Stress has negative effects on the health of the body and the health of economies. When a person is feeling stress, the body releases certain chemicals that lower the immune system, increasing the likelihood that illnesses will form in the body. Stress can also cause negative effects in the workplace, as when workers take time off and seek treatment, the companies they work for lose money as well. The Mental Health Foundation estimates that around 12 million adults living in the United Kingdom visit their General Practitioner every year with concerns of their mental health that have been brought on by stress. Due to these illnesses, an estimated 13.3 million work days are lost every year. The World Health Organization estimates that around 8.4 million GBP (10.5 million USD) are lost by UK businesses due to these health concerns. Moreover, the average wait time to get treatment is 3-6 months. Because of these factors, over the last decade studies performed on stress and mental health have increased in popularity.
Researchers, Mozos, Oscar Martinez, et al. aimed to detect stressful behaviors by having their participants wear noninvasive physiological monitoring systems to find what activities elicited stress. These experiments were performed in a laboratory setting, using the TSST (Tier Social Stress Test) to manage levels of anxiety in each participant in a controlled situation; this popular method was used in over 4,000 settings over the last decade. The total sample size the researchers used was 18, male and female volunteers from the School of Psychology at the University of Lincoln in the UK.
The social task presented was for the subject to prepare a presentation for a mock job interview, and tasked to speak continuously for five minutes. When subjects paused the first time, the experimenter would tell them their remaining time and ask them to continue. The second time the participant paused they would be asked a set of predefined interview questions. For the cognition task, the experimenter asked the participant to count down from 1022 in sets of 13, for five minutes. If the participant made a mistake, they were asked to start from 1022 again.
Using the wearable, dual-signal BioNomadix, researchers wirelessly recorded electrodermal activity (EDA) and pulse plethysmograms (PPG), then analyzed the data to gather the Heart Rate Variability (HRV) between different tasks the subjects were doing, and extrapolate the stress the subjects were feeling in those conditions. The goal of the researchers was to show that using wearable monitoring systems can help detect stress, and with variations, can be applied outside of the lab, and into everyday interactions to get a firmer grip on what is causing anxiety and stress in millions of people.
Cardiovascular risk factors (obesity, metabolism, hypertension, etc.) can significantly impact a person’s lifespan. While obesity rates are currently stagnant, they still remain very high. Cardiovascular risk factors have thus become the focus of health research to understand what behaviors might contribute to increased risk. The majority of these studies have been aimed specifically at adults, but little is understood about the origins of these risk factors in childhood. Laurie Wideman, et al thus sought to create a longitudinal study that investigated social and emotional development, called the RIGHT Track Health Study. The RIGHT track study followed participants from infancy to young adult to understand how their self-regulation and increased autonomy via their health behaviors might contribute to cardiovascular risks factors. Participants were measured at five ages: two, four, five, seven, and ten. Participants performed a variety of assessments including body composition, fitness tests, orthostatic challenge (while having heart rate variability recorded), 7-day accelerometry for physical activity and sleep, 24-hour dietary recalls, and blood analysis for various related biomarkers. Researchers also had participants complete extensive self-report measures related to diet, sleep, physical activity, and medical history. Heart rate variability (HRV) was measured using BIOPAC’s dual-channel BioNomadix wireless ECG and Respiration transmitter while participants performed the orthostatic challenge. Heart rate variability measured from the orthostatic challenge was compared to HRV collected during the early years of the study where the infants underwent psychological stressors. Through their RIGHT track health study, the researchers were able to provide valuable about the influence of childhood regulatory abilities on youth healthcare. The researchers hope that their findings will help illuminate potential critical “windows,” or specific points in childhood where people may be more at risk. Assessing when these windows occur could help greatly reduce certain risk factors and help our understanding of how to prevent chronic disease earlier in life.
One of the foundational concepts making virtual reality (VR) video games of high interest is the game’s ability to transport the user to a flow state. The visual and auditory stimuli presented are highly immersive, leading the user to focus entirely on the game, often losing track of time. Flow states are characterized by this lack of time awareness, as participants are able to match their abilities with the demands of the game (i.e., the game is not too easy or too hard). But what qualities reflect a flow state as it is happening in the moment?
