Psychological Predictors of Self-reported COVID-19 Outcomes: Results From a Prospective Cohort Study

Background Previous research has shown that psychological factors, such as stress and social support, are associated with greater susceptibility to viral respiratory illnesses and more severe symptoms. During the COVID-19 pandemic there has been a well-documented deterioration in psychological well-being and increased social isolation. This raises questions as to whether those experiencing psychological adversity during the pandemic are more at risk of contracting and/or experiencing COVID-19 symptoms. Purpose To examine the relationship between psychological factors and the risk of COVID-19 self-reported infection and the symptomatic experience of SARS-CoV-2 (indicated by the number and severity of symptoms). Methods As part of a longitudinal prospective observational cohort study, 1,087 adults completed validated measures of psychological well-being during April 2020 and self-reported incidence of COVID-19 infection and symptom experience across the pandemic through to December 2020. Regression models were used to explore these relationships controlling for demographic and occupational factors. Results Greater psychological distress during the early phase of the pandemic was significantly associated with subsequent self-reported SARS-CoV-2 infection as well as the experience of a greater number and more severe symptoms. Conclusions COVID-19 infection and symptoms may be more common among those experiencing elevated psychological distress. Further research to elucidate the mechanisms underlying these associations is needed.

Symptoms COVID 19 Positive Vapers Compared to COVID 19 Positive Non-vapers

Objectives: The purpose of the present study was to assess and describe the severity of symptoms reported by Covid-19 positive patients who vaped (smoked e-cigarettes) when compared to those who did not vape or smoke at the time of the diagnosis of Covid-19. Methods: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between March 1, 2020 and February 28, 2021; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Among the 1734 eligible patients, 289 patients reported current vaping. The cohort of vapers (N = 289) was age and gender matched to 1445 covid-19 positive patients who did not vape. The data analyzed included: date of birth, gender, ethnicity, race, marital status, as well as lifestyle history such as vaping and smoking and reported covid-19 symptoms experienced. Results: A logistic regression analysis was performed separately for each symptom using generalized estimating equations (GEE) with robust variance estimates in order to account for the 1:5 age, sex, and race matched set study design. Patients who vaped and developed Covid-19 infection were more likely to have chest pain or tightness (16% vs 10%, vapers vs non vapers, P = .005), chills (25% vs 19%, vapers vs non vapers, P = .0016), myalgia (39% vs 32%, vapers vs non vapers, P = .004), headaches (49% vs 41% vapers vs non vapers, P = .026), anosmia/dysgeusia (37% vs 30%, vapers vs non vapers, P = .009), nausea/vomiting/abdominal pain (16% vs 10%, vapers vs non vapers, P = .003), diarrhea (16% vs 10%, vapers vs non vapers, P = .004), and non-severe light-headedness (16% vs 9%, vapers vs non vapers, P < .001). Conclusion: Vapers experience higher frequency of covid-19 related symptoms when compared with age and gender matched non-vapers. Further work should examine the impact vaping has on post-covid symptom experience.

Good Self-Rated Health as an Indicator of Personal Capability in Old Age

Self-rated health, or self-assessed health, is based on asking individuals to evaluate their general health status on a four- or five-point scale, with response options ranging from ‘very good’ to ‘very poor’. This simple question has been one of the most frequently used health indicators for decades. In nursing research, the voices, interpretations and understanding of humans, as well as their ability to shape their experiences, are studied through the collection and analysis of primarily qualitative materials that are subjective and narrative in nature. However, knowledge about subjective experiences of health and illness, situated and filtered through the life-world of the individual, can also be sought using other approaches. The aim of this chapter is twofold. Firstly, it aims to outline perspectives on how epidemiology and population-based studies of self-rated health as an indicator of subjective experiences can generate new evidence to solve nursing problems and expand nursing knowledge. Secondly, based on the hypothesis that there is an association between good self-rated health and a person’s capability to master the gains and losses of late life, the chapter also aims to describe how personal capability can be operationalised as self-rated health, given that this seemingly simple question delegates to the individual the task of synthesising, in a single evaluation, the many dimensions that make up the complex concept of health and wellbeing in old age. Although a person’s capabilities are dependent on a large variety of factors, at the individual level, symptom experience, chronic illnesses and functional disability are paramount. Therefore, in this chapter, the focus will be on self-rated health as an indicator of personal capability in the fourth age – the period of late life characterised by illness, frailty, impairment and dependence on others. To study self-rated health during this period of life is especially interesting in that the discrepancy between subjective and objective health seems to increase with age, and older olds tend to rate their health as better than younger olds given the same level of disease and functioning.

Internal Tremors and Vibration Symptoms Among People with Post-Acute Sequelae of SARS-CoV-2: A narrative review of patient reports

To introduce the perspective of patients who have PASC with vibrations and tremors as a prominent component, we leveraged the efforts by Survivor Corps, a grassroots COVID-19 patient advocacy group, to gather information from people in their Facebook group suffering from vibrations and tremors. Survivor Corps collected 140 emails and 450 Facebook comments from members. From the emails, we identified 22 themes and 7 broader domains based on common coding techniques for qualitative data and the constant comparative method of qualitative data analysis. Facebook comments were analyzed using Word Clouds to visualize frequency of terms. The respondents’ emails reflected 7 domains that formed the basis of characterizing their experience with vibrations and tremors. These domains were: (1) symptom experience, description, and anatomic location; (2) initial symptom onset; (3) symptom timing; (4) symptom triggers or alleviators; (5) change from baseline health status; (6) experience with medical establishment; and (7) impact on people’s lives and livelihood. There were 22 themes total, each corresponding to one of the broader domains. The Facebook comments Word Cloud revealed that the 10 most common words used in comments were: tremors (64), covid (55), pain (51), vibrations (43), months (36), burning (29), feet (24), hands (22), legs (21), back (20). Overall, these patient narratives described intense suffering, and there is still no diagnosis or treatment available.

Factors Affecting Quality of Life of Hemodialysis Patients - with Focus on Resilience, Symptom Experience and Self-management

Objectives: The purpose of this study was to investigate the factors affecting quality of life of hemodialysis patients.Methods: As a descriptive study, the data were collected from 172 hemodialysis patients receiving hemodialysis at 4 medical institutions. Collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation analysis and multiple regression.Results: The influential variable of the quality of Life of hemodialysis patients were resilience, symptom experience and monthly income less than 2 million won. These factors explained for 48.7% of the quality of Life of hemodialysis patients.Conclusions: The most ideal method to increase the quality of hemodialysis patients’ lives is to develop an integrated nursing intervention that will increase patients’ resilience and reduce the intensity of symptoms.

Symptom Correlates Using Network Analysis in Pediatric Patients Undergoing Blood and Marrow Transplant

Introduction: Youth undergoing blood and marrow transplantation (BMT) experience significant distress. The widespread and mainstream use of mHealth technologies such as smartphone applications offer a unique opportunity to collect patient-generated health data that can enhance patients' and clinicians' understanding of symptom onset, trajectories, and inter-relationships. Network analysis (NA) is a useful tool that can illuminate inter-relationships between symptoms, leading to better collective understanding of the symptom experiences of these youths. This knowledge can lead to enhanced patient-caregiver interactions/collaborations, treatment management, and potentially support improved inference around adverse clinical outcomes. Objective: To determine the feasibility of using network analysis to evaluate mHealth symptom data in patients undergoing BMT. Aim 1. Estimate the network by creating a graphic model that depicts symptom inter-relationships. Aim 2. Identify influential symptoms in the network by evaluating centrality indices and assessing the stability of edges and centrality results. Methods : Study participants undergoing BMT recorded daily symptoms via a smartphone app from preconditioning through 120 days. Patients report their symptom experience (intensity and distress on a scale of 0-10). NA was conducted on the initial patients (n=3) to evaluate inter-relationships between reported symptoms. Each symptom is represented by a circle (node), while associations (regularized partial correlations coefficients) between symptoms are depicted as lines (edges). Stronger associations among symptoms present as thicker lines in the network and a higher value in the weighted matrix table. Centrality indices identify and quantify symptoms that exert more influence in the network. These symptoms are influential in the network due to their strength (sum of absolute values of its connections with other nodes), betweenness (number of times a node lies on the shortest path between two other nodes), or closeness (the summed average distance of a node to all other nodes). Other centrality measures also exist. Centrality tests aimed to evaluate symptoms for their reported importance to the youth and association of those symptoms with other reported symptoms. The more "central" a symptom, the higher the potential to transmit effects to and from other symptoms in the network. This can make them important foci for intervention. Stability testing was used to assess the network's accuracy. Results/Discussion: Descriptive statistics are summarized in Table 1. The estimated network (Fig. 1) provided details on the eight most often reported symptoms; nausea, tired (intensity and distress), vomiting, pain, mouth pain, and sore throat (intensity). The network shows strong mutual associations (regularized partial correlations) between the intensity and distress of nausea (.596) and being tired (.722). There was also a strong relationship between mouth pain and sore throat (.791). A less strong relationship was noted between nausea intensity and tired intensity (.145), and tired intensity and pain intensity (.187). A slight negative relationship was noted between vomiting intensity and pain intensity (-.086). The centrality indices (Fig. 2) revealed vomiting intensity (-1.917) as the strongest symptom with the highest closeness (-1.715). The symptom with the highest betweenness centrality was equal for both nausea intensity and tired intensity at 1.286. To assess confidence in the network estimation we replicated this test 1000 times (non-parametric bootstrapping, n=1000) on the edge stability and centrality results (Fig. 3). Results indicate areas with wide confidence intervals (instability) especially in the edge between vomiting intensity and mouth pain. Conclusion: It is feasible to use mHealth data from youth who experience symptom distress during BMT. However, efforts to obtain more data is necessary if we hope to make accurate inferences from the data. Future work will focus on enriching data collection, examining clinically important sign and symptom patterns and interrelationships, and to explore feasibility of using mHealth data for individualized/precision care and possible predictive uses. Figure 1 Figure 1. Disclosures Shah: Novartis: Research Funding, Speakers Bureau; GBT: Consultancy, Research Funding, Speakers Bureau; CSL Behring: Consultancy; Guidepoint Global: Consultancy; Alexion: Speakers Bureau; Bluebird Bio: Consultancy; Emmaus: Consultancy; GLG: Consultancy.