Mobile health applications (MHAs) have been widely used by community health workers (CHWs) in primary care to facilitate their workflow, but the continuance of these applications by CHWs remains ...inadequate. This article employs expectation confirmation theory as the overarching theory, complemented by the information system success model and the consumer expectations model, to propose a research model aimed at examining the factors influencing CHWs’ MHA continuance. To validate this research model, a survey was conducted involving 459 CHWs who used MHAs. The analysis results confirm many hypothetical relationships within the research model. First, we propose an extended expectation confirmation model remedying deficiency of the expectation confirmation theory. Second, this study uncovers an inverted U-shaped relationship between CHWs’ expectations and expectation confirmation. Third, we study continuance from a novel perspective – that of CHWs. Fourth, we reveal one boundary condition of our research model by considering the moderating role of health institution type. In addition, practical implications for both policymakers in healthcare systems and MHA providers are delivered.
Mobile devices are increasingly becoming an indispensable part of people's daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing ...to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.
This review centers upon two-dimensional (2D) nanomaterials beyond graphene and their utilization in the development of novel electrochemical (bio)sensors and analytical devices. The text is ...organized in three main sections including the presentation of the most important families of 2D nanomaterials, an outline of “top-down” and hydro(solvo)thermal methods which are commonly employed for the production of 2D nanomaterials, and finally a detailed overview on the progress had been made the last three years on the use of 2D nanomaterials on the development of electrochemical (bio)sensors and analytical devices in water & food analysis, drug sensing, diabetes monitoring, cancer diagnostics, and virus sensing. Critical discussion on the effect of the 2D nanomaterials on various sensing buildups along with the perspectives of further improving the utility of 2D nanomaterials in (bio)sensing applications, in real world samples, are discussed.
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•Recent advances of 2D materials beyond graphene in (bio)sensing are reviewed.•Focus is on water & food analysis and health applications (H.Ap).•H.Ap include drug analysis, diabetes monitoring, cancer diagnostics and virus sensing.•MXenes is the most extensively used 2D material among those considered in the review.•Further exploration of the antifouling properties of 2D nanomaterials is warranted.
Oral health is one of the most important components of health that can affect the quality of life of children. Oral problems affect over 600 million children, globally. Evidence showed that ...mobile-based applications have the potential to facilitate self-care processes for oral health in children. Despite the importance of children's oral health and the role of mobile health in facilitating parental education, few systematic reviews were conducted regarding child oral health and mobile-based applications.
This study aimed to investigate the characteristics and capabilities of m-health applications in children's oral health.
A systematic search was completed using keywords alongside thesaurus and MeSH terms on Web of Science, Scopus, and PubMed databases from 2015 to 2020. The present study was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist based on inclusion and exclusion criteria. A synthesis was done to report study findings and patterns identified across studies. All results were analyzed and summarized into general and specific categories.
Out of 23 articles included, only nine studies were RCTs (Randomized controlled trials), two studies were cross-sectional studies, one study was evidence-based, and one study was a cluster-randomized trial. According to the findings, most studies were conducted in Brazil, the United Kingdom, and the United States. In terms of the field of dentistry and the application of studies related to dental science, studies fall into three domains, including oral health (61%), orthodontics (4%), and periodontology (26%). Our analysis showed that the developed mobile health applications are equipped with some features to help patients improve their oral health. The most popular features include educational multimedia, game-based stories, reminders, and sending SMS.
This study could be the first step towards enhancing our understanding and knowledge regarding applications of mobile health applications in children's oral health. These applications should be developed by involving the end users in the design phase and should be evaluated in terms of usability and effects on the oral health of children.
The aim of this article is to discuss how different factors affect the decision of intention to use and adopt mobile health applications using the extended technology acceptance model (TAM) among ...older adults in Iraq. “Perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), and facilitating conditions (FC)” were four key predictors. Gender and age were included as factors for moderating the impact of two key TAM components in the proposed model (PU and PEU) on intention to use and adoption behaviors. The results of the past studies indicated that PU, PEU and SN were important predictors of adoption of mobile health applications among older adults in Iraq, While PU, SN, and FC were important predictors of the intention to use mobile health applications. Previous studies highlighted a strong impact of PEU on the intention to use mobile health applications on older adults than for younger adults. Implications are discussed for future research and practices.
