Objective: The objective of this review was to assess published literature relating to health literacy and older adults. Method: The current review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses. Results: Eight articles met inclusion criteria. All studies were conducted in urban settings in the United States. Study sample size ranged from 33 to 3,000 participants. Two studies evaluated health-related outcomes and reported significant associations between low health literacy and poorer health outcomes. Two other studies investigated the impact of health literacy on medication management, reporting mixed findings. Discussion: The findings of this review highlight the importance of working to improve health care strategies for older adults with low health literacy and highlight the need for a standardized and validated clinical health literacy screening tool for older adults.

The purpose of this study was to describe population knowledge and beliefs about COVID-19 and current social media coverage to address a gap in what is known about risk communication during health crises. A survey with 27 questions was developed. Twenty-three percent ( N = 1,136) of respondents started the survey. Less than half of the students reported a high health literacy level (43%, n = 365/855). When asked where students have heard about COVID-19, the majority reported the Internet and social media. Students reported a basic level of COVID-19 knowledge, but few students (18%, n = 173/966) correctly identified all three signs and/or symptoms of COVID-19. Results highlight the need for an increased public health presence on social media and the urgent need to remain diligent in educating community members about COVID-19 myths.

A comprehensive and integrated primary care and public health community effort is needed to support and improve adolescent breastfeeding. Further examination of integrated interventions focused on adolescent breastfeeding behaviors through an environmental approach is needed.

Objectives: Health Literacy skills are important for people of all ages. Older adults have the lowest health literacy rates. The purpose of this study was to assess health literacy rates and validate the use of a screening tool with older adults. Methods: Participants included a convenience sample, age 65 years or older, English speaking with corrected vision of 20/100 or better and typical cognitive skills. Participants completed the 36-item Short Test of Functional Health Literacy Assessment (STOFHLA) and a single item screening (SIS) tool. Results of STOFHLA and SIS were compared using nonparametric statistics. Results: Of the 64 participants, 94% had adequate scores on the STOFHLA, while 64% self-reported confidence in filling out medical forms, p = .006, χ2 = 7.606, df(1). Conclusion: Results suggest that use of health literacy screening tools for older adults may be of value. Additional studies are needed to expand the study sample and validate the findings of this study.

Community capacity may be enhanced through intermediary supports that provide training and technical assistance (TA). This study used a randomized pre/posttest design to assess the impact of training and TA on coalition capacity. Seven community coalitions from the Midwest participated in the 2-year study, which included 36 hours of training, followed by monthly TA calls to support action planning implementation for prioritized processes. Collaborative processes most commonly identified as high-need areas for TA were Developing Organizational Structure, Documenting Progress, Making Outcomes Matter, and Sustaining the Work. Based on a coalition survey, the average change for processes prioritized through TA across all seven coalitions was .27 (SD = .29), while the average change for non-prioritized processes was .09 (SD = .20) (t(6) = 4.86, p = .003, d = 1.84). The findings from this study suggest that TA can increase coalition capacity for implementing collaborative processes using a participatory approach.

Health literacy continues to be an important research topic as part of population-based assessments for overall health issues. The objective of this continuation study was to examine the health literacy rates and health outcomes as measured by the Kansas Behavioral Risk Factor Surveillance System (BRFSS) survey. A cross-sectional research design was used. Health literacy data were extracted from the state-specific module of the BRFSS telephone survey. Demographic and health status variables were extracted from the core BRFSS dataset. The association between demographic and health status characteristics with health literacy was obtained using weighted samples in multivariable logistic regression models. As in the previous study, most respondents had moderate health literacy (61.1%), followed by high health literacy (31.4%) and low health literacy (7.5%). The demographic variables of interest included race, marital status, home ownership, insurance status, metropolitan status code, survey language, veteran status, education, employment, income, sex, and age. The health status variables included general health rating, presence of chronic conditions, and length of time since last check-up. Findings include individuals with low levels of health literacy were nearly 7 times as likely to be unsure of at least one health condition than those with high health literacy and demonstrate a broad gap in people’s ability to communicate accurate information to health-care providers. Results can inform future efforts to build programs that address health disparities issues including low health literacy to provide equitable health-care services. There is a continued need for support for the creation of health literate programs.

Community prevention coalitions are a commonly utilized mechanism for supporting community-based prevention efforts. The effectiveness of community coalitions to foster change and improvements in outcomes is inconclusive and often influenced by other factors such as community readiness and coalition capacity. Limited studies have examined the effects of technical assistance (TA) models on coalition and community capacity to facilitate change and improvements in outcomes. The present study analyzed the effects of a capacity-building TA model on the implementation of both key coalition processes (e.g., strategic planning) and the facilitation of community changes (i.e., program, policy, and practice changes) by prevention coalitions. A between-group randomized controlled trial, with a delayed intervention control group design, was used with eight coalitions in the Midwest. The results suggest that although internal coalition capacity increased, it did not immediately result in greater facilitation of external community changes by the community prevention coalitions. C Capacity Building for Change Model r 763 adolescent substance use. Since coalitions are likely to continue as a common mechanism for addressing issues of critical community and public health concern, it is important to further examine and refine the prevention support system models to better assure support for enhancing the capacity and effectiveness of prevention coalitions.

Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on machine learning have been proposed to develop an obesity prediction model or to determine key determinants of obesity for designing intervention tools. Despite having a rich and diverse set of literature on obesity prediction models, obesity rates are at an all-time high for both children and adolescents. This paper surveys the growing body of recent literature on machine and deep learning models for obesity prediction by providing a coherent view (critical analysis) of the limitations of the existing systems. The taxonomy of the existing literature on obesity prediction into methods used, predicted outcome, factors used, type of datasets, and the associated purpose, is discussed for analysis of the state-of-the-art. This analysis revealed that a) prediction-focused models do not use variables from as many domains as predictor-focused models do, b) very few studies proposed gender-specific and race-specific obesity prediction models, c) lack of large-scale multimodal datasets and d) existing predictor-focused models obtain an accuracy range of [53.7%, 96%] with an optimum set of predictors. Further, computer vision-based methods for obesity prediction and interpretable techniques for understanding the outcome of the models are discussed as well. In addition, we have also identified novel research directions. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.