The Obemat2.0 Study: A Clinical Trial of a Motivational Intervention for Childhood Obesity Treatment.

The primary aim of the Obemat2.0 trial was to evaluate the efficacy of a multicomponent motivational program for the treatment of childhood obesity, coordinated between primary care and hospital specialized services, compared to the usual intervention performed in primary care. This was a cluster randomized clinical trial conducted in Spain, with two intervention arms: motivational intervention group vs. usual care group (as control), including 167 participants in each. The motivational intervention consisted of motivational interviewing, educational materials, use of an eHealth physical activity monitor and three group-based sessions. The primary outcome was body mass index (BMI) z score increments before and after the 12 (+3) months of intervention. Secondary outcomes (pre-post intervention) were: adherence to treatment, waist circumference (cm), fat mass index (z score), fat free mass index (z score), total body water (kg), bone mineral density (z score), blood lipids profile, glucose metabolism, and psychosocial problems. Other assessments (pre and post-intervention) were: sociodemographic information, physical activity, sedentary activity, neuropsychological testing, perception of body image, quality of the diet, food frequency consumption and foods available at home. The results of this clinical trial could open a window of opportunity to support professionals at the primary care to treat childhood obesity. The clinicaltrials.gov identifier was NCT02889406

Body composition assessment in paediatric patients. Validation of new methods of body composition measurements in obese children

L'obesitat es defineix com un excés de greix en el cos, però generalment es diagnostica mitjançant mètodes que en realitat no poden mesurar o estimar el teixit adipós del cos, com l'índex de massa corporal (IMC). Hi ha moltes tècniques que poden diferenciar els compartiments corporals in vivo i després, el greix es pot estimar amb un nivell relativament alt de precisió, per exemple, absorciometria de raigs X de doble energia (DXA), pletismografia per desplaçament d'aire (ADP), dilucions isotòpiques, models multi-components, entre d'altres. El mètode de referència per avaluar la composició corporal in vivo és el model de 4-components. No obstant això, aquestes tècniques tenen algunes limitacions, principalment, totes són cares i inadequades per a la pràctica clínica. L'anàlisi d'impedància bioelèctrica (BIA) s'ha proposat com una tècnica adequada per avaluar la composició corporal en diverses poblacions, inclosos nens obesos. No obstant això, hi ha investigacions que van mostrar una baixa precisió de les mesures amb BIA en obesos. Els nostres resultats van mostrar que aquesta imprecisió podria ser deguda a l'ús de valors constants de les propietats de massa lliure de greix, hidratació i densitat, quan s’avalua la composició corporal mitjançant tècniques basades en 2-components (ADP, BIA). Tenint en compte aquest fet, aquest treball va proposar dos nous mètodes per avaluar la composició corporal en nens obesos: el primer va suggerir que utilitzar valors calculats de la densitat de la massa lliure de greix, amb la nova equació predictiva, en lloc dels valors constants publicats per mesurar la composició corporal mitjançant tècniques basades en 2-components (per exemple, ADP) millora la precisió de la tècnica; el segon mètode mostra una nova equació per calcular la massa lliure de greix a partir de les mesures d'impedància, millorant la precisió de les equacions del fabricant dels analitzadors d'impedància en població obesa.La obesidad se define como un exceso de grasa en el cuerpo, pero generalmente se diagnostica mediante métodos que en realidad no pueden medir o estimar el tejido adiposo del cuerpo como el índice de masa corporal (IMC). Existen muchas técnicas que pueden diferenciar los compartimentos corporales in vivo y luego, la grasa se puede estimar con un nivel relativamente alto de precisión, por ejemplo, absorciometría de rayos X de doble energía (DXA), pletismografía por desplazamiento de aire (ADP), diluciones isotópicas, modelos multi-componentes, entre otros. El método de referencia para evaluar la composición corporal in vivo es el modelo de 4-componentes. Sin embargo, estas técnicas tienen algunas limitaciones, principalmente, todas son caras e inadecuadas para la práctica clínica. El análisis de impedancia bioeléctrica (BIA) se ha propuesto como una técnica adecuada para evaluar la composición corporal en diversas poblaciones, incluidos niños obesos. Sin embargo, existen investigaciones que mostraron una baja precisión de las medidas con BIA en obesos. Nuestros resultados mostraron que esta imprecisión podría deberse al uso de valores constantes de las propiedades de masa libre de grasa, hidratación y densidad, al evaluar la composición corporal mediante técnicas basadas en 2-componentes (ADP, BIA). Teniendo en cuenta este hecho, este trabajo propuso dos nuevos métodos para evaluar la composición corporal en niños obesos: el primero sugirió que usar valores calculados de la densidad de la masa libre de grasa, con la nueva ecuación predictiva, en lugar de los valores constantes publicados al evaluar composición corporal mediante técnicas basadas en 2-componentes (por ejemplo, ADP) mejora la precisión de la técnica; el segundo método muestra una nueva ecuación para calcular la masa libre de grasa a partir de las mediciones de impedancia, lo que mejora la precisión de las ecuaciones del fabricante de los analizadores de impedancia en la población obesa.Obesity is defined as an excess of fat in the body but it is usually diagnosed by methods which cannot actually measure or estimate the adipose tissue of the body, i.e. body mass index (BMI). There are many existing techniques which can differentiate body compartments in vivo and then, fat can be estimated with a relative high level of accuracy, i.e. dual energy X-ray absorptiometry (DXA), air-displacement plethysmography (ADP), isotopic dilutions, multi-component models, among others. The gold standard method to assess body composition in vivo is the four-component model. However, these techniques have some limitations, and mainly, all of them are expensive and implausible for clinical practice. Bioelectrical impedance analysis (BIA) has been proposed as a suitable technique to assess body composition in a wide range of populations, including obese children. However, there are research evidences that showed a poor accuracy of BIA body composition assessments in this population. Our results showed that this lack of accuracy might be due to the assumption of constant values of the fat-free mass properties, hydration and density, when assessing body composition by 2-component based techniques (e.g. ADP and BIA). Considering this fact, this work proposed two new methods to assess body composition in obese children: the first one suggested that using calculated values of the density of the fat-free mass, with the new predictive equation, instead the published constant values when assessing body composition by 2-component based techniques (e.g. ADP) improves the accuracy of the technique; the second method shows a new equation to calculate the fat-free mass from whole-body impedance measurements, which improves the accuracy of the impedance analysers manufacturer’s equations in obese population

Body composition assessment in paediatric patients. Validation of new methods of body composition measurements in obese children

L'obesitat es defineix com un excés de greix en el cos, però generalment es diagnostica mitjançant mètodes que en realitat no poden mesurar o estimar el teixit adipós del cos, com l'índex de massa corporal (IMC). Hi ha moltes tècniques que poden diferenciar els compartiments corporals in vivo i després, el greix es pot estimar amb un nivell relativament alt de precisió, per exemple, absorciometria de raigs X de doble energia (DXA), pletismografia per desplaçament d'aire (ADP), dilucions isotòpiques, models multi-components, entre d'altres. El mètode de referència per avaluar la composició corporal in vivo és el model de 4-components. No obstant això, aquestes tècniques tenen algunes limitacions, principalment, totes són cares i inadequades per a la pràctica clínica. L'anàlisi d'impedància bioelèctrica (BIA) s'ha proposat com una tècnica adequada per avaluar la composició corporal en diverses poblacions, inclosos nens obesos. No obstant això, hi ha investigacions que van mostrar una baixa precisió de les mesures amb BIA en obesos. Els nostres resultats van mostrar que aquesta imprecisió podria ser deguda a l'ús de valors constants de les propietats de massa lliure de greix, hidratació i densitat, quan s’avalua la composició corporal mitjançant tècniques basades en 2-components (ADP, BIA). Tenint en compte aquest fet, aquest treball va proposar dos nous mètodes per avaluar la composició corporal en nens obesos: el primer va suggerir que utilitzar valors calculats de la densitat de la massa lliure de greix, amb la nova equació predictiva, en lloc dels valors constants publicats per mesurar la composició corporal mitjançant tècniques basades en 2-components (per exemple, ADP) millora la precisió de la tècnica; el segon mètode mostra una nova equació per calcular la massa lliure de greix a partir de les mesures d'impedància, millorant la precisió de les equacions del fabricant dels analitzadors d'impedància en població obesa.La obesidad se define como un exceso de grasa en el cuerpo, pero generalmente se diagnostica mediante métodos que en realidad no pueden medir o estimar el tejido adiposo del cuerpo como el índice de masa corporal (IMC). Existen muchas técnicas que pueden diferenciar los compartimentos corporales in vivo y luego, la grasa se puede estimar con un nivel relativamente alto de precisión, por ejemplo, absorciometría de rayos X de doble energía (DXA), pletismografía por desplazamiento de aire (ADP), diluciones isotópicas, modelos multi-componentes, entre otros. El método de referencia para evaluar la composición corporal in vivo es el modelo de 4-componentes. Sin embargo, estas técnicas tienen algunas limitaciones, principalmente, todas son caras e inadecuadas para la práctica clínica. El análisis de impedancia bioeléctrica (BIA) se ha propuesto como una técnica adecuada para evaluar la composición corporal en diversas poblaciones, incluidos niños obesos. Sin embargo, existen investigaciones que mostraron una baja precisión de las medidas con BIA en obesos. Nuestros resultados mostraron que esta imprecisión podría deberse al uso de valores constantes de las propiedades de masa libre de grasa, hidratación y densidad, al evaluar la composición corporal mediante técnicas basadas en 2-componentes (ADP, BIA). Teniendo en cuenta este hecho, este trabajo propuso dos nuevos métodos para evaluar la composición corporal en niños obesos: el primero sugirió que usar valores calculados de la densidad de la masa libre de grasa, con la nueva ecuación predictiva, en lugar de los valores constantes publicados al evaluar composición corporal mediante técnicas basadas en 2-componentes (por ejemplo, ADP) mejora la precisión de la técnica; el segundo método muestra una nueva ecuación para calcular la masa libre de grasa a partir de las mediciones de impedancia, lo que mejora la precisión de las ecuaciones del fabricante de los analizadores de impedancia en la población obesa.Obesity is defined as an excess of fat in the body but it is usually diagnosed by methods which cannot actually measure or estimate the adipose tissue of the body, i.e. body mass index (BMI). There are many existing techniques which can differentiate body compartments in vivo and then, fat can be estimated with a relative high level of accuracy, i.e. dual energy X-ray absorptiometry (DXA), air-displacement plethysmography (ADP), isotopic dilutions, multi-component models, among others. The gold standard method to assess body composition in vivo is the four-component model. However, these techniques have some limitations, and mainly, all of them are expensive and implausible for clinical practice. Bioelectrical impedance analysis (BIA) has been proposed as a suitable technique to assess body composition in a wide range of populations, including obese children. However, there are research evidences that showed a poor accuracy of BIA body composition assessments in this population. Our results showed that this lack of accuracy might be due to the assumption of constant values of the fat-free mass properties, hydration and density, when assessing body composition by 2-component based techniques (e.g. ADP and BIA). Considering this fact, this work proposed two new methods to assess body composition in obese children: the first one suggested that using calculated values of the density of the fat-free mass, with the new predictive equation, instead the published constant values when assessing body composition by 2-component based techniques (e.g. ADP) improves the accuracy of the technique; the second method shows a new equation to calculate the fat-free mass from whole-body impedance measurements, which improves the accuracy of the impedance analysers manufacturer’s equations in obese population

The EMPOWER Occupational e–Mental Health Intervention Implementation Checklist to Foster e–Mental Health Interventions in the Workplace:Development Study

Background: Occupational e–mental health (OeMH) interventions significantly reduce the burden of mental health conditions. The successful implementation of OeMH interventions is influenced by many implementation strategies, barriers, and facilitators across contexts, which, however, are not systematically tracked. One of the reasons is that international consensus on documenting and reporting the implementation of OeMH interventions is lacking. There is a need for practical guidance on the key factors influencing the implementation of interventions that organizations should consider. Stakeholder consultations secure a valuable source of information about these key strategies, barriers, and facilitators that are relevant to successful implementation of OeMH interventions. Objective: The objective of this study was to develop a brief checklist to guide the implementation of OeMH interventions. Methods: Based on the results of a recently published systematic review, we drafted a comprehensive checklist with a wide set of strategies, barriers, and facilitators that were identified as relevant for the implementation of OeMH interventions. We then used a 2-stage stakeholder consultation process to refine the draft checklist to a brief and practical checklist comprising key implementation factors. In the first stage, stakeholders evaluated the relevance and feasibility of items on the draft checklist using a web-based survey. The list of items comprised 12 facilitators presented as statements addressing “elements that positively affect implementation” and 17 barriers presented as statements addressing “concerns toward implementation.” If a strategy was deemed relevant, respondents were asked to rate it using a 4-point Likert scale ranging from “very difficult to implement” to “very easy to implement.” In the second stage, stakeholders were interviewed to elaborate on the most relevant barriers and facilitators shortlisted from the first stage. The interview mostly focused on the relevance and priority of strategies and factors affecting OeMH intervention implementation. In the interview, the stakeholders’ responses to the open survey’s questions were further explored. The final checklist included strategies ranked as relevant and feasible and the most relevant facilitators and barriers, which were endorsed during either the survey or the interviews. Results: In total, 26 stakeholders completed the web-based survey (response rate=24.8%) and 4 stakeholders participated in individual interviews. The OeMH intervention implementation checklist comprised 28 items, including 9 (32.1%) strategies, 8 (28.6%) barriers, and 11 (39.3%) facilitators. There was widespread agreement between findings from the survey and interviews, the most outstanding exception being the idea of proposing OeMH interventions as benefits for employees. Conclusions: Through our 2-stage stakeholder consultation, we developed a brief checklist that provides organizations with a guide for the implementation of OeMH interventions. Future research should empirically validate the effectiveness and usefulness of the checklist.</p

The EMPOWER Occupational e–Mental Health Intervention Implementation Checklist to Foster e–Mental Health Interventions in the Workplace: Development Study

BackgroundOccupational e–mental health (OeMH) interventions significantly reduce the burden of mental health conditions. The successful implementation of OeMH interventions is influenced by many implementation strategies, barriers, and facilitators across contexts, which, however, are not systematically tracked. One of the reasons is that international consensus on documenting and reporting the implementation of OeMH interventions is lacking. There is a need for practical guidance on the key factors influencing the implementation of interventions that organizations should consider. Stakeholder consultations secure a valuable source of information about these key strategies, barriers, and facilitators that are relevant to successful implementation of OeMH interventions. ObjectiveThe objective of this study was to develop a brief checklist to guide the implementation of OeMH interventions. MethodsBased on the results of a recently published systematic review, we drafted a comprehensive checklist with a wide set of strategies, barriers, and facilitators that were identified as relevant for the implementation of OeMH interventions. We then used a 2-stage stakeholder consultation process to refine the draft checklist to a brief and practical checklist comprising key implementation factors. In the first stage, stakeholders evaluated the relevance and feasibility of items on the draft checklist using a web-based survey. The list of items comprised 12 facilitators presented as statements addressing “elements that positively affect implementation” and 17 barriers presented as statements addressing “concerns toward implementation.” If a strategy was deemed relevant, respondents were asked to rate it using a 4-point Likert scale ranging from “very difficult to implement” to “very easy to implement.” In the second stage, stakeholders were interviewed to elaborate on the most relevant barriers and facilitators shortlisted from the first stage. The interview mostly focused on the relevance and priority of strategies and factors affecting OeMH intervention implementation. In the interview, the stakeholders’ responses to the open survey’s questions were further explored. The final checklist included strategies ranked as relevant and feasible and the most relevant facilitators and barriers, which were endorsed during either the survey or the interviews. ResultsIn total, 26 stakeholders completed the web-based survey (response rate=24.8%) and 4 stakeholders participated in individual interviews. The OeMH intervention implementation checklist comprised 28 items, including 9 (32.1%) strategies, 8 (28.6%) barriers, and 11 (39.3%) facilitators. There was widespread agreement between findings from the survey and interviews, the most outstanding exception being the idea of proposing OeMH interventions as benefits for employees. ConclusionsThrough our 2-stage stakeholder consultation, we developed a brief checklist that provides organizations with a guide for the implementation of OeMH interventions. Future research should empirically validate the effectiveness and usefulness of the checklist

Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y.

BACKGROUND & AIMS: The aim was to generate a predictive equation to assess body composition (BC) in children with obesity using bioimpedance (BIA), and avoid bias produced by different density levels of fat free mass (FFM) in this population. METHODS: This was a cross-sectional validation study using baseline data from a randomized intervention trial to treat childhood obesity. Participants were 8 to 14y (n = 315), underwent assessments on anthropometry and BC through Air Displacement Plethysmography (ADP), Dual X-Ray Absorptiometry and BIA. They were divided into a training (n = 249) and a testing subset (n = 66). In addition, the testing subset underwent a total body water assessment using deuterium dilution, and thus obtained results for the 4-compartment model (4C). A new equation to estimate FFM was created from the BIA outputs by comparison to a validated model of ADP adjusted by FFM density in the training subset. The equation was validated against 4C in the testing subset. As reference, the outputs from the BIA device were also compared to 4C. RESULTS: The predictive equation reduced the bias from the BIA outputs from 14.1% (95%CI: 12.7, 15.4) to 4.6% (95%CI: 3.8, 5.4) for FFM and from 18.4% (95%CI: 16.9, 19.9) to 6.4% (95% CI: 5.3, 7.4) for FM. Bland-Altman plots revealed that the new equation significantly improved the agreement with 4C; furthermore, the observed trend to increase the degree of bias with increasing FM and FFM also disappeared. CONCLUSION: The new predictive equation increases the precision of BC assessment using BIA in children with obesity

The Obemat2.0 Study: A Clinical Trial of a Motivational Intervention for Childhood Obesity Treatment.

The primary aim of the Obemat2.0 trial was to evaluate the efficacy of a multicomponent motivational program for the treatment of childhood obesity, coordinated between primary care and hospital specialized services, compared to the usual intervention performed in primary care. This was a cluster randomized clinical trial conducted in Spain, with two intervention arms: motivational intervention group vs. usual care group (as control), including 167 participants in each. The motivational intervention consisted of motivational interviewing, educational materials, use of an eHealth physical activity monitor and three group-based sessions. The primary outcome was body mass index (BMI) z score increments before and after the 12 (+3) months of intervention. Secondary outcomes (pre-post intervention) were: adherence to treatment, waist circumference (cm), fat mass index (z score), fat free mass index (z score), total body water (kg), bone mineral density (z score), blood lipids profile, glucose metabolism, and psychosocial problems. Other assessments (pre and post-intervention) were: sociodemographic information, physical activity, sedentary activity, neuropsychological testing, perception of body image, quality of the diet, food frequency consumption and foods available at home. The results of this clinical trial could open a window of opportunity to support professionals at the primary care to treat childhood obesity. The clinicaltrials.gov identifier was NCT02889406

Jornadas Nacionales de Robótica y Bioingeniería 2023: Libro de actas

Las Jornadas de Robótica y Bioingeniería de 2023 tienen lugar en la Escuela Técnica Superior de Ingeniería Industrial de la Universidad Politécnica de IVIadrid, entre los días 14 y 16 de junio de 2023. En este evento propiciado por el Comité Español de Automática (CEA) tiene lugar la celebración conjunta de las XII Jornadas Nacionales de Robótica y el XIV Simposio CEA de Bioingeniería. Las Jornadas Nacionales de Robótica es un evento promovido por el Grupo Temático de Robótica (GTRob) de CEA para dar visibilidad y mostrar las actividades desarrolladas en el ámbito de la investigación y transferencia tecnológica en robótica. Asimismo, el propósito de Simposio de Bioingeniería, que cumple ahora su decimocuarta dicción, es el de proporcionar un espacio de encuentro entre investigadores, desabolladores, personal clínico, alumnos, industriales, profesionales en general e incluso usuarios que realicen su actividad en el ámbito de la bioingeniería. Estos eventos se han celebrado de forma conjunta en la anualidad 2023. Esto ha permitido aunar y congregar un elevado número de participantes tanto de la temática robótica como de bioingeniería (investigadores, profesores, desabolladores y profesionales en general), que ha posibilitado establecer puntos de encuentro, sinergias y colaboraciones entre ambos. El programa de las jornadas aúna comunicaciones científicas de los últimos resultados de investigación obtenidos, por los grupos a nivel español más representativos dentro de la temática de robótica y bioingeniería, así como mesas redondas y conferencias en las que se debatirán los temas de mayor interés en la actualidad. En relación con las comunicaciones científicas presentadas al evento, se ha recibido un total de 46 ponencias, lo que sin duda alguna refleja el alto interés de la comunidad científica en las Jornadas de Robótica y Bioingeniería. Estos trabajos serán expuestos y presentados a lo largo de un total de 10 sesiones, distribuidas durante los diferentes días de las Jornadas. Las temáticas de los trabajos cubren los principales retos científicos relacionados con la robótica y la bioingeniería: robótica aérea, submarina, terrestre, percepción del entorno, manipulación, robótica social, robótica médica, teleoperación, procesamiento de señales biológicos, neurorehabilitación etc. Confiamos, y estamos seguros de ello, que el desarrollo de las jornadas sea completamente productivo no solo para los participantes en las Jornadas que podrán establecer nuevos lazos y relaciones fructíferas entre los diferentes grupos, sino también aquellos investigadores que no hayan podido asistir. Este documento que integra y recoge todas las comunicaciones científicas permitirá un análisis más detallado de cada una de las mismas