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Abstract Xenon dual-phase time projections chambers (TPCs) have proven to be a successful technology in studying physical phenomena that require low-background conditions. With $$40\,\textrm{t}$$ 40 t of liquid xenon (LXe) in the TPC baseline design, DARWIN will have a high sensitivity for the detection of particle dark matter, neutrinoless double beta decay ( $$0\upnu \upbeta \upbeta $$ 0 ν β β ), and axion-like particles (ALPs). Although cosmic muons are a source of background that cannot be entirely eliminated, they may be greatly diminished by placing the detector deep underground. In this study, we used Monte Carlo simulations to model the cosmogenic background expected for the DARWIN observatory at four underground laboratories: Laboratori Nazionali del Gran Sasso (LNGS), Sanford Underground Research Facility (SURF), Laboratoire Souterrain de Modane (LSM) and SNOLAB. We present here the results of simulations performed to determine the production rate of $${}^{137}$$ 137 Xe, the most crucial isotope in the search for $$0\upnu \upbeta \upbeta $$ 0 ν β β of $${}^{136}$$ 136 Xe. Additionally, we explore the contribution that other muon-induced spallation products, such as other unstable xenon isotopes and tritium, may have on the cosmogenic background.

Abstract We correct an overestimation of the production rate of $$^{137}$$ 137 Xe in the DARWIN detector operated at LNGS. This formerly dominant intrinsic background source is now at a level similar to the irreducible background from solar $$^8$$ 8 B neutrinos, thus unproblematic at the LNGS depth. The projected half-life sensitivity for the neutrinoless double beta decay ( $$0\nu \beta \beta $$ 0 ν β β ) of $$^{136}$$ 136 Xe improves by $$22\%$$ 22 % compared to the previously reported number and is now $$T^{0\nu }_{1/2}= {3.0\times 10^{27}} \hbox { yr}$$ T 1 / 2 0 ν = 3.0 × 10 27 yr (90% C.L.) after 10 years of DARWIN operation.

Introduction Clinicians in mental healthcare have few objective tools to identify and analyse their patient’s care needs. Clinical decision aids are tools that can support this process. Objectives This study examines whether 1) clinicians working with a clinical decision aid (TREAT) discuss more of their patient’s care needs compared to usual treatment, and 2) agree on more evidence-based treatment decisions. Methods Clinicians participated in consultations (n=166) with patients diagnosed with psychotic disorders from four Dutch mental healthcare institutions. Primary outcomes were measured with the modified Clinical Decision-making in Routine Care questionnaire and combined with psychiatric, physical and social wellbeing related care needs. A multilevel analysis compared discussed care needs and evidence-based treatment decisions between treatment as usual (TAU) before, TAU after and the TREAT-condition. Results First, a significant increase in discussed care needs for TREAT compared to both TAU conditions (b = 20.2, SE = 5.2, p = 0.00 and b = 15.8, SE = 5.4, p = 0.01) was found. Next, a significant increase in evidence-based treatments decisions for care needs was observed for TREAT compared to both TAU conditions (b = 16.7, SE = 4.8, p = 0.00 and b = 16.0, SE = 5.1, p = 0.01). Conclusions TREAT improved the discussion about physical health issues and social wellbeing related topics. It also increased evidence-based treatment decisions for care needs which are sometimes overlooked and difficult to treat. Our findings suggest that TREAT makes sense of ROM data and improves guideline-informed care. Disclosure No significant relationships.

Abstract Long-lived radon daughters are a critical background source in experiments searching for low-energy rare events. Originating from radon in ambient air, radioactive polonium, bismuth and lead isotopes plate-out on materials that are later employed in the experiment. In this paper, we examine cleaning procedures for their capability to remove radon daughters from PTFE surfaces, a material often used in liquid xenon TPCs. We find a large difference between the removal efficiency obtained for the decay chains of $$^{222}$$ 222 Rn and $$^{220}$$ 220 Rn. This indicates that the plate-out mechanism has an effect on the cleaning success. While the long-lived $$^{222}$$ 222 Rn daughters can be reduced by a factor of 2, the removal of $$^{220}$$ 220 Rn daughters is up to 10 times more efficient depending on the treatment. Furthermore, the impact of a nitric acid based PTFE cleaning on the liquid xenon purity is investigated in a small-scale liquid xenon TPC.

Abstract We detail the sensitivity of the proposed liquid xenon DARWIN observatory to solar neutrinos via elastic electron scattering. We find that DARWIN will have the potential to measure the fluxes of five solar neutrino components: pp, $$^7$$ 7 Be, $$^{13}$$ 13 N, $$^{15}$$ 15 O and pep. The precision of the $$^{13}$$ 13 N, $$^{15}$$ 15 O and pep components is hindered by the double-beta decay of $$^{136}$$ 136 Xe and, thus, would benefit from a depleted target. A high-statistics observation of pp neutrinos would allow us to infer the values of the electroweak mixing angle, $$\sin ^2\theta _w$$ sin 2 θ w , and the electron-type neutrino survival probability, $$P_{ee}$$ P ee , in the electron recoil energy region from a few keV up to 200 keV for the first time, with relative precision of 5% and 4%, respectively, with 10 live years of data and a 30 tonne fiducial volume. An observation of pp and $$^7$$ 7 Be neutrinos would constrain the neutrino-inferred solar luminosity down to 0.2%. A combination of all flux measurements would distinguish between the high- (GS98) and low-metallicity (AGS09) solar models with 2.1–2.5 $$\sigma $$ σ significance, independent of external measurements from other experiments or a measurement of $$^8$$ 8 B neutrinos through coherent elastic neutrino-nucleus scattering in DARWIN. Finally, we demonstrate that with a depleted target DARWIN may be sensitive to the neutrino capture process of $$^{131}$$ 131 Xe.

