Digital Remote Monitoring Using an mHealth Solution for Survivors of Cancer: Protocol for a Pilot Observational Study

BACKGROUND

Healthy lifestyle interventions have a positive impact on multiple disease trajectories, including cancer-related outcomes. Specifically, appropriate habitual physical activity, adequate sleep, and a regular wholesome diet are of paramount importance for the wellness and supportive care of survivors of cancer. Mobile health (mHealth) apps have the potential to support novel tailored lifestyle interventions.

OBJECTIVE

This observational pilot study aims to assess the feasibility of mHealth multidimensional longitudinal monitoring in survivors of cancer. The primary objective is to test the compliance (user engagement) with the monitoring solution. Secondary objectives include recording clinically relevant subjective and objective measures collected through the digital solution.

METHODS

This is a monocentric pilot study taking place in Bangor, Wales, United Kingdom. We plan to enroll up to 100 adult survivors of cancer not receiving toxic anticancer treatment, who will provide self-reported behavioral data recorded via a dedicated app and validated questionnaires and objective data automatically collected by a paired smartwatch over 16 weeks. The participants will continue with their normal routine surveillance care for their cancer. The primary end point is feasibility (eg, mHealth monitoring acceptability). Composite secondary end points include clinically relevant patient-reported outcome measures (eg, the Edmonton Symptom Assessment System score) and objective physiological measures (eg, step counts). This trial received a favorable ethical review in May 2023 (Integrated Research Application System 301068).

RESULTS

This study is part of an array of pilots within a European Union funded project, entitled "GATEKEEPER," conducted at different sites across Europe and covering various chronic diseases. Study accrual is anticipated to commence in January 2024 and continue until June 2024. It is hypothesized that mHealth monitoring will be feasible in survivors of cancer; specifically, at least 50% (50/100) of the participants will engage with the app at least once a week in 8 of the 16 study weeks.

CONCLUSIONS

In a population with potentially complex clinical needs, this pilot study will test the feasibility of multidimensional remote monitoring of patient-reported outcomes and physiological parameters. Satisfactory compliance with the use of the app and smartwatch, whether confirmed or infirmed through this study, will be propaedeutic to the development of innovative mHealth interventions in survivors of cancer.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/52957.

Towards a common European ethical and legal framework for conducting clinical research: the GATEKEEPER experience

This paper examines the ethical and legal challenges encountered during the GATEKEEPER Project and how these challenges informed the development of a comprehensive framework for future Large-Scale Pilot (LSP) projects. GATEKEEPER is a LSP Project with 48 partners conducting 30 implementation studies across Europe with 50,000 target participants grouped into 9 Reference Use Cases. The project underscored the complexity of obtaining ethical approval across various jurisdictions with divergent regulations and procedures. Through a detailed analysis of the issues faced and the strategies employed to navigate these challenges, this study proposes an ethical and legal framework. This framework, derived from a comparative analysis of ethical application forms and regulations, aims to streamline the ethical approval process for future LSP research projects. By addressing the hurdles encountered in GATEKEEPER, the proposed framework offers a roadmap for more efficient and effective project management, ensuring smoother implementation of similar projects in the future.

Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

Hypertension Diagnosis and Management in Africa Using Mobile Phones: A Scoping Review

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear.

The use of smart environments and robots for infection prevention control: A systematic literature review

BACKGROUND

Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings METHODS: The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis.

RESULTS

The search strategy returned 1520 citations and 17 papers were included. This review identified 3 main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (ie, air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field.

CONCLUSIONS

Increasing the awareness of healthcare workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.

Pandemic Preparedness: A Scoping Review of Best and Worst Practices from COVID-19

The COVID-19 pandemic highlighted the scale of global unpreparedness to deal with the fast-arising needs of global health threats. This problem was coupled with a crisis of governance and presented in the context of globally hitting climate crisis and disasters. Although such a pandemic was predictable due to the known effects of human intervention on the surrounding environment and its devastating secondary effects, such as climate change and increased zoonoses, most countries were unprepared to deal with the scale and scope of the pandemic. In this context, such as that of the climate crisis, the Global North and Global South faced several common challenges, including, first and foremost, the scarcity of resources required for health, policy, wellbeing and socioeconomic wellness. In this paper, we review the most recent evidence available in the literature related to pandemic preparedness and governance, focusing on principles and practices used during the COVID-19 pandemic, and we place it in the context of a European Parliament Interest Group meeting (this event took place on 21 March 2023 during the "European Health Tech Summit") to ground it within ongoing discussions and narratives of policy and praxis. The review identified key practices and principles required to better face future health threats and emergencies. Beyond health practices relying on technology and innovation, it is useful to mention the importance of contextualising responses and linking them to clear goals, improving the agreement between science and policymaking, thus building trust and enabling transparent communication with the general public based on clear ethical frameworks.

