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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.

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. 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. 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. 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. 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.

BackgroundAs 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.MethodsTo 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.ResultsThe 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.

Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures. Methods Forty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs. Results Acoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes 𝜼2 between 0.183 and 0.3, p  < 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (𝜼2 between 0.062 and 0.143, p  < 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p  = 0.01). Conclusions This study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.

IntroductionShared 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.MethodolgyUsing 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.ResultsProjected 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.ConclusionsOur 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.

Sustainability research is frequently tasked with the development of concrete solutions that can be directly applied to socio-environmental problems as such this paper presents and discusses an inter- and transdisciplinary approach to developing and testing a mobility-on-demand-system in a “real world laboratory” set up in Schorndorf, Germany. This paper addresses the following questions: (1) How can stakeholders be involved in the research and development process and become co-designers? (2) What are the suitable ways of supporting and facilitating interdisciplinary exchange and joint work at different places? The main contribution of this paper is the description of a methodological approach. It thereby reflects on the process of inter- and transdisciplinary work in the development phase and pilot operation. In addition, a joint working document, a so called “Specification Book”, is utilized to facilitate teamwork and enable the exchange of scientific knowledge within the team. The experiences in the project are also reflected upon and specific recommendations are determined. The paper further reflects on the possibilities and challenges of the methodology and provides recommendations for its application. The originality of the paper lies in its description and reflection of a method that goes beyond the participation of users in the design phase of the project.

BackgroundRidepooling 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.MethodsIn 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.ResultsThe 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.

Background Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. Methods We included n =  118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0–60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. Results In our sample, n =  93 individuals scored below and n =  25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. Conclusions Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.

Moving is an indispensable component of travelling. This paper discusses the experiences of persons with disabilities when moving around cities on foot or wheels, based on research conducted during the EU-funded project TRIPS. Findings comprise participants’ vignettes from 49 interviews in seven European cities, views on smart assistive technologies (e.g., Augmented Reality) from a pan-European quantitative survey, and design concepts related to walking based on a co-creation workshop that actively engaged persons with various types of disabilities in ideation. Findings suggest that people need reliable and clear wayfaring information on accessible travel routes featuring the coordinated design of streets, pavement, stops, stations, and vehicles to ensure seamless, step-free, and obstacle-free access, as well as disability-sensitive management of disruptions such as maintenance works, for example. Findings also suggest that users are open to using any assistive technology that can enable them to live more independently, assuming it is accessible, and are keen to co-innovate. Finally, we make recommendations for policy changes that can facilitate the redesign of urban infrastructure to make cities more accessible for people with disabilities and drive structural changes in urban planning.

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. There is an urgent need to identify prognostic markers for patients undergoing curative resection of CRC. The detection of circulating tumor cells in peripheral blood is a promising approach to identify high-risk patients with disseminated disease in colorectal cancer. This study aims to evaluate the prognostic relevance of preoperative CTCs using the Cellsearch® system (CS) in patients, who underwent resection with curative intent of different stages (UICC I-IV) of colorectal cancer. Out of 91 Patients who underwent colorectal resection, 68 patients were included in this study. CTC analysis was performed in patients with CRC UICC stages I-IV immediately before surgery. Data were correlated with clinicopathological parameters and patient outcomes. One or more CTCs/7.5 mL were detected in 45.6% (31/68) of patients. CTCs were detected in all stages of the Union of International Cancer Control (UICC), in stage I (1/4, 25%), in stage II (4/12, 33.3%), in stage III (5/19, 26.3%) and in stage IV (21/33, 63.6%). The detection of ≥ 1 CTCs/ 7.5ml correlated to the presence of distant overt metastases (p = 0.014) as well as with shorter progression-free ( p = 0.008) and overall survival ( p = 0.008). Multivariate analyses showed that the detection of ≥ 1 CTCs/ 7.5ml is an independent prognostic indicator for overall survival (HR, 3.14; 95% CI, 1.18–8.32; p = 0.021). The detection of CTCs is an independent and strong prognostic factor in CRC, which might improve the identification of high-risk patients in future clinical trials.