Aurélie Fischer

Background: Cognitive impairment is among the most frequently reported and disabling persistent symptoms following SARS-CoV-2 infection. Despite growing evidence of its prevalence, the characterization of domain-specific cognitive profiles and the identification of modifiable factors influencing cognitive outcomes in post-COVID-19 condition (PCC) remains scarce. Digital cognitive assessment tools have emerged as scalable and examiner-independent alternatives to traditional neuropsychological testing, but their validation in post-infectious contexts is still limited. Objective: The DigiCog study aims to characterize cognitive profiles associated with PCC, to evaluate a novel digital cognitive assessment technology (ViewMind, VMTech) against standardized neuropsychological measures, and to explore the potential role of cognitive reserve (CR) proxies in shaping cognitive outcomes following SARS-CoV-2 infection. Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort ( NCT04380987 ). Participants aged 25–65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen’s kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored. Results: Participant recruitment began in May 2023. As of March 2025, 199 participants had been enrolled. Data collection has been completed. Data processing and statistical analyses are ongoing. Conclusions: The DigiCog study will provide new insights into cognitive functioning in PCC and evaluate the validity of a novel digital cognitive assessment approach. By integrating neuropsychological testing, digital assessment, and cognitive reserve, the study may inform scalable and more personalized approaches to cognitive screening and monitoring in post-infectious conditions. Study registration: ClinicalTrials.gov NCT06391970 ; Luxembourg National Research Ethics Committee approval No. 202208/05.

Background: Long COVID, defined as unexplained persistent symptoms experienced after COVID-19 infection, is a complex multi-organ condition with unclear aetiology but considerable societal implications. While research on pathophysiological mechanisms underlying established medical risk factors is ongoing, a literature on socio-economic determinants has emerged but remains limited in geographical scope and quality. We enrich the evidence by reporting an interpretable battery of patient-level socio-economic, demographic and medical risk factors for 12-month Long COVID, in Luxembourg’s Predi-COVID cohort study.Methods: Participants, enrolled after PCR-confirmed SARS-CoV-2 infection, completed comprehensive questionnaires at baseline and 12-month follow-up covering socio-demographics, medical history, and persistent symptoms. As a primary outcome, Long COVID was defined as occurrence of any persistent symptom at follow-up, while a secondary outcome based on at least five symptoms was defined to explore robustness to symptom misclassification. Candidate risk factors included age, sex, low-income status, family size, educational attainment, employment, comorbidities, body mass index and, for a sub-sample, vaccination status. Risk ratios were estimated using quasi-Poisson regression, reweighted to Luxembourg’s age-sex structure.Findings: The main analytical sample, made of 585 complete cases (54·4%), is younger and slightly skewed towards males vis-à-vis the general population, with 237 (40·51%) and 135 (23·07%) patients reporting at least one or five persisting symptoms, respectively. Analyses replicate established findings on the role of sex, age, BMI, comorbidities, and vaccination. They also identify a protective role of higher educational attainment: conditional on other factors, post-secondary and tertiary education are associated with a 30% (IRR: 0·70; 95%CI: 0·50–0·97) and 20% (IRR: 0·79; 95%CI: 0·64–0·97) lower risk of Long COVID. Associations were robust to the use of the secondary outcome.Interpretation: In this test-positive, re-weighted cohort, higher educational attainment was independently protective against Long COVID at 12-month, alongside established demographic and medical risk factors. The findings support a role for social determinants, suggesting low education as a possible marker of life-course chronic inflammation and antecedent of inflammatory processes in Long COVID.

