Improving Real-Life Estimates of Emotion Based on Heart Rate: A Perspective on Taking Metabolic Heart Rate Into Account

Ecological momentary assessment of the unique effects of trait worry on daily negative emotionality: does arousal matter?

Worry proneness is a transdiagnostic trait that predicts increased negative affect (NA), potentially in the service of preventing negative emotional contrasts. Although discrete types of NA vary along the dimension of arousal, the extent to which trait worry predicts high vs. low arousal forms of NA in daily life is unclear. This distinction has important implications for conceptualising how worry may perturb adaptive emotionality in various disorders. The present study (not pre-registered) aimed to isolate the effects of trait worry on high (N = 88) and low (N = 122) arousal NA in daily life using ecological momentary assessment while controlling for potential physical and psychological confounds. Participants were assessed for trait worry and depressive symptoms at baseline then reported their affect, heart rate, and exercise three times per day for one week. Multilevel models revealed that trait worry predicted both increased high and low arousal NA after controlling for momentary heart rate, daily exercise, and depression. In contrast, baseline depressive symptoms only predicted low arousal NA in daily life. Findings support the contrast avoidance model of worry and suggest that worry is linked to increased state NA in daily life, independent of arousal.

Travel experience in public transport: Experience sampling and cardiac activity data for spatial analysis

The transportation sector has the potential to enable a greener future if aligned with increasing mobility needs. Making public transport an attractive alternative to individual transportation requires real-world data to investigate reasons and indicators of positive and negative travel experiences. These experiences manifest not only in subjective evaluations but also in physiological reactions like cardiac activity. We present a geo-referenced dataset where participants wore electrocardiograms and reported real-time stress, satisfaction, events, and emotions while traveling by tram, train, and bus. An interactive experience map helps to visually explore the data, with benchmark analyses identifying significant stress hot spots and satisfaction cold spots during journeys. Events and emotions in these spots highlight positive and negative travel experiences in an ecologically valid setting. Data on age and self-identified gender provide insights into differences between user groups. Despite including only 44 participants, the dataset offers a valuable foundation for transportation researchers and mobility providers to combine qualitative and quantitative methods for identifying public transportation users' needs.

Accelerometer-based heart rate adjustment for ambulatory stress research

Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task-baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between-subject correlations of aHR with task-baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within-subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.

Physical activity and heart rate in PTSD inpatients: Moderation by custody of a service dog

Posttraumatic stress disorder is associated with autonomic hyperarousal often shown to involve elevated resting heart rate and, simultaneously if somewhat paradoxically, reduced physical activity. Both are risk factors for cardiovascular disease and so may contribute to its elevated prevalence in persons with this diagnosis. Epidemiological studies have observed dog owners to exhibit lower rates of cardiovascular disease. Non-randomized between-group studies have found service dog companionship to be associated with increased physical activity and with lower resting heart rate. A challenge to research in this area is its vulnerability to selection biases in group assignment which could lead to over-estimation of advantages or disadvantages associated with pet or service dog contact and companionship. The current study executed a within-subjects design investigating physical activity and heart rate in a sample of U.S. military Veterans engaged in residential treatment for PTSD and a concurrent service animal training intervention. A mean of 37 days of continuously-recorded day-time physical activity and heart rate were obtained from 45 participants. On days when they had custody of a familiar service-dog-in-training, participants exhibited increased physical activity accompanied by a small reduction in concurrent, activity-adjusted heart rate. Though observed in a specialized context, these short-term findings align with prior observations of lowered risk of cardiovascular disease in association with pet dog ownership. Extended longitudinal designs will be necessary to determine whether such short-term effects truly mediate lowered long-term risk.

Emotional Awareness Is Correlated With Ambulatory Heart Rate Variability: A Replication and Extension

OBJECTIVE

In healthy volunteers, a positive association has previously been observed between emotional awareness (EA), the ability to identify and describe emotional experiences in oneself and others, and resting heart rate variability (HRV), which is dominated by vagus nerve activity. The current study aimed to investigate the EA-HRV association across multiple assessments in a "real-world" ambulatory context in patients with long QT syndrome (LQTS) who are at genetic risk for sudden cardiac death.

METHODS

Participants (157 LQTS patients; Mean Age = 35.1, SD Age = 10.4; 115 women) completed the levels of emotional awareness scale (LEAS) on one occasion, which served as our measure of EA. In an ecological momentary assessment study involving 10 assessments per day over 3 days, multiple 5-minute ECG assessments (mean = 24.6, SD = 5.1) were obtained in each patient using a Holter monitor, from which high-frequency HRV (HF-HRV) was computed on each occasion.

RESULTS

There was a significant positive association between LEAS scores and HF-HRV controlling for biobehavioral covariates. We also detected a similar inverse relation between EA and mean heart rate.

CONCLUSION

These findings suggest that, in patients with a well-defined genetic risk for ventricular arrhythmia and sudden death, the ability to experience emotions in a complex and differentiated way covaries with greater parasympathetic influences on the heart. These findings are consistent with the overlapping neural substrates of EA and HRV and their common contribution to adaptive emotional responding, consistent with the Neurovisceral Integration Model.

