Impact of a comprehensive nutrition and lifestyle education intervention on body weight and health-related outcomes in morbidly-obese Hispanic-Americans following laparoscopic Roux-en-Y gastric bypass

As morbid obesity increasingly affects Hispanic-Americans, the incidence of Roux-en-Y gastric bypass procedures (RYGB) among this population rises. Prospective research on the impact of postoperative educational interventions focused on Hispanic- Americans is needed to prevent premature weight loss plateau, weight regain, nutritional deficiencies, and relapse of obesity-related comorbidities. This randomized-controlled study evaluated the impact of a comprehensive nutrition and lifestyle education intervention (6 biweekly postoperative sessions that incorporated motivational strategies for behavioral change) as compared to a non-comprehensive approach (printed guidelines for healthy lifestyle). The variables to consider are body weight, obesity-related comorbidities (depression, diabetes, dyslipidemia, and others), nutrient status, physical activity, and eating habits in 144 morbidly-obese adult Hispanic-Americans 6 to 12 months following RYGB. Patients were randomly assigned to either the comprehensive intervention (n=72) or the comparison group (n=72). Participants (mean age 44.5 ± 13.5 years) were mainly Cuban-born females (83.3%). Intervention sessions attendance was 64%. At 12 months, both groups lost weight significantly, but those in the comprehensive intervention experienced greater excess weight loss than those in the comparison group (80% vs. 64% from preoperative excess weight, P<.001). Intervention participants were significantly more involved in physical activity (+ 14 min/week vs. – 4 min/week), had decreased depression, joint illness, and required less medication for comorbidities than comparison participants. Additionally, those in the comprehensive intervention had sustained supplement intake experiencing less folate deficiency (P=.014). The non-comprehensive intervention group significantly decreased their protein and supplement intake compared to the intervention group. Patients in the comprehensive intervention had significantly better eating habits reflected by fewer episodes of dumping syndrome, constipation, and night eating, than those in the comparison group who reported greater eating in response to negative emotions (P=.003). These findings support the importance of a comprehensive educational approach to achieve more effective weight reduction and health-related outcomes to prevent relapse of obesity-related comorbidities and nutritional deficiencies in Hispanic-Americans 6 to 12 months following RYGB.

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].

Factors associated with critical care nurses' communication with non-communicative patients in the ICU

Hospitalized individuals are isolated from their familiar environment at the onset of illness. Those individuals who are non-communicative are detached from the world and from life, as they previously knew it. Although nurses have long since recognized the importance of communication, patients still report the lack of iy. This study was done to identify factors influencing critical care nurses to communicate with their noncommunicative patients. The overall results of the study indicate that nurses are aware of the importance of verbal communication with patients who may be intubated, paralyzed, unconscious, comatose or neurologically impaired and are not deterred by them. Despite these results, some significant observations emerged identified. CCRN certified nurses and nurses with more years of experience were less likely to have verbal communication with noncommunicative patients. Nurses with children, spouses and those working full-time were more likely to communicate with non-communicative patients.

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].^