Disease Prediction via Graph Neural Networks Sun, Zhenchao; Yin, Hongzhi; Chen, Hongxu ...
IEEE journal of biomedical and health informatics,
2021-March, 2021-Mar, 2021-3-00, 20210301, Letnik:
25, Številka:
3
Journal Article
Recenzirano
With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input ...prediction signals (e.g., symptoms, patient demographics, etc.) to the estimated diseases for each patient. However, existing machine learning-based solutions heavily rely on abundant manually labeled EMR training data to ensure satisfactory prediction results, impeding their performance in the existence of rare diseases that are subject to severe data scarcity. For each rare disease, the limited EMR data can hardly offer sufficient information for a model to correctly distinguish its identity from other diseases with similar clinical symptoms. Furthermore, most existing disease prediction approaches are based on the sequential EMRs collected for every patient and are unable to handle new patients without historical EMRs, reducing their real-life practicality. In this paper, we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs. By aggregating information from directly connected neighbor nodes, the proposed neural graph encoder can effectively generate embeddings that capture knowledge from both data sources, and is able to inductively infer the embeddings for a new patient based on the symptoms reported in her/his EMRs to allow for accurate prediction on both general diseases and rare diseases. Extensive experiments on a real-world EMR dataset have demonstrated the state-of-the-art performance of our proposed model.
Besides surfactants, which decrease the interfacial tension between two immiscible liquids, also interfacially active particles can successfully stabilize an emulsion system by attaching at the ...liquid–liquid interface. The preparation of the resulting Pickering emulsions has been so far investigated starting from the study of the interactions arising between the dispersed droplets and the stabilizers, till the application of these systems in a wide range of different fields. This work is intended to provide an overall overview about the development of Pickering emulsions by considering the most general aspects and scanning the diverse types of solid stabilizers. Among them, Halloysite nanotubes play a major role as naturally derived clay with emulsifying capability owing to their cheap, abundant, green and biocompatible properties. Therefore, the design of Halloysite stabilized Pickering emulsions is the main content of this review, which will survey the role of nanotubes in providing colloidal stability and will comprehensively sum up the use of these particles in technological and industrial purposes: from environmental to catalytic, from health to cultural heritage related applications.
This review provides meaningful insights about the design of Halloysite stabilized Pickering emulsions. The general aspects about their properties are enlightened by focusing many different factors. Most importantly, the applications of the clay nanotubes stabilized droplets in both technological and industrial fields are considered: from environmental remediation to catalysis, from health science to cultural heritage conservation.
•Using speech materials recorded from speakers with different voice disorders this paper shows: how continuous speech recordings recorded in voice clinics can be automatically annotated to identify ...different phonetic regions.•That consideration of how voice disorders differentially affect phonetic regions can improve the discrimination between types of disorder.•That automated differential diagnosis of voice disorders from a read passage is possible at sensitivities and specificities typical of diagnostic screening tests.
In this paper we evaluate the hypothesis that automated methods for diagnosis of voice disorders from speech recordings would benefit from contextual information found in continuous speech. Rather than basing a diagnosis on how disorders affect the average acoustic properties of the speech signal, the idea is to exploit the possibility that different disorders will cause different acoustic changes within different phonetic contexts. Any differences in the pattern of effects across contexts would then provide additional information for discrimination of pathologies. We evaluate this approach using two complementary studies: the first uses a short phrase which is automatically annotated using a phonetic transcription, the second uses a long reading passage which is automatically annotated from text. The first study uses a single sentence recorded from 597 speakers in the Saarbrucken Voice Database to discriminate structural from neurogenic disorders. The results show that discrimination performance for these broad pathology classes improves from 59% to 67% unweighted average recall when classifiers are trained for each phone-label and the results fused. Although the phonetic contexts improved discrimination, the overall sensitivity and specificity of the method seems insufficient for clinical application. We hypothesise that this is because of the limited contexts in the speech audio and the heterogeneous nature of the disorders. In the second study we address these issues by processing recordings of a long reading passage obtained from clinical recordings of 60 speakers with either Spasmodic Dysphonia or Vocal fold Paralysis. We show that discrimination performance increases from 80% to 87% unweighted average recall if classifiers are trained for each phone-labelled region and predictions fused. We also show that the sensitivity and specificity of a diagnostic test with this performance is similar to other diagnostic procedures in clinical use. In conclusion, the studies confirm that the exploitation of contextual differences in the way disorders affect speech improves automated diagnostic performance, and that automated methods for phonetic annotation of reading passages are robust enough to extract useful diagnostic information.
