Background: Lower quantity and poorer sleep quality are common in most older adults, especially for those who live in a nursing home. The use of wearable devices, which measure some parameters such as the sleep stages, could help to determine the influence of sleep quality in daily activity among nursing home residents. Therefore, this study aims to analyse the influence of sleep and its changes concerning the health status and daily activity of older people who lived in a nursing home, by monitoring the participants for a year with Xiaomi Mi Band 2. Methods: This is a longitudinal study set in a nursing home in [Details omitted for double-anonymized peer reviewed]. The Xiaomi Mi Band 2 will be used to measure biomedical parameters and different assessment tools will be administered to participants for evaluating their quality of life, sleep quality, cognitive state, and daily functioning. Results: A total of 21 nursing home residents participated in the study, with a mean age of 86.38 ± 9.26. The main outcomes were that sleep may influence daily activity, cognitive state, quality of life, and level of dependence in activities of daily life. Moreover, environmental factors and the passage of time could also impact sleep. Conclusions: Xiaomi Mi Band 2 could be an objective tool to assess the sleep of older adults and know its impact on some factors related to health status and quality of life of older nursing homes residents. Trial Registration: NCT04592796 (Registered 16 October 2020) Available on: https://clinicaltrials.gov/ct2/show/NCT04592796.

(1) Background: Sleep disorders are a common problem for public health since they are considered potential triggers and predictors of some mental and physical diseases. Evaluating the sleep quality of a person may be a first step to prevent further health issues that diminish their independence and quality of life. Polysomnography (PSG) is the “gold standard” for sleep studies, but this technique presents some drawbacks. Thus, this study intends to assess the capability of the new Xiaomi Mi Smart Band 5 to be used as a tool for sleep self-assessment. (2) Methods: This study will be an observational and prospective study set at the sleep unit of a hospital in A Coruña, Spain. Forty-three participants who meet the inclusion criteria will be asked to participate. Specific statistical methods will be used to analyze the data collected using the Xiaomi Mi Smart Band 5 and PSG. (3) Discussion: This study offers a promising approach to assess whether the Xiaomi Mi Smart Band 5 correctly records our sleep. Even though these devices are not expected to replace PSG, they may be used as an initial evaluation tool for users to manage their own sleep quality and, if necessary, consult a health professional. Further, the device may help users make simple changes to their habits to improve other health issues as well. Trial registration: NCT04568408 (Registered 23 September 2020).

(1) Background: Work stress is one of the most relevant issues in public health. It has a significant impact on health, especially the development of mental disorders, causing occupational imbalance. There is a growing interest in the development of tools with a positive effect on workers. To this end, wearable technology is becoming increasingly popular, as it measures biometric variables like heartbeat, activity, and sleep. This information may be used to assess the stress a person is suffering, which could allow the development of stress coping strategies, both at a professional and personal level. (2) Methods: This paper describes an observational, analytical, and longitudinal study which will be set at a research center in A Coruña, Spain. Various scales and questionnaires will be filled in by the participants throughout the study. For the statistical analysis, specific methods will be used to evaluate the association between numerical and categorical variables. (3) Discussion: This study will lay the foundation for a bigger, more complete study to assess occupational stress in different work environments. This will allow us to begin to understand how occupational stress influences daily life activity and occupational balance, which could directly enhance the quality of life of workers if the necessary measures are taken.

Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.