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Objectives: This case intended to explore the effectiveness of cognitive behavioral approach via electronic means in treating symptoms associated with Panic Disorder and pre-occupation with health-related concerns. Design of the study: It's a single-case study design for an in-depth understanding of client and the disorder dynamics. Place and Duration of the study: The case study was done via electronic means during Covid-19, from June 2020 to October 2020 in Lahore, Pakistan. Sample and Method: Case study was done on a 24 years old female having Panic Disorder, along with sub-threshold features of Illness Anxiety Disorder. Psycho-diagnostic interview (DSM-V Criteria), CBT assessment form, Panic Disorder Checklist, HFD and TAT used in the initial phase for case conceptualization. Further, techniques from the Cognitive Behavioral Therapy were used for the client's catastrophic thoughts and cognitive distortions to alter her overestimation of threat in reducing her panic attacks. Cognitive Behavior Therapy techniques such as thought reconstruction, grounding techniques along with mindful relaxation techniques also helped the client gain control over her anxious thinking process and pre-occupation with health. Results and Conclusion: CBT techniques helped improve the client's overall functioning, panic attacks were eliminated and preoccupation with health was reduced. The results and client's recovery established that Cognitive Behavior Therapy via online means is an effective approach to treat Panic Disorder and negative thinking process.
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Many researchers attribute factors of identity, acculturation, sexual orientation, and substance use and other maladaptive behaviors as potential risk factors associated with increased mental illnesses reported by college students (Castillo & Schwartz, 2013;Wyatt & Oswalt, 2013). [...]the COVID-19 pandemic has further exacerbated mental health problems in college students (Lopez Steinmetz et al., 2021;Son et al., 2020), with 71% experiencing more stress, anxiety, problems with concentration, and worry about a loved one's health as well as their own (Son et al., 2020). Approximately 75% of first-year college students with any mental illness do not seek help (Bruffaerts et al, 2019). [...]there is a growing need to address possible factors contributing to reduced help seeking and to identify vulnerable groups in college populations (Castillo & Schwartz, 2013). Men's decreased treatment utilization may be in part due to shame, stigma, and parental and peer norms toward seeking out mental health services (Seehuus et al, 2021). Given the impact that gender-related stressors play in the rise of mental illness in college students, this study aims to bridge the gap in mental health clinicians' and researchers' understanding of how diverse gender identities influence mental health-related outcomes and mental illness. [...]this study addresses two research questions: (1) Do gender differences (i.e., between cisgender men, cisgender women, and TGNC individuals) exist among college students in the prevalence of mental illness, utilization of psychological services, and mental health-related outcomes (i.e., psychological distress, stress, resilience, overall health, and loneliness)? (2) Do mental health-related variables differentially predict mental illness diagnoses among college students? METHOD Participants and Data Collection The ACHA (2020) National College Health Assessment-Ill Fall 2020 (NCHA-III) is a survey that gathers information regarding students' health.
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Stress and mental health have become a global concern as a result of the COVID-19 pandemic. This review highlights the technologies applied to stress monitoring with up-to -date literature on psychological and behavioural evaluation for stress detection. The working principle and potential of these stress identifiers, particularly physiological signals are explored. Researchers have been directing their interest in producing reliable and wearable devices to detect and prevent stress and panic attacks. The breakthrough of biochemical-near-infrared stress monitoring devices will facilitate medical practitioners in the early detection of stress and panic attacks. The review seeks to explore the types of methods used to detect stress as well as the pros and cons of the available technology while providing a new solution for future implications of stress monitoring detection devices. [ FROM AUTHOR] Copyright of IIUM Medical Journal Malaysia is the property of International Islamic University Malaysia, Faculty of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The purpose of this qualitative phenomenological study was to explore the lived postdeployment experiences of U.S. women veterans with posttraumatic stress disorder (PTSD) and their perception of the impact their diagnosis had on their immediate families. The theoretical framework was Heider's causal attribution theory. The first two research questions directly addressed the purpose of the study, and the third research question addressed each participant's experience with accessing mental healthcare services. NVivo was used to transcribe, code, and analyze data derived from semistructured