PRESENT AND FUTURE PERVASIVE HEALTHCARE METHODOLOGIES: INTELLIGENT BODY DEVICES, PROCESSING AND MODELING TO SEARCH FOR NEW CARDIOVASCULAR AND PHYSIOLOGICAL BIOMARKERS

The motivation behind this work comes from the area of pervasive computing technologies for healthcare and wearable healthcare IT systems, an emerging field of research that brings in revolutionary paradigms for computing models in the 21st century. The aim of this thesis is focused on emerging personal health technologies and pattern recognition strategies for early diagnosis and personalized treatment and rehabilitation for individuals with cardiovascular and neurophysiological diseases. Attention was paid to the development of an intelligent system for the automatic classification of cardiac valve disease for screening purposes. Promising results were reported with the possibility to implement a new screening strategy for the diagnosis of cardiac valve disease in developing countries. A novel assistive architecture for the elderly able to non-invasively assess muscle fatigue by surface electromyography using wireless platform during exercise with an ergonomic platform was presented. Finally a wearable chest belt for ECG monitoring to investigate the psycho-physiological effects of the autonomic system and a wearable technology for monitoring of knee kinematics and recognition of ambulatory activities were characterized to evaluate the reliability for clinical purposes of collected data. The potential impact in the clinical arena of this research would be extremely important, since promising data show how such emerging personal technologies and methodologies are effective in several scenarios to early screening and discovery of novel diagnostic and prognostic biomarkers

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

The Co-regulatory Effects of Emotionally Focused Therapy

Mental health literature emphasizes the necessity of expanding emotional regulation to improve symptomology of a variety of mental health disorders. Coregulatory experiences have been shown to expand individual emotional regulation and are more likely in relationships with secure attachment. Emotionally Focused Therapy (EFT) is focused on developing secure attachment between partners. This study examined the coregulatory effects of EFT with a single couple over the course of couples’ counseling. The participating couple received eight couples’ counseling sessions from a clinician who is a certified EFT trainer, supervisor, and therapist while having their heart rate, electrodermal activity, and skin temperature taken during the sessions. Surrogate synchrony analysis (SUSY) was used to assess physiological synchrony between the couple over their sessions. Heart rate variability was also calculated. The degree of relationship between the heart rate variabilities of the couple was measured using linear regression. While the results of the study did not show significant increases in physiological synchrony between the couple, the results emphasize the presents of in and out of synchrony pendulation occurring between the couple as well as the presence of lagged synchrony as the couple responds according to the partner’s experience. Implications for future research are also included

Dissociation and interpersonal autonomic physiology in psychotherapy research: an integrative view encompassing psychodynamic and neuroscience theoretical frameworks

Interpersonal autonomic physiology is an interdisciplinary research field, assessing the relational interdependence of two (or more) interacting individual both at the behavioral and psychophysiological levels. Despite its quite long tradition, only eight studies since 1955 have focused on the interaction of psychotherapy dyads, and none of them have focused on the shared processual level, assessing dynamic phenomena such as dissociation. We longitudinally observed two brief psychodynamic psychotherapies, entirely audio and video-recorded (16 sessions, weekly frequency, 45 min.). Autonomic nervous system measures were continuously collected during each session. Personality, empathy, dissociative features and clinical progress measures were collected prior and post therapy, and after each clinical session. Two-independent judges, trained psychotherapist, codified the interactions\u2019 micro-processes. Time-series based analyses were performed to assess interpersonal synchronization and de-synchronization in patient\u2019s and therapist\u2019s physiological activity. Psychophysiological synchrony revealed a clear association with empathic attunement, while desynchronization phases (range of length 30-150 sec.) showed a linkage with dissociative processes, usually associated to the patient\u2019s narrative core relational trauma. Our findings are discussed under the perspective of psychodynamic models of Stern (\u201cpresent moment\u201d), Sander, Beebe and Lachmann (dyad system model of interaction), Lanius (Trauma model), and the neuroscientific frameworks proposed by Thayer (neurovisceral integration model), and Porges (polyvagal theory). The collected data allows to attempt an integration of these theoretical approaches under the light of Complex Dynamic Systems. The rich theoretical work and the encouraging clinical results might represents a new fascinating frontier of research in psychotherapy