Hintergrund: Menschen mit Long COVID (LC) haben weiterhin Schwierigkeiten, Zugang zu einer angemessenen Versorgung zu erhalten und fühlen sich oft unverstanden, wobei die dahinterliegenden Mechanismen dieser multisystemischen Erkrankung weiterhin nicht geklärt sind. Aufgrund dieser Herausforderungen soll eine bedürfnisgerechte App entwickelt werden, welche Betroffene in ihrem Alltag unterstützt, eine bessere Selbstorganisation ermöglicht, die Kommunikation mit Behandelnden verbessert und den Zugang zu relevanten Informationen erleichtert. Methoden: Es wurden quantitative Umfragen (N = 216) und qualitative Interviews (N = 20) durchgeführt, um den Einfluss von LC auf Gesundheit und Alltagsgestaltung von LC-Betroffenen und Professionellen zu analysieren. Die Umfragen enthielten Fragen zur Symptomlast, den Alltagsbelastungen sowie den Bedürfnissen an digitalen Hilfsmitteln. Zudem wurden Vor- und Nachteile einer digitalen App sowie gewünschte Funktionalitäten erfasst. Anschließend wurde die App entwickelt und systematisch Rückmeldungen eingeholt (N = 9). Ergebnisse: Die Ergebnisse zeigten eine allgemeine Befürwortung einer kostenfreien App für den LC-Bereich, insbesondere in den Bereichen Symptomüberwachung und administrative Unterstützung. Datenschutz, Zugänglichkeit und einfache Handhabung wurden von den Teilnehmenden als essenzielle Anforderungen genannt. Diskussion: Neben dem wertschätzenden Entwicklungsprozess der App wurden insbesondere deren Funktionalitäten und die Visualisierung der Ergebnisse als sehr positiv bewertet. Die App wurde von den LC-Betroffenen und den LC-Professionellen als bedeutsames Hilfsmittel zur Selbstorganisation und zur Dokumentation des Krankheitsverlaufs angesehen. Schlussfolgerungen: Weitere Studien sind erforderlich, um die langfristige Akzeptanz, Benutzerfreundlichkeit und Wirksamkeit der App zu evaluieren.

Introduction: Long COVID is a multisystemic, fluctuating condition inducing a high burden on affected people. Despite the existence of some guidelines, its management remains complicated. We aimed to demonstrate that symptoms after a COVID-19 infection evolve following different trajectories from the initial infection until 24 months after, to identify the determinants of these trajectories, and the quality of life of people in these trajectories. Methods: Study participants from the Predi-COVID cohort were digitally followed from their acute SARS-CoV-2 infection until a maximum of 24 months. Data from 10 common symptoms collected at study inclusion, and months 12, 15, and 24 awere used to create a total symptom score. Impact of symptoms on quality of life was assessed at month 24 using standardized questionnaires and ad-hoc questions. Latent classes mixed models were used to identify total score symptom trajectories and individual symptoms trajectories. Results: We included 555 participants with at least 2 different time points available during follow-up (Baseline and at least one of the M12, M15 or M24 questionnaires). We identified 2 total symptom score trajectories: T1 “Mild symptoms, fast resolution” (N = 376; 67.7%), and T2 “Elevated and persisting symptoms” (N = 179; 32.3%). The main determinants of being in T2 were: older age (OR = 1.86; p = 0.003), to be a woman (OR = 1.81; p = 0.001)), elevated BMI (OR = 3.97; p < 0.001), and the presence of multi comorbidities (OR = 2.67; p = 0.005). Symptoms impacted the quality of life more in T2 than in T1 at 24 months (high fatigue level: 64.8% vs 19.5%, altered respiratory quality of life: 42.6% vs 4.6%, anxiety: 24.1% vs 4.6%, stress: 57.4% vs 35.6%, and bad sleep: 75.9% vs 51.1%). Conclusion: A third of our study population was in the T2 “Elevated and persisting symptoms” trajectory, presenting high symptom frequencies up to 24 months after initial infection, with a significant impact on quality of life. This work underlined the urgent need to better identify individuals most vulnerable to long-term complications to develop tailored interventions for them. Trial registration: Clinicaltrials.gov NCT04380987 (date of registration: 2020–05-07).

