Web-based Tool Research Articles

Vibration vulnerability assessment of heritage structures in Lahore: a step towards sustainable conservation

Lahore is among the most important historical cities in Pakistan, and many cultural heritage structures, such as Shalamar Gardens and Lahore Fort, which are recognised as World Heritage Sites by UNESCO, are located there. The rapid increase in transit operations and infrastructure development in Lahore can increase the vulnerability of nearby heritage structures to ground-borne vibrations. In this study, a vibration vulnerability assessment of the heritage structures present in Lahore was performed with the help of the framework provided by the Federal Transit Administration (FTA), USA. A remote survey was carried out to gather valuable information regarding the distance between the heritage structures and potential vibrational sources, as well as the latitude and longitude of the heritage structures, via the web-based mapping tool Google Earth. A vibration vulnerability map of heritage structures was developed on the basis of a vibration vulnerability assessment to make it convenient for policymakers to visualise the present state of heritage structures with respect to vibration vulnerability. The performed vibration vulnerability assessment was validated by experimentally measuring vibrations at the Buddha’s Tomb (Buddha Ka Awa) heritage structure. The study revealed that 17% of the heritage structures present in Lahore are vulnerable to vibrations at present, with potential vibrational sources such as road traffic, metro trains, and rail traffic. The vibration vulnerability assessment revealed that 72% of the vibrationally vulnerable heritage structures in Lahore are most influenced by vibrations from road traffic. The results of this study can aid policymakers in creating intervention packages for vibrationally vulnerable heritage structures in Lahore in relation to their respective needs, which will make it possible to achieve the sustainable conservation of heritage structures.

National Fatality Review Case Reporting System: Twenty Years of Data Collection.

The National Fatality Review Case Reporting System (NFR-CRS) is a web-based data collection tool for child death review and fetal and infant mortality review teams. The NFR-CRS captures information from the multidisciplinary review, including the social and community risk factors that may have impacted the death. The NFR-CRS is a nimble data system that has evolved throughout the past 20 years. The most recent enhancements include a life stressors section focused on collecting contextual information such as racism and poverty, revised cause of death sections to better align data collected with risk factors identified in the research, and enhanced data visualizations. The NFR-CRS has improved data quality since the launch of a data quality initiative in 2015. As a result of the data quality initiative, the completeness, consistency, accuracy, and timeliness of the NFR-CRS data have improved. Limitations for the NFR-CRS include the fact that data entered are not population-based, data fields have evolved over time, and there are high percentages of missing and unknown data. Despite its limitations, the NFR-CRS remains a valuable research tool, especially when paired with other data sources.

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data, RNA expression, and tumor microenvironment data, using fusion techniques to enhance model performance. We obtained data from the SEER database to assess the impact of nine demographic and clinical features on the survival of 58,495 GBM patients and built predictive machine learning models. Additionally, mRNA expression data from 600 GBM patients from TCGA, CGGA, and GEO were analyzed. We used Cox regression and LASSO to create a gene signature, which was compared against 13 published signatures for accuracy. Twenty-one machine learning models were applied to predict survival at multiple time points. Finally, we integrated multiple feature types using fusion techniques and developed a Shiny app to provide survival predictions for GBM patients. Using the SEER database, we constructed machine learning models based on nine clinical variables: age, gender, marital status, race, tumor site, laterality, surgery, chemotherapy, and radiation therapy. The best-performing model achieved AUC values of 0.775, 0.728, 0.692, and 0.683 for predicting survival at 6, 12, 18, and 24months in the testing cohort. In the merged TCGA, CGGA, and GEO cohorts, we identified 11 genes to develop predictive models. These 11 genes outperformed 13 other published gene signatures in predicting the prognosis of GBM. When incorporating mRNA features, tumor microenvironment features, and clinical variables into the fusion models, the AUC values for predicting survival at 6, 12, 18, and 24months were 0.641, 0.624, 0.655, and 0.637, respectively. A user-friendly tool for predicting the survival curve of individual GBM patients is available at https://zzubioinfo.shinyapps.io/mlGBM/ . Our study provides a web-based tool that includes two modules: one for predicting survival curves using only clinical variables, and another that integrates multiple feature types for more comprehensive predictions.

