Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Exploring Potential Referral Pathways for Renal Artery Denervation and Developing a Centre of Excellence in Ireland.

Arterial hypertension is one of the most significant and prevalent risk factors for cardiovascular disease. Despite widespread awareness of the condition, as well as a multitude of available antihypertensive drug classes, rates of uncontrolled hypertension remain high on a global scale. Frequently, poor compliance with anti-hypertensive medication plays a big role in patients' inability to attain adequate blood pressure control. In individuals with resistant and/or uncontrolled hypertension, renal denervation is an emerging device-based therapy that has shown to be efficacious and safe in reducing blood pressure in several sham controlled trials. Additionally, it represents a treatment option for patients intolerant to oral pharmacotherapy. University Hospital Galway has been performing renal denervation procedures over the past number of years within multicentre, international sham-controlled trials and registries. Representing a novel and emerging antihypertensive treatment option, sources of referral for renal denervation are diverse and multiple; thus, there is an unmet need for standardised referral structures in Ireland. Herein, we review current and developing referral pathways for renal denervation at our institution, and discuss streamlined patient management and requirements to establish a centre of excellence.

Modelling the relationship between continuously measured glucose and electrocardiographic data in adults with type 1 diabetes mellitus.

INTRODUCTION: Type 1 diabetes mellitus (T1DM) is associated with earlier onset of cardiovascular disease. Recent evidence has found hyperglycaemia appears to play a greater role in this association among T1DM compared to T2DM. This study investigates the relationship between glucose and QTc (a key cardiovascular measure) using data from continuous electrocardiogram (ECG) and glucose monitors. METHODS: Seventeen adults with T1DM were recruited at a clinical facility in Ireland. A continuous glucose monitoring system was fitted to each participant that measured glucose every 5 min for 7 days. The participants simultaneously wore a vest with sensors to measure 12-lead ECG data every 10 min for 7 days. Area under the glucose curve (AUC), proportion of time spent in hypoglycaemia and hyperglycaemia, and mean daily absolute deviation of glucose were calculated. Mixed effects ANOVA and functional regression models were fitted to the data to investigate the aggregate and time-dependent association between glucose and QTc. RESULTS: All participants were male with an average age of 52.5 (SD 3.8) years. Those with neuropathy had a significantly higher mean QTc compared to their counterparts. Mean QTc was significantly longer during hyperglycaemia. There was a significant positive association between QTc and time spent in hyperglycaemia. A negative association was found between QTc and time spent in hypoglycaemia. A functional model suggested a positive relationship between glucose and QTc at several times during the 7-day follow-up. CONCLUSION: This study used sensor technology to investigate, with high granularity, the temporal relationship between glucose and ECG data over one week. QTc was found to be longer on average during hyperglycaemia.

Cross-validation of a manual form of the Letter Memory Test using a known-groups methodology.

A manual form of the Letter Memory Test (LMT: Orey, Cragar, & Berry, 2000) was compared in neuropsychological evaluees classified as honest (HON: n = 39) or probable cognitive feigners (PCF: n = 10) using results from two well-validated motivational tests. With the exception of lower educational level and higher rate of compensation seeking in the PCF, the groups were equivalent on most important demographic and injury severity parameters. PCF participants scored significantly lower on most neuropsychological tests (median Cohen's d = 1.2), as well as on the manual LMT (Cohen's d = 4.2). Operating characteristics of the manual LMT in the present sample were comparable to those reported in a similar study using the computerized version of the LMT in neuropsychiatric patients (Vagnini et al., 2006).