Assessing physiological response mechanisms and the role of psychosocial job resources in the physical activity health paradox : study protocol for the Flemish Employees' Physical Activity (FEPA) study

Background: In the current labour system many workers are still exposed to heavy physical demands during their job. In contrast to leisure time physical activity (LTPA), occupational physical activity (OPA) is associated with an increased risk of cardiovascular diseases and all-cause mortality, termed the physical activity (PA) health paradox. In order to gain more insight into the PA health paradox, an exploration of structural preventive measures at the workplace is needed and therefore objective field measurements are highly recommended. The objective of this paper is to provide an overview of the protocol of the Flemish Employees' Physical Activity (FEPA) study, including objective measurements of PA, heart rate (HR) and cardiorespiratory fitness (CRF) to gain more insight into the PA health paradox. Methods: A total of 401 workers participated in the FEPA study across seven companies in the service and production sector in Belgium. The participants comprised 167 men and 234 women, aged 20 to 65years. OPA and LTPA were assessed by two Axivity AX3 accelerometers on the thigh and upper back. Ambulatory HR was measured by the Faros eMotion 90 degrees monitor. Both devices were worn during two to four consecutive working days. In addition, CRF was estimated by using the Harvard Step Test. Statistical analyses will be performed using Pearson correlation, and multiple regression adjusted for possible confounders. Discussion: This study aims to provide a better insight in the PA health paradox and the possible buffering factors by using valid and objective measurements of PA and HR (both during LTPA and OPA) over multiple working days. The results of the study can contribute to the prevention of cardiovascular disease by providing tailored recommendations for participants with high levels of OPA and by disseminating the results and recommendations to workplaces, policy makers and occupational health practitioners

A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

BeMonitored: monitorização psicofisiológica usando dispositivos móveis

Mestrado em Engenharia de Computadores e TelemáticaThe daily life in modern societies has a high impact in individuals. Long-term stress, changes, traumas and life experiences are some of environmental factors that lead to the development of anxiety disorders. Anxiety disorders affects many people in their daily lives, since they may lead to social isolation, clinical depression, and can impair a person’s ability to work, study and routine activities. Nevertheless, there are many effective therapies available for such disease, sufferers do not seek for treatment, because they underestimate the problem, the treatments duration, cost or difficult in access. In result, it is of the utmost importance that researchers can recreate, as accurately as possible, real life conditions in psychological studies. However, that is not always possible. Recent improvements in sensors technology make then a straightforward solution to gather physiological data. However, their standalone use is quite limited. Nevertheless, combining those sensors with a Smartphone creates an independent solution that without any more requirements has an enormous potential, due to the advanced computing power and connectivity features available. In this dissertation it is proposed the BeMonitored, a Smartphone based solution to support more ecological valid monitoring of psychological experiments. BeMonitored delivers customizable specific context dependent audio-visual stimuli and using external resources connected via Bluetooth or Smartphone own resources (camera, gps), is able to capture the subject’s behavior, physiology and environment. As a proof of concept, BeMonitored was tested in a spider phobia population, where it was found that spider phobic was separated from control subjects using solely the face motion captured with the Smartphone camera. Also, heart rate differences were found between spider and neutral stimuli. Although current study focused only on spider phobia, the results support the validity and the potential of using BeMonitored in other phobias related, especially in cognitive behavioral therapy (CBT) scenarios, either for assessment of the phobia “stage” or to deliver a stepwise sequence of video stimuli according to accepted psychology guidelines.O dia a dia nas sociedades modernas, tem um grande impacto nos indivíduos. O stress continuado, mudanças, traumas e as experiências de vida, são alguns dos fatores ambientais que potenciam o desenvolvimento de doenças de ansiedade. Este tipo de doenças podem conduzir ao isolamento social, a depressões, à diminuição da capacidade de trabalhar, estudar ou executar tarefas do quotidiano. Apesar de existirem inúmeras terapias eficazes no tratamento deste tipo de doenças, os sofredores, não procuram tratamento, ou por desvalorizarem o problema, ou devido à duração e custo associado ou pelo difícil acesso. Deste modo, é da extrema importância que os investigadores consigam recriar as condições da vida real no estudo de doenças do foro psicológico.Contudo, tal nem sempre é possível. As recentes evoluções ao nível dos sensores biomédicos fazem deles uma solução simples para adquirir sinais biológicos. Contudo, o seu uso isolado é de certa forma limitado. Por outro lado, combinando estes sensores com um Smartphone, criamos uma solução independente, com enorme potencial, devido ao avançado poder computacional e conectividade destes dispositivos. Nesta dissertação propomos o sistema BeMonitored: uma solução baseada em Smartphone para suportar um estudo ecologicamente válido a nível da monitorização de doenças do foro psicológico. BeMonitored é uma solução que permite expor os sujeitos a um estímulo audiovisual configurável, que usando sensores biomédicos ligados por Bluetooth ao Smartphone, juntamente com os seus recursos de hardware (ex: câmera, GPS), é capaz de adquirir o comportamento e a fisiologia dos sujeitos, bem como o contexto envolvente. Como prova de conceito, o BeMonitored foi testado num estudo de fobia a aranhas, onde foi possível obter resultados que nos permitem separar os sujeitos fóbicos dos sujeitos de controlo usando apenas o movimento facial capturado com a camara do smartphone. Encontraram-se também diferenças na frequência cardiaca entre os segmentos de vídeo com aranhas e neutros. Apesar do estudo ser focado nas fobias a aranhas, os resultados obtidos confirmam a validade e o potencial de utilização do BeMonitored em outras fobias, bem como em cenários de terapia cognitivo-comportamental(CBT), quer para a avaliação do nível de fobia quer na exposição gradual de estímulos de video de acordo com as directizes aceites na área da psicologia