interviews with six U.S. women veterans who were diagnosed with PTSD by a licensed mental health professional postdeployment. Findings emerged PTSD diagnosis, U.S. women veterans experienced effects of PTSD on themselves and their immediate family members, access challenges to mental health care services. Eight subordinate themes (codes) also emerged history of traumatic events during active duty (i.e., sexual assault);communicative challenges, elevated verbal exchanges/angry outbursts with immediate family members;lengthy process of claims to begin Veterans Affairs mental health services, prescription medication to treat PTSD symptoms;denied mental health care services due to peacetime service;desire for improved U.S. women veterans' mental health care services;anxiety/panic attacks;and difficulty rescheduling therapy sessions and/or distractible teletherapy sessions due to Covid-19 pandemic. The positive social change implications include increasing awareness for the need of rapid response and access to mental health care services for U.S. women veterans living with PTSD. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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PurposeThe crude oil market has experienced an unprecedented overreaction in the first half of the pandemic year 2020. This study aims to show the performance of the global crude oil market amid Covid-19 and spillover relations with other asset classes.Design/methodology/approachThe authors employ various pandemic outbreak indicators to show the overreaction of the crude oil market due to Covid-19 infection. The analysis also presents market connectedness and spillover relations between the crude oil market and other asset classes.FindingsOne of the essential findings the authors report is that the crude oil market remains more responsive to pandemic fake news. The shock of the global pandemic panic index and pandemic sentiment index appears to be more promising. It has also been noticed that the energy trader's sentiment (OVX and OIV) was measured at a too high level within the Covid-19 outbreak. Volatility spillover analysis shows that crude oil and other market are closely connected, and the total connectedness index directs on average 35% contribution from spillover. During the initial growth of the infection, other macroeconomic and political events remained to favor the market. The second phase amidst the pandemic outbreak harms the global crude oil market. The authors find that infectious diseases increase investor panic and anxiety.Practical implicationsThe crude oil investors' sentiment index OVX indicates fear and panic due to infectious diseases and lack of hedge funds to protect energy investments. The unparalleled overreaction of the investors gauged in OVX indicates market participants have paid an excessive put option (protection) premium over the contagious outbreak of the infectious disease.Originality/valueThe empirical model and result reported amid Covid-19 are novel in terms of employing a news-based index of the pandemic, which are based on the content analysis and text search using natural processing language with the aid of computer algorithms.
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This chapter discusses a panoply of practical ways to make the work of therapists more ecologically sustainable. It points out that with growing eco-anxiety worldwide, the need for sensitivity to ecological dynamics is increasing. Drawing on the psychological literature, the chapter identifies pragmatic steps therapists can take, such as integrating plants into therapeutic settings. It also touches upon one specific outcome of the COVID-19 crisis: the vast increase in videoconferencing, which has also been used in therapy. With the rise of eco-anxiety, therapists can anticipate seeing an increase of clients concerned about the state of the environment. Eco-anxiety is, put simply, "a specific form of anxiety relating to stress or distress caused by environmental changes and our knowledge of them". Some symptoms include panic attacks, insomnia, obsessive thinking, appetite changes, and anxiety about the future of the planet. The chapter covers ways that therapists (and anyone who works in an office) can make their settings more eco-sensitive to meet the needs of their clients and the planet. It concludes with suggestions for maintaining an eco-conscious practice during a pandemic. The chapter discusses the importance of both the office environment and the crucial role of nature in design. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Cyberchondria is one of the major concern now-a-days. Cyberchondria refers to the excessive and repeated digital health-related searching of information resulting in increased in anxiety. Such searches are compulsive and obsessional in nature. Amid the COVID-19 pandemic, cyberchondria became regular among population, as they surfed internet sites related to COVID-19 symptomatology, management etc. Surfing for digital information about symptoms and illnesses on the Internet is very common and very beneficial. However, now-a-days it is seen that this online surfing is distressing population making them more prone to hypochondriasis, panic attacks, anxiety and non-compliance with treatment. Cyberchondria management is the major among health care professionals.
ABSTRACT
The purpose of this qualitative phenomenological study was to explore the lived postdeployment experiences of U.S. women veterans with posttraumatic stress disorder (PTSD) and their perception of the impact their diagnosis had on their immediate families. The theoretical framework was Heider's causal attribution theory. The first two research questions directly addressed the purpose of the study, and the third research question addressed each participant's experience with accessing mental healthcare services. NVivo was used to transcribe, code, and analyze data derived from semistructured interviews with six U.S. women veterans who were diagnosed with PTSD by a licensed mental health professional postdeployment. Findings emerged PTSD diagnosis, U.S. women veterans experienced effects of PTSD on themselves and their immediate family members, access challenges to mental health care services. Eight subordinate themes (codes) also emerged history of traumatic events during active duty (i.e., sexual assault);communicative challenges, elevated verbal exchanges/angry outbursts with immediate family members;lengthy process of claims to begin Veterans Affairs mental health services, prescription medication to treat PTSD symptoms;denied mental health care services due to peacetime service;desire for improved U.S. women veterans' mental health care services;anxiety/panic attacks;and difficulty rescheduling therapy sessions and/or distractible teletherapy sessions due to Covid-19 pandemic. The positive social change implications include increasing awareness for the need of rapid response and access to mental health care services for U.S. women veterans living with PTSD. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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BACKGROUND: Both the global COVID-19 pandemic situation, as well as the current political situation in Eastern Europe may exacerbate anxiety and contribute to stress-related disorders such as panic disorder. Electroencephalography (EEG)-based neurofeedback provides both assessment of the subject's brainwave activity as well as the possibility of its therapeutic correction. It is possible that it can be implemented as an auxiliary treatment in panic disorders of different origin. The aim of this feasibility study was to demonstrate (both short- and long-term) effectiveness of neurofeedback therapy in a patient with previously diagnosed panic attacks, related to fear of COVID-19 infection. METHODS: We report the case study of a 47-year-old man affected by panic attacks, related to his profound, constant fear of COVID-19 infection and its sequelae. For the initial diagnostic workup, several clinical and research tools were used: 1. Baseline psychological exam; 2. Anxiety-targeted interview performed by miniQEEG therapist; 3. Analysis of previous clinical test results (EEG record/lab blood test); and 4. The miniQEEG exam (central strip recording Cz-C3-C4), The patient was subjected to regular EEG Neurofeedback sessions for two consecutive months. After completing the treatment, follow-up tests, as listed above were repeated immediately after completing the whole treatment program, as well as 1 and 2 years later. MiniQEEG results were compared with healthy control (age-matched male subject not affected with panic attacks) and evaluated over the time that the subject was involved in the study. RESULTS: Initially, the patient was suffering from severe panic attacks accompanied by vegetative symptoms and from destructive and negative thoughts. After 8 consecutive weeks of treatment encompassing sixteen QEEG neurofeedback training sessions (each lasting 30 min), a subjective improvement of his complaints was reported. More importantly, QEEG records of the patient also improved, approximating the pattern of QEEG recorded in the healthy control. CONCLUSION: In this single case-based feasibility analysis, we demonstrate that systematic application of QEEG-Neurofeedback may result in manifest and durable therapeutic effect. Of note, use of this treatment may be a valuable option for patients with panic attack/panic disorder, especially if related to the psychological burden of the COVID-19/war era. Future studies on a larger patient population, especially with a longitudinal/prospective design, are warranted.
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As COVID-19 crisis is settling down in countries, whether or not a person has been affected personally by the disease, he fights with issues such as anxiety, panic attacks, grief, low mood, and many other psychotic disorders. Mental fitness is one of the major strengths in the development of the individual. Development of social sites turns out to be one platform where the person feels free to vent out their thoughts and to easily interact with people. Extracting useful information from those posts is a part of sentimental analysis, which is the technique of machine learning that helps to know the mental condition of the individual. In this paper, various machine learning algorithms such as random forest, Naive Bayes, decision tree, multilayer perceptron, maximum entropy, KNN, gradient boosted decision tree, adaptive boosting, bagged logistic regression, tree ensemble model, Liblinear, convolutional neural network, and long short-term memory are applied on the dataset, and different mathematical scales such as accuracy, precision, recall, and F1 score concluded that bagged logistic regression has given the better accuracy results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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[...]death anxiety has been proposed to be a transdiagnostic construct, underpinning numerous mental health conditions (for a review, see Iverach, Menzies, & Menzies, 2014). [...]the underlying fear of death itself remains unaddressed by current gold standard treatments for mental health conditions. [...]recent research found that death anxiety is associated with how many disorders are experienced prior to current disorder onset, at least for obsessive-compulsive disorder (Menzies, Zuccala, Sharpe, & Dar-Nimrod, 2020b) Despite the importance of targeting death anxiety in treatment, at present, no self-help treatments for this construct exist. [...]to the best of the authors' knowledge, only two published therapist manuals exist, each of which only specifically address treating death anxiety in a single brief chapter (Furer, Walker, & Stein, 2007;Willson & Veale, 2009).
