Automated Assistive Health Care System for Disabled Patients utilizing Things

Anisha, M.; Sindhuja, J. Francis Felix; Elliot, Chris; R, Rani; Devi, J. Durga; Monal, K.; Chezhiyan, Ponmozhi; Majeeth, A. Abdul; Shalini, U.

doi:10.25103/jestr.134.20

Cited by 5 publications

(2 citation statements)

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“…The evolution of digital healthcare services is further propelled by patients who, as informed health consumers, are increasingly harnessing digital devices for health management [2]. Leveraging Information and Communication Technology (ICT), digital healthcare services transcend traditional boundaries, offering cost-effective medical solutions that are not solely reliant on medical personnel [3]. A notable advancement in this domain is remote health monitoring, which empowers patients to access healthcare services within the comfort of their homes, thereby fostering a sense of independence [3 4].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Mobile-Centered Digital Health Readiness Scale (mDiHERS): Health Literacy and Equity Scale (Preprint)

Kim,

Schnall,

Yoon

et al. 2024

Preprint

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BACKGROUND There has been a rapid expansion of digital healthcare services, making the need for measuring and improving digital health readiness a priority. In response, our study team developed the Mobile-Centered Digital Health Readiness Scale: Health Literacy and Equity Scale (mDiHERS), to measure digital health readiness. OBJECTIVE To develop and validate a scale that assesses digital health readiness, encompassing literacy and equity, and to ensure the effective use of mobile-centered digital health services. METHODS This study was conducted from October 2021 to October 2022 to develop and validate the mDiHERS. Participants included patients with inflammatory bowel disease (IBD), a chronic condition requiring continuous management, and experts in medical and nursing informatics. The scale development involved a literature review, focus group interviews (FGIs), and content validity evaluations. A total of 440 IBD patients were recruited for the validation phase, with 403 completing the survey. The scale's validity and reliability were assessed through exploratory factor analysis and Cronbach's alpha. The scale was translated into English by translators and bilingual and native researchers, ensuring its applicability in diverse settings. RESULTS The mDiHERS consists of 36 items across six domains, with a 5-point Likert scale for responses. The validation process confirmed the scale's construct validity, with four factors explaining 65.05% of the total variance. The scale's reliability was established with Cronbach's alpha values ranging from .84 to .91. The scale's development considered the technical proficiency necessary for engaging with health mobile applications and devices, reflecting the importance of subjective confidence and objective skills in digital health literacy. CONCLUSIONS The mDiHERS is a validated tool that quantifies patients' readiness and influencing factors to engage with digital health services. It also considers the users' characteristics, capabilities, and perceptions that influence these areas. It emphasizes the importance of considering cultural and educational backgrounds in digital health readiness and advocates for comprehensive support to enhance accessibility and equity. The mDiHERS is meaningful because it is essential to use and widely adopt health mobile applications and devices.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Mobile-Centered Digital Health Readiness Scale (mDiHERS): Health Literacy and Equity Scale (Preprint)

Kim,

Schnall,

Yoon

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…As the world marches into the era of the Internet of Things (IoT) and 5G technologies, APT devices have become more interactive and have the potential to provide user-generated data continuously at the Gigabyte level per person per day. − Culling effective parameters from such large data sets for APT devices has been particularly challenging for practical employment. Machine learning (ML) is a widely used artificial intelligence (AI) technology for big data processing. − Machine-learning boundaries are a deep-learning domain that can classify and process a large amount of sensor data. − A deep-learning method is an algorithm that can mimic human cognitive processes. − It can learn rules from data and automatically improve the learning process without explicit programming.…”

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confidence: 99%

Machine-Learning-Aided Self-Powered Assistive Physical Therapy Devices

Xiao

Fang

et al. 2021

ACS Nano

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An expanding elderly population and people with disabilities pose considerable challenges to the current healthcare system. As a practical technology that integrates systems and services, assistive physical therapy devices are essential to maintain or to improve an individual’s functioning and independence, thus promoting their well-being. Given technological advancements, core components of self-powered sensors and optimized machine-learning algorithms will play innovative roles in providing assistive services for unmet global needs. In this Perspective, we provide an overview of the latest developments in machine-learning-aided assistive physical therapy devices based on emerging self-powered sensing systems and a discussion of the challenges and opportunities in this field.

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