Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Opportunities and challenges of a dynamic consent-based application: personalized options for personal health data sharing and utilization

BACKGROUND

The principles of dynamic consent are based on the idea of safeguarding the autonomy of individuals by providing them with personalized options to choose from regarding the sharing and utilization of personal health data. To facilitate the widespread introduction of dynamic consent concepts in practice, individuals must perceive these procedures as useful and easy to use. This study examines the user experience of a dynamic consent-based application, in particular focusing on personalized options, and explores whether this approach may be useful in terms of ensuring the autonomy of data subjects in personal health data usage.

METHODS

This study investigated the user experience of MyHealthHub, a dynamic consent-based application, among adults aged 18 years or older living in South Korea. Eight tasks exploring the primary aspects of dynamic consent principles-including providing consent, monitoring consent history, and managing personalized options were provided to participants. Feedback on the experiences of testing MyHealthHub was gathered via multiple-choice and open-ended questionnaire items.

RESULTS

A total of 30 participants provided dynamic consent through the MyHealthHub application. Most participants successfully completed all the provided tasks without assistance and regarded the personalized options favourably. Concerns about the security and reliability of the digital-based consent system were raised, in contrast to positive responses elicited in other aspects, such as perceived usefulness and ease of use.

CONCLUSIONS

Dynamic consent is an ethically advantageous approach for the sharing and utilization of personal health data. Personalized options have the potential to serve as pragmatic safeguards for the autonomy of individuals in the sharing and utilization of personal health data. Incorporating the principles of dynamic consent into real-world scenarios requires remaining issues, such as the need for powerful authentication mechanisms that bolster privacy and security, to be addressed. This would enhance the trustworthiness of dynamic consent-based applications while preserving their ethical advantages.

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

Wellness or medicine? Use and perception of Ayurveda in Germany: data from an online-representative cross-sectional study

Introduction

Ayurveda, South Asia's largest and most relevant system of Traditional Medicine, holds a legal status akin to conventional Western medicine in India and elsewhere. There is an almost complete lack of data on the use of Ayurveda in Germany. The aim of this study was to investigate Ayurveda's utilization patterns, entry points, and factors influencing its use and the perception of Ayurveda among the German population.

Methods

Basis of this manuscript was an online-representative survey which involved 4,065 participants aged 18-75 about the use and acceptance of Traditional, Complementary and Integrative Medicine (TCIM) in Germany. The survey was conducted online using Computer Assisted Web Interview (CAWI) in 2022. The dataset was analyzed descriptively and inferentially.

Results

Altogether 9.3% (n = 377) of all survey participants (n = 4,065) had already used Ayurveda somehow, either more often (1.7%) or at least once in a lifetime (7.6%). Responders associated Ayurveda primarily with Indian Medicine (27.7%) and wellness (18%). Commonly used Ayurvedic services included non-medical treatments at wellness resorts/spas (48.3%), in outpatient practices (27.1%), and hotels (23.6%). 30.2% of the participants believe in Ayurveda's therapeutic potential. 76.7% of Ayurveda users find healthy nutrition important or very important. Nine predictors were found to classify Ayurveda users vs. non-users with spirituality and belief in Ayurveda's therapeutic efficacy as the most relevant ones. Ayurveda seems to be primarily used by well-educated and female individuals, often from higher-income groups and with a rather modern social milieu-orientation.

Conclusion

Study results suggest that about every tenth German citizen has used Ayurveda in the past and about one third believes in its therapeutic potential. Because Ayurvedic therapies are often not evidence-based, there is an urgent need to perform high quality randomized controlled trials to investigate potential effects and safety of Ayurveda and how evidence-based Ayurveda treatments can be integrated into the German healthcare system.

The paradoxes of telehealth platforms: what did we learn from the use of telehealth platforms?

This article is an overview and reflection of the findings of an evaluative study conducted on a program called "Techno-Covid Partnership" (TCP) implemented in April 2020 at the Centre Hospitalier de l'Université de Montréal (CHUM) in Montreal, Canada. In the context of the COVID-19 pandemic, the CHUM decided in April 2020 to implement telehealth, virtual care, and telemonitoring platforms and technologies to maintain access to care and reduce the risks of contamination and spread of COVID-19 as well as to protect users of health services and health professionals. Three technological platforms for telehealth and remote care and monitoring have been developed, implemented, and evaluated in real-time within the framework of the TCP program. A cross-sectional study was carried out in which a questionnaire was used and administered to users of telehealth platforms including patients and healthcare professionals. The methods and results of the study have been published previously published. In the completion of the two articles published in this context, in this paper, we briefly recall the context of the study and the method performed. The main focus of the paper is on presenting a critical overview and reflection on the major findings of our evaluation of the use of telehealth platforms from the point of view of patients and health professionals and discuss certain paradoxes i.e., the advantages, challenges, recommendations, and other perspectives that emerged in this study.