Background: People living with Long COVID (PWLC), which is still a poorly understood disease, often face major issues accessing proper care and frequently feel abandoned by the healthcare system. PWLC frequently report impaired quality of life because of the medical burden, the variability and intensity of symptoms, and insecurity toward the future. These particular needs justify the development of innovative, minimally disruptive solutions to facilitate the monitoring of this complex and fluctuating disease. Voice-based interactions and vocal biomarkers are promising digital approaches for such health monitoring. Methods: Based on a mixed-method approach, this study describes the entire co-design process of Long COVID Companion, a voice-based digital health app to monitor Long COVID symptoms. Potential end-users of the app, both PWLC and healthcare professionals (HCP) were involved to (1) understand the unmet needs and expectations related to Long COVID care and management, (2) to assess the barriers and facilitators regarding a health monitoring app, (3) to define the app characteristics, including future potential use of vocal biomarkers and (4) to develop a first version of the app. Results: This study revealed high needs and expectations regarding a digital health app to monitor Long COVID symptoms and the readiness to use vocal biomarkers from end-users. The main expectations included improved care and daily life, and major concerns were linked to accessibility and data privacy. Long COVID Companion was developed as a web application and is composed of a health monitoring component that allows auto-evaluation of symptoms, global health, and scoring relevant symptoms and quality of life using standardized questionnaires. Conclusions: The Long COVID Companion app will address a major gap and provide day-to-day support for PWLC. However, further studies will be needed following its release, to evaluate its acceptability, usability and effectiveness.

Background: Between 10% and 20% of people with a COVID-19 infection will develop the so-called long COVID syndrome, which is characterized by fluctuating symptoms. Long COVID has a high impact on the quality of life of affected people, who often feel abandoned by the health care system and are demanding new tools to help them manage their symptoms. New digital monitoring solutions could allow them to visualize the evolution of their symptoms and could be tools to communicate with health care professionals (HCPs). The use of voice and vocal biomarkers could facilitate the accurate and objective monitoring of persisting and fluctuating symptoms. However, to assess the needs and ensure acceptance of this innovative approach by its potential users—people with persisting COVID-19–related symptoms, with or without a long COVID diagnosis, and HCPs involved in long COVID care—it is crucial to include them in the entire development process. Objective: In the UpcomingVoice study, we aimed to define the most relevant aspects of daily life that people with long COVID would like to be improved, assess how the use of voice and vocal biomarkers could be a potential solution to help them, and determine the general specifications and specific items of a digital health solution to monitor long COVID symptoms using vocal biomarkers with its end users. Methods: UpcomingVoice is a cross-sectional mixed methods study and consists of a quantitative web-based survey followed by a qualitative phase based on semistructured individual interviews and focus groups. People with long COVID and HCPs in charge of patients with long COVID will be invited to participate in this fully web-based study. The quantitative data collected from the survey will be analyzed using descriptive statistics. Qualitative data from the individual interviews and the focus groups will be transcribed and analyzed using a thematic analysis approach. Results: The study was approved by the National Research Ethics Committee of Luxembourg (number 202208/04) in August 2022 and started in October 2022 with the launch of the web-based survey. Data collection will be completed in September 2023, and the results will be published in 2024. Conclusions: This mixed methods study will identify the needs of people affected by long COVID in their daily lives and describe the main symptoms or problems that would need to be monitored and improved. We will determine how using voice and vocal biomarkers could meet these needs and codevelop a tailored voice-based digital health solution with its future end users. This project will contribute to improving the quality of life and care of people with long COVID. The potential transferability to other diseases will be explored, which will contribute to the deployment of vocal biomarkers in general. Trial Registration: ClinicalTrials.gov NCT05546918; https://clinicaltrials.gov/ct2/show/NCT05546918 International Registered Report Identifier (IRRID): DERR1-10.2196/46103

