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Correlation of the questionnaire technostress level results and feature values obtained at different artifact correction levels.
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Technostress has become an important topic in the scientific literature, particularly in Information Systems (IS) research. Heart rate variability (HRV) has been proposed as a measure of (techno)stress and is widely used in scientific investigations. The objective of the pilot study reported in this paper is to showcase how the preprocessing/cleani...
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... can be attributed to the fact that the SDANN feature is defined as the standard devia- tion of the average RR interval calculated over 5 minute periods [13]; this additional aver- aging smooths out the effect of the artifact corrections. Figure 2 illustrates the influence of the artifact correction level on the correlation coef- ficient between feature values and the self-reported technostress levels. We can observe that changing the correction level results, for two features, in a change of sign of the cor- relation coefficient between raw and corrected data. ...
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... can be attributed to the fact that the SDANN feature is defined as the standard deviation of the average RR interval calculated over 5 min periods [13]; this additional averaging smooths out the effect of the artifact corrections. Figure 2 illustrates the influence of the artifact correction level on the correlation coefficient between feature values and the self-reported technostress levels. We can observe that changing the correction level results, for two features, in a change of sign of the correlation coefficient between raw and corrected data. ...
Citations
... При аналізі ВСР оцінювали такі показники [1,10]: І. Часові: ...
... Висновки та перспективи подальших розробок 1. Встановлено, що в жінок вищий рівень психічної напруженості, ніж у чоловіків, і свідчить про здатність окремих опитаних проявляти "корисну (здорову)" тривожність, яка є складовою природної частини особи. ...
Annotation. The analysis of heart rate variability indicators makes it possible to explain the mechanisms of adaptation of the organism to changing environmental conditions, to changing environmental conditions. The goal isto establish the psychological characteristics of stress, to determine changes in heart rate variability in students of higher medical education under martial law, to provide measures for the correction of psychological features. We examined 54 people – 38 women and 16 men, average age – 23.32±0.08 years, who had the application “Air Alarm” application installed on a mobile device. The level of anxiety was determined by the D. Spielberger – Y. L. Khanin scale and the assessment of heart rate variability rhythm variability using Holter electrocardiogram monitoring (portable system DiaCard 2.0 (Solveig JSC, Kyiv, Ukraine), the indicators of which were presented in the form of mean values and their average error (M±m). In group 1 (38 patients), heart rate variability was determined for 5 minutes twice during the day and once at night. In group 2 (16 people) – for 5 minutes at the beginning, middle and at the end of the "air raid" signal, and the average value of the three indicators was taken. The reliability of differences between groups was assessed using the t-test Student’s t-test. It was found that among the respondents, reactive anxiety was determined in 7.41% of female higher education students, personal anxiety – in women – 11.11%. That is, women have a higher level of mental tension than men. In applicants for higher medical education in the final year under the influence of the “air raid” signal a decrease in the time course of heart rate variability and an increase in LF and LF/HF among the spectral ones indicates the predominance of the sympathetic vegetative nervous system, respectively, a decrease in HF characterizes the suppression in the tone of the parasympathetic regulation of the heart rhythm. An increase in VLF indicates an increase in humoral regulation of heart function. Stress caused by the “air raid” signal statistically significantly increases the heart rate in higher education students of the 2nd group (95.8±3.4 vs. 76.2±2.6 beats/min (p≤0.05)). In 50% of people of the 2nd group during the "air raid" signal, heart rhythm disturbances were detected. Recommendations and suggestions are given in the work, that can be used in the process of psychocorrectional work on to increase stress resistance in martial law, in psychological and counseling practice, in research, and in the educational process.
... Detection of stress may help improve self-awareness and inform the development of effective and timely therapeutic activities (Alberdi et al., 2016). Current stress detection methods include using self-reports, chest-wearable and wearing jacket sensors, which may be intrusive, costly, and impractical (Baumgartner et al., 2019;Castaldo et al., 2015;Pallauf et al., 2011;Park et al., 2018). ...
The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.
... Since the intermediate beat intervals are determined by successive "normal" heartbeats, these intervals are often referred to as "normal to normal interval" (NN interval) or "R to R interval" (RR interval) (ibit.) ( [34] ...
... The relative change from the second to the first measurement was used to interpret the changed stress level (referred to as RMSSD − DIF). As in Baumgartner et al. [34], a chest strap equipped with a heart rate sensor was used for recording. The measurement data was recorded with the Suunto Ambit3 Run heart rate monior. ...
Gamification, i.e., the use of game elements in non-game contexts, aims to increase peoples’ motivation and productivity in professional settings. While previous work has shown both positive as well as negative effects of gamification, there have been barely any studies so far that investigate the impact different gamification elements may have on perceived stress. The aim of the experimental study presented in this paper was thus to explore the relationship between (1) leaderboards, a gamification element which exchanges and compares results, (2) heart rate variability (HRV), used as a relatively objective measure for stress, and (3) task performance. We used a coordinative smartphone game, a manipulated web-based leaderboard, and a heart rate monitor (chest strap) to investigate respective effects. A total of n = 34 test subjects participated in the experiment. They were split into two equally sized groups so as to measure the effect of the manipulated leaderboard positions. Results show no significant relationship between the measured HRV and leaderboard positions. Neither did we find a significant link between the measured HRV and subjects’ task performance. We may thus argue that our experiment did not yield sufficient evidence to support the assumption that leaderboard positions increase perceived stress and that such may negatively influence task performance.
... Besides, current measures do not address some recent issues related to AI and the impact of misinformation because of the poor quality of the information available online [3,13,30,34,40,59,74]. Therefore, more multimethod studies are needed to validate the different dimensions but also to develop new dimensions and strengthen the structure of current measurements [5,14,22,23]. These new dimensions are necessary to better understand the phenomenon and advance current frameworks of technostress [42]. ...
... Finally, a fifth avenue of research is related to the psychological assessment of the phenomenon by the participants in a self-reported instrument. More studies should be conducted to cross-reference this psychological measure with physiological measures [5]. The physiological measures can help explain more variance in the outcomes of technostress such as burnout [70]. ...
Current measures of technostress do not consider some recent techno-stressors generated by information and communications technologies. Such stressors may affect knowledge professionals by challenging their credibility, such as insecurity induced by artificial intelligence and websites that misinform clients. This paper presents the development and validation of a 25-item self-report instrument (Techno-Stressors-Index; eight dimensions) based on established guidelines and including new and adapted dimensions of techno-stressors in a professional context. This study was conducted in four phases using a multimethod approach: qualitative exploratory ia pretest, a final validation using exploratory factor analysis and confirmatory factor analysis, and the assessment of nomological validity by testing the relationship between reactions to techno-stressors and psychological distress. The results confirm the relevance of insecurity induced by artificial intelligence and of websites that misinform clients as contributing factors to the technostress process.
... Again, the NeuroIS field may serve as an example. In this field, methodological contributions related to technostress (i.e., stress caused by the use and ubiquity of digital technologies) have been published, such as a paper on blood pressure measurement and an article on heart rate variability (Baumgartner et al., 2019). ...
In the past decade, brain and autonomic nervous system activity measurement received increasing attention in the study of software engineering (SE). This paper presents a systematic literature review (SLR) to survey the existing NeuroSE literature. Based on a rigorous search protocol, we identified 89 papers (hereafter denoted as NeuroSE papers). We analyzed these papers to develop a comprehensive understanding of who had published NeuroSE research and classified the contributions according to their type. The 47 articles presenting completed empirical research were analyzed in detail. The SLR revealed that the number of authors publishing NeuroSE research is still relatively small. The thematic focus so far has been on code comprehension, while code inspection, programming, and bug fixing have been less frequently studied. NeuroSE publications primarily used methods related to brain activity measurement (particularly fMRI and EEG), while methods related to the measurement of autonomic nervous system activity (e.g., pupil dilation, heart rate, skin conductance) received less attention. We also present details of how the empirical research was conducted, including stimuli and independent and dependent variables, and discuss implications for future research. The body of NeuroSE literature is still small. Yet, high quality contributions exist constituting a valuable basis for future studies.