Objective quantification and analysis of eating behaviour associated with obesity development - from lab to real-life

Introduction: The last four decades have seen a marked increase in childhood and adult obesity prevalence, attributed to an “obesogenic” environment. Several genetical, environmental and behavioural factors have been identified that increase the risk of obesity, but treatment outcomes are usually modest and the risk of relapse high. One limitation responsible for these moderate results could be methodological, with researchers questioning both the external validity of eating behaviour measures in the laboratory (controlled) and the internal validity of eating behaviour measures in free-living (real-life) settings. Technological advances could solve some of these issues, allowing for accurate methods, similar to those used in controlled settings, to be used in real- life. Deploying accurate methods in both controlled and real-life settings would in turn enable the estimation of external validity, determining the limits of generalization between settings. In turn enabling the deployment of these methods in settings which allow large scale screening, for early identification of individuals at risk of becoming obese. Aim: The overarching aim of the thesis was to: i) evaluate the stability of human eating behaviour and ii) investigate the usability and feasibility of methods developed for controlled settings, when deployed in semi-controlled and real-life settings. Paper I – Determine if individuals maintain their eating behaviour, in relation to the group, despite experimental manipulations to meal conditions (i.e., unit sizes and serving occasion). Paper II – Feasibility of employing novel technology for baseline eating behaviour collection in adolescents eating school lunches in a school cafeteria setting (semi-controlled). Paper III – Feasibility of employing novel technology in an experimental manipulation study, to determine the effect of proximity in a semi-controlled school setting. Paper IV – By use of novel technology, examine the maintenance of eating behaviours in adolescents, from semi-controlled to real-life settings, both at group- and individual-level. Methods: Paper I – Three randomised crossover studies, of which two compared eating behaviour across different unit sizes, while one compared eating behaviour between lunch and dinner in healthy young adults. Performed in a controlled setting, employing traditional laboratory methods. Paper II – An observational study of healthy adolescents, performed at lunch in a school cafeteria, employing traditional laboratory methods in a semi-controlled setting. Paper III – A randomised experimental study of healthy adolescents, performed in a semi- controlled, comparing the eating behaviour between two groups seated at different proximity to food items. Paper IV – An observational study on eating behaviour of healthy adolescents, divided into two parts; i) collection of eating behaviour data, performed at lunch in a school cafeteria, using a similar protocol to that of Paper II and ii) collection of eating behaviour data by the participants in real-life settings, using the same devices as in the controlled setting. Results: In all papers the distribution of eating behaviour values between individuals were large. In Paper I, the largest increase in unit size significantly increased meal duration and chews and while there was a trend for both increased meal duration and number of chews the larger the food unit sizes were, it did not lead to a significant reduction in food intake. Meanwhile, the correlation coefficient of all eating behaviours across all conditions was high (except for number of bites between the largest and smallest food unit size condition). In Paper II, male participants ate significantly more, mediated by significantly larger bites. The bite sizes of both men and women were reduced as the meal progressed. In Paper III, increased distance to food led to a significant reduction in intake, caused by individuals taking less chocolate. In Paper IV, there was no significant difference in eating behaviour characteristics between the semi- controlled and real-life meals. In addition, the correlation coefficient of food intake and eating rate was high between settings, while the correlation of meal duration was low. Also, on an individual level, 50%, 32% and 27% of the food intake, eating rate and meal duration measures, respectively, from the semi-controlled meal fell within the confidence interval of the real-life meals. In the semi-controlled and real-life settings (Papers II-IV), the agreement between subjective and objective eating behaviour measures were very low. Meanwhile, in both semi- controlled and real-life settings the method could be deployed within the time schedule imposed by the school, with high data retention. Also, participants rated the comfortability participating in the semi-controlled and real-life settings very high and the usability of the system as “Good” or higher. Conclusions: Human eating behaviour appears stable in comparison to the group when unit size and serving occasion is manipulated in a controlled setting and when eating in different settings (semi- controlled and real-life). Suggesting generalisations can be made between settings and conditions and that risk behaviours may be measured in settings other than real-life, at least on group level. However, although individual prediction rates of eating behaviour characteristics from semi-controlled setting to real-life settings appears higher than subjective ratings, they are still too low for use in the design of tailored interventions. In addition, compared to controlled studies, the method allowed recruitment of a younger age group, since it enabled measurements in a different location. The thesis also provides evidence that the employed methods are usable, feasible and acceptable, with high data retention in adolescent users, in semi-controlled and real-life settings. Methods similar to the ones used in this thesis can provide previously unattainable information (primarily temporal) in settings that are less controlled than the laboratory, such as semi-controlled and real-life

