An overview of the research evidence on ethnicity and communication in healthcare

• The aim of the present study was to identify and review the available research evidence on 'ethnicity and communication' in areas relevant to ensuring effective provision of mainstream services (e.g. via interpreter, advocacy and translation services); provision of services targeted on communication (e.g. speech and language therapy, counselling, psychotherapy); consensual/ participatory activities (e.g. consent to interventions), and; procedures for managing and planning for linguistic diversity

Neural Activation on Guided Imagery and Music: A Functional MRI Study

Music and imagery have been used for working emotions such as awareness, arousal, enhancement, reflection and transformation of emotions in therapeutic relationship; these are crucial processes in music psychotherapy. To illuminate the empirical adequacy of concepts of theory in guided imagery and music (GIM) as one of music psychotherapeutic methods, the present study investigated the neural bases of arousal and emotional processing in response to recall and re-experience of personal negative emotional episodes via GIM and the efficacy of GIM for arousal and emotional processing via functional magnetic resonance imaging (fMRI) data. For this study, classical music and verbal stimuli were presented to 24 righthanded healthy participants, to measure the blood oxygen level dependent (BOLD) signal changes during arousal and emotional processing through GIM. Volume analyses for the contrast of GIM to guided imagery (GI) or music and region of interest (ROI) analysis for the difference of three conditions - GIM, music, and GI – were conducted in the regions of bilateral amygdala, insula, and anterior cingulate gyrus. Results included that in the contrast of GIM to music, 11 neural regions (left anterior cingulate gyrus, left amygdala, left thalamus, left claustrum, left insula, bilateral precentral gyrus, left superior temporal gyrus, bilateral middle temporal gyrus, left inferior parietal lobule, right cuneus, and bilateral culmen) were activated, whereas there was no activated neural regions in the contrast of music to GIM. In the contrast of GIM to GI, 9 neural regions (right posterior cingulate gyrus, bilateral parahippocampal gyrus, bilateral precentral gyrus, left superior frontal gyrus, left middle frontal gyrus, bilateral middle occipital gyrus, bilateral cuneus, right lingual gyrus, and inferior parietal lobule) were activated, whereas 3 neural regions 10 (right superior temporal gyrus, bilateral middle temporal gyrus, and left inferior parietal lobule) were activated in the contrast of GI to GIM. The ROI analysis revealed statistically significant differences among three conditions in bilateral amygdala, insula, and anterior cingulate gyrus. Findings suggest that guided imagery and music as multimodal stimuli are effective approach in emotional work with personal episodic memories, indicating activation of various neural regions functioning in emotions, various kinds of sensory modalities, integration of cross-modal sensory, episodic memory, empathy, and out-ofbody experience

Innovative Approach for Depression Treatment: Two Ways of Modifying the Gut-Microbiome to Treat Major Depressive Disorder

Depression is a debilitating disorder that affects people worldwide. However, at least one third of patients do not respond to existing therapies, and novel treatment methods are necessary. A promising new approach for treating depression targets the microbiota-gut-brain axis, which is linked to physiological, behavioral, and cognitive functions affected in major depressive disorder. Probiotics and fecal microbiota transplantation are two possible ways to modify the microbiota-gut-brain axis. The first study in this thesis (Doll et al., 2022) presents two patients with diagnosed depression treated with fecal microbiota transplantation as an add-on therapy. Depressive symptoms decreased for both patients four weeks after the transplantation, with the effects lasting up to eight weeks in one patient. Gastrointestinal symptoms were reflected in microbiota changes and improved in one patient. In the second study (Schaub et al., 2022), we examined the effect of a probiotic supplementation on depressive symptoms, the gut microbiota, and the brain in a randomized controlled trial. Depressive symptoms decreased over time in both groups, but the decrease was stronger in the probiotic group. Probiotics increased the abundance of the genus Lactobacillus, which was associated with decreased depressive symptoms in the probiotics group. Finally, putamen activation in response to neutral faces was significantly decreased after the probiotic intervention. In the third study (Schneider et al., submitted), we investigated the effect of the probiotic supplementation on cognitive functions, brain-derived neurotrophic factor, and the brain. We found a significantly improved immediate recall for the probiotics group immediately after intervention and a trend for a time-group-interaction considering all timepoints. We also found a time-group-interaction in hippocampus activation during working memory processing, revealing a remediated hippocampus function in the probiotic group. However, we did not find significant changes of brain-derived neurotrophic factor. The results of the three studies highlight the role of the microbiota-gut-brain axis in depression and emphasize the potential of adjuvant microbiota-related treatment approaches as accessible, pragmatic, and non-stigmatizing therapies for depression. Nonetheless, the safety issues of fecal microbiota transplantation and the modest sample size of our studies imply that large-scale studies are needed to better understand the underlying mechanisms of depression and the microbiota-gut-brain axis and to replicate and validate our results

