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The coronavirus disease (COVID-19) pandemic has created an urgent need for coordinated mechanisms to respond to the outbreak across health sectors, and digital health solutions have been identified as promising approaches to address this challenge. This editorial discusses the current situation regarding digital health solutions to fight COVID-19 as well as the challenges and ethical hurdles to broad and long-term implementation of these solutions. To decrease the risk of infection, telemedicine has been used as a successful health care model in both emergency and primary care. Official communication plans should promote facile and diverse channels to inform people about the pandemic and to avoid rumors and reduce threats to public health. Social media platforms such as Twitter and Google Trends analyses are highly beneficial to model pandemic trends as well as to monitor the evolution of patients’ symptoms or public reaction to the pandemic over time. However, acceptability of digital solutions may face challenges due to potential conflicts with users’ cultural, moral, and religious backgrounds. Digital tools can provide collective public health benefits; however, they may be intrusive and can erode individual freedoms or leave vulnerable populations behind. The COVID-19 pandemic has demonstrated the strong potential of various digital health solutions that have been tested during the crisis. More concerted measures should be implemented to ensure that future digital health initiatives will have a greater impact on the epidemic and meet the most strategic needs to ease the life of people who are at the forefront of the crisis.

Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual’s voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.

Visceral adiposity is a major risk factor of cardiometabolic diseases. Visceral adipose tissue (VAT) is usually measured with expensive imaging techniques which present financial and practical challenges to population-based studies. We assessed whether cardiometabolic conditions were associated with VAT by using a new and easily measurable anthropometric index previously published and validated. Data (1529 participants) came from the European Health Examination Survey in Luxembourg (2013–2015). Logistic regressions were used to study associations between VAT and cardiometabolic conditions. We observed an increased risk of all conditions associated with VAT. The total adjusted odds ratio (AOR, [95% CI]) for hypertension, prediabetes/diabetes, hypercholesterolemia, and hypertriglyceridemia for the fourth quartile of VAT compared to the lowest were 10.22 [6.75, 15.47]), (5.90 [4.02, 8.67]), (3.60 [2.47, 5.25]) and (7.67 [5.04, 11.67]. We observed higher odds in women than in men for all outcomes with the exception of hypertension. Future studies should investigate the impact of VAT changes on cardiometabolic health and the use of anthropometrically predicted VAT as an accurate outcome when no biomedical imaging is available.

Background Spatial inequalities in health result from different exposures to health risk factors according to the features of geographical contexts, in terms of physical environment, social deprivation, and health care accessibility. Using a common geographical referential, which combines indices measuring these contextual features, could improve the comparability of studies and the understanding of the spatial dimension of health inequalities. Methods We developed the Geographical Classification for Health studies (GeoClasH) to distinguish French municipalities according to their ability to influence health outcomes. Ten contextual scores measuring physical and social environment as well as spatial accessibility of health care have been computed and combined to classify French municipalities through a K-means clustering. Age-standardized mortality rates according to the clusters of this classification have been calculated to assess its effectiveness. Results Significant lower mortality rates compared to the mainland France population were found in the Wealthy Metropolitan Areas (SMR = 0.868, 95% CI 0.863–0.873) and in the Residential Outskirts (SMR = 0.971, 95% CI 0.964–0.978), while significant excess mortality were found for Precarious Population Districts (SMR = 1.037, 95% CI 1.035–1.039), Agricultural and Industrial Plains (SMR = 1.066, 95% CI 1.063–1.070) and Rural Margins (SMR = 1.042, 95% CI 1.037–1.047). Conclusions Our results evidence the comprehensive contribution of the geographical context in the constitution of health inequalities. To our knowledge, GeoClasH is the first nationwide classification that combines social, environmental and health care access scores at the municipality scale. It can therefore be used as a proxy to assess the geographical context of the individuals in public health studies.

This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.

As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research. This scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods. We followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence. After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest. Since their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective. RR2-10.1136/bmjopen-2020-040671.

Aims/hypothesisRecent evidence suggests that oxidative stress may contribute to the pathogenesis of type 2 diabetes. The diet, and especially fruit and vegetables, contains a variety of compounds with antioxidant activity, which may have cumulative/synergistic antioxidant effects. The total antioxidant capacity, an index derived from dietary intake, is a single estimate of antioxidant capacity from all dietary antioxidants. The main aim of this study was to investigate the relationship between total antioxidant capacity and risk of type 2 diabetes.MethodsAmong 64,223 women (mean age 52 ± 7 years) from the French E3N-European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, 1751 women had validated type 2 diabetes during 15 years of follow-up. The total antioxidant capacity was estimated with the ferric ion-reducing antioxidant power (FRAP) method. Adjusted Cox proportional hazards regression models were used to calculate HRs and 95% CIs for the associations between total antioxidant capacity and type 2 diabetes risk, adjusted for potential confounders.ResultsIn multivariable models, higher levels of total antioxidant capacity were associated with a lower risk of type 2 diabetes. Compared with women in the lowest quintile, women in the third, fourth and fifth quintiles for total antioxidant capacity had HRs of 0.74 (95% CI 0.63, 0.86), 0.70 (95% CI 0.59, 0.83) and 0.73 (95% CI 0.60, 0.89), respectively. The inverse association between total antioxidant capacity and risk of type 2 diabetes was linear up to values of 15 mmol/day, after which the effect reached a plateau.Conclusions/interpretationOur findings suggest that the total antioxidant capacity may play an important role in reducing the risk of type 2 diabetes in middle-aged women. More studies are warranted to better understand the biological mechanisms underlying this inverse association.

IntroductionStress is omnipresent in our everyday lives and a key risk factor for our physical and mental health. Yet little is known about the impact of geographic life environments, linked to our daily activities and mobility patterns, on our momentary and daily stress levels.We propose this review to gather evidence on the spatio-temporal determinants of momentary or daily stress in studies using ecological momentary assessment (EMA) or experience sampling methods (ESM) in addition to global positioning systems (GPS) tracking. We will focus on the spatio-temporal definition and modelling of environmental exposures accounting for participant daily activities and mobility patterns and their association with stress.Methods and analysisThis scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework for scoping reviews (2018). We will search the PubMed/Medline, Web of Science, PsycInfo and Scopus databases. We will include papers using EMA or ESM and GPS measuring chronic, daily or momentary stress as an outcome; these methods are also referred to as geographically-explicit ecological momentary assessment.Articles published from January 2000–June 2025 will be screened. Two independent reviewers will screen titles and abstracts to agree on the inclusion of articles. No geographical or population limitation will be imposed.Ethics and disseminationThis study is a scoping review based on previously published and publicly available literature. It does not involve the collection of primary data, human participants, or the processing of personal or sensitive information. Therefore, ethical approval is not required in accordance with institutional and international research ethics guidelines. The results will be submitted in peer-reviewed journals and presented at international conferences.

Aims/hypothesisDiet is one of the main lifestyle-related factors that can modulate the inflammatory process. Surprisingly the dietary inflammatory index (DII) has been little investigated in relation to type 2 diabetes, and the role of BMI in this relationship is not well established. We studied this association and the role of BMI in the inflammatory process in a large population-based observational study.MethodsA total of 70,991 women from the E3N (Etude Epidémiologique auprès de femmes de la Mutuelle Générale de l’Education Nationale) cohort study were followed for 20 years. Incident type 2 diabetes cases were identified using diabetes-specific questionnaires and drug reimbursement insurance databases, and 3292 incident cases were validated. The DII was derived from a validated food frequency questionnaire. Multivariable Cox regression models estimated HRs and 95% CIs between DII and incident type 2 diabetes. Interactions were tested between DII and BMI on incident type 2 diabetes and a mediation analysis of BMI was performed.ResultsHigher DII scores, corresponding to a higher anti-inflammatory potential of the diet, were associated with a lower risk of type 2 diabetes. Compared with the 1st quintile group, women from the 2nd quintile group (HR 0.85 [95% CI 0.77, 0.94]) up to the 5th quintile group (HR 0.77 [95% CI 0.69, 0.85]) had a lower risk of type 2 diabetes before adjustment for BMI. There was an interaction between DII and BMI on type 2 diabetes risk (pInteraction < 0.0001). The overall association was partly mediated by BMI (58%).Conclusions/interpretationOur findings suggest that a higher anti-inflammatory potential of the diet is associated with a lower risk of type 2 diabetes, and the association may be mediated by BMI. These results may improve our understanding of the mechanisms underlying the role of diet-related anti-inflammation in the pathogenesis of type 2 diabetes in women. Further studies are warranted to validate our results and evaluate whether the results are similar in men.

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.