BACKGROUND: Assistive technology, in the form of a smart home environment, is employed to support people with dementia. OBJECTIVES: To propose a system for continuous and objective remote monitoring of problematic daily living activity areas and design personalized interventions based on system feedback and clinical observations for improving cognitive function and health-related quality of life. METHODS: The assistive technology of the proposed system, including wearable, sleep, object motion, presence, and utility usage sensors, was methodically deployed at four different home installations of people with cognitive impairment. Detection of sleep patterns, physical activity, and activities of daily living, based on the collected sensor data and analytics, was available at all times through comprehensive data visualization solutions. Combined with clinical observation, targeted psychosocial interventions were introduced to enhance the participants' quality of life and improve their cognitive functions and daily functionality. Meanwhile, participants and their caregivers were able to visualize a reduced set of information tailored to their needs. RESULTS: Overall, paired-sample t-test analysis of monitored qualities revealed improvement for all participants in neuropsychological assessment. Moreover, improvement was detected from the beginning to the end of the trial, in physical condition and in the domains of sleep. Detecting abnormalities via the system, for example in sleep quality, such as REM sleep, has proved to be critical to assess current status, drive interventions, and evaluate improvements in a reliable manner. CONCLUSION: It has been proved that the proposed system is suitable to support clinicians to reliably drive and evaluate clinical interventions toward quality of life improvement of people with cognitive impairment.
BACKGROUND: Assistive technology, in the form of a smart home environment, is employed to support people with dementia. OBJECTIVES: To propose a system for continuous and objective remote monitoring of problematic daily living activity areas and design personalized interventions based on system feedback and clinical observations for improving cognitive function and health-related quality of life. METHODS: The assistive technology of the proposed system, including wearable, sleep, object motion, presence, and utility usage sensors, was methodically deployed at four different home installations of people with cognitive impairment. Detection of sleep patterns, physical activity, and activities of daily living, based on the collected sensor data and analytics, was available at all times through comprehensive data visualization solutions. Combined with clinical observation, targeted psychosocial interventions were introduced to enhance the participants' quality of life and improve their cognitive functions and daily functionality. Meanwhile, participants and their caregivers were able to visualize a reduced set of information tailored to their needs. RESULTS: Overall, paired-sample t-test analysis of monitored qualities revealed improvement for all participants in neuropsychological assessment. Moreover, improvement was detected from the beginning to the end of the trial, in physical condition and in the domains of sleep. Detecting abnormalities via the system, for example in sleep quality, such as REM sleep, has proved to be critical to assess current status, drive interventions, and evaluate improvements in a reliable manner. CONCLUSION: It has been proved that the proposed system is suitable to support clinicians to reliably drive and evaluate clinical interventions toward quality of life improvement of people with cognitive impairment.
Authors: Patricia Gual-Montolio; Verónica Martínez-Borba; Juana María Bretón-López; Jorge Osma; Carlos Suso-Ribera Journal: Int J Environ Res Public Health Date: 2020-05-02 Impact factor: 3.390
Authors: Stefan Teipel; Alexandra König; Jesse Hoey; Jeff Kaye; Frank Krüger; Julie M Robillard; Thomas Kirste; Claudio Babiloni Journal: Alzheimers Dement Date: 2018-06-21 Impact factor: 21.566