Nutritional intake and navicular bone well being

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BACKGROUND Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context-defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed. read more OBJECTIVE The goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for tiver operating characteristic curve (P=.40). CONCLUSIONS We used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data. ©Andrew J King, Gregory F Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F Sittig, Shyam Visweswaran. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 02.04.2020.BACKGROUND The worldwide expansion of preexposure prophylaxis (PrEP) with oral tenofovir-disoproxil-fumarate/emtricitabine will be critical to ending the HIV epidemic. However, maintaining daily adherence to PrEP can be difficult, and the accuracy of self-reported adherence is often limited by social desirability bias. Pharmacologic adherence monitoring (measuring drug levels in a biomatrix) has been critical to interpreting PrEP trials, but testing usually requires expensive equipment and skilled personnel. read more We have recently developed a point-of-care (POC) immunoassay to measure tenofovir in urine, allowing real-time adherence monitoring for the first time. OBJECTIVE The goal of this study is to examine a point-of-care adherence metric in PrEP to support and increase adherence via a randomized controlled trial. METHODS The paper describes the protocol for a pilot randomized controlled trial to test the acceptability, feasibility, and impact on long-term adherence of implementing a POC urine test to provide reir in hair. RESULTS This study has been funded by the National Institute of Health, approved by the Kenya Medical Research Institute Institutional Review Board, and will commence in June 2020. CONCLUSIONS A novel urine assay to measure and deliver information on adherence to PrEP in real-time will be tested for the first time in this trial planned among women on PrEP in Kenya. Study findings will inform a larger-scale trial assessing the impact of real-time adherence monitoring/feedback on HIV prevention. Improving adherence to PrEP will have long-term implications for efforts to end the HIV epidemic worldwide. TRIAL REGISTRATION ClinicalTrials.gov NCT03935464; https//clinicaltrials.gov/ct2/show/NCT03935464. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/15029. ©Paul Drain, Kenneth Ngure, Nelly Mugo, Matthew Spinelli, Purba Chatterjee, Peter Bacchetti, David Glidden, Jared Baeten, Monica Gandhi. Originally published in JMIR Research Protocols (http//www.researchprotocols.org), 02.04.2020.BACKGROUND Acute respiratory infections (ARIs), primarily pneumonia, are the leading infectious cause of under-5 mortality worldwide. Manually counting respiratory rate (RR) for 60 seconds using an ARI timer is commonly practiced by community health workers to detect fast breathing, an important sign of pneumonia. However, correctly counting breaths manually and classifying the RR is challenging, often leading to inappropriate treatment. A potential solution is to introduce RR counters, which count and classify RR automatically. OBJECTIVE This study aims to determine how the RR count of an Automated Respiratory Infection Diagnostic Aid (ARIDA) agrees with the count of an expert panel of pediatricians counting RR by reviewing a video of the child's chest for 60 seconds (reference standard), for children aged younger than 5 years with cough and/or difficult breathing. METHODS A cross-sectional study aiming to enroll 290 children aged 0 to 59 months presenting to pediatric in- and outpatient departments at a teacols (http//www.researchprotocols.org), 02.04.2020.BACKGROUND By enabling frequent, sensitive, and economic remote assessment, smartphones will facilitate the detection of early cognitive decline at scale. Previous studies have sustained participant engagement with remote cognitive assessment over a week; extending this to a period of 1 month clearly provides a greater opportunity for measurement. However, as study durations are increased, the need to understand how participant burden and scientific value might be optimally balanced also increases. OBJECTIVE This study explored the little but often approach to assessment employed by the Mezurio app when prompting participants to interact every day for over a month. Specifically, this study aimed to understand whether this extended duration of remote study is feasible, and which factors promote sustained participant engagement over such periods. METHODS A total of 35 adults (aged 40-59 years) with no diagnosis of cognitive impairment were prompted to interact with the Mezurio smartphone app platform for up to Chinner, Leona Wolters, Chris Hinds. Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 02.04.2020.BACKGROUND Smoking remains a leading cause of preventable death and illness. Internet interventions for smoking cessation have the potential to significantly impact public health, given their broad reach and proven effectiveness. Given the dose-response association between engagement and behavior change, identifying strategies to promote engagement is a priority across digital health interventions. Text messaging is a proven smoking cessation treatment modality and a powerful strategy to increase intervention engagement in other areas of health, but it has not been tested as an engagement strategy for a digital cessation intervention. OBJECTIVE This study examined the impact of 4 experimental text message design factors on adult smokers' engagement with an internet smoking cessation program. METHODS We conducted a 2×2×2×2 full factorial screening experiment wherein 864 participants were randomized to 1 of 16 experimental conditions after registering with a free internet smoking cessation program and enrolling in its automated text message program.