Determining the factors that contribute to making a reliable prediction of the metabolic syndrome will provide a deeper understanding of the medical indices involved in the prediction and assist in early diagnosis and treatment of patients. The study examined the optimal number of National cholesterol education program adult treatment panel (NCEP ATP) III indices needed to make a reliable prediction of the syndrome, whether each of the five NCEP ATP III indices for predicting the syndrome is equally important and whether a reliable prediction can be made using calculated blood pressure indices - estimated mean arterial pressure and pulse pressure - instead of NCEP ATP III blood pressure indices. The results show that NCEP ATP III indices for determination of the syndrome are not equally important. Moreover, the indices importance and their prediction quality vary according to gender. Optimal results are obtained by using all five NCEP ATP III indices for prediction.

IntroductionTelemedicine services saw substantial surges in their use during the COVID-19 pandemic due to the lockdowns and characteristics of the pandemic. Therefore, the authors aimed to systematically review the telemedicine services provided during the COVID-19 pandemic and their potential applications.MethodsThe authors searched PubMed, Scopus, and Cochrane databases on September 14, 2021. Then, the retrieved records underwent two-step title/abstract and full-text screening processes, and the eligible articles were included for qualitative synthesis.ResultsThe review of studies demonstrated that the telephone is listed 38 times, making it the most common technology used in telemedicine. Video conferencing is also mentioned in 29 articles, as well as other technologies: Mobile-health (n = 15), Virtual reality (n = 7). According to the findings of the present study, Tele-follow-up (n = 24), Tele-consulting (n = 20), Virtual visits (20), and Tele-monitoring (n = 18) were the most widely used telemedicine applications.ConclusionTelemedicine has been an effective approach to COVID-19 management. Telemedicine technology is going to play a key role in the future of health medicine, patient consultation, and many other extended applications of health care in remote rural locations.

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method.

Background/Objectives: Unsupervised topic models are often used to facilitate improved understanding of large unstructured clinical text datasets. In this study we investigated how ICD-9 diagnostic codes, collected alongside clinical text data, could be used to establish concurrent-, convergent- and discriminant-validity of learned topic models. Design/Setting: Retrospective open cohort design. Data were collected from primary care clinics located in Toronto, Canada between 01/01/2017 through 12/31/2020. Methods: We fit a non-negative matrix factorization topic model, with K = 50 latent topics/themes, to our input document term matrix (DTM). We estimated the magnitude of association between each Boolean-valued ICD-9 diagnostic code and each continuous latent topical vector. We identified ICD-9 diagnostic codes most strongly associated with each latent topical vector; and qualitatively interpreted how these codes could be used for external validation of the learned topic model. Results: The DTM consisted of 382,666 documents and 2210 words/tokens. We correlated concurrently assigned ICD-9 diagnostic codes with learned topical vectors, and observed semantic agreement for a subset of latent constructs (e.g. conditions of the breast, disorders of the female genital tract, respiratory disease, viral infection, eye/ear/nose/throat conditions, conditions of the urinary system, and dermatological conditions, etc.). Conclusions: When fitting topic models to clinical text corpora, researchers can leverage contemporaneously collected electronic medical record data to investigate the external validity of fitted latent variable models.

Boosted by the COVID-19 pandemic, as well as the tightened General Data Protection Regulation (GDPR) legislation within the European Union (EU), individuals have become increasingly concerned about privacy. This is also reflected in how willing individuals are to consent to sharing personal data, including their health data. To understand this behaviour better, this study focuses on willingness to consent in relation to genomic data. The study explores how the provision of educational information relates to willingness to consent, as well as differences in privacy concerns, information sensitivity and the perceived trade-off value between individuals willing versus unwilling to consent to sharing their genomic data. Of the respondents, 65% were initially willing to consent, but after educational information 89% were willing to consent and only 11% remained unwilling to consent. Educating individuals about potential health benefits can thus help to correct the beliefs that originally led to the unwillingness to share genomic data.