Autonomic nervous system biomarkers from multi-modal and model-based signal processing in mental health and illness

Esta tesis se centra en técnicas de procesado multimodal y basado en modelos de señales para derivar parámetros fisiológicos, es decir, biomarcadores, relacionados con el sistema nervioso autónomo (ANS). El desarrollo de nuevos métodos para derivar biomarcadores de ANS no invasivos en la salud y la enfermedad mental ofrece la posibilidad de mejorar la evaluación del estrés y la monitorización de la depresión. Para este fin, el presente documento se estructura en tres partes principales. En la Parte I, se proporciona unaintroducción a la salud y la enfermedad mental (Cap. 1). Además, se presenta un marco teórico para investigar la etiología de los trastornos mentales y el papel del estrés en la enfermedad mental (Cap. 2). También se destaca la importancia de los biomarcadores no invasivos para la evaluación del ANS, prestando especial atención en la depresión clínica (Cap. 3, 4). En la Parte II, se proporciona el marco metodológico para derivar biomarcadores del ANS. Las técnicas de procesado de señales incluyen el análisis conjunto de la variabilidad del rítmo cardíaco (HRV) y la señal respiratoria (Cap. 6), técnicas novedosas para derivar la señal respiratoria del electrocardiograma (ECG) (Cap. 7) y un análisis robusto que se basa en modelar la forma de ondas del pulso del fotopletismograma (PPG) (Ch. 8). En la Parte III, los biomarcadores del ANS se evalúan en la quantificacióndel estrés (Cap. 9) y en la monitorización de la depresión (Ch. 10).Parte I: La salud mental no solo está relacionada con ese estado positivo de bienestar, en el que un individuo puede enfrentar a las situaciones estresantes de la vida, sino también con la ausencia de enfermedad mental. La enfermedad o trastorno mental se puede definir como un trastorno emocional, cognitivo o conductual que causa un deterioro funcional sustancial en una o más actividades importantes de la vida. Los trastornos mentales más comunes, que muchas veces coexisten, son la ansiedad y el trastorno depresivo mayor (MDD). La enfermedad mental tiene un impacto negativo en la calidad de vida, ya que se asocia con pérdidas considerables en la salud y el funcionamiento, y aumenta ignificativamente el riesgo de una persona de padecer enfermedades ardiovasculares.Un instigador común que subyace a la comorbilidad entre el MDD, la patologíacardiovascular y la ansiedad es el estrés mental. El estrés es común en nuestra vida de rítmo rapido e influye en nuestra salud mental. A corto plazo, ANS controla la respuesta cardiovascular a estímulos estresantes. La regulación de parámetros fisiológicos, como el rítmo cardíaco, la frecuencia respiratoria y la presión arterial, permite que el organismo responda a cambios repentinos en el entorno. Sin embargo, la adaptación fisiológica a un fenómeno ambiental que ocurre regularmente altera los sistemas biológicos involucrados en la respuesta al estrés. Las alteraciones neurobiológicas en el cerebro pueden alterar lafunción del ANS. La disfunción del ANS y los cambios cerebrales estructurales tienen un impacto negativo en los procesos cognitivos, emocionales y conductuales, lo que conduce al desarrollo de una enfermedad mental.Parte II: El desarrollo de métodos novedosos para derivar biomarcadores del ANS no invasivos ofrece la posibilidad de mejorar la evaluacón del estrés en individuos sanos y la disfunción del ANS en pacientes con MDD. El análisis conjunto de varias bioseñales (enfoquemultimodal) permite la cuantificación de interacciones entre sistemas biológicos asociados con ANS, mientras que el modelado de bioseãles y el análisis posterior de los parámetros del modelo (enfoque basado en modelos) permite la cuantificación robusta de cambios en mecanismos fisiológicos relacionados con el ANS. Un método novedoso, quetiene en cuenta los fenómenos de acoplo de fase y frecuencia entre la respiración y las señales de HRV para evaluar el acoplo cardiorrespiratorio no lineal cuadrático se propone en el Cap. 6.3. En el Cap. 7 se proponen nuevas técnicas paramejorar lamonitorización de la respiración. En el Cap. 8, para aumentar la robustez de algunas medidas morfológicas que reflejan cambios en el tonno arterial, se considera el modelado del pulso PPG como una onda principal superpuesta con