Background: Parkinson’s disease presents unique challenges in healthcare delivery due to its symptom complexity and the progressive nature of the condition, requiring coordinated care across multiple healthcare disciplines. In response to the limitations of traditional, fragmented care models, the healthcare system is increasingly shifting towards integrated, Value-Based Care (VBC), highlighting the need for innovative quality monitoring frameworks and real-world evidence supported by digital health technologies. Effective monitoring and evaluation are thus essential to ensure evidence-based program design that addresses the complex needs of all people with Parkinson's (PwP). Here, we present the process and results of the quality monitoring framework by outlining key domains, indicators and data sources to monitor equity and the quality of person-centred and digitally-enabled integrated care in Parkinson’s. Approach: The quality monitoring and evaluation framework for Parkinson's Disease (Réseaux de Compétences: Maladies Neurodégénératives) was developed in a multiphased approach in collaboration with the Ministry of Health as part of a national initiative to implement integrated care models for various health conditions across Luxembourg. The development of the PD-relevant indicators involved a consultative process within a multidisciplinary team, including neurologists, nurses, allied healthcare professionals, patient representatives, and researchers of various backgrounds and expertise. A core set of indicators was proposed for baseline data collection and annual project monitoring, along with key patient characteristics and various data sources to support real-world monitoring. A feasibility study was conducted to determine the digital readiness among healthcare professionals and a minimal set of indicators that could be collected in the first year. Results: A core set of 20 indicators for digitally enabled integrated care was proposed, directly contributing to the healthcare system's quadruple aim: enhancing population well-being, improving patient and healthcare provider experiences, and reducing costs. These indicators were organised into four primary domains: (1) Patient Outcomes, Experiences, and Capabilities, encompassing clinical data, quality of life, patient-reported outcomes and experience, and empowerment; (2) Process Outcomes, covering healthcare utilisation, adherence to clinical guidance, and digital engagement among patients and healthcare professionals; (3) System Structure and Capacity, which includes healthcare professional training and competencies, operational and coordination costs, and related infrastructure; and (4) Healthcare Professional Work Satisfaction, focused on job fulfilment and well-being. Patient characteristics included measurements of sociodemographic status, digital (health) literacy and disease severity. Real-world data sources included patient and healthcare professional questionnaires via digital apps, electronic health records, health claims, and administrative data. Initial assessment showed challenges in adopting digital health technologies and a need to implement fewer indicators at the first stage. Implications: This framework presents a comprehensive model for quality monitoring in digitally enabled, integrated care for Parkinson’s disease, offering essential indicators to evaluate and optimise VBC. By focusing on patient-centred outcomes and integrating digital health technologies, the framework aligns with healthcare systems' goals to provide effective, efficient, equitable, timely, and safe services in an era of digital transformation. While the model has significant potential, challenges remain in ensuring the feasibility of consistent data collection, integration across various healthcare providers, and maintaining engagement with digital health technologies.

BackgroundPeople with Parkinson's disease (PwPD) require coordinated, multidisciplinary care, which can be facilitated by Electronic Health Records (EHRs) enabling efficient information exchange and personalized decision-making. Understanding which patient-, healthcare provider- (HCP), and technology-related factors drive EHR engagement among older population with complex health needs is crucial for the successful adoption and advancement of digital transformation in medicine.MethodsGuided by the digital health empowerment framework, this cross-sectional study explored patient engagement with the national EHRs among 191 PwPD in Luxembourg, using validated instruments, including eHealth Literacy Questionnaire (eHLQ) and Health Information National Trends Survey (HINTS).ResultsFindings from the descriptive and regression analysis showed that only 29.8% respondents engaged multiple times with their personal EHR in the previous year, 70.2% have not used it, including 40.8 % who have never access it since the launch of EHR system. Key factors associated with higher engagement with personal EHR included being born in Luxembourg, milder disease severity, and higher digital health literacy, as well as receiving support from HCPs to use personal EHR. Surprisingly, higher trust in HCPs and greater health literacy were linked to lower personal EHR usage.ConclusionsPersonal EHR engagement among the vulnerable aging population is influenced by a complex interplay of patient, HCP, and technology-related factors, which must be addressed holistically to ensure inclusive usage and adherence to digital health tools.

