Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
In an increasingly urbanised world where mental health is currently in crisis, interventions\nto increase human engagement and connection with the natural environment are one of the fastest\ngrowing, most widely accessible, and cost-effective ways of improving human wellbeing. This study\naimed to provide an evaluation of a smartphone app-based wellbeing intervention. In a randomised\ncontrolled trial study design, the app prompted 582 adults, including a subgroup of adults classified\nby baseline scores on the Recovering Quality of Life scale as having a common mental health problem\n(n = 148), to notice the good things about urban nature (intervention condition) or built spaces (active\ncontrol). There were statistically significant and sustained improvements in wellbeing at one-month\nfollow-up. Importantly, in the noticing urban nature condition, compared to a built space control,\nimprovements in quality of life reached statistical significance for all adults and clinical significance\nfor those classified as having a mental health difficulty. This improvement in wellbeing was partly\nexplained by significant increases in nature connectedness and positive affect. This study provides\nthe first controlled experimental evidence that noticing the good things about urban nature has strong\nclinical potential as a wellbeing intervention and social prescription....
This study investigated whether 10 month telephone follow-up intervention effectively\nstabilizes reductions in %body fat, and markers of inflammation and oxidative stress obtained from\nsummer camp in obese Hispanic children. Fifty-six obese children (19 SUTI: summer camp and\n10 months of follow-up telephone intervention, 18 SU: summer camp intervention only, and 19 CON:\nno intervention) completed this study. Anthropometric data and blood samples were obtained before\n(PRE), after 8 weeks of summer camp, and a 10month follow-up telephone intervention to measure\nmarkers of inflammation and oxidative stress. Eight weeks of summer camp significantly reduced\n%body fat, and levels of tumor necrosis factor-alpha, C-reactive protein and 8-hydroxydeoxyguanosine���...
Background: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The\nAKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information,\nand accessible online. In order to evaluate its clinical value, the AKIpredictor was compared to physiciansâ?? predictions.\nMethods: Prospective observational study in five ICUs of a tertiary academic center. Critically ill adults without endstage\nrenal disease or AKI upon admission were considered for enrollment. Using structured questionnaires, physicians\nwere asked upon admission, on the first morning, and after 24 h to predict the development of AKI stages 2 or 3 (AKI-\n23) during the first week of ICU stay. Discrimination, calibration, and net benefit of physiciansâ?? predictions were\ncompared against the ones by the AKIpredictor.\nResults: Two hundred fifty-two patients were included, 30 (12%) developed AKI-23. In the cohort of patients with\npredictions by physicians and AKIpredictor, the performance of physicians and AKIpredictor were respectively upon ICU\nadmission, area under the receiver operating characteristic curve (AUROC) 0.80 [0.69-0.92] versus 0.75 [0.62-0.88] (n =\n120, P = 0.25) with net benefit in ranges 0-26% versus 0-74%; on the first morning, AUROC 0.94 [0.89-0.98] versus 0.89\n[0.82-0.97] (n = 187, P = 0.27) with main net benefit in ranges 0-10% versus 0-48%; after 24 h, AUROC 0.95 [0.89-1.00]\nversus 0.89 [0.79-0.99] (n = 89, P = 0.09) with main net benefit in ranges 0-67% versus 0-50%.\nConclusions: The machine-learning-based AKIpredictor achieved similar discriminative performance as physicians for\nprediction of AKI-23, and higher net benefit overall, because physicians overestimated the risk of AKI. This suggests an\nadded value of the systematic risk stratification by the AKIpredictor to physiciansâ?? predictions, in particular to select\nhigh-risk patients or reduce false positives in studies evaluating new and potentially harmful therapies. Due to the low\nevent rate, future studies are needed to validate these findings....
Objective: To evaluate a new management model using mobile health for senile\nhypertension. Methods: This medical service combined traditional medical\ntreatment with Mobile Health. We use it to explore a new and effective\nmodel of elderly hypertension management and the most effective and lowest\ncost management crowd. According to the randomized controlled design of\ntrial, 105 old hypertensive patients participated in the study voluntarily in the\nQingdao Municipal Hospital were randomly divided into the experimental\ngroup (75 cases) and control group (30 cases). Experimental group is divided\ninto geriatric specialist group (25 cases), general practitioner group (25 cases)\nand nurse group (25 cases). Blood pressure was administered in experimental\n(with the new model) and control groups (with the traditional model) for 2\nmonths to compare their blood pressure and the decrease of them. Results:\nBlood pressure was compared between the two groups before and after administration.\nThe systolic blood pressure (SBP) of experimental group is �����....
Background: Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data\ncovering all aspects of patientsâ?? journeys into and through intensive care are now collected and stored in electronic\nhealth records: machine learning has been used to analyse such data in order to provide decision support to clinicians.\nMethods: Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science\nand MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The\nstudy aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated,\nand measures of predictive accuracy were extracted.\nResults: Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting\ncomplications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43\n[16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108-4099): 41 studies\nanalysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to\npredict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161\n(95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%)\nvalidated predictions using independent data. The median AUC was 0.83 in studies of 1000-10,000 patients,\nrising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks\n(72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015\n(125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random\nforests (29 [23.2%]).\nConclusions: The rate of publication of studies using machine learning to analyse routinely collected ICU data is\nincreasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these\nmethods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and\nvalidation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use\nin clinical practice....
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