Barriers to Remote Health Interventions for Type 2 Diabetes: A Systematic Review and Proposed Classification Scheme

BACKGROUND: Diabetes self-management involves adherence to healthy daily habits typically involving blood glucose monitoring, medication, exercise, and diet. To support self-management, some providers have begun testing remote interventions for monitoring and assisting patients between clinic visits. Although some studies have shown success, there are barriers to widespread adoption. OBJECTIVE: The objective of our study was to identify and classify barriers to adoption of remote health for management of type 2 diabetes. METHODS: The following 6 electronic databases were searched for articles published from 2010 to 2015: MEDLINE (Ovid), Embase (Ovid), CINAHL, Cochrane Central, Northern Light Life Sciences Conference Abstracts, and Scopus (Elsevier). The search identified studies involving remote technologies for type 2 diabetes self-management. Reviewers worked in teams of 2 to review and extract data from identified papers. Information collected included study characteristics, outcomes, dropout rates, technologies used, and barriers identified. RESULTS: A total of 53 publications on 41 studies met the specified criteria. Lack of data accuracy due to input bias (32%, 13/41), limitations on scalability (24%, 10/41), and technology illiteracy (24%, 10/41) were the most commonly cited barriers. Technology illiteracy was most prominent in low-income populations, whereas limitations on scalability were more prominent in mid-income populations. Barriers identified were applied to a conceptual model of successful remote health, which includes patient engagement, patient technology accessibility, quality of care, system technology cost, and provider productivity. In total, 40.5% (60/148) of identified barrier instances impeded patient engagement, which is manifest in the large dropout rates cited (up to 57%). CONCLUSIONS: The barriers identified represent major challenges in the design of remote health interventions for diabetes. Breakthrough technologies and systems are needed to alleviate the barriers identified so far, particularly those associated with patient engagement. Monitoring devices that provide objective and reliable data streams on medication, exercise, diet, and glucose monitoring will be essential for widespread effectiveness. Additional work is needed to understand root causes of high dropout rates, and new interventions are needed to identify and assist those at the greatest risk of dropout. Finally, future studies must quantify costs and benefits to determine financial sustainability

Synergistic Effect of 3 ',4 '-Dihidroxifenilglicol and Hydroxytyrosol on Oxidative and Nitrosative Stress and Some Cardiovascular Biomarkers in an Experimental Model of Type 1 Diabetes Mellitus

The objective of this study was to assess a possible synergistic effect of two extra-virgin olive oil polyphenols, 3,4,-dyhydroxyphenylglycol (DHPG) and hydroxytyrosol (HT), in an experimental model of type 1 diabetes. Seven groups of animals were studied: (1) Nondiabetic rats (NDR), (2) 2-month-old diabetic rats (DR), (3) DR treated with 5 mg/kg/day p.o. HT, (4) DR treated with 0.5 mg/kg/day p.o. DHPG, (5) DR treated with 1 mg/kg/day p.o. DHPG, (6) DR treated with HT + DHPG (0.5), (7) DR treated with HT + DHPG (1). Oxidative stress variables (lipid peroxidation, glutathione, total antioxidant activity, 8-isoprostanes, 8-hydroxy-2-deoxyguanosine, and oxidized LDL), nitrosative stress (3-nitrotyrosine), and some cardiovascular biomarkers (platelet aggregation, thromboxane B2, prostacyclin, myeloperoxidase, and vascular cell adhesion protein 1 (VCAM-1)) were analyzed. The diabetic animals showed an imbalance in all of the analyzed variables. HT exerted an antioxidant and downregulatory effect on prothrombotic biomarkers while reducing the fall of prostacyclin. DHPG presented a similar, but quantitatively lower, profile. HT plus DHPG showed a synergistic effect in the reduction of oxidative and nitrosative stress, platelet aggregation, production of prostacyclin, myeloperoxidase, and VCAM-1. This synergism could be important for the development of functional oils enriched in these two polyphenols in the proportion used in this study

Challenges and recommendations for magnetic hyperthermia characterization measurements

PURPOSE: The localized heating of magnetic nanoparticles (MNPs) via the application of time-varying magnetic fields - a process known as magnetic field hyperthermia (MFH) - can greatly enhance existing options for cancer treatment; but for broad clinical uptake its optimization, reproducibility and safety must be comprehensively proven. As part of this effort, the quantification of MNP heating - characterized by the specific loss power (SLP), measured in W/g, or by the intrinsic loss power (ILP), in Hm2/kg - is frequently reported. However, in SLP/ILP measurements to date, the apparatus, the analysis techniques and the field conditions used by different researchers have varied greatly, leading to questions as to the reproducibility of the measurements. MATERIALS AND METHODS: An interlaboratory study (across N = 21 European sites) of calorimetry measurements that constitutes a snapshot of the current state-of-the-art within the MFH community has been undertaken. Identical samples of two stable nanoparticle systems were distributed to all participating laboratories. Raw measurement data as well as the results of in-house analysis techniques were collected along with details of the measurement apparatus used. Raw measurement data was further reanalyzed by universal application of the corrected-slope method to examine relative influences of apparatus and results processing. RESULTS: The data show that although there is very good intralaboratory repeatability, the overall interlaboratory measurement accuracy is poor, with the consolidated ILP data having standard deviations on the mean of ca. ± 30% to ± 40%. There is a strong systematic component to the uncertainties, and a clear rank correlation between the measuring laboratory and the ILP. Both of these are indications of a current lack of normalization in this field. A number of possible sources of systematic uncertainties are identified, and means determined to alleviate or minimize them. However, no single dominant factor was identified, and significant work remains to ascertain and remove the remaining uncertainty sources. CONCLUSION: We conclude that the study reveals a current lack of harmonization in MFH characterization of MNPs, and highlights the growing need for standardized, quantitative characterization techniques for this emerging medical technology

