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To individualize nutritional interventions for the prevention and treatment of malnutrition and sarcopenia, it is required to understand the nutritional needs of older adults. This study explores the nutritional needs of geriatric outpatients. We hypothesized that inadequate energy and protein intake is common in geriatric outpatients. Data were retrieved from 2 cohort studies encompassing community-dwelling older adults referred to geriatric outpatient mobility clinics in Amsterdam, The Netherlands and Melbourne, Australia. Indirect calorimetry and a food diary, respectively, were used to assess resting metabolic rate (RMR) and energy and protein intake. Total energy expenditure (TEE) was calculated by the RMR multiplied by an activity factor of 1.4. An energy deficit was defined as a relative difference >10% between TEE and energy intake. A protein deficit was defined as protein intake <1.2 g/kg body weight per day. Bland-Altman analysis assessed the agreement between energy and protein requirements versus intake at an individual level. Seventy-four outpatients were included (25 males, median age 78.9 [IQR: 72.8-86.1] years). The mean difference between TEE and energy intake was 292 (SD 481) kcal/d. An energy deficit was present in 46 outpatients. The median protein intake was 1.00 (IQR: 0.87-1.19) g/kg body weight per day and a protein deficit was present in 57 outpatients. There was a low agreement between energy and protein requirements versus intake at an individual level. In conclusion, over half of the outpatients had energy and/or protein deficits. Integrating dietetic services at geriatric outpatient mobility clinics could potentially improve nutrition- and muscle-related outcomes in a multidisciplinary approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • First decision-support system developed for pasture-based automatic milking systems. • Uses data from 37 farms across Australia, Ireland, New Zealand, Chile and Argentina. • Differences between actual and predicted physical performance ranged from 2 to 14%. • Farmers agreed that the decision-support system is useful and easy to use. • Applications include investment planning and system performance optimisation. There is a significant opportunity to improve profitability and productivity in pasture-based automatic milking systems (AMS). A decision-support system (DSS) is required for AMS that can integrate key mechanics of dynamic biological processes with farm economics. Here we developed and evaluated a web-based DSS named the Integrated Management Model (IMM) designed for assisting AMS farmers and their advisors to evaluate and improve the physical and economic performance of their businesses (https://bit.ly/MilkingEdgeAMSTool). The IMM comprises a series of empirically determined predictive equations derived from the main drivers of productivity and profitability in AMS, together with stochastic simulation and optimisation modelling. The equations and models in the IMM were developed using two data sources available through the Milking Edge project: (a) an annual physical and economic dataset collected from 14 AMS farms across Australia (from years 2015 to 2018), and (b) a monthly physical dataset from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile (years 2015 to 2019). Model predictions were evaluated using two similar datasets: (a) an annual physical and economic dataset from 11 Australian AMS farms (2018 to 2020) and a monthly physical dataset from 20 AMS from Australia, New Zealand, Ireland, and Argentina (2019 to 2020). The DSS was tested by 11 AMS farmers that provided feedback using the technology acceptance model framework. Results from the model evaluation showed the accuracy of the equations and simulations to predict physical variables such as milk harvested (kg milk/robot.d) or milking frequency (milkings/cow.d) was reasonably good (2–14% differences between observed and predicted values). The economic equations, which predicted operating profit margin (OP) (%) and return on total assets (ROTA) (%), could determine the relative changes and direction of profitability when physical variables change. However, the accuracy of these equations to estimate absolute values was low (ROTA: R2 0.26 and MAE 2.0%; OP: R2 0.36 and MAE 13.5%), probably due to the empirical nature of the equations and the relatively small number of farms involved in their development. Overall, farmers agreed that the IMM was a useful and easy-to-use DSS that can be used confidently to simulate physical scenarios and optimise the system performance. Whilst this DSS was designed for AMS farmers and farmers considering investing in AMS, future applications could include research, training, extension, teaching, consultancy or even innovation and development of new technology. [ABSTRACT FROM AUTHOR]

Mobilisation alarms are a falls prevention strategy used in hospitals to alert staff when an at risk patient is attempting to mobilise. Mobilisation alarms have an estimated annual cost of $AUD58MIL in Australia. There is growing evidence from randomised controlled trials indicating mobilisation alarms are unlikely to prevent falls. The primary aim of this study was to describe the rate of mobilisation alarm false triggers and staff response time across different health services. The secondary aim was to compare pre to post mobilisation alarm utilisation following the introduction of policy to reduce or eliminate mobilisation alarms. This descriptive and comparative study was conducted through Monash Partners Falls Alliance across six health services in Melbourne, Australia. This study described true and false alarm triggers and trigger response times across three health services and usual care mobilisation alarm utilisation across six health services; and then compared alarm utilisation across two health services following the introduction of policy to reduce (<2.5%) or eliminate (0.0%) mobilisation alarms in the acute and rehabilitation settings. The most frequent observation was a false alarm (n = 74, 52%), followed by a true alarm (n = 67, 47%) and no alarm (n = 3, 2%). Time to respond to the true and false alarms was an average of 37 seconds (SD 92) and this included 61 occasions of 0 seconds as a member of staff was present when the alarm triggered. If the 61 occasions of staff being present when the alarm triggered were removed, the average time to respond was 65 seconds (SD114). Usual care mobilisation alarm utilisation in acute was 7% (n = 171/2,338) and in rehabilitation was 11% (n = 286/2,623). Introducing policy for reduced and eliminated mobilisation alarm conditions was successful with a reduced utilisation rate of 1.8% (n = 11/609) and an eliminated utilisation rate of 0.0% (n = 0/521). Half of mobilisation alarm triggers are false and when alarms trigger without staff present, staff take about a minute to respond. While usual care has one in fourteen patients in acute and one in nine patients in rehabilitation using a mobilisation alarm, it is possible to introduce policy which will change practice to reduce or eliminate the use of mobilisation alarms, providing evidence of feasibility for future disinvestment effectiveness studies that it is feasible to disinvest in the alarms. [ABSTRACT FROM AUTHOR]