Background: Prolonged hours of standing are a central part of a nurse's job and can potentially affect their foot health. Nurses often address these problems by choosing functional stockings (FS) and/or compression stockings (CS). However, it remains to be established whether nurses' perceptions regarding their foot health are related to the choice of hosiery products, furthermore, the decision between FS or CS may impact the arterial health of nurses. The aim of this study was to investigate nurses' awareness of foot health and determine whether FS or CS genuinely benefited the arterial health of the nurses' feet. Methods: A descriptive cross-sectional study was conducted among nurses worked regular nursing shifts in a tertiary medical center in Taipei, Taiwan after obtaining ethical approval from the Institutional Review Committee. Convenience sampling method was used. Point estimate was calculated at a 95% confidence interval. Results: Nurses wearing FS exhibited lower brachial mean arterial pressure (MAP) (92.3±11.1 mmHg; P=0.03), end-systolic blood pressure (SBP) (103.4±13.1 mmHg; P=0.02), and pressure-time index (PTI) during systole (2,338.0±378.1 mmHg/s per min; P=0.04) compared with those wearing CS. Nurses wearing CS exhibited lower end-SBP (103.5±14.6 mmHg; P=0.03), prolongation in left ventricular ejection time (LVET) (316.4±19.3 ms; P=0.01), and a reduction in PTI during diastole (3,194.0±414.3 mmHg/s per min; P=0.03). Conclusions: Nurses without varicose vein issues, seeking to avoid the negative effects of CS, may find that alternative forms of comfortable FS can also contribute to arterial and cardiovascular health.
Background: As low heart rate variability (HRV) has been shown to be associated with cardiovascular diseases (CVDs), attempts have been made to increase HRV through a variety of methods. It has recently been suggested that HRV might be increased by nerve stimulation. The aim of this study was to investigate the effect of transcutaneous median nerve stimulation (MNS) on HRV on healthy human subjects with low HRV. Methods: This is a double-blind randomized control trial. Sixty-one healthy adults, male and female, were recruited to the study. Among the 61 participants, only 38 participants with lower than 40 standard deviation of NN interval (SDNN), mean age 49.5±6.8 years, were selected to further participate in the study. Participants underwent two sessions of either MNS (5 Hz, 100–300 μs for 20 minutes) or a sham treatment (control). Their HRV measurements were compared before, immediately after treatment, and then again 10 minutes later. Results: SDNN increased following both treatments, yet there were no effects on other HRV variables, blood pressure (BP), or resting heart rate (HR) (P>0.05). Conclusions: Transcutaneous stimulation of the left median nerve on healthy subjects, as applied in the present study, was safe and tolerable, and SDNN was increased on healthy subjects with low HRV.
Background: The traditional methods of acquiring new knowledge have seen a dramatic shift globally as technology has advanced our means of delivering content, and social media use has become a primary means of communication in this generation of healthcare students.Academic engagement through online discussion boards has provided students with the critical reasoning skills to challenge concepts, articulate objective perspectives and develop the notion of interactive peer learning.The aim of this project was to evaluate student engagement and the effectiveness of an interactive discussion board when discussing the topic of confidentiality and telehealth among population health management graduate students.This was to determine intelligent interactions, the appropriateness of the learning environment, and active online learning.Methods: We conducted a retrospective process evaluation on the effectiveness of an online discussion board.The analysis was carried out through rigorous, systematic reading of the discussion board, evaluating the frequency and depth of student interactions and manual coding.Results: Intelligent critical discussions and immersive active online learning were the two main themes identified.Spontaneous independent learning promoted student leadership with asynchronous online learning.Conclusions: Online interactive discussion boards created an environment that emphasized content and supported social engagement and peer learning when discussing the topic of confidentiality and telehealth.Online learning engagement is dependent on student participation, and the presentation of debatable questions on an interactive discussion board to heighten the student learning experience.