Narrating a path: Digital humanities tools in the linguistics classroom

This paper embraces the premise in Mehl (2021: 331) that “linguists should care about the digital humanities … because collaborations between … linguistics and DH will be fruitful for all of us.” I discuss my incorporation of a selection of DH tools and practices into my teaching of three undergraduate linguistics courses, where in lieu of the traditional “research paper”, students learn about free web-based tools to create interactive exhibits and digitally edited volumes. These tools make multimodal writing and data presentation easy, and they are ideal for interactive presentation of ideas. They also allow students to weave concepts in linguistics with ways in which linguistics, usually more embedded in the social sciences, finds footing in the humanities, including language and identity, language endangerment and revitalization, or specific languages that they speak or are spoken in their worlds. These initiatives address justice, equity, diversity, and inclusion (JEDI) initiatives promoting cross-linguistic and cross-cultural understanding. They also allow students to actively interrogate the ways in which the discipline may perpetuate or challenge existing power structures and their biases.

Predictors of Food Insecurity and Food Assistance Program Usage Among Puerto Ricans Before and During the COVID-19 Pandemic in Holyoke, Massachusetts

Background/Objectives: Puerto Rican households often face elevated rates of food insecurity. Frequently, households experiencing food insecurity turn to federal and emergency nutrition assistance for urgent or prolonged aid. This study analyzes factors influencing food insecurity and food assistance program involvement among Puerto Ricans in Holyoke, Massachusetts, amidst the COVID-19 pandemic. Methods: Using a combination of community-based participatory recruitment, purposive sampling, and web-based tools, we collected 284 survey responses. Our paper employed Pearson’s chi-square, logistic regression, and hierarchical linear models to assess relationships between demographic and household attributes and food access outcomes. Results: Variables such as having children in the household and age significantly influenced food relief participation and food insecurity outcomes. Puerto Rican heritage and low educational attainment were strong predictors of obtaining federal food aid. Low-income participants disproportionately faced food shortages and depended on emergency food initiatives. Our findings indicate that demographic and household factors significantly influence food insecurity and accessibility. Conclusions: Our study demonstrates that the pandemic made it challenging for households to obtain consistent, safe, and affordable nourishment. The impacts were unequally spread; hence, Puerto Rican communities and low-income groups were most affected.

A comparison of operating room toric placement tools: CALLISTO eye vs. e Wavetec AnalyzOR (CORTCO)

BackgroundTo evaluate procedure times for two cataract planning systems (ZEISS CALLISTO eye and the Wavetec AnalyzOR) in predicting residual astigmatism (prediction error) and other visual outcomes in patients with corneal astigmatism (maximum allowable up to 3.0D) at postoperative month 1.MethodsThis was a prospective, single center, parallel treatment group, bilateral and unilateral, randomized, 1-month study on patients scheduled to undergo routine, small-incision cataract surgery with a toric intraocular lens implantation. Both groups underwent preop measurements with the IOLMaster 700 (Zeiss, Jena, Germany) and surgery with the LenSx device (Alcon). Lens selection in the CALLISTO eye group was based on Zeiss VERACITY Surgery Planner (a web-based tool) and on the Wavetec AnalyzOR component of the ORA system (a real-time intraoperative aberrometer) for those eyes in the ORA group. All procedure and intraoperative times were measured with a stopwatch. Postoperative visual outcomes were evaluated between 1 and 2 months after surgery.ResultsThere were 23 eyes in the CALLISTO group and 28 eyes in the ORA group. The mean surgical time for the CALLISTO group was 28.09 ± 1.72 min compared to 34.41 ± 1.52 min for the ORA group (P = 0.01). Toric lens placement mean time in the CALLISTO group was 2.47 ± 0.34 min compared to 3.88 ± 0.29 min in the ORA group (P = 0.0034). At month 1 postoperatively, the manifest refractive spherical error (MRSE) in the CALLISTO eye group 0.022 ± 0.388 diopters (D) compared to -0.174 ± 0.322 D in the ORA group; these were not statistically different. There was a higher percentage (75%) of eyes with an MRSE within 0.25D in the ORA group compared to the CALLISTO eye group (56.5%); at all other levels outcomes were numerically higher in the CALLISTO eye group.ConclusionsLess surgical time was needed when using the CALLISTO eye than the ORA when performing cataract surgery with toric lens implantation. There were similar visual outcomes between the groups and no statistical differences.