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Disasters around the world have adversely affected every aspect of life and panic-health of stranded persons is one such category. An effective and on-time evacuation from disaster-affected areas can avoid any panic-related health problems of the stranded persons. Although the nature of disasters differ in terms of how they occur, the evacuation of stranded persons faces approximately same set of issues related to the communication, time-sensitive computation and energy efficiency of the devices operated in the disaster-affected areas. In this paper, a cyber physical system (CPS) is proposed that takes into account various challenges of the disaster evacuation, so an efficient on-time and orderly evacuation of stranded panicked persons could be realized. The system employs fog-assisted mobile and UAV devices for time-sensitive computation services, data relaying and energy-aware computation. The system uses a fog-assisted two-factor energy-aware computation approach using data reduction, which enables the energy-efficient data reception and transmission (DRecTrans) operations at the fog nodes and compensates to extend the period for other functionalities. The data reduction at fog devices employs Novel Events Identification (NEI) and Principal Component Analysis (PCA) for detecting consecutive duplicate traffic and data summarization of high dimensional data, respectively. The proposed system operates in two spaces: physical and cyber. Physical space facilitates real-world data acquisition and information sharing with the concerned stakeholders (stranded persons, evacuation teams and medical professionals). The cyber space houses various data-analytics layers and comprises of two subspaces: fog and cloud. The fog space helps in providing real-time panic-health diagnostic and alert services and enables the optimized energy consumption of devices operate in disaster-affected areas, whereas the cloud space facilitates the monitoring and prediction of panic severity of the stranded persons, using a conditional probabilistic model and seasonal auto regression integrated moving average (SARIMA), respectively. Cloud space also facilitates the disaster mapping for converging the evacuation map to the actual situation of the disaster-affected area, and geographical population analysis (GPA) for the identification of the panic severity-based critical regions. The performance evaluation of the proposed CPS acknowledges its Logistic Regression-based panic-well being determination and real-time alert generation efficiency. The simulated implementation of NEI and PCA depicts the fog-assisted energy efficiency of the DRecTrans operations of the fog nodes. The performance evaluation of the proposed CPS also acknowledges the prediction efficiency of the SARIMA and disaster mapping accuracy through GPA. The proposed system also discusses a case study related to the pandemic disaster of coronavirus disease 2019 (COVID-19), where the system can help in panic-based selective testing of the persons, and preventing panic due to distressing period of COVID-19 outbreak.
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In the current scenario of the COVID-19 pandemic and worldwide health emergency, one of the major challenges is to identify and predict the panic health of persons. The management of panic health and on-time evacuation prevents COVID-19 infection incidences in educational institutions and public places. Therefore, a system is required to predict the infection and suggests a safe evacuation path to people that control panic scenarios with mortality. In this paper, a fog-assisted cyber physical system is introduced to control panic attacks and COVID-19 infection risk in public places. The proposed model uses the concept of physical and cyber space. The physical space helps in real time data collection and transmission of the alert generation to the stakeholders. Cyberspace consists of two spaces, fog space, and cloud-space. The fog-space facilitates panic health and COVID-19 symptoms determination with alert generation for risk-affected areas. Cloud space monitors and predicts the person's panic health and symptoms using the SARIMA model. Furthermore, it also identifies risk-prone regions in the affected place using Geographical Population Analysis. The performance evaluation acknowledges the efficiency related to panic health determination and prediction based on the SARIMA with risks mapping accuracy. The proposed system provides an efficient on time evacuation with priority from risk-affected places that protect people from attacks due to panic and infection caused by COVID-19.