Environmental effects on brain functional networks in a juvenile twin population

: The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored

Adding expressiveness to unit selection speech synthesis and to numerical voice production

La parla és una de les formes de comunicació més naturals i directes entre éssers humans, ja que codifica un missatge i també claus paralingüístiques sobre l’estat emocional del locutor, el to o la seva intenció, esdevenint així fonamental en la consecució d’una interacció humà-màquina (HCI) més natural. En aquest context, la generació de parla expressiva pel canal de sortida d’HCI és un element clau en el desenvolupament de tecnologies assistencials o assistents personals entre altres aplicacions. La parla sintètica pot ser generada a partir de parla enregistrada utilitzant mètodes basats en corpus com la selecció d’unitats (US), que poden aconseguir resultats d’alta qualitat però d’expressivitat restringida a la pròpia del corpus. A fi de millorar la qualitat de la sortida de la síntesi, la tendència actual és construir bases de dades de veu cada cop més grans, seguint especialment l’aproximació de síntesi anomenada End-to-End basada en tècniques d’aprenentatge profund. Tanmateix, enregistrar corpus ad-hoc per cada estil expressiu desitjat pot ser extremadament costós o fins i tot inviable si el locutor no és capaç de realitzar adequadament els estils requerits per a una aplicació donada (ex: cant en el domini de la narració de contes). Alternativament, nous mètodes basats en la física de la producció de veu s’han desenvolupat a la darrera dècada gràcies a l’increment en la potència computacional. Per exemple, vocals o diftongs poden ser obtinguts utilitzant el mètode d’elements finits (FEM) per simular la propagació d’ones acústiques a través d’una geometria 3D realista del tracte vocal obtinguda a partir de ressonàncies magnètiques (MRI). Tanmateix, atès que els principals esforços en aquests mètodes de producció numèrica de veu s’han focalitzat en la millora del modelat del procés de generació de veu, fins ara s’ha prestat poca atenció a la seva expressivitat. A més, la col·lecció de dades per aquestes simulacions és molt costosa, a més de requerir un llarg postprocessament manual com el necessari per extreure geometries 3D del tracte vocal a partir de MRI. L’objectiu de la tesi és afegir expressivitat en un sistema que genera veu neutra, sense haver d’adquirir dades expressives del locutor original. Per un costat, s’afegeixen capacitats expressives a un sistema de conversió de text a parla basat en selecció d’unitats (US-TTS) dotat d’un corpus de veu neutra, per adreçar necessitats específiques i concretes en l’àmbit de la narració de contes, com són la veu cantada o situacions de suspens. A tal efecte, la veu és parametritzada utilitzant un model harmònic i transformada a l’estil expressiu desitjat d’acord amb un sistema expert. Es presenta una primera aproximació, centrada en la síntesi de suspens creixent per a la narració de contes, i es demostra la seva viabilitat pel que fa a naturalitat i qualitat de narració de contes. També s’afegeixen capacitats de cant al sistema US-TTS mitjançant la integració de mòduls de transformació de parla a veu cantada en el pipeline del TTS, i la incorporació d’un mòdul de generació de prosòdia expressiva que permet al mòdul de US seleccionar unitats més properes a la prosòdia cantada obtinguda a partir de la partitura d’entrada. Això resulta en un framework de síntesi de conversió de text a parla i veu cantada basat en selecció d’unitats (US-TTS&S) que pot generar veu parlada i cantada a partir d'un petit corpus de veu neutra (~2.6h). D’acord amb els resultats objectius, l’estratègia de US guiada per la partitura permet reduir els factors de modificació de pitch requerits per produir veu cantada a partir de les unitats de veu parlada seleccionades, però en canvi té una efectivitat limitada amb els factors de modificació de les durades degut a la curta durada de les vocals parlades neutres. Els resultats dels tests perceptius mostren que tot i òbviament obtenir una naturalitat inferior a la oferta per un sintetitzador professional de veu cantada, el framework pot adreçar necessitats puntuals de veu cantada per a la síntesis de narració de contes amb una qualitat raonable. La incorporació d’expressivitat s’investiga també en la simulació numèrica 3D de vocals basada en FEM mitjançant modificacions de les senyals d’excitació glotal utilitzant una aproximació font-filtre de producció de veu. Aquestes senyals es generen utilitzant un model Liljencrants-Fant (LF) controlat amb el paràmetre de forma del pols Rd, que permet explorar el continu de fonació lax-tens a més del rang de freqüències fonamentals, F0, de la veu parlada. S’analitza la contribució de la font glotal als modes d’alt ordre en la síntesis FEM de les vocals cardinals [a], [i] i [u] mitjançant la comparació dels valors d’energia d’alta