varias ondas reflejadas.Parte III: Los biomarcadores del ANS se evalúan en la cuantificación de diferentes tipos de estrés, ya sea fisiológico o psicológico, en individuos sanos, y luego, en la monitorización de la depresión. En presencia de estrés mental (Cap. 9.1), inducido por tareas cognitivas, los sujetos sanos muestran un incremento en la frecuencia respiratoria y un mayor número de interacciones no lineales entre la respiración y la seãl de HRV. Esto podría estar asociado con una activación simpática, pero también con una respiración menos regular. En presencia de estrés hemodinámico (Cap. 9.2), inducido por un cambio postural, los sujetos sanos muestran una reducción en el acoplo cardiorrespiratoriono lineal cuadrático, que podría estar relacionado con una retracción vagal. En presencia de estrés térmico (Cap. 9.3), inducido por la exposición a emperaturas ambientales elevadas, los sujetos sanos muestran un aumento del equilibrio simpatovagal. Esto demuestra que los biomarcadores ANS son capaces de evaluar diferentes tipos de estrés y pueden explorarse más en el contexto de la monitorización de la depresión. En el Cap. 10, se evalúan las diferencias en la función del ANS entre elMDD y los sujetos sanos durante un protocolo de estrés mental, no solo con los valores brutos de los biomarcadores del ANS, sino también con los índices de reactividad autónoma, que reflejan la capacidad deun individuo para afrontar con una situación desafiante. Los resultados muestran que la depresión se asocia con un desequilibrio autonómico, que se caracteriza por una mayor actividad simpática y una reducción de la distensibilidad arterial. Los índices de reactividad autónoma cuantificados por cambios, entre etapas de estrés y de recuperación, en los sustitutos de la rigidez arterial, como la pérdida de amplitud de PPG en las ondas reflejadas, muestran el mejor rendimiento en términos de correlación con el grado de la depresión, con un coeficiente de correlación r = −0.5. La correlación negativa implicaque un mayor grado de depresión se asocia con una disminución de la reactividadautónoma. El poder discriminativo de los biomarcadores del ANS se aprecia también por su alto rendimiento diagnóstico para clasificar a los sujetos como MDD o sanos, con una precisión de 80.0%. Por lo tanto, se puede concluir que los biomarcadores del ANS pueden usarse para evaluar el estrés y que la distensibilidad arterial deteriorada podría constituir un biomarcador de salud mental útil en el seguimiento de la depresión.This dissertation is focused on multi-modal and model-based signal processing techniques for deriving physiological parameters, i.e. biomarkers, related to the autonomic nervous system (ANS). The development of novel approaches for deriving noninvasive ANS biomarkers in mental health and illness offers the possibility to improve the assessment of stress and the monitoring of depression. For this purpose, the present document is structured in three main parts. In Part I, an introduction to mental health and illness is provided (Ch. 1). Moreover, a theoretical framework for investigating the etiology of mental disorders and the role of stress in mental illness is presented (Ch. 2). The importance of noninvasive biomarkers for ANS assessment, paying particular attention in clinical depression, is also highlighted (Ch. 3, 4). In Part II, themethodological framework for deriving ANS biomarkers is provided. Signal processing techniques include the joint analysis of heart rate variability (HRV) and respiratory signals (Ch. 6), novel techniques for deriving the respiratory signal from electrocardiogram (ECG) (Ch. 7), and a robust photoplethysmogram(PPG)waveform analysis based on amodel-based approach (Ch. 8). In Part III, ANS biomarkers are evaluated in stress assessment (Ch. 9) and in the monitoring of depression (Ch. 10). Part I:Mental health is not only related to that positive state ofwell-being, inwhich an individual can cope with the normal stresses of life, but also to the absence of mental illness. Mental illness or disorder can be defined as an emotional, cognitive, or behavioural disturbance that causes substantial functional impairment in one or more major life activities. The most common mental disorders, which are often co-occurring, are anxiety and major depressive disorder (MDD). Mental illness