Classical methods for eliciting emotional responses, including the use of emotionally-charged pictures and films, have been used to study the influence of affective states on human decision-making and other cognitive processes. Advanced multisensory display systems, such as Virtual Reality (VR) headsets, offer a degree of immersion that may support more reliable elicitation of emotional experiences than less-immersive displays, and can provide a powerful yet relatively safe platform for inducing negative emotions such as fear and anger. However, it is not well understood how the presentation medium influences the degree to which emotions are elicited. In this study, emotionally-charged stimuli were introduced via two display configurations – on a desktop computer and on a VR system –and were evaluated based on performance in a decision task. Results show that the use of VR can be a more effective method for emotion elicitation when study decision-making under the influence of emotions.
Background Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms. Objective This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD. Methods A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non–English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review. Results We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling. Conclusions All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models.
Objective We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate. Background PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown. Method Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case. Results Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified. Conclusion Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events. Application These findings show promise for future work to detect the onset of PTSD symptoms.
A patient portal is a secure online tool that provides patients with convenient access to personal health information. Such information may include test results, notes written by a healthcare provider, or a list of prescribed medications. The system can also be used to communicate with a healthcare provider, schedule appointments and upload or record health documents. While there seems to be an increase over the last decade in patient activation of individual portal accounts, less than half of patients repeatedly utilize its functions (Ralston, 2007; Ancker, 2010). The aim of this research was to evaluate previous studies on patient portals and to derive user and functional requirements for such portals. For this effort, we conducted a series of semi-structured prospective interviews with variety of patients to derive objective assessments and user requirements for an effective and usable patient portal. A group of 10 participants were recruited from students and staff at Texas A&M University (age M = 34, age STD = 14). Participants were asked about the prevalence and context of the usage of patient portals and their expectations from such system in terms of functionality. The findings from the literature review and patient interviews were used to conduct a Functional Information Requirements analysis to derive a set of objective requirements for patient portals. The information requirements were presented in three stages. The first stage is the high-level functionality that is expected. These include communication with the clinic, documenting and accessing medical history, scheduling, accessing general medical information, security, uploading documents, paying bills, notifications, and help feature. The high-level functions were further decomposed into more specific low-level functions, for example, the method of communication between the patient and provider such as texting option and online messaging system incorporating chatting option, emailing option, and recording audios. The third stage is the information requirements that identify specific pieces of information that user should input or systems should provide as feedback (Scott & Sasangohar, 2009). For the communication function this may include patient's contact information for the texting option or patient's email address for online messaging system. The information requirements are a set of objectively derived design-independent requirements that can be used to evaluate current patient portals against patient expectations. Work is in progress to evaluate several major patient portal systems for variety of large hospitals in Texas. Combined with heuristic analysis and usability testing, information requirements analysis shows promise in understanding user needs and improving patient portal adoption and usability.
The workplace environment for nurses is highly stressful, with long working hours (3 or more 12-hour shifts) and a dynamic workload that may induce fatigue. These factors reduce nurses’ efficiency and may contribute to medical errors. The Institute of Medicine (IOM) estimates that in the United States (U.S.) 100,000 deaths are caused by preventable medical errors (Kohn et al., 2002). In the U.S. Intensive Care Units (ICU) alone, 1.7 errors per patient per day are reported (Donchin et al., 1995; Wu et al., 2002). Moreover, it is documented in the literature that stress and fatigue are two important factors that contribute to medical errors in nursing (Wu et al., 2002). Factors that affect nurses’ stress and fatigue in the workplace are also well documented (e.g., Foxall et al., 1990; Sawatzky, 1996; Erlen & Sereika, 1997; Meltzer & Huckabay, 2004; McHugh et al., 2011). In previous studies, Khamisa et al., (2016) conducted a longitudinal study of 277 nurses from four hospitals in South Africa. Findings revealed health workforce wellbeing is not prioritized and mostly lacking with existing policies failing to address psychosocial stressors among nurses. The authors suggested the need for further studies using biomarker assessments and other cellular variables to investigate the health impact of stress, burnout and job satisfaction. In recent years, the advancement in technology has made wearable tools such as smartwatches easily accessible and widely used (Jovanov, 2015). Despite these advances, there are no validated intervention, continuous monitoring systems or tools to mitigate ill effects of stress and fatigue among nurses in critical care areas such as the ICU (Khanade et al., 2017). It is evident in the literature that accurate detection of stress and fatigue levels remains a research gap; one explanation for such gap would be that such tools could potentially be intrusive and interrupt an already complex task of working in a critical care area. In spite of the challenges, a system that provides continuous monitoring and alerts regarding abnormal physiological reactions might help in increasing nurses’ awareness regarding personal responses to their tasks and environment and may contribute to improved patient safety and nurses’ well-being. By making this information visible, these systems may also help nurse managers and administrators to improve work environment practices to reduce stressful tasks and reduce effects of fatigue and stress on their nurses. This current study focus is to address the research gap of accurate detection of stress and fatigue levels. A smart wearable system is being designed to help nurses who experience high levels of stress and workload at work. This paper documents the systematic process of deriving information requirements from two focus groups conducted with delivery care nurses and nurse managers working in various Southeastern Texas hospitals. A focus group was conducted to obtain a more in-depth understanding of nurses’ expectations of a tool that can help with periods of high-stress and fatigue as well as some of the problems nurses face in their daily work life. The second focus group was conducted to inform the design of an information display for nurse managers to monitor the ICU unit’s status in terms of collective stress and fatigue levels. A moderator and two co-moderators led the focus group interviews. Previously formulated questions were presented to the group to guide the discussions. There were 13 questions followed by probing questions to obtain more information or clarification. Questions were organized into four groups to investigate 1) participants’ task/roles, 2) situations where high-levels of stress/fatigue are experienced and their effects on performance, 3) expectation from a tool to help in those instances, and 4) specific expectations from a smart-watch (or supervisory-control) interface. The feedback from participants was documented as FIRs. The FIR method provides a set of design-independent requirements that can be used as objective assessment of needs for displays. Additionally FIRs served to inform the design of a smartwatch-based tool for nurses and supervisory-level interface for nurse managers. While the overall findings from the focus groups are discussed in the paper, the FIRs are out of the scope of this short paper and will be reported elsewhere. The study also sought to determine how the use of technology could assist nurses during the periods of high stress and/or workload.
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