freqüència (HFE) obtinguts amb geometries realistes i simplificades del tracte vocal. Les simulacions indiquen que els modes d’alt ordre es preveuen perceptivament rellevants d’acord amb valors de referència de la literatura, particularment per a fonacions tenses i/o F0s altes. En canvi, per a vocals amb una fonació laxa i/o F0s baixes els nivells d’HFE poden resultar inaudibles, especialment si no hi ha soroll d’aspiració en la font glotal. Després d’aquest estudi preliminar, s’han analitzat les característiques d’excitació de vocals alegres i agressives d’un corpus paral·lel de veu en castellà amb l’objectiu d’incorporar aquests estils expressius de veu tensa en la simulació numèrica de veu. Per a tal efecte, s’ha usat el vocoder GlottDNN per analitzar variacions d’F0 i pendent espectral relacionades amb l’excitació glotal en vocals [a]. Aquestes variacions es mapegen mitjançant la comparació amb vocals sintètiques en valors d’F0 i Rd per simular vocals que s’assemblin als estils alegre i agressiu. Els resultats mostren que és necessari incrementar l’F0 i disminuir l’Rd respecte la veu neutra, amb variacions majors per a alegre que per agressiu, especialment per a vocals accentuades. Els resultats aconseguits en les investigacions realitzades validen la possibilitat d’afegir expressivitat a la síntesi basada en corpus US-TTS i a la simulació numèrica de veu basada en FEM. Tanmateix, encara hi ha marge de millora. Per exemple, l’estratègia aplicada a la producció numèrica de veu es podria millorar estudiant i desenvolupant mètodes de filtratge invers així com incorporant modificacions del tracte vocal, mentre que el framework US-TTS&S es podria beneficiar dels avenços en tècniques de transformació de veu incloent transformacions de la qualitat de veu, aprofitant l’experiència adquirida en la simulació numèrica de vocals expressives.El habla es una de las formas de comunicación más naturales y directas entre seres humanos, ya que codifica un mensaje y también claves paralingüísticas sobre el estado emocional del locutor, el tono o su intención, convirtiéndose así en fundamental en la consecución de una interacción humano-máquina (HCI) más natural. En este contexto, la generación de habla expresiva para el canal de salida de HCI es un elemento clave en el desarrollo de tecnologías asistenciales o asistentes personales entre otras aplicaciones. El habla sintética puede ser generada a partir de habla gravada utilizando métodos basados en corpus como la selección de unidades (US), que pueden conseguir resultados de alta calidad, pero de expresividad restringida a la propia del corpus. A fin de mejorar la calidad de la salida de la síntesis, la tendencia actual es construir bases de datos de voz cada vez más grandes, siguiendo especialmente la aproximación de síntesis llamada End-to-End basada en técnicas de aprendizaje profundo. Sin embargo, gravar corpus ad-hoc para cada estilo expresivo deseado puede ser extremadamente costoso o incluso inviable si el locutor no es capaz de realizar adecuadamente los estilos requeridos para una aplicación dada (ej: canto en el dominio de la narración de cuentos). Alternativamente, nuevos métodos basados en la física de la producción de voz se han desarrollado en la última década gracias al incremento en la potencia computacional. Por ejemplo, vocales o diptongos pueden ser obtenidos utilizando el método de elementos finitos (FEM) para simular la propagación de ondas acústicas a través de una geometría 3D realista del tracto vocal obtenida a partir de resonancias magnéticas (MRI). Sin embargo, dado que los principales esfuerzos en estos métodos de producción numérica de voz se han focalizado en la mejora del modelado del proceso de generación de voz, hasta ahora se ha prestado poca atención a su expresividad. Además, la colección de datos para estas simulaciones es muy costosa, además de requerir un largo postproceso manual como el necesario para extraer geometrías 3D del tracto vocal a partir de MRI. El objetivo de la tesis es añadir expresividad en un sistema que genera voz neutra, sin tener que adquirir datos expresivos del locutor original. Per un lado, se añaden capacidades expresivas a un sistema de conversión de texto a habla basado en selección de unidades (US-TTS) dotado de un corpus de voz neutra, para abordar necesidades específicas y concretas en el ámbito de la narración de cuentos, como son la voz cantada o situaciones de suspense. Para ello, la voz se parametriza utilizando un modelo harmónico y se transforma al estilo expresivo deseado de acuerdo con un sistema experto. Se presenta una primera aproximación, centrada en la síntesis de suspense creciente para la narración de cuentos, y se demuestra su viabilidad en cuanto a naturalidad y calidad de narración de cuentos. También se añaden capacidades de canto al sistema US-TTS mediante la integración de módulos de transformación de habla a voz cantada en el pipeline del TTS, y la incorporación