Continuous electronic data capture of physiology, behavior and experience in real life: towards ecological momentary assessment of emotion

Emotions powerfully influence our physiology, behavior, and experience. A comprehensive assessment of affective states in health and disease would include responses from each of these domains in real life. Since no single physiologic parameter can index emotional states unambiguously, a broad assessment of physiologic responses is desirable. We present a recently developed system, the LifeShirt, which allows reliable ambulatory monitoring of a wide variety of cardiovascular, respiratory, metabolic, motor-behavioral, and experiential responses. The system consists of a garment with embedded inductive plethysmography and other sensors for physiologic data recording and a handheld computer for input of experiential data via touch screen. Parameters are extracted offline using sophisticated analysis and display software. The device is currently used in clinical studies and to monitor effects of physical and emotional stress in naturalistic settings. Further development of signal processing and pattern recognition algorithms will enhance computerized identification of type and extent of physical and emotional activatio

Methodological Implications of Nonlinear Dynamical Systems Models for Whole Systems of Complementary and Alternative Medicine

This paper focuses on the worldview hypotheses and research design approaches from nonlinear dynamical complex systems (NDS) science that can inform future studies of whole systems of complementary and alternative medicine (WS-CAM), e.g., Ayurveda, traditional Chinese medicine, and homeopathy. The worldview hypotheses that underlie NDS and WS-CAM (contextual, organismic, interactive-integrative - Pepper, 1942) overlap with each other, but differ fundamentally from those of biomedicine (formistic, mechanistic). Differing views on the nature of causality itself lead to different types of study designs. Biomedical efficacy studies assume a simple direct mechanistic cause-effect relationship between a specific intervention and a specific bodily outcome, an assumption less relevant to WS-CAM outcomes. WS-CAM practitioners do not necessarily treat a symptom directly. Rather, they intervene to modulate an intrinsic central imbalance of the person as a system and to create a more favorable environmental context for the emergence of health, e. g., with dietary changes compatible with the constitutional type. The rebalancing of the system thereby fosters the emergence of indirect, diffuse, complex effects throughout the person and the person\u27s interactions with his/her environment. NDS theory-driven study designs thus have the potential for greater external and model validity than biomedically driven efficacy studies (e. g., clinical trials) for evaluating the indirect effects of WS-CAM practices. Potential applications of NDS analytic techniques to WS-CAM include characterizing different constitutional types and documenting the evolution and dynamics of whole-person healing and well-being over time. Furthermore, NDS provides models and methods for examining interactions across organizational scales, from genomic/proteomic/metabolomic networks to individuals and social groups

Does Breath Pattern Influence Reacting and Feeling?

This experimental within-participant reversal paradigm quantified effects of breath manipulation on emotional reactivity and inhibition. Participants were assessed for inhibitory ability and emotional reactivity at baseline and following three breathing conditions: controlled neutral, resonance frequency, and variable breathing; selected to assess a range of breathing behavior from anxious breathing, vegetative breathing, and meditative breathing. Emotional reactivity was elicited using the International Affective Picture System and inhibition utilizing a verbal Stop Signal task. Dependent variables for emotion induction included self-reported mood and arousal using the Self-Assessment Manikin of Valence and Arousal, and for inhibition was response time and accuracy. For twenty-six healthy participants, emotion induction demonstrated no statistical findings across breathing condition. However, for inhibition tasks, a significant reduction in inhibitory response time and increase in response accuracy was found following resonance frequency breathing. Breath manipulation effects inhibitory control and could be a tool for improving efficacy of behavioral therapies addressing aspects of inhibition

Anxolotl - An Anxiety Companion App

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresA Organização Mundial de Saúde apresentou as perturbações mentais como os maiores contribuintes para incapacidade global em 2015, com os distúrbios de ansiedade a ocuparem a sexta posição. Distúrbios de ansiedade têm um alta prevalência na sociedade, e apresentam sintomas precoces que podem ser detetados. Nesta tese, produzimos um sistema capaz de detetar sintomas de distúrbios de ansiedade antes que a doença se instale por completo. Adicionalmente, queremos dar outra opção a portadores, monitorizando o seu estado mental e oferecendo a hipótese de tratarem dos seus níveis de ansiedade antes que apareçam mais sintomas. Aqui introduzimos um sistema de saúde móvel, entitulado de Anxolotl, que pode detetar e classificar níveis de ansiedade multiclasse e detetar níveis binários de estados de pânico . A nossa solução é composta por: datacenter, com o objectivo de guardar dados fisiológicos anónimos, e aplicar modelos de aprendizagem automática; broker de mensagens, que irá providenciar escalaabilidade e habilidade de desacoplamento no sistema; aplicação móvel, que funcionará em conjunto com um wearable para capturar dados fisiológicos. A nossa applicação é capaz de detetar e monitorizar diariamente, os níveis de ansiedade e pânico do utilizador, filtrando dados dúbios com base em atividade física. A aplicação também apresenta múltiplos exercícios de respiração guiados, bem como meditações acompanhadas para vários cenários de saúde mental. O nosso modelo de deteção de ansiedade foi capaz de apresentar uma precisão de 92% e um f1-Score de 90% na classificação de ansiedade multiclasse, treinando com um dataset com 124 entradas, enquanto que o nosso modelo de deteção de pânico apresenta uma precisão de 94% e um f1-Score de 94%. Estes valores foram atingindos utilizando maioritariamente dados de ritmo cardíaco. O código dos modelos está disponível em https://github.com/nunogoms/Anxolotl-engines.World Health Organization referred that common mental health disorders were the biggest contributors to global disability during the year of 2015, with anxiety disorders occupying the 6th position. Currently, anxiety disorders have high prevalence in society, and present early symptoms that are suited to be detected. With this thesis, we intend to produce a system capable of detecting the anxiety disorder early symptoms before the onset of the full range of symptoms. Additionally, we want to give another option to people already affected, in the form of monitoring their mental health, and the ability for them to react to their anxiety state quickly. Herein, we are introducing a mobile health system — Anxolotl, that can detect and classify multi class anxiety levels and detect binary panic states. Our solution is composed by: a datacenter, intended to store anonymous physiological data and applying the machine learning models; a message broker, aiming to provide scalability and decoupling to the system; and, finally a mobile app, which will work in tandem with a wearable to capture physiological data. The app is able to track and monitor, on a daily basis, its user’s anxiety and panic levels, filtering when the data is unreliable based on activity. It also presents the users with guided breathing exercises for multiple mental health scenarios as well as some guided meditations, in an effort to help its users. The Anxiety Engine model provided a 92% accuracy and 90% f1-Score in classifying multi-class anxiety levels, training and testing with a dataset containing 124 entries, and our binary Panic Engine had an accuracy of 94% and a f1-Score of 94%. Both these scenarios were mainly achieved by using heart rate data, activity context was also used in some scenarios. The code for these models is available at https://github.com/nunogoms/Anxolotl-engines.N/