Background: On-medication motor assessment in Parkinson's Disease confounds treatment response with the disease's motor state. To properly quantify an off-medication state, we predicted the patient's underlying motor severity and characterized their response to treatment through individualized trajectories without relying on medication washout. Methods: A retrospective, longitudinal analysis was conducted on participants enrolled in the Parkinson's Progression Markers Initiative. We developed and internally validated a linear mixed-effects model using a subject-wise nested cross-validation framework. The primary outcome was MDS-UPDRS III in the off-medication state, approximated by levodopa equivalent daily dose, disease duration, presence of dyskinesia, and MDS-UPDRS III while on medication, as primary predictors. Principal component analysis was applied to model residuals to characterize treatment response patterns across off-medication predictions. Findings: The model predicted off-medication MDS-UPDRS III within a 4-point margin. Up to 12% of observations exhibit a limited treatment response, indicating potential under-recognized treatment resistance or dosage ceiling effects. In support of the treatment response patterns, principal component analysis uncovered clinically relevant differences between patterns, including statistically significant MDS-UPDRS III subitems, activities of daily living, and affective state scores between limited and excessive treatment response profiles. Thereby supporting the clinical utility in providing an individualized method of characterizing treatment response. Interpretation: By quantifying the underlying disease severity in the off-medication state, we can enhance the characterization of treatment efficacy and support improved treatment decisions in conventional clinical practice. This capability enables the detection of limited treatment responses and highlights potential underrecognized effects in individuals with more severe diseases, which can lead to undertreatment or dosage limitations. Furthermore, assessing medication response is achievable using routinely collected clinical measures.

REM Sleep Behaviour Disorder (RBD) is a hallmark of the prodromal phase of α-synucleinopathies. We aimed to describe the prevalence of probable RBD and to assess its associations with demographics, cognition, and location in a large sample of older adults in Luxembourg, as a first step toward identifying individuals with RBD symptoms for future prodromal-marker assessment. In 2021, residents of Luxembourg aged 55–75 were invited to complete an online survey including the RBD Screening Questionnaire (RBDSQ); with a threshold of ≥ 7 defining screen-positive probable RBD (sppRBD). Screen-positive participants underwent a telephone interview, and those confirmed were categorised as telephone-assessed probable RBD (pRBD). Bayesian spatial mapping assessed the geographical distribution of pRBD, and logistic regression identified determinants of sppRBD and pRBD. Among 15,915 participants (54% male; median age 62 [IQR 58–67]), 12.4% had sppRBD. The telephone interview confirmed only 34.8% of these as pRBD, yielding a projected prevalence of 4.3%. Self-reported cognitive impairment, male sex, and Portuguese as questionnaire language were associated with pRBD, which showed heterogeneous geographical distribution. Online questionnaires may yield false positives, potentially reflecting e-health literacy issues; therefore, a confirmation step is essential. This analysis identifies individuals with RBD symptoms warranting further prodromal-marker assessment, a candidate group for, rather than a validated instance of, an at-risk-for-α-synucleinopathy cohort.

While health technology assessment (HTA) aims to quantify the quality of digital health apps, app stores offer unstructured information on app quality to users like patients. This provides barriers for people with Parkinson's disease (PD) and Alzheimer's disease (AD) to identify high-quality apps. This study explores the discoverability of AD/PD-related apps and their quality information in app stores. We applied descriptive statistics and text mining such as large language models (LLMs) and topic modelling to analyze app descriptions and user reviews of 1237 apps. Only ∼2% of apps were discoverable as holding a CE-mark. Most apps were "Care Support", followed by "Health & Wellness" and "Patient Monitoring" patient-facing categories. User reviews addressed "user experience", "health improvement", and "costs". To help patients identify high-quality apps, quality information should be presented in a structured and trustworthy way. The use of user feedback for patient-reported measures should be explored in future work.