PurposeAccording to the literature of International Diabetes Federation reports in 2019, one of the causes of adulthood death is diabetes. Diabetes is a chronic metabolic disease that has a long-term impact on the individual's well-being. Insulin resistance and abnormal glucose metabolism are hallmarks of type 2 diabetes mellitus (T2DM). Noncommunicable Diseases (NCD), such as diabetes, are caused by poor eating habits and way of life.MethodsIn this work, several risk prediction models for Type 2 Diabetes Mellitus have been proposed. Using multivariate analysis to assist patients with risk stratification within a population, this study also attempts to determine the relationship between lifestyle behavior patterns and diabetes. Considering that a number of diabetes predictive models proposed by previous researchers rely primarily on specific medical measurement data, but lack external diabetic factors, external diabetic factors are necessary for the development of diabetes predictive models. Glycated Hemoglobin (HbA1c) is utilized in the form to diagnose diabetes due to its efficiency and patient-friendliness. In addition, contrary to the widespread belief that machine learning is superior in many ways, a number of issues, such as racial bias, must be considered when deploying machine learning in the healthcare industry. Because HbA1c is influenced by external factors such as race and ethnicity, the Asia-Pacific region has a variety of HbA1c cut-off points. As a result, restricting the population scope must be viewed as the best approach in this task to permit improved accuracy and assurance.ResultsA detailed experimental analysis of various machine learning model performances was evaluated on a standard Type-2 Diabetes dataset. Random forest with SMOTE oversampling and PCA technique outperformed the other methods for Type-2 Diabetes prediction with a recall of 65%, precision of 89%, and f1-score of 75%. The machine learning models performances were evaluated on various test cases during testing.ConclusionsThis study leveraging a dataset of medical report and lifestyle behavior factor to predict the HbA1c category. The proposed approach can be deployed as a tool at medical centers to serve as an early Type-2 disease diagnosis tool and in addition, the tool can assist the medical experts in accurately predicting the Type-2 diabetes disease.
AbstractBlood pressure is an important cardiovascular parameter. Currently, the cuff-based sphygmomanometer is a popular, reliable, measurement method, but blood pressure monitors without cuffs have become popular and are now available without a prescription. Blood pressure monitors must be approved by regulatory authorities. Current cuffless blood pressure (CL-BP) monitors are not suitable for at-home management and prevention of hypertension. This paper proposes simple criteria for over-the-counter CL-BP monitoring. First, the history of the sphygmomanometer and current standard blood pressure protocol are reviewed. The main components of CL-BP monitoring are accuracy during the resting condition, accuracy during dynamic blood pressure changes, and long-term stability. In this proposal we recommend intermittent measurement to ensure that active measurement accuracy mirrors resting condition accuracy. A new experimental protocol is proposed to maintain long-term stability. A medically approved automated sphygmomanometer was used as the standard device in this study. The long-term accuracy of the test device is based on the definition of propagation error, i.e., for an oscillometric automated sphygmomanometer (5 ± 8 mmHg) ± the error for the test device static accuracy (–0.12 ± 5.49 mmHg for systolic blood pressure and − 1.17 ± 5.06 mmHg for diastolic blood pressure). Thus, the long-term stabilities were − 3.38 ± 7.1 mmHg and − 1.38 ± 5.4 mmHg, which satisfied propagation error. Further research and discussion are necessary to create standards for use by manufacturers; such standards should be readily evaluated and ensure high-quality evidence.
Purpose Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully. Methods This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing. Results Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities. Conclusions This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.
PurposeWe generated methods for evaluating clinical outcomes including treatment response in oncology using the unstructured data from electronic health records (EHR) in Japanese language.MethodsThis retrospective analysis used medical record database and administrative data of University of Miyazaki Hospital in Japan of patients with lung/breast cancer. Treatment response (objective response [OR], stable disease [SD] or progressive disease [PD]) was adjudicated by two evaluators using clinicians' progress notes, radiology reports and pathological reports of 15 patients with lung cancer (training data set). For assessing key terms to describe treatment response, natural language processing (NLP) rules were created from the texts identified by the evaluators and broken down by morphological analysis. The NLP rules were applied for assessing data of other 70 lung cancer and 30 breast cancer patients, who were not adjudicated, to examine if any difference in using key terms exist between these patients.ResultsA total of 2,039 records in progress notes, 131 in radiology reports and 60 in pathological reports of 15 patients, were adjudicated. Progress notes were the most common primary source data for treatment assessment (60.7%), wherein, the most common key terms with high sensitivity and specificity to describe OR were "reduction/shrink", for SD were "(no) remarkable change/(no) aggravation)" and for PD were "(limited) effect" and "enlargement/grow". These key terms were also found in other larger cohorts of 70 patients with lung cancer and 30 patients with breast cancer.ConclusionThis study demonstrated that assessing response to anticancer therapy using Japanese EHRs is feasible by interpreting progress notes, radiology reports and Japanese key terms using NLP.
This scoping review compiled information concerning digital health technologies (DHTs) evolution to support primary health care (PHC) during COVID-19 and lessons for the future of PHC. The identified literature was published during the COVID-19 peak years (2019–2021), retrieved from PubMed, Scopus, and Google Scholar, as well as hand searched on the internet. Predefined inclusion criteria were used, thematic analysis was applied, and reporting followed the PRISMA for Scoping Reviews. A total of 46 studies were included in the final synthesis (40 articles, one book, two book chapters, one working paper, and two technical reports). These studies scrutinized various aspects of DHTs, entailing 19 types of DHTs with 20 areas of use that can be compressed into five bigger PHC functions: general PHC service delivery (teleconsultations, e-diagnosis, e-prescription, etc.); behavior promotion and digital health literacy (e.g., combating vaccine hesitancy); surveillance functions; vaccination and drugs; and enhancing system decision-making for proper follow-up of ongoing PHC interventions during COVID-19. DHTs have the potential to solve some of the problems that have plagued us even prior to COVID-19. Therefore, this study uses a forward-looking viewpoint to further stimulate the use of evidence-based DHT, making it more inclusive, educative, and satisfying to people’s needs, both under normal conditions and during outbreaks. More research with narrowed research questions is needed, with a particular emphasis on quality assurance in the use of DHTs, technical aspects (standards for digital health tools, infrastructure, and platforms), and financial perspectives (payment for digital health services and adoption incentives).
Purpose There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool. Methods Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective. Results Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores. Conclusions This is the world’s first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.
Purpose The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. Sparse Representation Classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical Mode Decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of attributes. Methods In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use Multilayer Perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Attribute selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Results Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. The SRC achieves an average accuracy of 83.07% while the MLP is 71.71%, representing a gain of over 15.84%. The use of EMD in relation to other attribute processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP etc.) do not achieve the performance of other conventional models. The best sparse models achieve an average accuracy of 66.7% among the subjects in the base, while other models reach 76.05%. Conclusion The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.
Purpose Extracorporeal ultrafiltration is an attractive alternative to diuretics for removing excess plasma water in critically ill patients suffering from fluid overload. In continuous renal replacement therapy (CRRT), ultrafiltration occurs in isolated form (SCUF) or supplemented by replacement fluid infusion (CVVH) and the net fluid removal rate is controlled by peristaltic pumps. In this work, a pump-free solution for regulating the ultrafiltration rate in CRRT applications is presented. Methods The system consists of a motorized clamp on the ultrafiltration line, whose intermittent opening is modulated with a closed-loop control system based on monitoring of ultrafiltrate collected and any replacement fluid infused. The system was tested on two platforms for SCUF and CVVH, with “low-flux” and “high-flux” hemofilter, with various ultrafiltration setpoints and patient net weight loss targets. Results In all configurations the set ultrafiltration rate was achieved with a maximum error of 5% and the values recorded were kept within ± 100 ml/h with respect to the setpoint, as recommended by international standard IEC 60601-2-16. The net fluid removal trend was highly correlated with that expected (95%<R 2 <99%) and the weight loss target was reached in the expected time. For low ultrafiltration rates (60-150 ml/h) the system accuracy was better with the “low-flux” hemofilter. Conclusion The developed clamp system represents a valid alternative to state-of-the-art solutions with peristaltic pumps in terms of performance, with potential usability advantages. The compliance with safety requirements given by international standard IEC 60601-2-16 is a prerequisite for clinical use.
Purpose The goal of this review is to provide a comprehensive overview of ML technologies used to diagnose, detect, predict, monitor, treat, control, and manage TB. In addition, the study aimed to present future challenges, research directions, and recommendations for diagnosing, detecting, predicting, and monitoring TB treatment using ML technologies. Methods Review of published papers regarding diagnosis, detection, prediction, and monitoring of TB treatment, using ML technologies. In line with other TB case studies and reports of organizational institutionalism and implementation studies for a digital health. Results The reviewed related research has successfully demonstrated that the application of ML technologies in the diagnosis, detection, prediction, monitoring, treatment, control and management of TB plays an important role in improving the quality of TB care and human health. The literature analyzed identified the key areas including future challenges, research directions and recommendations for diagnosing, detecting, predicting and monitoring TB treatment using ML technologies. Conclusions Knowledge of the state-of-the-art in the application of ML technologies in TB management and the identified research directions is beneficial for researchers and healthcare experts. It is recommended that policymakers should develop a mechanism to support the adoption of best practice of ML technologies regulations in the country's healthcare sector.
