Health beliefs and health care options of disparate black communities in the United States

According to the 1999 U.S. Census, there were approximately thirty-three million African Americans and approximately 1.8 million non-Hispanic black immigrants in the United States. The 1997 U.S. Census estimated that there were as many as 554,000 foreign-born Haitians and 505,000 foreign-born Jamaicans living in the United States, mainly residing in Florida and New York. The U.S. Census claims that blacks are one of the largest emerging ethnic groups in America constituting at least twelve percent of this country's population. Because of this increase, their specific health beliefs and health care options are increasingly nationally significant. ^ In the present intra-black and inter-ethnic study, two hundred seventy African Americans, Haitian immigrants, and Jamaican immigrants residing in South Florida were quantitatively and qualitatively surveyed in order to investigate their health beliefs and health care options. According to the reviewed literature, the three black ethnic groups researched in this study have not been compared or contrasted before in relation to these beliefs and health care choices. ^ As was discovered by the present research, differing health beliefs and health care options were the cultural products of the ethnic differences of the researched communities. It was expected that differing health beliefs among the researched black groups might indicate disparate patterns of health care utilization of either western or non-western models. Additionally, it was projected that by investigating the health beliefs and the health care options of these emerging black ethnic groups, western health care providers in the United States could become better versed in medically servicing growing ethnically-disparate black populations. The study yielded results about the researched groups that supported major findings in the reviewed literature. The data were reported and examined, and theoretical generalizations from the data were discussed. The most important of these findings was that, within a race, health beliefs and health care options were determined by specific ethno-cultural variables dependent on national origins. ^

Food customs of rural and urban Inupiaq elders and their relationships to select nutrition parameters, food insecurity, health, and physical and mental functioning

The Inupiaq Tribe resides north of the Arctic Circle in northwestern Alaska. The people are characterized by their continued dependence on harvested fish, game and plants, known as a subsistence lifestyle (Lee 2000:35-45). Many are suggesting that they leave their historical home and move to urban communities, places believed to be more comfortable as they age. Tribal Elders disagree and have stated, "Elders need to be near the river where they were raised" (Branch 2005:1). The research questions focused on differences that location had on four groups of variables: nutrition parameters, community support, physical functioning and health. A total of 101 Inupiaq Elders ≥ 50 years were surveyed: 52 from two rural villages, and 49 in Anchorage. Location did not influence energy intake or intake of protein; levels of nutrition risk and food insecurity; all had similar rates between the two groups. Both rural and urban Elders reported few limitations of ADLs and IADLs. Self-reported general health scores (SF-12.v2 GH) were also similar by location. Differences were found with rural Elders reporting higher physical functioning summary scores (SF-12.v2 PCS), higher mental health scores (SF-12.v2 MH), higher vitality and less pain even though the rural mean ages were five years older than the urban Elders. Traditional food customs appear to support the overall health and well being of the rural Inupiaq Elders as demonstrated by higher intakes of Native foods, stronger food sharing networks and higher family activity scores than did urban Elders. The rural community appeared to foster continued physical activity. It has been said that when Elders are in the rural setting they are near "people they know" and it is a place "where they can get their Native food" (NRC 2005). These factors appear to be important as Inupiaq Elders age, as rural Inupiaq Elders fared as well or better than Inupiaq Elders in terms of diet, mental and physical health.

Health changes in Hispanic older adults in a Spanish arthritis self -management education program

Arthritis is the most common chronic condition affecting older people and is a major cause of limited activity. Arthritis education programs in English have demonstrated a positive impact on health but these programs have not reached the Hispanic communities where arthritis is the leading cause of disability. Minorities, such as Hispanics, have traditionally been reluctant to pursue self-help programs, and have been identified as an under-served population in terms of medical care. This study examined the effectiveness of one community health adult education program targeting Hispanic older adults with arthritis, the Spanish Arthritis Self Management Education Program (SASMEP), by evaluating changes in the participants' general health, pain, disability, self-efficacy, health perceptions, frequency of physician visits, and exercise. A pre and post control group experimental design and analyses of covariance were used to determine the pre and post differences in health status and health behaviors for a group participating in the SASMEP and a group who did not using gender and age as covariates. A repeated measures design was also used, and repeated measures analyses of variance and post hoc tests were done on health status and health behavior data collected pre, post and one-year post education to determine long-term differences. ^ Results indicated the participants' health status significantly improved in general health, significantly decreased in pain, and significantly decreased in arthritic disability immediately following the education. Self-efficacy and health perceptions increased for both groups but not significantly. The participants' health behaviors showed significantly fewer physician visits and significantly increased time spent performing stretching and strengthening exercise and time spent performing aerobic exercise. No group differences were found in the frequency of arthritis physician visits. ^ The improvements seen immediately after the SASMEP participation were not reflected in the post one-year scores. No significant differences were found for the participants' health status or health behaviors one year following the education. Health status and health behaviors did not return below baseline scores after one year suggesting the participants' health, although not improved, did not deteriorate. Therefore, the SASMEP education provided short-term health benefits for older Hispanic adults with arthritis, but not long-term health benefits. ^

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

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].

Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

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].^