BackgroundRoutine outpatient care of patients with coronary artery disease (CAD) lacks a simple measure of physical fitness and risk of mortality. Heart rate recovery (HRR) is noninvasive and easily obtainable in outpatient settings. Prior studies have suggested that delayed postexercise HRR in the first minutes is associated withmortality in several types of populations. However, a comprehensive overview of the prognostic value of delayed HRR for time to mortality specifically in CAD patients is not available. The purpose of the current meta-analysis is to evaluate the prognostic value of delayed HRR in CAD patients.MethodsWe conducted a systematic search in OVID MEDLINE and OVID EMBASE to identify studies reporting on HRR and risk of incident cardiovascular events or mortality in CAD patients. Hazard ratios for delayed versus nondelayed HRR were pooled using random-effects meta-analysis.Results Four studies were included, comprising 2,428 CAD patients. The study quality of the included studies was rated moderate (n = 2) to high (n = 2). Delayed HRR was defined by ≤12 to ≤21 beat/min in the recovery period. During follow-up (range 2.0-9.8 years), 151 patients died (6.2% [range 2.5%-19.5%]). Only data on mortality could be pooled. Heterogeneity was limited (I² = 32%; P = .23); pooled unadjusted hazard ratio for mortality, based on 3 studies, was 5.8 (95% CI 3.2-10.4).CoclusionsIn CAD patients, delayed HRR is significantly associated with all-cause mortality. As exercise testing is performed routinely in CAD patients, HRR can be considered in monitoring exercise; still, further research must investigate the addition of HRR in current risk scores.
The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R 2 = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.
Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee’s own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented, as the basis for the WearMe project. The included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system.
We propose to do an experimental study in which we will use 360 video and still photo simulations that portray varying levels of crowding. Simulations will be presented to 25 student participants and 25 older adult participants (65+; a lucrative tourist segment) in an experimental setting while signals of their emotional responses are recorded from their brain (EEG) and body (skin conductivity and heart rate) at our Experience Measurement Lab. A questionnaire will measure their intent to recommend and their willingness to pay for the ‘experiences’ (simulations) they have viewed. Analyses will determine optimal levels of crowding for the quality of the tourist experience, but also for income at the destination, accounting for the fact that a more crowded destination features more potential sources of income (visitors), but each a (possibly) different level of willingness to pay, including potential implications for local tourist taxes. Models will also account for possibly different processes in the two different age groups. Furthermore, modelling word-of-mouth/mouse marketing based on intent to recommend will also make it possible to predict how crowding affects demand long-term. Partner: KU Leuven.
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is used as an important tool for biomedical application (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this tool and its application was recently published (2018), as an especial edition of the Journal of Aerosol Sciences. One of the main known bottlenecks of this technique, it is the fact that the necessary strong electric fields create a risk for electric discharges. Such discharges destabilize the process but can also be an explosion risk depending on the application. The goal of this project is to develop a reliable tool to prevent discharges in electrospray applications.