Zinc intake, status and indices of cognitive function in adults and children: a systematic review and meta-analysis

In developing countries, deficiencies of micronutrients are thought to have a major impact on child development; however, a consensus on the specific relationship between dietary zinc intake and cognitive function remains elusive. The aim of this systematic review was to examine the relationship between zinc intake, status and indices of cognitive function in children and adults. A systematic literature search was conducted using EMBASE, MEDLINE and Cochrane Library databases from inception to March 2014. Included studies were those that supplied zinc as supplements or measured dietary zinc intake. A meta-analysis of the extracted data was performed where sufficient data were available. Of all of the potentially relevant papers, 18 studies met the inclusion criteria, 12 of which were randomised controlled trials (RCTs; 11 in children and 1 in adults) and 6 were observational studies (2 in children and 4 in adults). Nine of the 18 studies reported a positive association between zinc intake or status with one or more measure of cognitive function. Meta-analysis of data from the adult’s studies was not possible because of limited number of studies. A meta-analysis of data from the six RCTs conducted in children revealed that there was no significant overall effect of zinc intake on any indices of cognitive function: intelligence, standard mean difference of <0.001 (95% confidence interval (CI) –0.12, 0.13) P=0.95; executive function, standard mean difference of 0.08 (95% CI, –0.06, 022) P=0.26; and motor skills standard mean difference of 0.11 (95% CI –0.17, 0.39) P=0.43. Heterogeneity in the study designs was a major limitation, hence only a small number (n=6) of studies could be included in the meta-analyses. Meta-analysis failed to show a significant effect of zinc supplementation on cognitive functioning in children though, taken as a whole, there were some small indicators of improvement on aspects of executive function and motor development following supplementation but high-quality RCTs are necessary to investigate this further

Latent Patient Network Learning for Automatic Diagnosis

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications

Molecular characterisation of protist parasites in human-habituated mountain gorillas (Gorilla beringei beringei), humans and livestock, from Bwindi impenetrable National Park, Uganda

Over 60 % of human emerging infectious diseases are zoonotic, and there is growing evidence of the zooanthroponotic transmission of diseases from humans to livestock and wildlife species, with major implications for public health, economics, and conservation. Zooanthroponoses are of relevance to critically endangered species; amongst these is the mountain gorilla (Gorilla beringei beringei) of Uganda. Here, we assess the occurrence of Cryptosporidium, Cyclospora, Giardia, and Entamoeba infecting mountain gorillas in the Bwindi Impenetrable National Park (BINP), Uganda, using molecular methods. We also assess the occurrence of these parasites in humans and livestock species living in overlapping/adjacent geographical regions

Niobium and niobium-iron coatings on API 5LX 70 steel applied with HVOF

The present study aimed to create and characterize niobium and niobium-iron60% coatings applied to steel API 5L X70 using the hypersonic thermal spray process (HVOF). The morphologies of the coatings were analyzed using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD) and profilometry, while the coatings’ hardnesses was evaluated using the Vickers hardness test. The coatings’ corrosion resistance was evaluated by monitoring their open circuit potential and potentiodynamic polarization and performing electrochemical impedance spectroscopy in a 0.05 M NaCl solution. The results showed that the niobium-iron coating contained minor porosity regions, while such defects occurred over large regions of the niobium coating. In terms of corrosion resistance, the coatings obtained in this work promoted a reduction in the substrate’s corrosion rate, but the presence of discontinuities such as porosity compromised the barrier effects of these coatings

Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb

The reshaping and decorrelation of similar activity patterns by neuronal networks can enhance their discriminability, storage, and retrieval. How can such networks learn to decorrelate new complex patterns, as they arise in the olfactory system? Using a computational network model for the dominant neural populations of the olfactory bulb we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The decorrelation is quite robust with respect to various types of perturbations of the reciprocity. The model parsimoniously captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures

Heterogeneity in Health Insurance Coverage Among US Latino Adults

We sought to determine the differences in observed and unobserved factors affecting rates of health insurance coverage between US Latino adults and US Latino adults of Mexican ancestry. Our hypothesis was that Latinos of Mexican ancestry have worse health insurance coverage than their non-Mexican Latino counterparts. The National Health Interview Survey (NHIS) database from 1999–2007 consists of 33,847 Latinos. We compared Latinos of Mexican ancestry to non-Mexican Latinos in the initial descriptive analysis of health insurance coverage. Disparities in health insurance coverage across Latino categories were later analyzed in a multivariable logistic regression framework, which adjusts for confounding variables. The Blinder-Oaxaca technique was applied to parse out differences in health insurance coverage into observed and unobserved components. US Latinos of Mexican ancestry consistently had lower rates of health insurance coverage than did US non-Mexican Latinos. Approximately 65% of these disparities can be attributed to differences in observed characteristics of the Mexican ancestry population in the US (e.g., age, sex, income, employment status, education, citizenship, language and health condition). The remaining disparities may be attributed to unobserved heterogeneity that may include unobserved employment-related information (e.g., type of employment and firm size) and behavioral and idiosyncratic factors (e.g., risk aversion and cultural differences). This study confirmed that Latinos of Mexican ancestry were less likely to have health insurance than were non-Mexican Latinos. Moreover, while differences in observed socioeconomic and demographic factors accounted for most of these disparities, the share of unobserved heterogeneity accounted for 35% of these differences