Cognitive impairment (CI) is common in Parkinson’s disease (PD), affecting up to 30% of patients at diagnosis and increasing over time. This study evaluated and compared existing risk prediction tools for screening mild cognitive impairment (MCI) and assessing risk of subsequent cognitive decline in people with PD by validating and extending them with PD-specific and modifiable risk factors. Data from 201 PwPD and 231 controls from the Luxembourg Parkinson’s study were analyzed; 46.77% of PwPD had MCI. The Lifestyle for Brain Health (LIBRA) and preliminary disease Risk Estimator for Decline in Cognition Tool (pPREDICT) risk scores were evaluated and expanded to create a new tool called “ Modi fiable factors for cog nitive decline in PD” (ModiCogPD), and predictive performance was assessed using ROC–AUC, logistic regression, and Cox models. PD-specific scores (pPREDICT and ModiCogPD) showed better performance than LIBRA, with higher scores associated with increased risk of cognitive decline over time, while LIBRA was more informative in early disease stages.

The digital transformation of healthcare is rapidly reshaping neurology, particularly in the field of movement disorders, where continuous monitoring, long disease trajectories, and complex multimodal care create a high demand for innovative solutions. Wearable sensors, digital diagnostics, app-based therapeutics, and integrated hybrid care networks and digital supported care pathways promise earlier diagnosis, personalized treatment and care management, and improved long-term outcomes. However, real-world implementation in managed outpatient care remains fragmented and faces major barriers beyond pure technological feasibility. This position paper critically reviews the current state of digital technologies in movement disorder care, identifies key systemic, ethical, and economic roadblocks, and proposes a pragmatic roadmap toward a realistic and ethically sound digital outpatient clinic. We argue that the future digital clinic will not be defined solely by technical progress, but by how consciously healthcare systems integrate digital tools to hybrid care solutions while preserving human-centered, equitable, and evidence-based care.

Background: People with Parkinson’s disease suffer from gait impairments. Clinical scales provide a limited and rater-dependent assessment of gait. Wearable sensors allow an objective characterization by capturing rhythm, pace, and signature patterns. This study investigated if sensor-derived gait parameters have prognostic value for short-term progression of gait impairments. Methods: A total of 111 longitudinal visit pairs were analyzed, where participants underwent clinical evaluation and a 4 × 10 m walking test instrumented with wearable sensors. Changes in the UPDRSIII gait score between baseline and follow-up were used to classify participants as Improvers, Stables, or Deteriorators. Baseline group differences were assessed statistically. Machine-learning classifiers were trained to predict group membership using clinical variables alone, sensor-derived gait features alone, or a combination of both. Results: Significant between-group differences emerged. In participants with UPDRSIII gait score = 1, Improvers showed higher median gait velocity (0.81 m/s) and stride length (0.80 m) than Stables (0.68 m/s; 0.70 m) and Deteriorators (0.59 m/s; 0.68 m), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all p<0.05). The combined sensor-based and clinical model showed the best performance (AUC 0.82). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future.

Background: Digital solutions offer the possibility to improve the management of patient care by increasing and facilitating the communication within the multidisciplinary care team. However, integrating digital medical devices and services into integrated care models, such as those for Parkinson’s disease (PD), comes with several challenges such as the reservations to change by the healthcare professionals (HCPs), the disruption of the care pathway workflows, the management of highly sensitive information and the lack of systems interoperability, among others. Approach: Taken this into consideration, we aim to improve the communication between the multidisciplinary care team and patients of ParkinsonNet Luxembourg by leveraging the use of digital management solutions and the Luxembourgish Electronic Health Records known as the Dossier de Soins Partagé (DSP). This initiative was co-designed with local stakeholders, including an expert group in PD (n=6), PD patient representatives, and members from the Luxembourgish eHealth agency (eSanté). To test feasibility and engage users, we conducted an anonymous online survey among HCPs of ParkinsonNet Luxembourg (n=85) to understand their perspectives on the usage of digital solutions to collect, store and exchange clinical information. Results: In total 45 out of 85 invited HCPs answered the online survey. 82.2% of participants confirmed that they would like to use a medical software to document clinical information, and among these, 78.4% are willing to collect clinical information in a structured way. Furthermore, 72.7% of HCPs agreed that clinical information should be stored in the DSP. However, 51.16% believe that the lack of time during a consultation or the complexity of the DSP will be the biggest barriers to integrate DSP into their daily work. These results demonstrate that the planned initiative to exchange structured clinical information via the DSP is feasible within ParkinsonNet Luxembourg, provided that enough guidance is given to the HCPs and that the integration of DSP into their daily work is done without major adjustments to the care pathway and related workflows. Implications: Addressing potential challenges of the integration of digital solutions into integrated and managed healthcare models requires a strategic planning that considers the local context, the existing resources and the preferences of the care teams. This can be achieved through the integration of different stakeholders very early in the planning stage, the investigation of the preferences of the local care teams, the continuous education of HCPs and the availability of robust funding models to ensure that digital solutions can be seamlessly and sustainably integrated into such comprehensive healthcare models.

Objectives: To characterize Real-World fall-risk dynamics in Luxembourgish long-term care (LTC) through multistate models and assess whether the fall-risk state (FRS) is associated with the planning and delivery of healthcare resources at the provider level. Design: Retrospective observational study Setting and Participants: 69 geriatric participants living in Luxembourgish LTC facilities (median age 83 years, 32% female, and median time in LTC of 1 year). Methods: A multistate model was fitted to characterize FRS transitions across three states: low, medium, and high risk of falls. FRS were estimated from POMA scores using clinically defined cutoffs. Healthcare resource utilization was approximated using reimbursement-based therapy minutes and linked to FRS via a mixed model. Finally, a counterfactual projection estimating the population-level impact of a targeted clinical intervention was performed. Results: FRS transitions were modeled with weight, age, and grip strength. Lower weight increased the rate of transition from low to moderate risk (HR = 2.584; 95% CI, 1.338–4.992), whereas higher grip strength decreased the rate of transition from moderate to high risk (HR = 0.352; 95% CI, 0.179–0.694). Age (HR = 2.536; 95% CI, 1.171–5.496) was associated with a higher transition rate for the latter. Moderate- and high-risk patients received approximately 32 and 41 min/week more therapy than their low-risk counterparts, respectively. Counterfactual projections with a 2 kg improvement in grip strength showed a 19% relative reduction in the probability of reaching high fall risk at 36 months, saving 86 minutes of therapy per week per patient. Conclusions and Implications: We found that weight and grip strength are the most important modifiable factors influencing FRS transitions, with higher FRS associated with greater delivery of individual therapy. Using these as clinical endpoints could help improve the impact of therapy and thus positively reflect on the adverse quality-of-care indicators

PD patients present with diverse symptoms, complicating timely diagnosis. We analyzed 1124 PD trajectories using a novel model-based approach to estimate whether diagnosis was early or late compared to cohort averages. Higher age, specific non-motor symptoms, and fast disease progression were linked to later diagnosis, while gait impairment led to earlier diagnosis. Our findings are in line with a biological definition of PD that extends beyond classical motor symptoms.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson's dementia (PDD) post-diagnosis, further validated by data from the Parkinson's Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson's patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention.

BACKGROUND:Patient empowerment is widely recognized for improving health outcomes, increasing patient satisfaction, and enhancing the overall effectiveness of health care. Digital health technologies (DHTs) contribute to this empowerment by keeping patients informed, involved, and engaged in their own health. However, more evidence is needed to better understand which aspects of empowerment patients value when using DHTs and how DHTs can support these values. OBJECTIVE:Drawing on Sen's capability approach, this paper conceptualizes patient empowerment in digital health by defining distinct capabilities, resources, and conversion factors that contribute to patient empowerment through DHTs. METHODS:We based our scoping review on the methodology recommended by the Joanna Briggs Institute Manual for evidence synthesis and an a priori registered protocol. Papers were included if they focused on patient empowerment in relation to DHTs among patients with chronic diseases (cardiovascular diseases, diabetes, cancer, chronic respiratory diseases, and neurodegenerative diseases), with particular emphasis on the patient perspective. PubMed, Scopus, and Web of Science were searched for evidence published from January 2013 to April 2024. Data were extracted and thematically analyzed via a multidisciplinary workshop to identify empowerment components relevant to the capability framework, such as capabilities, DHTs as resources, and conversion factors. RESULTS:Our analysis identified 3 core capabilities to achieve patient empowerment supported by DHTs: health information and knowledge management, self-management, and emotional and social support. DHTs as resources supported these capabilities through distinct functional components, including informing patients, communication with the health care team, monitoring, behavior change interventions, individualized feedback, or peer support, each contributing to a varying degree. Conversion factors such as demographic and socioeconomic status, digital literacy, disease status, perceived value of DHTs, sociocultural values and norms, doctor-patient relationship, connectivity, and cost influenced the development of empowering capabilities resulting from using DHTs. CONCLUSIONS:While the capabilities related to patient empowerment in DHTs were clearly distinguishable, our analysis revealed a notable interconnectedness among these components. Our conceptualization of patient empowerment serves as a valuable resource for researchers seeking to understand or assess patient empowerment via DHTs. It also provides guidance for DHT developers, helping them design DHTs that enhance valued capabilities and account for the conversion factors and ultimately promote patient empowerment across diverse population groups.

