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.

Introduction A few major clinical factors such as sex, obesity or comorbidities have already been associated with COVID-19 severity, but there is a need to identify new epidemiological, clinical, digital and biological characteristics associated with severity and perform deep phenotyping of patients according to severity. The objectives of the Predi-COVID study are (1) to identify new determinants of COVID-19 severity and (2) to conduct deep phenotyping of patients by stratifying them according to risk of complications, as well as risk factors for infection among household members of Predi-COVID participants (the Predi-COVID-H ancillary study). Methods and analysis Predi-COVID is a prospective, hybrid cohort study composed of laboratory-confirmed COVID-19 cases in Luxembourg who will be followed up remotely for 1 year to monitor their health status and symptoms. Predi-COVID-H is an ancillary cohort study on household members of index cases included in Predi-COVID to monitor symptoms and household clusters in this high-risk population. A subcohort of up to 200 Predi-COVID and 300 Predi-COVID-H participants with biological samples will be included. Severity of infection will be evaluated by occurrence and duration of hospitalisation, admission and duration of stay in intensive care units or equivalent structures, provision of and duration of supplemental oxygen and ventilation therapy, transfer to another hospital, as well as the impact of infection on daily activities following hospital discharge. Ethics and dissemination The study has been approved by the National Research Ethics Committee of Luxembourg (study number 202003/07) in April 2020. An informed consent is signed by study participants. Scientific articles will be submitted to international peer-reviewed journals, along with press releases for lay audience for major results. Trial registration number NCT04380987.

Background: Patient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols.Objective: The aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research.Methods: We conducted a review on PPI methods. We mirrored it with a patient advocate’s viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case.Results: We analyzed data from 97 studies, of which 58 (60%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62%), co-design (42/97, 43%), defining priorities (31/97, 32%), and participation in data analysis (25/97, 26%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies.Conclusions: PPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies.

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.

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: 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