Over the past 20years, the identification of interventions related to healthcare management has been greatly facilitated by improvements in the well-being and health of the entire population. However, regardless of the positive developments in smart health applications and e-health research, there are two important gaps, (1) the role of gamification variables in the continued use of eHealth applications has not been adequately assessed, and (2) the extent to which people's perception of the continued use of e-health applications is encouraged through habit. Customers and companies can derive considerable value from exploring E-Health applications' health self-management services. Accordingly, estimating such services' ongoing adoption by customers is aimed for in this research, with habits, intrinsic and extrinsic variables incorporated into a study model which is then tested. This paper examined perceived autonomy, perceived competence, perceived relatedness has positively related to enjoyment and habit. Reward has positively related to perceived autonomy and continued to use. Enjoyment and Habit have positively associated with the decision to continue to use in e-Health Apps. 269 individuals who have used Chinese e-health applications comprised the data collection sample, being reached via an online questionnaire. Data analysis was undertaken using Partial Least Squares Structural Equation Modelling (PLS-SEM). It was found that the ongoing adoption of e-health self-management services was perpetuated to a greater extent by intrinsic variables; in terms of strategizing for companies' e-services, the results can inform this process.

This study assesses the perceived impact and benefits of Project ECHO (Extension for Community Healthcare Outcomes), a tele-mentoring intervention for health and social care providers, patients and the health system in Northern Ireland. Having access to a specialist, a space to share experiences, and being able to disseminate up-to-date best practice were all cited as improving provider knowledge as well as improving quality of care for patients. Healthcare providers reported being more confident in managing patients and that relationships had been improved between different levels of the health system. ECHO was described as improving access to education and training by removing geographic and time barriers. This is one of the first studies to qualitatively analyse impact across a number of different clinical and social care ECHO networks. The results strongly indicate the perceived benefit of ECHO in improving provider, patient and health system outcomes such as increased healthcare provider knowledge and confidence to manage patients at primary levels of the health system. This has implications for future service design, particularly within the context of COVID-19 in which virtual and online training is necessitated by social distancing requirements.

The ability to develop and evaluate approaches to the management of advanced cancer in sub-Saharan Africa is limited by the lack of local, reliable and valid data to ensure that practice is evidence-based, replicable and reflects the needs of the population served. A secondary qualitative analysis of in-depth interviews with 59 health professionals delivering palliative cancer care in Nigeria, Uganda and Zimbabwe were conducted to determine the use and needs for data and information for patient care and service delivery. Framework analysis was used, informed by a conceptual model for data use in low and middle-income countries. Three meta-themes include: (1) Current practice in data gathering and use; (2) Gaps for capturing, storing information and supporting communication, and; (3) Needs and opportunities for data use. Deficits in current data access and use were identified, alongside targets for improving the quality, accessibility and utility of data to inform the development of palliative cancer care. The availability and use of relevant and reliable data relating to the current provision of palliative cancer care are requisite for the contextually appropriate and effective development of health services. The requirements and constraints articulated by participants can guide future development and optimisation of digital health approaches for palliative cancer care in the participating countries, with relevance to the wider sub-Saharan Africa region.

Reminiscence therapy is beneficial for improving the quality of life for older adults living with dementia. However, it is difficult for caregivers in care homes to make this part of their daily practice because remembering a resident's individual stories is difficult. Proximity-aware technologies can make rapid connections between residents, their histories, and nearby artefacts. Through a user-centred iterative design approach, we designed Memento, a prototype technology system that makes it easy and convenient for caregivers to connect residents with real stories from their past and engage them in meaningful conversations. Memento displays connections between a resident's stories and their surroundings on a caregiver smartphone. Caregivers can then subtly use this information to start spontaneous reminiscences with residents, as they go about their everyday activities. Remembering their histories was highly enjoyable for residents, giving them a renewed sense of self-confidence and well-being.