Traditional clinical assessments in Parkinson's disease (PD) trials are limited by subjectivity and inter-rater variability. Digital health technologies (DHT) offer an objective continuous assessment of motor symptoms and are increasingly used in clinical research. This review evaluated the role of DHTs as outcome tools in pharmacological trials for PD. A systematic search of MEDLINE and Embase was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, covering studies up to August 31, 2025. Eligible studies included randomized controlled trials, open-label or crossover designs, and observational studies using DHTs to assess motor outcome variables in PD. Studies focusing only on technology development or with fewer than 10 participants were excluded. Data extracted included study design, DHT type, assessment setting, and motor parameters measured. Study quality was appraised using an eight-criterion tool, and level of evidence was rated using the Oxford Centre for Evidence-Based Medicine framework. A total of 42 studies were included, covering 26 distinct DHTs. These comprised 11 wearable sensors and 15 nonwearable systems such as motion capture platforms and force-sensing assessments. DHTs were used to measure bradykinesia, tremor, gait, balance, and nocturnal motor symptoms in both supervised and unsupervised settings. Fifteen studies were rated as high quality, 14 moderate, and 13 low. Among currently available tools, only Opal reached the threshold of Level 1a evidence. Other validated tools included the Parkinson's Kinetigraph, Actiwatch, and Roche PD Mobile Application (Level 1b). DHTs offer valuable tools for objective assessment in PD trials, though broader adoption requires greater standardization and regulatory alignment. © 2025 International Parkinson and Movement Disorder Society.

A major challenge in diagnosing post COVID lies in differentiating symptoms following a confirmed SARS-CoV-2 infection from those that may also occur in uninfected individuals (post COVID mimics) and be associated with a broader impact of the pandemic. The WHO post COVID definition was applied to the Luxembourgish longitudinal CON-VINCE cohort, where SARS-CoV-2 infection was confirmed via either a positive RT-qPCR or a serology test. Risk factor analysis was conducted on 1,865 individuals. Female gender, lower resilience, greater loneliness, and a higher number of comorbidities were associated with symptoms persistence. The symptomatology and comorbidity profiles of 559 participants (including 50 post COVID and 66 post COVID mimics) were investigated. Two distinct clusters of persistent symptoms were identified: (1) depression with anxiety, present in both infected and non-infected groups, and (2) memory impairment with fatigue, unique to the post COVID group. Therefore, presence of both memory impairment and fatigue may help differentiate post COVID patients from post COVID mimics. Yet, verification that memory impairment was newly developed was not possible, as this symptom was not recorded at baseline. Our findings suggest that future studies should consider factors affecting development of persistent post COVID-like symptoms observed in individuals that were never infected.