has a negative impact on the quality of life, since it is associated with considerable losses in health and functioning, and increases significantly a person’s risk for cardiovascular diseases. A common instigator underlying the co-morbidity between MDD, cardiovascular pathology, and anxiety is mental stress. Stress is common in our fast-paced society and strongly influences our mental health. In the short term, ANS controls the cardiovascular response to stressful stimuli. Regulation of physiological parameters, such as heart rate, respiratory rate, and blood pressure, allows the organism to respond to sudden changes in the environment. However, physiological adaptation to a regularly occurring environmental phenomenon alters biological systems involved in stress response. Neurobiological alterations in the brain can disrupt the function of the ANS. ANS dysfunction and structural brain changes have a negative impact on cognitive, emotional, and behavioral processes, thereby leading to development of mental illness. Part II: The development of novel approaches for deriving noninvasive ANS biomarkers offers the possibility to improve the assessment of stress in healthy individuals and ANS dysfunction in MDD patients. Joint analysis of various biosignals (multi-modal approach) allows for the quantification of interactions among biological systems associated with ANS, while the modeling of biosignals and subsequent analysis of the model’s parameters (model-based approach) allows for the robust quantification of changes in physiological mechanisms related to the ANS. A novel method, which takes into account both phase and frequency locking phenomena between respiration and HRV signals, for assessing quadratic nonlinear cardiorespiratory coupling is proposed in Ch. 6.3. Novel techniques for improving the monitoring of respiration are proposed in Ch. 7. In Ch. 8, to increase the robustness for some morphological measurements reflecting arterial tone changes, the modeling of the PPG pulse as amain wave superposed with several reflected waves is considered. Part III: ANS biomarkers are evaluated in the assessment of different types of stress, either physiological or psychological, in healthy individuals, and then, in the monitoring of depression. In the presence of mental stress (Ch. 9.1), induced by cognitive tasks, healthy subjects show an increment in the respiratory rate and higher number of nonlinear interactions between respiration and HRV signal, which might be associated with a sympathetic activation, but also with a less regular breathing. In the presence of hemodynamic stress (Ch. 9.2), induced by a postural change, healthy subjects show a reduction in strength of the quadratic nonlinear cardiorespiratory coupling, whichmight be related to a vagal withdrawal. In the presence of heat stress (Ch. 9.3), induced by exposure to elevated environmental temperatures, healthy subjects show an increased sympathovagal balance. This demonstrates that ANS biomarkers are able to assess different types of stress and they can be further explored in the context of depression monitoring. In Ch. 10, differences in ANS function between MDD and healthy subjects during a mental stress protocol are assessed, not only with the raw values of ANS biomarkers but also with autonomic reactivity indices, which reflect the ability of an individual to copewith a challenging situation. Results show that depression is associated with autonomic imbalance, characterized by increased sympathetic activity and reduced arterial compliance. Autonomic reactivity indices quantified by changes, from stress to recovery, in arterial stiffness surrogates, such as the PPG amplitude loss in wave reflections, show the best performance in terms of correlation with depression severity, yielding to correlation coefficient r = −0.5. The negative correlation implies that a higher degree of depression is associated with a decreased autonomic reactivity. The discriminative power of ANS biomarkers is supported by their high diagnostic performance for classifying subjects as having MDD or not, yielding to accuracy of 80.0%. Therefore, it can be concluded that ANS biomarkers can be used for assessing stress and that impaired arterial compliance might constitute a biomarker of mental health useful in the monitoring of depression.<br /