BACKGROUND Between 10% and 20% of people with a COVID-19 infection will develop the so-called long COVID syndrome, which is characterized by fluctuating symptoms. Long COVID has a high impact on the quality of life of affected people, who often feel abandoned by the health care system and are demanding new tools to help them manage their symptoms. New digital monitoring solutions could allow them to visualize the evolution of their symptoms and could be tools to communicate with health care professionals (HCPs). The use of voice and vocal biomarkers could facilitate the accurate and objective monitoring of persisting and fluctuating symptoms. However, to assess the needs and ensure acceptance of this innovative approach by its potential users—people with persisting COVID-19–related symptoms, with or without a long COVID diagnosis, and HCPs involved in long COVID care—it is crucial to include them in the entire development process. OBJECTIVE In the UpcomingVoice study, we aimed to define the most relevant aspects of daily life that people with long COVID would like to be improved, assess how the use of voice and vocal biomarkers could be a potential solution to help them, and determine the general specifications and specific items of a digital health solution to monitor long COVID symptoms using vocal biomarkers with its end users. METHODS UpcomingVoice is a cross-sectional mixed methods study and consists of a quantitative web-based survey followed by a qualitative phase based on semistructured individual interviews and focus groups. People with long COVID and HCPs in charge of patients with long COVID will be invited to participate in this fully web-based study. The quantitative data collected from the survey will be analyzed using descriptive statistics. Qualitative data from the individual interviews and the focus groups will be transcribed and analyzed using a thematic analysis approach. RESULTS The study was approved by the National Research Ethics Committee of Luxembourg (number 202208/04) in August 2022 and started in October 2022 with the launch of the web-based survey. Data collection will be completed in September 2023, and the results will be published in 2024. CONCLUSIONS This mixed methods study will identify the needs of people affected by long COVID in their daily lives and describe the main symptoms or problems that would need to be monitored and improved. We will determine how using voice and vocal biomarkers could meet these needs and codevelop a tailored voice-based digital health solution with its future end users. This project will contribute to improving the quality of life and care of people with long COVID. The potential transferability to other diseases will be explored, which will contribute to the deployment of vocal biomarkers in general. CLINICALTRIAL ClinicalTrials.gov NCT05546918; https://clinicaltrials.gov/ct2/show/NCT05546918 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/46103

The increasing number of people living with Long COVID requires the development of more personalized care; currently, limited treatment options and rehabilitation programs adapted to the variety of Long COVID presentations are available. Our objective was to design an easy-to-use Long COVID classification to help stratify people with Long COVID. Individual characteristics and a detailed set of 62 self-reported persisting symptoms together with quality of life indexes 12 months after initial COVID-19 infection were collected in a cohort of SARS-CoV-2 infected people in Luxembourg. A hierarchical ascendant classification (HAC) was used to identify clusters of people. We identified three patterns of Long COVID symptoms with a gradient in disease severity. Cluster-Mild encompassed almost 50% of the study population and was composed of participants with less severe initial infection, fewer comorbidities, and fewer persisting symptoms (mean = 2.9). Cluster-Moderate was characterized by a mean of 11 persisting symptoms and poor sleep and respiratory quality of life. Compared to the other clusters, Cluster-Severe was characterized by a higher proportion of women and smokers with a higher number of Long COVID symptoms, in particular vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified into three subcategories in terms of severity. If replicated in other populations, this simple classification will help clinicians improve the care of people with Long COVID.

The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.

Background: The COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75% to 95% and from 50% to 80% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner. Objective: We hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them. Methods: This study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research. Results: This study included 259 participants. Younger (aged <35 years) and female participants showed higher rates of ageusia and anosmia. Participants were aged 41 (SD 13) years on average, and the data set was balanced for sex (female: 134/259, 51.7%; male: 125/259, 48.3%). The analyzed symptom was present in 94 (36.3%) out of 259 participants and in 450 (27.5%) out of 1636 audio recordings. In all, 2 machine learning models were built, one for Android and one for iOS devices, and both had high accuracy—88% for Android and 85% for iOS. The final biomarker was then calculated using these models and internally validated. Conclusions: This study demonstrates that people with COVID-19 who have anosmia and ageusia have different voice features from those without these symptoms. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance the telemonitoring of COVID-19–related symptoms. Trial Registration: Clinicaltrials.gov NCT04380987; https://clinicaltrials.gov/ct2/show/NCT04380987