ChineseCVD: a web-based Chinese-specifi c cardiovascular risk calculator incorporating long COVID, COVID-19vaccination, SGLT2i and PCSK9i treatment effects

Abstract Background Web-based risk prediction tools for cardiovascular diseases are crucial for providing rapid risk estimates for busy clinicians, but there is none available specifically for Chinese subjects. This study developed ChineseCVD, a risk calculator for cardiovascular disease. Methods Adult patients attending government-funded family medicine clinics in Hong Kong between 1st January 2000 and 31st December 2003 were included. The primary outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, transient ischaemic attacks/ischaemic strokes, and cardiovascular mortality. Results A total of 155,066 patients were used as the derivation cohort. Over a median follow-up of 16.1 (11.6-17.8) years, 31,061 (20.44%) had MACE. Cox regression identified male gender, age, comorbidities, cardiovascular medications, systolic and diastolic blood pressure, and laboratory test results (neutrophil-lymphocyte ratio, creatinine, ALP, AST, ALT, HbA1c, fasting glucose, triglyceride, LDL and HDL) as significant predictors of the above outcomes. ChineseCVD further incorporates the impact of smoking status, COVID-19 infection, number of COVID-19 vaccination doses, and modifier effects of newest medication classes of PCSK9i and SGLT2i. The calculator enables clinicians to demonstrate to patients how risks vary with different medications. Conclusions The ChineseCVD risk calculator enables rapid web-based risk assessment for adverse cardiovascular outcomes, thereby facilitating clinical decision-making at the bedside or in the clinic.

Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination

Abstract Background Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis. Purpose The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical decision-making. Methods We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC). Results The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2). Conclusions This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.Figure 1Figure 2

A New Objective Diagnostic Tool for Attention-Deficit Hyper-Activity Disorder (ADHD): Development of the Distractor-Embedded Auditory Continuous Performance Test

Background/Objectives: Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. Traditional diagnostic methods, which depend on subjective assessments, often lack precision. This study evaluates the validity and reliability of a newly developed diagnostic tool, the Distractor-Embedded Auditory Continuous Performance Test (da-CPT), which integrates auditory stimuli with distractors to enhance the clinical utility of ADHD diagnosis. Methods: The study included 160 children aged 6–12 years, comprising 80 with a confirmed ADHD diagnosis and 80 controls. All participants completed the da-CPT, a web-based tool designed to assess inattention, hyperactivity, impulsivity, and timing via an auditory-based task. To validate the da-CPT, participants also completed the Conners’ Parent Rating Scale—Revised Short Form (CPRS-R) and either the MOXO or IVA-2 tests. Data were analyzed using ROC curves and statistical correlations to assess sensitivity, specificity, and overall diagnostic accuracy. Results: The da-CPT demonstrated high diagnostic accuracy, with a sensitivity of 91.25% and specificity of 83.75%. ROC analysis indicated that the inattention index had the highest discriminatory power (AUC = 0.881), followed by timing, impulsivity, and hyperactivity (all p < 0.01). Furthermore, the da-CPT scores were strongly correlated with ADHD severity (p < 0.01). Conclusions: This study confirms that the da-CPT is a valid and reliable tool for diagnosing ADHD in children aged 6–12. By incorporating auditory stimuli and distractors, the tool offers a more ecologically valid assessment of ADHD symptoms in clinical settings, improving diagnostic precision and utility.

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

50 Co-designing an intervention with asylum seeking youth to improve access to healthcare in Canada using human-centered design

Abstract Background Asylum seeking youth in Canada and the United States face multiple barriers to accessing healthcare, leaving them at greater risk of worse health outcomes. In a previous study, we found that limited knowledge of the Canadian health care system and few navigation supports were a key barrier to care for asylum seeking youth, yet no youth-developed intervention existed to support this population. Human-centered design (HCD) develops meaningful interventions with communities who have been historically marginalized. Objectives This study sought to co-design an intervention to increase youth empowerment and access to health care for asylum seeking youth in Canada. Design/Methods This was a community-based participatory-action research (CBPAR) study, and combined human-centered design (HCD) methodologies. We obtained research ethics board approval for this study (#SMH REB 22-117). In partnership with two community organizations that supported asylum seeking youth in a large Canadian city, a youth advisory board was created. In multiple co-creation sessions over a calendar year, an intervention was developed in rapid prototyping sessions and refined with feedback, to develop a final pilot intervention. Results A total of 7 asylum-seeking youth participated in the youth advisory board, ranging in age from 12-19, representing 3 different languages and included youth who self-identified with chronic medical conditions. Over a 15-month period, they participated in 6 co-creation sessions to develop a multi-lingual, web-based tool; youth specifically stated they did not believe an app would be helpful, but rather a resource that could be easily accessed. The youth advisory board shared they had limited understanding of the Canadian healthcare system, as well as interim and provincial health insurance. Youth felt the tool should answer questions about how health insurance worked, be brightly colored, use dyslexia-friendly font and be easily saved on a smartphone as an image. A multi-lingual wallet card was developed for youth to easily present at healthcare encounters, particularly if they did not have provincial health insurance, to help youth communicate their coverage, language and needs. Conclusion To our knowledge, we developed the first co-designed tool in Canada for youth asylum seekers, by youth, to improve and empower youth to receive health care services they are entitled to through navigation support. Given the high degree of marginalization faced by asylum seeking youth, this intervention has the promise to improve health outcomes. Feasibility and acceptability of this study will be evaluated in subsequent studies.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Economic and environmental pollutant impact of Maternal-Fetal Telemedicine.

The global issue of greenhouse gas emissions has significant implications for the environment and human health. Telemedicine provides a valuable tool for delivering healthcare while reducing gas emissions by limiting the need for patient travel. However, the environmental effects of telemedicine in high-risk pregnancy populations remain unassessed. The aim of this study was to estimate the economic and environmental impact of an outpatient teleMFM program. This retrospective cohort study examined all visits at 3 teleMFM clinics more than 90 miles away from the nearest in-person MFM office between 10/1/2021 and 6/1/2022. Travel distances and times were calculated for each appointment between the patient's home, telemedicine clinic, and nearest in-person clinics, using zip code data and Google Maps web-based map calculator tools. Travel cost savings and environmental impact were calculated by determining differences in mileage reimbursement rate and emissions between those incurred in attending telemedicine appointments and those that would have been incurred if in-person using inflation-adjusted Internal Revenue Service annual standard mileage reimbursement rate ($0.58 per mile), and the U.S. Environmental Protection Agency Office of Transportation and Air Quality's average annual emissions and fuel consumption for gasoline-fueled passenger vehicles. During the study period a total number of 2,712 appointments were scheduled, of which 2,454 were kept (cancellations removed) and analyzed. Visiting a teleMFM clinic resulted in 204 miles, 200 minutes and 118.32 dollars saved per patient visit compared to visiting the nearest in-person clinic. Over a 7 month period a total of 96.6 metric tons of emissions were saved. This study demonstrates the positive economic and environmental impact of teleMFM utilization in communities remote from in-person care. Given the contribution of greenhouse gas emissions to climate change, such findings may provide strategies for our specialty make informed policy, advocacy, and business decisions.

Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation

IntroductionThe prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies.MethodsUtilizing data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018, this study identified independent prognostic factors influencing Overall Survival (OS) in ASC using Boruta analysis. Employing Gradient Boosting, Random Forest, and Neural Network algorithms, predictive models were constructed. Model performance was assessed through key metrics, including Area Under the Receiver Operating Characteristic Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA).ResultsAmong 241 eligible patients, seven clinical parameters—age, sex, primary tumor size, N stage, primary tumor site, chemotherapy, and systemic therapy—were identified as significant predictors of OS. Advanced age, male gender, larger tumor size, absence of chemotherapy, and lack of systemic therapy were associated with poorer survival. The Random Forest model outperformed others, achieving 3- and 5-year AUCs of 0.80/0.79 (training) and 0.74/0.65 (validation). It also demonstrated better calibration, lower Brier scores (training: 0.189/0.171; validation: 0.207/0.199), and more favorable DCA. SHAP values enhanced model interpretability by highlighting the impact of each parameter on survival predictions. To facilitate clinical application, the Random Forest model was deployed on a web-based server for accessible prognostic assessments.ConclusionsThis study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.

Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder

Autistic Spectrum Disorder (ASD) is a challenging neurological development disorder, which involves poor social interaction, communication, and repetitive behaviours. If autism is identified early enough it can be treated with better outcomes but present diagnostic tests are dependent on subjective opinion, consume a lot of time, and are vague. This study is aimed at optimizing one-dimensional convolutional neural networks (1D CNN) to improve the precision and speed of early ASD diagnosis. Four ASD datasets representing different age groups — toddlers, children, adolescents, and adults were modelled using one-dimensional convolutional neural networks (1D CNN). These datasets are accessible to the public on the UCI Machine Learning Repository and Kaggle, they consist of behavioural features relevant to ASD diagnosis. Each dataset underwent feature selection, categorical encoding, and missing value handling. Then, baseline 1D CNN with predefined hyperparameters was modelled on each of the datasets. Subsequently, the baseline models were optimized using the Tree-structured Parzen Estimator (TPE). An interactive web-based ASD diagnostic tool was developed, where user inputs are processed through age-specific pre-trained optimized models to determine ASD probability. The optimized 1D CNN models significantly outperformed the baseline models across all age groups and achieved scores of 100% in accuracy, precision, recall, F1-score, MCC, and AUC ROC. This implies that the optimized models can reliably identify people in various age groups who have and do not have ASD. The development of an interactive web-based diagnostic tool extends the practical utility of the models, making them accessible for clinical and at-home use.

Evaluating User Experiences and Preferred Features of a Web-Based 24-Hour Dietary Assessment Tool: Usability Study.

Intake24, a web-based 24-hour dietary recall tool developed in the United Kingdom, was adapted for use in New Zealand (Intake24-NZ) through the addition of a New Zealand food list, portion size images, and food composition database. Owing to the customizations made, a thorough evaluation of the tool's usability was required. Detailed qualitative usability studies are well suited to investigate any challenges encountered while completing a web-based 24-hour recall and provide meaningful data to inform enhancements to the tool. This study aims to evaluate the usability of Intake24-NZ and identify improvements to enhance both the user experience and the quality of dietary intake data collected. We used a mixed methods approach comprising two components: (1) completion of a single 24-hour dietary recall using Intake24-NZ with both screen observation recordings and collation of verbal participant feedback on their experience and (2) a survey. A total of 37 participants aged ≥11 years self-completed the dietary recall and usability survey (men and boys: 14/37, 38% and women and girls: 23/37, 62%; Māori: 10/37, 27% and non-Māori: 27/37, 73%). Although most (31/37, 84%) reported that Intake24-NZ was easy to use and navigate, data from the recorded observations and usability survey revealed challenges related to the correct use of search terms, search results obtained (eg, type and order of foods displayed), portion size estimation, and associated food prompts (eg, did you add milk to your tea?). This comprehensive usability study identified challenges experienced by users in completing a dietary recall in Intake24-NZ. The results informed a series of improvements to enhance user experience and the quality of dietary data collected with Intake24-NZ, including adding new foods to the food list, optimizing the search function and ordering of search results, creating new portion size images, and providing clearer instructions to the users.

