Objective: To characterize the systemic phenotype of primary Sjögren’s syndrome at diagnosis by analysing the EULAR-SS disease activity index (ESSDAI) scores. Methods: The Sjögren Big Data Consortium is an international, multicentre registry based on worldwide data-sharing cooperative merging of pre-existing databases from leading centres in clinical research in Sjögren’s syndrome from the five continents. Results: The cohort included 10 007 patients (9352 female, mean 53 years) with recorded ESSDAI scores available. At diagnosis, the mean total ESSDAI score was 6.1; 81.8% of patients had systemic activity (ESSDAI score ≥1). Males had a higher mean ESSDAI (8.1 vs 6.0, P < 0.001) compared with females, as did patients diagnosed at 65 years, P < 0.001). The highest global ESSDAI score was reported in Black/African Americans, followed by White, Asian and Hispanic patients (6.7, 6.5, 5.4 and 4.8, respectively; P < 0.001). The frequency of involvement of each systemic organ also differed between ethnic groups, with Black/African American patients showing the highest frequencies in the lymphadenopathy, articular, peripheral nervous system, CNS and biological domains, White patients in the glandular, cutaneous and muscular domains, Asian patients in the pulmonary, renal and haematological domains and Hispanic patients in the constitutional domain. Systemic activity measured by the ESSDAI, clinical ESSDAI (clinESSDAI) and disease activity states was higher in patients from southern countries (P < 0.001). Conclusion: The systemic phenotype of primary Sjögren’s syndrome is strongly influenced by personal determinants such as age, gender, ethnicity and place of residence, which are key geoepidemiological players in driving the expression of systemic disease at diagnosis.

Objectives: To characterize the phenotypic presentation at diagnosis of childhood-onset primary SS. Methods: The Big Data Sjögren Project Consortium is an international, multicentre registry using worldwide data-sharing cooperative merging of pre-existing clinical SS databases from the five continents. For this study, we selected those patients in whom the disease was diagnosed below the age of 19 years according to the fulfilment of the 2002/2016 classification criteria. Results: Among the 12 083 patients included in the Sjögren Big Data Registry, 158 (1.3%) patients had a childhood-onset diagnosis (136 girls, mean age of 14.2 years): 126 (80%) reported dry mouth, 111 (70%) dry eyes, 52 (33%) parotid enlargement, 118/122 (97%) positive minor salivary gland biopsy and 60/64 (94%) abnormal salivary US study, 140/155 (90%) positive ANA, 138/156 (89%) anti-Ro/La antibodies and 86/142 (68%) positive RF. The systemic EULAR Sjögren’s syndrome disease activity index (ESSDAI) domains containing the highest frequencies of active patients included the glandular (47%), articular (26%) and lymphadenopathy (25%) domains. Patients with childhood-onset primary SS showed the highest mean ESSDAI score and the highest frequencies of systemic disease in 5 (constitutional, lymphadenopathy, glandular, cutaneous and haematological) of the 12 ESSDAI domains, and the lowest frequencies in 4 (articular, pulmonary, peripheral nerve and CNS) in comparison with patients with adult-onset disease. Conclusions: Childhood-onset primary SS involves around 1% of patients with primary SS, with a clinical phenotype dominated by sicca features, parotid enlargement and systemic disease. Age at diagnosis plays a key role in modulating the phenotypic expression of the disease.

Aim: To describe the impact of the COVID‐19 pandemic on stress, resilience and depression in health professionals from a public hospital in Barcelona, Spain after the first peak of pandemic.Background: The COVID‐19 pandemic in Spain has pushed boundaries in health systems and, especially, for health professionals. Analysis of resilience as an individual resource and it is essential to understand the mechanisms that make staff react unfavourably to stressors caused by the pandemic.Design: A descriptive cross‐sectional study was designed.Participants: Health professionals supervised by the nursing department, including registered nurses, health care assistants, health technicians, final year nurse student nurses, foreign nurses, and other nurse‐related health workers.Methods: The study complies with the STROBE checklist for cross‐sectional studies. An online survey was administered to all health professionals supervised by the nursing department between 6 and 27 May 2020. The survey included the ER‐14 Resilience Scale, the widely‐used PHQ‐9 depression scale, the Spanish version of the Nursing Stress Scale, and an ad‐hoc questionnaire to obtain sociodemographic and occupational variables.Results: A total of 686 participants answered the survey. Resilience was high or very high in health professionals, with an inverse correlation with stress and depression scores. Personal on fixed shifts showed better resilience. The most stressed health professionals were full‐time registered nurses, followed by health care assistants. Up to 25% of nursing professionals had depression.Conclusion: Our study showed a high degree of resilience among nurse professionals despite the overwhelming nature of the COVID‐19 pandemic. Relevant signs of depression and stress were detected among participants. Occupational factors heavily influenced nurses’ resilience, stress and depression.Implications for Nursing & Health Policy: Government policy shifts are needed in Spain to improve nurses’ workforce conditions, enhance the ratio of nurses to patient numbers, and avoid workforce losses. Maintaining the resilience of health professionals would assist in improving their health and their capacity to possible future emergency situations.

This study aimed to observe if quinoa could produce a benefit on postprandial glycemia that would result in less progression to type 2 diabetes (T2D). A cross-over design pilot clinical study with a nutritional intervention for 8 weeks was performed: 4 weeks on a regular diet (RD) and 4 weeks on a quinoa diet (QD). Nine subjects aged ≥65 years with prediabetes were monitored during the first 4 weeks of RD with daily dietary records and FreeStyle Libre^®. Subsequently, participants started the QD, where quinoa and 100% quinoa-based products replaced foods rich in complex carbohydrates that they had consumed in the first 4 weeks of RD. The glycemic measurements recorded by the sensors were considered as functions of time, and the effects of nutrients consumed at the intended time period were analyzed by means of a function-on-scalar regression (fosr) model. With QD participants, decreased body weight (−1.6 kg, p = 0.008), BMI (−0.6 kg/m²p = 0.004) and waist circumference (−1.5 cm, p = 0.015) were observed. Nutrients intake changed during QD, namely, decreased carbohydrates (p = 0.004) and increased lipids (p = 0.004) and some amino acids (p < 0.05). The fosr model showed a reduction in postprandial glycemia in QD despite intrapersonal differences thanks to the joint action of different nutrients and the suppression of others consumed on a regular diet. We conclude that in an old age and high T2D-risk population, a diet rich in quinoa reduces postprandial glycemia and could be a promising T2D-preventive strategy.

Wearable devices and sensors have recently become a popular way to collect data, especially in the health sciences. The use of sensors allows patients to be monitored over a period of time with a high observation frequency. Due to the continuous-on-time structure of the data, novel statistical methods are recommended for the analysis of sensor data. One of the popular approaches in the analysis of wearable sensor data is functional data analysis. The main objective of this paper is to review functional data analysis methods applied to wearable device data according to the type of sensor. In addition, we introduce several freely available software packages and open databases of wearable device data to facilitate access to sensor data in different fields.