This diploma thesis represents a sum of theoretical findings about correlates and antecedents of boredom and an empirical investigation of opened questions about boredom, according to the literature. This was done through a laboratory experiment in which the author of the thesis, in order to investigate the research questions, induced boredom in the research participants. The research aim of this thesis involves three topics. The primary purpose of the experiment is to assess the experimental manipulation effects of causal attribution of attentional failure on the intensity of boredom. In order to investigate the main research questions, the author of this thesis created a specific experimental design. This includes a fictive subliminal distracting element representing an alternative cause explaining attentional failure during a task reliably eliciting boredom. Experimental design was also developed to fulfil a secondary aim of this thesis, to explore some of the correlates of boredom with emphasis on the question of relation between activation and boredom. The third aim is to investigate hypothesis about the type of activities preferred after the boredom induction by giving a choice to participants between interesting and an enjoyable videoclip. The results of the experiment do not support the main...

Trichotillomania is defined as recurrent pulling of one’s hair that results in distress and negative effects on general functioning and quality of life. Estimates of trichotillomania lifetime prevalence generally range from approximately 1% to 3% and it is likely as common as disorders such as obsessive-compulsive disorder and agoraphobia. Yet, quality treatment for trichotillomania is often difficult to find as many mental health professionals are uninformed about the disorder and its treatment. Moreover, mental health services in general are inaccessible to many with estimates suggesting that 96.5 million people do not have access to adequate services. The use of telepsychology has been an effective method for disseminating treatment services for a variety of mental health conditions. However, no research has examined the effectiveness of telepsychology to treat trichotillomania. The current study reports the results of a randomized clinical trial of Acceptance and Commitment Therapy Enhanced Behavior Therapy for the treatment of trichotillomania delivered by way of telepsychology. The study compared an active treatment condition (n = 12) to a waitlist control condition (n = 10). Results showed significant reductions in hair pulling severity from pre- to post-treatment compared to the waitlist condition. Participants in the waitlist condition received the same treatment as participants in the treatment condition following the waitlist period. All participants were then combined to examine overall treatment effects from pre-treatment to a 12-week follow-up. The effect of treatment on hair pulling severity was still significant at follow-up, however the effect was not as strong as at post-treatment. Conversely, the effect on quality of life was maintained and even increased following post-treatment. Additional measures of psychological flexibility, perceived shame, and valued action also saw significant changes from pre-treatment to follow-up. The findings demonstrate that telepsychology is a viable option to disseminate treatment for trichotillomania. Implications, limitations, and future research directions are discussed.

À l'ère de la numérisation, les données devient pas cher et facile à obtenir. Cela se traduit par de nombreux nouveaux problèmes d'optimisation avec de très grandes tailles. En particulier, pour le même genre de problèmes, le nombre de variables et de contraintes sont énormes. En outre, dans de nombreux paramètres d'application tels que ceux dans l'apprentissage de la machine, une solution précise est moins préférée que celles approximatives mais robustes. Il est un véritable défi pour les algorithmes traditionnels, qui sont utilisés pour bien travailler avec des problèmes de taille moyenne, pour faire face à ces nouvelles circonstances.Au lieu de développer des algorithmes qui évoluent bien à résoudre ces problèmes directement, une idée naturelle est de les transformer en problèmes de petite taille qui se rapporte fortement aux originaux. Étant donné que les nouvelles sont de tailles gérables, ils peuvent encore être résolus efficacement par des méthodes classiques. Les solutions obtenues par ces nouveaux problèmes, cependant, nous donner un aperçu des problèmes originaux. Dans cette thèse, nous allons exploiter l'idée ci-dessus pour résoudre certains problèmes de grande dimension optimisation. En particulier, nous appliquons une technique spéciale appelée projection aléatoire pour intégrer les données du problème dans les espaces de faible dimension, et de reformuler environ le problème de telle manière qu'il devient très facile à résoudre, mais capte toujours l'information la plus importante.Dans le chapitre 3, nous étudions les problèmes d'optimisation dans leurs formes de faisabilité. En particulier, nous étudions le problème que l'on appelle l'adhésion linéaire restreint. Cette classe contient de nombreux problèmes importants tels que la faisabilité linéaire et entier. Nous proposonsd'appliquer une projection aléatoire aux contraintes linéaires etnous voulons trouver des conditions sur T, de sorte que les deux problèmes de faisabilité sont équivalentes avec une forte probabilité.Dans le chapitre 4, nous continuons à étudier le problème ci-dessus dans le cas où l'ensemble restreint est un ensemble convexe. Nous établissons les relations entre les problèmes originaux et projetés sur la base du concept de la largeur gaussienne, qui est populaire dans la détection comprimé. En particulier, nous montrons que les deux problèmes sont équivalents avec une forte probabilité aussi longtemps que pour une projection aléatoire échantillonné à partir ensemble sous-gaussienne avec grande dimension suffisante (dépend de la largeur gaussienne).Dans le chapitre 5, nous étudions le problème de l'adhésion euclidienne:.. `` Étant donné un vecteur b et un euclidienne ensemble fermé X, décider si b est en Xor pas "Ceci est une généralisation du problème de l'appartenance linéaire restreinte précédemment considéré. Nous employons une gaussienne projection aléatoire T pour l'intégrer à la fois b et X dans un espace de dimension inférieure et étudier la version projetée correspondant. Lorsque X est fini ou dénombrable, en utilisant un argument simple, nous montrons que les deux problèmes sont équivalents presque sûrement quelle que soit la dimension projetée. Dans le cas où X peut être indénombrable, nous prouvons que les problèmes initiaux et prévus sont également équivalentes si la dimension d projetée est proportionnelle à une dimension intrinsèque de l'ensemble X. En particulier, nous employons la définition de doubler la dimension estimer la relation entre les deux problèmes.Dans le chapitre 6, nous proposons d'appliquer des projections aléatoires pour la zone de confiance sous-problème. Nous réduisons le nombre de variables en utilisant une projection aléatoire et prouver que des solutions optimales pour le nouveau problème sont en fait des solutions approchées de l'original. Ce résultat peut être utilisé dans le cadre de confiance-région pour étudier l'optimisation de boîte noire et l'optimisation des produits dérivés libre. / In the digitization age, data becomes cheap and easy to obtain. That results in many new optimization problems with extremely large sizes. In particular, for the same kind of problems, the numbers of variables and constraints are huge. Moreover, in many application settings such as those in Machine learning, an accurate solution is less preferred as approximate but robust ones. It is a real challenge for traditional algorithms, which are used to work well with average-size problems, to deal with these new circumstances.Instead of developing algorithms that scale up well to solve these problems directly, one natural idea is to transform them into small-size problems that strongly relates to the originals. Since the new ones are of manageable sizes, they can still be solved efficiently by classical methods. The solutions obtained by these new problems, however, will provide us insight into the original problems. In this thesis, we will exploit the above idea to solve some high-dimensional optimization problems. In particular, we apply a special technique called random projection to embed the problem data into low dimensional spaces, and approximately reformulate the problem in such a way that it becomes very easy to solve but still captures the most important information. Therefore, by solving the projected problem, we either obtain an approximate solution or an approximate objective value for the original problem.We will apply random projection to study a number of important optimization problems, including linear and integer programming (Chapter 3), convex optimization with linear constraints (Chapter 4), membership and approximate nearest neighbor (Chapter 5) and trust-region subproblems (Chapter 6).In Chapter 3, we study optimization problems in their feasibility forms. In particular, we study the so-called restricted linear membership problem. This class contains many important problems such as linear and integer feasibility. We proposeto apply a random projection to the linear constraints, andwe want to find conditions on T, so that the two feasibility problems are equivalent with high probability.In Chapter 4, we continue to study the above problem in the case the restricted set is a convex set. Under that assumption, we can define a tangent cone at some point with minimal squared error. We establish the relations between the original and projected problems based on the concept of Gaussian width, which is popular in compressed sensing. In particular, we prove thatthe two problems are equivalent with high probability as long as for some random projection sampled from sub-gaussian ensemble with large enough dimension (depends on the gaussian width).In Chapter 5, we study the Euclidean membership problem: ``Given a vector b and a Euclidean closed set X, decide whether b is in Xor not". This is a generalization of the restricted linear membership problem considered previously. We employ a Gaussian random projection T to embed both b and X into a lower dimension space and study the corresponding projected version: ``Decide whether Tb is in T(X) or not". When X is finite or countable, using a straightforward argument, we prove that the two problems are equivalent almost surely regardless the projected dimension. In the case when X may be uncountable, we prove that the original and projected problems are also equivalent if the projected dimension d is proportional to some intrinsic dimension of the set X. In particular, we employ the definition of doubling dimension estimate the relation between the two problems.In Chapter 6, we propose to apply random projections for the trust-region subproblem. We reduce the number of variables by using a random projection and prove that optimal solutions for the new problem are actually approximate solutions of the original. This result can be used in the trust-region framework to study black-box optimization and derivative-free optimization.

Par l’intermédiaire d’Esther Duflo et de son laboratoire le J-PAL, les expérimentations aléatoires ont connu un essor remarquable depuis les années 2000 en économie et sont présentées par leurs promoteurs comme une méthode particulièrement robuste dans l’évaluation d’impact. Combinant méthodologies quantitatives et qualitatives, cette thèse examine la construction sociale de la preuve expérimentale et apporte une contribution à une épistémologie sociale et historique des expérimentations aléatoires, ainsi qu’à la socio-économie de la quantification. Dans une première partie, nous développons une socio-histoire de cette méthode. Les origines des expérimentations aléatoires sont pluridisciplinaires et antérieures à leur utilisation massive en médecine depuis les années 1940, puis en économie depuis la fin des années 1960. Nous en tirons des enseignements méthodologiques éclairant la pratique actuelle des expérimentations aléatoires. Dans un second temps, nous nous intéressons aux acteurs de cette méthode, en nous penchant sur les chercheurs du J-PAL. En procédant à une analyse prosopographique, complétée par une analyse de réseau, nous montrons que les capitaux académiques élevés de ces chercheurs et l’existence de leaders permettent de contrôler et promouvoir la diffusion de la méthode. Dans une dernière partie, nous interrogeons la production de la preuve par les expérimentations aléatoires. En nous attachant à saisir les pratiques expérimentales, nous montrons que les validités interne et externe sont souvent problématiques. Enfin, nous analysons les liens contrariés entre expérimentations aléatoires et politique(s). / With Esther Duflo and her lab (the J-PAL), randomized controlled trials (RCTs) became trendy from the the 2000’s onward in economics and are presented by their advocates as the most robust method for impact evaluation. Relying on mixed methods, this thesis investigates the social construction of experimental evidence and contributes to a social and historical epistemology of RCTs and to the socio-economy of quantification.The first part develops a socio-history of this method. The origins of RCTs are multidisciplinary and precede their extensive use in medicine from the 1940s and in economics from the 1960s onward. This allows us to gain a deeper undestanding of the current use of RCTs.In the second part, we examine the stakeholders of this method, chiefly J-PAL researchers. Our prosopographical analysis, supplemented by a network analysis, demonstrates that their high level of academic capital and the presence of leaders allow for the control and the diffusion of RCTs.In the last part, we scrutinize the production of experimental evidence. By examining RCTs in operation, we show that both their internal and external validity are in many cases compromized. Finally, we explore the convoluted links between RCTs, policy and politics.

abstract: Excessive gestational weight gain (EGWG) affects 50% of US pregnant women and may be an important contributor to obesity in both the mother and child. Novel strategies to prevent EGWG are needed to reduce the risk of adverse health outcomes for the mother and child. This dissertation presents three manuscripts that 1) propose a novel model to explain how prenatal yoga may prevent EGWG through behavioral, psychological/emotional, and physical factors, 2) test the feasibility and preliminary efficacy of a prenatal yoga intervention to prevent EGWG compared to a pregnancy education comparison group, and 3) qualitatively investigate pregnant women’s experiences participating in a prenatal yoga intervention to prevent EGWG. In manuscript two, 49 women were recruited and randomized to a 12-week prenatal yoga intervention (n=23) or a time-matched pregnancy education comparison group (n=26). A satisfaction survey was administered at post-intervention to assess feasibility outcomes (e.g., acceptability, demand). Mindfulness, emotion regulation, self-awareness, sleep quality, depression, anxiety, and perceived stress were assessed at baseline and post-intervention (12-weeks) and GWG was assessed weekly. Linear mixed models were used to analyze pre-post changes in primary (i.e., GWG during pregnancy) and secondary (i.e., mindfulness, emotion regulation, self-awareness, sleep quality, depression, anxiety, and stress) outcomes. In manuscript three, interviews were conducted with pregnant women who participated in the prenatal yoga intervention (n=13). Interview responses were summarized using an inductive approach to thematic analysis. Findings in manuscript two suggest that prenatal yoga was a feasible method to prevent EGWG with high enjoyment and satisfaction reported among participants. The average number of prenatal yoga sessions attended was 8.84 (SD = 3.85). There was no significant group differences on the rate of GWG or total GWG throughout the intervention and a significant group x time interaction effect for stress (p=.03). In manuscript three, twelve themes were identified among the data and were organized into the following categories (three themes each): 1) experiences of prenatal yoga, 2) prenatal yoga and weight, 3) barriers to prenatal yoga, and 4) facilitators of prenatal yoga. This initial evidence suggests that prenatal yoga has potential as a strategy to prevent EGWG in pregnant women. / Dissertation/Thesis / Embargo form / Doctoral Dissertation Exercise and Nutritional Sciences 2019

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京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第18516号 / 社医博第57号 / 新制||社医||8(附属図書館) / 31402 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 川上 浩司, 教授 横出 正之, 教授 佐藤 俊哉 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DGAM

This is the peer reviewed version of the following article: [J Am Geriatr Soc 63:1355–1363, 2015.], which has been published in final form at [doi: 10.1111/jgs.13481]. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. / 京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第19642号 / 人健博第34号 / 新制||人健||3(附属図書館) / 32678 / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 三谷 章, 教授 黒木 裕士, 教授 市橋 則明 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第20289号 / 社医博第78号 / 社新制||医||9(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 山田 亮, 教授 中山 健夫, 教授 古川 壽亮 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM