Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring

Ianculescu, Marilena; Paraschiv, Elena-Anca; Alexandru, Adriana

doi:10.3390/healthcare10071214

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…Staff providing interventions in the early stages of MCI can help patients with MCI maintain a functional and healthy lifestyle and may prevent them from becoming severely and chronically unwell 9 . Furthermore, intervention programs that are managed and deployed independently by older adults are expected to maintain their mental health, 17 and remote monitoring of their health status and activities using Internet of Things reportedly supports independent living for older adults 18 . Therefore, staff should be aware of their roles in post‐diagnosis support for patients with MCI and initiate support immediately after diagnosis to facilitate proactive self‐management and promote wellbeing.…”

Section: Discussionmentioning

confidence: 99%

Staff‐rated ability to provide post‐diagnosis support for patients with mild cognitive impairment and related factors

Iiyama,

Kaba,

Kusaba

et al. 2023

Psychogeriatrics

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BackgroundProactive interventions for patients with mild cognitive impairment (MCI) are required. We aimed to determine the staff‐rated ability to provide post‐diagnosis support for patients with MCI at a medical centre for dementia and the related factors.MethodsWe conducted a web‐based survey on post‐diagnosis support for patients with MCI among healthcare personnel, such as mental health social workers and public health nurses (hereafter referred to as ‘staff’), in consultation and support roles at medical centres for dementia nationwide. The latent characteristic value for ‘staff's self‐rated ability to provide post‐diagnosis support to patients with MCI’, which was estimated using the one‐parameter logistic model of item response theory, was used as the dependent variable. Multivariate linear regression analysis was used to examine the factors associated with the dependent variable.ResultsWe conducted the study at 482 medical centres for dementia. We received responses from 162 participants, 158 of which were valid. We applied item response theory to 45 staff‐rated items regarding post‐diagnosis support for patients with MCI and found that item difficulty ranged from −2.56 to 1.02; 40 items had negative values and were deemed relatively easy. The staff‐rated ability to provide post‐diagnosis support was significantly higher for ‘The role in assisting patients with MCI is clear’ (P < 0.005), ‘A reasonable number of personnel is available to assist immediately after MCI diagnosis’ (P = 0.001), and ‘Collaboration with family physician available immediately after MCI diagnosis’ (P < 0.001).ConclusionsThe results of this study showed that staff rated their ability to provide post‐diagnosis support for patients with MCI as relatively easy. The staff‐rated ability to provide post‐diagnosis support to patients with MCI may be enhanced by increased availability of staff immediately after MCI diagnosis, clarification of staff roles, and collaboration with family doctors.

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Section: Discussionmentioning

confidence: 99%

Staff‐rated ability to provide post‐diagnosis support for patients with mild cognitive impairment and related factors

Iiyama,

Kaba,

Kusaba

et al. 2023

Psychogeriatrics

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“…The sensor network is made of permeable and moisture‐proof textile triboelectric sensors, which can collect highly accurate respiratory signals with a decent signal‐to‐noise ratio, response time, and sensitivity. RO‐SmartAgeing 34 was developed to address mild cognitive impairment (MCI) in old age. It offers personalized remote monitoring and assistance for the elderly, including predictive models for detecting MCI onset and its progression toward dementia.…”

Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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“…The sensor network is made of permeable and moisture-proof textile triboelectric sensors, which can collect highly accurate respiratory signals with a decent signal-to-noise ratio, response time, and sensitivity. RO-SmartAgeing [30] was developed to address mild cognitive impairment (MCI) in old age. It offers personalized remote monitoring and assistance for the elderly, including predictive models for detecting MCI onset and its progression toward dementia.…”

Section: Figure 2 Iomt-based Personalized Health Monitoring Solutionmentioning

confidence: 99%

Deep Learning for Personalized Health Monitoring and Prediction: A Review

Damaševicˇius,

Jagatheesaperumal

et al. 2023

Preprint

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Personalized health monitoring and prediction have become essential for improving health- care delivery, especially with the growing prevalence of chronic diseases and an aging population. Deep learning (DL) has emerged as a promising approach for developing personalized health mon- itoring systems that can predict health outcomes accurately and efficiently. With the increasing availability of personal health data, DL-based methods have emerged as a promising approach to improve healthcare delivery by providing accurate and timely predictions of health outcomes. This article provides a comprehensive review of the recent developments in the application of DL for personalized health monitoring and prediction. It summarizes various DL architectures and their applications for personalized health monitoring, including wearable devices, electronic health records, and social media data. Furthermore, the article also explores the challenges and future directions for the application of DL in personalized health monitoring. valuable insights into the potential of DL for personalized health monitoring and prediction.

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Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring

Cited by 11 publications

References 40 publications

Staff‐rated ability to provide post‐diagnosis support for patients with mild cognitive impairment and related factors

Staff‐rated ability to provide post‐diagnosis support for patients with mild cognitive impairment and related factors

Deep learning for personalized health monitoring and prediction: A review

Deep Learning for Personalized Health Monitoring and Prediction: A Review

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