Background: The death toll of COVID-19 topped 170,000 in Europe by the end of May 2020. COVID-19 has caused an immense psychological burden on the population, especially among doctors and nurses who are faced with high infection risks and increased workload. Objective: The aim of this study was to compare the mental health of medical professionals with nonmedical professionals in different European countries during the COVID-19 pandemic. We hypothesized that medical professionals, particularly those exposed to COVID-19 at work, would have higher levels of depression, anxiety, and stress. We also aimed to determine their main stressors and most frequently used coping strategies during the crisis. Methods: A cross-sectional online survey was conducted during peak COVID-19 months in 8 European countries. The questionnaire included demographic data and inquired whether the participants were exposed to COVID-19 at work or not. Mental health was assessed via the Depression Anxiety Stress Scales32 (23.53)–21 (DASS-21). A 12-item checklist on preferred coping strategies and another 23-item questionnaire on major stressors were completed by medical professionals. Results: The sample (N=609) consisted of 189 doctors, 165 nurses, and 255 nonmedical professionals. Participants from France and the United Kingdom reported experiencing severe/extremely severe depression, anxiety, and stress more often compared to those from the other countries. Nonmedical professionals had significantly higher scores for depression and anxiety. Among medical professionals, no significant link was reported between direct contact with patients with COVID-19 at work and anxiety, depression, or stress. “Uncertainty about when the epidemic will be under control” caused the most amount of stress for health care professionals while “taking protective measures” was the most frequently used coping strategy among all participants. Conclusions: COVID-19 poses a major challenge to the mental health of working professionals as a considerable proportion of our participants showed high values for depression, anxiety, and stress. Even though medical professionals exhibited less mental stress than nonmedical professionals, sufficient help should be offered to all occupational groups with an emphasis on effective coping strategies.

Alzheimer’s disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent “generalizable” signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.

Background: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. Objective: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy. Methods: A group of apathetic and non-apathetic patients ( n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. Results: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females. Conclusions: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy.

Background: As a reaction to the novel coronavirus disease (COVID-19), countries around the globe have implemented various measures to reduce the spread of the virus. The transportation sector is particularly affected by the pandemic situation. The current study aims to contribute to the empirical knowledge regarding the effects of the coronavirus situation on the mobility of people by (1) broadening the perspective to the mobility rural area’s residents and (2) providing subjective data concerning the perceived changes of affected persons’ mobility practices, as these two aspects have scarcely been considered in research so far. Methods: To address these research gaps, a mixed-methods study was conducted that integrates a qualitative telephone interview study (N = 15) and a quantitative household survey (N = 301). The rural district of Altmarkkreis Salzwedel in Northern Germany was chosen as a model region. Results: The results provide in-depth insights into the changing mobility practices of residents of a rural area during the legal restrictions to stem the spread of the virus. A high share of respondents (62.6%) experienced no changes in their mobility behavior due to the COVID-19 pandemic situation. However, nearly one third of trips were also cancelled overall. A modal shift was observed towards the reduction of trips by car and bus, and an increase of trips by bike. The share of trips by foot was unchanged. The majority of respondents did not predict strong long-term effects of the corona pandemic on their mobility behavior.

Introduction: Shared on-demand mobility services emerge at a fast pace, changing the landscape of public transport. However, shared mobility services are largely designed without considering the access needs of people with disabilities, putting these passengers at risk of exclusion. Recognising that accessibility is best addressed at the design stage and through direct participation of persons with disabilities, the objective of this study was to explore disabled users’ views on the following emerging shared mobility services: (a) ride pooling, (b) microtransit, (c) motorbike taxis, (d) robotaxis, (f) e-scooter sharing, and (g) bike sharing. Methodolgy: Using an online mobility survey, we sampled disabled users’ (1) views on accessibility, (2) use intention, and (3) suggestions for improving accessibility. The results reflect the responses of 553 individuals with different types of disabilities from 21 European countries. Results: Projected accessibility and use intention were greatest for microtransit, robotaxis, and ride pooling across different disabilities. In contrast, motorbike taxis, e-scooter sharing, and bike sharing were viewed as least accessible and least attractive to use, especially by persons with physical, visual, and multiple disabilities. Despite differences in projected accessibility, none of the shared mobility services would fulfil the access needs of disabled persons in their current form. Suggestions for increasing the accessibility of these services included (a) an ondemand door-to-door service, (b) an accessible booking app, (c) real-time travel information, and (d) the necessity of accommodating wheelchairs. Conclusions: Our findings highlight the need for improving both vehicles and service designs to cater for the access needs of persons with disabilities and provide policymakers with recommendations for the design of accessible mobility solutions.

Background: Ridepooling services have been predicted a bright future since they promise a flexible and user-centered mobility service. However, there is a research gap in examining the travelers’ perception of ridepooling service characteristics since findings concerning fixed-scheduled public transport are hardly transferable. Methods: In order to shed some light on the human factors of ridepooling services a Discrete Choice Experiment (N = 410) was performed to identify travelers’ preferences concerning ridepooling’s service features. The study thereby focusses on the effect of trip purpose on the appraisal of the service attributes. Based on a literature review and a focus group six attributes of the operational concept were determined: fare, walking distance to the pick-up point, time of booking in advance, shift of departure time, travel time and information. Results: The results underline that all of the six attributes significantly affected choice behavior. The appraisal of the service characteristics differed depending on the presented trip purpose. The willingness to pay was calculated for each service characteristics. The results give guidance for the user-centered design and operation of ridepooling systems that meet the requirements of the prospective passengers and thus facilitate behavioral shifts towards more sustainable mobility systems.