Comprehensive review on sexual dimorphism to improve scalp acupuncture in nervous system disease

BACKGROUND

With the development of modern medicine, the Traditional Chinese Medicine (TCM) combined with western medicine began to be produced and applied. Scalp acupuncture (SA) as a Chinese medicine based on neurological theory, has a great advantage compared with TCM in the treatment of nervous system diseases.

METHOD

In this paper, we analyze the physiological and pathological manifestations of sexual dimorphism (SD) to illustrate the necessity of SD treatment. In addition, we review the factors that can affect SD and analyze in physiological structure, function, and pathological neurons. Diseases (pathological basis, pathological manifestations, and incidence) and factors leading to gender differences, which to analyze the possibility of gender differences in SA.

RESULT

Furthermore, we creatively a new insight of SD-SA and provide the complete SD treatment cases on the basis of the existing SA in different kinds of diseases including stroke, migraine, attention deficit hyperactivity disorder (ADHD), and depression.

CONCLUSION

In summary, we believe that it is feasible to improve the clinical effectiveness of SA, which is able to promote the development of SA, and then provides an actionable evidence for the promotion of precision medicine in the future.

Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

BACKGROUND

Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios.

METHODS

This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria.

RESULTS

The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent.

CONCLUSIONS

Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.

Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine

As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.

Data processing pipeline for cardiogenic shock prediction using machine learning

Introduction

Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.

Methods

We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.

Results

We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.

Conclusion

We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

One Digital Health for more FAIRness

BACKGROUND

 One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

OBJECTIVES

 This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

METHODS

 An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

RESULTS

 A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

CONCLUSIONS

 ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Systematic literature review of adopting eHealth in pharmaceutical care during COVID-19 pandemic: recommendations for strengthening pharmacy services

OBJECTIVES

The study aimed to determine how eHealth was adopted in pharmaceutical care (PC), the outcome reported and the contextual factors.

DESIGN

Systematic literature review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

DATA SOURCES

Literature was searched in six databases including PubMed, Scopus, Medline, Web of Science, Science Direct and China National Knowledge Infrastructure.

ELIGIBILITY CRITERIA

Studies which reported the usage experiences of eHealth in any aspects of PC by pharmacists during the COVID-19 pandemic, written in English or Chinese, and published in peer-reviewed journals between December 2019 and March 2022 were included. Opinion articles, conference abstracts, correspondence, letters and editorials were excluded.

DATA EXTRACTION AND SYNTHESIS

The literature search was completed on 15 April 2022. Two researchers independently conducted the literature search and extracted the data into an Excel table informed by the logic model with the key components of goals, input, activities, output and contextual factors.

RESULTS

Forty-three studies were included in this review. During the COVID-19 pandemic, hospital pharmacists, community pharmacists and specialist pharmacists in 17 countries continued to educate, consult, monitor and manage the patients and the general public via phone calls, videoconferences, mobile applications, social media, websites and/or enhanced interoperability of electronic medical records. Assuring the continuity of pharmacy care, reduced need for hospital visits, and improved work accuracy and efficiency were the benefits of eHealth mostly reported. Contextual factors affecting the adoption of eHealth were multifaceted, prompting supporting actions at the levels of government, hospital/pharmacy, pharmacists and patients.

CONCLUSION

This study revealed the wide adoption of eHealth in PC during the pandemic and the emerging evidence for its importance. Proper adoption of eHealth will help reshape the mode of pharmacy services to ensure continuity, quality and efficiency of care amid the challenges of the pandemic.

PROSPERO REGISTRATION NUMBER

CRD42022299812.

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

In.To. COVID-19 socio-epidemiological co-causality

Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.

Readiness of healthcare providers for e-hospitals: a cross-sectional analysis in China before the COVID-19 period

OBJECTIVES

The growth and development of smartphones and eHealth technologies have enabled the potential for extended care hospitals (e-hospitals) in China in order to facilitate the success of a primary healthcare centre (PHC)-based integrated delivery model. Although the adoption of e-hospitals is essential, few studies have directed their research towards understanding the perspectives of healthcare providers. This study aims to identify the current readiness of healthcare providers to adopt e-hospital technologies, determine the factors influencing this adoption and describe the perceived facilitators and barriers in regard to working at e-hospitals.

