Although the association between teacher-student relations, teacher emotions, and burnout has been proven on a general level, we do not know the exact processes underlying these associations. Recently there has been a call for intra-individual process measures that assess what happens from moment-to-moment in class in order to better understand inter-individual differences in emotions and burnout between teachers. This paper explored the use of process measures of teachers’ heart rate and their interpersonal behavior during teaching. Our aim was to illustrate different ways of analyzing and combining physiological and observational time-series data and to explore their potential for understanding between-teacher differences. In this illustration, we focused on two teachers who represented contrasting cases in terms of their self-reported teaching-related emotions (i.e., anxiety and relaxation) and burnout. We discuss both univariate process analyses (i.e., trend, autocorrelation, stability) as well as state-of-the-art multivariate process analyses (i.e., cross-correlations, dynamic structural equation modeling). Results illustrate how the two teachers differed in the nature of their physiological responses, their interpersonal behavior, and the association between these two process measures over time. Along implications and suggestions for further research, it is discussed how the process-based, dynamic assessment of physiology and interpersonal behavior may ultimately help to understand differences in more general teaching-related emotions and burnout.

Objective: This study utilized a novel multisystem approach to investigate the effect of observed parental behavior on the relationship between biological mechanisms associated with disease processes (i.e., autonomic physiology and immune response) among their adolescent children. Method: Thirty-three adolescents (23 males), aged 11–13, and their parents participated in a laboratory session in which adolescents provided baseline measures of autonomic (sympathetic) activity, and adolescents and 1 parent participated in a laboratory based dyadic conflict resolution interaction task. This included 3 male parent/male adolescent dyads, 20 female parent/male adolescent dyads, 3 male parent/female adolescent dyads, and 7 female parent/female adolescent dyads. Approximately 3 years later, adolescents provided a salivary measure of C-Reactive Protein (sCRP) to index inflammation. Results: Analyses revealed a positive association between sympathetic activity and sCRP, as well as a moderating role of positive parental behavior in this relationship, such that the association between sympathetic activity and sCRP was greater among adolescents whose parents displayed shorter duration of positive affect. Conclusions: Overall findings indicate parental behavior may influence the association between adolescent sympathetic activity and inflammatory processes. These findings have important implications for understanding the impact of psychosocial factors on biological mechanisms of disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The aim of this study is to investigate whether the change in (sub)maximal heart rate after intensified training is associated with the change in performance. Thirty subjects were recruited who performed cardiopulmonary exercise tests to exhaustion 2 weeks before (pre), 1 week after (post) and 5 weeks after (follow-up) an 8-day non-competitive amateur cycling event (TFL). The exercise volume during the TFL was 7.7 fold the volume during the preparation period. Heart rate and cardiopulmonary parameters were obtained at standardised absolute submaximal workloads (low, medium and high intensity) and at peak level each test. Subjects were classified as functionally overreached (FOR) or acute fatigued (AF) based on the change in performance. No differences between FOR and AF were observed for heart rate (P = .51). On total group level (AF + FOR), post-TFL heart rate decreased significantly at low (−4.4 beats·min−1, 95% CI [−8.7, −0.1]) and medium (−5.5 beats·min−1 [−8.5, −2.4]), but not at high intensity. Peak heart rate decreased −3.4 beats·min−1 [−6.1, −0.7]. O2pulse was on average 0.49 ml O2·beat−1 [0.09, 0.89] higher at all intensities after intensified training. No changes in ⩒O2 (P = .44) or the ventilatory threshold (P = .21) were observed. Pearson’s correlation coefficients revealed negative associations between heart rate and O2pulse at low (r = −.56, P < .01) and medium intensity (r = −.54, P < .01), but not with ⩒O2 or any other submaximal parameter. (Sub)maximal heart rate decreased after the TFL. However, this decrease is unrelated to the change in performance. Therefore, heart rate seems inadequate to prescribe and monitor intensified training.