Abstract The DARWIN observatory is a proposed next-generation experiment to search for particle dark matter and for the neutrinoless double beta decay of $$^{136}$$ 136 Xe. Out of its 50 t total natural xenon inventory, 40 t will be the active target of a time projection chamber which thus contains about 3.6 t of $$^{136}$$ 136 Xe. Here, we show that its projected half-life sensitivity is $$2.4\times {10}^{27}\,{\hbox {year}}$$ 2.4×1027year , using a fiducial volume of 5 t of natural xenon and 10 year of operation with a background rate of less than 0.2 events/(t $$\cdot $$ · year) in the energy region of interest. This sensitivity is based on a detailed Monte Carlo simulation study of the background and event topologies in the large, homogeneous target. DARWIN will be comparable in its science reach to dedicated double beta decay experiments using xenon enriched in $$^{136}$$ 136 Xe.

AbstractQuality and performance are current issues and a strategic priority for the Portuguese National Health Service. Currently, there is a lack of studies in primary health care (PHC) that assess the relationship between the quality and the level of performance of the Primary Health Care Teams. In Portugal, those teams are monitored through the Global Performance Index (GPI), which demonstrates, through a score, the procedures and results obtained by electronic records and the metrics defined for each indicator. This paper aims to analyze the relationship between the perception of the quality of health professionals (service culture; leadership; service strategy; infrastructure and external resources; information and knowledge; processes; service management; human resource planning and skills development, commitment; conditions, satisfaction, performance, and recognition; and results) in the different Primary Health Care teams of the PHC of Central Alentejo—Portugal, and the level of performance of the given teams (care performance; professional training; organizational quality; and services). Self-Perception of Quality Questionnaire for Primary Health Care (SPQQ4PHC) was used as an assessment tool. The population comprises 324 health professionals (Family Doctors, Nurses, and Medical Secretaries) distributed over 34 Primary Health Care Teams. When analyzing the global correlation of the variables in the SPQQ4PHC questionnaire with the GPI, it´s possible to confirm that twenty-one out of twenty-five correlates with the GPI. Thus, this study allowed us to conclude that, as health professionals perceive the quality of the variables increases, the GPI also increases, demonstrating that the dimensions of quality and performance are closely related.

Reprocessing of regional-scale airborne electromagnetic data is used to build a 3D geological model of the Nasia sub-basin, northern Ghana. The resulting 3D geological model consistently integrates all the prior pieces of information brought by electromagnetic data, lithologic logs, ground-based geophysical surveys, and geological knowledge of the terrain. The geo-modeling process is aimed at defining the lithostratigraphy of the area, chiefly to improve the stratigraphic definition of the area, and for hydrogeological purposes. The airborne electromagnetic measurements, consisting of GEOTEM B-field data, were originally collected for mineral exploration purposes. Thus, those B-field data had to be (re)processed and properly inverted as the original survey and data handling were designed for the detection of potential mineral targets and not for detailed geological mapping. These new geophysical inversion results, compared with the original conductivity–depth images, provided a significantly different picture of the subsurface. The new geophysical model led to new interpretations of the geological settings and to the construction of a comprehensive 3D geo-model of the basin. In this respect, the evidence of a hitherto unexposed system of paleovalleys could be inferred from the airborne data. The stratigraphic position of these paleovalleys suggests a distinctly different glaciation history from the known Marinoan events, commonly associated with the Kodjari formation of the Voltaian sedimentary basin. Indeed, the presence of the paleovalleys within the Panabako may be correlated with mountain glaciation within the Sturtian age, though no unequivocal glaciogenic strata have yet been identified. Pre-Marinoan glaciation is recorded in rocks of the Wassangara group of the Taoudéni Basin. The combination of the Marinoan and, possibly, Sturtian glaciation episodes, both of the Cryogenian period, can be an indication of a Neoproterozoic Snowball Earth. Hence, the occurrence of those geological features not only has important socioeconomic consequences – as the paleovalleys can act as reservoirs for groundwater – but also from a scientific point of view, they could be extremely relevant as their presence would require a revision of the present stratigraphy of the area.

We report the performances of a 0.7-μm InP/GaInAs DHBT developed in III-V Lab demonstrating both fT and f MAX of 400 GHz as well as a high fabrication yield and homogeneity on a 3-inch wafer. This technology is used for the fabrication of a very high speed 2:1 multiplexing selector operating up to 212-Gb/s, establishing a speed record. A 5.4-Vpp 100-Gb/s distributed differential selector-driver, as well as a 4.3-Vpp 64-GBd 8-pulse-amplitude-modulation (PAM) (192 Gb/s) high-speed power digital-to-analog converter (DAC) were also realized in this technology.

Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art multiple-point statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted, Denmark, using the snesim algorithm. The goal is to study how changes to the underlying datasets propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the training image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried out exhaustively, and realizations are compared with each other as well as with the cognitive geological model.The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology, or TI, results in a significant increase in realization uncertainty. The airborne transient electromagnetic SkyTEM data used in this study cover a large portion of the Kasted model area and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparser, and 410 boreholes were present in this study. The borehole lithology logs infer local changes in the immediate vicinity of the boreholes, thus, in areas with a high degree of geological heterogeneity, boreholes only provide limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. The importance of the TI was also studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological settings are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km2. The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.

Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

Uncertainty of groundwater model predictions has in the past mostly been related to uncertainty in the hydraulic parameters, whereas uncertainty in the geological structure has not been considered to the same extent. Recent developments in theoretical methods for quantifying geological uncertainty have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS) integrated in the Stanford Geostatistical Modeling Software (SGeMS) for exploring the impact of geological uncertainty on groundwater flow patterns for a site in Denmark. Realizations from the geostatistical model were used as input to a groundwater model developed from Modular three-dimensional finite-difference ground-water model (MODFLOW) within the Groundwater Modeling System (GMS) modeling environment. The uncertainty analysis was carried out in three scenarios involving simulation of groundwater head distribution and travel time. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model optimization, where the hydraulic parameters for each of them were estimated by calibration against observations of hydraulic head and stream discharge. In the third scenario each geological model was run with 216 randomized sets of parameters. The analysis documented that the uncertainty on the conceptual geological model was as significant as the uncertainty related to the embedded hydraulic parameters.