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

Emotion, mood, and stress recognition (EMSR) has been studied in laboratory settings for decades. In particular, physiological signals are widely used to detect and classify affective states in lab conditions. However, physiological reactions to emotional stimuli have been found to differ in laboratory and natural settings. Thanks to recent technological progress (e.g., in wearables) the creation of EMSR systems for a large number of consumers during their everyday activities is increasingly possible. Therefore, datasets created in the wild are needed to insure the validity and the exploitability of EMSR models for real-life applications. In this paper, we initially present common techniques used in laboratory settings to induce emotions for the purpose of physiological dataset creation. Next, advantages and challenges of data collection in the wild are discussed. To assess the applicability of existing datasets to real-life applications, we propose a set of categories to guide and compare at a glance different methodologies used by researchers to collect such data. For this purpose, we also introduce a visual tool called Graphical Assessment of Real-life Application-Focused Emotional Dataset (GARAFED). In the last part of the paper, we apply the proposed tool to compare existing physiological datasets for EMSR in the wild and to show possible improvements and future directions of research. We wish for this paper and GARAFED to be used as guidelines for researchers and developers who aim at collecting affect-related data for real-life EMSR-based applications

Large-scale wearable data reveal digital phenotypes for daily-life stress detection

Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects' demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

Introduction: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. Method: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. Results: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that – although more systematic studies are necessary – task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. Conclusion: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

INTRODUCTION: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. METHOD: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. RESULTS: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. CONCLUSION: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies

Aware : monitorização psicológica com realidade aumentada em smartphone

Mestrado em Engenharia de Computadores e TelemáticaA exposição a agressões psicológicas subsequentes do estilo de vida na sociedade atual, como o stress, trauma, ou a deslocalização constante, têm um forte impacto na condição psicológica e comportamental das pessoas, podendo mesmo conduzir a distúrbios de ansiedade. Desta forma, é imperativo que os processos de terapia recriem ambientes semelhantes aos do quotidiano do indivíduo (“contextos ecologicamente válidos”), para que este possa ser exposto aos seus receios de forma controlada. No entanto, existe alguma dificuldade em obter dados ecologicamente precisos, uma vez que as ferramentas de medição ou métodos de monitorização utilizados induzem alterações no contexto real, colocando em causa a viabilidade da execução do processo num ambiente natural para o utilizador. Nesta dissertação, propomos o sistema AWARE, uma solução que permite avaliar as condições fisiológicas e comportamentais do(s) participante(s) de uma experiência. Este sistema permite integrar o individuo num contexto ecologicamente válido (fora de laboratório), durante a execução de terapia, recorrendo aos recursos de localização, deteção de movimento e conectividade disponíveis num smartphone, bem como a dispositivos dedicados de medição de sinais vitais, de uma forma não intrusiva, abstraindo o sujeito do mesmo. A existência de uma estrutura de persistência de dados suportada na Cloud garante também um acesso remoto a estes resultados. Terapias associadas a distúrbios comportamentais têm usualmente como componente fundamental a exposição a estímulos que despoletam reações de ansiedade. Desta forma, e paralelamente à monitorização, o sistema proposto está preparado para a apresentação de estímulos, permitindo a avaliação da consequente reação do utilizador. Graças à implementação de tecnologias de realidade aumentada, torna-se possível apresentar modelos 3D virtuais, integrados na perspetiva do mundo real do sujeito. Esta proposta de solução constitui, assim, uma ferramenta flexível e de utilização intuitiva, com a qual podemos avaliar as alterações provocadas no comportamento em cada sujeito, quando confrontado com estímulos específicos. O sistema AWARE foi testado em dois contextos diferentes: 1. A componente de monitorização foi utilizada em artistas aquando da representação de variados temas de música clássica; 2. As componentes de monitorização e realidade aumentada foram aplicadas em simultâneo no contexto do estudo e tratamento de fobias. No primeiro caso de estudo, verificou-se um aumento da frequência cardíaca em momentos cruciais das peças, como a subida ao palco e o início da atuação. No segundo contexto, verificou-se que o aparecimento de um estímulo despoleta alterações fisiológicas (exemplo: alterações na frequência cardíaca). Em terapias de tratamento de fobias, os indivíduos são repetidamente sujeitos ao mesmo tipo de testes/estímulos, de forma a avaliar a reação à exposição continuada aos mesmos (cuja variação irá, em teoria, sofrer uma diminuição, caso o sujeito seja fóbico). Na nossa experiência, dois voluntários não fóbicos realizaram dois testes sucessivos, não havendo um padrão de variação da frequência cardíaca do primeiro para o segundo teste. Este valor vai de encontro ao esperado, dada a caracterização não fobica dos sujeitos. Os resultados apresentados sugerem que o sistema proposto pode revelar-se uma ferramenta útil quer na monitorização não intrusiva, quer em terapias de exposição, sendo de fácil integração num contexto real.The exposure to psychologically hostile situations in today's society, like stress, trauma or constant life event changing have a strong negative influence on people's psychological condition and general behavior, possibly leading to anxietyrelated disorders. Therefore, it is of extreme importance that the therapy processes are able to recreate a natural and familiar ("ecological") environment, according to the individual everyday life, allowing him/her to face his/her fear in a controlled environment. However, up to this day, there are issues related to the gathering of ecologically precise data, due to the real world variations induced by the measuring tools or the viability issues related to the therapy's execution. In this dissertation, we propose the AWARE system, a solution that allows the assessment of a subject's physiological and behavioral condition in an ecologically valid context, using the location, movement description and connectivity resources available on a smartphone, as well as dedicated vital signal measuring devices. The whole process is executed outside of the lab, in a real ecosystem, while keeping the subject oblivious about the whole monitoring activity. The existence of a data persistence based on the Cloud assures the remote availability of this results. Parallel to the monitoring, the proposed solution is also ready to present stimuli, providing a means of evaluating the subject's reaction. Augmented reality technologies are employed in order to present different 3D models embedded into the subject's real world perspective. Our system establishes itself as a flexible and intuitive tool, with which we can study the behavioral changes caused by the confrontation of a person with a specific set of stimuli. AWARE was tested in two different contexts: 1. Independently, the monitoring features were used in musicians during the performance of classical plays; 2. On a different scenario, both the augmented reality and the monitoring modules were tested together in phobia analysis and treatment. In the first study case, a heart rate frequency increase was detected in crucial moments of the plays (like going to the stage, or beginning the performance). In the second study case, some physiological reactions to the presented stimuli (e.g. heart rate frequency variation) were observed. In phobia treatment therapies, subjects repeat the same series of tests multiple times, in order to evaluate the reaction to continuous exposure to the same stimuli (which will, in theory, have a progressively lower variation in phobic subjects). In our experiments, the volunteers executed two tests each, and there was not any visible heart rate standard deviation decrease, an expected result, given their non-phobic condition. The presented results suggest that the proposed system may be a useful tool in non-intrusive monitoring, and in exposure therapies, being the system integration in real contexts of straightforward use

Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving

Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioural data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world