Stress Detection on Social Network: Public Mental Health Surveillance: Public mental health surveillance

Many nations of the world struggle with the COVID-19 pandemic, as the disease causes wide sweeping changes to society and the economy. One of the consequences of the pandemic is its effect on mental health stress. Gauging stress levels at scale is challenging to implement, as traditional methods require administrative labour and time. However, a combination of supervised Machine Learning (ML) and social media analytics could provide a faster and aggregated way to detect the stress levels of a population. This study investigates the potential clinical usage of ML practices for detecting stress in Twitter content, as a quantitative measure of stress at scale. The stress scores obtained by the models will be compared to the COVID-19 timeline of daily new cases. © 2023 ACM.

Teman Berlindung: Mobile-Based Student Counseling Service Application

The existence of Covid-19, which is sweeping the whole world, has caused Indonesia to make a policy to change the learning system that was previously face-to-face to online. As a result, students are prevented from interacting directly with their friends, making them bored and stressed. Increased stress levels can also be influenced by other factors, such as an unsupportive home environment and the number of tasks assigned with relatively few deadlines. In addition, other problems, from friends and family, are often a burden for students, significantly affecting stress levels and mental health. The data collection method used in this study uses observation and literature studies on object-oriented applications. This study uses the Waterfall method to make information systems in the designed application and Unified Modeling Language as the language used in analyzing needs. The application's design can explain in detail how this application will run, and the appearance of this application will be seen. So, the purpose of designing this application is expected to reduce stress levels by accommodating students who need counselling services. Based on the design of the Teman Berlindung that the author carried out, the author stated that there were many cases where college students had no space to tell stories or just a place to complain. Regarding mental health, Teman Berlindung is expected to be useful not only during the pandemic but can also be used in the long term and help students channel complaints to related parties. © 2022 IEEE.

Stress-Coping Tweets Acquisition: A Two-phase Bootstrapping Method on Patterns and Semantic Features

Stress is integral to biological survival. However, without an appropriate coping response, high stress levels and long-term stressful situations may lead to negative mental health outcomes. Since the COVID-19 pandemic, remote assessment of mental health has become imperative. The majority of past studies focused on detecting users' stress levels rather than coping responses using social media. Because of the diversity of human expression and because people do not usually express stress and the corresponding coping response simultaneously, it is challenging to extract users' tweets about their coping responses to stressful events from their daily tweets. Consequently, there are two goals being pursued in this study: to anchor users' stress statuses and to detect their stress responses based on the existing stressful conditions. In order to accomplish these goals, we propose a framework that consists of two phases: the construction of stress dataset and the extraction of coping responses. Since the stressed users' data are lacking, the first phase is to construct a stress dataset based on stress-related hashtags, personal pronouns, and emotion recognition. In addition, to ensure the collection of enough tweets to observe the coping responses of stressed users, we broadened the survey's scope by collecting all tweets from the same user. In the second phase, stress-coping tweets were extracted by utilizing bootstrapping-based patterns and semantic features. The bootstrapping method was used to enrich word patterns for text expression and the semantic feature to assess the meaning of sentences. The collected data included the tweets of the stressed users identified in Phase 1 and the various coping responses from Phase 2 can contribute to developing a tool for the remote assessment of mental health. The experimental results show that our two-phase method outperforms the baseline and can help improve the efficiency of extracting stress-coping tweets. © 2022 IEEE.

Deep Learning and IoT Based Assistance System for Autism Spectrum Disorder People

During the Covid-19 pandemic situation, the economic and social disruption is devastating. People could not get out of their homes and lead their normal life. Schools across the country have switched to remote learning, which is also inevitable. Though there are some advantages in the online classes, the fact that many of the children suffered mental stress due to these classes could not be denied. Especially, when speaking about the autism people, they could not handle the stress like normal children. The caretaker is necessary to assist them all the time. Therefore, an assistance system is needed to monitor the stress level. In this research, prototype of deep learning and Internet of Things (IoT) based assistance system has been proposed. It monitors the stress parameters namely body temperature, pulse rate, skin conductivity and the facial emotion of the autism disorder people. Further, the hardware model (Raspberry Pi) has been developed to measure the stress level. The processed data from the model has been stored in the Thingspeak cloud platform for monitoring the autism people remotely. From the threshold stress parameters, the level of the stress can be predicted by the proposed stress management algorithm. © 2022 IEEE.

