Browsing by Subject "sleep disturbance"
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Item Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations(MDPI, 2023-01-09) Ikram, Mohammad; Shaikh, Nazneen F.; Vishwanatha, Jamboor K.; Sambamoorthi, UshaDuring the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income >/= $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.Item The SPADE symptom cluster and physical disability in chronic low back pain patients(2019-12) Hendrix, Zachary N.; Cross, Deanna S.; Licciardone, John C.; Kearns, Cathleen; Mathew, Stephen O.Introduction: Chronic pain is a major healthcare issue. It is debilitating and often occurs simultaneously with other health issues (Murray et al., 2013; Shmagel et al., 2016). The SPADE symptom cluster (sleep disturbance, pain interference, anxiety, depression, and low energy/fatigue) is common in chronic low back pain (cLBP) patients and may interact with their disability (Alamam et al., 2019; Davis et al., 2016; Tavares et al., 2019). Methods: This cross-sectional study utilized data from the Pain Registry for Epidemiological, Clinical, and Interventional Studies and Innovation (PRECISION). The PROMIS-29 v2.0 was used to assess SPADE symptoms, and the Roland-Morris Disability Questionnaire was used to measure disability. The Spearman-Rho correlation between each SPADE symptom and disability was calculated. The correlations were then tested for significant differences and ranked in order of strongest to weakest correlation. Lastly, groups were assigned based on the number of presenting symptoms and tested for between-groups differences in mean disability. Results: Each of the five SPADE symptoms and the composite SPADE score were all positively and significantly correlated with disability. Pain Interference was most strongly correlated with disability. SPADE comorbidity was related to disability. Conclusion: SPADE symptoms greatly increase disability in chronic low back pain patients.