Exploring the Neuropsychiatric Sequalae of Perceived COVID-19 Exposure in College Students: A Pilot Digital Phenotyping Study

The urgency to understand the long-term neuropsychiatric sequala of COVID-19, a part of the Post-Acute COVID-19 Syndrome (PACS), is expanding as millions of infected individuals experience new unexplained symptoms related to mood, anxiety, insomnia, headache, pain, and more. Much research on PACS involves cross sectional surveys which limits ability to understand the dynamic trajectory of this emerging phenomenon. In this secondary analysis, we analyzed data from a 4-week observational digital phenotyping study using the mindLAMP app for 695 college students with elevated stress who specified if they were exposed to COVID-19. Students also completed a biweekly survey of clinical assessments to obtain active data. Additionally, passive data streams like GPS, accelerometer, and screen state were extracted from phone sensors and through features the group built. Three hundred and eighty-second number participants successfully specified their COVID-19 exposure and completed the biweekly survey. From active smartphone data, we found significantly higher scores for the Prodromal Questionnaire (PQ) and the Pittsburgh Sleep Quality Index (PSQI) for students reporting exposure to COVID-19 compared to those who were not (ps < 0.05). Additionally, we found significantly decreased sleep duration as captured from the smartphone via passive data for the COVID-19 exposed group (p < 0.05). No significant differences were detected for other surveys or passive sensors. Smartphones can capture both self-reported symptoms and behavioral changes related to PACS. Our results around changes in sleep highlight how digital phenotyping methods can be used in a scalable and accessible manner toward better capturing the evolving phenomena of PACS. The present study further provides a foundation for future research to implement improving digital phenotyping methods.

Reflection imaging of complex geology in crystalline environment using virtual-source seismology: case study from the Kylylahti polymetallic mine, Finland

For the first time, we apply a full-scale 3D seismic virtual-source survey (VSS) for the purpose of near-mine mineral exploration. The data was acquired directly above the Kylylahti underground mine in Finland. Recorded ambient noise (AN) data is characterized using power-spectral density (PSD) and beamforming. Data has most energy at frequencies 25–90 Hz and arrivals with velocities higher than 4 km/s have wide range of azimuths. Based on the PSD and beamforming results, we created 10-days subset of AN recordings that were dominated by multi-azimuth high-velocity arrivals. We use illumination-diagnosis technique and location procedure to show that the AN recordings associated with high apparent velocities are related to body-wave events. Next, we produce 994 virtual-source gathers by applying seismic-interferometry processing by cross-correlating AN at all receivers resulting in full 3D VSS. We apply standard 3D time-domain reflection seismic data processing and imaging using both a selectively stacked subset and full passive data, and validate the results against a pre-existing detailed geological information and 3D active-source survey data processed in the same way as the passive data. The resulting post-stack migrated sections show agreement of reflections between the passive and active data and indicate that VSS provide images where the active-source data are not available due to terrain restrictions. We conclude that while the all-noise approach provides some higher quality reflections related to the inner geological contacts within the target formation and the general dipping trend of the formation, the selected subset is most efficient in resolving the base of formation.

Surface-wave tomography for mineral exploration: a successful combination of passive and active data (Siilinjärvi phosphorus mine, Finland)

Surface wave (SW) methods are ideal candidates for an effective and sustainable development of seismic exploration, but still remain under-exploited in hard rock sites. We present a successful application of active and passive surface wave tomography for the characterization of the southern continuation of the Siilinjärvi phosphate deposit (Finland). A semi-automatic workflow for the extraction of the path-average dispersion curves (DCs) from ambient seismic noise data is proposed, including identification of time windows with strong coherent SW signal, azimuth analysis and two-station method for DC picking. DCs retrieved from passive data are compared with active SW tomography results recently obtained at the site. Passive data are found to carry information at longer wavelengths, thus extending the investigation depth. Active and passive DCs are consequently inverted together to retrieve a deep pseudo-3D shear-wave velocity model for the site, with improved resolution. The seismic results are compared with the latest available geological models to both validate the proposed workflow and improve the interpretation of the geometry, extent and contacts of the mineralization. Important large-scale geological boundaries and structural discontinuities are recognized from the results, demonstrating the effectiveness and advantages of the methods for mineral exploration perspectives.

Examining the Discrepancies between Self-Reported and Actual Commuting Behavior at the Individual Level

For decades, transportation researchers have used survey data to understand the factors that affect travel-related choices. Nowadays, travel surveys lay the foundation of travel behavior analysis for transportation modeling, planning, and policy-making. The development of information technology for urban sensing has enabled substantial improvements to be made in survey-elicited and passive mobility data collection. Actively collected and passive data are very different, and being able to compare and integrate them could allow stakeholders to achieve a greater understanding of human mobility. The comparison between survey self-reported travel behavior and actual travel behavior revealed by urban and mobile systems provides us with the opportunity to find potential discrepancies. Previous work has examined these discrepancies mostly at the population level. An individual-level investigation of these discrepancies could provide many benefits, from increasing our understanding of survey and passive data accuracy and collection, to designing personalized transportation services. In this study, the discrepancies between self-reported and observed travel behavior are analyzed at both the individual and aggregated level by utilizing the available mobility data, namely, survey-based commuting diaries and passive mobility records. We propose a group of discrepancy metrics for commuting activities for which we have available and comparable data, and apply the framework to an empirical analysis at the Massachusetts Institute of Technology in Cambridge, U.S.A. Our results show that survey-elicited commuting diaries are quite reliable when examining overall commuting trends, whereas passive mobility data are more suitable for investigating individual-level commuting behavior. Furthermore, we identify the association between discrepancies in commuting behavior and certain individual characteristics, for example, employee type and age.