Effects of COVID-19 on Stress and Mental Health of Community College Pre-Engineering Students

This research full paper presents screening rates for mental health issues and life-stress events in engineering-focused community college students during the initial phases of the COVID-19 pandemic in the US. Specifically, it attempts to answer the following research questions: 1) What is the overall rate of various mental health conditions among engineering-focused community college students, 2) What effects has the pandemic had on baseline stress levels engineering-focused community college, and 3) What effects has the pandemic had on quality of life, such as sleep habits and financial security of engineering-focused community college students?Data for this paper was collected via survey from May-July 2020 and includes responses from 84 students at 24 community colleges. The survey itself was a compilation of several widely-used instruments for measuring overall mental health and stress levels in a population. These instruments include the Kessler-6 for psychological distress, the PHQ for anxiety, depression, and eating disorders, the PC-PTSD for PTSD-like symptoms, and the SRRS for inventorying stressful life events.Among the major findings, 32% of respondents reported a major change in financial situation, 27% reported loss of employment, and 13% reported ceasing formal schooling because of the COVID-19 pandemic. Additionally, 32% of respondents reported that the COVID-19 pandemic worsened their housing security situation, 38% reported that COVID-19 has worsened their food security situation, and 36% report that COVID-19 has decreased their ability to access instruction, course materials, or course supplies. Finally, of respondents who completed at least one mental health screening instrument, 70% screened positive for at least one potentially diagnosable condition, while only 9% reported ever receiving a mental health diagnosis. © 2022 IEEE.

Impact of Coherent breathing on academic stress levels during COVID-19 Pandemic in king Abdulaziz University students

Academic stress is a condition that develops when pressures of academic challenging situations are facing students;expressly during COVID- 19 outbreak. Through learning breathing methods, students can manage stress circumstances and attain a high awareness level. Objective: the present study was done to measure the effects of coherent breathing on female and male students' academic stress levels. A pilot study and randomized controlled trail included 100 participant (50 males, 50 females) from King Abdul-Aziz University (KAU), their age ranged from 23 to 25 years old. We evaluated their academic stress levels by Academic Stress Scale "ASS". Once we detected that they have at least slight stress, those students were included and randomly separated to two equal groups, (control and experimental). The control group had their BP, HR and RR recorded at normal breathing. While for the experimental group, we measured their BP, HR and RR before and after the coherent breathing. We had done re-assessment after two weeks of doing this exercise. We instructed them to do it twice a day daily. There was a highly significant reduction in RR in the experimental group after intervention, and there was no significant difference between the academic stress levels pre and post intervention. Coherent breathing has shown a significant change in the ASS in one element, which is the lack of concentration at home/ hostel when studying, between the two studied groups. In addition, for the physiological parameters there was only a highly significant reduction in RR

Heart Rate Variability of Healthcare Workers during Covid-19

The outbreak of Covid-19 has exacerbated the mental health of Healthcare Workers (HCWs), caused by an increase in their stress levels owing to an exponential rise in their workloads. Previous works have revealed visible changes in Heart Rate Variability (HRV), in response to increased/decreased stress levels. This study focused on analyzing HRV as a parameter to observe the impact of higher stress levels, on clinicians, due to the pandemic. Their responses to a Perceived Stress Score (PSS) questionnaire were used as a reference to determine their escalated stress levels. The responses showed that 40% of clinicians revealed increased levels of high chronic stress while the remaining were affected by moderate chronic stress. We computed HRV for each clinician from HR data obtained using a chest-based wearable device during sleep and ward sessions. Through detailed analysis of HRV, we observed clinicians with high chronic stress showed lower HRV when compared to clinicians with moderate chronic stress during both sleep and ward sessions. Later we did a close investigation of their HRV on Day 1 and Day 2 in Covid-IP (Inpatient) and compared the HRV features. Finally, we compared the HRV features of clinicians between Covid-IP Covid-OP (Outpatient) ward sessions. The above study validated that HRV is a reliable parameter for an objective assessment of stress levels. © 2022 IEEE.

