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Introduction:Total hip and total knee replacement (THR and TKR) are suggested for reducing joint pain resulting from hip and knee osteoarthritis (OA), especially when other interventions have not resulted in desired outcomes. Providing prehabilitation education can improve patients’ psychological and physical well-being before and after surgery. The use of electronic health (eHealth) tools can be considered an effective method to increase patients’ access to prehabilitation, particularly for those facing barriers to attending diagnosis-specific in-person education sessions. However, limited attention is paid to both caregiver and patient perspectives regarding the delivery formats, features, and characteristics of eHealth tools.Method:Patients with hip (n = 46) and knee OA (n = 14) and their family caregivers (n = 16) participated in in-person focus groups or phone interviews. Participants were shown a mock-up of an eHealth module, and asked to share their preferences regarding the formats, features, and characteristics of the eHealth prehabilitation tool. Data was transcribed verbatim and coded using primary thematic and secondary content analyses.Result:Analyses revealed 3 main themes: 1. “easier to understand” emphasizes patients’ preferences on delivery formats and features; 2. “what does that mean?” highlights requests for clear and simple information; and 3. “Preparation, right?” shows patients’ perspectives on the best time to have access to the eHealth tool.Discussion:Participants’ preferences for prehabilitation tools included offering eHealth tools in multiple mediums of delivery (eg, written materials, pictures, videos). Participants preferred simplified information that emphasized the key points and rationale for the knowledge. There were differences in preferred timeline for having access to prehabilitation education, such as some participants wanting to receive prehabilitation well in advance, while others stated just before surgery was adequate. Our findings provide novel and actionable information about patient and caregiver perspectives on features and characteristics of prehabilitation education for patients with hip and knee OA.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.

Social distancing measures due to the COVID-19 pandemic have accelerated the adoption and implementation of digital mental health tools. Psychiatry and therapy sessions are being conducted via videoconferencing platforms, and the use of digital mental health tools for monitoring and treatment has grown. This rapid shift to telehealth during the pandemic has given added urgency to the ethical challenges presented by digital mental health tools. Regulatory standards have been relaxed to allow this shift to socially distanced mental health care. It is imperative to ensure that the implementation of digital mental health tools, especially in the context of this crisis, is guided by ethical principles and abides by professional codes of conduct. This paper examines key areas for an ethical path forward in this digital mental health revolution: privacy and data protection, safety and accountability, and access and fairness.

The World Health Organization&rsquo, s Health Economic Assessment Tool (HEAT) for walking and cycling is a user-friendly web-based tool to assess the health impacts of active travel. HEAT, developed over 10 years ago, has been used by researchers, planners and policymakers alike in appraisals of walking and cycling policies at both national and more local scales. HEAT has undergone regular upgrades adopting the latest scientific evidence. This article presents the most recent upgrades of the tool. The health impacts of walking and/or cycling in a specified population are quantified in terms of premature deaths avoided (or caused). In addition to the calculation of benefits derived from physical activity, HEAT was recently expanded to include assessments of the burden associated with air pollution exposure and crash risks while walking or cycling. Further, the impacts on carbon emissions from mode shifts to active travel modes can now be assessed. The monetization of impacts using Value of Statistical Life and Social Costs of Carbon now uses country-specific values. As active travel inherently results in often substantial health benefits as well as not always negligible risks, assessments of active travel behavior or policies are incomplete without considering health implications. The recent developments of HEAT make it easier than ever to obtain ballpark estimates of health impacts and carbon emissions related to walking and cycling.

Background Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. Objective The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19–related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. Methods This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)–driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19–related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. Results During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. Conclusions Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

{Abstract] Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. The software to perform the calculation is not always available for general users. In this work, we developed the first library in R for the calculation of MCDs and we also report the first public web server for the calculation of MCDs online that include the calculation of a new class of MCDs called Markov Singular values. We also report the first Cheminformatics study of the biological activity of 5644 compounds against colorectal cancer.

BackgroundFive linked studies were undertaken to inform identified evidence gaps in the childhood obesity pathway.Objectives(1) To scope the impact of the National Child Measurement Programme (NCMP) (study A). (2) To develop a brief evidence-based electronic assessment and management tool (study B). (3) To develop evidence-based algorithms for identifying the risk of obesity comorbidities (study B). (4) To conduct an efficacy trial of the Healthy Eating and Lifestyle Programme (HELP) (study C). (5) To improve the prescribing of anti-obesity drugs in UK adolescents (study D). (6) To investigate the safety, outcomes and predictors of outcome of adolescent bariatric surgery in the UK (study E).MethodsFive substudies – (1) a parental survey before and after feedback from the National Childhood Measurement Programme, (2) risk algorithm development and piloting of a new primary care management tool, (3) a randomised controlled trial of the Healthy Eating and Lifestyle Programme, (4) quantitative and qualitative studies of anti-obesity drug treatment in adolescents and (5) a prospective clinical audit and cost-effectiveness evaluation of adolescent bariatric surgery in one centre.ResultsStudy A – before the National Childhood Measurement Programme feedback, three-quarters of parents of overweight and obese children did not recognise their child to be overweight. Eighty-seven per cent of parents found the National Childhood Measurement Programme feedback to be helpful. Feedback had positive effects on parental knowledge, perceptions and intentions. Study B – risk estimation models for cardiovascular and psychosocial comorbidities of obesity require further development. An online consultation tool for primary care practitioners is acceptable and feasible. Study C – the Healthy Eating and Lifestyle Programme, when delivered in the community by graduate mental health workers, showed no significant effect on body mass index at 6 months (primary outcome) when compared with enhanced usual care. Study D – anti-obesity drugs appear efficacious in meta-analysis, and their use has expanded rapidly in the last decade. However, the majority of prescriptions are rapidly discontinued after 1–3 months of treatment. Few young people described positive experiences of anti-obesity drugs. Prescribing was rarely compliant with the National Institute for Health and Care Excellence guidance. Study E – bariatric surgery appears safe, effective and highly cost-effective in adolescents in the NHS.Future work and limitationsWork is needed to evaluate behaviour and body mass index change in the National Childhood Measurement Programme more accurately and improve primary care professionals’ understanding of the National Childhood Measurement Programme feedback, update and further evaluate the Computer-Assisted Treatment of CHildren (CATCH) tool, investigate delivery of weight management interventions to young people from deprived backgrounds and those with significant psychological distress and obtain longer-term data on anti-obesity drug use and bariatric surgery outcomes in adolescence.Trial registrationCurrent Controlled Trials ISRCTN99840111.FundingThis project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 8, No. 3. See the NIHR Journals Library website for further project information.

Water demand strains supply capacity in the Mediterranean coastal areas, affecting negatively the maintenance of natural and cultural heritage. Tackling this subject, the “Coastal areas sustainable tourism water management in the Mediterranean - CASTWATER” project aims to support sustainable tourism water management in Mediterranean (MED) coastal areas, by improving the monitoring and assessment of the water sustainability performance of tourism. To this end, the elaboration of an online tool to monitor and assess sustainable tourism water management was envisaged and developed, primarily addressed to tourism sector small and medium-sized enterprises (SMEs). The CASTWATER online tool was designed to so as to provide two different functionalities. On the one hand, SMEs can self-evaluate their performance in sustainable water management and efficiently acknowledge possible actions that promote water efficiency in their tourism establishments. On the other hand, the tool measures, at both regional and local level, the levels of good governance and the effectiveness of water-tourism policies in order to improve sustainable water management. The approach presented in this work is heavily based on the initial technical specifications and end-user feedback, aiding SMEs in understanding, comparing, assessing and rating their performance regarding water efficiency and sustainable water management.

Background Most cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate HPV infection, cervical disease and prevention strategies. However, it is common for professionals who would need to perform these analyses to not be familiar with the models. This work introduces the Online Cost-Effectiveness ANalysis tool, featuring an easy-to-use web interface providing health professionals, researchers and decision makers involved in cervical cancer prevention programmes with a useful instrument to conduct complex cost-effectiveness analyses, which are becoming an essential tool as an approach for supporting decision-making that involves important trade-offs. Results The users can run cost-effectiveness evaluations of cervical cancer prevention strategies without deep knowledge of the underlying mathematical model or any programming language, obtaining the most relevant costs and health outcomes in a user-friendly format. The results provided by the tool are consistent with the existing literature. Conclusions Having such a tool will be an asset to the cervical cancer prevention community, providing researchers with an easy-to-use instrument to conduct cost-effectiveness analyses.