ACTIGRAPHIC ASSESSMENT OF SLEEP-ACTIVITY CYCLE IN PHYSIOPATHOLOGY: EXPERIMENTAL AND METHODOLOGICAL STUDIES

The principal objective of my research during my PhD has been the investigation of the rest-activity circadian rhythms in physiopathology, dealing with both experimental and methodological issues. On the experimental side, the focus of my research program was centered on the investigation of the rest-activity circadian rhythms in patients with binge eating disorders. On the methodological side, my activity was aimed at exploring the relationships between the actigraphy-based assessment of circadian rhythmicity and the questionnaire-based assessment of circadian typology. The thesis is organized in 11 Chapters. Chapter 1 provides a short introduction to chronobiology and to the components of a circadian rhythm. Chapter 2 describes the two most common methods used to evaluate the circadian rhythmicity, namely actigraphy and self-administered questionnaires. These two approaches have remarkable strengths and weaknesses. Actigraphy is a non-invasive method (usually based on a small, wearable actigraphic unit) that allows one to monitor the activity levels during the 24 hours, to detect the rest-activity circadian rhythm, to evaluate the activity levels during the nocturnal sleep and to assess the quality and quantity of sleep by specific sleep parameters. One alternative approach to assessment of the circadian typology of a subject is based on self-administered questionnaires. Questionnaires are obviously less objective than actigraphy-based assessments, but have the advantage of being simple and cost-effective. Chapter 3 provides a general overview of all the research projects I have taken part in throughout my PhD course. This chapter has been written with the reader in mind and aims to succinctly describe the structure and function of the subsequent chapters, 4 through 11. In Chapters 4 to 7, I will focus on the experimental core of my research activity during my PhD course, which is the chronobiological investigation of obese patients suffering from binge eating disorder. First, I will provide an overview of the features characterizing this disorder. Then, I will describe three experimental studies that were carried out in these patients with the purpose of i): quantifying their rest-activity circadian rhythm (RARs); ii) describing their sleep behaviour; iii) evaluating the effectiveness of a physical activity program as an auxiliary therapeutic approach to the traditional treatment for BED. In Chapters 8 to 10, I will illustrate the methodological core of my research activity during my PhD which aims to develop predictive formulas - based on linear regression - enabling investigators to use the questionnaire-based assessment of circadian typology (Morningness-Eveningness Questionnaire, MEQ) as a surrogate of the actigraphy-based assessment of circadian rhythmicity. A methodological project of this kind was successful is showing that both MEQ and its reduced version rMEQ are appropriate for the prediction of the actigraphy-based acrophase and this may prove useful when actigraphy-based measurements are not applicable, in so far as they result either too complex or time-consuming. Chapter 11, the final chapter, is concerned with providing concise summaries of the other studies I have been involved in during my PhD course. Seven experimental studies are described in relation to: i) the influence of chronotype on circadian rhythm (RARs), on sleep, on physical activity and on cardiac autonomic function; ii) the effects of aerobic physical activity on sleep and on markers of insulin resistance in breast cancer women; iii) the effects of short and prolonged exposure to cave environments on human physiology. The thesis also comprises an appendix containing the list of all the scientific papers that I co-authored in the course of my PhD thesis. The list reports both the published and the submitted articles