SummaryBackground-Although heart rate and respiratory rate are routinely measured in children in acute settings, current reference ranges are not evidence-based. The aim of this study is to derive new centile charts for heart rate and respiratory rate using systematic review data from existing studies, and to compare these with existing international ranges.

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H igh blood pressure is a major risk factor for global disease burden.1 Even modest reductions in blood pressure are important and would reduce the risk of associated morbidity and premature mortality. [2][3][4] In settings where health care and medicines are freely available, a substantial burden of cardiovascular disease may be attributable to suboptimal adherence to blood pressure-lowering treatments. 5 Missed appointments for collection of medicine and challenges with taking lifelong treatment are some of the major reasons for suboptimal Background-We assessed the effect of automated treatment adherence support delivered via mobile phone short message system (SMS) text messages on blood pressure. Methods and Results-In this pragmatic, single-blind, 3-arm, randomized trial (SMS-Text Adherence Support [StAR]) undertaken in South Africa, patients treated for high blood pressure were randomly allocated in a 1:1:1 ratio to information only, interactive SMS text messaging, or usual care. The primary outcome was change in systolic blood pressure at 12 months from baseline measured with a validated oscillometric device. All trial staff were masked to treatment allocation. Analyses were intention to treat. Between June 26, 2012, and November 23, 2012, 1372 participants were randomized to receive information-only SMS text messages (n=457), interactive SMS text messages (n=458), or usual care (n=457). Primary outcome data were available for 1256 participants (92%). At 12 months, the mean adjusted change in systolic blood pressure compared with usual care was −2.2 mm Hg (95% confidence interval, −4.4 to −0.04) with informationonly SMS and −1.6 mm Hg (95% confidence interval, −3.7 to 0.6) with interactive SMS. Odds ratios for the proportion of participants with a blood pressure <140/90 mm Hg were 1.42 (95% confidence interval, 1.03-1.95) for information-only messaging and 1.41 (95% confidence interval, 1.02-1.95) for interactive messaging compared with usual care. Conclusions-In this randomized trial of an automated adherence support program delivered by SMS text message in a general outpatient population of adults with high blood pressure, we found a small reduction in systolic blood pressure control compared with usual care at 12 months. There was no evidence that an interactive intervention increased this effect. Clinical Trial Registration-URL: http://www.clinicaltrials.gov.

The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when a priori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10,172 s of PCG recorded from 112 patients (including 12,181 first and 11,627 second heart sounds). The proposed method achieved an average F1 score of 95.63 ± 0.85%, while the current state of the art achieved 86.28 ± 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.