Meaningful digital biomarkers derived from wearable sensors to predict daily fatigue in multiple sclerosis patients and healthy controls.

Fatigue is the most common symptom among multiple sclerosis (MS) patients and severely affects the quality of life. We investigate how perceived fatigue can be predicted using biomarkers collected from an arm-worn wearable sensor for MS patients (n = 51) and a healthy control group (n = 23) at an unprecedented time resolution of more than five times per day. On average, during our two-week study, participants reported their level of fatigue 51 times totaling more than 3,700 data points. Using interpretable generalized additive models, we find that increased physical activity, heart rate, sympathetic activity, and parasympathetic activity while awake and asleep relate to perceived fatigue throughout the day-partly affected by dysfunction of the ANS. We believe our analysis opens up new research opportunities for fine-grained modeling of perceived fatigue based on passively collected physiological signals using wearables-for MS patients and healthy controls alike.

Comparing Synchronous and Asynchronous Task Delivery in Mixed Reality Environments.

Asynchronous digital communication is a widely applied and well-known form of information exchange. Most pieces of technology make use of some variation of asynchronous communication systems, be it messaging or email applications. This allows recipients to process digital messages immediately (synchronous) or whenever they have time (asynchronous), meaning that purely digital interruptions can be mitigated easily. Mixed Reality systems have the potential to not only handle digital interruptions but also interruptions in physical space, e.g., caused by co-workers in workspaces or learning environments. However, the benefits of such systems previously remained untested in the context of Mixed Reality. We conducted a user study ($\mathrm{N}=26$) to investigate the impact that the timing of task delivery has on the participants' performance, workflow, and emotional state. Participants had to perform several cognitively demanding tasks in a Mixed Reality workspace. Inside the virtual workspace, we simulated in-person task delivery either during tasks (i.e., interrupting the participant) or between tasks (i.e., delaying the interruption). Our results show that delaying interruptions has a significant impact on subjective metrics like the perceived performance and workload.

Modeling multiple sclerosis using mobile and wearable sensor data.

Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the leading cause of non-traumatic disability in young adults. Clinical laboratory tests and neuroimaging studies are the standard methods to diagnose and monitor MS. However, due to infrequent clinic visits, it is fundamental to identify remote and frequent approaches for monitoring MS, which enable timely diagnosis, early access to treatment, and slowing down disease progression. In this work, we investigate the most reliable, clinically useful, and available features derived from mobile and wearable devices as well as their ability to distinguish people with MS (PwMS) from healthy controls, recognize MS disability and fatigue levels. To this end, we formalize clinical knowledge and derive behavioral markers to characterize MS. We evaluate our approach on a dataset we collected from 55 PwMS and 24 healthy controls for a total of 489 days conducted in free-living conditions. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS. Our findings open up opportunities for continuous monitoring of MS in free-living conditions and can be used to evaluate and guide the effectiveness of treatments, manage the disease, and identify participants for clinical trials.

Video-based sympathetic arousal assessment via peripheral blood flow estimation.

Electrodermal activity (EDA) is considered a standard marker of sympathetic activity. However, traditional EDA measurement requires electrodes in steady contact with the skin. Can sympathetic arousal be measured using only an optical sensor, such as an RGB camera? This paper presents a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically. We contribute a self-recorded dataset of 21 participants, comprising synchronized videos of participants' faces and palms and gold-standard EDA and photoplethysmography (PPG) signals. Our results show that we can measure peripheral sympathetic responses that closely correlate with the ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers. We also show that sympathetic arousal is best inferred from the forehead, finger, or palm.

Controllers or Bare Hands? A Controlled Evaluation of Input Techniques on Interaction Performance and Exertion in Virtual Reality.

Virtual Reality (VR) systems have traditionally required users to operate the user interface with controllers in mid-air. More recent VR systems, however, integrate cameras to track the headset's position inside the environment as well as the user's hands when possible. This allows users to directly interact with virtual content in mid-air just by reaching out, thus discarding the need for hand-held physical controllers. However, it is unclear which of these two modalities-controller-based or free-hand interaction-is more suitable for efficient input, accurate interaction, and long-term use under reliable tracking conditions. While interacting with hand-held controllers introduces weight, it also requires less finger movement to invoke actions (e.g., pressing a button) and allows users to hold on to a physical object during virtual interaction. In this paper, we investigate the effect of VR input modality (controller vs. free-hand interaction) on physical exertion, agency, task performance, and motor behavior across two mid-air interaction techniques (touch, raycast) and tasks (selection, trajectory-tracing). Participants reported less physical exertion, felt more in control, and were faster and more accurate when using VR controllers compared to free-hand interaction in the raycast setting. Regarding personal preference, participants chose VR controllers for raycast but free-hand interaction for mid-air touch. Our correlation analysis revealed that participants' physical exertion increased with selection speed, quantity of arm motion, variation in motion speed, and bad postures, following ergonomics metrics such as consumed endurance and rapid upper limb assessment. We also found a negative correlation between physical exertion and the participant's sense of agency, and between physical exertion and task accuracy.

