BACKGROUND: Patients with primary brain tumors (PBTs) face uncertainty related to prognosis, symptoms, treatment response, and toxicity. The authors of this report examined the direct/indirect relations among patients' uncertainty, mood states, and symptoms.METHODS: In total, 186 patients with PBTs were accrued at various points in the illness trajectory. Data‐collection tools included an investigator‐completed clinician checklist, a patient‐completed demographic data sheet, the Mishel Uncertainty in Illness Scale‐Brain Tumor Form (MUIS‐BT), the MD Anderson Symptom Inventory‐Brain Tumor Module (MDASI‐BT), and the Profile of Mood States‐Short Form (POMS‐SF). Structural equation modeling was used to explore correlations among variables.RESULTS: Participants were primarily white (80%) men (53%) with a variety of brain tumors. They ranged in age from 19 to 80 years (mean ± standard deviation, 44.2 ± 12.6 years). Lower functional status and earlier point in the illness trajectory were associated with greater uncertainty (P < .01 for both). Uncertainty (P < .05), except in the model of “confusion,” and the 5 negative mood states measured by the POMS‐SF were directly associated with symptom severity perceived by patients (P < .01 for all). The impact of uncertainty on perceived symptom severity also was mediated significantly by mood states.CONCLUSIONS: The results from the study clearly demonstrated distinct pathways for the relations between uncertainty‐mood states‐symptom severity for patients with PBTs. Uncertainty in patients with PBTs is higher for those who have a poor performance status and directly impacts negative mood states, which mediate patient‐perceived symptom severity. This conceptual model suggests that interventions designed to reduce uncertainty or that target mood states may help lessen patients' perception of symptom severity, which, in turn, may result in better treatment outcomes and quality of life. Cancer 2013;119:2796–2806. © 2013 American Cancer Society.

Background: Military personnel are confronted with physiological and psychological changes caused by stress and exposure to trauma. Although resilience may be protective against psychopathology, very few studies have explored the relationships between the resilience and coping strategies. The study aims to assess how different coping strategies affect resilience, psychological well-being (PWB), and perceived health among military personnel. Subjects and Methods: This study was a cross-sectional survey. Nuclear, biological, and chemical (NBC) soldiers and nurses in the military medical center were recruited in Taiwan in November 2015. The survey comprised the Brief COPE Scale, Ryff's PWB Scale, and the Resilience Scale for Adults, which examined the relationships among coping strategies, PWB, resilience, and perceived health. Path analysis was applied. Results: We recruited 200 participants (145 male and 177 single) aged 24.6 ± 4.7 years (range, 18–46 years). Resilience (coefficient = 0.60, P < 0.001) and PWB (coefficient = 0.33, P < 0.001) were better when using more approach-oriented coping strategies and fewer avoidant coping strategies, whereas the opposite pattern was seen when using avoidant coping (coefficient = −0.31, P < 0.001 for resilience and coefficient = −.20, P < 0.1 for PWB). PWB significantly predicted perceived health (coefficient = 0.45, P < 0.001). Conclusions: Resilience is higher when positive approach-oriented coping strategies are used, which directly affects PWB, and in turn, predicts better-perceived health. Our conceptual model indicates that interventions designed to promote approach-oriented coping strategies may help military personnel develop improved resilience, PWB, and perceived health status.

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

Purpose: Secondary prevention of coronary artery disease, self‐management behavior, and blood pressure control are important to cardiovascular event prevention and promotion of quality of life (QOL), but they are underutilized. The purpose of this study was to investigate the effects of a self‐efficacy theory–based health information technology intervention implemented through blood control and patient self‐management.Design: A clinical randomized waitlist‐controlled trial.Methods: The study was conducted at a medical center in Taipei, Taiwan. A total of 60 subjects were randomly assigned to either the immediate intervention (experimental) group or the waitlist control group. The primary endpoint was systolic blood pressure at 3 months; secondary end points included self‐management behavior and QOL. Treatment for the immediate intervention group lasted 3 months, while the waitlist control group received routine care for the first 3 months, at which point they crossed over to the intervention arm and received the same intervention as the experimental group for another 3 months. Both groups were evaluated by questionnaires and physiological measurements at both 3 and 6 months postadmission. The results were analyzed using generalized estimating equations.Results: Systolic blood pressure significantly improved for the intervention group participants at 3 months, when there was also significant improvement in self‐management behavior and QOL. There was no significant or appreciable effect of time spent in the waitlist condition, with treatments in the two conditions being similarly effective.Conclusion: The use of a theory‐based health information technology treatment compared with usual care resulted in a significant improvement in systolic blood pressure, self‐management behavior, and QOL in patients with coronary artery disease.Clinical Relevance: This treatment would be a useful strategy for clinical care of cardiovascular disease patients, improving their disease self‐management. It also may help guide further digital health care strategies during the COVID‐19 pandemic.