ABSTRACT
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.
Subject(s)
Artificial Intelligence , Biomedical Engineering , Humans , Bioengineering , Engineering , Focus GroupsABSTRACT
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.
Subject(s)
Bioethical Issues , Medical Device Legislation , Benin , Delivery of Health Care , HumansABSTRACT
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.
Subject(s)
Biomedical Engineering/trends , Internationality , Technology Assessment, Biomedical/trends , Awards and Prizes , Humans , Intersectoral CollaborationABSTRACT
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.
Subject(s)
Electrocardiography/methods , Heart Rate/physiology , Machine Learning , Models, Theoretical , Stress, Psychological/diagnosis , Adult , Feasibility Studies , Female , Humans , Male , Young AdultABSTRACT
Mental stress may cause cognitive dysfunctions, cardiovascular disorders and depression. Mental stress detection via short-term Heart Rate Variability (HRV) analysis has been widely explored in the last years, while ultra-short term (less than 5 minutes) HRV has been not. This study aims to detect mental stress using linear and non-linear HRV features extracted from 3 minutes ECG excerpts recorded from 42 university students, during oral examination (stress) and at rest after a vacation. HRV features were then extracted and analyzed according to the literature using validated software tools. Statistical and data mining analysis were then performed on the extracted HRV features. The best performing machine learning method was the C4.5 tree algorithm, which discriminated between stress and rest with sensitivity, specificity and accuracy rate of 78%, 80% and 79% respectively.
Subject(s)
Electrocardiography/methods , Heart Rate/physiology , Stress, Psychological/diagnosis , Adolescent , Adult , Algorithms , Data Mining , Humans , Rest , Sensitivity and Specificity , Signal Processing, Computer-Assisted , Stress, Psychological/physiopathology , Young AdultABSTRACT
BACKGROUND: Standing from a bed or chair may cause a significant lowering of blood pressure (ΔBP), which may have severe consequences such as, for example, falls in older subjects. The goal of this study was to develop a mathematical model to predict the ΔBP due to standing in healthy subjects, based on their Heart Rate Variability, recorded in the 5 minutes before standing. METHODS: Heart Rate Variability was extracted from an electrocardiogram, recorded from 10 healthy subjects during the 5 minutes before standing. The blood pressure value was measured before and after rising. A mathematical model aiming to predict ΔBP based on Heart Rate Variability measurements was developed using a robust multi-linear regression and was validated with the leave-one-subject-out cross-validation technique. RESULTS: The model predicted correctly the ΔBP in 80% of experiments, with an error below the measurement error of sphygmomanometer digital devices (± 4.5 mmHg), a false negative rate of 7.5% and a false positive rate of 10%. The magnitude of the ΔBP was associated with a depressed and less chaotic Heart Rate Variability pattern. CONCLUSIONS: The present study showes that blood pressure lowering due to standing can be predicted by monitoring the Heart Rate Variability in the 5 minutes before standing.
Subject(s)
Blood Pressure/physiology , Heart Rate/physiology , Hypotension, Orthostatic , Models, Theoretical , Adult , Electrocardiography , Female , Healthy Volunteers , Humans , Male , Pilot Projects , Young AdultABSTRACT
BACKGROUND: Falls in the elderly is a major problem. Although falls have a multifactorial etiology, a commonly cited cause of falls in older people is poor vision. This study proposes a method to discriminate fallers and non-fallers among ophthalmic patients, based on data-mining algorithms applied to health and socio-demographic information. METHODS: A group of 150 subjects aged 55 years and older, recruited at the Eye Clinic of the Second University of Naples, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. A subject who reported at least one fall within one year was considered as faller, otherwise as non-faller. Different tree-based data-mining algorithms (i.e., C4.5, Adaboost and Random Forest) were used to develop automatic classifiers and their performances were evaluated by assessing the receiver-operator characteristics curve estimated with the 10-fold-crossvalidation approach. RESULTS: The best predictive model, based on Random Forest, enabled to identify fallers with a sensitivity and specificity rate of 72.6% and 77.9%, respectively. The most informative variables were: intraocular pressure, best corrected visual acuity and the answers to the total difficulty score of the Activities of Daily Vision Scale (a questionnaire for the measurement of visual disability). CONCLUSIONS: The current study confirmed that some ophthalmic features (i.e. cataract surgery, lower intraocular pressure values) could be associated with a lower fall risk among visually impaired subjects. Finally, automatic analysis of a combination of visual function parameters (either self-evaluated either by ophthalmological tests) and other health information, by data-mining algorithms, could be a feasible tool for identifying fallers among ophthalmic patients.
Subject(s)
Accidental Falls , Decision Support Systems, Clinical , Vision Disorders/diagnosis , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Pilot Projects , Sensitivity and SpecificityABSTRACT
The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.
