Usha Sambamoorthi, Ph.D.
Permanent URI for this communityhttps://hdl.handle.net/20.500.12503/31896
Associate Professor, Pharmacotherapy
UNT System College of Pharmacy
Email: Usha.Sambamoorthi@unthsc.edu
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Browsing Usha Sambamoorthi, Ph.D. by Subject "Adult"
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Item A Multi-Level Analysis of Individual and Neighborhood Factors Associated with Patient Portal Use among Adult Emergency Department Patients with Multimorbidity(MDPI, 2023-01-22) Wang, Hao; Shen, Chan; Barbaro, Michael; Ho, Amy F.; Pathak, Mona; Dunn, Cita; Sambamoorthi, UshaBACKGROUND: Patient portals tethered to electronic health records (EHR) have become vital to patient engagement and better disease management, specifically among adults with multimorbidity. We determined individual and neighborhood factors associated with patient portal use (MyChart) among adult patients with multimorbidity seen in an Emergency Department (ED). METHODS: This study adopted a cross-sectional study design and used a linked database of EHR from a single ED site to patients' neighborhood characteristics (i.e., zip code level) from the American Community Survey. The study population included all adults (age > 18 years), with at least one visit to an ED and multimorbidity between 1 January 2019 to 31 December 2020 (N = 40,544). Patient and neighborhood characteristics were compared among patients with and without MyChart use. Random-intercept multi-level logistic regressions were used to analyze the associations of patient and neighborhood factors with MyChart use. RESULTS: Only 19% (N = 7757) of adults with multimorbidity used the patient portal. In the fully adjusted multi-level model, at the patient level, having a primary care physician (AOR = 5.55, 95% CI 5.07-6.07, p < 0.001) and health insurance coverage (AOR = 2.41, 95% CI 2.23-2.61, p < 0.001) were associated with MyChart use. At the neighborhood level, 4.73% of the variation in MyChart use was due to differences in neighborhood factors. However, significant heterogeneity existed in patient portal use when neighborhood characteristics were included in the model. CONCLUSIONS: Among ED patients with multimorbidity, one in five adults used patient portals. Patient-level factors, such as having primary care physicians and insurance, may promote patient portal use.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 Association of Mobile Health Applications with Self-Management Behaviors among Adults with Chronic Conditions in the United States(MDPI, 2021-09-30) Wang, Hao; Ho, Amy F.; Wiener, R. Constance; Sambamoorthi, UshaBACKGROUND: Mobile applications related to health and wellness (mHealth apps) are widely used to self-manage chronic conditions. However, research on whether mHealth apps facilitate self-management behaviors of individuals with chronic conditions is sparse. We aimed to evaluate the association of mHealth apps with different types of self-management behaviors among patients with chronic diseases in the United States. METHODS: This is a cross-sectional observational study. We used data from adult participants (unweighted n = 2340) of the Health Information National Trends Survey in 2018 and 2019. We identified three self-management behaviors: (1) resource utilization using electronic personal health records; (2) treatment discussions with healthcare providers; and (3) making healthcare decisions. We analyzed the association of mHealth apps to self-management behaviors with multivariable logistic and ordinal regressions. RESULTS: Overall, 59.8% of adults (unweighted number = 1327) used mHealth apps. Adults using mHealth apps were more likely to use personal health records (AOR = 3.11, 95% CI 2.26-4.28), contact healthcare providers using technology (AOR = 2.70, 95% CI 1.93-3.78), and make decisions on chronic disease management (AOR = 2.59, 95% CI 1.93-3.49). The mHealth apps were associated with higher levels of self-management involvement (AOR = 3.53, 95% CI 2.63-4.72). CONCLUSION: Among individuals with chronic conditions, having mHealth apps was associated with positive self-management behaviors.