BackgroundAlthough 24‐hour blood pressure (BP) variability (BPV) is predictive of cardiovascular outcomes independent of absolute BP levels, it is not regularly assessed in clinical practice. One possible limitation to routine BPV assessment is the lack of standardized methods for accurately estimating 24‐hour BPV. We conducted a systematic review to assess the predictive power of reported BPV indexes to address appropriate quantification of 24‐hour BPV, including the average real variability (ARV) index.Methods and ResultsStudies chosen for review were those that presented data for 24‐hour BPV in adults from meta‐analysis, longitudinal or cross‐sectional design, and examined BPV in terms of the following issues: (1) methods used to calculate and evaluate ARV; (2) assessment of 24‐hour BPV determined using noninvasive ambulatory BP monitoring; (3) multivariate analysis adjusted for covariates, including some measure of BP; (4) association of 24‐hour BPV with subclinical organ damage; and (5) the predictive value of 24‐hour BPV on target organ damage and rate of cardiovascular events. Of the 19 assessed studies, 17 reported significant associations between high ARV and the presence and progression of subclinical organ damage, as well as the incidence of hard end points, such as cardiovascular events. In all these cases, ARV remained a significant independent predictor (P<0.05) after adjustment for BP and other clinical factors. In addition, increased ARV in systolic BP was associated with risk of all cardiovascular events (hazard ratio, 1.18; 95% confidence interval, 1.09–1.27). Only 2 cross‐sectional studies did not find that high ARV was a significant risk factor.ConclusionsCurrent evidence suggests that ARV index adds significant prognostic information to 24‐hour ambulatory BP monitoring and is a useful approach for studying the clinical value of BPV.
With the rapid deployment of the Internet of Things and cloud computing, it is necessary to enhance authentication protocols to reduce attacks and security vulnerabilities which affect the correct performance of applications. In 2019 a new lightweight IoT-based authentication scheme in cloud computing circumstances was proposed. According to the authors, their protocol is secure and resists very well-known attacks. However, when we evaluated the protocol we found some security vulnerabilities and drawbacks, making the scheme insecure. Therefore, we propose a new version considering login, mutual authentication and key agreement phases to enhance the security. Moreover, we include a sub-phase called evidence of connection attempt which provides proof about the participation of the user and the server. The new scheme achieves the security requirements and resists very well-known attacks, improving previous works. In addition, the performance evaluation demonstrates that the new scheme requires less communication-cost than previous authentication protocols during the registration and login phases.
Despite the potential benefits that computer approaches could provide for children with cognitive disabilities, research and implementation of emerging approaches to learning supported by computing technology has not received adequate attention. We conducted a pilot study to assess the effectiveness of a computer‐assisted learning tool, named “HATLE,” for children with Down syndrome. The tool helps to improve reading and writing abilities in Spanish, through mobile computing, multimedia design, and computer speech‐recognition techniques. An experimental design with nonequivalent groups was used to assess the effectiveness of HATLE. The treatment group was taught using HATLE; the control group received typical instructions with the same material. Individual literacy achievement was assessed for both groups, before and after therapy sessions. The dependent variables in all analyses were posttest scores, adjusted via Analysis of Covariance (ANCOVA) for pretest variance. Differences between treatment and control groups were statistically significant in favor of the HATLE group on measures of Single‐Word Reading (p = 0.048) and Handwriting‐Form (p = 0.046) with large effect sizes (d > 0.8). Results indicate that HATLE might be effective in supporting computer‐aided learning for children with intellectual disabilities. The results are discussed in terms of limitations and implications.
Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.
The ARVmobile v1.0 is a multiplatform mobile personal health monitor (PHM) application for ambulatory blood pressure (ABP) monitoring that has the potential to aid in the acquisition and analysis of detailed profile of ABP and heart rate (HR), improve the early detection and intervention of hypertension, and detect potential abnormal BP and HR levels for timely medical feedback. The PHM system consisted of ABP sensor to detect BP and HR signals and smartphone as receiver to collect the transmitted digital data and process them to provide immediate personalized information to the user. Android and Blackberry platforms were developed to detect and alert of potential abnormal values, offer friendly graphical user interface for elderly people, and provide feedback to professional healthcare providers via e-mail. ABP data were obtained from twenty-one healthy individuals (>51 years) to test the utility of the PHM application. The ARVmobile v1.0 was able to reliably receive and process the ABP readings from the volunteers. The preliminary results demonstrate that the ARVmobile 1.0 application could be used to perform a detailed profile of ABP and HR in an ordinary daily life environment, bedsides of estimating potential diagnostic thresholds of abnormal BP variability measured as average real variability.
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