Supplementation with spinach-derived thylakoid augments the benefits of high intensity training on adipokines, insulin resistance and lipid profiles in males with obesity.

Introduction: This study investigated the effects of 12 weeks of high-intensity functional training (HIFT) combined with spinach-derived thylakoid supplementation on some selected Adipokines and insulin resistance in males with obesity. Method: Sixty-eight participants (mean age: 27.6 ± 8.4 yrs.; mean height: 168.4 ± 2.6 cm; mean weight: 95.7 ± 3.8 kg, mean BMI: 32.6 ± 2.6 kg/m2) were randomly divided into four groups of 17 per group: Control group (CG), Supplement group (SG), Training group (TG), and Training + supplement group (TSG). Following baseline measurements, the two training groups (TG and TSG) started the 12 weeks of exercise training program (3 sessions per week). A total of 36 sessions lasting up to 60 min were included in the HIFT program using the CrossFit program. The eligible participants received 5 g/day of thylakoid-rich spinach extract or matching placebo as 5 g/day of raw corn starch (one sachet, 30 min before lunch) for 12 weeks. Baseline assessments were obtained 48 hours before the start of the training protocols and 48 hours after the last training session in all groups. Results: There were significant interactions (p<0.001 for all) between exercise and time for adiponectin (ES:0.48), leptin (ES:0.46), resistin (ES:0.3), omentin (ES:0.65), vaspin (ES:0.46), visfatin (ES:0.62), apelin (ES:0.42), RBP4 (ES:0.63), chemrin (0.36) and semaphorin3c (ES: 0.5). Plasma levels of semaphorin3c were significantly correlated (p<0.05) with body weight (r= 0.57), BMI (r= 0.43), FFM (r= -0.612), FAT (r= 0.768), VO2peak (r=-0.53), insulin (r= 0.756), glucose (r= 0.623), and HOMA-IR (r= 0.727). There were also significant group differences in insulin (ES: 0.77), glucose (ES: 0.21), and HOM-IR (ES: 0.44) (p<0.05). Discussion: Our findings indicate that 12 weeks of HIFT supplemented with spinach-derived thylakoid reduced levels of leptin, resistin, vaspin, visfatin, apelin, RBP4, chemrin, semaphorin3c and insulin resistance while increasing adiponectin and omentin levels in men with obesity.

Matching medical staff to long term care facilities to respond to COVID-19 outbreak.

BACKGROUND: Staff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies. METHODS: For the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback. RESULTS: We used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to. CONCLUSION: Developing a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

Impact of using a centralized matching process on nursing home staffing.

OBJECTIVE: To examine the effectiveness of adopting a novel centralized matching process for reducing staff shortages in Massachusetts nursing homes during the COVID-19 pandemic. METHODS: This study involved several datasets and 216 Massachusetts nursing homes that used a novel online portal to enter demand for nursing staff from May 2020 to April 2021. RESULTS: There were significant associations between the staff-to-resident ratio and demand entries lagged by three and four weeks, and no significant associations between the staff-to-resident ratio and demand entries lagged by one and two weeks. In contrast, we found significant associations between the staff-to-resident ratio and the number of generated staff matches lagged by one, two and three weeks, with larger impacts overall. CONCLUSION: This study shows how adopting a centralized matching process may expedite and increase improvement in the staff-to-resident ratio in nursing homes, compared with the setup in which nursing homes need to seek nurses on their own.