Experiencing herd immunity in virtual reality increases COVID-19 vaccination intention: Evidence from a large-scale field intervention study.

This study investigates the impact of an immersive virtual reality (VR) simulation of herd immunity on vaccination intentions and its potential underlying mechanisms. In this preregistered field study, N = 654 participants were randomly assigned to one of the three VR conditions: (1) Gamified Herd Immunity; (2) Gamified Herd Immunity + Empathy (with additional narrative elements); (3) Control (gamified with no vaccination-related content). In the Gamified Herd Immunity simulation, participants embodied a vulnerable person and navigated a wedding venue trying to avoid getting infected. A total of 455 participants with below maximum intentions to take a novel vaccine and without severe cybersickness were analyzed. The Gamified Herd Immunity + Empathy and the Gamified Herd Immunity conditions increased vaccination intentions by 6.68 and 7.06 points on a 0-100 scale, respectively, compared to 1.91 for the Control condition. The Gamified Herd Immunity + Empathy condition enhanced empathy significantly more than the Gamified Herd Immunity condition but did not result in higher vaccination intentions. Experienced presence was related to the change in vaccination intentions. The results suggest that VR vaccination communication can effectively increase vaccination intentions; the effect is not solely due to the technological novelty and does not depend on empathy.

Characterizing Physiological Responses to Fear, Frustration, and Insight in Virtual Reality.

Physiological sensing often complements studies of human behavior in virtual reality (VR) to detect users' affective and cognitive states. Some psychological states, such as fear and frustration, can be particularly hard to differentiate from a physiological perspective as they are close in the arousal and valence emotional space. Moreover, it is largely unclear how users' physiological reactions are expressed in response to transient psychological states such as fear, frustration, and insight-especially since these are rich indicators for characterizing users' responses to dynamic systems but are hard to capture in highly interactive settings. We conducted a study ($N=24$) to analyze participants' pulmonary, electrodermal, cardiac, and pupillary responses to moments of fear, frustration, and insight in immersive settings. Participants interacted in five VR environments, throughout which we measured their physiological reactions and analyzed the patterns we observed. We also measured subjective fear and frustration using questionnaires. We found differences between fear and frustration pupillary, respiratory, and electrodermal responses, as well as between the pupillary changes that followed fear in a horror game and those that followed fear in a vertigo experiment. We present the relationships between fear levels, frustration levels, and their physiological responses. To detect these affective events and states, we introduce user-independent binary classification models that achieved an average micro $F_{1}$ score of 71% for detecting fear in a horror game, 75% for fear of vertigo, 76% for frustration, and 75% for insight, showing the promise for detecting these states from passive and objective signals.

Virtual reality reduces COVID-19 vaccine hesitancy in the wild: a randomized trial.

Vaccine hesitancy poses one of the largest threats to global health. Informing people about the collective benefit of vaccination has great potential in increasing vaccination intentions. This research investigates the potential for engaging experiences in immersive virtual reality (VR) to strengthen participants' understanding of community immunity, and therefore, their intention to get vaccinated. In a pre-registered lab-in-the-field intervention study, participants were recruited in a public park (tested: [Formula: see text], analyzed: [Formula: see text]). They were randomly assigned to experience the collective benefit of community immunity in a gamified immersive virtual reality environment ([Formula: see text] of sample), or to receive the same information via text and images ([Formula: see text] of sample). Before and after the intervention, participants indicated their intention to take up a hypothetical vaccine for a new COVID-19 strain (0-100 scale) and belief in vaccination as a collective responsibility (1-7 scale). The study employs a crossover design (participants later received a second treatment), but the primary outcome is the effect of the first treatment on vaccination intention. After the VR treatment, for participants with less-than-maximal vaccination intention, intention increases by 9.3 points (95% CI: 7.0 to [Formula: see text]). The text-and-image treatment raises vaccination intention by 3.3 points (difference in effects: 5.8, 95% CI: 2.0 to [Formula: see text]). The VR treatment also increases collective responsibility by 0.82 points (95% CI: 0.37 to [Formula: see text]). The results suggest that VR interventions are an effective tool for boosting vaccination intention, and that they can be applied "in the wild"-providing a complementary method for vaccine advocacy.

