Nutrition plays an important role in bone health. The aim of our study was to update the evidence regarding dairy intake, osteoporotic fracture (OF) risk, and prospective bone mass density (BMD) evolution assessed by dual-energy X-ray absorptiometry in Europeans and non-Hispanic whites from North America. A systematic search was conducted in MEDLINE, EMBASE, and Scopus for papers published from 1 January, 2000 to 30 April, 2018. The eligibility criteria were as follows: healthy adults; measurable dairy exposure; hip, vertebral, wrist or OF as outcomes; and cohort or case-control studies. Two independent investigators conducted the search and the data extraction. A pooled analysis was conducted with random-effects models. Publication bias and meta-regression were considered. Ten cohort studies relating to OF risk were selected for meta-analysis. Three papers reporting BMD changes associated with dairy intake could not be aggregated in the meta-analysis. The pooled HRs of the highest compared with the lowest levels of dairy intake were 0.95 (95% CI: 0.87, 1.03; I² = 82.9%; P-heterogeneity < 0.001) for OF at any site; 0.87 (95% CI: 0.75, 1.01; I² = 86.7%; P-heterogeneity < 0.001) for hip fractures; and 0.82 (95% CI: 0.68, 0.99; I² = 0.0%; P-heterogeneity = 0.512) for vertebral fractures. Concerning BMD, the selected studies described a 1.7–3% lower hip BMD in young and postmenopausal women with poor intake of milk in their youth, a positive relationship between baseline milk ingestion and the percentage of trochanter BMD change in elderly people, and a positive correlation between milk consumption and BMD change at the radius in women aged >65 y. In conclusion, in the studied population, the highest consumption of dairy products did not show a clear association with the total OF or hip fracture risks; however, a diminished risk of vertebral fracture could be described. The results regarding BMD change were heterogeneous and did not allow for a definitive conclusion.

To examine independent and combined associations of sleep duration and sleep variability with body composition, obesity and type 2 diabetes (T2D) in elders at high cardiovascular risk. Cross-sectional analysis of 1986 community-dwelling elders with overweight/obesity and metabolic syndrome from PREDIMED-Plus trial. Associations of accelerometry-derived sleep duration and sleep variability with body mass index (BMI), waist circumference (WC) and body composition were assessed fitting multivariable-adjusted linear regression models. Prevalence ratios (PR) and 95% confidence intervals (CI) for obesity and T2D were obtained using multivariable-adjusted Cox regression with constant time. “Bad sleepers” (age-specific non-recommended sleep duration plus sleep variability above the median) and “good sleepers” (age-specific recommended sleep duration plus sleep variability below the median) were characterized by combining sleep duration and sleep variability, and their associations with these outcomes were examined. One hour/night increment in sleep duration was inversely associated with BMI (β −0.38 kg/m² [95% CI −0.54, −0.23]), WC (β −0.86 cm [95% CI −1.25, −0.47]), obesity (PR 0.96 [95% CI 0.93, 0.98]), T2D (PR 0.93 [95% CI 0.88, 0.98]) and other DXA-derived adiposity-related measurements (android fat and trunk fat, all p < .05). Each 1-hour increment in sleep variability was positively associated with T2D (PR 1.14 [95% CI 1.01, 1.28]). Compared with “good sleepers,” “bad sleepers” were positively associated with obesity (PR 1.12 [95% CI 1.01, 1.24]) and T2D (PR 1.62 [95% CI 1.28, 2.06]). This study revealed cross-sectional associations of sleep duration with adiposity parameters and obesity. Sleep duration and sleep variability were associated with T2D. Considering simultaneously sleep duration and sleep variability could have additional value, particularly for T2D, as they may act synergistically.

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals.

This article aims to estimate the differences in environmental impact (greenhouse gas [GHG] emissions, land use, energy used, acidification and potential eutrophication) after one year of promoting a Mediterranean diet (MD). Baseline and 1-year follow-up data from 5800 participants in the PREDIMED-Plus study were used. Each participant's food intake was estimated using validated semi-quantitative food frequency questionnaires, and the adherence to MD using the Dietary Score. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The influence of diet on environmental impact was assessed through the EAT-Lancet Commission tables. The association between MD adherence and its environmental impact was calculated using adjusted multivariate linear regression models. After one year of intervention, the kcal/day consumed was significantly reduced (-125,1 kcal/day), adherence to a MD pattern was improved (+0,9) and the environmental impact due to the diet was significantly reduced (GHG: -361 g/CO2-eq; Acidification:-11,5 g SO2-eq; Eutrophication:-4,7 g PO4-eq; Energy use:-842,7 kJ; and Land use:-2,2 m2). Higher adherence to MD (high vs. low) was significantly associated with lower environmental impact both at baseline and one year follow-up. Meat products had the greatest environmental impact in all the factors analysed, both at baseline and at one-year follow-up, in spite of the reduction observed in their consumption. A program promoting a MD, after one year of intervention, significantly reduced the environmental impact in all the factors analysed. Meat products had the greatest environmental impact in all the dimensions analysed.

Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM’s health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: “bed margin” (available beds minus occupied beds, expressed as a percentage) and “flexibility” (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions: This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital’s management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.