Interpersonal aspects of teaching have repeatedly been linked to teacher emotions and well-being on a general level. However, it is unclear how teachers’ moment-to-moment interpersonal behavior is associated with their physiological arousal during teaching and how this contributes to their lesson-focused emotional outcomes. Eighty secondary education teachers with a mean age of 43.7 years (SD = 11.5) and 13.4 years of teaching experience (SD = 9.7) participated during one real-life lesson. We coded teacher behavior from an interpersonal perspective on the dimensions of agency (i.e., social influence) and communion (i.e., friendliness). Teachers’ physiology (in terms of heart rate) was measured as a proxy for their affective arousal. Teachers differed widely in their behaviors and in how behavior and physiology were associated from moment to moment. Being generally agentic was associated with higher levels of self-reported positive emotions after the lesson, also when being agentic went together with a high heart rate. In contrast, the stronger and the more positively a teacher’s physiological arousal was associated with displaying communal behavior, the more likely a teacher was to report negative emotions. We conclude that combining moment-to-moment data of teachers’ interpersonal behavior and physiological arousal has the potential to explain differences in teachers’ emotional outcomes. Such an approach might ultimately provide teachers and teacher educators with the fine-grained and personalized information needed to foster teacher well-being.

Ambulatory wearable monitoring of human physiology is increasingly utilized in the fields of psychology, movement sciences, and medicine. With the rapid growth of available consumer- and research-oriented wearables, researchers are faced with a multitude of devices to choose from. It is unfeasible timewise for researchers to determine all relevant technical specifications, available signals, signal sampling details, and (raw) data availability, and conduct a search of studies regarding the reliability, validity, and usability of wearables. Thus, selection of wearables for a given study proves highly challenging and will often be unsystematic and uninformed. The 10-year research program Stress in Action initiated a publicly accessible database of wearable ambulatory monitoring devices. We outline the genesis and final structure of the first version of the Stress in Action Wearables Database (SiA-WD) and a summary of the characteristics of the wearables it currently contains. Furthermore, one short-term (2 days) and one long-term (3 months) scenario from the field of stress research are provided with walkthroughs of how the SiA-WD can help select the optimal wearable for a specific research project. Insights gathered include the scarceness of studies testing wearable user-friendliness, inconsistencies in reported validity statistics, and imprecise manufacturer documentation on recorded physiological data such as sampling rate (or window) of signals and parameter extraction. The SiA-WD is the first open-access database to simultaneously include physiological sampling information and technical specifications along with a systematic reliability, validity, and usability search. It will be iteratively expanded to facilitate informed and time-efficient wearable selection. For access to the database, see the following: https://osf.io/umgvp/.

BackgroundEarly detection of elevated acute stress is necessary if we aim to reduce consequences associated with prolonged or recurrent stress exposure. Stress monitoring may be supported by valid and reliable machine-learning algorithms. However, investigation of algorithms detecting stress severity on a continuous scale is missing due to high demands on data quality for such analyses. Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. We aimed to detect laboratory-induced stress using multimodal data and identify challenges researchers may encounter when conducting a similar study.MethodsWe conducted a preliminary exploration of performance of a machine-learning algorithm trained on multimodal data, namely visual, acoustic, verbal, and physiological features, in its ability to detect stress severity following a partially automated online version of the Trier Social Stress Test. College students (n = 42; M age = 20.79, 69% female) completed a self-reported stress visual analogue scale at five time-points: After the initial resting period (P1), during the three stress-inducing tasks (i.e., preparation for a presentation, a presentation task, and an arithmetic task, P2-4) and after a recovery period (P5). For the whole duration of the experiment, we recorded the participants’ voice and facial expressions by a video camera and measured cardiovascular and electrodermal physiology by an ambulatory monitoring system. Then, we evaluated the performance of the algorithm in detection of stress severity using 3 combinations of visual, acoustic, verbal, and physiological data collected at each of the periods of the experiment (P1-5).ResultsParticipants reported minimal (P1, M = 21.79, SD = 17.45) to moderate stress severity (P2, M = 47.95, SD = 15.92), depending on the period at hand. We found a very weak association between the detected and observed scores (r2 = .154; p = .021). In our post-hoc analysis, we classified participants into categories of stressed and non-stressed individuals. When applying all available features (i.e., visual, acoustic, verbal, and physiological), or a combination of visual, acoustic and verbal features, performance ranged from acceptable to good, but only for the presentation task (accuracy up to.71, F1-score up to.73).ConclusionsThe complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. These are difficult to recruit for laboratory setting, due to high time and effort demands on the side of both researcher and participant. Resources needed may be decreased using automatization of experimental procedures, which may, however, lead to additional technological challenges, potentially causing other recruitment setbacks. Further investigation is necessary, with the emphasis on quality ground truth, i.e., gold standard (self-report) instruments, but also outside of laboratory experiments, mainly in general populations and mental health care patients.