The use of mindfulness practices to reduce perceived stress levels in an elementary special education environment

The research and literature behind teaching mindfulness practices to support self-regulation are overwhelmingly favorable-many journals currently available report positive outcomes when using mindfulness practice interventions with research subjects. However, the majority of research now focuses on adult practitioners. This study proposed that elementary special education students would have similar benefits when engaging in the practices, and through learned self-regulation, perceived stress rates would decrease. Students participated in a six-week cycle of mindfulness practice (meditation, positive affirmations, and gratitude journaling), and perceived stress levels were measured prior to and upon the completion of the cycle. The results were favorable as many participants reported positive outcomes in perceived student stress decreasing, and an ability to control one's thoughts and slow them down increasing. The initial PDSA cycle steps have been outlined in this paper;the outcomes have been delineated. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments

Work stress can have serious deleterious effects for individuals and society and therefore its management is of great importance. Work environment has been demonstrated as one of the significant factors effecting work stress. Recently, COVID-19 has led to an increased frequency of individuals working in hybrid work environments mainly comprising of home and office environments. The effects these work environments have on individuals’ mental stress is important to understand for both employers and employees so they can mitigate and effectively manage the mental stress. In this paper, we present an intelligent approach to predict the stress occurrences using the physiological data acquired from individuals working in both remote and office locations. Multiple factors are collected related to physiological indicators of stress and subjective performance level. We developed a boosted tree ensemble model which produced binary stress classification accuracy of 99.9%. The statistical outcomes indicate that there is no overall correlation between mental stress and productivity, however there is some indication of mental stress being is influenced by the work environment, the time of day and the day of the week. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Collective Stress Visualization Enabled by Smart Cushions for Office Chairs

This paper explores the possibility of combining sensory data of multiple individuals into a collective visualization. Using a smart cushion for office chairs that collects several stress-related parameters, namely: heart rate, respiratory rate, and heart-rate variability, individuals’ data can be aggregated into a collective stress visualization. Three different visualizations are designed which ly, grouped and aggregated, and metaphorically visualize the collective stress. Additionally, two more visualizations are explored for the ‘new way of working’ during the COVID-19 epidemic, where people work remotely and from the office. Through expert and user interviews, these visualizations are evaluated. Additionally, there is researched on whether measured heart-rate variability can predict perceived stress levels. The results found an inversed correlation than hypothesized. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Is There a Relationship Between Pilot Stress and Short- and Long- Haul Flights?

Past studies have been conducted to identify whether short-haul (SH) or long-haul (LH) pilots experience a higher level of stress during a single flight. An extensive literature review revealed high stress levels in both groups (i.e., LH pilots were more stressed than SH pilots, and vice versa). To investigate these mixed results, quantitative and qualitative survey data were collected from 49 international commercial airline pilots from various countries in the Asia-Pacific, Europe and in North America. The General Health Questionnaire–12 (GHQ-12) was used to measure the stress levels of pilots during the pandemic. The study found that there was no significant difference between the stress levels of SH pilots compared to the stress levels of medium-, long-, and ultra long-haul pilots. To further investigate stress levels, pilots’ qualitative responses indicated that 75.5% of pilots were impacted by factors related to the COVID-19 pandemic, including increased stress associated with the uncertain future of the aviation industry, and income instability. In summary, this study aims to raise the attention of industry stakeholders such as aviation authorities and airlines of the need for targeted initiatives to support pilots who are most vulnerable to high-stress levelsas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Team Lavender Supports Healthcare Workers: "Our Spiritual, Emotional and Mental Health Matters".