Measurement-based care (MBC) comprises collecting patient-reported outcomes data using validated assessments and using that information to support treatment. The Veterans Health Administration (VHA) ...has developed technology platforms to support MBC, including the Mental Health Checkup (MHC) mobile health application (app). Our objective was to examine VHA mental health provider perspectives on the MHC app. We completed a mixed-methods, sequential explanatory evaluation of MHC. We surveyed 284 VHA mental health providers who used MHC, then conducted semistructured telephone interviews with a purposefully selected subset of survey respondents ( n = 20). Approximately half of survey respondents agreed that MHC allowed them to collect assessment data from veterans more frequently than before (51%) and that they more frequently discussed assessment results with veterans because of MHC (50%) and used those results to inform goal-setting discussions (50%) and treatment decision making (51%). Bivariate analyses indicated a positive relationship between frequency of MHC use and the aforementioned impacts on care. Interview data conveyed both advantages (e.g., increased treatment efficiency, improved treatment decision making) and challenges (e.g., limited assessment availability, difficulties engaging veterans in completing assessments through the app) to using MHC. This evaluation demonstrated how MHC supported providers working to implement MBC. The app enhanced their ability to reach and engage veterans and incorporate assessment data into clinical encounters. Still, many did not perceive that MHC was impactful on mental health care delivery; given that providers who used MHC more frequently reported more positive impressions of MHC, this may be related to how frequently they used the app. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: journal abstract)
Commercial nutrition apps are increasingly used to evaluate diet. Evaluating the comparative validity of nutrient data from commercial nutrition app databases is important to determine the merits of ...using these apps for dietary assessment.
Nutrient data from four commercial nutrition apps were compared with a research-based food database, Nutrition Data System for Research (NDSR) (version 2017).
Comparative validation study.
An investigator identified the 50 most frequently consumed foods (22% of total reported foods) from a weight-loss study in Chicago, IL, during 2017. Nutrient data were compared between four commercial databases with NDSR.
Comparative validity of energy, macronutrients, and other nutrient data (ie, total sugars, fiber, saturated fat, cholesterol, calcium, and sodium).
Intraclass correlation coefficients (ICCs) evaluated agreement between commercial databases with the NDSR for foods that were primarily un- and minimally processed and by the three most frequently consumed food groups. Bland-Altman plots determined degree of bias for calories between commercial databases and NDSR.
This study observed excellent agreement between NDSR and CalorieKing (ICC range = 0.90 to 1.00). Compared with NDSR, agreement for Lose It! and MyFitnessPal ranged from good to excellent (ICC range = 0.89 to 1.00), with the exception of fiber in MyFitnessPal (ICC = 0.67). Fitbit showed the widest variability with NDSR (ICC range = 0.52 to 0.98). When evaluating by food group, Fitbit had poor agreement for all food groups, with the lowest agreement observed for fiber within the vegetable group (ICC = 0.16). Bland-Altman plots confirmed ICC energy results but also found that MyFitnessPal had the poorest agreement to NDSR (mean 8.35 SD 133.31 kcal) for all food items.
Degree of agreement varied by commercial nutrition app. CalorieKing and Lose It! had mostly excellent agreement with NDSR for all investigated nutrients. Fitbit showed the widest variability in agreement with NDSR for most nutrients, which may reflect how well the app can accurately capture diet.
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