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The prevalence of type-2 diabetes(T2D) has increased globally. This has led to greater mortality, morbidity and disability in the general population. It is thus crucial to identify methods to prevent its onset among the healthy, and to also discover solutions to adequately manage the complications among those affected. Most research in this area has focused on the role of diet and exercise. More recently, different exercise types and their relationship with T2D has received considerable attention. In our work, we investigate the association between T2D (primary outcome) and two types of exercises cardio (CR) and weight lifting (WL). Specifically, the relationship between duration of time spent in the two exercises and the odds of T2D is explored. Data are obtained from the Behavioural Risk Factor Surveillance System (BRFSS) survey, USA. click here Three ethnic populations are considered White American, Black American and Hispanic American. Both WL and CR are found to be associated with negative log-odds of diabetes across all three ethnicities (WL p less then ; 0.0001 and CR p=0.00431). The association between WL and T2D is found to be modified for females (interaction-term coefficient -0.096 (p=0.0115)).Heart rate (HR) monitoring under real-world activities of daily living conditions is challenging, particularly, using peripheral wearable devices integrated with simple optical and acceleration sensors. The study presents a novel technique, named as CurToSS CURve Tracing On Sparse Spectrum, for continuous HR estimation in daily living activity conditions using simultaneous photoplethysmogram (PPG) and triaxial-acceleration signals. The performance validation of HR estimation using the CurToSS algorithm is conducted in four public databases with distinctive study groups, sensor types, and protocols involving intense physical and emotional exertions. The HR performance of this time-frequency curve tracing method is also compared to that of contemporary algorithms. The results suggest that the CurToSS method offers the best performance with significantly (P less then 0.01) lowest HR error compared to spectral filtering and multi-channel PPG correlation methods. The current HR performances are also consistently better than a deep learning approach in diverse datasets. The proposed algorithm is powerful for reliable long-term HR monitoring under ambulatory daily life conditions using wearable biosensor devices.In-silico clinical platforms have been recently used as a new revolutionary path for virtual patients (VP) generation and further analysis, such as, drug development. Advanced individualized models have been developed to enhance flexibility and reliability of the virtual patient cohorts. This study focuses on the implementation and comparison of three different methodologies for generating virtual data for in-silico clinical trials. Towards this direction, three computational methods, namely (i) the multivariate log-normal distribution (log- MVND), (ii) the supervised tree ensembles, and (iii) the unsupervised tree ensembles are deployed and evaluated against their performance towards the generation of high-quality virtual data using the goodness of fit (gof) and the dataset correlation matrix as performance evaluation measures. Our results reveal the dominance of the tree ensembles towards the generation of virtual data with similar distributions (gof values less than 0.2) and correlation patterns (average difference less than 0.03).Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.Nocturnal pulse oximetry has been proposed as a tool for diagnosing sleep apnea. We established criteria in determining previous occurrences of apnea events by extracting quantitative characteristics caused by apnea events over the duration of changes in blood oxygen saturation values in our previous studies. In addition, the apnea-hypopnea index was estimated by regression modeling. In this paper, the algorithm presented in the previous study was applied to the data collected from the sleep medicine center of other hospitals to verify its performance. As a result of applying the algorithm to pulse oximetry data of 15 polysomnographic recordings, the minute-by-minute apneic segment detection exhibited an average accuracy of 87.58% and an average Cohen's kappa coefficient of 0.6327. In addition, the correlation coefficient between the estimated apnea-hypopnea index and the reference was 0.95, and the average absolute error was 5.02 events/h. When the algorithm is evaluated on the data collected by the other sleep medicine center, they still detected semi real-time sleep apnea events and showed meaningful results in estimating apnea-hypopnea index, although their performance was somewhat lower than before. With the recent popularity of devices for mobile healthcare, such as the wearable pulse oximeter, the results of this study are expected to improve the user value of devices by implementing mobile sleep apnea diagnosis and monitoring functions.
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