Background & aims: Low muscle mass and -quality on ICU admission, as assessed by muscle area and -density on CT-scanning at lumbar level 3 (L3), are associated with increased mortality. However, CT-scan analysis is not feasible for standard care. Bioelectrical impedance analysis (BIA) assesses body composition by incorporating the raw measurements resistance, reactance, and phase angle in equations. Our purpose was to compare BIA- and CT-derived muscle mass, to determine whether BIA identified the patients with low skeletal muscle area on CT-scan, and to determine the relation between raw BIA and raw CT measurements. Methods: This prospective observational study included adult intensive care patients with an abdominal CT-scan. CT-scans were analysed at L3 level for skeletal muscle area (cm2) and skeletal muscle density (Hounsfield Units). Muscle area was converted to muscle mass (kg) using the Shen equation (MMCT). BIA was performed within 72 h of the CT-scan. BIA-derived muscle mass was calculated by three equations: Talluri (MMTalluri), Janssen (MMJanssen), and Kyle (MMKyle). To compare BIA- and CT-derived muscle mass correlations, bias, and limits of agreement were calculated. To test whether BIA identifies low skeletal muscle area on CT-scan, ROC-curves were constructed. Furthermore, raw BIA and CT measurements, were correlated and raw CT-measurements were compared between groups with normal and low phase angle. Results: 110 patients were included. Mean age 59 ± 17 years, mean APACHE II score 17 (11–25); 68% male. MMTalluri and MMJanssen were significantly higher (36.0 ± 9.9 kg and 31.5 ± 7.8 kg, respectively) and MMKyle significantly lower (25.2 ± 5.6 kg) than MMCT (29.2 ± 6.7 kg). For all BIA-derived muscle mass equations, a proportional bias was apparent with increasing disagreement at higher muscle mass. MMTalluri correlated strongest with CT-derived muscle mass (r = 0.834, p < 0.001) and had good discriminative capacity to identify patients with low skeletal muscle area on CT-scan (AUC: 0.919 for males; 0.912 for females). Of the raw measurements, phase angle and skeletal muscle density correlated best (r = 0.701, p < 0.001). CT-derived skeletal muscle area and -density were significantly lower in patients with low compared to normal phase angle. Conclusions: Although correlated, absolute values of BIA- and CT-derived muscle mass disagree, especially in the high muscle mass range. However, BIA and CT identified the same critically ill population with low skeletal muscle area on CT-scan. Furthermore, low phase angle corresponded to low skeletal muscle area and -density. Trial registration: ClinicalTrials.gov (NCT02555670).
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Background The gait modification strategies Trunk Lean and Medial Thrust have been shown to reduce the external knee adduction moment (EKAM) in patients with knee osteoarthritis which could contribute to reduced progression of the disease. Which strategy is most optimal differs between individuals, but the underlying mechanism that causes this remains unknown. Research question Which gait parameters determine the optimal gait modification strategy for individual patients with knee osteoarthritis? Methods Forty-seven participants with symptomatic medial knee osteoarthritis underwent 3-dimensional motion analysis during comfortable gait and with two gait modification strategies: Medial Thrust and Trunk Lean. Kinematic and kinetic variables were calculated. Participants were then categorized into one of the two subgroups, based on the modification strategy that reduced the EKAM the most for them. Multiple logistic regression analysis with backward elimination was used to investigate the predictive nature of dynamic parameters obtained during comfortable walking on the optimal modification gait strategy. Results For 68.1 % of the participants, Trunk Lean was the optimal strategy in reducing the EKAM. Baseline characteristics, kinematics and kinetics did not differ significantly between subgroups during comfortable walking. Changes to frontal trunk and tibia angles correlated significantly with EKAM reduction during the Trunk Lean and Medial Thrust strategies, respectively. Regression analysis showed that MT is likely optimal when the frontal tibia angle range of motion and peak knee flexion angle in early stance during comfortable walking are high (R2Nagelkerke = 0.12). Significance Our regression model based solely on kinematic parameters from comfortable walking contained characteristics of the frontal tibia angle and knee flexion angle. As the model explains only 12.3 % of variance, clinical application does not seem feasible. Direct assessment of kinetics seems to be the most optimal strategy for selecting the most optimal gait modification strategy for individual patients with knee osteoarthritis.
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The model of the Best Practice Unit (BPU) is a specific form of practice based research. It is a variation of the Community of Practice (CoP) as developed by Wenger, McDermott and Snyder (2002) with the specific aim to innovate a professional practice by combining learning, development and research. We have applied the model over the past 10 years in the domain of care and social welfare in the Netherlands. Characteristics of the model are: the interaction between individual and collective learning processes, the development of (new or better) working methods, and the implementation of these methods in daily practice. Multiple knowledge sources are being used: experiential knowledge, professional knowledge and scientific knowledge. Research is serving diverse purposes: articulating tacit knowledge, documenting the learning and innovation process, systematically describing the revealed or developed ways of working, and evaluating the efficacy of new methods. An analysis of 10 different research projects shows that the BPU is an effective model.
