A Remote Home Monitoring System to Support Informal Caregivers of People with Dementia

Lentelink, Stefan; Tabak, Monique; Schooten, B.W. van; Hofs, Dennis; Akker, Harm op den; Hermens, Hermanus J.

doi:10.5220/0006565300940102

Cited by 3 publications

(1 citation statement)

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“…Several very impressive projects already described the development of monitoring systems for people with MCI. Among these are INLIFE (Independent Living support Functions for the Elderly) cofounded by the European Union [ 28 ], HELMA eHealth monitoring application [ 29 ] and the Remote Home Monitoring System to Support Informal Caregivers of People with Dementia [ 30 ]. The main global aim of those projects is to support the elderly with MCI in a variety of daily activities through supporting their caregivers and enabling improved service to the elderly.…”

Section: Introductionmentioning

confidence: 99%

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease

Rosenblum

Richardson

Meyer

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog—a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual–spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

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Section: Introductionmentioning

confidence: 99%

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease

Rosenblum

Richardson

Meyer

et al. 2021

Sensors

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Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study

Schütz¹,

Saner²,

Botros³

et al. 2021

JMIR Mhealth Uhealth

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Background Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

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Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study (Preprint)

Schütz¹,

Saner²,

Botros³

et al. 2020

Preprint

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BACKGROUND Population ageing is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased healthcare expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs, and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnoea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted, evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. OBJECTIVE In this work we aim to evaluate which contactlessly measurable sleep parameter, is best suited to monitor perceived and actual health status changes in older adults. METHODS We analysed real-world longitudinal data from 37 community-dwelling older adults, including more than 6000 nights of sleep. Sleep data was measured by a pressure sensor placed beneath the mattress and corresponding health status information was acquired through weekly questionnaires and reports by healthcare personnel. Association with perceived health, evaluated by EuroQol EQ-VAS ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to real-world health incidents was investigated through manual case-by-case analysis. RESULTS Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed - measured by the number toss-and-turn events - as the most predictive sleep parameter (t-Value=-0.435, p-adj=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be linked to reported health incidents. CONCLUSIONS Our results suggest that nightly body movements in bed, such as toss and turns, could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier as well as more personalized and precise treatment options. CLINICALTRIAL

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A Remote Home Monitoring System to Support Informal Caregivers of People with Dementia

Cited by 3 publications

References 0 publications

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease

Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study

Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study (Preprint)

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