Rationale: Diagnosis of sarcopenia in older adults is essential for early treatment in clinical practice. Bio-electrical impedance analysis (BIA) may be a valid means to assess appendicular lean mass (ALM) in older adults, but limited evidence is available. Therefore, we aim to evaluate the validity of BIA to assess ALM in older adults.Methods: In 215 community dwelling older adults (age ≥ 55 years), ALM was measured by BIA (Tanita MC-780; 8-points) and compared with dual-energy X-ray absorptiometry (DXA, Hologic Discovery A) as reference. Validity for assessing absolute values of ALM was evaluated by: 1) bias (mean difference), 2) percentage of accurate predictions (within 5% of DXA values), 3) individual error (root mean squared error (RMSE), mean absolute deviation), 4) limits of agreement (Bland-Altman analysis). For diagnosis of low ALM, the lowest quintile of ALM by DXA was used (below 21.4 kg for males and 15.4 for females). Sensitivity and specificity of detecting low ALM by BIA were assessed.Results: Mean age of the subjects was 71.9 ± 6.4, with a BMI of 25.8 ± 4.2 kg/m2, and 70% were females. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 0.2 kg. The percentage accurate predictions was 54% with RMSE 1.6 kg and limits of agreements −3.0 to +1.8 kg. Sensitivity was 79%, indicating that 79% of subjects with low ALM according to DXA also had low ALM with the BIA. Specificity was 90%, indicating that 90% of subjects with ‘no low’ ALM according to DXA also had ‘no low’ ALM with the BIA.Conclusions: This comparison showed a poor validity of BIA to assess absolute values of ALM, but a reasonable sensitivity and specificity to diagnose a low level of ALM in community-dwelling older adults in clinical practice.Disclosure of interest: None declared.
De laatste decennia is tijd een strategische concurrentiefactor geworden in de maakindustrie (Demeter, 2013; Godinho Filho et al., 2017a; Gromova, 2020). Naast tijdige levering verwacht de klant ook keuze, maatwerk, hoge kwaliteit en een lage prijs (Siong et al., 2018; Suri, 2020). Om de door de klant gewenste korte doorlooptijd te kunnen realiseren en daarbij ook te voldoen aan zijn andere eisen, zijn flexibiliteit en aanpassingsvermogen essentieel geworden (Godinho Filho et al., 2017b; Siong et al., 2018). Quick Response Manufacturing (QRM) heeft als doel de doorlooptijd te verkorten in productieomgevingen die gekenmerkt worden door een hoge variëteit in producten en maatwerk (Suri, 2020; Siong et al., 2018). QRM kent zijn oorsprong begin jaren negentig van de vorige eeuw (Suri, 2020) en vertoont sterke gelijkenis met lean manufacturing. Het verschil met lean manufacturing is echter dat QRM zich richt op bedrijven in een omgeving met veel productvariatie. Daarnaast heeft QRM nieuwe elementen toegevoegd, zoals Paired-cell Overlapping Loops of Cards with Authorization (POLCA) en Manufacturing Critical Path Time’ (MCT)’ (Godinho Filho et al., 2017b).
Rationale: Lean body mass, including muscle, is known to decrease with age, which may contribute to loss of physical function, an indicator of frailty. Moreover, low muscle thickness is considered an indicator of frailty in critically ill patients. However, little is known about the relationship between muscle thickness and frailty in community dwelling adults. Therefore, we studied the association between frailty and whole body lean body mass index (LBMi) and muscle thickness of the rectus femoris (RF) in community dwelling older adults. Methods: In older adults aged ≥55y, who participated in the Hanze Health and Ageing Study, frailty status was assessed with a multidimensional instrument, measuring frailty on a cognitive, psychosocial en physical level, i.e., the Groningen Frailty Indicator (GFI), using ≥4 as cut-off score for frailty. LBMi (kg/m2) was estimated with BIA (Quadscan 4000©, Bodystat), using the build-in equation. Muscle thickness (mm) of the RF was measured with ultrasound, using the Bodymetrix© (Intelametrix). Univariate and multivariate binary logistic regression analyses were performed for LBMi and for RF thickness. Multivariate analysis corrected for age, sex, body mass index (kg/m2), and handgrip strength (handgrip dynamometer; kg). A p-level of <0.05 was considered significant and Odds Ratios (OR; [95% CI]) were presented. Results: 93 participants (age 65.2±7.7 years; male 46 %; LBMi 17.2±2.6 kg/m2; RF 14.6±4.4 mm; median GFI =1 (interquartile range=0-3; frail: n=18) were included in the analysis. In both the univariate and multivariate analysis, LBMi (p=0.082, OR=0.82 [0.66-1.03]; p=0.077, OR=0.55 [0.28-1.07] respectively) and muscle thickness of RF (p=0.436, OR=0.95 [0.84-1.08]; p=0.796, OR= 1.02 [0.88-1.18] respectively) were not significantly associated with frailty. None of the co-variables were significantly associated with frailty either. Conclusion: In this sample of older adults aged ≥55 years, LBMi and RF thickness are not associated with frailty. However, frail participants scored at cut-off or just above, and measurements in a population with higher scores for frailty may provide further insight in the association between lean body mass and muscle thickness and frailty.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
The research, supported by our partners, sets out to understand the drivers and barriers to sustainable logistics in port operations using a case study of drone package delivery at Rotterdam Port. Beyond the technical challenges of drone technology as an upcoming technology, it needs to be clarified how drones can operate within a port ecosystem and how they could contribute to sustainable logistics. KRVE (boatmen association), supported by other stakeholders of Rotterdam port, approached our school to conduct exploratory research. Rotterdam Port is the busiest port in Europe in terms of container volume. Thirty thousand vessels enter the port yearly, all needing various services, including deliveries. Around 120 packages/day are delivered to ships/offices onshore using small boats, cars, or trucks. Deliveries can take hours, although the distance to the receiver is close via the air. Around 80% of the packages are up to 20kg, with a maximum of 50kg. Typical content includes documents, spare parts, and samples for chemical analysis. Delivery of packages using drones has advantages compared with traditional transport methods: 1. It can save time, which is critical to port operators and ship owners trying to reduce mooring costs. 2. It can increase logistic efficiency by streamlining operations. 3. It can reduce carbon emissions by limiting the use of diesel engines, boats, cars, and trucks. 4. It can reduce potential accidents involving people in dangerous environments. The research will highlight whether drones can create value (economic, environmental, social) for logistics in port operations. The research output links to key national logistic agenda topics such as a circular economy with the development of innovative logistic ecosystems, energy transition with the reduction of carbon emissions, societal earning potential where new technology can stimulate the economy, digitalization, key enabling technology for lean operations, and opportunities for innovative business models.
Mattresses for the healthcare sector are designed for robust use with a core foam layer and a polyurethane-coated polyester textile cover. Nurses and surgeons indicate that these mattresses are highly uncomfortable to patients because of poor microclimatic management (air, moisture, temperature, friction, pressure regulation, etc) across the mattress, which can cause pressure ulcers (in less than a day). The problem is severe (e.g., extra recovery time, medication, increased risk, and costs) for patients with wounds, infection, pressure-sensitive decubitus. There are around 180,000 waterproof mattresses in the healthcare sector in the Netherlands, of which yearly 40,000 mattresses are discarded. Owing to the rapidly aging population it is expected to increase the demand for these functional mattresses from 180,000 to 400,000 in the next 10 years in the healthcare sector. To achieve a circular economy, Dutch Government aims for a 50% reduction in the use of primary raw materials by 2030. As of January 1, 2022, mattress manufacturers and importers are obliged to pay a waste management contribution. Within the scope of this project, we will design, develop, and test a circular & functional mattress for the healthcare (cure & care) sector. The team of experts from knowledge institutes, SMEs, hospital(s), branch-organization joins hands to design and develop a functional (microclimate management, including ease of use for nurses and patients) mattress that deals with uncomfortable sleeping and addresses the issue of pressure ulcers thereby overall accelerating the healing process. Such development addresses the core issue of circularity. The systematic research with proper demand articulation leads to V-shape verification and validation research methodology. With design focus and applied R&D at TRL-level (4-6) is expected to deliver the validated prototype(s) offering SMEs an opportunity to innovate and expand their market. The knowledge will be used for dissemination and education at Saxion.
