With a Little Help from My Friend: Emotional Expressiveness in a Female Digital Human and User Gender Interact to Affect Psychological and Physiological Outcomes (Preprint)

2021 ◽  
Author(s):  
Kate Loveys ◽  
Mark Sagar ◽  
Xueyuan Zhang ◽  
Gregory Fricchione ◽  
Elizabeth Broadbent
Keyword(s):  
Social Support ◽  
Heart Rate ◽  
Skin Temperature ◽  
Emotional Expression ◽  
Electrodermal Activity ◽  
Emotional Expressiveness ◽  
Subjective Stress ◽  
Digital Humans ◽  
Physiological Outcomes ◽  
Digital Human

BACKGROUND Loneliness is a growing public health problem that has been exacerbated in vulnerable groups during the COVID-19 pandemic. Social support interventions have been shown to improve loneliness and may be delivered through technology. Digital humans are a new type of computer agent that show promise as supportive peers in healthcare. For digital humans to be effective and engaging support persons, it is important that they can develop closeness with people. Closeness can be improved by emotional expressiveness, particularly in female relationships. However, it is unknown whether emotional expressiveness improves closeness in relationships with digital humans and affects physiological responses. OBJECTIVE This study investigated whether emotional expression by a digital human can affect psychological and physiological outcomes, and whether the effects are moderated by user gender. METHODS 198 healthy adults (101 females, 95 males, 2 gender-diverse individuals) were block-randomized by gender to complete a 15-minute self-disclosure conversation with a female digital human, in one of six conditions. In these conditions, the digital human varied in modality richness and emotional expression in the face and voice (emotional/ neutral/ no face; emotional/ neutral voice). Perceived loneliness, closeness, social support, caring perceptions, and stress were measured after the interaction. Physiological measures including heart rate, skin temperature, and electrodermal activity were collected during the interaction using an Empatica E4 watch. Three-way factorial ANOVA with post hoc tests were conducted to analyse the effect of face type, voice type, and user gender on outcomes. RESULTS Overall, emotional expression in the voice was associated with greater caring perceptions and physiological arousal during the interaction, and unexpectedly, lower feelings of support. Gender was found to moderate the effect of emotional expressiveness on loneliness, social, and certain physiological outcomes. For females, an emotional voice digital human was associated with improved perceptions of closeness, social support, and caring perceptions, whereas for males, a neutral voice digital human was associated with improvements in closeness, social support, and caring perceptions. For females, a neutral face was associated with lower loneliness and subjective stress compared to no face. Whereas interacting with no face (i.e., a voice only black screen) resulted in lower loneliness and subjective stress for males compared to a neutral or emotional face digital human. No significant results were found for heart rate or skin temperature. However, average electrodermal activity was significantly higher for males while interacting with the emotional voice digital human. CONCLUSIONS Findings suggest that emotional expressiveness in a female digital human has different effects on loneliness, social, and physiological outcomes for males and females. Results may inform the design of digital human support persons, and have theoretical implications. Further research is needed to evaluate how more pronounced emotional facial expressions in a digital human might impact results. CLINICALTRIAL Australia New Zealand Clinical Trials Registry (ANZCTR) registration application Id: 381816

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

2021 ◽  
pp. 001872082110408
Author(s):  
Nima Ahmadi ◽  
Farzan Sasangohar ◽  
Tariq Nisar ◽  
Valerie Danesh ◽  
Ethan Larsen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Heart Rate ◽  
Intensive Care ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Laboratory Research ◽  
Prior Literature ◽  
High Level ◽  
Intensive Care Unit Nurses ◽  
Skin Temperatures

Objective To identify physiological correlates to stress in intensive care unit nurses. Background Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap. Method Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4). Results Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008). Discussion The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses. Application Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

Salivary cortisol, heart rate, electrodermal activity and subjective stress responses to the Mannheim Multicomponent Stress Test (MMST)

2012 ◽  
Vol 198 (1) ◽  
pp. 106-111 ◽  
Author(s):  
Tatyana Reinhardt ◽  
Christian Schmahl ◽  
Stefan Wüst ◽  
Martin Bohus
Keyword(s):  
Heart Rate ◽  
Stress Responses ◽  
Salivary Cortisol ◽  
Electrodermal Activity ◽  
Stress Test ◽  
Subjective Stress

scholarly journals Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

2021 ◽  
Vol 9 (1) ◽  
pp. e002027
Author(s):  
Brinnae Bent ◽  
Peter J Cho ◽  
April Wittmann ◽  
Connie Thacker ◽  
Srikanth Muppidi ◽  
...  
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Validation Cohort ◽  
Electrodermal Activity ◽  
External Validation ◽  
Glucose Variability ◽  
Proof Of Concept ◽  
Concept Study ◽  
Non Invasive ◽  
The Relationship

IntroductionDiabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.Research design and methodsWe recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8–10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2–6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.ResultsA total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor’s importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).ConclusionsThis study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

scholarly journals Cardiorespiratory and Thermoregulatory Parameters Are Good Surrogates for Measuring Physical Fatigue during a Simulated Construction Task

2020 ◽  
Vol 17 (15) ◽  
pp. 5418
Author(s):  
Shahnawaz Anwer ◽  
Heng Li ◽  
Maxwell Fordjour Antwi-Afari ◽  
Waleed Umer ◽  
Arnold Y. L. Wong
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Physical Workload ◽  
Breathing Rate ◽  
Physical Fatigue ◽  
Local Skin ◽  
Local Skin Temperature ◽  
The Right ◽  
Construction Task

Cardiorespiratory (e.g., heart rate and breathing rate) and thermoregulatory (e.g., local skin temperature and electrodermal activity) responses are controlled by the sympathetic nervous system. To cope with increased physical workload, the sympathetic system upregulates its activity to generate greater sympathetic responses (i.e., increased heart rate and respiratory rate). Therefore, physiological measures may have the potential to evaluate changes in physical condition (including fatigue) during functional tasks. This study aimed to quantify physical fatigue using wearable cardiorespiratory and thermoregulatory sensors during a simulated construction task. Twenty-five healthy individuals (mean age, 31.8 ± 1.8 years) were recruited. Participants were instructed to perform 30 min of a simulated manual material handling task in a laboratory. The experimental setup comprised a station A, a 10-metre walking platform, and a station B. Each participant was asked to pick up a 15 kg ergonomically-designed wooden box from station A and then carried it along the platform and dropped it at station B. The task was repeated from B to A and then A to B until the participants perceived a fatigue level > 15 out of 20 on the Borg-20 scale. Heart rate, breathing rate, local skin temperature, and electrodermal activity at the wrist were measured by wearable sensors and the perceived physical fatigue was assessed using the Borg-20 scale at baseline, 15 min, and 30 min from the baseline. There were significant increases in the heart rate (mean changes: 50 ± 13.3 beats/min), breathing rate (mean changes: 9.8 ± 4.1 breaths), local skin temperature (mean changes: 3.4 ± 1.9 °C), electrodermal activity at the right wrist (mean changes: 7.1 ± 3.8 µS/cm), and subjective physical fatigue (mean changes: 8.8 ± 0.6 levels) at the end of the simulated construction task (p < 0.05). Heart rate and breathing rate at 15 and 30 min were significantly correlated with the corresponding subjective Borg scores (p < 0.01). Local skin temperature at 30 min was significantly correlated with the corresponding Borg scores (p < 0.05). However, electrodermal activity at the right wrist was not associated with Borg scores at any time points. The results implied cardiorespiratory parameters and local skin temperature were good surrogates for measuring physical fatigue. Conversely, electrodermal activity at the right wrist was unrelated to physical fatigue. Future field studies should investigate the sensitivity of various cardiorespiratory and thermoregulatory parameters for real time physical fatigue monitoring in construction sites.

scholarly journals Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

2021 ◽  
Author(s):  
William Romine ◽  
Noah Schroeder ◽  
Anjali Edwards ◽  
Tanvi Banerjee
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental Effort ◽  
Physiological Data ◽  
Cognitive State ◽  
Switching Regression ◽  
Longitudinal Case Study ◽  
Time Duration

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

scholarly journals Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

2021 ◽  
Author(s):  
William Romine ◽  
Noah Schroeder ◽  
Anjali Edwards ◽  
Tanvi Banerjee
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental Effort ◽  
Physiological Data ◽  
Cognitive State ◽  
Switching Regression ◽  
Longitudinal Case Study ◽  
Time Duration

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

Perceived Social Support and Heart Rate Variability: An Integrative Review

2021 ◽  
pp. 019394592110289
Author(s):  
Madison P. Goodyke ◽  
Patricia E. Hershberger ◽  
Ulf G. Bronas ◽  
Susan L. Dunn
Keyword(s):  
Social Support ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Response ◽  
Health Care Professionals ◽  
Acute Stress ◽  
Perceived Social Support ◽  
Included Study ◽  
Integrative Review ◽  
Stress Buffering

The purpose of this integrative review is to explore and synthesize literature about the relationship between perceived social support and cardiac vagal modulation, measured by heart rate variability (HRV), during phases of an acute stress response to assess this potential relationship underlying the stress-buffering effects of perceived social support. A systematic search of seven databases was conducted, including MEDLINE, CINAHL, PsychINFO, Embase, ProQuest, medRxiv, and clinicaltrials.gov. Eight studies met the inclusion criteria and were systematically synthesized. A quality appraisal was completed for each included study. Majority of studies focused on time and frequency domain measures of HRV thought to reflect parasympathetic modulation of heart rate and identified them as positively associated with perceived social support during rest, stress induction, and recovery from an acute stressor. Results highlight the importance for nurses and other health care professionals to assess patients’ perceived social support, as increased perceived social support may contribute to an adaptive stress response.

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

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