This technical report describes "validation of the 'Target system definition"
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A review has been completed for a verification and validation (V&V) of the (Excel) BioGas simulator or EBS model. The EBS model calculates the environmental impact of biogas production pathways using Material and Energy Flow Analysis, time dependent dynamics, geographic information, and Life Cycle analysis. Within this article a V&V method is researched, selected and applied to validate the EBS model. Through the use of the method described within this article: mistakes in the model are resolved, the strengths and weaknesses of the model are found, and the concept of the model is tested and strengthened. The validation process does not only improve the model but also helps the modelers in widening their focus and scope. This article can, therefore, also be used in the validation process of similar models. The main result from the V&V process indicates that the EBS model is valid; however, it should be considered as an expert model and should only be used by expert users.
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This report describes the validation of a "framework that delivers insight into the tangible and intangible effects of a mobile (IT) system, before it is being implemented".
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Due to a lack of transparency in both algorithm and validation methodology, it is diffcult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (+/- 10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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Abstract: Clinicians find it challenging to engage with patients who engage in self-harm. Improving the self-efficacy of professionals who treat self-harm patients may be an important step toward accomplishing better treatment of self-harm. However, there is no instrument available that assesses the self-efficacy of clinicians dealing with self-harm. The aim of this study is to describe the development and validation of the Self-Efficacy in Dealing with Self-Harm Questionnaire (SEDSHQ). This study tests the questionnaire’s feasibility, test-retest reliability, internal consistency, content validity, construct validity (factor analysis and convergent validity) and sensitivity to change. The Self-Efficacy in Dealing with Self-Harm Questionnaire is a 27-item instrument which has a 3-factor structure, as found in confirmatory factor analysis. Testing revealed high content validity, significant correlation with a subscale of the Attitude Towards Deliberate Self-Harm Questionnaire (ADSHQ), satisfactory test-retest correlation and a Cronbach’s alpha of 0.95. Additionally, the questionnaire was able to measure significant changes after an intervention took place, indicating sensitivity to change. We conclude that the present study indicates that the Self-Efficacy in Dealing with Self-Harm Questionnaire is a valid and reliable instrument for assessing the level of self-efficacy in response to self-harm.
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This report describes "a framework that delivers insight into the tangible and intangible effects of a mobile (IT) system, before it is being implemented".
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The aim of this study was to develop a valid instrument to measure student nurses’ perceptions of community care (SCOPE). DeVellis’ staged model for instrument development and validation was used. Scale construction of SCOPE was based on existing literature. Evaluation of its psychometric properties included exploratory factor analysis and reliability analysis. After pilot-testing, 1062 bachelor nursing students from six institutions in the Netherlands (response rate 81%) took part in the study. SCOPE is a 35-item scale containing: background variables, 11 measuring the affective component, 5 measuring community care perception as a placement, 17 as a future profession, and 2 on the reasons underlying student preference. Principal axis factoring yielded two factors in the affective component scale reflecting ‘enjoyment’ and ‘utility’, two in the placement scale reflecting ‘learning possibilities’ and ‘personal satisfaction’, and four in the profession scale: ‘professional development’, ‘collaboration’, ‘caregiving’, and ‘complexity and workload’. Cronbach’s α of the complete scale was .892 and of the subscales .862, .696, and .810 respectively. SCOPE is a psychometrically sound instrument for measuring students’ perceptions of community care. By determining these perceptions, it becomes possible to positively influence them with targeted curriculum redesign, eventually contributing to decreasing the workforce shortage in community nursing.
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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.
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INTRODUCTION: The conceptual ambiguity of the integrated care concept precludes a full understanding of what constitutes a well-integrated health system, posing a significant challenge in measuring the level of integrated care. Most available measures have been developed from a disease-specific perspective and only measure certain aspects of integrated care. Based on the Rainbow Model of Integrated Care, which provides a detailed description of the complex concept of integrated care, a measurement tool has been developed to assess integrated care within a care system as a whole gathered from healthcare providers' and managerial perspectives. This paper describes the methodology of a study seeking to validate the Rainbow Model of Integrated Care measurement tool within and across the Singapore Regional Health System. The Singapore Regional Health System is a recent national strategy developed to provide a better-integrated health system to deliver seamless and person-focused care to patients through a network of providers within a specified geographical region.METHODS: The validation process includes the assessment of the content of the measure and its psychometric properties.CONCLUSION: If the measure is deemed to be valid, the study will provide the first opportunity to measure integrated care within Singapore Regional Health System with the results allowing insights in making recommendations for improving the Regional Health System and supporting international comparison.
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