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Service design is literally the design of services. Service designers improve existing services or design completely new ones. Nothing new so far. Services have been around for centuries, and every service was conceived and designed by someone. However, service design takes a different angle; a different perspective as its starting point: it is a process of creative inquiry aimed at the experiences of the individual user. ‘Service design, insights from 9 case studies’ is the final publication of the Innovation in Services programme. During this programme, creative design agencies applied the methods of service design in nine different projects.
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How and where can Dutch design entrepreneurs find work in Germany? This was the question DutchDFA put to the research team at Inholland University of Applied Sciences in February 2010. But the researchers took a different angle, and generated unexpected data, revealing patterns, and valuable new insights into practicing design and architecture abroad.
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BACKGROUND: Hemodialysis patients experience an elevated risk of malnutrition associated with increased morbidity and mortality. Nocturnal hemodialysis (NHD) results in more effective removal of waste products and fluids. Therefore, diet and fluid restrictions are less restricted in NHD patients. However, it is ambiguous whether transition from conventional hemodialysis (CHD) to NHD leads to improved intake and nutritional status. We studied the effect of NHD on protein intake, laboratory indices of nutritional status, and body composition.STUDY DESIGN: Systematic review with meta-analysis.POPULATION: NHD patients.SEARCH STRATEGY: Systematic literature search from databases, Medline, Cinahl, EMBASE and The Cochrane Library, to identify studies reporting on nutritional status post-transition from CHD to NHD.INTERVENTION: Transition from CHD to NHD.OUTCOMES: Albumin, normalized protein catabolic rate (nPCR), dry body weight (DBW), body mass index (BMI), phase angle, protein intake, and energy intake.RESULTS: Systematic literature search revealed 13 studies comprising 282 patients that made the transition from CHD to NHD. Meta-analysis included nine studies in 229 patients. In control group controlled studies (n = 4), serum albumin increased significantly from baseline to 4-6 months in NHD patients compared with patients that remained on CHD (mean difference 1.3 g/l, 95% CI 0.02; 2.58, p = 0.05). In baseline controlled studies, from baseline to 4-6 months of NHD treatment, significant increases were ascertained in serum albumin (mean difference (MD) 1.63 g/l, 95% CI 0.73-2.53, p<0.001); nPCR (MD 0.16 g/kg/day; 95% CI 0.04-0.29, p = 0.01); protein intake (MD 18.9 g, 95% CI 9.7-28.2, p<0.001); and energy intake (MD 183.2 kcal, 95% CI 16.8-349.7, p = 0.03). Homogeneity was rejected only for nPCR (baseline versus 4-6 months). DBW, BMI, and phase angle did not significantly change. Similar results were obtained for comparison between baseline and 8-12 months of NHD treatment.LIMITATIONS: Most studies had moderate sample sizes; some had incomplete dietary records and relatively brief follow-up period. Studies markedly differed with regard to study design.CONCLUSIONS: NHD is associated with significantly higher protein and energy intake as well as increases in serum albumin and nPCR. However, the data on body composition are inconclusive.
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BACKGROUND: Implicit (IF) and explicit (EF) feedback are two motor learning strategies demonstrated to alter movement patterns. There is conflicting evidence on which strategy produces better outcomes. The purpose of this study was to examine the effects of reduced IF and EF video feedback on lower extremity landing mechanics. METHODS: Thirty participants (24 ± 2 years, 1.7 ± 0.1 m, 70 ± 11 kg) were randomly assigned to three groups: IF (n = 10), EF (n = 10), and control (CG) (n = 10). They performed twelve box-drop jumps three times a week on the training sessions for six weeks. Only IF and EF groups received video feedback on the training sessions. IF was cued to focus their attention on the overall jump, while EF was cued to focus on position of their knees. 3D lower extremity biomechanics were tested on testing sessions with no feedback. All sessions were at least 24 h apart from another. Testing sessions included baseline testing (pretest), testing after 3 training sessions with 100% feedback (pst1), testing after 6 training sessions with 33.3% feedback (pst2), testing after 6 training sessions with 16.6% feedback (Pst3), and testing 1 month after with no feedback (retention - ret). ANOVA compared differences between groups and time at initial contact and peak for hip flexion (HF, °) and abduction angle (HA, °), hip abduction moment (HAM, Nm/kgm), knee flexion (KF, °) and abduction angle (KA, °), knee abduction moment (KAM, Nm/kgm) and VGRF (N) (p < 0.05). RESULTS: A significant main effect for group was found between IF and EF groups for HA (IF = - 6.7 ± 4; EF = - 9.4 ± 4.1) and KAM (IF = 0.05 ± 0.2; EF = - 0.07 ± 0.2) at initial contact, and peaks HA (IF = - 3.5 ± 4.5; EF = - 7.9 ± 4.7) and HAM (IF = 1.1 ± 0.6; EF = 0.9 ± 0.4). A significant main effect for time at initial contact for HF (pre = 32.4 ± 3.2; pst2 = 36.9 ± 3.2; pst3 = 37.9 ± 3.7; ret. = 34.1 ± 3.7), HAM (pre = 0.1 ± 0.1; pst1 = 0.04 ± 0.1; pst3 = 0.1 ± 0.01), KA (pre = 0.7 ± 1.1; pst1 = 0.2 ± 1.2; pst3 = 1.7 ± 1), and KAM (pre = 0.003 ± 0.1; pst3 = 0.01 ± 0.1) was found. DISCUSSION/CONCLUSION: We found that implicit feedback produced positive changes in landing mechanics while explicit feedback degraded motor learning. Our results indicate that implicit feedback should be used in programs to lower the ACL injury risk. We suggest that implicit feedback should be frequent in the beginning and not be reduced as much following the acquisition phase.
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BACKGROUND: In critically ill patients, muscle loss is associated with adverse outcomes. Raw bioelectrical impedance analysis (BIA) parameters (eg, phase angle [PA] and impedance ratio [IR]) have received attention as potential markers of muscularity, nutrition status, and clinical outcomes. Our objective was to test whether PA and IR could be used to assess low muscularity and predict clinical outcomes.METHODS: Patients (≥18 years) having an abdominal computed tomography (CT) scan and admitted to intensive care underwent multifrequency BIA within 72 hours of scan. CT scans were landmarked at the third lumbar vertebra and analyzed for skeletal muscle cross-sectional area (CSA). CSA ≤170 cm(2) for males and ≤110 cm(2) for females defined low muscularity. The relationship between PA (and IR) and CT muscle CSA was evaluated using multivariate regression and included adjustments for age, sex, body mass index, Charlson Comorbidity Index, and admission type. PA and IR were also evaluated for predicting discharge status using dual-energy X-ray absorptiometry-derived cut-points for low fat-free mass index.RESULTS: Of 171 potentially eligible patients, 71 had BIA and CT scans within 72 hours. Area under the receiver operating characteristic (c-index) curve to predict CT-defined low muscularity was 0.67 (P ≤ .05) for both PA and IR. With covariates added to logistic regression models, PA and IR c-indexes were 0.78 and 0.76 (P < .05), respectively. Low PA and high IR predicted time to live ICU discharge.CONCLUSION: Our study highlights the potential utility of PA and IR as markers to identify patients with low muscularity who may benefit from early and rigorous intervention.
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For this exploratory study we aim to provide knowledge and insights concerning the processes of setting up, implementing and managing online communities as a part of the product/services offer of media companies. The goal is to increase their reach amongst target groups, to strengthen involvement with their audiences and to entice their audiences to participate. This information should help us to understand the many different aspects important for developing and managing online communities. The research question for this phase is: Which critical success factors play a role in the process of setting up and managing online communities using social media in order to activate and/or engage target audiences? In this exploratory first phase we looked into literature relating to general guidelines and critical success factors in setting up and managing online communities. These aspects include, communication and interaction options, functionalities for sharing information, the content structure given, the importance of socialization within the community, the policies used and the usability of the platform (Ning Shen & Khalifa, 2008).
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Training-induced adaptations in muscle morphology, including their magnitude and individual variation, remain relatively unknown in elite athletes. We reported changes in rowing performance and muscle morphology during the general and competitive preparation phases in elite rowers. Nineteen female rowers completed 8 weeks of general preparation, including concurrent endurance and high-load resistance training (HLRT). Seven rowers were monitored during a subsequent 16 weeks of competitive preparation, including concurrent endurance and resistance training with additional plyometric loading (APL). Vastus lateralis muscle volume, physiological cross-sectional area (PCSA), fascicle length, and pennation angle were measured using 3D ultrasonography. Rowing ergometer power output was measured as mean power in the final 4 minutes of an incremental test. Rowing ergometer power output improved during general preparation [+2 ± 2%, effect size (ES) = 0.22, P = 0.004], while fascicle length decreased (−5 ± 8%, ES = −0.47, P = 0.020). Rowing power output further improved during competitive preparation (+5 ± 3%, ES = 0.52, P = 0.010). Here, morphological adaptations were not significant, but demonstrated large ESs for fascicle length (+13 ± 19%, ES = 0.93), medium for pennation angle (−9 ± 15%, ES = −0.71), and small for muscle volume (+8 ± 13%, ES = 0.32). Importantly, rowers showed large individual differences in their training-induced muscle adaptations. In conclusion, vastus lateralis muscles of elite female athletes are highly adaptive to specific training stimuli, and adaptations largely differ between individual athletes. Therefore, coaches are encouraged to closely monitor their athletes' individual (muscle) adaptations to better evaluate the effectiveness of their training programs and finetune them to the athlete's individual needs.
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