The impact of dieting status and restraint on post-exercise energy intake in overweight females

This study investigated the impact of an acute bout of physical activity on postexercise energy intake (PE-EI) in overweight females who were dieting with high restraint (D-HR) and non-dieting with either high restraint (ND-HR) or low restraint (ND-LR). PE-EI at lunch and 12-hours after was compared on the exercise (E) and a nonexercise (NE) day. There was a significant interaction (F (2,33)= 4.12, p = 0.025) of dieting/restraint status and condition (E vs. NE day) on the 12-hour El. The D-HR ate 519 ± 596 kcal more on the E than on the NE day; while the ND-HR ate 177 ± 392 kcal less on the E than on the NE day. The results of this study demonstrate that the impact of exercise on PE-EI is determined by both a physiological and psychological response. Dieting status, dietary restraint, level of disinhibition and cognitive factors may influence PE-EI and weight.

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].^

The impact of the New University Code on the physical education curriculum of four-year colleges and universities in Taiwan, Republic of China

With the establishment of The New University Code in 1994, Taiwan's colleges and universities were mandated to shift physical education from a required course to an elective. The four-year colleges and universities are now responsible for either developing new physical education programs or removing existing programs from their curriculum. Planned change and curriculum leadership are considered in light of policy changes regarding required physical education programs enacted by the Ministry of Education. ^ This study compared the organizational structure and the curriculum of physical education at accredited colleges and universities in Taiwan. Chairpersons of physical education departments from 60 four-year colleges and universities were surveyed using a modified version of the Hensley's Basic Instruction Program (BIP) Questionnaire. Results were analyzed using analysis of variance (ANOVA) and crosstabs. The findings confirmed that physical education programs were effected by declining enrollment and administrative decisions to eliminate them. However, at the same time, chairpersons expressed strong support for the maintenance of the traditional physical education curriculum. ^

Psychological and cognitive factors that influence post -exercise energy intake in normal weight and overweight sedentary males

Physical activity is recommended to facilitate weight management. However, some individuals may be unable to successfully manage their weight due to certain psychological and cognitive factors that trigger them to compensate for calories expended in exercise. The primary purpose of this study was to evaluate the effect of moderate-intensity exercise on lunch and 12-hour post-exercise energy intake (PE-EI) in normal weight and overweight sedentary males. Perceived hunger, mood, carbohydrate intake from beverages, and accuracy in estimating energy intake (EI) and energy expenditure (EE) were also assessed. The study consisted of two conditions, exercise (treadmill walking) and rest (sitting), with each participant completing each condition, in a counterbalanced-crossover design on two days. Eighty males, mean age 30 years (SD=8) were categorized into five groups according to weight (normal-/overweight), dietary restraint level (high/low), and dieting status (yes/no). Results of repeated measures, 5x2 ANOVA indicated that the main effects of condition and group, and the interaction were not significant for lunch or 12-hour PE-EI. Among overweight participants, dieters consumed significantly (p<0.05) fewer calories than non-dieters at lunch (M=822 vs. M=1149) and over 12 hours (M=1858 vs. M =2497). Overall, participants’ estimated exercise EE was significantly (p<0.01) higher than actual exercise EE, and estimated resting EE was significantly (p<0.001) lower than actual resting EE. Participants significantly (p<0.001) underestimated EI at lunch on both experimental days. Perceived hunger was significantly (p<0.05) lower after exercise (M=49 mm, SEM=3) than after rest (M=57 mm, SEM=3). Mood scores and carbohydrate intake from beverages were not influenced by weight, dietary restraint, and dieting status. In conclusion, a single bout of moderate-intensity exercise did not influence PE-EI in sedentary males in reference to weight, dietary restraint, and dieting status, suggesting that this population may not be at risk for overeating in response to exercise. Therefore, exercise can be prescribed and used as an effective tool for weight management. Results also indicated that there was an inability to accurately estimate EI (ad libitum lunch meal) and EE (60 minutes of moderate-intensity exercise). Inaccuracies in the estimation of calories for EI and EE could have the potential to unfavorably impact weight management.