DESIGN

A cross-sectional study conducted in Sichuan, China, between June and September 2019.

SETTINGS

Information was collected from healthcare providers who have more than 3 years of work experience from a tertiary hospital, secondary hospital, PHCs and private hospital.

PARTICIPANTS

2298 medical professionals were included in this study.

OUTCOME MEASURE

This study included a self-administered questionnaire that was used to assess participants' sociodemographic characteristics, online medical practices, willingness to use e-hospitals and perceived facilitators/barriers to working at e-hospitals. Multivariate regression analysis was performed in order to evaluate the independent factors associated with e-hospital work.

RESULTS

Overall, 86.3% had a positive response towards working at e-hospitals. Age (p<0.05), familiarity with e-hospitals (p<0.001) and prior work practices in online healthcare settings (p<0.001) were associated with participants' readiness to work at e-hospitals. Gender, education level, professional level, the tier of their affiliated hospital and workload were not statistically associated. Healthcare providers who had positive attitudes towards e-hospitals considered improved efficiency, patient satisfaction, communication among physicians, increased reputation and income, and alleviated workload to be advantages of adoption. The participants who were unwilling to work at e-hospitals perceived lack of time, insufficient authenticity/reliability and underdeveloped policies as potential barriers.

CONCLUSION

Improving operative proficiency in electronic devices, accommodating to work schedules, increasing familiarity with e-hospitals and regulating practices will improve the readiness of healthcare providers to work at e-hospitals.

Remote Patient Monitoring Program for COVID-19 Patients Following Hospital Discharge: A Cross-Sectional Study

Background: The COVID-19 pandemic created an urgent need to act to reduce the spread of the virus and alleviate congestion from healthcare services, protect healthcare providers, and help them maintain satisfactory quality and safety of care. Remote COVID-19 monitoring platforms emerged as potential solutions. Objective: The purpose of this study was to evaluate the capacity and contribution of two different platforms used to remotely monitor patients with COVID-19 to maintain quality, safety, and patient engagement in care, as well as their acceptability, usefulness, and user-friendliness from the user's perspective. The first platform is focused on telecare phone calls (Telecare-Covid), and the second is a telemonitoring app (CareSimple-Covid). Methods: We performed a cross-sectional study. The data were collected through a phone survey from May to August 2020. Data were analyzed using descriptive statistics and t-test analysis. Participants' responses and comments on open-ended questions were analyzed using content analysis to identify certain issues and challenges and potential avenues for improving the platforms. Results: Fifty one patients participated in the study. Eighteen participants used the CareSimple-Covid platform and 33 participants used the Telecare-Covid platform. Overall, the satisfaction rate for quality and safety of care for the two platforms was 80%. Over 88% of the users on each platform considered the platforms' services to be engaging, useful, user-friendly, and appropriate to their needs. The survey identified a few significant differences in users' perceptions of each platform: empathy toward users and the quality and safety of the care received were rated significantly higher on the CareSimple-Covid platform than on the Telecare-Covid platform. Users appreciated four aspects of these telehealth approaches: (1) the ease of access to services and the availability of care team members; (2) the user-friendliness of the platforms; (3) the continuity of care provided, and (4) the wide range of services delivered. Users identified some technical limitations and raised certain issues, such as the importance of maintaining human contact, data security, and confidentiality. Improvement suggestions include promoting access to connected devices; enhancing communications between institutions, healthcare users, and the public on confidentiality and personal data protection standards; and integrating a participatory approach to telehealth platform development and deployment efforts. Conclusion: This study provides preliminary evidence that the two remote monitoring platforms are well-received by users, with very few significant differences between them concerning users' experiences and views. This type of program could be considered for use in a post-pandemic era and for other post-hospitalization clienteles. To maximize efficiency, the areas for improvement and the issues identified should be addressed with a patient-centered approach.

Personalized multicomponent exercise programs using smartphone technology among older people: protocol for a randomized controlled trial

BACKGROUND

Optimal application of the recently updated World Health Organization (WHO) guidelines for exercise in advanced age necessitates an accurate adjustment for the age-related increasing variability in biological age and fitness levels, alongside detailed recommendations across a range of motor fitness components, including balance, strength, and flexibility. We previously developed and validated a novel tool, designed to both remotely assess these fitness components, and subsequently deliver a personalized exercise program via smartphone. We describe the design of a prospective randomized control trial, comparing the effectiveness of the remotely delivered personalized multicomponent exercise program to either WHO exercise guidelines or no intervention.