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Gastrointestinal tract-based implicit measures for cognition, emotion and behavior

Implicit physiological measures such as heart rate and skin conductance convey information about someone's cognitive or affective state. Currently, gastrointestinal (GI) tract-based markers are not yet considered while both the organs involved as well as the microbiota populating the GI tract are bidirectionally connected to the brain and have a relation to emotion, cognition and behavior. This makes GI tract-based measures relevant and interesting, especially because the relation may be causal, and because they have a different timescale than current physiological measures. This perspective paper (1) presents the (mechanistic) involvement of the GI tract and its microbiota in emotion, cognition and behavior; (2) explores the added value of microbiome-based implicit measures as complementary to existing measures; and (3) sets the priorities to move forward. Five potential measures are proposed and discussed in more detail: bowel movement, short-chain fatty acids, tyrosine and tryptophan, GI tract flora composition, and cytokine levels. We conclude (1) that the involvement of the GI tract in emotion, cognition and behavior is undisputed, (2) that GI tract-based implicit measures are still in a conceptual phase of development but show potential and (3) that the first step to bring this field forward is to start validation studies in healthy humans and that are designed in the context of implicit measurements.

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

How can we better prepare new doctors for the tasks and challenges of ward rounds?: An observational study of junior doctors' experiences

PURPOSE

Ward rounds play a crucial role in the delivery of patient care in inpatient settings, but involve a complex mix of tasks, skills and challenges for junior doctors to negotiate. This study informs the development of high-quality training by identifying the activities that junior doctors perform, and those associated with stress during real-life ward rounds.

MATERIALS AND METHODS

All activities performed by FY1 doctors (n = 60) over 2 ward rounds were coded in real-time by a trained observer using the work observation method by activity timing (WOMBAT). Doctors' heart rate was continuously recorded and non-metabolic peaks in heart rate used as a physiological indicator of stress.

RESULTS

During ward rounds, FY1 doctors commonly engaged in indirect patient care, professional communication, documentation and observation. Very little time was spent on direct patient care (6%) or explicit supervision/education (0.01%). Heart rate data indicated that stress was highest during administrative tasks while interacting directly with patients while stepping out of rounds to complete personal tasks, when answering bleeps and while multi-tasking.

CONCLUSIONS

Training that specifically covers the activities involved, skills required, and challenges inherent in real-life ward rounds may better prepare FY doctors for this complex area of practice.

Effects of Heat Stress on Heart Rate Variability in Free-Moving Sheep and Goats Assessed With Correction for Physical Activity

Heart rate variability (HRV) is the heart beat-to-beat variation under control of the cardiovascular function of animals. Under stressed conditions, cardiac activity is generally regulated with an upregulated sympathetic tone and withdrawal of vagal tone; thus, HRV monitoring can be a non-invasive technique to assess stress level in animals especially related to animal welfare. Among several stress-induced factors, heat stress is one of the most serious causes of physiological damage to animals. The aim of this study was to assess the effects of heat stress on HRV in small ruminants under free-moving conditions. In three experimental periods (June, August, and October), inter-beat intervals in sheep and goats (three for each) in two consecutive days were measured. HRV parameters were calculated from the inter-beat interval data by three types of analyses: time domain, frequency domain, and non-linear analyses. The temperature–humidity index (THI) was used as an indicator of heat stress, and vectorial dynamic body acceleration (VeDBA) was calculated to quantify the physical activity of the animals tested. First, we investigated correlations of THI and VeDBA with HRV parameters; subsequently, THI was divided into five categories according to the values obtained (≤ 65, 65–70, 70–75, 75–80, and >80), and the effects of the THI categories on HRV parameters were investigated with and without correcting for the effects of physical activity based on the VeDBA. The results indicated that HRV significantly decreased with increasing THI and VeDBA. For non-linear HRV parameters that were corrected for the effects of physical activity, it was suggested that there would be a threshold of THI around 80 that strongly affected HRV; high heat stress can affect the autonomic balance of animals non-linearly by inducing the sympathetic nervous system. In conclusion, to assess psychophysiological conditions of unrestrained animals by HRV analysis, the confounding effect of physical activity on HRV should be minimized for a more precise interpretation of the results.

Embodied Emotion Recognition Based on Life-Logging

Embodied emotion is associated with interaction among a person’s physiological responses, behavioral patterns, and environmental factors. However, most methods for determining embodied emotion has been considered on only fragmentary independent variables and not their inter-connectivity. This study suggests a method for determining the embodied emotion considering interactions among three factors: the physiological response, behavioral patterns, and an environmental factor based on life-logging. The physiological response was analyzed as heart rate variability (HRV) variables. The behavioral pattern was calculated from features of Global Positioning System (GPS) locations that indicate spatiotemporal property. The environmental factor was analyzed as the ambient noise, which is an external stimulus. These data were mapped with the emotion of that time. The emotion was evaluated on a seven-point scale for arousal level and valence level according to Russell’s model of emotion. These data were collected from 79 participants in daily life for two weeks. Their relationships among data were analyzed by the multiple regression analysis, after pre-processing the respective data. As a result, significant differences between the arousal level and valence level of emotion were observed based on their relations. The contributions of this study can be summarized as follows: (1) The emotion was recognized in real-life for a more practical application; (2) distinguishing the interactions that determine the levels of arousal and positive emotion by analyzing relationships of individuals’ life-log data. Through this, it was verified that emotion can be changed according to the interaction among the three factors, which was overlooked in previous emotion recognition.