Prevalence of Cardiovascular Disease and Comparison of Risk Category Predictions of Systemic Coronary Risk Evaluation Score-2 and 4 Other Cardiovascular Disease Risk Assessment Tools Among People Living with Human Immunodefficiency Virus in Türkiye.

Cardiovascular disease (CVD) is a major cause of mortality among people living with HIV (PLWH). We aimed to assess the prevalence of diagnosed CVD and the risk of CVD among PLWH using 5 different tools. This retrospective, cross-sectional study was conducted in 20 tertiary centers in Türkiye between October 2021 and March 2022, among 1425 PLWH aged 40-75 years. About 82.7% were male, with a median age of 51. Web-based tools for each score were used for CVD risk calculations. Of 1425 PLWH enrolled, 10.8% had confirmed CVD, and 1132 had their risk scores evaluated. Of those participants, 42.8% had a higher risk of CVD (10-year risk of atherosclerotic CVD risk score (ASCVD) above 7.5%), and according to the European Society of Cardiology systemic coronary risk evaluation 2 (SCORE2), 71.7% had a high- to very high-risk rate. The agreement between various CVD risk tools varied, with Framingham heart study risk score (FRS), modified FRS, data collection on adverse effects of anti-HIV drugs (DAD), and SCORE2 for high-risk countries showing overall agreement rates of 82%, 94%, 91%, and 36%, respectively, compared to ASCVD. According to the 2021 European and 2019 American Cardiology guidelines, 75.3% and 47.1% of PLWH would be eligible for lipid-lowering agents, respectively. The diagnosed CVD prevalence highlighted the importance of monitoring cardiovascular health and comorbidities in this population. SCORE2 identified a greater number of individuals at high/very high risk compared to other prediction tools. The implementation of CVD prevention through lipid-lowering therapy was far from desired levels in our cohort.

Web-based vs. conventional: a comprehensive analysis of visual acuity assessment using the PocDoc tool in a tertiary eye care centre.

Visual acuity (VA) represents a fundamental measure of visual function. The significant prevalence of underdiagnosed ocular disorders underscores the importance of effective VA assessment. This study evaluates the efficacy of a web-based VA assessment tool ("PocDoc") versus conventional VA testing. Prospective observational study including 353 participants recruited from various eye clinics in a tertiary referral centre. Age, diagnosis, and VA related information (i.e. VA measurements from PocDoc and conventional VA test [Snellen chart], test type, etc) were collected. Spearman's rank correlation, Intraclass Correlation, and Bland-Altman plot compared outcomes of both tests. One-way ANOVA and paired-T test were used to compare means. Most patients were males (59.2%) with a mean age of 52.2 ± 20.6 years. PocDoc had moderate positive correlation to conventional testing (rho = 0.50, p < 0.001). PocDoc led to higher logMAR scores compared to conventional testing (mean logMAR 0.19 and 0.13 respectively, p < 0.01). Moreover, PocDoc demonstrated a sensitivity of 82.8% and specificity of 79% for detecting visual impairment. The discrepancy between PocDoc and conventional VA testing increased with higher logMAR values, indicating greater inconsistency between the tests for patients with poorer VA. Age, test type, and disease type contributed to this variability. The concordance between PocDoc and conventional testing for VA measurement across various ages and conditions makes it a suitable screening tool. Future technological inventions should consider age, test type, and disease type as critical factors related to the level of agreement and correlation between digital and conventional VA testing methods.

Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community’s needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs.