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.

Background: The COVID-19 pandemic disrupted behavior within populations, affecting physical activity (PA), heart rate (HR), and sleep characteristics in particular. Activity trackers provide unique insights into these changes, enabling large-scale, real-time monitoring. Objective: This study aims to analyze the associations between the features of the COVID-19 pandemic worldwide and PA, HR, and sleep parameters, using data collected from activity trackers over a 3-year period. Methods: We performed a retrospective analysis using anonymized data collected from the 208,818 users of Withings Steel HR activity trackers, spanning 34 countries, over a 3-year period from January 2019 to March 2022. Key metrics analyzed included daily step counts, average heart rate, and sleep duration. The statistical methods used included descriptive analyses, time-trend analysis, and mixed models to evaluate the impact of restriction measures, controlling for potential confounders such as sex, age, and seasonal variations. Results: We detected a significant decrease in PA, with a 12.3% reduction in daily step count (from 5802 to 5082 steps/d) over the 3 years. The proportion of sedentary individuals increased from 38% (n=14,177) in 2019 to 52% (n=19,510) in 2020 and remained elevated at 51% (n=18,972) in 2022, while the proportion of active individuals dropped from 8% (n=2857) to 6% (n=2352) in 2020 before returning to 8% (n=2877) in 2022. In 2022, the global population had not returned to prepandemic PA levels, with a noticeable persistence of inactivity. During lockdowns, HR decreased by 1.5%, which was associated with lower activity levels. Sleep duration increased during restrictions, particularly in the countries with the most severe lockdowns (eg, an increase of 15 min in countries with stringent measures compared to 5 min in less restricted regions). Conclusions: The sustained decrease in PA and its physiological consequences highlight the need for public health strategies to mitigate the long-term effects of the measures taken during the pandemic. Despite the gradual lifting of restrictions, PA levels have not fully recovered, with lasting implications for global health. If similar circumstances arise in the future, priority should be given to measures for effectively increasing PA to counter the increase in sedentary behavior, mitigate health risks, and prevent the rise of chronic diseases.

The increasing number of people living with Long COVID requires the development of more personalized care, as for now 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 stratifying 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 3 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 a poor sleep and respiratory quality of life. Cluster-Severe was characterized by a higher proportion of women and smokers as in the other clusters, with a higher number of Long COVID symptoms, in particular of vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified in 3 sub-categories in terms of severity. If replicated in other populations, this simple classification will help clinicians to personalize the care of people with Long COVID.

Introduction: Negative menstrual cycle–related symptoms have been shown to perceptually impact well-being, as well as training and competition performance, yet they are rarely daily monitored over extended periods. The aim is to compare menstrual cycle (MC) and hormonal contraception (HC) related symptoms reported through retrospective and daily questionnaires in elite female athletes and to assess their association with well-being and performance metrics. Methods: Data were collected from 108 elite female athletes across seven sports over 554 full cycles. Participants completed a retrospective questionnaire on regular symptoms and provided daily prospective entries for 6 months ( n = 16,491) about their symptoms, sleep, fitness and mood. Symptom reporting methods were compared, differences in symptom frequency across cycle phases were analyzed, and associations between symptom count and well-being were explored. Performance metrics were collected for sports providing objective data and analyzed comparing training with vs. without reported symptoms. Results: Athletes reported more symptoms retrospectively than in daily questionnaires. Mood swings, tiredness, and pelvic pain were the most common retrospective symptoms, while bloating, tiredness, and pelvic pain were most frequent in daily entries. MC athletes reported more symptoms than HC users in both methods. According to the daily follow-up, symptoms were more frequent during menstruation and the prebleeding phase for MC athletes and the break phase for HC users. A significant negative correlation was observed between symptoms and well-being indicators. In football players, high-speed running distance significantly declined on symptomatic days. Conclusion: Retrospective questionnaires shows a greater symptom prevalence than daily monitoring. Symptoms, more frequent during bleeding phases, negatively impacted athletes' well-being and also high-speed running performance for football players. These findings highlight the importance of individualized monitoring and tailored interventions to optimize athlete health, well-being, and performance.

The purpose of the study is to identify variations in wellness parameters among elite female athletes, considering simultaneously the influence of natural menstrual cycle (MC) or hormonal contraception (HC) phases and weekly and seasonal variations. A total of 54 elite female athletes were followed up with through a 6-month daily monitoring of their reported fitness, sleep quality, mood, and menstrual symptoms. Estimated cycle phases, days of the week, and seasons were all integrated into a single model to study cyclicities concomitantly. A total of 263 cycles were analyzed, totaling 7370 pieces of daily self-reported data. We observed more frequent symptoms during the menstruation and the pause phase. Reported fitness was greater in the mid-cycle and mood was lower at the end of the cycle for the MC group. Wellness parameters were rather stable for HC users. For both groups, symptoms were more frequent in summer. Reported fitness levels were lower on Monday, while mood scores were highest on Saturday and Sunday. In summer and autumn, fitness, sleep quality, and mood were greater. A cyclical wellness rhythm was identified in the menstrual cycle and persists even when considering weekdays and seasons cyclicities. This comprehensive approach helps to identify physiological influence on human behaviors, considering the specificities of women.

Background: Wearable and connected digital devices continuously generate large volumes of real-world behavioral, physiological, and environmental data, offering new opportunities for health monitoring and personalized care. Digital phenotyping has emerged as a promising paradigm; yet, there is limited consensus regarding how such data should be analyzed. Inconsistent analytical practices and insufficient methodological reporting may compromise reproducibility, comparability, and the validity of findings. Objective: This scoping review examined current analytical practices in digital phenotyping in health research, identified methodological gaps, and highlighted the need for transparency and standardized approaches. Methods: Literature searches were conducted in PubMed, Scopus, Embase, and Web of Science, with Google Scholar used as a complementary source. Eligible studies were published up to December 31, 2024, involved human populations, and used wearable digital devices in longitudinal health research. Studies were screened independently by multiple reviewers using predefined eligibility criteria. Data extraction focused on 6 methodological domains: sample size determination, variable selection and definition, data cleaning and preprocessing, digital phenotyping methods, predictive modeling, and statistical significance handling in large-scale data contexts. Results: A total of 162 studies were included. Most studies were published from 2018 onward (n=144, 89%) and were conducted primarily in North America (n=92, 57%). Activity trackers (n=81, 50%), smartphones (n=35, 22%), accelerometers (n=26, 16%), and smartwatches (n=24, 15%) were the most frequently used devices. The most common outcomes were activity level (n=67, 41%), sleep (n=65, 40%), step counts (n=63, 39%), and heart rate (n=46, 28%). Heterogeneity and limited reporting were observed across all methodological domains. Preprocessing was the most frequently reported component (n=88, 54%), although specific aspects such as missing-data handling remained inconsistently described (n=35, 22%). Sample size determination methods were reported in only 30% (n=48) of studies, and variable selection methods were described in 27% (n=43). Digital phenotyping approaches were identified in 30% (n=48) of studies and predominantly used regression-based models. Predictive modeling approaches were reported in 17% (n=28) of studies, with substantial diversity in algorithms and limited reporting of validation procedures. Only 4.3% (n=7) of studies explicitly discussed statistical challenges related to large or high-dimensional datasets. Practices varied across health fields, with no domain consistently demonstrating comprehensive reporting across all methodological components. Conclusions: Digital phenotyping research is expanding rapidly, but methodological practices remain heterogeneous and insufficiently standardized. By providing a cross-domain overview of how wearable-derived longitudinal data are currently processed and analyzed in health research, this review highlights recurring gaps in the reporting and justification of analytical choices, particularly regarding sample size determination, preprocessing, variable definition, predictive modeling, and statistical inference. These findings emphasize the need for clearer analytical frameworks and more consistent reporting practices to improve transparency, reproducibility, and methodological rigor in wearable-based digital health research. Trial Registration: PROSPERO CRD420251234000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251234000