Validation of an interview for study the process of formation of elite judokas

Con el objetivo de analizar el proceso de formación deportiva de los judokas españoles de élite, se diseñó una entrevista semiestructurada de 52 preguntas agrupadas en 6 dimensiones (entorno social, entorno deportivo, psicológica, técnico-táctica, condición física y otros aspectos). Se llevó a cabo un análisis cualitativo y cuantitativo mediante la valoración de 10 expertos. Se analizó la validez de contenido a través del coeficiente V de Aiken, estableciendo un intervalo de confianza del 99 %, y el coeficiente de variación. Para conocer la fiabilidad se pasó la entrevista a judokas de alto nivel en dos momentos distintos y se analizó la consistencia interna por el método del Alfa de Cronbach (0,915) y la fiabilidad test-retest utilizando el coeficiente de correlación intraclase resultando 0,843 (p<0,01). La entrevista reúne suficientes propiedades como para ser considerada una herramienta válida y fiable para estudiar y analizar el proceso de formación de los judokas de éliteIn order to analyze the process of sports training of the Spanish elite judokas , a semistructured interview of 52 questions grouped into 6 domains ( social, sporting environment , psychological , technical and tactical, physical condition and other aspects ) was designed. It was conducted a qualitative and quantitative analysis with 10 experts´ valuation. The validity of content was analysed through the coefficient V Aiken , establishing a 99 % confidence interval, and so the coefficient of variation was analyzed. To know reliability the interview was given to senior judokas in two different moments , and internal consistency was analysed by the Cronbach's alpha method ( 0.915 ) and so test-retest reliability by using the intraclass correlation coefficient resulting 0.843 (p < 0.01 ) . The interview brings enough properties to be considered a valid and reliable tool to study and analyze the formation of elite judoka