Researchers from Shandong University tested five first-level physiological functions to determine their efficacy in reflecting a flow state, including EMG, EDA, EEG, respiratory rate, and cardiovascular activity. Thirty-six students participated in a VR game while having their physiological responses monitored. Prior to the experiment, the researchers placed BIOPAC’s wearable dual-signal BioNomadix transmitters with appropriate electrodes on the participants to wirelessly record Respiration and ECG data. Participants were then seated in the designated gaming chair for five minutes to record baseline responses. After this, they played the game for six minutes, followed by a questionnaire about flow experience. The results showed that the five physiological functions, as a whole, indicated flow state, though respiratory rate was most effective. The authors note that, as a physiologic arousal marker, respiratory rate best predicted flow state.
Having physiologic indicators of a flow state not only assists future research, but also provides a method for real-time feedback on the efficacy of the game. The authors note that with better biometric data comes the opportunity to provide a better gaming experience, with real-time adjustments. If the physiologic responses and adjustments could be integrated with the gaming software, games would be far more realistic.
Wouldn’t it be nice to be able to regulate your emotional reactions? Birk and Bonanno (2016) studied the adaptiveness of modifying emotional regulation (ER) strategies based on affective and physiological feedback. They wanted to understand how people use emotional regulation to adjust from thinking about emotional situation to more neutral information (distraction). To develop a more coherent comprehensive understanding, researchers studied whether people use internal feedback (negative intensity, corrugator’s activity, heart rate) to guide emotional regulation. Birk and Bonanno performed two studies: Study 1 examined switching from reappraisal to distraction and Study 2 examined switching from distraction to reappraisal.
The studies explored whether internal feedback influenced people to shift to an optimal strategy (distraction), instead of a nonoptimal (reappraisal) strategy, when regulating strong emotional reactions. In addition, both researchers tested whether the frequency of switching and response to internal feedback (RIF) predicted well-being. The researchers found that negative intensity, corrugator’s activity, and the magnitude of heart rate deceleration were higher on switching rather than maintained strategies. In Study 1, researchers found that a greater switching frequency showed higher rates of higher and lower life satisfaction.
Birk and Bonanno were able to record and analyze data using a BIOPAC Research System with wireless BioNomadix and AcqKnowledge software. Facial electromyography, electrocardiography, and electrodermal activity were the physiological signals measured. Wireless BioNomadix data recording allows researchers to assess ambulatory subjects in an environment most appropriate for their study. BioNomadix amplifiers are capable of interfacing with MP Systems and AcqKnowledge software for a complete, wireless solution that supports advanced analysis for multiple applications.
In conclusion, Birk and Bonanno collectively found that internal feedback about the experience of intense negative emotion guides the decision to switch form reappraisal to distraction in Study 1, but not the reverse order of strategies in Study 2.
Wouldn’t it be nice to be able to regulate your emotional reactions? Birk and Bonanno (2016) studied the adaptiveness of modifying emotional regulation (ER) strategies based on affective and physiological feedback. They wanted to understand how people use emotional regulation to adjust from thinking about emotional situation to more neutral information (distraction). To develop a more coherent comprehensive understanding, researchers studied whether people use internal feedback (negative intensity, corrugator’s activity, heart rate) to guide emotional regulation. Birk and Bonanno performed two studies: Study 1 examined switching from reappraisal to distraction and Study 2 examined switching from distraction to reappraisal.
The studies explored whether internal feedback influenced people to shift to an optimal strategy (distraction), instead of a nonoptimal (reappraisal) strategy, when regulating strong emotional reactions. In addition, both researchers tested whether the frequency of switching and response to internal feedback (RIF) predicted well-being. The researchers found that negative intensity, corrugator’s activity, and the magnitude of heart rate deceleration were higher on switching rather than maintained strategies. In Study 1, researchers found that a greater switching frequency showed higher rates of higher and lower life satisfaction.
Birk and Bonanno were able to record and analyze data using a BIOPAC Research System with wireless BioNomadix and AcqKnowledge software. Facial electromyography, electrocardiography, and electrodermal activity were the physiological signals measured. Wireless BioNomadix data recording allows researchers to assess ambulatory subjects in an environment most appropriate for their study. BioNomadix amplifiers are capable of interfacing with MP Systems and AcqKnowledge software for a complete, wireless solution that supports advanced analysis for multiple applications.
In conclusion, Birk and Bonanno collectively found that internal feedback about the experience of intense negative emotion guides the decision to switch form reappraisal to distraction in Study 1, but not the reverse order of strategies in Study 2.