Symptom Experience and Treatment Delay during Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Dissertation

Chronic obstructive pulmonary disease (COPD) is a major health problem in the United States. Acute exacerbations of COPD are primarily responsible for the physical, psychological and economic burden of this disease. Early identification and treatment of exacerbations is important to improve patient and healthcare outcomes. Little is known about how patients with COPD recognize an impending exacerbation and subsequently decide to seek treatment. The purpose of this qualitative descriptive study was to explore and describe symptom recognition and treatment delay in individuals experiencing an acute exacerbation of chronic obstructive pulmonary disease (COPD). Leventhal’s Common Sense Model of illness representation undergirded this study. Using semi-structured interviews, adults hospitalized with an acute exacerbation of COPD were asked to describe their symptom experience and self care behaviors, including treatment seeking, in the days to weeks prior to hospitalization. Data analysis revealed one main theme: Recognizing, responding and reacting to change, and six subthemes: Something’s coming, Here we go again, Seeking urgent treatment, Riding it out, Not in charge anymore and My last day that richly described the COPD exacerbation experience. The study revealed that patients experience an illness prodrome prior to exacerbation and have a recurrent exacerbation symptom pattern that was self-recognized. Treatment seeking was most influenced by the speed and acuity of exacerbation onset, severity of breathlessness, fears of death, nature of patient-provider relationship and the perception of stigmatization during prior healthcare encounters. These findings are important for the development of interventions to improve patient recognition and management of COPD exacerbations in the future

Application of an evidence-based, out-patient treatment strategy for COVID-19: Multidisciplinary medical practice principles to prevent severe disease☆

The COVID-19 pandemic has devastated individuals, families, and institutions throughout the world. Despite the breakneck speed of vaccine development, the human population remains at risk of further devastation. The decision to not become vaccinated, the protracted rollout of available vaccine, vaccine failure, mutational forms of the SARS virus, which may exhibit mounting resistance to our molecular strike at only one form of the viral family, and the rapid ability of the virus(es) to hitch a ride on our global transportation systems, means that we are will likely continue to confront an invisible, yet devastating foe. The enemy targets one of our human physiology’s most important and vulnerable life-preserving body tissues, our broncho-alveolar gas exchange apparatus. Notwithstanding the fear and the fury of this microbe\u27s potential to raise existential questions across the entire spectrum of human endeavor, the application of an early treatment intervention initiative may represent a crucial tool in our defensive strategy. This strategy is driven by evidence-based medical practice principles, those not likely to become antiquated, given the molecular diversity and mutational evolution of this very clever “world traveler

Effectiveness of Physiological Alarm Management Strategies to Prevent Alarm Fatigue

There is limited clinical research on the effectiveness of alarm management strategies and nursing behaviors related to alarms in clinical settings. As many as 76% of physiological monitor alarms are overlooked as clinically insignificant by nursing staff. Excessive alarms may impact patient outcomes and cause cognitive overload for nurses that can result in medical errors and missed patient resuscitations. The purpose of this systematic review was to rate alarm management studies on level of evidence for interventions, nursing responses to alarms, and impact on alarm fatigue behavior. The nursing role effectiveness model guided this project. Twenty-seven studies were reviewed to analyze outcome effectiveness by addressing structure, process, and outcomes related to how the roles of the nurse affect nurse-sensitive patient outcomes. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and the Cochrane guidelines guided study selection and analysis. A second reviewer collaborated on the search strategy and provided an independent review of the identified literature. The effectiveness of alarm management was difficult to determine because most studies were descriptive, cohort, or nonrandomized trials. Review findings did not support a relationship between the amount of alarms and increased alarm fatigue behaviors. Findings indicated that nurses\u27 attitudes and alarm fatigue behaviors are present globally and have not significantly altered since reduction strategies were implemented. The findings may impact social change by decreasing nurses\u27 stress levels related to cognitive workloads, improving patient outcomes, and supporting increased levels of nurses\u27 workforce satisfaction

Fear recognition for women using a reduced set of physiological signals

This article belongs to the Section Biomedical Sensors.Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.This activity is partially supported by Community of Madrid in the pluri-annual agreement with Universidad Carlos III de Madrid, in the line of action "Excelence with the University Faculty", V Regional Plan of Scientific Research and Technology Innovation 2016-2020, and by the Community of Madrid Region Government under the Synergic Program: EMPATIA-CM, Y2018/TCS-5046