The development of an algorithm to track blood pressure accurately based on the pulse transit time method

Oscillometric blood pressure (BP) monitors are currently used to diagnose hypertension both in home and clinical settings. These monitors take BP measurements once every 15 minutes over a 24 hour period and provide a reliable and accurate system that is minimally invasive. Although intermittent cuff measurements have proven to be a good indicator of BP, a continuous BP monitor is highly desirable for the diagnosis of hypertension and other cardiac diseases. However, no such devices currently exist. A novel algorithm has been developed based on the Pulse Transit Time (PTT) method, which would allow non-invasive and continuous BP measurement. PTT is defined as the time it takes the BP wave to propagate from the heart to a specified point on the body. After an initial BP measurement, PTT algorithms can track BP over short periods of time, known as calibration intervals. After this time has elapsed, a new BP measurement is required to recalibrate the algorithm. Using the PhysioNet database as a basis, the new algorithm was developed and tested using 15 patients, each tested 3 times over a period of 30 minutes. The predicted BP of the algorithm was compared to the arterial BP of each patient. It has been established that this new algorithm is capable of tracking BP over 12 minutes without the need for recalibration, using the BHS standard, a 100% improvement over what has been previously identified. The algorithm was incorporated into a new system based on its requirements and was tested using three volunteers. The results mirrored those previously observed, providing accurate BP measurements when a 12 minute calibration interval was used. This new system provides a significant improvement to the existing method allowing BP to be monitored continuously and non-invasively, on a beat-to-beat basis over 24 hours, adding major clinical and diagnostic value.

Physical function, psychosocial adaptation, and hardiness post stroke

Introduction: Stroke is a chronic condition that significantly impacts on morbidity and mortality (Balanda et al. 2010). Globally, the complexity of stroke is well documented and more recently, in Ireland, as part of the National Survey of Stroke Survivors (Horgan et al. 2014). There are a number of factors that are known to influence adaptation post stroke. However, there is a lack of research to explain the variability in how survivors adapt post stroke. Hardiness is a broad personality trait that leads to better outcome. This study investigated the influence of hardiness and physical function on psychosocial adaptation post stroke. Methods: A quantitative cross-sectional, correlational, exploratory study was conducted between April and November 2013. The sample consisted of stroke survivors (n=100) who were recruited from three hospital outpatient departments and completed a questionnaire package. Results: The mean age of participants was 76 years (range 70-80), over half (56%) of the participants achieved the maximum score of 20 on the Barthel Index indicating independence in activities of daily living. The median number of days since stroke onset was 91 days (range 74-128). The total mean score and standard deviation for hardiness was 1.89 (0.4) as measured by the Dispositional Resilience Scale, indicating medium hardiness (possible range 0-3). Psychosocial adaptation was measured using the Psychosocial Adjustment to Illness Scale, the total weighted mean and standard deviation was 0.54 (0.3) indicating a satisfactory level of psychosocial adaptation (possible range 0-3). A hierarchical multiple linear regression was performed which contained 6 independent variables (hardiness, living arrangement, and length of hospital stay, number of days since stroke onset, physical function and self-rated recovery). Findings demonstrated that physical function (p<0.001) and hardiness (p=0.008) were significantly related to psychosocial adaptation. Altogether, 65% of the variation in psychosocial adaptation can be explained by the combined effect of the independent variables. Physical functioning had the highest unique contribution (11%) to explain the variance in psychosocial adaptation while self-rated recovery, hardiness, and living arrangements contributed 3% each. Conclusion: This research provides important information regarding factors that influence psychosocial adaptation post stroke at 3 months. Physical function significantly contributed to psychosocial adaptation post stroke. The personality trait of hardiness provides insight into how behaviour influenced adaptation post stroke. While hardiness also had a strong relationship with psychosocial adaptation, further research is necessary to fully comprehend this process.