METHODS

Participants (n = 300) are community dwelling, healthy, functionally independent, cognitively intact volunteers aged ≥65 at low risk for serious fall injuries, assigned using permuted block randomization (age/gender) to intervention, active-control, or control group. The intervention is an 8-week program including individually tailored exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular); active-controls receive exercising counselling according to WHO guidelines; controls receive no guidance. Primary outcome is participant fitness level, operationalized as 42 digital markers generated from 10 motor fitness measures (balance, strength, flexibility); measured at baseline, mid-trial (4-weeks), trial-end (8-weeks), and follow-up (12-weeks). Target sample size is 300 participants to provide 99% power for moderate and high effect sizes (Cohen's f = 0.25, 0.40 respectively).

DISCUSSION

The study will help understand the value of individualized motor fitness assessment used to generate personalized multicomponent exercise programs, delivered remotely among older adults.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04181983.

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

Defining participatory health informatics - a scoping review

Healthcare has been shifting toward individuals participating in decision-making and empowered to be active in their treatment, and health monitoring. The term "participatory health informatics" (PHI) started to appear in literature. A clear definition of PHI is missing, and facets of PHI still have to be shaped. The objective of this paper is to offer a definition of PHI considering themes and technologies that make healthcare participatory. We searched Pubmed, ACM Digital Library, IEEE Xplore, EMBASE, and conference proceedings for articles that reported about use of information technology or informatics in the context of PHI. We performed qualitative synthesis and reported summary statistics. 39 studies were eligible after screening 382 titles and abstracts and reviewing 82 full texts. The top 5 person-centered key themes related to PHI included empowerment, decision-making, informed patient, collaboration, and disease management. Finally, we propose to define PHI as multidisciplinary field that uses information technology as provided through the web, smartphones, or wearables to increase participation of individuals in their care process and to enable them in self-care and shared decision-making. Goals to be achieved through PHI include maintaining health and well-being; improving the healthcare system and health outcomes; sharing experiences; achieving life goals; and self-education.

Patient-centred e-health supports value cocreation and quality of medical care in Taiwan

BACKGROUND

Patient-centred e-health (PCEH) focusses on the interaction between patients and physicians. However, only a limited number of studies have focussed on the design of physician-patient value cocreation mechanisms in the PCEH context. Thus, we extend Grönroos' concept of value cocreation to understand how PCEH might improve the quality of care.

OBJECTIVES

This study proposes a theoretical framework to embody PCEH-supported value cocreation and presents some empirical validation. We expect that PCEH-supported value cocreation should comprise capabilities for patient empowerment, intention for information sharing, complementation for checking and verifying information, and interaction for shared understanding.

METHODS

This study surveyed a small group of patients that have used PCEH, 'My Health Bank' in Taiwan. The questionnaires were delivered to patients in hospitals (n = 167 questionnaires, 98% response rate).

RESULTS

Results indicate that certain PCEH-supported value cocreation mechanisms-capabilities for patient empowerment and interaction for shared understanding-affect the perceived quality of medical care.

LIMITATIONS

The survey only considered patient perceptions of value cocreation.

CONCLUSION

This study shows the patient perception of value cocreation in patient-centred e-Health. Further research needs to validate the framework for health professionals and in other e-Health record information sharing settings.

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.

Spanish Society of Pulmonology and Thoracic Surgery positioning on the use of telemedine in sleep-disordered breathing and mechanical ventilation

The rapid introduction of new information and communication technologies into medical practice has prompted Spanish Society of Pulmonology and Thoracic SurgeryR to publish a position paper on sleep-disordered breathing, especially in relation to positive pressure treatment. It should be pointed out that the scientific literature is to some extent controversial due to a paucity of large randomized multicenter studies with long-term follow-up. Moreover, the telematics devices and systems on the market vary widely. As a result, the recommendations are based primarily on a consensus of expert professionals. Another very important aspect addressed extensively in this document is the obvious lack of regulations on legal matters and the operations of commercial companies. The most important recommendations included in this position paper are that telemedicine is primarily advocated in subjects with travel problems or who live far from the hospital, in patients with poor CPAP compliance, and in most cases treated with non-invasive mechanical ventilation. A key element is patient individualization. It is imperative that the relevant technical, legal and ethical requirements (medical device regulations, data protection, and informed consent) are met. Finally, expert professionals from our society must contribute to and become involved in spearheading this technological change.

