A roadmap for remote digital health technology for Motor Neuron Disease (Preprint)

Mapping Intimacies ◽

10.2196/preprints.28766 ◽

2021 ◽

Author(s):

Ruben P.A. van Eijk ◽

Anita Beelen ◽

Esther T. Kruitwagen ◽

Deirdre Murray ◽

Ratko Radakovic ◽

...

Keyword(s):

Motor Neuron ◽

Motor Neuron Disease ◽

Real World ◽

Clinical Decision Making ◽

Digital Health ◽

Health Technology ◽

Innovative Technology ◽

Progression Rate ◽

Healthcare Technology ◽

Digital Healthcare

UNSTRUCTURED Despite recent and compelling technological advances, the real-world implementation of remote digital health technology in care and monitoring of patients with motor neuron disease (MND) has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of the patients’ ability to visit the clinic. To facilitate wide-scale adoption of digital healthcare technology, and to align current initiatives, we outline a roadmap that (1) will identify clinically relevant digital parameters, (2) mediate the development of benefit-to-burden criteria for innovative technology and (3) direct the validation, harmonization and adoption of digital healthcare technology in real-world settings. We define two key end-products of the roadmap: (1) a set of reliable digital parameters to capture data, collected under free-living conditions, that reflect patient-centric measures and facilitate clinical decision-making, and (2) an integrated, open-source system that provides personalized feedback to patients, healthcare providers, clinical researchers and caregivers, linked to a flexible and adaptable ICT platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of MND, the adoption of digital healthcare technology will significantly benefit the delivery of care and accelerate the development of effective treatments.

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A roadmap for remote digital health technology for Motor Neuron Disease (Preprint)

Journal of Medical Internet Research ◽

10.2196/28766 ◽

2021 ◽

Author(s):

Ruben P.A. van Eijk ◽

Anita Beelen ◽

Esther T. Kruitwagen ◽

Deirdre Murray ◽

Ratko Radakovic ◽

...

Keyword(s):

Motor Neuron ◽

Motor Neuron Disease ◽

Digital Health ◽

Health Technology

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The healthcare technology needs you

Innovation in Health for Society ◽

10.31603/ihs.5265 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Sumarno Adi Subrata ◽

Jonathan Bayuo ◽

Busra Sahin

Keyword(s):

Artificial Intelligence ◽

3D Printing ◽

Standard Treatment ◽

Healthcare Services ◽

Fundamental Aspect ◽

Innovative Technology ◽

Community Settings ◽

Healthcare Technology ◽

Health Related ◽

Digital Healthcare

The growing evidence and technology in healthcare lead to an improvement in the patient's health across a continuum of services in clinical and community settings. A multidisciplinary team should work in tandem on this phenomenon. Therefore, innovative healthcare technology must be designed intensively to optimize productivity and provide new insight along with support the standard treatment for particular diseases. In the coming years, technology is needed to change the way of caring for the patient. This is a fundamental aspect because the recent technology has shaped up in front of our practice with advances in digital healthcare services, such as 3D printing, robotics, nanotechnology and even artificial intelligence (The Medical Futurist, 2021). To respond to this, updated studies should be developed and published focusing on innovative technology including in Medicine, Nursing, Pharmacy, and other health-related topics.

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Attitudes Towards Digital Health Technology: Introducing the Digital Health Scale

10.1101/2021.09.03.21262482 ◽

2021 ◽

Author(s):

Ridley Cassidy

Keyword(s):

Digital Health ◽

Health Technology ◽

Healthcare Services ◽

Older Individuals ◽

Cross Sectional ◽

Frequent Use ◽

Age Related ◽

Frequency Of Use ◽

The Uk ◽

Digital Healthcare

Objective: The study sought to investigate the relationship between attitude towards digital health technology and age, gender and frequency of use of digital health technology and to consider whether age, gender and frequency of use present potential barriers to accessing future healthcare in the UK. Differences in technological affinity are likely to lead to differences in the adoption of digital health technology and subsequent inequalities in healthcare between older and younger people and between men and women. Design: The study represents an example of a technology adoption study employing a survey-based cross sectional correlational design. Attitude towards digital health technology was measured using the 20 item Digital Health Scale. Age, gender, frequency of use of health technology and employment status data were gathered using a demographics questionnaire. The opportunity sample (N = 247) included volunteer participants aged 16-84 years (M = 31.7, SD = 19.35, 156 females and 91 males). Results: Results indicated a significant negative correlation between age and positive attitude towards digital health technology (r = -0.24, p < .01). Gender differences in attitudes towards digital health technology were non-significant (p > .05). Significant differences in frequency of use were also found, where occasional and frequent use resulted in more positive attitudes than never having used digital health technology (p < 0.05) and participants reporting frequent use were significantly older than those reporting never or occasional use (p < .05) Conclusion: Findings identified age, but not gender, as a significant factor in attitude towards digital health technology, suggesting that continued and increased reliance on digital technology in healthcare may lead to age, but not gender, related inequalities in access to healthcare in the UK. That frequent users of digital health technology were also older, highlights the greater demand for healthcare services by older individuals and is further evidence for the potential of digital healthcare to lead to age related inequalities in access to and provision of healthcare. Recommendations for successful application of digital healthcare technology are considered in the light of these findings.

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MEDBIZ Big Data Platform Based on Enterprise Support for Digital Healthcare Service using Internet of Medical Things (Preprint)

10.2196/preprints.34100 ◽

2021 ◽

Author(s):

Hee Young LEE ◽

Kang Hyun LEE ◽

Kyu Hee LEE ◽

Urtnasan Erdenbayar ◽

Sangwon HWANG ◽

...

Keyword(s):

Big Data ◽

Real World ◽

Digital Health ◽

Healthcare Services ◽

Mobile App ◽

Chronic Obstructive ◽

Real World Data ◽

Data Platform ◽

Internet Of Medical Things ◽

Digital Healthcare

UNSTRUCTURED The aim of this study is to introduce the implemented big data platform (MEDBIZ) based on the internet of medical things (IoMT) supporting digital healthcare services and to discuss about application cases of this platform. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, Core, Analytics, and Services. IoMT component is used for lifelog data acquisition and collection from the IoMT devices. The core components are composed of the main functional operations including the metadata, resource brokers and computing elements, virtual file system, security, and system logs. Analytics component is covered data analyzing frameworks such as Hadoop, Spark, R, and TensorFlow. Finally, the service component can support the web-based or mobile app-based digital healthcare services through the Open API to the end-users. As a result, an implemented big data platform can provide various digital healthcare services using various IoMT devices. Among them, we are focusing on detailed empirical studies about chronic obstructive pulmonary disease (COPD), metabolic syndrome, vital sign, arrhythmia, and diabetes monitoring services. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting the real-world evidence. And then through this platform, we are developing Software as a Medical Device (SaMD), digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

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Developing the accuracy of vital signs measurements by the LifelightTM software application in comparison to standard of care methods: an observational study protocol (Preprint)

10.2196/preprints.14326 ◽

2019 ◽

Author(s):

Thomas L Jones ◽

Emily Heiden ◽

Felicity Mitchell ◽

Carole Fogg ◽

Sharon McCready ◽

...

Keyword(s):

Vital Sign ◽

Large Scale ◽

Clinical Decision Making ◽

Digital Health ◽

Clinical Care ◽

Population Sample ◽

Standard Of Care ◽

Health Technology ◽

Software Application ◽

Health And Social Care

BACKGROUND Vital sign measurements are an integral component of clinical care, but current challenges with the accuracy and timeliness of patient observations can impact appropriate clinical decision making. Advanced technologies using techniques such as photoplethysmography have the potential to automate non-contact physiological monitoring and recording, improving the quality and accessibility of this essential clinical information. OBJECTIVE To develop the algorithm used in the LifelightTM software application and improve the accuracy of its estimated heart rate, respiratory rate, oxygen saturation and blood pressure measurements METHODS This preliminary study will compare measurements predicted by the LifelightTM software with standard of care measurements for an estimated population sample of 2000 inpatients, outpatients and healthy people attending a large acute hospital. Both training datasets and validation datasets will be analysed to assess the degree of correspondence between the vital sign measurements predicted by the LifelightTM software and the direct physiological measurements taken using standard of care methods. Sub group analyses will explore how the performance of the algorithm varies with particular patient characteristics, including age, sex, health condition and medication. RESULTS Recruitment of participants to this study began in July 2018 and data collection will continue for a planned study period of 12 months. CONCLUSIONS Digital health technology is a rapidly evolving area for health and social care. Following this initial exploratory study to develop and refine the LifelightTM software application, subsequent work will evaluate its performance across a range of health characteristics and extended validation trials will support its pathway to registration as a medical device. Innovations in health technology such as this may provide valuable opportunities for increasing the efficiency and accessibility of vital sign measurements and improve healthcare services on a large scale across multiple health and care settings. CLINICALTRIAL

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Neurofilament markers for ALS correlate with extent of upper and lower motor neuron disease

Neurology ◽

10.1212/wnl.0000000000004029 ◽

2017 ◽

Vol 88 (24) ◽

pp. 2302-2309 ◽

Cited By ~ 85

Author(s):

Koen Poesen ◽

Maxim De Schaepdryver ◽

Beatrice Stubendorff ◽

Benjamin Gille ◽

Petra Muckova ◽

...

Keyword(s):

Motor Neuron ◽

Motor Neuron Disease ◽

Prognostic Value ◽

Added Value ◽

Progression Rate ◽

Neurofilament Light Chain ◽

Neurofilament Light ◽

Diagnostic Biomarkers ◽

Disease Progression Rate

Objective:To determine the diagnostic performance and prognostic value of phosphorylated neurofilament heavy chain (pNfH) and neurofilament light chain (NfL) in CSF as possible biomarkers for amyotrophic lateral sclerosis (ALS) at the diagnostic phase.Methods:We measured CSF pNfH and NfL concentrations in 220 patients with ALS, 316 neurologic disease controls (DC), and 50 genuine disease mimics (DM) to determine and assess the accuracy of the diagnostic cutoff value for pNfH and NfL and to correlate with other clinical parameters.Results:pNfH was most specific for motor neuron disease (specificity 88.2% [confidence interval (CI) 83.0%–92.3%]). pNfH had the best performance to differentially diagnose patients with ALS from DM with a sensitivity of 90.7% (CI 84.9%–94.8%), a specificity of 88.0% (CI 75.7%–95.5%) and a likelihood ratio of 7.6 (CI 3.6–16.0) at a cutoff of 768 pg/mL. CSF pNfH and NfL levels were significantly lower in slow disease progressors, however, with a poor prognostic performance with respect to the disease progression rate. CSF pNfH and NfL levels increased significantly as function of the number of regions with both upper and lower motor involvement.Conclusions:In particular, CSF pNfH concentrations show an added value as diagnostic biomarkers for ALS, whereas the prognostic value of pNfH and NfL warrants further investigation. Both pNfH and NfL correlated with the extent of motor neuron degeneration.Classification of evidence:This study provides Class II evidence that elevated concentrations of CSF pNfH and NfL can accurately identify patients with ALS.

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