This study aims to model training adaptation using Artificial Neural Network (ANN) geometric optimisation. Over 26 weeks, 38 swimmers recorded their training and recovery data on a web platform. Based on these data, ANN geometric optimisation was used to model and graphically separate adaptation from maladaptation (to training). Geometric Activity Performance Index (GAPI), defined as the ratio of the adaptation to the maladaptation area, was introduced. The techniques of jittering and ensemble modelling were used to reduce overfitting of the model. Correlation (Spearman rank) and independence (Blomqvist &beta, ) tests were run between GAPI and performance measures to check the relevance of the collected parameters. Thirteen out of 38 swimmers met the prerequisites for the analysis and were included in the modelling. The GAPI based on external load (distance) and internal load (session-Rating of Perceived Exertion) showed the strongest correlation with performance measures. ANN geometric optimisation seems to be a promising technique to model training adaptation and GAPI could be an interesting numerical surrogate to track during a season.

Background High-throughput technologies have brought tremendous changes to biological domains, and the resulting high-dimensional data has also posed enormous challenges to computational science. A Bayesian network is a probabilistic graphical model represented by a directed acyclic graph, which provides concise semantics to describe the relationship between entities and has an independence assumption that is suitable for sparse omics data. Bayesian networks have been broadly used in biomedical research fields, including disease risk assessment and prognostic prediction. However, the inference and visualization of Bayesian networks are unfriendly to the users lacking programming skills. Results We developed an R/Shiny application, shinyBN, which is an online graphical user interface to facilitate the inference and visualization of Bayesian networks. shinyBN supports multiple types of input and provides flexible settings for network rendering and inference. For output, users can download network plots, prediction results and external validation results in publication-ready high-resolution figures. Conclusion Our user-friendly application (shinyBN) provides users with an easy method for Bayesian network modeling, inference and visualization via mouse clicks. shinyBN can be used in the R environment or online and is compatible with three major operating systems, including Windows, Linux and Mac OS. shinyBN is deployed at https://jiajin.shinyapps.io/shinyBN/. Source codes and the manual are freely available at https://github.com/JiajinChen/shinyBN.

Background Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. Objective This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. Methods We compared the number of sessions by users with a COVID-19–positive contact and users with COVID-19–compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. Results We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. Conclusions Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.

Background Genetic testing for germline cancer susceptibility genes is widely available. The Ask2Me.org (All Syndromes Known to Man Evaluator) tool is a clinical decision support tool that provides evidence-based risk predictions for individuals with pathogenic variants in cancer susceptibility genes. Objective The aim of this study was to understand the search behavior of the Ask2Me.org tool users, identify the patterns of queries entered, and discuss how to further improve the tool. Methods We analyzed the Ask2Me.org user-generated queries collected between December 12, 2018, and October 8, 2019. The gene frequencies of the user-generated queries were compared with previously published panel testing data to assess the correspondence between usage and prevalence of pathogenic variants. The frequencies of prior cancer in the user-generated queries were compared with the most recent US population–based cancer incidence. Results A total of 10,085 search queries were evaluated. The average age submitted in the queries was 48.8 (SD 16.5) years, and 84.1% (8478/10,085) of the submitted queries were for females. BRCA2 (1671/10,085, 16.6%), BRCA1 (1627/10,085, 16.1%), CHEK2 (994/10,085, 9.9%), ATM (662/10,085, 6.6%), and APC (492/10,085, 4.9%) were the top 5 genes searched by users. There was a strong linear correlation between genes queried by users and the frequency of pathogenic variants reported in published panel testing data (r=0.95, r2=0.90, P Conclusions The patients entered in the Ask2Me.org tool are a representative cohort of patients with pathogenic variants in cancer susceptibility genes in the United States. While a majority of the queries were on breast cancer susceptibility genes, users also queried susceptibility genes with lower prevalence, which may represent a transformation from single gene testing to multigene panel testing. Owing to these changing tides, more efforts are needed to improve evidence-based clinical decision support tools to better aid clinicians and their practice.

Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data

Bayesian Networks (BNs) are commonly used to model socio-ecological systems, as their graphical structure supports participatory modelling, they can integrate quantitative data and qualitative knowledge, and account for uncertainty. Although the spatial and temporal dimensions are important in socio-ecological systems, there is a lack of openly available and easy-to-use tools to run BNs with spatial data over time. Here, we present gBay (gbay.ethz.ch), an online platform where users can link their BNs to spatial data, run the network iteratively to incorporate dynamics and feedbacks, and add geo-processing calculations to account for spatial interactions. We demonstrate the use of this tool on the examples of a modelling a regulating ecosystem service, where we account for neighbourhood effects, and land-use decisions, where we include regional-level boundary conditions. The gBay platform supports users with its graphical interface and instructive wiki page, and provides a step towards more accessible and flexible socio-ecological modelling., Environmental Modelling & Software, 127, ISSN:1364-8152, ISSN:1873-6726

Low-cost phenotyping using proximal sensors is increasingly becoming popular in plant breeding. As these techniques generate a large amount of data, analysis pipelines that do not require expertise in computer programming can benefit a broader user base. In this work, a new online tool Specalyzer is presented that allows interactive analysis of the spectral reflectance data generated by proximal spectroradiometers. Specalyzer can be operated from any web browser allowing data uploading, analysis, interactive plots and exporting by point and click using a simple graphical user interface. Specalyzer is evaluated with case study data from a winter wheat fertilizer trial with two fertilizer treatments. Specalyzer can be accessed online at http://www.specalyzer.org.

Background: Understanding and addressing the needs of frail patients has been identified as an important strategy by the Nova Scotia Health Authority (NSHA). Primary care (PC) providers are in a key position to aid in the identification of, and response to frailty as part of routine care. Unlike singular chronic conditions such as diabetes and hypertension which garner a disease-based approach and identification as part of standard practice, frailty is only just emerging as a concept for PC. The web-based Frailty Portal was developed to aid in the identification of, assessment and care planning for frail patients in PC practice. In this study we assess the implementation feasibility and impact of the Frailty Portal by: (1) identifying factors influencing the Frailty Portal’s use in community PC practice, and (2) examination of the immediate impact of the ‘Frailty Portal’ on frail patients, their caregivers and PC providers. Methods: A convergent mixed method approach was implemented among PC providers in community-based practice in the NSHA, Central Zone. Quantitative and qualitative data were collected concurrently over a 9-month period. A sample of patients who underwent assessment and/or their caregiver were approached for survey participation. Results: Fourteen community PC providers (10 family physicians, 4 nurse practitioners) completed 48 patient assessments and completed or begun 41 care plans; semi-structured interviews were conducted among 9 providers. Nine patients and 5 caregivers participated in the survey. PC providers viewed frailty as an important concept but implementation challenges were met, primarily with respect to the time required for use and lack of fit with traditional practice routines. Additional barriers included tool usability and accessibility, training and care planning steps, and privacy. Impacts of the tools use with respect to confidence and knowledge showed early promise. Conclusion: This feasibility study highlights the need for added health system supports, resources and financial incentives for successful implementation of the Frailty Portal in community PC practice. We suggest future implementation integrate the Frailty Portal to practice electronic medical records (EMRs) and target providers with largely geriatric practice populations and those practicing within interdisciplinary, collaborative primary healthcare (PHC) teams.

Agricultural non-point source (NPS) pollution is a source of water quality impairment, and demonstrates widely varying spatial and temporal pollution potential. Many efforts to protect water quality are based on seasonal and annual estimates of pollutant loss potential (NRCS 590 nutrient management standard, P-Index) that inadequately address the hydrologic processes driving NPS pollution. One barrier to adopting practices that address NPS pollution is a lack of tools capable of transferring information at sufficient spatial and temporal resolution so that end-users can make informed decisions. We introduce a web-based system displaying distributed hydrologic forecasts using free and open source software. The system consists of three primary components: (1) a hydrology model that provides short-term distributed forecasts; (2) a data structure capable of re-structuring large, high resolution rasters; (3) an interface employing adaptive map-viewing technology that allows end-users to interact with the data to avoid high-risk areas when planning agricultural practices.

Damages to urban systems as a result of water-related natural disasters have escalated in recent years. The observed trend is expected to increase in the future as the impacts of population growth, rapid urbanization and climate change persist. To alleviate the damages associated with these impacts, it is recommended to integrate disaster management methods into planning, design and operational policies under all levels of government. This manuscript proposes the concept of ResilSIM: A decision support tool that rapidly estimates the resilience (a modern disaster management measure that is dynamic in time and space) of an urban system to the consequences of natural disasters. The web-based tool (with mobile access) operates in near real-time. It is designed to assist decision makers in selecting the best options for integrating adaptive capacity into their communities to protect against the negative impacts of a hazard. ResilSIM is developed for application in Toronto and London, Ontario, Canada; however, it is only demonstrated for use in the city of London, which is susceptible to riverine flooding. It is observed how the incorporation of different combinations of adaptation options maintain or strengthen London’s basic structures and functions in the event of a flood.

Objective To explore the acceptability of implementing an online tool for the assessment and management of childhood obesity (Computer-Assisted Treatment of CHildren, CATCH) in primary care. Design and setting An uncontrolled pilot study with integral process evaluation conducted at three general practices in northwest London, UK (November 2012–April 2013). Participants Families with concerns about excess weight in a child aged 5–18 years (n=14 children). Intervention Families had a consultation with a doctor or nurse using CATCH, which assessed child weight status, cardiometabolic risk and risk of emotional and behavioural difficulties and provided personalised lifestyle advice. Families and practitioners completed questionnaires to assess the acceptability and usefulness of the consultation, and participated in semistructured interviews which explored user experiences. Outcome measures The primary outcome was family satisfaction with the tool-assisted consultation. Secondary outcomes were practitioners’ satisfaction, and acceptability and usefulness of the intervention to families and practitioners. Results The majority of families (86%, n=12) and all practitioners (n=4) were satisfied with the consultation. Participants reported that the tool was easy to use, the personalised lifestyle advice useful and the use of visual aids beneficial. Families and practitioners identified a need for practical, structured support for weight management following the consultation. Conclusions The results of this pilot study indicate that an online tool for assessment and management of childhood obesity can be implemented in primary care, and is acceptable to patients, families and practitioners. Further development and evaluation of the tool is warranted.

The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/ . Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of each group of predictions. Finally, It is also possible to specify a list of genes tracking down all the drugs that may have an indirect influence on them based on a multi-drug, multi-target, multi-pathway analysis, which aims to discover drugs for future follow-up studies.

Participatory design (PD) is mostly conducted face-to-face with the support of physical props. Although this is a valid and very beneficial approach, it is unfortunately not applicable in every project context. It is especially challenging when a project's stakeholders are widely distributed; software tools can address this challenge. A review of existing online annotation tools indicates that they do not meet the essential requirements for distributed PD tools. To address this issue, a prototype called Pdot has been developed and evaluated with some users whose feedback will inform its future development.

Since seafood is a significant source of nutrients with known health benefits, its consumption is promoted as a healthy food choice. However, seafood can also contain potentially hazardous environmental pollutants. In the context of the ECsafeSEAFOOD FP7 project, FishChoice (www.fishchoice.eu) was developed as a communication tool to help to the consumers to take the most appropriate decisions on their seafood consumption habits. FishChoice relies on scientific information that allows calculating, on an individual basis, intakes of nutrients and pollutants derived from seafood consumption. In the framework of the EU-H2020 funded SEAFOODTOMORROW project, an optimized version of the online tool has been released. FishChoice is available in 25 EU languages with a customized list of seafood species per EU country, considering specific (national) consumption habits. The list of nutrients has been extended according to the latest EFSA recommendations, while pollutants data incorporate results from recent studies. The sustainability of seafood consumption has been also implemented, providing recommendations to help preserve the marine environment. Finally, FishChoice is suitable not only for consumers, but also health professionals, schools and academia, as well as the industrial sector and public health providers.