Geophysical techniques are increasingly being used as tools for characterising the subsurface, and they are generally required to develop subsurface models that properly delineate the distribution of aquifers and aquitards, salt/freshwater interfaces, and geological structures that affect groundwater flow. In a study area covering 730 km2 across the border between Germany and Denmark, a combination of an airborne electromagnetic survey (performed with the SkyTEM system), a high-resolution seismic survey and borehole logging has been used in an integrated mapping of important geological, physical and chemical features of the subsurface. The spacing between flight lines is 200–250 m which gives a total of about 3200 line km. About 38 km of seismic lines have been collected. Faults bordering a graben structure, buried tunnel valleys, glaciotectonic thrust complexes, marine clay units, and sand aquifers are all examples of geological structures mapped by the geophysical data that control groundwater flow and to some extent hydrochemistry. Additionally, the data provide an excellent picture of the salinity distribution in the area and thus provide important information on the salt/freshwater boundary and the chemical status of groundwater. Although the westernmost part of the study area along the North Sea coast is saturated with saline water and the TEM data therefore are strongly influenced by the increased electrical conductivity there, buried valleys and other geological elements are still revealed. The mapped salinity distribution indicates preferential flow paths through and along specific geological structures within the area. The effects of a future sea level rise on the groundwater system and groundwater chemistry are discussed with special emphasis on the importance of knowing the existence, distribution and geometry of the mapped geological elements, and their control on the groundwater salinity distribution is assessed.

Background: The prevalence of Major Depressive Disorder (MDD) is twice as high in women as in men and this difference already emerges during adolescence. Because the mechanisms underlying this sex-difference remain poorly understood, we took a bottom-up approach to identify factors explaining the sex-MDD relationship., Methods: Data came from the TRacking Adolescents' Individual Lives Survey (TRAILS), a population study investigating youths' development from age 11 into adulthood. We assessed multiple baseline covariates (e.g., demographic, social and psychological) at ages 11-13 years and MDD onset at ages 19 and 25 years. In regression analyses, each covariate's role in the sex-MDD association as an effect modifier or confounder/explanatory variable was investigated. Replicability was evaluated in an independent sample., Results: The analyses identified no effect-modifiers. Baseline internalizing problems, behavioral inhibition, dizziness, comfort in classroom, physical complaints, attention problems, cooperation, self/effortful control, interpersonal life events and computer use partially explained the association between sex and MDD at age 19. The association between sex and MDD at age 25 was explained by largely the same variables, but also by shyness, acne, antisocial behavior, aggression, affection from peers and time spent shopping. The explanatory roles of internalizing problems, behavioral inhibition, negative events involving gossip/rumors and leisure-time spending (computer-use/shopping) were replicated., Limitations: Potentially important baseline variables were not included or had low response rates. Gender roles or identification were not considered. Baseline MDD was not adjusted for., Conclusion: The sex-MDD association is partially explained by sex differences in symptoms and vulnerability factors already present in early adolescence., Competing Interests: Declaration of competing interest None of the authors declare any conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Combining non-specialists and digital technologies in mental health interventions could decrease the mental healthcare gap in resource scarce countries. This systematic review examined different combinations of non-specialists and digital technologies in mental health interventions and their effectiveness in reducing the mental healthcare gap in low-and middle-income countries., Methods: Literature searches were conducted in four databases (September 2023), three trial registries (January-February 2022), and using forward and backward citation searches (May-June 2022). The review included primary studies on mental health interventions combining non-specialists and digital technologies in low-and middle-income countries. The outcomes were: (1) the mental health of intervention receivers and (2) the competencies of non-specialists to deliver mental health interventions. Data were expressed as standardised effect sizes (Cohen's d) and narratively synthesised. Risk of bias assessment was conducted using the Cochrane risk-of-bias tools for individual and cluster randomised and non-randomised controlled trials., Results: Of the 28 included studies (n = 32 interventions), digital technology was mainly used in non-specialist primary-delivery treatment models for common mental disorders or subthreshold symptoms. The competencies of non-specialists were improved with digital training (d ≤ 0.8 in 4/7 outcomes, n = 4 studies, 398 participants). The mental health of receivers improved through non-specialist-delivered interventions, in which digital technologies were used to support the delivery of the intervention (d > 0.8 in 24/40 outcomes, n = 11, 2469) or to supervise the non-specialists' work (d = 0.2-0.8 in 10/17 outcomes, n = 3, 3096). Additionally, the mental health of service receivers improved through digitally delivered mental health services with non-specialist involvement (d = 0.2-0.8 in 12/27 outcomes, n = 8, 2335). However, the overall certainty of the evidence was poor., Conclusion: Incorporating digital technologies into non-specialist mental health interventions tended to enhance non-specialists' competencies and knowledge in intervention delivery, and had a positive influence on the severity of mental health problems, mental healthcare utilization, and psychosocial functioning outcomes of service recipients, primarily within primary-deliverer care models. More robust evidence is needed to compare the magnitude of effectiveness and identify the clinical relevance of specific digital functions. Future studies should also explore long-term and potential adverse effects and interventions targeting men and marginalised communities., (© 2023. The Author(s).)

Background: More knowledge on the cost-effectiveness of various depression treatment programmes can promote efficient treatment allocation and improve the quality of depression care., Objective: This study aims to compare the real-world cost-effectiveness of an algorithm-guided programme focused on remission to a predefined duration, patient preference-centred treatment programme focused on response using routine care data., Methods: A naturalistic study (n=6295 in the raw dataset) was used to compare the costs and outcomes of two programmes in terms of quality-adjusted life years (QALY) and depression-free days (DFD). Analyses were performed from a healthcare system perspective over a 2-year time horizon. Incremental cost-effectiveness ratios were calculated, and the uncertainty of results was assessed using bootstrapping and sensitivity analysis., Findings: The algorithm-guided treatment programme per client yielded more DFDs (12) and more QALYs (0.013) at a higher cost (€3070) than the predefined duration treatment programme. The incremental cost-effectiveness ratios (ICERs) were around €256/DFD and €236 154/QALY for the algorithm guided compared with the predefined duration treatment programme. At a threshold value of €50 000/QALY gained, the programme had a probability of <10% of being considered cost-effective. Sensitivity analyses confirmed the robustness of these findings., Conclusions: The algorithm-guided programme led to larger health gains than the predefined duration treatment programme, but it was considerably more expensive, and hence not cost-effective at current Dutch thresholds. Depending on the preferences and budgets available, each programme has its own benefits., Clinical Implication: This study provides valuable information to decision-makers for optimising treatment allocation and enhancing quality of care cost-effectively., Competing Interests: Competing interests: SOdV was involved in the design of the algorithm-guided treatment programme investigated in this study. BG was involved in the running of the predefined duration treatment programme investigated in this study. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. Published by BMJ.)

Background: Despite growing concerns about mental health during the COVID-19 pandemic, particularly in people with pre-existing mental health disorders, research has shown that symptoms of depression and anxiety were generally quite stable, with modest changes in certain subgroups. However, individual differences in cumulative exposure to COVID-19 stressors have not been yet considered., Aims: We aimed to quantify and investigate the impact of individual-level cumulative exposure to COVID-19-pandemic-related adversity on changes in depressive and anxiety symptoms and loneliness. In addition, we examined whether the impact differed among individuals with various levels of pre-pandemic chronicity of mental health disorders., Method: Between April 2020 and July 2021, 15 successive online questionnaires were distributed among three psychiatric case-control cohorts that started in the 2000s ( N = 1377). Outcomes included depressive and anxiety symptoms and loneliness. We developed a COVID-19 Adversity Index (CAI) summarising up to 15 repeated measures of COVID-19-pandemic-related exposures (e.g. exposure to COVID-19 infection, negative economic impact and quarantine). We used linear mixed linear models to estimate the effects of COVID-19-related adversity on mental health and its interaction with pre-pandemic chronicity of mental health disorders and CAI., Results: Higher CAI scores were positively associated with higher increases in depressive symptoms, anxiety symptoms and loneliness. Associations were not statistically significantly different between groups with and without (chronic) pre-pandemic mental health disorders., Conclusions: Individual differences in cumulative exposure to COVID-19-pandemic-related adversity are important predictors of mental health, but we found no evidence for higher vulnerability among people with (chronic) pre-pandemic mental health disorders.

Background: Limited evidence exists regarding the association between early symptom change and later outcomes of cognitive behavioral therapy (CBT). This study aimed to apply machine learning algorithms to predict continuous treatment outcomes based on pre-treatment predictors and early symptom changes and to uncover whether additional variance could be explained compared to regression methods. Additionally, the study examined early subscale symptom changes to determine the most significant predictors of treatment outcome., Methods: We investigated CBT outcomes in a large naturalistic dataset (N = 1975 depression patients). The sociodemographic profile, pre-treatment predictors, and early symptom change, including total and subscale scores were used to predict the Symptom Questionnaire (SQ)48 score at the 10th session as a continuous outcome. Different machine learners were compared to linear regression., Results: Early symptom change and baseline symptom score were the only significant predictors. Models with early symptom change explained 22.0 % to 23.3 % more variance than those without early symptom change. Specifically, the baseline total symptom score, and the early symptom score changes of the subscales pertaining to depression and anxiety were the top three predictors of treatment outcome., Limitation: Excluded patients with missing treatment outcomes had slightly higher symptom scores at baseline, indicating possible selection bias., Conclusion: Early symptom change improved the prediction of treatment outcomes. The prediction performance achieved is far from clinical relevance: the best learner could only explain 51.2 % of the variance in outcomes. Compared to linear regression, more sophisticated preprocessing and learning methods did not substantially improve performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Precise radon measurements are a requirement for various applications, ranging from radiation protection over environmental studies to material screening campaigns for rare-event searches. All of them ultimately depend on the availability of calibration sources with a known and stable radon emanation rate. A new approach to produce clean and dry radon sources by implantation of 226 Ra ions into stainless steel has been investigated. In a proof of principle study, two stainless steel plates have been implanted in collaboration with the ISOLDE facility located at CERN. We present results from a complete characterization of the sources. Each sample provides a radon emanation rate of about 2 Bq, which has been measured using electrostatic radon monitors as well as miniaturized proportional counters. Additional measurements using HPGe and alpha spectrometry as well as measurements of the radon emanation rate at low temperatures were carried out., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Clinicians in mental healthcare have few objective tools to identify and analyze their patient's care needs. Clinical decision aids are tools that support this process. This study examines whether 1) clinicians working with a clinical decision aid (TREAT) discuss more of their patient's care needs compared to usual treatment, and 2) agree on more evidence-based treatment decisions., Methods: Clinicians participated in consultations (n = 166) with patients diagnosed with psychotic disorders from four Dutch mental healthcare institutions (research registration number 201700763). Primary outcomes were measured with the modified Clinical Decision-making in Routine Care questionnaire and combined with psychiatric, physical and social wellbeing related care needs. A multilevel analysis compared discussed care needs and evidence-based treatment decisions between treatment as usual (TAU) before, TAU after and the TREAT condition., Results: First, a significant increase in discussed care needs for TREAT compared to both TAU conditions (β = 20.2, SE = 5.2, p = 0.00 and β = 15.8, SE = 5.4, p = 0.01) was found. Next, a significant increase in evidence-based treatments decisions for care needs was observed for TREAT compared to both TAU conditions (β = 16.7, SE = 4.8, p = 0.00 and β = 16.0, SE = 5.1, p = 0.01)., Conclusion: TREAT improved the discussion about physical health issues and social wellbeing related topics. It also increased evidence-based treatment decisions for care needs which are sometimes overlooked and difficult to treat. Our findings suggest that TREAT makes sense of routine outcome monitoring data and improves guideline-informed care., Competing Interests: Declaration of competing Interest The authors declare they do not have any conflicting interests., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: People with severe mental illness (SMI) often suffer from long-lasting symptoms that negatively influence their social functioning, their ability to live a meaningful life, and participation in society. Interventions aimed at increasing physical activity can improve social functioning, but people with SMI experience multiple barriers to becoming physically active. Besides, the implementation of physical activity interventions in day-to-day practice is difficult. In this study, we aim to evaluate the effectiveness and implementation of a physical activity intervention to improve social functioning, mental and physical health., Methods: In this pragmatic stepped wedge cluster randomized controlled trial we aim to include 100 people with SMI and their mental health workers from a supported housing organization. The intervention focuses on increasing physical activity by implementing group sports activities, active guidance meetings, and a serious game to set physical activity goals. We aim to decrease barriers to physical activity through active involvement of the mental health workers, lifestyle courses, and a medication review. Participating locations will be divided into four clusters and randomization will decide the start of the intervention. The primary outcome is social functioning. Secondary outcomes are quality of life, symptom severity, physical activity, cardiometabolic risk factors, cardiorespiratory fitness, and movement disturbances with specific attention to postural adjustment and movement sequencing in gait. In addition, we will assess the implementation by conducting semi-structured interviews with location managers and mental health workers and analyze them by direct content analysis., Discussion: This trial is innovative since it aims to improve social functioning in people with SMI through a physical activity intervention which aims to lower barriers to becoming physically active in a real-life setting. The strength of this trial is that we will also evaluate the implementation of the intervention. Limitations of this study are the risk of poor implementation of the intervention, and bias due to the inclusion of a medication review in the intervention that might impact outcomes., Trial Registration: This trial was registered prospectively in The Netherlands Trial Register (NTR) as NTR NL9163 on December 20, 2020. As the The Netherlands Trial Register is no longer available, the trial can now be found in the International Clinical Trial Registry Platform via: https://trialsearch.who.int/Trial2.aspx?TrialID=NL9163 ., (© 2022. The Author(s).)

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level., (© 2022. The Author(s).)

Understanding liquid xenon scintillation and ionization processes is of great interest to improve analysis methods in current and future detectors. In this paper, we investigate the dynamics of the scintillation process for excitation by O(10 keV) electrons from a 83m Kr source and O(6 MeV) α-particles from a 222 Rn source, both mixed with the xenon target. The single photon sampling method is used to record photon arrival times in order to obtain the corresponding time distributions for different applied electric fields between about 0.8 V cm -1 to 1.2 kV cm -1 . Energy and field dependencies of the signals, which are observed in the results, are discussed.

Dissolution dynamic nuclear polarization (dDNP) is a versatile hyperpolarization technique to boost signal intensities in nuclear magnetic resonance (NMR) spectroscopy. The possibility to dissolve biomolecules in a hyperpolarized aqueous buffer under mild conditions has recently widened the scope of NMR by dDNP. The water-to-target hyperpolarization transfer mechanisms remain yet unclear, not least due to an often-encountered dilemma of dDNP experiments: The strongly enhanced signal intensities are accompanied by limited structural information as data acquisition is restricted to short time series of only one-dimensional spectra or a single correlation spectrum. Tackling this challenge, we combine dDNP with molecular dynamics (MD) simulations and predictions of cross-relaxation rates to unravel the spin dynamics of magnetization flow in hyperpolarized solutions.

Background: Mental health was only modestly affected in adults during the early months of the COVID-19 pandemic on the group level, but interpersonal variation was large., Aims: We aim to investigate potential predictors of the differences in changes in mental health., Method: Data were aggregated from three Dutch ongoing prospective cohorts with similar methodology for data collection. We included participants with pre-pandemic data gathered during 2006-2016, and who completed online questionnaires at least once during lockdown in The Netherlands between 1 April and 15 May 2020. Sociodemographic, clinical (number of mental health disorders and personality factors) and COVID-19-related variables were analysed as predictors of relative changes in four mental health outcomes (depressive symptoms, anxiety and worry symptoms, and loneliness), using multivariate linear regression analyses., Results: We included 1517 participants with ( n = 1181) and without ( n = 336) mental health disorders. Mean age was 56.1 years (s.d. 13.2), and 64.3% were women. Higher neuroticism predicted increases in all four mental health outcomes, especially for worry ( β = 0.172, P = 0.003). Living alone and female gender predicted increases in depressive symptoms and loneliness ( β = 0.05-0.08), whereas quarantine and strict adherence with COVID-19 restrictions predicted increases in anxiety and worry symptoms ( β = 0.07-0.11).Teleworking predicted a decrease in anxiety symptoms ( β = -0.07) and higher age predicted a decrease in anxiety ( β = -0.08) and worry symptoms ( β = -0.10)., Conclusions: Our study showed neuroticism as a robust predictor of adverse changes in mental health, and identified additional sociodemographic and COVID-19-related predictors that explain longitudinal variability in mental health during the COVID-19 pandemic.

Background: Little is known about the longer-term impact of the Covid-19 pandemic beyond the first months of 2020, particularly for people with pre-existing mental health disorders. Studies including pre-pandemic data from large psychiatric cohorts are scarce., Methods: Between April 2020 and February 2021, twelve successive online questionnaires were distributed among participants of the Netherlands Study of Depression and Anxiety, Netherlands Study of Depression in Older Persons, and Netherlands Obsessive Compulsive Disorder Association Study (N = 1714, response rate 62%). Outcomes were depressive symptoms, anxiety, worry, loneliness, perceived mental health impact of the pandemic, fear of Covid-19, positive coping, and happiness. Using linear mixed models we compared trajectories between subgroups with different pre-pandemic chronicity of disorders and healthy controls., Results: Depressive, anxiety and worry symptoms were stable since April-May 2020 whereas happiness slightly decreased. Furthermore, positive coping steadily decreased and loneliness increased - exceeding pre-Covid and April-May 2020 levels. Perceived mental health impact and fear of Covid-19 fluctuated in accordance with national Covid-19 mortality rate changes. Absolute levels of all outcomes were poorer with higher chronicity of disorders, yet trajectories did not differ among subgroups., Limitations: The most vulnerable psychiatric groups may have been underrepresented and results may not be generalizable to lower income countries., Conclusions: After a year, levels of depressive and worry symptoms remained higher than before the pandemic in healthy control groups, yet not in psychiatric groups. Nevertheless, persistent high symptoms in psychiatric groups and increasing loneliness in all groups are specific points of concern for mental health care professionals., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Mismatch between need and mental healthcare (MHC) use (under-and overuse) has mainly been studied with cross-sectional designs, not accurately capturing patterns of persistence or change in clinical burden and MHC-use among persons with depressive and/or anxiety disorders., Aims: Determining and describing [mis]match of longitudinal trajectories of clinical burden and MHC-use., Methods: Six-year longitudinal burden and MHC-use data came from the Netherlands Study of Depression and Anxiety (n=2981). The sample was split into four subgroups: I) no clinical burden but constant MHC use, II) constant clinical burden but no MHC-use, III) changing clinical burden and MHC-use, and IV) healthy non-users. Within subgroups I)-III), specific clinical burden and MHC trajectories were identified (growth mixture modeling). The resulting classes' associations with predisposing, enabling, and need factors were investigated (regression analysis)., Results: Subgroups I-III revealed different trajectories. I) increasing MHC without burden (4.1%). II) slightly increasing (1.9%), strongly increasing (2.4%), and decreasing (9.5%) burden without MHC. III) increasing (41.4%) or decreasing (19.4%) burden and concurrently increasing MHC use (first underuse, then matched care), thus revealing delayed MHC-use. Only having suicidal ideation (p<.001, Cohen's d= .6-1.5) was a significant determinant of being in latter classes compared to underusers (strongly increasing burden without MHC-use)., Limitations: More explanatory factors are needed to explain [mis]match., Conclusion: Mismatch occurred as constant underuse or as delayed MHC-use in a high-income country (Netherlands). Additionally, no meaningful class revealed constantly matched care on average. Presence of suicidal ideation could influence the probability of symptomatic individuals receiving matched MHC or not., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: This study examines satisfaction with social connectedness (SSC) as predictor of positive and negative symptoms in people with a psychotic disorder., Methods: Data from the Pharmacotherapy Monitoring and Outcome Survey (PHAMOUS) was used from patients assessed between 2014 and 2019, diagnosed with a psychotic disorder (N = 2109). Items about social connectedness of the Manchester short assessment of Quality of Life (ManSA) were used to measure SSC. Linear mixed models were used to estimate the association of SSC with the Positive and Negative Syndrome Scale (PANSS) after one and two years against α = 0.01. Analyses were adjusted for symptoms, time since onset, gender and age. Additionally, fluctuation of positive and negative symptom scores over time was estimated., Results: The mean duration of illness of the sample was 18.8 years (SD 10.7) with >65% showing only small variation in positive and negative symptoms over a two to five-year time period. After adjustment for covariates, SSC showed to be negatively associated with positive symptoms after one year (β = -0.47, p < 0.001, 95% CI = -0.70, -025) and two years (β = -0.59, p < 0.001, 95% CI = -0.88, -0.30), and for negative symptoms after one year (β = -0.52, p < 0.001, 95% CI = -0.77, -0.27). The prediction of negative symptoms was not significant at two years., Conclusion: This research indicates that interventions on SSC might positively impact mental health for people with psychosis. SSC is a small and robust predictor of future levels of positive symptoms. Negative symptoms could be predicted by SSC at one year., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Apart from cost-effectiveness, considerations like equity and acceptability may affect health-care priority setting. Preferably, priority setting combines evidence evaluation with an appraisal procedure, to elicit and weigh these considerations., Objective: To demonstrate a structured approach for eliciting and evaluating a broad range of assessment criteria, including key stakeholders' values, aiming to support decision makers in priority setting., Methods: For a set of cost-effective substitute interventions for depression care, the appraisal criteria were adopted from the Australian Assessing Cost-Effectiveness initiative. All substitute interventions were assessed in an appraisal, using focus group discussions and semi-structured interviews conducted among key stakeholders., Results: Appraisal of the substitute cost-effective interventions yielded an overview of considerations and an overall recommendation for decision makers. Two out of the thirteen pairs were deemed acceptable and realistic, that is investment in therapist-guided and Internet-based cognitive behavioural therapy instead of cognitive behavioural therapy in mild depression, and investment in combination therapy rather than individual psychotherapy in severe depression. In the remaining substitution pairs, substantive issues affected acceptability. The key issues identified were as follows: workforce capacity, lack of stakeholder support and the need for change in clinicians' attitude., Conclusions: Systematic identification of stakeholders' considerations allows decision makers to prioritize among cost-effective policy options. Moreover, this approach entails an explicit and transparent priority-setting procedure and provides insights into the intended and unintended consequences of using a certain health technology., Patient Contribution: Patients were involved in the conduct of the study for instance, by sharing their values regarding considerations relevant for priority setting., (© 2021 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

Background: The impact on survival of complete resection (CR) in patients with malignant glioma and MGMT promoter methylation on adjuvant therapy strategies has been proven in the past. However, it is not known whether a MGMT promoter methylation can compensate a subtotal resection. Therefore, we analyzed the progress of postoperative residual tumor tissue depending on the molecular tumor status., Methods: We included all glioblastoma, IDH-wildtype (WHO grade IV) patients with postoperative residual tumor tissue, who were treated at our neurooncological department between 2010 and 2018. Correlation of molecular patterns with clinical data and survival times was performed. The results were compared to patients following CR., Results: 267 patients with glioblastoma, IDH-wildtype (WHO grade IV) received surgery of whom 81 patients with residual tumor were included in the analysis. MGMT promoter was methylated in 31 patients (38.27%). Median OS and PFS were significantly increased in patients with methylated MGMT promoter (mOS: 16 M vs. 13 M, p = 0.009; mPFS: 13 M vs. 5 M, p = 0.003). In comparison to survival of patients following CR, OS was decreased in patients with residual tumor regardless MGMT methylation., Conclusion: Our data confirm impact of MGMT promoter methylation in patients with glioblastoma, IDH-wildtype on OS and PFS. However, in comparison to patients after CR, a methylated MGMT promoter cannot compensate the disadvantage due to residual tumor volume. In terms of personalized medicine and quality of life as major goal in oncology, neuro-oncologists have to thoroughly discuss advantages and disadvantages of residual tumor volume versus possible neurological deficits in CR., (© 2021. The Author(s).)

Background: The majority of patients with major depressive disorder (MDD) have comorbid mental conditions., Objectives: Since most cost-of-illness studies correct for comorbidity, this study focuses on mental healthcare utilization and treatment costs in patients with MDD including psychiatric comorbidities in specialist mental healthcare, particularly patients with a comorbid personality disorder (PD)., Methods: The Psychiatric Case Register North Netherlands contains administrative data of specialist mental healthcare providers. Treatment episodes were identified from uninterrupted healthcare use. Costs were calculated by multiplying care utilization with unit prices (price level year: 2018). Using generalized linear models, cost drivers were investigated for the entire cohort., Results: A total of 34,713 patients had MDD as a primary diagnosis over the period 2000-2012. The number of patients with psychiatric comorbidities was 24,888 (71.7%), including 13,798 with PD. Costs were highly skewed, with an average ± standard deviation cost per treatment episode of €21,186 ± 74,192 (median €2320). Major cost drivers were inpatient days and daycare days (50 and 28% of total costs), occurring in 12.7 and 12.5% of episodes, respectively. Compared with patients with MDD only (€11,612), costs of patients with additional PD and with or without other comorbidities were, respectively, 2.71 (p < .001) and 2.06 (p < .001) times higher and were 1.36 (p < .001) times higher in patients with MDD and comorbidities other than PD. Other cost drivers were age, calendar year, and first episodes., Conclusions: Psychiatric comorbidities (especially PD) in addition to age and first episodes drive costs in patients with MDD. Knowledge of cost drivers may help in the development of future stratified disease management programs.

Background: The impact of the COVID-19 pandemic on mental health in people with pre-existing mental health disorders is unclear. In three psychiatry case-control cohorts, we compared the perceived mental health impact and coping and changes in depressive symptoms, anxiety, worry, and loneliness before and during the COVID-19 pandemic between people with and without lifetime depressive, anxiety, or obsessive-compulsive disorders., Methods: Between April 1 and May 13, 2020, online questionnaires were distributed among the Netherlands Study of Depression and Anxiety, Netherlands Study of Depression in Older Persons, and Netherlands Obsessive Compulsive Disorder Association cohorts, including people with (n=1181) and without (n=336) depressive, anxiety, or obsessive-compulsive disorders. The questionnaire contained questions on perceived mental health impact, fear of COVID-19, coping, and four validated scales assessing depressive symptoms, anxiety, worry, and loneliness used in previous waves during 2006-16. Number and chronicity of disorders were based on diagnoses in previous waves. Linear regression and mixed models were done., Findings: The number and chronicity of disorders showed a positive graded dose-response relation, with greater perceived impact on mental health, fear, and poorer coping. Although people with depressive, anxiety, or obsessive-compulsive disorders scored higher on all four symptom scales than did individuals without these mental health disorders, both before and during the COVID-19 pandemic, they did not report a greater increase in symptoms during the pandemic. In fact, people without depressive, anxiety, or obsessive-compulsive disorders showed a greater increase in symptoms during the COVID-19 pandemic, whereas individuals with the greatest burden on their mental health tended to show a slight symptom decrease., Interpretation: People with depressive, anxiety, or obsessive-compulsive disorders are experiencing a detrimental impact on their mental health from the COVID-19 pandemic, which requires close monitoring in clinical practice. Yet, the COVID-19 pandemic does not seem to have further increased symptom severity compared with their prepandemic levels., Funding: Dutch Research Council., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Introduction: This study assessed the cost-effectiveness and budget impact of a lifestyle intervention to improve cardiometabolic health in severe mentally ill (SMI) patients in the LION trial., Methods: Patients (n = 244) were randomized to receive either care-as-usual or a lifestyle intervention in which mental health nurses coached patients in changing their lifestyle by using a web tool. Costs and quality of life were assessed at baseline and at 6 and 12 months. Incremental costs per centimeter waist circumference (WC) lost and per Quality-Adjusted Life Year (QALY) gained were assessed. Budget impact was estimated based on three intervention-uptake scenarios using a societal and a third-party payer perspective., Results: Costs and reduction in WC were higher in the intervention (n = 114) than in the control (n = 94) group after 12 months, although not statistically significant, resulting in €1,370 per cm WC lost. QALYs did not differ between the groups, resulting in a low probability of the intervention being cost-effective in cost/QALY gained. The budget impact analysis showed that for a reasonable participation of 43%, total costs were around €81 million over 5 years, or on average €16 million annually (societal perspective)., Conclusions: The intervention is not cost-effective at 12 months and the budget impact over 5 years is substantial. Possibly, 12 months was too short to implement the intervention, improve cardiometabolic health, and reduce care costs. Therefore, the incentive for this intervention cannot be found in short-term financial advantages. However, there may be benefits associated with lifestyle interventions in the long term that remain unclear., Competing Interests: Conflict of interest: The authors declare no conflict of interest., (Copyright © 2020, The Authors.)

Background: Doubts exist on whether effects found in randomized controlled trials (RCTs) are directly generalizable to daily clinical practice. This study aimed (a) to investigate the effectiveness of treatment options within an algorithm-guided treatment (AGT) program for depression and compare their effectiveness with outcomes of efficacy trials and (b) to assess the relation between treatment continuity and outcomes., Methods: This naturalistic study linked treatment data from January 2012 to November 2014 from a Dutch mental healthcare provider, to routine outcome monitoring (ROM) data (N = 351). Effectiveness of the treatment options (pharmacotherapy, psychotherapy and their combination) was compared to the efficacy reported in the meta-analyses. We included treatment continuity as binary variable "early terminators versus completers of the recommended number of treatment sessions"., Results: Remission rates for psychotherapy (38% [95% CI: 32-45]), pharmacotherapy (31% [95% CI: 22-42]) and combination therapy (46% [95% CI: 19-75]) were respectively lower, comparable, and comparable to those reported in the meta-analyses. Similarly, response rates were respectively lower (24% [95% CI: 19-30]), lower (21% [95% CI: 13-31]), and comparable (46% [95% CI: 19-75]) to meta-analyses results. A similar share of early terminators and completers achieved remission and response., Limitations: A substantial proportion of patients had incomplete ROM data after data linkage. Limited set of patient characteristics to check for selection bias., Conclusions: Despite the more heterogeneous patient population in clinical practice, the effectiveness of an AGT program, emphasizing strict guideline adherence, approached that found in RCTs. A fixed number of treatment sessions may not suit all individual patients., Competing Interests: Declaration of Competing Interest Sybolt O. de Vries was involved in the design of the algorithm-guided treatment program investigated in this study. All other authors declare that they have no conflicts of interest. Furthermore, De Friesland Zorgverzekeraar, a health insurance company, was part of the research consortium, although grant conditions guarantee full freedom of research and no influence on publications., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Although symptomatic remission is considered the optimal outcome in depression, this is not always achieved. Furthermore, symptom indicators do not fully capture patients' and clinicians' perspectives on remission. Broader indicators of (partial) remission from depression should be considered., Aims: To investigate relevant outcomes of depression treatment in specialist care from patients' and clinicians' perspectives and to investigate whether these perspectives differ from each other., Method: Three focus groups with 11 patients with depression and seven semi-structured interviews with clinicians were conducted exploring their perspectives on remission. All interviews were audio-recorded and transcribed verbatim. We analysed the transcripts thematically using the phenomenologist approach., Results: Independently, both patients and clinicians perceived the following outcomes relevant: restoring social functioning and interpersonal relations, regaining quality of life and achieving personal goals. All clinicians emphasised symptom reduction and satisfaction with treatment as relevant outcomes, whereas the former was not an obvious theme in patients. Unlike clinicians, patients made a clear distinction between treatment outcomes in first versus recurrent/chronic depression., Conclusions: Classically defined study outcomes based on symptom resolution only partly reflect issues considered important by patients and clinicians in specialist depression treatment. Incorporating patients' and clinicians' perspectives in the development of measurable end-points makes them more suitable for use in trials and subsequent translation to clinical practice. Furthermore, evaluating patients' perspectives on treatment outcomes helps in the development of tailored interventions according to patients' needs.

Background: An increasing number of studies in people with mental illness have demonstrated the efficacy of lifestyle interventions for both mental and physical health. However, implementing and sustaining such interventions in routine mental healthcare is challenging.
AIM: To describe implementation barriers and facilitators of various lifestyle interventions in routine mental healthcare.
METHOD: In this paper we summarise the results of three recent doctoral theses and provide recommendations for routine mental healthcare.
RESULTS: Enabling factors and barriers for successful implementation appeared to be universal across various settings and patient populations. Mental healthcare professionals and patients appreciated and supported the lifestyle interventions. A lack of priority, recognition and support on the organisational level were reported as barriers in all the studies. Addressing various lifestyle behaviours simultaneously (e.g. physical activity and diet), personalised to the patients' abilities and preferences, and setting measurable and attainable goals, were important for success. Moreover, involving qualified professionals (e.g. exercise professionals and dietitians with expertise in mental healthcare) and ensuring cooperation between patients and healthcare professionals and disciplines within and outside mental healthcare appeared crucial.
CONCLUSION: Successful implementation of lifestyle interventions in mental healthcare requires changes in culture and behaviour, a long-term strategy and endurance.

Background: Unhealthy lifestyle behaviours contribute to alarming cardiometabolic risk in patients with serious mental illness (SMI). Evidence-based practical lifestyle tools supporting patients and staff in improving patient lifestyle are lacking., Methods: This multi-site randomized controlled pragmatic trial determined the effectiveness of a twelve-month multimodal lifestyle approach, including a web-based tool to improve patients' cardiometabolic health, versus care-as-usual. Using the web tool, nurses (trained in motivational interviewing) assisted patients in assessing their lifestyle behaviours, creating a risk profile and constructing lifestyle goals, which were discussed during fortnightly regular care visits. Twenty-seven community-care and sheltered-living teams were randomized into intervention (N = 17) or control (N = 10) groups, including 244 patients (140 intervention/104 control, 49.2% male, 46.1 ± 10.8 years) with increased waist circumference (WC), BMI or fasting glucose. The primary outcomes concerned differences in WC after six and twelve months intervention, while BMI and metabolic syndrome Z-score were secondary outcome measures., Results: General multilevel linear mixed models adjusted for antipsychotic medication showed that differences in WC change between intervention and control were - 0.15 cm (95%CI: - 2.49; 2.19) after six and - 1.03 cm (95%CI: - 3.42; 1.35) after twelve months intervention; however, the differences were not statistically significant. No intervention effects were found for secondary outcome measures. The intervention increased patients' readiness to change dietary behaviour., Conclusion: A multimodal web-based intervention facilitating nurses to address lifestyle changes in SMI patients did not improve patient cardiometabolic health. Web-tool use was lower than expected and nurses need more lifestyle coaching knowledge and skills. The type of intervention and delivery mode need optimization to realize effective lifestyle care for SMI patients., Trial Registration: Dutch Trial Registry, www.trialregister.nl , NTR3765, 21 December 2012.