Diabetes Self-management Behaviors, Medical Care, Glycemic Control, and Self-rated Health in US Men by Race/Ethnicity

Men, particularly minorities, have higher rates of diabetes as compared with their counterparts. Ongoing diabetes self-management education and support by specialists are essential components to prevent the risk of complications such as kidney disease, cardiovascular diseases, and neurological impairments. Diabetes self-management behaviors, in particular, as diet and physical activity, have been associated with glycemic control in the literature. Recommended medical care for diabetes may differ by race/ethnicity. This study examined data from the National Health and Nutrition Examination Surveys, 2007 to 2010 for men with diabetes (N = 646) from four racial/ethnic groups: Mexican Americans, other Hispanics, non-Hispanic Blacks, and non-Hispanic Whites. Men with adequate dietary fiber intake had higher odds of glycemic control (odds ratio = 4.31, confidence interval [1.82, 10.20]), independent of race/ethnicity. There were racial/ethnic differences in reporting seeing a diabetes specialist. Non-Hispanic Blacks had the highest odds of reporting ever seeing a diabetes specialist (84.9%) followed by White non-Hispanics (74.7%), whereas Hispanics reported the lowest proportions (55.2% Mexican Americans and 62.1% other Hispanics). Men seeing a diabetes specialist had the lowest odds of glycemic control (odds ratio = 0.54, confidence interval [0.30, 0.96]). The results of this study suggest that diabetes education counseling may be selectively given to patients who are not in glycemic control. These findings indicate the need for examining referral systems and quality of diabetes care. Future studies should assess the effectiveness of patient-centered medical care provided by a diabetes specialist with consideration of sociodemographics, in particular, race/ethnicity and gender.

Reducing Health Disparities: Medical advice and health behavior of minorities with diabetes

OBJECTIVE: to examine the relationships among reported medical advice, diabetes education, health insurance and health behavior of individuals with diabetes by race/ethnicity and gender. METHOD: Secondary analysis of data (N = 654) for adults ages > or = 21 years with diabetes acquired through the National Health and Nutrition Examination Survey (NHANES) for the years 2007-2008 comparing Black, non-Hispanics (BNH) and Mexican-Americans (MA) with White, non-Hispanics (WNH). The NHANES survey design is a stratified, multistage probability sample of the civilian noninstitutionalized U.S. population. Sample weights were applied in accordance with NHANES specifications using the complex sample module of IBM SPSS version 18. RESULTS: The findings revealed statistical significant differences in reported medical advice given. BNH [OR = 1.83 (1.16, 2.88), p = 0.013] were more likely than WNH to report being told to reduce fat or calories. Similarly, BNH [OR = 2.84 (1.45, 5.59), p = 0.005] were more likely than WNH to report that they were told to increase their physical activity. Mexican-Americans were less likely to self-monitor their blood glucose than WNH [OR = 2.70 (1.66, 4.38), p < 0.001]. There were differences by race/ethnicity for reporting receiving recent diabetes education. Black, non-Hispanics were twice as likely to report receiving diabetes education than WNH [OR = 2.29 (1.36, 3.85), p = 0.004]. Having recent diabetes education increased the likelihood of performing several diabetes self-management behaviors independent of race. CONCLUSIONS: There were significant differences in reported medical advice received for diabetes care by race/ethnicity. The results suggest ethnic variations in patient-provider communication and may be a consequence of their health beliefs, patient-provider communication as well as length of visit and access to healthcare. These findings clearly demonstrate the need for government sponsored programs, with a patient-centered approach, augmenting usual medical care for diabetes. Moreover, the results suggest that public policy is needed to require the provision of diabetes education at least every two years by public health insurance programs and recommend this provision for all private insurance companies

Perceived Stress and Self‑rated Health of Haitian and African Americans with and without Type 2 Diabetes

Background: Blacks have a higher incidence of diabetes and its related complications. Self-rated health (SRH) and perceived stress indicators are associated with chronic diseases. The aim of this study was to examine the associations between SRH, perceived stress and diabetes status among two Black ethnicities. Materials and Methods: The cross-sectional study included 258 Haitian Americans and 249 African Americans with (n = 240) and without type 2 diabetes (n = 267) (N = 507). Recruitment was performed by community outreach. Results: Haitian-Americans were less likely to report ‘fair to poor’ health as compared to African Americans [OR=0.58 (95% CI: 0.35, 0.95), P = 0.032]; yet, Haitian Americans had greater perceived stress than African Americans (P = 0.002). Having diabetes was associated with ‘fair to poor’ SRH [OR=3.14 (95% CI: 2.09, 4.72),P < 0.001] but not perceived stress (P = 0.072). Haitian-Americans (P = 0.023), females (P = 0.003) and those participants having ‘poor or fair’ SRH (P < 0.001) were positively associated with perceived stress (Nagelkerke R2=0.151). Conclusion: Perceived stress associated with ‘poor or fair’ SRH suggests that screening for perceived stress should be considered part of routine medical care; albeit, further studies are required to confirm our results. The findings support the need for treatment plans that are patient-centered and culturally relevant and that address psychosocial issues.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^