This pilot study investigated the effects of digital collaboration technologies on heart rate variability (HRV), fatigue, and perceived stress. Experimental data were collected from university students who performed a digital collaboration task in either the metaverse or MS Teams. Heart rate (HR) was measured at baseline and throughout the task using an electrocardiogram-based measurement device (Polar H7 chest strap). HRV data (time domain metrics) and self-reported data were compared during and after the task and between groups. The results show that digital collaboration technologies cause a decrease in parasym-pathetic activity (RMSSD) with higher self-reported stress levels of individuals collaborating in metaverse compared to those working with the videoconferenc-ing tool MS Teams. These results suggest that digital collaboration technologies are related to variations in parasympathetic nervous system activity and perceived stress, suggesting that monitoring autonomic nervous system activity during digital collaboration needs to be considered to counteract symptoms of fatigue or digital stress.
The revolution in technology has impacted the work and personal lives of human beings greatly. While it has introduced the mankind to a more comfortable life, it has brought in the stress too in the form of technostress, the situation where a person fails to cope up with the ever-advancing technology and experiences stress symptoms. The increasing intensity of technostress calls for more research on technostress diving deeper into the causes and coping mechanisms. However, technostress research requires successful and reliable assessment of stress. It has been observed in recent years that biomarkers such as cortisol and salivary alpha amylase are reliable indicators of stress. There are several reports where the researchers have used questionnaires and surveys to assess the technostress, but the number of studies using biomarkers for technostress assessment is limited. It has been established that biomarker assessment is an important complement to the surveys to study the technostress. Here, we summarize the important studies done on technostress using the biomarkers along with the rationale of using these biomarkers.
Annotation. Today, the adaptation of people during the warin Ukraine is an important medical and social problem, and for many it serves as an extreme factor affecting changes in the dynamics of physiological processes. Stress is a universal adaptive reaction that causes changes in the functioning of all body systems. One of the adverse consequences of chronic psychological stress is the development of cardiovascular diseases. The purpose of the work is to systematize and analyze the existing problematic aspects of the influence of stress on the activity of the cardiovascular system and to separate the most substantiated approaches to assessing the effects of stress. From the GoogleScholar, PubMed data bases, 45 recent publications on this issue were selected and reviewed. The analysis of literary sources determines the growing interest in the problem of reactivity of the cardiovascular system to psycho-emotional stress. The impact of stress on the human body can be both positive and negative. When stress is short-lived and very strong, it has a beneficial effect, and, on the contrary, if it is intense, acute and long-lasting, it has an adverse effect. One of the tools for objective assessment of stress is heart rate variability, which is recognized as an indicator of autonomic nervous activity. The work examines the invariance of heart rate variability indicators as indicators of the body's stress resistance in the modern distressed anthropogenic environment. Thus, the determination of changes in the regulation of the activity of the cardiovascular system caused by stress at the initial stages has an important prognostic value regarding the development and prevention of possible cardiovascular complications. Observation of stress-related changes in heart rate variability can be used to objectively assess stress. It is worth emphasizing the predictive value of the heart rate variability assessment method, rather than its physiological interpretation.
Electrocardiography (ECG) offers a lot of information that can be processed to make inferences about levels of arousal, stress, and emotions. One of the most popular measures is the Heart Rate Variability (HRV), a measure of the variation on the heart beats, which is only taken from one heart movement of the cardiac cycle, the R-wave. We explore the other heart movements of the cardiac cycle observed in the ECG with the aim of deriving new proxy measures for stress and arousal to enrich and complement HRV analysis. This article discusses existing approaches, suggests new measurements for stress and arousal detected in an ECG, and examines their potential to contribute new information based on their correlations with two HRV measures.
