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

Assessment of canal walls after biomechanical preparation of root canals instrumented with protaper universalTM rotary system

OBJECTIVE: The aim of this study was to examine the instrumented walls of root canals prepared with the ProTaper UniversalTM rotary system. MATERIAL AND METHODS: Twenty mesiobuccal canals of human first mandibular molars were divided into 2 groups of 10 specimens each and embedded in a muffle system. The root canals were transversely sectioned 3 mm short of the apex before preparation and remounted in their molds. All root canals were prepared with ProTaper UniversalTM rotary system or with NitiflexTM files. The pre and postoperative images of the apical thirds viewed with a stereoscopic magnifier (X45) were captured digitally for further analysis. Data were analyzed statistically by Fisher's exact test and Chi-square test at 5% significance level. RESULTS: The differences observed between the instrumented and the noninstrumented walls were not statistically significant (p<0.05). CONCLUSIONS: The NitiflexTM files and the ProTaper UniversalTM rotary system failed to instrument all the root canal walls

2013 WSES guidelines for management of intra-abdominal infections

Peer reviewe

Pattern Sampling in Distributed Databases

International audienceMany applications rely on distributed databases. However, only few discovery methods exist to extract patterns without centralizing the data. In fact, this centralization is often less expensive than the communication of extracted patterns from the different nodes. To circumvent this difficulty, this paper revisits the problem of pattern mining in distributed databases by benefiting from pattern sampling. Specifically , we propose the algorithm DDSampling that randomly draws a pattern from a distributed database with a probability proportional to its interest. We demonstrate the soundness of DDSampling and analyze its time complexity. Finally, experiments on benchmark datasets highlight its low communication cost and its robustness. We also illustrate its interest on real-world data from the Semantic Web for detecting outlier entities in DBpedia and Wikidata