de un módulo de generación de prosodia expresiva que permite al módulo de US seleccionar unidades más cercanas a la prosodia cantada obtenida a partir de la partitura de entrada. Esto resulta en un framework de síntesis de conversión de texto a habla y voz cantada basado en selección de unidades (US-TTS&S) que puede generar voz hablada y cantada a partir del mismo pequeño corpus de voz neutra (~2.6h). De acuerdo con los resultados objetivos, la estrategia de US guiada por la partitura permite reducir los factores de modificación de pitch requeridos para producir voz cantada a partir de las unidades de voz hablada seleccionadas, pero en cambio tiene una efectividad limitada con los factores de modificación de duraciones debido a la corta duración de las vocales habladas neutras. Los resultados de las pruebas perceptivas muestran que, a pesar de obtener una naturalidad obviamente inferior a la ofrecida por un sintetizador profesional de voz cantada, el framework puede abordar necesidades puntuales de voz cantada para la síntesis de narración de cuentos con una calidad razonable. La incorporación de expresividad se investiga también en la simulación numérica 3D de vocales basada en FEM mediante modificaciones en las señales de excitación glotal utilizando una aproximación fuente-filtro de producción de voz. Estas señales se generan utilizando un modelo Liljencrants-Fant (LF) controlado con el parámetro de forma del pulso Rd, que permite explorar el continuo de fonación laxo-tenso además del rango de frecuencias fundamentales, F0, de la voz hablada. Se analiza la contribución de la fuente glotal a los modos de alto orden en la síntesis FEM de las vocales cardinales [a], [i] y [u] mediante la comparación de los valores de energía de alta frecuencia (HFE) obtenidos con geometrías realistas y simplificadas del tracto vocal. Las simulaciones indican que los modos de alto orden se prevén perceptivamente relevantes de acuerdo con valores de referencia de la literatura, particularmente para fonaciones tensas y/o F0s altas. En cambio, para vocales con una fonación laxa y/o F0s bajas los niveles de HFE pueden resultar inaudibles, especialmente si no hay ruido de aspiración en la fuente glotal. Después de este estudio preliminar, se han analizado las características de excitación de vocales alegres y agresivas de un corpus paralelo de voz en castellano con el objetivo de incorporar estos estilos expresivos de voz tensa en la simulación numérica de voz. Para ello, se ha usado el vocoder GlottDNN para analizar variaciones de F0 y pendiente espectral relacionadas con la excitación glotal en vocales [a]. Estas variaciones se mapean mediante la comparación con vocales sintéticas en valores de F0 y Rd para simular vocales que se asemejen a los estilos alegre y agresivo. Los resultados muestran que es necesario incrementar la F0 y disminuir la Rd respecto la voz neutra, con variaciones mayores para alegre que para agresivo, especialmente para vocales acentuadas. Los resultados conseguidos en las investigaciones realizadas validan la posibilidad de añadir expresividad a la síntesis basada en corpus US-TTS y a la simulación numérica de voz basada en FEM. Sin embargo, hay margen de mejora. Por ejemplo, la estrategia aplicada a la producción numérica de voz se podría mejorar estudiando y desarrollando métodos de filtrado inverso, así como incorporando modificaciones del tracto vocal, mientras que el framework US-TTS&S desarrollado se podría beneficiar de los avances en técnicas de transformación de voz incluyendo transformaciones de la calidad de la voz, aprovechando la experiencia adquirida en la simulación numérica de vocales expresivas.Speech is one of the most natural and direct forms of communication between human beings, as it codifies both a message and paralinguistic cues about the emotional state of the speaker, its mood, or its intention, thus becoming instrumental in pursuing a more natural Human Computer Interaction (HCI). In this context, the generation of expressive speech for the HCI output channel is a key element in the development of assistive technologies or personal assistants among other applications. Synthetic speech can be generated from recorded speech using corpus-based methods such as Unit-Selection (US), which can achieve high quality results but whose expressiveness is restricted to that available in the speech corpus. In order to improve the quality of the synthesis output, the current trend is to build ever larger speech databases, especially following the so-called End-to-End synthesis approach based on deep learning techniques. However, recording ad-hoc corpora for each and every desired expressive style can be extremely costly, or even unfeasible if the speaker is unable to properly perform the styles required for a given application (e.g., singing in the storytelling domain). Alternatively, new methods based on the physics of voice production have been developed