Subject(s)
Accidental Falls , Cloud Computing , Hypertension/physiopathology , Monitoring, Ambulatory/instrumentation , Telemetry/instrumentation , Aged , Aged, 80 and over , Artificial Intelligence , Cardiovascular Diseases/physiopathology , Computer Security , Electrocardiography , Female , Humans , Male , Movement , Prospective Studies , Retrospective Studies , Risk Assessment , Risk Factors , User-Computer InterfaceABSTRACT
Successful management of health conditions in older population is determined by strategic involvement of a professional team of careers and by empowering patients and their caregivers to take over a central role and responsibility in the daily management of condition. Identifying, structuring and ranking the most important needs related to these aspects could pave the way for improved strategies in designing systems and technological solutions supporting user empowerment. This paper presents the preliminary results of a study aiming to elicit these needs. Healthcare professionals, working together in the European and Innovation Partnership on Active and Healthy Ageing (EIP-AHA) initiative, have defined a set of needs and factors that have been organized in two hierarchies around the concepts of patient activation and proactive and prepared care team, defined in the Chronic Care Model. The two hierarchies have been mapped, by a team of experts in computer science, with technologies and solutions that could facilitate the achievement of the identified needs.
Subject(s)
Aging , Health Education/methods , Health Knowledge, Attitudes, Practice , Patient Participation/methods , Power, Psychological , Humans , Patient Care Team/organization & administration , Patient Participation/psychologyABSTRACT
INTRODUCTION: In inguinal hernia repair, many complications are due to mesh fixation technique. Therefore, new types of atraumatic methods of fixation have been proposed. In this article, we present the results of a prospective multicentric parallel randomized controlled trial aiming to compare two mesh fixation techniques: fibrin sealant (QUIXIL(®), Omrix Biopharmaceuticals S.A., Belgium) and Lichtenstein technique. METHOD: Adult patients with primary uncomplicated inguinal hernia were randomized in two groups: fibrin sealant group (FSG) and Lichtenstein group (LTG). The two groups underwent a follow-up of 15 months. Operative time is the primary outcome. Intraoperative and postoperative outcomes were analyzed. Moreover, a differential cost analysis was performed. Patients and evaluators (with exception of the surgeon who treated the patient) were blinded. RESULTS: A total of 102 patients, 50 in FSG and 52 in LTG, were enrolled from January 2009 to June 2010, and two patients were lost to follow-up at the twelfth month. No significant differences in baseline and clinical characteristics were observed in the two groups. Operative time was longer in LTG (median/ interquartile range: 35 min/30-42.5 min vs. 31 min/28-35 min; effect size: 0.65/95% CI 0.50-0.91; p < 0.05). No differences in intraoperative complications were observed. No significant differences were observed in early complication rate (RR = 0.62; p > 0.05). Numbness rate was lower in the FSG at 1 week (RR = 0.43; p < 0.01) and at 1 month (RR = 0.17; p < 0.05). No significant differences were observed after 6 months. Postoperative pain was lower in the FSG at 1 week (0/0-1 vs. 1/0-2; p < 0.05) and at 1 month (0/0-0 vs. 0/0-1; p < 0.05). Pain disappeared in all patients after 6 months. Analgesic assumption rate was lower in the FSG (RR = 0.42; p < 0.05). Twenty per cent of FSG and 9.62% of LTG patients were discharged within 12 h; 78% of FSG and 90.38% of LTG patients were discharged within 24 h. The only one recurrence we observed was in FSG group. About costs, although fibrin sealant needed for one mesh fixation is about 10 times more costly than the needed sutures, the total costs of the two procedures did not change significantly. This was mainly due to reduction in operative time. CONCLUSIONS: The use of fibrin sealant determined a significant reduction in short-term numbness rate and postoperative pain. There was no relevant difference in total costs per patient between the two procedures.
Subject(s)
Fibrin Tissue Adhesive/therapeutic use , Hernia, Inguinal/surgery , Herniorrhaphy/methods , Surgical Mesh , Cost-Benefit Analysis , Female , Fibrin Tissue Adhesive/economics , Herniorrhaphy/economics , Humans , Italy , Male , Middle Aged , Operative Time , Pain Measurement , Pain, Postoperative , Prospective Studies , Surgical Mesh/economics , Treatment OutcomeABSTRACT
BACKGROUND: A gap exists between evidence-based medicine and clinical-practice. Every day, healthcare professionals (HCPs) combine empirical evidence and subjective experience in order to maximize the effectiveness of interventions. Consequently, it is important to understand how HCPs interpret the research evidence and apply it in everyday practice. We focused on the prevention of falls, a common cause of injury-related morbidity and mortality in later life, for which there is a wide range of known risk factors. OBJECTIVES: To use the Analytic Hierarchy Process (AHP) to investigate the opinions of HCPs in prioritizing risk factors for preventing falls. METHODS: We used the AHP to develop a hierarchy of risk factors for falls based on the knowledge and experience of experts. We submitted electronic questionnaires via the web, in order to reach a wider number of respondents. With a web service, we pooled the results and weighted the coherence and the experience of respondents. RESULTS: Overall, 232 respondents participated in the study: 32 in the technical pilot study, nine in the scientific pilot study and 191 respondents in the main study. We identified a hierarchy of 35 risk factors, organized in two categories and six sub-categories. CONCLUSIONS: The hierarchy of risk factors provides further insights into clinicians' perceptions of risk factors for falls. This hierarchy helps understand the relative importance that clinicians place on risk factors for falls in older people and why evidence-based guidelines are not always followed. This information may be helpful in improving intervention programs and in understanding how clinicians prioritize multiple risk factors in individual patients. The AHP method allows the opinions of HCPs to be investigated, giving appropriate weight to their coherence, background and experience.