Cognitive fatigability assessment test (cFAST): Development of a new instrument to assess cognitive fatigability and pilot study on its association to perceived fatigue in multiple sclerosis.

Background: Fatigue is a common symptom of many diseases, including multiple sclerosis. It manifests as a cognitive or physical condition. Fatigue is poorly understood, and effective therapies are missing. Furthermore, there is a lack of methods to measure fatigue objectively. Fatigability, the measurable decline in performance during a task, has been suggested as a complementary method to quantify fatigue. Objective: To develop a new and objective measurement of cognitive fatigability and investigate its association with perceived fatigue. Methods: We introduced the cognitive fatigability assessment test (cFAST), a novel smartphone-based test to quantify cognitive fatigability. Forty-two people with multiple sclerosis (23 fatigued and 19 non-fatigued, defined by the Fatigue Scale for Motor and Cognitive Functions) took part in our validation study. Patients completed cFAST twice. We used t-tests, Monte Carlo sampling, and area under the receiver operating characteristic curves to evaluate our approach using two sets of proposed metrics. Results: When classifying fatigue, our fatigability metric Δresponse time has a mean area under the receiver operating characteristic curve of 0.74 (95% CI 0.64-0.84), making it the best performing metric for this task. Furthermore, Δresponse time shows a statistically significant difference between the fatigued and non-fatigued groups (t = 2.27, P = .03). Particularly, cognitively-fatigued patients decline in performance, while non-fatigued patients do not. Conclusions: We introduce cFAST, a new instrument to quantify cognitive fatigability. Our pilot study provides evidence that cognitive fatigability assessment test produces a quantifiable drop in cognitive performance in a short period. Furthermore, our results indicate that cFAST may have the potential to serve as a surrogate for subjective cognitive fatigue. cFAST is significantly shorter than the existing fatigability assessments and does not require specialized equipment. Thus, it could enable frequent and remote monitoring, which could substantially aid clinicians in better understanding and treating fatigue.

Continuous monitoring with wearables in multiple sclerosis reveals an association of cardiac autonomic dysfunction with disease severity.

Background: Dysfunction of the autonomic nervous system is common in multiple sclerosis patients, and probably present years before diagnosis, but its role in the disease is poorly understood. Objectives: To study the autonomic nervous system in patients with multiple sclerosis using cardiac autonomic regulation measured with a wearable. Methods: In a two-week study, we present a method to standardize the measurement of heart rate variability using a wearable sensor that allows the investigation of circadian trends. Using this method, we investigate the relationship of cardiac autonomic dysfunction with clinical hallmarks and subjective burden of fatigue and autonomic symptoms. Results: In 55 patients with multiple sclerosis and 24 healthy age- and gender-matched controls, we assessed the cumulative circadian heart-rate variability trend of two weeks. The trend analysis revealed an effect of inflammation (P = 0.0490, SMD = -0.5466) and progressive neurodegeneration (P = 0.0016, SMD = 1.1491) on cardiac autonomic function. No association with subjective symptoms could be found. Conclusions: Trend-based heart rate variability measured with a wearable provides the opportunity for unobtrusive long-term assessment of autonomic functions in patients with multiple sclerosis. It revealed a general dysregulation in patients with multiple sclerosis.

A self-administered virtual reality intervention increases COVID-19 vaccination intention.

Effective interventions for increasing people's intention to get vaccinated are crucial for global health, especially considering COVID-19. We devised a novel intervention using virtual reality (VR) consisting of a consultation with a general practitioner for communicating the benefits of COVID-19 vaccination and, in turn, increasing the intention to get vaccinated against COVID-19. We conducted a preregistered online experiment with a 2×2 between-participant design. People with eligible VR headsets were invited to install our experimental application and complete the ten minute virtual consultation study at their own discretion. Participants were randomly assigned across two age conditions (young or old self-body) and two communication conditions (with provision of personal benefit of vaccination only, or collective and personal benefit). The primary outcome was vaccination intention (score range 1-100) measured three times: immediately before and after the study, as well as one week later. Five-hundred-and-seven adults not vaccinated against COVID-19 were recruited. Among the 282 participants with imperfect vaccination intentions (<100), the VR intervention increased pre-to-post vaccination intentions across intervention conditions (mean difference 8.6, 95% CI 6.1 to 11.1,p<0.0001). The pre-to-post difference significantly correlated with the vaccination intention one week later, ρ=0.20,p<0.0001. The VR intervention was effective in increasing COVID-19 vaccination intentions both when only personal benefits and personal and collective benefits of vaccination were communicated, with significant retention one week after the intervention. Utilizing recent evidence from health psychology and embodiment research to develop immersive environments with customized and salient communication efforts could therefore be an effective tool to complement public health campaigns.

Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System-Based Semantic Analysis and Experts' Review.

BACKGROUND: For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. OBJECTIVE: This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. METHODS: Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. RESULTS: A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts' occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. CONCLUSIONS: Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

Differentiation of Aneugens and Clastogens in the In Vitro Micronucleus Test by Kinetochore Scoring Using Automated Image Analysis.

The in vitro micronucleus test according to OECD Test Guideline 487 (TG 487) is widely used to investigate the genotoxic potential of drugs. Besides the identification of in vitro genotoxicants, the assay can be complemented with kinetochore staining for the differentiation between clastogens and aneugens. This differentiation constitutes a major contribution to risk assessment as especially aneugens show a threshold response. Thus, a novel method for automated MN plus kinetochore (k+) scoring by image analysis was developed based on the OECD TG 487. Compound-induced increases in MN frequency can be detected using the cytokinesis-block (cytochalasin B) method in V79 cells after 24 h in a 96-well format. Nuclei, MN, and kinetochores were labeled with nuclear counterstain and anti-kinetochore antibodies, respectively, to score MN in binuclear or multinuclear cells and to differentiate compound-induced MN by the presence of kinetochores. First, a reference data set was created by manual scoring using two clastogens and aneugens. After developing the automated scoring process, a set of 14 reference genotoxicants were studied. The automated image analysis yielded the expected results: 5/5 clastogens and 6/6 aneugens (sensitivity: 100%) as well as 3/3 non-genotoxicants (specificity: 100%) were correctly identified. Further, a threshold was determined for identifying aneugens. Based on the data for our internally characterized reference compounds, unknown compounds that induce ≥53.8% k+ MN are classified as aneugens. The current data demonstrate excellent specificity and sensitivity and the methodology is superior to manual microscopic analysis in terms of speed and throughput as well as the absence of human bias. Environ. Mol. Mutagen. 60:227-242, 2019. © 2018 Wiley Periodicals, Inc.

Doubling the Signal Quality of Smartphone Camera Pulse Oximetry Using the Display Screen as a Controllable Selective Light Source.

Recent smartphones have the potential to bring camera oximetry to everyone using their powerful sensors and the capability to process measurements in real-time, potentially augmenting people's lives through always-available oximetry monitoring everywhere. The challenge of camera oximetry on smartphones is the low contrast between reflections from oxyhemoglobin and deoxyhemoglobin. In this paper, we show that this is the result of using the camera flash for illumination, which illuminates evenly across bands than thus leads to the diminished contrast in reflections. Instead, we propose capturing pulse using the front-facing camera and illuminating with the phone's display, a selective illuminant in the red, green, and blue band. We evaluate the spectral characteristics of the phone display using a spectroradiometer in a controlled experiment, convolve them with the sensitivity curves of the phone's camera, and show that the screen's narrow-band display illumination increases the contrast between the reflections in the desired bands by a factor of two compared to flash illumination. Our preliminary evaluation showed further support for our approach and findings.

Key Data Elements in Myeloid Leukemia.

Data standards consisting of key data elements for clinical routine and trial documentation harmonize documentation within and across different health care institutions making documentation more efficient and improving scientific data analysis. This work focusses on the field of myeloid leukemia (ML), where a semantic core of common data elements (CDEs) in routine and trial documentation is established by automatic UMLS-based form analysis of existing documentation models. These CDEs (n = 227) were initially reviewed and commented by leukemia experts before they were systematically surveyed by an international voting process through seven hematologists of four countries. The total agreement score was 86%. 116 elements (51%) of these share an agreement score of 100%. This work generated CDEs with language-independent semantic codes and international clinical expert review to build a first approach towards an international data standard for ML. A first version of the CDE list is implemented in the data standard Operational Data Model and additional other data formats for reuse in different medical information systems.