Ethics and biomedical engineering for well-being: a cocreation study of remote services for monitoring and support

The well-being of students and staff directly affects their output and efficiency. This study presents the results of two focus groups conducted in 2022 within a two-phase project led by the Applied Biomedical and Signal Processing Intelligent e-Health Lab, School of Engineering at the University of Warwick, and British Telecom within "The Connected Campus: University of Warwick case study" program. The first phase, by involving staff and students at the University of Warwick, aimed at collecting preliminary information for the subsequent second phase, about the feasibility of the use of Artificial Intelligence and Internet of Things for well-being support on Campus. The main findings of this first phase are interesting technological suggestions from real users. The users helped in the design of the scenarios and in the selection of the key enabling technologies which they considered as the most relevant, useful and acceptable to support and improve well-being on Campus. These results will inform future services to design and implement technologies for monitoring and supporting well-being, such as hybrid, minimal and even intrusive (implantable) solutions. The user-driven co-design of such services, leveraging the use of wearable devices and Artificial Intelligence deployment will increase their acceptability by the users.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

BACKGROUND

The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs.

OBJECTIVE

We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform.

METHODS

The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities.

RESULTS

Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors.

CONCLUSIONS

This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions

INTRODUCTION

Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.

METHODS AND ANALYSIS

This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.

ETHICS AND DISSEMINATION

This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.

TRIAL REGISTRATION NUMBER

NCT05461144.

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol

Purpose

Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device.

Methods

This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.

Results

Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm.

Conclusion

This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions.

Trial registration

ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.

Supplementary information

The online version contains supplementary material available at 10.1007/s12553-022-00719-x.

A robotic arm for safe and accurate control of biomedical equipment during COVID-19

Purpose

Hospital facilities and social life, along with the global economy, have been severely challenged by COVID-19 since the World Health Organization (WHO) declared it a pandemic in March 2020. Since then, countless ordinary citizens, as well as healthcare workers, have contracted the virus by just coming into contact with infected surfaces. In order to minimise the risk of getting infected by contact with such surfaces, our study aims to design, prototype, and test a new device able to connect users, such as common citizens, doctors or paramedics, with either common-use interfaces (e.g., lift and snack machine keyboards, traffic light push-buttons) or medical-use interfaces (e.g., any medical equipment keypad).

Method

To this purpose, the device was designed with the help of Unified Modelling Language (UML) schemes, and was informed by a risk analysis, that highlighted some of its essential requirements and specifications. Consequently, the chosen constructive solution of the robotic system, i.e., a robotic-arm structure, was designed and manufactured using computer-aided design and 3D printing.

Result

The final prototype included a properly programmed micro-controller, linked via Bluetooth to a multi-platform mobile phone app, which represents the user interface. The system was then successfully tested on different physical keypads and touch screens. Better performance of the system can be foreseen by introducing improvements in the industrial production phase.

Conclusion

This first prototype paves the way for further research in this area, allowing for better management and preparedness of next pandemic emergencies.

Recurrence quantification analysis of center of pressure trajectories for balance and fall-risk assessment in young and older adults

The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions. Participants were retrospectively classified as young adults (age<60, n=85), non-fallers (age≥60, falls=0, n=56), and fallers (age≥60, falls≥1, n=18). Mixed ANOVA and post hoc analyzes were performed to test for differences between groups. For CoP fluctuations in the anterior-posterior direction, all RQA measures showed significantly higher values for young than older adults when standing on a compliant surface, indicating less predictable and stable balance control among seniors under testing conditions where sensory information is restricted or altered. However, no significant differences between non-fallers and fallers were observed. These results support the use of RQA to characterize balance control in young and old adults, but not to discriminate between different fall risk groups.

Predictors of adverse events after endoscopic ultrasound-guided through-the-needle biopsy of pancreatic cysts: a recursive partitioning analysis

BACKGROUND AND STUDY AIMS : Endoscopic ultrasound-guided through-the-needle biopsy (TTNB) of pancreatic cystic lesions (PCLs) is associated with a non-negligible risk for adverse events (AEs). We aimed to identify the hierarchic interaction among independent predictors for TTNB-related AEs and to generate a prognostic model using recursive partitioning analysis (RPA). PATIENTS AND METHODS : Multicenter retrospective analysis of 506 patients with PCLs who underwent TTNB. RPA of predictors for AEs was performed and the model was validated by means of bootstrap resampling. RESULTS : Mean cysts size was 36.7 mm. Most common diagnoses were intraductal papillary mucinous neoplasm (IPMN, 45 %), serous cystadenoma (18.8 %), and mucinous cystadenoma (12.8 %). Fifty-eight (11.5 %) AEs were observed. At multivariate analysis, age (odds ratio [OR] 1.32, 1.09-2.14; p = 0.05), number of TTNB passes (OR from 2.17, 1.32-4.34 to OR 3.16, 2.03-6.34 with the increase of the number of passes), complete aspiration of the cyst (OR 0.56, 0.31-0.95; p = 0.02), and diagnosis of IPMN (OR 4.16, 2.27-7.69; p < 0.001) were found to be independent predictors of AEs, as confirmed by logistic regression and random forest analyses. RPA identified three risk classes: high-risk (IPMN sampled with multiple microforceps passes, 28 % AEs rate), low-risk (1.4 % AE rate, including patients < 64 years with other-than-IPMN diagnosis sampled with ≤ 2 microforceps passes and with complete aspiration of the cyst) and middle-risk class (6.1 % AEs rate, including the remaining patients). CONCLUSION : TTNB should be selectively used in the evaluation of patients with IPMN. The present model could be applied during patient selection as to optimize the benefit/risk of TTNB.

Exploring the misalignment on the value of further research between payers and manufacturers. A case study on a novel total artificial heart

Payers and manufacturers can disagree on the appropriate level of evidence that is required for new medical devices, resulting in high societal costs due to decisions taken with sub-optimal information. A cost-effectiveness model of a hypothetical total artificial heart was built using data from the literature and the (simulated) results of a pivotal study. The expected value of perfect information (EVPI) was calculated from both the payer and manufacturer perspectives, using net monetary benefit and the company's return on investment respectively. A function was also defined, linking effectiveness to market shares. Additional constraints such as a minimum clinical difference or maximum budget impact were introduced into the company's decisions to simulate additional barriers to adoption. The difference in the EVPI between manufacturers and payers varied greatly depending on the underlying decision rules and constraints. The manufacturer's EVPI depends on the probability of being reimbursed, the uncertainty on the (cost-)effectiveness of the technology, as well as other parameters relating to initial investments, operating costs and market dynamics. The use of Value of information for both perspectives can outline potential misalignments and can be particularly useful to inform early dialogs between manufacturers and payers, or negotiations on conditional reimbursement schemes.

On the universality of medical device regulations: the case of Benin

Background

Regulatory frameworks surrounding medical devices (MDs) and medical locations are of utter importance for safeguarding patients and users, and for granting a universal access to healthcare.

Currently, as the main existing regulatory frameworks are drafted by high-income countries, they pretend to be general and applicable globally, but fail to understand particular contexts, specifically those in low-resource settings (LRSs), resulting, therefore, inapplicable. In particular, LRSs present a varied situation, with legal transplants of guidelines from their previous colonial regimes. This apparently theoretical issue, is, effectively, a tangible and rising matter of concern, given the ever-increasing number of MD patent applications per year, as well as the appearance of low- and middle-income countries (LMICs) on the MD market itself.

This article will focus on the European Regulation on MDs 745/2017 and its applicability in LRSs, specifically presenting the case of Benin, a Sub-Saharan African country.

Methods

This work is based on a field study conducted in 2019 in Benin, which is particularly exemplar to show the complexity of the “legal transplantation” concept. A multidisciplinary approach, comprising the standard tools and methods of ethics, law, and biomedical engineering, was used to draft a heuristic hermeneutic framework, and to analyse related bioethical issues concerning Medical Device Regulations (MDRs) in LRSs, the role of Maintenance, and other sociological questions; as well as the rural population’s perception on MDs and health technologies, and the role of ethics in the hospitals of LRSs.

Results

The definition of these themes helped approach the local perspective and define the research questions. Downstream of the analysis of the Medical Devices Regulations, the Maintenance and other bioethical issues in Benin, the heuristic hermeneutic framework was created to guide a shift in the paradigm of law and regulation making, so as to make them more contextualised and inclusive, globally.

Conclusion

This article proposes a framework that will help policymakers take into account the particularism of each context, especially those of the most vulnerable countries, when drafting and issuing regulatory frameworks, promoting an ever-evolving model of universalism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08396-2.

COVID-19 preparedness and social dynamics in a Sub-Saharan Africa country, Benin

This project aims to assess and analyse the perception and impact of the COVID-19 pandemic in Benin. The applied research methodology was interdisciplinary and combined field studies that used ethnographic and social research methods with coding and data analysis, leading to theoretical dilemmas, which were analysed from the viewpoint of bioethical reflection. Furthermore, biomedical engineering approaches were used to assess the preparedness to COVID-19. Despite the preparedness to COVID-19 due to the promoted governmental measures, a peculiar management of the pandemic emerged. The latter, although noteworthy, did not overcome the typical challenges of medical locations in low-resource settings. This, together with the controversial spread of information and local beliefs, caused significant economic and social consequences, exceeding the benefits related to the containment of the virus. This research highlights how the emotion of fear, in this specific situation, was herald of dramatic consequences, rather than having a heuristic and empowering effect.

From COVID-19 Pandemic to Patient Safety: A New “Spring” for Telemedicine or a Boomerang Effect?

During the Covid-19 health emergency, telemedicine was an essential asset through which health systems strengthened their response during the critical phase of the pandemic. According to the post-pandemic economic reform plans of many countries, telemedicine will not be limited to a tool for responding to an emergency condition but it will become a structural resource that will contribute to the reorganization of Healthcare Systems and enable the transfer of part of health care from the hospital to the home-based care. However, scientific evidences have shown that health care delivered through telemedicine can be burdened by numerous ethical and legal issues. Although there is an emerging discussion on patient safety issues related to the use of telemedicine, there is a lack of reseraches specifically designed to investigate patient safety. On the contrary, it would be necessary to determine standards and specific application rules in order to ensure safety. This paper examines the telemedicine-risk profiles and proposes a position statement for clinical risk management to support continuous improvement in the safety of health care delivered through telemedicine.

Social Engagement in the Fight Against COVID-19 in the Urban and Peri-Urban Areas of Cotonou (Benin, Sub-saharan Africa): Acceptability of the Vaccination and Tracking Program

Introduction

This article aims at investigating social engagement in the fight against the COVID-19 pandemic in low-resource settings (LRSs). In particular, it focuses on Benin (Sub-Saharan Africa), and reports the results of a field study that investigated the local people's acceptance of the vaccine and the tracking program.

Methods

This project is the product of a collaboration between the ABSPIE (Applied Biomedical and Signal Processing E-Health) Lab of the University of Warwick (UK) and the LAMA (Laboratoire d'Antropologie Medical Appliqué) of the University of Abomey Calavi (Benin). This international multidisciplinary collaboration brought together engineers, sociologists, anthropologists, and bioethicists. In light of the aims of the project, a qualitative methodology was deemed appropriate. The research team prepared two questionnaires that provided the basis for semi-structured interviews that took place between June and August 2021.

Results

The research team interviewed 34 Beninese respondents, comprising people aged 60+ (with multiple comorbidities), who were primarily healthcare workers and/or traditional therapists. The results of this work highlight the fact that there is widespread reticence about the vaccination program in Benin, both due to local beliefs and uncertainty about governmental management. In this study, we uncovered several local reasons interfering with the involvement of the population in the vaccination campaign against COVID-19, e.g., the existence of traditional medical practices considered as valid alternatives to vaccines, and many beliefs showing a fear of neo-colonialism hidden in the pandemic threat. Yet, another hindrance can be traced to shortcomings in the management of the vaccination campaign which resulted in obstacles to the implementation of the program.

Conclusions

This work does not intend to denounce any governmental effort or foster a regressive mindset, but shows how the overall confusion (defined by the World Health Organization as infodemic) linked to the pandemic and its management has caused even more dramatic consequences in LRSs. In addition, the paper proposes a specific framework for the interpretation and management of bioethical and biomedical issues in LRSs that the authors are validating in their current research.

Use of technology to prevent, detect, manage and control hypertension in sub-Saharan Africa: a systematic review

OBJECTIVE

To identify and assess the use of technologies, including mobile health technology, internet of things (IoT) devices and artificial intelligence (AI) in hypertension healthcare in sub-Saharan Africa (SSA).

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase, Scopus and Web of Science.