Modelling of batteries for application in light electric urban vehicles

[EN] In this paper a dynamic model of a battery that lets simulate different types of batteries in light electric urban vehicles applications is proposed. The model is directly parameterizable from discharging experimental curves in test facilities. It properly fits to the particular behaviour observed in the charging/discharging curves in LiFePo 4 batteries. For the calibration of the proposed model experimental data from an experimental facility have been used and validation results are presented. The model is implemented in the object oriented modelling language Modelica reusing classes from the Modelica Standard Library. The calibration and the calibration has been performed with Dymola modelling tool.[ES] En este artículo se propone un modelo dinámico de batería que permite simular el comportamiento de distintos tipos de baterías para su aplicación en vehículos eléctricos urbanos ligeros. El modelo es fácilmente parametrizable a partir de las curvas de descarga experimentales del equipo real y se ajusta adecuadamente al comportamiento particular de la curva de carga/descarga de las baterías de Litio-Ferrofosfato (LiFePo4). Se han utilizado los datos obtenidos sobre una instalación experimental para la calibración del modelo propuesto y se presentan resultados de la validación del mismo. El modelo se ha implementado en el lenguaje de modelado orientado a objetos Modelica reutilizando clases de su librería estándar Modelica Standard Library. La calibración y validación se ha realizado con la herramienta de modelado Dymola.Al personal técnico del Grupo de Automática, Robótica y Mecatrónica de la Universidad de Almería (TEP-197) a cargo de la microrred experimental, por su inestimable ayuda en la obtención de los registros experimentales utilizados. El presente trabajo ha sido parcialmente financiado por el Proyecto DPI2017-85007-R del Plan Nacional R+D+i del Ministerio de Ciencia, Innovación y Universidades del Reino de España y por el Fondo Europeo de Desarrollo Regional (FEDER).Gómez, F.; Yebra, L.; Giménez, A.; Torres-Moreno, J. (2019). Modelado de baterías para aplicación en vehículos urbanos eléctricos ligeros. Revista Iberoamericana de Automática e Informática. 16(4):459-466. https://doi.org/10.4995/riai.2019.10609SWORD459466164A123 Systems, 2012. Nanophosphate High Power Lithium Ion Cell ANR26650M1-B.Ahmed, M., 2016. Modeling Lithium-ion Battery Chargers in PLECS R . Tech.rep.Ansean, D., Gonzalez, M., Viera, J. C., Alvarez, J. C., Blanco, C., García, V. M., 2013. Evaluation of LiFePO4batteries for Electric Vehicle applications. In: 2013 Int. Conf. New Concepts Smart Cities Foster. Public Priv. Alliances. IEEE, Gijon, Spain, p. 8. URL: https://ieeexplore.ieee.org/document/6708211 http://doi.org/10.1109/SmartMILE.2013.6708211Berecibar, M., Garmendia, M., Gandiaga, I., Crego, J., Villarreal, I., 2016. State of health estimation algorithm of LiFePO4battery packs based on differential voltage curves for battery management system application. Energy 103, 784-796. https://doi.org/10.1016/j.energy.2016.02.163Brondani, M. D. F., Sausen, A. T. Z. R., Sausen, P. S., Binelo, M. O., 2017. Battery Model Parameters Estimation Using Simulated Annealing. TEMA(Sao Carlos) 18 (1), 127. URL: https://tema.sbmac.org.br/tema/article/view/1003 https://doi.org/10.5540/tema.2017.018.01.0127Dempsey, M., Gäfvert, M., Harman, P., Kral, C., Otter, M., Treffinger, P., 2006. Coordinated automotive libraries for vehicle system modelling. In: 5thModel. Conf. 2006. The Modelica Association, Vienna, Austria, pp. 33-41.URL: https://www.modelica.org/events/modelica2006/Proceedings/sessions/Session1b2.pdfDizqah,A.M.,Busawon,K.,Fritzson,P.,2012.ACAUSALMODELINGAND SIMULATION OF THE STANDALONE SOLAR POWER SYSTEMS AS HYBRID DAEs. In: 53rd Int. Conf. Scand. Simul. Soc. pp. 1-7.Dymola - Dynamic Modeling Laboratory - User Manual, 2018. Dymola. URL: http://www.dymola.comElmqvist, H., Olsson, H., Mattsson, S. E., Brück, D., Schweiger, C., Joos, D., Otter, M., 2005. Optimization for design and parameter estimation. In: In4th International Modelica Conference.Fritzson, P., 2015. Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach, 2nd Edition. Wiley. https://doi.org/10.1002/9781118989166Gómez, F.J., Yebra, L.J., Giménez, A., 2018. Modelling a Smart-Grid for a Solar Powered Electric Vehicle. In: Technische Universität Wien (Ed.), 9th Vienna Conf. Math. Model. Vol. 55. ARGESIM Publisher, Vienna, Vienna,Austria, pp. 5-6. URL: https://www.asim-gi.org/fileadmin/user_upload_argesim/ARGESIM_Publications_OA/MATHMOD_Publications_OA/MATHMOD_2018_AR55/articles/a55113.arep.55.pdf DOI: 10.11128/arep.55.a55113. https://doi.org/10.11128/arep.55.a55113Hausmann, A., Depcik, C., 2013. Expanding the Peukert equation for battery capacity modeling through inclusion of a temperature dependency. J. Power Sources 235, 148-158. URL: https://www.sciencedirect.com/science/article/pii/S0378775313002322. https://doi.org/10.1016/j.jpowsour.2013.01.174Kroeze, R. C., Krein, P. T., 2008. Electrical battery model for use in dynamic electric vehicle simulations. In: 2008 IEEE Power Electron. Spec. Conf. IEEE, Rhodes, Greece, pp. 1336-1342. URL: http://ieeexplore.ieee.org/document/4592119/. https://doi.org/10.1109/PESC.2008.4592119NREL, 2015. Technoeconomic Modeling of Battery Energy Storage in SAM. Tech. Rep.September.URL: http://www.nrel.gov/docs/fy15osti/64641.pdfOlsson, H., Mattsson, S. E., Hilding Elmqvist, 2006. Calibration of Static Models using Dymola. In: Proc. 5th Int. Model. Conf. The Modelica Association (http://www.modelica.org/) and Arsenal Research (http://www.arsenal.ac.at/), Vienna, Austria, pp. 615-620.URL: https://modelica.org/events/modelica2006/Proceedings/sessions/Session6a3.pdfPetzl, M., Danzer, M. A., 2013. Advancements in OCV measurement and analysis for lithium-ion batteries. IEEE Trans. Energy Convers. 28 (3), 675-681. https://doi.org/10.1109/TEC.2013.2259490Seaman, A., Dao, T.-S., McPhee, J., jun 2014. A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation. J. Power Sources 256, 410-423. URL: https://www.sciencedirect.com/science/article/pii/S0378775314000810. https://doi.org/10.1016/j.jpowsour.2014.01.057Torres-Moreno, J. L., Gimenez-Fernandez, A., Perez-Garcia, M., Rodriguez, F., 2018. Energy management strategy for micro-grids with pv-battery systemsand electric vehicles. Energies 11 (3). URL: http://www.mdpi.com/1996-1073/11/3/522 DOI: 10.3390/en11030522. https://doi.org/10.3390/en11030522Tremblay, O., Dessaint, L., 2009. Experimental validation of a battery dynamic model for EV applications. World Electr. Veh. J. 3, 1-10. https://doi.org/10.3390/wevj3020289TÜV SÜD Certification and Testing (China) Co. Ltd., 2016. Test Report IEC-62619A BYD B-Box. Tech. rep., TÜV SÜD Certification and Testing (China) Co. Ltd., Shenzhen (China). URL: https://www1.fenecon.de/web/content/34638van Baten, J., 2017. ScanIt. URL: https://www.amsterchem.com/scanit.htmlWang, W., Chung, H. S. H., Zhang, J., 2014. Near-real-time parameter estimation of an electrical battery model with multiple time constants and SoCdependent capacitance. 2014 IEEE Energy Convers. Congr. Expo. ECCE 2014 29 (11), 3977-3984. 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Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance

[EN] Background: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. Methods: We performed an extensive analysis of sensor¿s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. Results: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). Conclusions: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government through Grants DPI 2013-46982-C2-1-R, DPI 2016-78831-C2-1-R, DPI 2013-46982-C2-2-R, and DPI 2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Hirata-Bertachi, A.; Conget, I.; Quirós, C.; Giménez, M.; Ampudia-Blasco, F.; Rossetti, P.... (2017). Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance. Journal of Diabetes Science and Technology. 11(6):1089-1095. https://doi.org/10.1177/1932296817714272S10891095116Doyle, F. J., Huyett, L. M., Lee, J. B., Zisser, H. C., & Dassau, E. (2014). Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms. Diabetes Care, 37(5), 1191-1197. doi:10.2337/dc13-2108Cengiz, E., & Tamborlane, W. V. (2009). A Tale of Two Compartments: Interstitial Versus Blood Glucose Monitoring. Diabetes Technology & Therapeutics, 11(S1), S-11-S-16. doi:10.1089/dia.2009.0002Cobelli, C., Schiavon, M., Dalla Man, C., Basu, A., & Basu, R. (2016). Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study. Diabetes Technology & Therapeutics, 18(8), 505-511. doi:10.1089/dia.2016.0112Castle, J. R., & Ward, W. K. (2010). Amperometric Glucose Sensors: Sources of Error and Potential Benefit of Redundancy. Journal of Diabetes Science and Technology, 4(1), 221-225. doi:10.1177/193229681000400127Basu, A., Dube, S., Veettil, S., Slama, M., Kudva, Y. C., Peyser, T., … Basu, R. (2014). Time Lag of Glucose From Intravascular to Interstitial Compartment in Type 1 Diabetes. Journal of Diabetes Science and Technology, 9(1), 63-68. doi:10.1177/1932296814554797Keenan, D. B., Grosman, B., Clark, H. W., Roy, A., Weinzimer, S. A., Shah, R. V., & Mastrototaro, J. J. (2011). Continuous Glucose Monitoring Considerations for the Development of a Closed-Loop Artificial Pancreas System. Journal of Diabetes Science and Technology, 5(6), 1327-1336. doi:10.1177/193229681100500603Van Bon, A. C., Jonker, L. D., Koebrugge, R., Koops, R., Hoekstra, J. B. L., & DeVries, J. H. (2012). Feasibility of a Bihormonal Closed-Loop System to Control Postexercise and Postprandial Glucose Excursions. Journal of Diabetes Science and Technology, 6(5), 1114-1122. doi:10.1177/193229681200600516Rossetti, P., Quirós, C., Moscardó, V., Comas, A., Giménez, M., Ampudia-Blasco, F. J., … Vehí, J. (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics, 19(6), 355-362. doi:10.1089/dia.2016.0443Bailey, T., Zisser, H., & Chang, A. (2009). New Features and Performance of a Next-Generation SEVEN-Day Continuous Glucose Monitoring System with Short Lag Time. Diabetes Technology & Therapeutics, 11(12), 749-755. doi:10.1089/dia.2009.0075Zschornack, E., Schmid, C., Pleus, S., Link, M., Klötzer, H.-M., Obermaier, K., … Freckmann, G. (2013). Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring. Journal of Diabetes Science and Technology, 7(4), 815-823. doi:10.1177/193229681300700403Pleus, S., Schmid, C., Link, M., Zschornack, E., Klötzer, H.-M., Haug, C., & Freckmann, G. (2013). Performance Evaluation of a Continuous Glucose Monitoring System under Conditions Similar to Daily Life. Journal of Diabetes Science and Technology, 7(4), 833-841. doi:10.1177/193229681300700405Zisser, H. C., Bailey, T. S., Schwartz, S., Ratner, R. E., & Wise, J. (2009). Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements. Journal of Diabetes Science and Technology, 3(5), 1146-1154. doi:10.1177/193229680900300519Obermaier, K., Schmelzeisen-Redeker, G., Schoemaker, M., Klötzer, H.-M., Kirchsteiger, H., Eikmeier, H., & del Re, L. (2013). Performance Evaluations of Continuous Glucose Monitoring Systems: Precision Absolute Relative Deviation is Part of the Assessment. Journal of Diabetes Science and Technology, 7(4), 824-832. doi:10.1177/193229681300700404Clarke, W. L., Cox, D., Gonder-Frederick, L. A., Carter, W., & Pohl, S. L. (1987). Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. Diabetes Care, 10(5), 622-628. doi:10.2337/diacare.10.5.622Martin Bland, J., & Altman, D. (1986). STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT. The Lancet, 327(8476), 307-310. doi:10.1016/s0140-6736(86)90837-8Breton, M., & Kovatchev, B. (2008). Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. Journal of Diabetes Science and Technology, 2(5), 853-862. doi:10.1177/193229680800200517Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., … DeVries, J. H. (2014). Accuracy of two continuous glucose monitoring systems: a head‐to‐head comparison under clinical research centre and daily life conditions. Diabetes, Obesity and Metabolism, 17(4), 343-349. doi:10.1111/dom.12378Reddy, M., Herrero, P., Sharkawy, M. E., Pesl, P., Jugnee, N., Pavitt, D., … Oliver, N. S. (2015). Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology, 10(2), 405-413. doi:10.1177/1932296815616134Pleus, S., Schoemaker, M., Morgenstern, K., Schmelzeisen-Redeker, G., Haug, C., Link, M., … Freckmann, G. (2015). Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings. Journal of Diabetes Science and Technology, 9(4), 801-807. doi:10.1177/193229681557871

Satisfacción de los internos de centros penitenciarios con el programa deportivo educativo de baloncesto desarrollado por la Fundación Real Madrid

El deporte y la actividad física tienen, cada vez más, un papel muy importante en la vida de los centros penitenciarios, siendo usados, normalmente, con el fin de favorecer el proceso de reinserción de los reclusos. En este sentido, los objetivos de este trabajo fueron: analizar el grado de satisfacción de los internos de los centros penitenciarios con el programa deportivo educativo de baloncesto llevado a cabo por la Fundación Real Madrid, y destacar los resultados más relevantes de la aplicación del mismo. Con este n, se administró un cuestionario a 267 reclusos de un total de 21 centros penitenciarios de España. Entre los resultados más destacados, sobresale el hecho de que, en general, los encuestados estaban muy satisfechos con el programa deportivo educativo llevado a cabo. Además, los datos hallados manifestaron que la gran mayoría de los internos consideraban que habían aprendido mucho en relación con la actividad deportiva desarrollada (baloncesto) y con los valores educativos trabajados.

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

This is a copy of an article published in the Diabetes Technology & Therapeutics © 2017 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology & Therapeutics is available online at: https://www.liebertpub.com/.[EN] Background: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. Objective: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. Methods: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 -10.4 years, disease duration 22.6 +/- 9.9 years, and A1c 7.8% +/- 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithmdriven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (C-max), and time to C-max glucose concentrations and time in range (180 mg/dL). Results: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). Conclusions: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov: study number NCT02100488.This work was supported by the Spanish Ministry of Economy and Competitiveness through Grants DPI2013-46982-C2-1-R and DPI2013-46982-C2-2-R, and the EU through FEDER funds. C.Q. is the recipient of a grant from the Hospital Clinic i Universitari of Barcelona ("Ajut a la recerca Josep Font 2014-2017").Rossetti, P.; Quirós, C.; Moscardo-Garcia, V.; Comas, A.; Giménez, M.; Ampudia-Blasco, F.; León, F.... (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics. 19(6):355-362. https://doi.org/10.1089/dia.2016.0443S35536219

Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study

Ocean monitoring systems are designed for continuous monitoring to track their evolution and anticipate environmental issues. However, they are often based on IoT systems that offer little spatial coverage and are hard to maintain. Satellite remote sensing offers good geographical coverage but they also face several challenges to become a monitoring system. This paper introduces an easy-to-use software tool to crawl water-quality data from up to 6 satellite instruments from the ESA and NASA. Particularly, Chl-a data is deeply analyzed in terms of reliability and data coverage for a highly anthropised coastal lagoon (Mar Menor, Spain), where serious socio-environmental issues are happening. Our results show a good linear correlation between in situ data and SRS data, reaching values close to 0.9, and stating the relevance of organic matter inputs from ephemeral streams in Chl-a concentrations. Moreover, temporal granularity is increased from 5 to 1.5 days by combining SRS sources.Preprin

BRIVA-LIFE–A multicenter retrospective study of the long-term use of brivaracetam in clinical practice

Objectives: Evaluate long-term effectiveness and tolerability of brivaracetam in clinical practice in patients with focal epilepsy. Materials and Methods: This was a multicenter retrospective study. Patients aged =16 years were started on brivaracetam from November 2016 to June 2017 and followed over 1 year. Data were obtained from medical records at 3, 6 and 12 months after treatment initiation for evaluation of safety- and seizure-related outcomes. Results: A total of 575 patients were included in analyses; most had been treated with =4 lifetime antiepileptic drugs. Target dosage was achieved by 30.6% of patients on the first day. Analysis of primary variables at 12 months revealed that mean reduction in seizure frequency was 36.0%, 39.7% of patients were =50% responders and 17.5% were seizure-free. Seizure-freedom was achieved by 37.5% of patients aged =65 years. Incidence of adverse events (AEs) and psychiatric AEs (PAEs) was 39.8% and 14.3%, respectively, and discontinuation due to these was 8.9% and 3.7%, respectively. Somnolence, irritability, and dizziness were the most frequently reported AEs. At baseline, 228 (39.7%) patients were being treated with levetiracetam; most switched to brivaracetam (dose ratio 1:10-15). Among those who switched because of PAEs (n = 53), 9 (17%) reported PAEs on brivaracetam, and 3 (5.7%) discontinued because of PAEs. Tolerability was not highly affected among patients with learning disability or psychiatric comorbidity. Conclusions: In a large population of patients with predominantly drug-resistant epilepsy, brivaracetam was effective and well-tolerated; no unexpected AEs occurred over 1 year, and the incidence of PAEs was lower compared with levetiracetam

Cut-offs and response criteria for the Hospital Universitario la Princesa Index (HUPI) and their comparison to widely-used indices of disease activity in rheumatoid arthritis

Objective To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Methods Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. Results The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if =2, low disease activity if >2 and =5), moderate if >5 and <9 and high if =9. HUPI''s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28''s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. Conclusions The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis