicate that while the processed step count, heart rate, and breathing rate show strong correlations to the clinically accepted gold standard values, lung volume and activity level do not. This indicates the need to proceed cautiously when making clinical decisions using such sensors, and suggests that users should focus on the three strongly correlated parameters for further analysis, at least in the older population. The use of physiological measurement devices such as the Hexoskin may eventually become a non-intrusive way to continuously assess physiological measures in older adults with dementia who are at risk for distressing behavioral and psychological symptoms.

Entities:  

Keywords:  Bayesian analysis; activity analysis; physiological measurement; sensor validation; wearable health

Year:  2017        PMID: 29915807      PMCID: PMC6003715          DOI: 10.1016/j.smhl.2017.11.001

Source DB:  PubMed          Journal:  Smart Health (Amst)        ISSN: 2352-6483


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