in the last decade thanks to the increase in computing power. For instance, vowels or diphthongs can be obtained using the Finite Element Method (FEM) to simulate the propagation of acoustic waves through a 3D realistic vocal tract geometry obtained from Magnetic Resonance Imaging (MRI). However, since the main efforts in these numerical voice production methods have been focused on improving the modelling of the voice generation process, little attention has been paid to its expressiveness up to now. Furthermore, the collection of data for such simulations is very costly, besides requiring manual time-consuming postprocessing like that needed to extract 3D vocal tract geometries from MRI. The aim of the thesis is to add expressiveness into a system that generates neutral voice, without having to acquire expressive data from the original speaker. One the one hand, expressive capabilities are added to a Unit-Selection Text-to-Speech (US-TTS) system fed with a neutral speech corpus, to address specific and timely needs in the storytelling domain, such as for singing or in suspenseful situations. To this end, speech is parameterised using a harmonic-based model and subsequently transformed to the target expressive style according to an expert system. A first approach dealing with the synthesis of storytelling increasing suspense shows the viability of the proposal in terms of naturalness and storytelling quality. Singing capabilities are also added to the US-TTS system through the integration of Speech-to-Singing (STS) transformation modules into the TTS pipeline, and by incorporating an expressive prosody generation module that allows the US to select units closer to the target singing prosody obtained from the input score. This results in a Unit Selection based Text-to-Speech-and-Singing (US-TTS&S) synthesis framework that can generate both speech and singing from the same neutral speech small corpus (~2.6 h). According to the objective results, the score-driven US strategy can reduce the pitch scaling factors required to produce singing from the selected spoken units, but its effectiveness is limited regarding the time-scale requirements due to the short duration of the spoken vowels. Results from the perceptual tests show that although the obtained naturalness is obviously far from that given by a professional singing synthesiser, the framework can address eventual singing needs for synthetic storytelling with a reasonable quality. The incorporation of expressiveness is also investigated in the 3D FEM-based numerical simulation of vowels through modifications of the glottal flow signals following a source-filter approach of voice production. These signals are generated using a Liljencrants-Fant (LF) model controlled with the glottal shape parameter Rd, which allows exploring the tense-lax continuum of phonation besides the spoken vocal range of fundamental frequency values, F0. The contribution of the glottal source to higher order modes in the FEM synthesis of cardinal vowels [a], [i] and [u] is analysed through the comparison of the High Frequency Energy (HFE) values obtained with realistic and simplified 3D geometries of the vocal tract. The simulations indicate that higher order modes are expected to be perceptually relevant according to reference values stated in the literature, particularly for tense phonations and/or high F0s. Conversely, vowels with a lax phonation and/or low F0s can result in inaudible HFE levels, especially if aspiration noise is not present in the glottal source. After this preliminary study, the excitation characteristics of happy and aggressive vowels from a Spanish parallel speech corpus are analysed with the aim of incorporating this tense voice expressive styles into the numerical production of voice. To that effect, the GlottDNN vocoder is used to analyse F0 and spectral tilt variations associated with the glottal excitation on vowels [a]. These variations are mapped through the comparison with synthetic vowels into F0 and Rd values to simulate vowels resembling happy and aggressive styles. Results show that it is necessary to increase F0 and decrease Rd with respect to neutral speech, with larger variations for happy than aggressive style, especially for the stressed [a] vowels. The results achieved in the conducted investigations validate the possibility of adding expressiveness to both corpus-based US-TTS synthesis and FEM-based numerical simulation of voice. Nevertheless, there is still room for improvement. For instance, the strategy applied to the numerical voice production could be improved by studying and developing inverse filtering approaches as well as incorporating modifications of the vocal tract, whereas the developed US-TTS&S framework could benefit from advances in voice transformation techniques including voice quality modifications, taking advantage of the experience gained in the numerical simulation of expressive vowels

The feeling of anger: From brain networks to linguistic expressions.

This review of the neuroscience of anger is part of The Human Affectome Project, where we attempt to map anger and its components (i.e., physiological, cognitive, experiential) to the neuroscience literature (i.e., genetic markers, functional imaging of human brain networks) and to linguistic expressions used to describe anger feelings. Given the ubiquity of anger in both its normative and chronic states, specific language is used in humans to express states of anger. Following a review of the neuroscience literature, we explore the language that is used to convey angry feelings, as well as metaphors reflecting inner states of anger experience. We then discuss whether these linguistic expressions can be mapped on to the neural circuits during anger experience and to distinct components of anger. We also identify relationships between anger components, brain networks, and other affective research relevant to motivational states of dominance and basic needs for safety

Learning Disabilities

Learning disabilities are a heterogeneous group of disorders characterized by failure to acquire, retrieve, and use information competently. These disorders have a multifactorial aetiology and are most common and severe in children, especially when comorbid with other chronic health conditions. This book provides current and comprehensive information about learning disorders, including information on neurobiology, assessment, clinical features, and treatment. Chapters cover such topics as historical research and hypotheses of learning disorders, neuropsychological assessment and counselling, characteristics of specific disorders such as autism and ADHD, evidence-based treatment strategies and assistive technologies, and much more

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

Sex and gender in biomedicine: theories, methodologies, results

Sex and gender in biomedicine are innovative research concepts of theoretical and clinical medicine that enable a better understanding of health and disease, evidence-based knowledge, effective therapies, and better health outcomes for women and men. Gender Medicine stimulates new ways of doing research: that is to consider sex and gender at all levels of research, from basic research into gene polymorphisms to health behaviour. New research questions have been put forward that focus not on differences per se but on the development of differences. In this book, contributions from the field of neuroscience, addiction research, and organ transplantation exemplify concepts, approaches, methods and results in the field