Cognition, educational attainment and diabetes distress predict poor health literacy in diabetes: A cross-sectional analysis of the SHELLED study

Relationship between diabetes health literacy, distress, burnout, social support, complications, self-care behaviors, and quality of life among patients with type 2 diabetes: a path analysis study

INTRODUCTION

Improving the quality of life (QOL) is the most important goal of early diagnosis and treatment in patients with type 2 diabetes (T2D). Numerous studies have indicated the positive effects of health literacy, social support and self-care behaviors and the negative effects of diabetes distress and burnout on the QOL of patients with T2D. Understanding these factors is crucial for people with diabetes. However, no study has investigated the simultaneous effects of these variables on QOL. In this study, our goals were to find out how these variables are related to each other, in addition, which variables play the role of mediating variables, and finally, what is the cumulative effect of these variables in predicting the QOL of patients with T2D. So, this study aimed to examine the relationship between diabetes health literacy (DHL), distress, burnout, social support, complications of diabetes, self-care behaviors, and QOL among patients with T2D by application Path analysis method.

METHODS

In this study 929 participants were entered to study by cluster sampling method and finally, data were analyzed among 820 participants. Data were gathered by self-report and with seven tools of Demographic section, DHL Scale, Diabetes distress scale, Diabetes Burnout scale, Diabetes Self-Management Questionnaire (DSMQ), Perceived social support, Diabetes Quality of Life (DQOL) Questionnaire. The software's of SPSS version 24 and AMOS version 24 were used for analysis.

RESULTS

The variables of DHL, social support, diabetes distress, and complications of diabetes predicted 38% variance in diabetes burnout (R2 = 0.38). Greatest impact on diabetes burnout was related to diabetes distress (estimate total effect = 0.539). The variables of DHL, social support, diabetes distress, complications of diabetes, and diabetes burnout predicted 24% variance in self- care behaviors (R2 = 0.24). Greatest impact on self- care behaviors was related to DHL (estimate total effect = 0.354). The variables of DHL, social support, diabetes distress, diabetes burnout, complications of diabetes, and self- care behaviors predicted 49% variance in DQOL (R2 = 0.49). Greatest impact on DQOL was related to variables of diabetes distress (estimate total effect = -0.613), DHL (estimate total effect = 0.225), diabetes burnout (estimate total effect = -0.202), complications of diabetes (estimate total effect = - 0.173), social support (estimate total effect = 0.149), and self -care (estimate total effect = 0.149), respectively.

CONCLUSION

To improve QOL in patients with T2D, health care providers must develop interventions that increase DHL of diabetic. Because DHL can decrease distress and burnout, enhance self -care skills, create supportive networks, and ultimately improve QOL in patients with type 2 diabetes.

Changes in Obesity Prevalence Among U.S. Adults After the COVID-19 Pandemic by State and Territorial Stay-at-Home Order Level and Sociodemographic Characteristics

PURPOSE

To examine changes in obesity prevalence among US adults after the COVID-19 pandemic by level of stay-at-home order and sociodemographic characteristics.

DESIGN

Quasi-experimental study using repeated cross-sectional data.

SETTING

Behavioral Risk Factor Surveillance System (BRFSS).

SAMPLE

Pooled data for US adults ages ≥26 years (n = 1,107,673) from BRFSS (2018-2021).

MEASURES

States/territories were classified into three levels of stay-at-home order: none, advisory/only for persons at risk, or mandatory for all. Individual-level sociodemographic characteristics were self-reported.

ANALYSIS

The difference-in-differences method was conducted with weighted multiple logistic regression analysis to examine obesity (body mass index ≥30 kg/m2) prevalence by stay-at-home order level and sociodemographic characteristics before/after the COVID-19 pandemic (January 2018-February 2020 vs March 2020-February 2022).

RESULTS

After adjusting for a secular trend and multiple covariates, adults in states/territories with mandatory stay-at-home orders experienced a larger increase in obesity prevalence (adjusted odds ratio: 1.05; 95% confidence interval: 1.01, 1.11) than adults in states/territories with no stay-at-home order. Younger adults (vs ≥65 years) and individuals with

CONCLUSION

Increases in obesity prevalence were perpetuated 2 years after implementation of stay-at-home orders, indicating that longer-term health implications are co-occurring during the pandemic recovery period. Future research should focus on successful methods for staying active and healthy during social distancing and beyond.

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Key Words

• COVID-19
• age
• education
• health policy
• obesity
• sociodemographic characteristic
• stay-at-home order

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MESH Headings

• Humans
• COVID-19/epidemiology
• Obesity/epidemiology
• Male
• United States/epidemiology
• Female
• Prevalence
• Adult
• Middle Aged
• Cross-Sectional Studies
• Behavioral Risk Factor Surveillance System
• Sociodemographic Factors
• Aged
• SARS-CoV-2
• Socioeconomic Factors
• Pandemics
• Young Adult

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Affiliation(s)

Beomyoung Cho Department of Public Health, Brooks College of Health, University of North Florida, Jacksonville, FL, USA
Yining Pan Department of Public Health, Brooks College of Health, University of North Florida, Jacksonville, FL, USA
McKinley Chapman Department of Public Health, Brooks College of Health, University of North Florida, Jacksonville, FL, USA
Aaron Spaulding Division of Health Care Delivery Research, College of Medicine, Mayo Clinic, Jacksonville, FL, USA
Sericea Stallings-Smith Department of Public Health, Brooks College of Health, University of North Florida, Jacksonville, FL, USA

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COVID-19 pandemic-related healthcare interruptions and diabetes distress: a national study of US adults with diabetes

BACKGROUND

Early COVID-19 pandemic research found changes in health care and diabetes management, as well as increased diabetes distress. This study aims to determine the association between COVID-19 pandemic-related healthcare interruptions and diabetes distress among adults with Type 1 and Type 2 diabetes in the US in 2021.

METHODS

Multinomial logistic regression was used to analyze moderate and high levels of diabetes distress (reference = no diabetes distress) in 228 individuals with Type 1 diabetes and 2534 individuals with Type 2 diabetes interviewed in the National Health Interview Survey in 2021.

RESULTS

Among adults with Type 1 diabetes, 41.2% experienced moderate diabetes distress and 19.1% experienced high diabetes distress, and among adults with Type 2 diabetes, 40.8% experienced moderate diabetes distress and 10.0% experienced high diabetes distress. In adults with Type 1 diabetes, experiencing delayed medical care was associated with an adjusted odds ratio (aOR) of 4.31 (95% CI: 1.91-9.72) for moderate diabetes distress and 3.69 (95% CI: 1.20-11.30) for high diabetes distress. In adults with Type 2 diabetes, experiencing delayed medical care was associated with an aOR of 1.61 (95% CI: 1.25-2.07) for moderate diabetes distress and 2.27 (95% CI: 1.48-3.49) for high diabetes distress. Similar associations were observed between not receiving medical care due to the pandemic and diabetes distress.

CONCLUSION

Among people with diabetes, experiencing delayed medical care and not receiving care due to the pandemic were associated with higher reports of diabetes distress.

Assessment Tools for Measuring Health Literacy and Digital Health Literacy in a Hospital Setting: A Scoping Review

Assessment of (digital) health literacy in the hospital can raise staff awareness and facilitate tailored communication, leading to improved health outcomes. Assessment tools should ideally address multiple domains of health literacy, fit to the complex hospital context and have a short administration time, to enable routine assessment. This review aims to create an overview of tools for measuring (digital) health literacy in hospitals. A search in Scopus, PubMed, WoS and CINAHL, following PRISMA guidelines, generated 7252 hits; 251 studies were included in which 44 assessment tools were used. Most tools (57%) were self-reported and 27% reported an administration time of <5 min. Almost all tools addressed the domain 'understanding' (98%), followed by 'access' (52%), 'apply' (50%), 'appraise' (32%), 'numeracy' (18%), and 'digital' (18%). Only four tools were frequently used: the Newest Vital Sign (NVS), the Short Test of Functional Health Literacy for Adults ((S)TOFHLA), the Brief Health Literacy Screener (BHLS), and the Health Literacy Questionnaire (HLQ). While the NVS and BHLS have a low administration time, they cover only two domains. HLQ covers the most domains: access, understanding, appraise, and apply. None of these four most frequently used tools measured digital skills. This review can guide health professionals in choosing an instrument that is feasible in their daily practice, and measures the required domains.