Personalized, Predictive, Participatory, Precision, and Preventive (P5) Medicine in Rotator Cuff Tears

Rotator cuff (RC) disease is a common musculoskeletal disorder of the shoulder entailing pain, with reduced functionality and quality of life. The main objective of this study was to present a perspective of the current scientific evidence about personalized, predictive, participatory, precision, and preventive approaches in the management of RC diseases. The personalized, predictive, participatory, precision and preventive (P5) medicine model is an interdisciplinary and multidisciplinary approach that will provide researchers and clinicians with a comprehensive patrimony of knowledge in the management of RC diseases. The ability to define genetic predispositions in conjunction with the evaluation of lifestyle and environmental factors may boost the tailoring of diagnosis and therapy in patients suffering from RC diseases.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

One Digital Health: A Unified Framework for Future Health Ecosystems

One Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic.

Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization

Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician's ability to understand the obtained results in a standardized way for each particular patient. The review aims to lead the reader through the general conception of PGx and related issues of PGx testing efficiency, personal data security, and health safety at a current clinical level.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.

Big Data in Ophthalmology

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

Accelerating Telemedicine for Cerebral Palsy During the COVID-19 Pandemic and Beyond

The effects of COVID-19 extend beyond the pandemic and are expected to transform healthcare in various ways, many of which remain unknown. With social distancing, telemedicine may become the preferred communication channel between caregivers and patients. Implications for cerebral palsy (CP) children are that this will pose a challenge within this transformation. CP, as a discreet entity, is not considered a risk factor. However, specific comorbidities in individuals with CP, such as chronic lung disease, are known as COVID-19 risk factors. The overall risk for the CP population is probably a factor of age and comorbidities. Staying at home for CP children is both a challenge and an opportunity. Escalation of behavioral conflicts or improved participation and equality within the household may emerge. Interestingly, restricted mobility for the general population narrows existing gaps of ambulation. Telemedicine is the primary way of providing services for chronic conditions during the pandemic and is expected to expand beyond pre-Coronavirus era use. The advantages of telemedicine vary, more so during pandemic times, according to severity, restrictions, and availably of telemedicine. A multidisciplinary therapeutic presence is more accessible with telemedicine, bringing together various specialties and approaches to the child's natural environment. Accessible, continuous care is expected to lower comorbidities, as demonstrated for other chronic conditions. Enhanced monitoring is crucial for younger children as devastating complications, such as hip dysplasia, could be minimized. Last but not least, we will discuss digital health care as an accelerator for participatory medicine, including networked patients and families, as responsible drivers of their health as full partners.

Dried Blood Spot in Laboratory: Directions and Prospects

Over the past few years, dried blood spot (DBS) technology has become a convenient tool in both qualitative and quantitative biological analysis. DBS technology consists of a membrane carrier (MC) on the surface of which a biomaterial sample becomes absorbed. Modern analytical, immunological or genomic methods can be employed for analysis after drying the sample. DBS has been described as the most appropriate method for biomaterial sampling due to specific associated inherent advantages, including the small volumes of biomaterials required, the absence of a need for special conditions for samples’ storage and transportation, improved stability of analytes and reduced risk of infection resulting from contaminated samples. This review illustrates information on the current state of DBS technology, which can be useful and helpful for biomedical researchers. The prospects of using this technology to assess the metabolomic profile, assessment, diagnosis of communicable diseases are demonstrated.

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

Non-invasive human vital signs monitoring based on twin-core optical fiber sensors

Twin-core fiber (TCF)-based sensor was proposed for non-invasive vital sign monitoring, including respiration and heartbeat. The TCF was homemade and the corresponding sensor was fabricated by sandwiching single-mode fiber (SMF) on both ends. The offset distance between SMF and TCF was optimized while the length of TCF was identified from preliminary vital sign measurement results. Then, the TCF-based sensor was attached under a mattress to realize non-invasive vital sign monitoring. Both respiration and heartbeat signal can be obtained simultaneously, which is consistent with the reference signals. For further application, post-exercise physiological activitity characterization were realized based on this vital sign monitoring system. In discussion, mode coupling in TCF was analyzed and utilized for curvature sensing with achieved sensitivity as high as 18 nm/m^-1, which supported its excellent performance for vital signs monitoring. In conclusion, the TCF-based vital signs monitors can be a promising candidate for healthcare and biomedical applications.