ELIGIBILITY CRITERIA

Studies addressing outcomes related to the use of technologies for hypertension healthcare (all points in the healthcare cascade) in SSA.

METHODS

Databases were searched from inception to 2 August 2021. Screening, data extraction and risk of bias assessment were done in duplicate. Data were extracted on study design, setting, technology(s) employed and outcomes. Blood pressure (BP) reduction due to intervention was extracted from a subset of randomised controlled trials. Methodological quality was assessed using the Mixed Methods Appraisal Tool.

RESULTS

1717 hits were retrieved, 1206 deduplicated studies were screened and 67 full texts were assessed for eligibility. 22 studies were included, all reported on clinical investigations. Two studies were observational, and 20 evaluated technology-based interventions. Outcomes included BP reduction/control, treatment adherence, retention in care, awareness/knowledge of hypertension and completeness of medical records. All studies used mobile technology, three linked with IoT devices. Short Message Service (SMS) was the most popular method of targeting patients (n=6). Moderate BP reduction was achieved in three randomised controlled trials. Patients and healthcare providers reported positive perceptions towards the technologies. No studies using AI were identified.

CONCLUSIONS

There are a range of successful applications of key enabling technologies in SSA, including BP reduction, increased health knowledge and treatment adherence following targeted mobile technology interventions. There is evidence to support use of mobile technology for hypertension management in SSA. However, current application of technologies is highly heterogeneous and key barriers exist, limiting efficacy and uptake in SSA. More research is needed, addressing objective measures such as BP reduction in robust randomised studies.

PROSPERO REGISTRATION NUMBER

CRD42020223043.

Personalized Training via Serious Game to Improve Daily Living Skills in Pediatric Patients with Autism Spectrum Disorder

The majority of people with Autism Spectrum Disorder (ASD) exhibit difficulties in social communication and behavior, which hinder their learning capability, amid others. Among technological solutions for people with ASD, serious games are frequently used to enhance learning of specific skills and instructional contents. However, because of heterogeneity in applications and game design, few studies have investigated their use in training daily activities. This paper presents a 3D personalized serious game we developed and validated to help ASD patients practice with shopping activities. Personalized training is paramount in people with ASD, thus several elements of this game were personalized to improve engagement and therefore the effectiveness of the virtual training. In order to assess the validity of the game, ten subjects (age 11.9 2.7, 20% female) with ASD played ten sessions of the serious game, once per week. The participants underwent a real-life experience pre- and post-training in a real-life supermarket. Changes in daily living skills among participants were evaluated through specific tools: a form based on the International Classification of Functioning, Disability and Health for Children and Youth; and the Vineland Adaptive Behavior Scale II. Significant improvements (p<0.05) were detected in the main skills trained with the serious game, especially in learning the shopping procedure, directing attention, and problem-solving skills. These findings suggest that personalized serious games can represent a prominent tool to enhance daily living skills, but future work should clinically validate their efficacy.

3D-printed activated charcoal inlet filters for oxygen concentrators: A circular economy approach

As of May 2021, the current COVID-19 pandemic is still plaguing the world, challenging all the countries and their health systems, globally. In this context, conditions typical of low-resource settings surfaced also in high-resource ones (e.g., the lack of essential medical equipment, of resources etc.), while exacerbating in the already resource-scarce settings, because of COVID-19. This is the case of oxygen concentrators that are one of the first-line medical devices for treating COVID-19 patients. Since the beginning of 2020, their demand has been rapidly growing worldwide, aggravating the situation for low-resource settings, where the availability of devices providing oxygen-enriched air was already scarce. In fact, due to their delicacy, the lack of spare parts and of an appropriate health technology management system, oxygen concentrators can often be found broken or not working properly in these settings. The underlying problems have deep roots. The current regulatory frameworks and standards, which are set by high-income countries, are too stringent, and do not take into account the limited resources of poorer settings. Thus, they are often inapplicable in such settings. One of the main issues affecting the oxygen concentrators, is that related to the filters, which are designed to filter out dust, particles, bacteria, and to be used in medical locations complying with international standards (e.g., the air filtration level in a surgical theatre in Italy is at 99.97%). When used in low-resource settings, which do not comply with these standards and face several challenges (e.g., dust), these filters have a much-reduced lifespan. For these reasons, this paper aims to present the redesign of the inlet filter of an oxygen concentrator, which is used to prevent gross particles to enter the device. The redesign is based on a reverse engineering approach, and on the use of 3D-printing along with activated charcoal. After testing the filtration efficiency with a particle counter, the filter design has been refined through several iterations. The final prototype performs particularly well when filtering particles above 1 μm (with a filtration efficiency of 64.2%), and still has a satisfactory performance with any particle size over 0.3 μm (with a filtration efficiency of 38.8%). Following the United Nations Sustainable Development Goals, this project aims to empower local communities, and start a positive trend of self-sustained supply chain of simple spare parts for medical devices, leveraging on frugal engineering, 3D-printing, locally produced activated charcoal, and circular economy.

Health Technology Assessment of Intensive Care Ventilators for Pediatric Patients

This paper is aimed at addressing all the critical aspects linked to the implementation of intensive care ventilators in a pediatric setting, highlighting the most relevant technical features and describing the methodology to conduct health technology assessment (HTA) for supporting the decision-making process. Four ventilator models were included in the assessment process. A decision-making support tool (DoHTA method) was applied. Twenty-eight Key Performance Indicators (KPIs) were identified, defining the safety, clinical effectiveness, organizational, technical, and economic aspects. The Performance scores of each ventilator have been measured with respect to KPIs integrated with the total cost of ownership analysis, leading to a final rank of the four possible technological solutions. The final technologies' performance scores reflected a deliver valued, contextualized, and shared outputs, detecting the most performant technological solution for the specific hospital context. HTA results had informed and supported the pediatric hospital decision-making process. This study, critically identifying the pros and cons of innovative features of ventilators and the evaluation criteria and aspects to be taken into account during HTA, can be considered as a valuable proof of evidence as well as a reliable and transferable method for conducting decision-making processes in a hospital context.

Biomedical engineering and ethics: reflections on medical devices and PPE during the first wave of COVID-19

In March 2019, the World Health Organization (WHO) declared that humanity was entering a global pandemic phase. This unforeseen situation caught everyone unprepared and had a major impact on several professional categories that found themselves facing important ethical dilemmas. The article revolves around the category of biomedical and clinical engineers, which were among those most involved in dealing with and finding solutions to the pandemic. In hindsight, the major issues brought to the attention of biomedical engineers have raised important ethical implications, such as the allocation of resources, the responsibilities of science and the inadequacy and non-universality of the norms and regulations on biomedical devices and personal protective equipment. These issues, analyzed one year after the first wave of the pandemic, come together in the appeal for responsibility for thought, action and, sometimes, even silence. This highlights the importance of interdisciplinarity and the definitive collapse of the Cartesian fragmentation of knowledge, calling for the creation of more fora, where this kind of discussions can be promoted.

Enhanced Medical and Community Face Masks with Antimicrobial Properties: A Systematic Review

Face masks help to limit transmission of infectious diseases entering through the nose and mouth. Beyond reprocessing and decontamination, antimicrobial treatments could extend the lifetime of face masks whilst also further reducing the chance of disease transmission. Here, we review the efficacy of treatments pertaining antimicrobial properties to medical face masks, filtering facepiece respirators and non-medical face masks. Searching databases identified 2113 studies after de-duplication. A total of 17 relevant studies were included in the qualitative synthesis. Risk of bias was found to be moderate or low in all cases. Sixteen articles demonstrated success in avoiding proliferation (if not elimination) of viruses and/or bacteria. In terms of methodology, no two articles employed identical approaches to efficacy testing. Our findings highlight that antimicrobial treatment is a promising route to extending the life and improving the safety of face masks. In order to reach significant achievements, shared and precise methodology and reporting is needed.

Early health technology assessment using the MAFEIP tool. A case study on a wearable device for fall prediction in elderly patients

By using a case-study on a fall-prediction device for elderly patients with orthostatic hypotension we aim to demonstrate how the MAFEIP tool, developed as part of the European Innovation Programme on Active and Healthy Ageing (EIP on AHA), can be used to inform manufacturers on their product development based on a cost-effectiveness criterion. Secondly, we critically appraise the tool and suggest further improvements that may be needed for a larger-scale adoption of MAFEIP within and beside the EIP on AHA initiative. The model was implemented using the MAFEIP tool. Within the tool one way sensitivity analyses were performed to assess the robustness of the model against the relative effectiveness of the fall-prevention device at different price levels. The MAFEIP tool was applied to a novel fall-prediction device and used to estimate the expected cost-effectiveness and perform threshold analysis. In our case study, the device produced estimated gains of 0.035 QALYs per patient and incremental costs of £ 518 (incremental cost-effectiveness ratio £14,719). Based on the one-way sensitivity analysis, the maximum achievable price at a willingness to pay threshold of £20,000 per QALY is estimated close to £900. The MAFEIP allows to quickly create early economic models, and to explore model uncertainty by performing deterministic sensitivity analysis for single parameters. However, the integration within the MAFEIP of common analytical tools such as probabilistic sensitivity analysis and Value of information would greatly contribute to its relevance for evaluating innovative technologies within and beside the EIP on AHA initiative.

A framework for designing medical devices resilient to low-resource settings

Background

To date (April 2021), medical device (MD) design approaches have failed to consider the contexts where MDs can be operationalised. Although most of the global population lives and is treated in Low- and Middle-Income Countries (LMCIs), over 80% of the MD market share is in high-resource settings, which set de facto standards that cannot be taken for granted in lower resource settings. Using a MD designed for high-resource settings in LMICs may hinder its safe and efficient operationalisation. In the literature, many criteria for frameworks to support resilient MD design were presented. However, since the available criteria (as of 2021) are far from being consensual and comprehensive, the aim of this study is to raise awareness about such challenges and to scope experts’ consensus regarding the essentiality of MD design criteria.

Results

This paper presents a novel application of Delphi study and Multiple Criteria Decision Analysis (MCDA) to develop a framework comprising 26 essential criteria, which were evaluated and chosen by international experts coming from different parts of the world. This framework was validated by analysing some MDs presented in the WHO Compendium of innovative health technologies for low-resource settings.

Conclusions

This novel holistic framework takes into account some domains that are usually underestimated by MDs designers. For this reason, it can be used by experts designing MDs resilient to low-resource settings and it can also assist policymakers and non-governmental organisations in shaping the future of global healthcare.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12992-021-00718-z.

The role of ethics in science: a systematic literature review from the first wave of COVID-19

This paper proposes a systematic literature review on ethics and CoviD-19, aiming to understand the impact and the perception of the pandemic during the first wave (January-June 2020) and the consequences one year later. PubMed was systematically searched up May 2020 to identify studies that took into consideration various ethical issues that have been arising from the Covid-19 outbreak. The eligibility of the papers was determined by two authors, who screened the results mediated by a third author. In order to facilitate the screening, the titles were divided into five sub-thematic macro-areas, namely allocation, policy, specialist, clinical trials, and technology and, when possible, per geographical area. Specifically, a posteriori, we decided to focus on the papers referring to policies and technology, as they highlighted ethical issues that are not overused and worthy of particular attention. Thus, 38 studies out of 233 met our inclusion criteria and were fully analysed. Accordingly, this review touches on themes such as fairness, equity, transparency of information, the duty of care, racial disparities, the marginalisation of the poor, and privacy and ethical concerns. Overall, it was found that despite the increased awareness of interdisciplinarity and the essential reference to ethics, many scientific articles use it with little competence, considering it only a "humanitarian" enrichment. In fact, as we understand, reflecting a year after the outbreak of the pandemic, although Covid-19 is leading scientists to increasingly recognise the importance of ethical issues, there is still a lot of confusion that could be helped by establishing international guidelines to act as a moral compass in times of crisis.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review

BACKGROUND

Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.

OBJECTIVE

This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice.

RESULTS

In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.

CONCLUSIONS

The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Correction to: Clinical needs and technical requirements for ventilators for COVID-19 treatment critical patients: an evidence-based comparison for adult and pediatric age

[This corrects the article DOI: 10.1007/s12553-020-00467-w.].

Early Cost-effectiveness Analysis of Electrochemotherapy as a Prospect Treatment Modality for Skin Melanoma

PURPOSE

Electrochemotherapy is increasingly entering into national and international guidelines, requiring formal evaluation of treatment costs and cost-effectiveness to ensure that its uptake provides value to budget-constrained health care systems. This study analyzed the early cost-effectiveness of electrochemotherapy in patients with Stage IIIc/IV skin melanoma in clinical practice in Slovenia. The costs of electrochemotherapy were compared to those of the standard of care, consisting of palliative treatment and therapy for symptoms.

METHODS

wThe study enrolled 23 patients treated with electrochemotherapy at the Institute of Oncology (Ljubljana, Slovenia). The mean cost of electrochemotherapy was estimated using patient-specific cost data on electrochemotherapy procedures and subsequent follow-up. Quality-adjusted life-years (QALYs) were estimated by collecting EQ-5D-3L questionnaires at baseline, after complete or partial response following the treatment, and after a relapse of skin lesions. A discrete-time Markov model was built to estimate the lifetime costs and consequences of using electrochemotherapy compared to standard of care, from the perspective of the Slovenian health care system. The analysis was conducted separately in the whole patient sample and in the subset of patients with bleeding lesions. Deterministic and probabilistic sensitivity analyses were conducted to test model assumptions and to characterize the uncertainty around model parameters.

FINDINGS

In the whole patient population, electrochemotherapy for skin melanoma Stage IIIc/IV was expected to increase QALYs by 0.29 (95% credible interval [CrI], 0.10-0.50), at the higher cost of 6568 EUR (95% CrI, 4593-8928) in comparison to the standard of care. At the cost-effectiveness threshold of 20,000 EUR/QALY, the estimated probabilities of electrochemotherapy being cost-effective compared to standard of care were 0.30 and 0.91 in the whole patient sample and in patients with bleeding lesions, respectively. In the whole sample population, a 50% reduction in the price of the electrodes was expected to increase the probability of electrochemotherapy being cost-effective from 0.30 to ~0.64.

IMPLICATIONS

The findings from this cost-effectiveness analysis of data from clinical practice were based on a small sample size (ie, 23 patents), which made the subgroup of patients with bleeding lesions very small. Therefore, the findings in this patient population should be carefully interpreted.

A Framework for Assessing Healthcare Facilities in Low-Resource Settings: Field Studies in Benin and Uganda

Purpose

The aim of this paper is to present and validate a framework for assessing healthcare facilities in low-resource settings to collect evidence and inform policies on the harmonisation, regulation and contextualised design of medical devices.

Methods

A literature review and focus groups with several experts of medical device design, clinical engineering, health technology assessment and management, allowed the creation of a protocol, comprising two parts: a semi-structured interview and electrical safety measures.

Results

Three hospitals were assessed in Benin and three in Uganda. All the health centres resulted to be facing typical challenges for low-resource settings, including the lack of funding, expertise, a well-established maintenance program, spare parts and consumables, and unreliable power supplies.

Conclusion

As there is a paucity of information regarding low-resource settings, the proposed framework can be used by clinical or biomedical engineers to assess and thereby propose actions for improving the conditions of healthcare settings.

Health Technology Assessment and Biomedical Engineering: Global trends, gaps and opportunities

The diffusion of medical devices is expanding at an astonishing rate. The increasing number of novel patents per year suggests this growth will continue. In contrast to drugs, medical devices are intrinsically dependent on the environment in which they are used and how they are maintained. This created an unprecedented global need for well-trained biomedical engineers who can help healthcare systems to assess them. The International Federation for Medical and Biological Engineering (IFMBE) is the global scientific society of biomedical engineers in official relations with the United Nations World Health Organisation (WHO) and has been very active in promoting the role of the biomedical engineer in Health Technology Assessment (HTA). The IFMBE Health Technology Assessment Division (HTAD) is the IFMBE operative branch in this field, promoting studies, projects and activities to foster the growth of this specific and very important science sector, including summer schools, training material, an HTA eLearning platform, HTA guidelines, awards and more. This article describes the vision, the mission and the strategy of the HTAD, with a focus on the results achieved and the impact this is having on global policymaking.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.

Endovascular Treatment versus Medical Therapy for Hypertensive Patients with Renal Artery Stenosis: An Updated Systematic Review

The benefit of balloon angioplasty, with or without stenting, for the treatment of patients with renal artery stenosis remains controversial. A number of randomized controlled trials have attempted to determine its efficacy but the matter remains unclear. A 2014 Cochrane review, which combined data from 8 trials, showed homogeneity among the trials with no significant benefit shown. This systematic review replicates the same research methods and meta-analysis while expanding it to include papers between 2014 and 2018. One of the trials included in the previous review published results in the interim. Additionally, 2 ongoing trials identified in the 2014 review are yet to publish any result. Meta-analysis of the reports showed no heterogeneity between trials and no significant improvement shown by balloon angioplasty, with or without stenting, versus medical therapy. Further studies are recommended in order to assess the benefits of balloon angioplasty for patients with more severe renal artery stenosis.

Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

Background

This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.

Methods

ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.

Results

Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.

Conclusion

This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0742-y) contains supplementary material, which is available to authorized users.