Attitudes towards Participation in a Passive Data Collection Experiment

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.

Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework.

An alternative to the ‘light touch’ digital health remote study: The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study (Preprint)

BACKGROUND Background: Several app-based studies share similar characteristics of a ‘light touch’ approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active study tasks, while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies reporting low retention and adherence. OBJECTIVE To describe an alternative to a ‘light touch’ digital health study that involved a participant centric design including high friction app-based assessments, semi-continuous passive data from wearable sensors and a digital engagement strategy centered on providing knowledge and support to participants. METHODS The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study included US frontline healthcare workers followed between May-November 2020. The study comprised 3 main components: 1) active and passive assessments of stress and symptoms from a smartphone app; 2) objective measured assessments of acute stress from wearable sensors; and 3) a participant co-driven engagement strategy that centered on providing knowledge and support to participants. The daily participant time commitment was an average of 10-15 minutes. Retention and adherence are described both quantitatively and qualitatively. RESULTS Results: 365 participants enrolled and started the study and 81.0% (297/365) of them completed the study for a total study duration of 4 months. Average wearable sensor usage was 90.6% days of total study duration. App-based daily, weekly, and every other week surveys were completed on average 69.18%, 68.37%, 72.86% of the time, respectively. CONCLUSIONS Conclusions: This study found evidence for feasibility and acceptability of a participant centric digital health study approach that involved building trust and respect with participants and providing support through regular phone check-ins. In addition to high retention and adherence, the collection of large volumes of objective measured data alongside contextual self-reported subjective data was able to be collected that is often missing from ‘light touch’ digital health studies. CLINICALTRIAL Clinicaltrials.Gov (NCT04713111)

Near-surface structure of the Sodankylä area in Finland, obtained by passive seismic interferometry

Controlled-source seismic exploration surveys are not always possible in nature-protected areas. As an alternative, the application of passive seismic techniques in such areas can be proposed. In our study, we show results of passive seismic interferometry application for mapping the uppermost crust in the area of active mineral exploration in northern Finland. We utilize continuous seismic data acquired by the Sercel Unite wireless multichannel recording system along several profiles during XSoDEx (eXperiment of SOdankylä Deep Exploration) multidisciplinary geophysical project. The objective of XSoDEx was to obtain a structural image of the upper crust in the Sodankylä area of northern Finland in order to achieve a better understanding of the mineral system at depth. The key experiment of the project was a high-resolution seismic reflection experiment. In addition, continuous passive seismic data were acquired in parallel with reflection seismic data acquisition. Due to this, the length of passive data suitable for noise cross-correlation was limited from several hours to a couple of days. Analysis of the passive data demonstrated that dominating sources of ambient noise are non-stationary and have different origins across the XSoDEx study area. As the long data registration period and isotropic azimuthal distribution of noise sources are two major conditions for empirical Green function (EGF) extraction under the diffuse field approximation assumption, it was not possible to apply the conventional techniques of passive seismic interferometry. To find the way to obtain EGFs, we used numerical modelling in order to investigate properties of seismic noise originating from sources with different characteristics and propagating inside synthetic heterogeneous Earth models representing real geological conditions in the XSoDEx study area. The modelling demonstrated that scattering of ballistic waves on irregular shape heterogeneities, such as massive sulfides or mafic intrusions, could produce a diffused wavefield composed mainly of scattered surface waves. In our study, we show that this scattered wavefield can be used to retrieve reliable EGFs from short-term and non-stationary data using special techniques. One of the possible solutions is application of “signal-to-noise ratio stacking” (SNRS). The EGFs calculated for the XSoDEx profiles were inverted, in order to obtain S-wave velocity models down to the depth of 300 m. The obtained velocity models agree well with geological data and complement the results of reflection seismic data interpretation.

Passive Data Use for Digital Public Health Surveillance in a Post-Pandemic World (Preprint)

UNSTRUCTURED There is a fundamental need to establish the most ethical and effective way of tracking disease in the post-pandemic era. The ubiquity of mobile phones generating passive data (collected without active user participation) has become a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, sophisticated analyses of passive data for digital public health surveillance have yet to be attempted, and there is no consensus on the best method to do so. Therefore, four patient-reported areas of concern must be addressed: (1) informed consent (2) privacy, (3) equity, and (4) ownership. Preparations must be undertaken proactively using the lessons fresh in our collective consciousness.