Team Lavender, a coordinated response team addresses the spiritual, emotional, and psychological needs of healthcare workers following adverse events and accumulated stress, including Covid-19. Proven to be a valuable peer-to-peer support team in reducing stress levels. Team Lavender is modeled from Code Lavender in the United States. This article addresses the background to justify the need of Team Lavender, its' significance in a regional acute care setting, and justification for implementing Team Lavender.

COVID-19 Impact on Online Learning: A Statistical and Machine Learning Model Analysis for Stress Detection

The preventive measure to control the outbreak of COVID-19 pandemic compelled the government across the globe to close the educational premises. In order to fill the academic gap, and to follow the prescribed isolation in this outbreak, a shift of physical classroom interaction to virtual space becomes indispensable. This rushed shift is largely affecting the academicians, groups of students, and institutions. Although, students from different backgrounds may have different psychological impacts of online learning experiences depending upon their usage and comfortability with the e-learning technology. Many researchers reported that online learning has resulted in depression and anxiety disorder among students and in due course resulting in increased stress levels. Therefore, it is vital to comprehend and examine the impact of this unexpected shift in the learning environment on students’ psychology and stress levels. There are numerous studies associated with stress identification in controlled laboratory surroundings, but there is inadequate research related to stress measurement in general (Can et al. in J. Biomed. Inform. 92, 2019). The present study is an attempt to explore a variety of predictive analysis and statistical analysis techniques in identification of stress and also to analyze the perceived stress level of students in online learning experience using cross-sectional research. The data collected is analyzed in depth using the regression analysis and the relationship between various factors is established and other valuable insights are demonstrated. The outcome of this research would be helpful for the educational institutes, policymakers, and government bodies to appraise the challenges and the inadequacies of online teaching platforms and their impact on student’s stress levels. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Latent burnout profiles in a sample of frontline healthcare professionals after the peak of the Italian COVID-19 pandemic

Background: In Italy since February 2020, the unexpected massive afflux of COVID-19 patients exposed healthcare professionals to high work-related stress, high time pressure and increased the risk of being infected. This is the first study that aimed to investigate the psychological impact of COVID-pandemic at the end of the peak, by identifying latent burnout profiles in a sample of front-line healthcare professionals that worked in Italy during the peak of the pandemic. Methods: A total of 589 subjects filled in an online ad-hoc questionnaire and the Italian version of Maslach Burnout Inventory - Human Services Survey. Results: A higher presence of burnout profile in healthcare professionals who worked in frontline during the peak of the COVID-19 pandemic was highlighted. Furthermore, those professionals showed significantly higher perceived stress levels, increase of worries, and sleep problems, they were more likely to underline the importance of team spirit and to consider asking for psychological support. A multiple regression analysis revealed that age, managing COVID-19 patients, perceived stress levels, adequacy of training, and considering to ask for psychological support significantly predicted latent burnout profiles. Moreover, perceived stress levels mediate the relationship between those profiles and managing COVID-19 patients. Conclusions: These findings highlight how stressful and damaging the pandemic has been, especially for people directly involved in the care of patients tested positive for COVID-19. Furthermore, it provides evidence for the importance of investing in wellness for healthcare professionals, in order to avoid shortage due to burnout and to guarantee optimal standards of care to all patients © 2022. by the Author(s);licensee Mediterranean Journal of Clinical Psychology, Messina, Italy. This article is an open access article, licensed under a Creative Commons Attribution 4.0 Unported License

The Impact of Mental Health due to Covid 19 - A Mental Health Detector Using Machine Learning

Stress has become a major part of human life during this Covid-19 pandemic due to the various internal and external expectations placed upon their shoulders. In this situation, Covid-19 is a very common and dangerous issue in the entire world, but as a result, people are going to notice the changes in their mental state such as depression, stress, and mood swings due to home quarantine. Thus, our paper will predict the stress levels using several algorithms of Machine Learning like K-Nearest Neighbour, Support Vector Machine, Naïve Bayes and Artificial Neural Networks. © 2022 IEEE.