Breast cancer is the first common cancer and one of the deadliest cancers among women worldwide. The treatment of cancer patients using radiotherapy involves many risk factors which can lead to severe complications. The aim of this study was to investigate measured doses received by breast cancer patients during external beam radiotherapy (EBT) treatment and compare to doses prescribed, using EBT-3 films and TLDs as detectors. Dosimetric evaluation of entrance doses during patient irradiation and scattered doses to sensitive organs for patients requiring postmastectomy radiotherapy (PMRT) were carried-out. Total of 183 field measurements for left and right breasts were performed on a male and 60 females undergoing PMRT. Measurements of delivered doses to patients were performed by applying both EBT-3 film and TLD chips directly on patient skin along the central beam axis, and on the sensitive selected organs during irradiations to measure scattered radiations. Irradiated films were scanned with a flatbed scanner and analysed with ImageJ software. The TLDs were read using Hawshaw 6600 TLD reader. The percentage error between measured and prescribed doses ranged from 0.07
Purpose:A transition from paper to Electronic Health Records has numerous benefits, including better communication and information exchange and decreased errors by medical staff. However, if managed poorly, it can result in frustration, causing errors in patient care and reduced patient-clinician interaction. Furthermore, a drop in staff morale and clinician burnout due to familiarising themselves with the technology has been mentioned in previous studies. Therefore, the aim of this project is to monitor the change in morale of staff of the Oral and Maxillofacial Department in a hospital which underwent the change in October 2020. Objectives: To observe staff morale during transition from paper to Electronic Health Records; to encourage feedback.Methods:After carrying out a Patient & Public Involvement consultation and receiving local research and development approval, a questionnaire was distributed to all members of the maxillofacial outpatients department on a regular basis.Results:On average, around 25 members responded to the questionnaire during each collection. There was a noticeable divergence in responses week on week according to job role and age, but minimal difference is noted from gender point of view after the first week. The study emphasised the position that not all members were happy with the new system but only a small minority would want to return to paper notes.Conclusion:Staff members adapt to change at different rates, which are multifactorial in nature. A change of this scale should be monitored closely to allow for a smoother transition and ensure staff burnout is minimised.
Purpose Radiosurgery with the Gamma Knife is the golden standard for the treatment of brain metastasis cases but its accessibility however in many countries is limited. Modern radiotherapy has made this treatment possible using other equipment such as linear accelerator and Cyberknife. The objective of this study was to explore the distribution of available radiotherapy equipment for brain metastasis treatment in Africa and provide practical guidelines to the establishment of a Stereotactic Radiosurgery (SRS) Program. Materials and methods The International Atomic Energy Agency (IAEA)’s Division of Human Health’s Directory for Radiotherapy Centres (DIRAC), served as the primary source for the distribution of radiotherapy equipment throughout Africa and worldwide. Data on megavoltage radiotherapy equipment for the 54 African countries were extracted from this database. Cancer incidence and brain metastasis assumption were made using data from the GLOBOCAN 2020 database and country’s income was assessed using the Gross Domestic Product (GDP) per capita on the world economics database. Further literature search was also carried out in PubMed on the price and availability of dedicated equipment for brain metastasis management in Africa. All these searches were done in April, 2023. Results There was increase in the number of brain metastasis cases. There were only two Gamma Knife machines in Africa. Three Cyberknife; two in Egypt and one in Kenya and 432 other megavoltage units (66 Cobalt-60s, 366 Linacs) distributed across the continent. The cost of a Gamma Knife machine could be up to 7 million United States Dollars (USD) compared to that of Linac between 2.4 and 2.8 million USD and Cyberknife between 3 and 5 million USD. A country’s (GDP) per capita was a vital determinant of the number of these machines in countries which did not have any machines to ones which have at least one machine. Conclusion Access to radiosurgery treatment for brain metastasis with the Gamma Knife or Cyberknife is limited due to the low number of these equipment. With the increase in radiotherapy expansion with linear accelerators, it is likely that the continent will be able to increase its stereotactic radiosurgery treatment centers by implementing Linac-based SRS following suitable guidelines. This will help provide comprehensive care to patients and promote quality of life.
