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The atlas of global inequalities
\"Drawing on research from around the world, this atlas gives shape and meaning to statistics, making it an indispensable resource for understanding global inequalities and an inspiration for social and political action. Inequality underlies many of the challenges facing the world today, and The Atlas of Global Inequalities considers the issue in all its dimensions. Organized in thematic parts, it maps not only the global distribution of income and wealth, but also inequalities in social and political rights and freedoms. It describes how inadequate health services, unsafe water, and barriers to education hinder people's ability to live their lives to the full; assesses poor transport, energy, and digital communication infrastructures and their effect on economic development; and highlights the dangers of unclean and unhealthy indoor and outdoor environments. Through world, regional, and country maps, and innovative and intriguing graphics, the authors unravel the complexity of inequality, revealing differences between countries as well as illustrating inequalities within them. Topics include: the discrimination suffered by children with a disability; the impact of inefficient and dangerous household fuels on the daily lives and long-term health of those who rely on them; the unequal opportunities available to women; and the reasons for families' descent into, and reemergence from, poverty.\"--Publisher description.
Book
A Comparison of Eye Tracking Latencies Among Several Commercial Head-Mounted Displays
A number of virtual reality head-mounted displays (HMDs) with integrated eye trackers have recently become commercially available. If their eye tracking latency is low and reliable enough for gaze-contingent rendering, this may open up many interesting opportunities for researchers. We measured eye tracking latencies for the Fove-0, the Varjo VR-1, and the High Tech Computer Corporation (HTC) Vive Pro Eye using simultaneous electrooculography measurements. We determined the time from the occurrence of an eye position change to its availability as a data sample from the eye tracker (delay) and the time from an eye position change to the earliest possible change of the display content (latency). For each test and each device, participants performed 60 saccades between two targets 20° of visual angle apart. The targets were continuously visible in the HMD, and the saccades were instructed by an auditory cue. Data collection and eye tracking calibration were done using the recommended scripts for each device in Unity3D. The Vive Pro Eye was recorded twice, once using the SteamVR SDK and once using the Tobii XR SDK. Our results show clear differences between the HMDs. Delays ranged from 15 ms to 52 ms, and the latencies ranged from 45 ms to 81 ms. The Fove-0 appears to be the fastest device and best suited for gaze-contingent rendering.
Journal Article
Survey on Open Science Practices in Functional Neuroimaging
Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them.
Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire.
Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance.
The results suggest growing adoption of open science practices but also highlight a number of important impediments.
Journal Article
Looking from within: Comparing first-person approaches to studying experience
Fostering our understanding of how humans behave, feel and think is a fundamental goal of psychological research. Widely used methods in psychological research are self-report and behavioral measures which require an experimenter to collect data from another person. By comparison, first-person measures that assess more subtle facets of subjective experiences, are less widely used. Without integrating such more subtle first-person measures, however, fundamental aspects of psychological phenomena remain inaccessible to psychological theorizing. To explore the value and potential contribution of first-person methods, the current article aims to provide an overview over already established first-person methods and compare them on relevant dimensions. Based on these results, researchers can select suitable first-person methods to study different facets of subjective experiences. Overall, the investigation of psychological phenomena from a first-person perspective can complement and enrich existing research from a third-person perspective.
Journal Article
Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer
The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.
Journal Article
Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
Journal Article
Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field
Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data. In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems. In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings. Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.
Journal Article
Portable EEG in groups shows increased brain coupling to strong health messages
Abstract
Health messages are core building blocks of public health efforts. Neuroscientific measures offer insights into how target audiences receive health messages. To move towards real-world applications, however, challenges regarding costs, lab restraints, and slow data acquisition need to be addressed. Using portable electroencephalography (EEG) and inter-subject correlation (ISC) analysis as measure of message strength, we ask whether these challenges can be met. Portable EEG was recorded while participants viewed strong and weak video health messages against risky alcohol use. Participants viewed the messages either individually or in a focus group-like setting with six participants simultaneously. For both viewing conditions, three correlated components were extracted. The topographies of these components showed a high spatial correlation with previous high-density EEG results. Moreover, ISC was strongly enhanced when viewing strong as compared to weak health messages in both the group and individual viewing conditions. The findings suggest that ISC analysis shows sensitivity to message strength, even in a group setting using low-density portable EEG. Measuring brain responses to messages in group settings is more efficient and scalable beyond the laboratory. Overall, these results support a translational perspective for the use of neuroscientific measures in health message development.
Journal Article
Introducing the MusicLab Copenhagen Dataset
MusicLab Copenhagen was a unique research concert featuring the world-renowned Danish String Quartet in a naturalistic setting. The audience was split between one group physically located in the hall, another group listening to a radio broadcast, and a third group watching a live stream. Qualitative and quantitative data were captured from both musicians and audiences, resulting in a comprehensive dataset that can be used to address many research questions. This document introduces the dataset, explains its structure, and reflects on the related data collection, storing, publishing, and archiving processes.
Journal Article