Machine Learning based model for predicting Stress Level in Online Education Due to Coronavirus Pandemic: A Case Study of Bangladeshi Students

During the pre-pandemic era online education in Bangladesh was not popular and certificates achieved from online education were often discouraged by organizations. However, the scenario has changed a lot within the last one and half years. The covid-19 pandemic force almost all the countries to adapt to new norms in almost every aspect of life and that happened in Bangladesh also, especially in the education sector. Undoubtedly this caused psychological stress to almost every stakeholder of this system. Our paper aims to predict this stress level of students in the context of Bangladesh using machine learning techniques. To conduct the research primary data were collected using google form and after preprocessing the data several prominent supervised classifiers were applied to predict the stress levels of students due to online education. Among these classifiers, the proximity of the Random Forest algorithm was found to play the greatest role in predicting the stress level detection in online classes and the accuracy was 73.91%. © 2021 IEEE.

Personalized Stress Monitoring AI System for Healthcare Workers

In the current COVID-19 pandemic scenario, healthcare workers, in particular nurses, face prolonged exposure to stress. This intense duress takes a toll on their health overtime, affects their quality of life, and in turn impacts the quality of care provided to the patients. Hence, real-time detection and monitoring of stress is extremely important for early detection of stress patterns, prevention of burnouts and chronic conditions in healthcare workers as well as facilitate improved patient-care outcomes. In this paper, we present a proof-of-concept case study using machine learning (ML) and artificial intelligence (AI)-based stress detection model that determines a personalized assessment of stress level using heart rate, heart rate variability, and physical activity of the users. We used wearable electrocardiogram and inertial sensor to record heart activity and physical activity of nurses during their shifts. Our preliminary results indicate that the proposed stress tracking model can effectively predict any stress occurrences. This study is a pivotal attempt to emphasize the significance of stress-detection and relief for healthcare workers and provide them a tool for an effective assessment of personalized stress levels. © 2021 IEEE.

Stress Analysis and Care Prediction System for Online Workers

Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers. © 2021 IEEE.

Effect of Alpha Wave-Containing Music on the Psychological Stress Level of Hospital Staffs and their Cognitive Performance

This study aims to investigate the effect of alpha music therapy that is an affordable, easily implemented, and sustainable method on stress level, cognitive functions, and physiological response of hospital staff amidst the context of the COVID-19 pandemic. The testing group was required to listen to alpha music for two weeks. Stress questionnaires, cognitive tasks, and physiological data were collected before and after the intervention. Blood pressure and heart rate between the two groups do not differ significantly and change after intervention. The increase in PSS scores and fast response time in the Matrix Task of the Control group indicate increasing stress levels, reduced attention, and remembering ability. These results of the Control group explain the high workload at the year-end and COVID-19 outbreak in Vietnam occurring during the second data collection week. In contrast, both the PSS and Respond time measures suggest a positive effect of alpha music on the Testing group. © 2021 ECBIOS 2021. All rights reserved.

Guided Learning Sequences as an e-Learning Enhancer During COVID-19 Emergency Conditions

During 2020, COVID-19 dramatically changed the way in which students receive and analyze information from their teachers, classes have moved from face-to-face sessions into synchronous virtual meetings and asynchronous homework, increasing stress levels in students. This Evidence-based Practice paper explores Guided Learning Sequences as a content delivery strategy which allows the student to receive information, think about its meaning, put it into practice, and receive instantaneous feedback in order to reinforce their learning process. Empirical evidence from 108 students in a Business Mathematics course shows a statistically significant decrease of students' stress level when exposed to the proposed methodology. As well, a pre-test post-test analysis of a sample of 45 of those students shows evidence of positive impact in student's performance. © American Society for Engineering Education, 2021