Background Research over the past twenty years has shown that gait outcomes have a high sensitivity for diagnosing Parkinson's disease (PD), for detecting the effects of interventions, and for monitoring disease progression, even in early disease. However, the lack of standardization in protocols and reported gait measures is impeding data aggregation across study sites and contributes to heterogeneity in the results, thus limiting the adoption of gait outcomes in clinical trials. Objective To provide recommendations for a minimum set of gait measures to be adopted in projects evaluating people with PD to enhance standardization across the field. Methods The Gait Advisors Leading Outcomes for Parkinson's (GALOP) committee is an advisory committee for the MJFF. Based on a five-step approach, GALOP generated recommendations for standardizing protocols that assess quantitative gait measures, following expert consensus on best practices. Results Built on the literature and consensus amongst experts, we recommend a minimum set of meta-data to accompany gait protocols and a minimum gait assessment protocol to be performed at a comfortable speed. Suggestions on challenging testing are provided. Conclusions To support and empower the scientific community, we have generated recommendations to collect and share gait data gathered from people with PD using an open data repository. Standardizing gait protocols and outcomes in PD has the potential of accelerating research and clinical trials, harmonizing protocols across study sites, fostering collaborations, and in the long run, improving patient care and quality of life.

Digital mobility outcomes (DMOs) have emerged as novel biomarkers offering objective, quantitative, and examiner-independent outcome measures for clinical studies. Unfortunately, research efforts on DMOs have not yet investigated the domain of clinical utility in Parkinson’s disease, i.e. providing evidence of improvements in health outcomes, diagnosis, decision-making, or prevention when compared to e.g. standard-of-care procedures. This manuscript, via a consensus building approach, aims to create a structured conceptual framework to map the knowledge generated by DMOs with clinical domains that could benefit from it. We conducted a three-round consensus-building study with 12 experts recruited from the Mobilise-D consortium’s Parkinson’s Disease Working Group. The experts designed and ranked different aspects of the conceptual framework via a 5-level Likert scale for level of agreement. Consensus for the different points evaluated was based on a double threshold: the simultaneous presence of a high level of agreement had to be accompanied by a low level of disagreement. As secondary objectives, the experts were asked to rate the practical application of DMOs by evaluating the timeline to applicability, the foreseen challenges for their implementation in clinical settings, and their main role in the decision-making process. A full consensus on the clinical utility framework was achieved after three rounds. The final framework consisted of three main categories (Disease Diagnosis, Patient Evaluation, and Treatment Evaluation) and six underlying domains (Enhancing Diagnostic Procedure, Predicting Risk, Timely Detecting Deterioration, Enhancing Clinical Judgment, Selecting Treatment, and Monitoring Treatment Response). The experts believed in the next 1–5 years DMOs will play a relevant role in clinical decision making, complementing care knowledge with useful digital biomarkers information. However, the main challenge to address is the definition of clear reference value for DMOs interpretability. This framework provides a structure for subsequent studies to build into by diversifying expert cohorts and expand our findings beyond PD. Additionally, our results support researchers planning future clinical trials where DMOs can play a valuable role for clinical decision support. Ultimately, this is the first step toward developing guidelines to assess DMOs’ clinical utility and support their integration into Real World clinical practice.

Background During the COVID-19 pandemic swift implementation of research cohorts was key. While many studies focused exclusively on infected individuals, population based cohorts are essential for the follow-up of SARS-CoV-2 impact on public health. Here we present the CON-VINCE cohort, estimate the point and period prevalence of the SARS-CoV-2 infection, reflect on the spread within the Luxembourgish population, examine immune responses to SARS-CoV-2 infection and vaccination, and ascertain the impact of the pandemic on population psychological wellbeing at a nationwide level. Methods A representative sample of the adult Luxembourgish population was enrolled. The cohort was followed-up for twelve months. SARS-CoV-2 RT-qPCR and serology were conducted at each sampling visit. The surveys included detailed epidemiological, clinical, socio-economic, and psychological data. Results One thousand eight hundred sixty-five individuals were followed over seven visits (April 2020—June 2021) with the final weighted period prevalence of SARS-CoV-2 infection of 15%. The participants had similar risks of being infected regardless of their gender, age, employment status and education level. Vaccination increased the chances of IgG-S positivity in infected individuals. Depression, anxiety, loneliness and stress levels increased at a point of study when there were strict containment measures, returning to baseline afterwards. Conclusion The data collected in CON-VINCE study allowed obtaining insights into the infection spread in Luxembourg, immunity build-up and the impact of the pandemic on psychological wellbeing of the population. Moreover, the study holds great translational potential, as samples stored at the biobank, together with self-reported questionnaire information, can be exploited in further research. Trial registration Trial registration number: NCT04379297, 10 April 2020.