Designing an Intelligent Health Monitoring System and Exploring User Acceptance for the Elderly

Overview

Journal J Med Syst

Specialty Medical Informatics

Date 2013 Sep 17

PMID 24037138

Citations 24

Authors

Kevin C Tseng

Chien-Lung Hsu

Yu-Hao Chuang

Affiliations

Soon will be listed here.

Abstract

Recently, many healthcare or health monitoring systems are proposed to improve life quality of the elderly in the aging process. The elderly are generally with poor health and low information literacy. Low information literacy might be an obstacle of using such systems. This research considered the characteristics and the needs of the elderly and developed an intelligent health monitoring system for the elderly with low information literacy living in the nursing home. The system is intelligent since it can monitor the health status of the elderly based on clinical and medical knowledge, provide an easy-to-understand and easy-to-use user interface for the elderly, and automatically send important or emergency feedback to caregivers. Finally, we explored the user acceptance for the elderly using our proposed system based on the unified theory of acceptance and user of technology model. The experimental results indicate the developed system is highly accepted by the elderly in terms of performance expectation, endeavor expectation, social influence, and facilitating condition.

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References

Espinoza S, Walston J . Frailty in older adults: insights and interventions. Cleve Clin J Med. 2006; 72(12):1105-12. DOI: 10.3949/ccjm.72.12.1105. View

Newell K, Mayer-Kress G, Liu Y . Aging, time scales, and sensorimotor variability. Psychol Aging. 2009; 24(4):809-18. DOI: 10.1037/a0017911. View

Chan M, Esteve D, Escriba C, Campo E . A review of smart homes- present state and future challenges. Comput Methods Programs Biomed. 2008; 91(1):55-81. DOI: 10.1016/j.cmpb.2008.02.001. View

Celler B, Earnshaw W, Ilsar E, Harris M, Clark R, Hesketh T . Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Wales. Int J Biomed Comput. 1995; 40(2):147-55. DOI: 10.1016/0020-7101(95)01139-6. View

Rockwood K, Mogilner A, Mitnitski A . Changes with age in the distribution of a frailty index. Mech Ageing Dev. 2004; 125(7):517-9. DOI: 10.1016/j.mad.2004.05.003. View