This scoping review aims to describe the range of research studies using arts-based data collection methods with immigrant and racialized older adults. A secondary aim is to identify challenges and strengths of using these approaches with this population. This review uses Arksey and O\'Malley\'s five-stage scoping review framework with a final number of 16 references included for the study. Enhanced social connectedness, increased transparency and quality of findings, and self-empowerment were key strengths of using arts-based approaches for data collection. Challenges identified included resource limitations, cultural and language barriers, and barriers to meaningful engagement. Only a small number of studies have utilized arts-based methods with immigrant and racialized older adults. Arts-based approaches require unique methodological adaptations with this population but have the potential to increase engagement in research activities, authenticity of research findings and empowerment of older adults.

OBJECTIVE: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children\'s development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aimed to identify the early predictors of CP using machine learning (ML).
METHODS: This is a retrospective case-control study, using data from the two population-based databases, the Slovenian National Perinatal Information System and the Slovenian Registry of Cerebral Palsy. Multiple ML algorithms were evaluated to identify the best model for predicting CP.
METHODS: This is a population-based study of CP and control subjects born into one of Slovenia\'s 14 maternity wards.
METHODS: A total of 382 CP cases, born between 2002 and 2017, were identified. Controls were selected at a control-to-case ratio of 3:1, with matched gestational age and birth multiplicity. CP cases with congenital anomalies (n=44) were excluded from the analysis. A total of 338 CP cases and 1014 controls were included in the study.
METHODS: 135 variables relating to perinatal and maternal factors.
METHODS: Receiver operating characteristic (ROC), sensitivity and specificity.
RESULTS: The stochastic gradient boosting ML model (271 cases and 812 controls) demonstrated the highest mean ROC value of 0.81 (mean sensitivity=0.46 and mean specificity=0.95). Using this model with the validation dataset (67 cases and 202 controls) resulted in an area under the ROC curve of 0.77 (mean sensitivity=0.27 and mean specificity=0.94).
CONCLUSIONS: Our final ML model using early perinatal factors could not reliably predict CP in our cohort. Future studies should evaluate models with additional factors, such as genetic and neuroimaging data.

BACKGROUND: Atopic diseases, obesity and neuropsychiatric disorders are lifestyle-related and environmental-related chronic inflammatory disorders, and the incidences have increased in the last years.
OBJECTIVE: To outline the design of the 18-year follow-up of the Copenhagen Prospective Study on Asthma in Childhood (COPSAC2000) birth cohort, where risk factors of atopic diseases, obesity and neuropsychiatric disorders are identified through extensive characterisation of the environment, along with deep clinical phenotyping and biosampling for omics profiling.
METHODS: COPSAC2000 is a Danish prospective clinical birth cohort study of 411 children born to mothers with asthma who were enrolled at 1 month of age and closely followed at the COPSAC clinical research unit through childhood for the development of atopic diseases. At the 18-year follow-up visit, biomaterial (hair, blood, urine, faeces, throat, and skin swabs, nasal lining fluid and scraping, and hypopharyngeal aspirates) and extensive information on environmental exposures and risk behaviours were collected along with deep metabolic characterisation and multiorgan investigations including anthropometrics, heart, lungs, kidneys, intestines, bones, muscles and skin. Neuropsychiatric diagnoses were captured from medical records and registers accompanied by electronic questionnaires on behavioural traits and psychopathology.
RESULTS: A total of 370 (90%) of the 411 cohort participants completed the 18-year visit. Of these, 25.1% had asthma, 23.4% had a body mass index >25 kg/m2 and 16.8% had a psychiatric diagnosis in childhood. Of the 62 probands with a neuropsychiatric diagnosis in childhood, a total of 68.7% drank alcohol monthly, and when drinking, 22.2% drank >10 units. Of the participants, 31.4% were currently smoking, and of these, 24.1% smoked daily. A total of 23.8% had tried taking drugs, and 19.7% reported having done self-destructive behaviour. The mean screen time per day was 6.0 hours.
CONCLUSIONS: This huge dataset on health and habits, exposures, metabolism, multiorgan assessments and biosamples from COPSAC2000 by age 18 provides a unique opportunity to explore risk factors and underlying mechanisms of atopic disease and other lifestyle-related, non-communicable diseases such as obesity and neuropsychiatric disorders, which are highly prevalent in the community and our cohort.

The Industrial Internet of Things has enabled the integration and analysis of vast volumes of data across various industries, with the maritime sector being no exception. Advances in cloud computing and deep learning (DL) are continuously reshaping the industry, particularly in optimizing maritime operations such as Predictive Maintenance (PdM). In this study, we propose a novel DL-based framework focusing on the fault detection task of PdM in marine operations, leveraging time-series data from sensors installed on shipboard machinery. The framework is designed as a scalable and cost-efficient software solution, encompassing all stages from data collection and pre-processing at the edge to the deployment and lifecycle management of DL models. The proposed DL architecture utilizes Graph Attention Networks (GATs) to extract spatio-temporal information from the time-series data and provides explainable predictions through a feature-wise scoring mechanism. Additionally, a custom evaluation metric with real-world applicability is employed, prioritizing both prediction accuracy and the timeliness of fault identification. To demonstrate the effectiveness of our framework, we conduct experiments on three types of open-source datasets relevant to PdM: electrical data, bearing datasets, and data from water circulation experiments.

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology\'s complexity, costs, fabrication and implementation challenges often hinder engineers\' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.

This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.

With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient\'s health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

BACKGROUND: Syringe services programs (SSPs) provide harm reduction supplies and services to people who use drugs and are often required by funders or partners to collect data from program participants. SSPs can use these data during monitoring and evaluation (M&E) to inform programmatic decision making, however little is known about facilitators and barriers to collecting and using data at SSPs.
METHODS: Using the Consolidated Framework for Implementation Research (CFIR), we conducted 12 key informant interviews with SSP staff to describe the overall landscape of data systems at SSPs, understand facilitators and barriers to data collection and use at SSPs, and generate recommendations for best practices for data collection at SSPs. We used 30 CFIR constructs to develop individual interview guides, guide data analysis, and interpret study findings.
RESULTS: Four main themes emerged from our analysis: SSP M&E systems are primarily designed to be responsive to perceived SSP client needs and preferences; SSP staffing capacity influences the likelihood of modifying M&E systems; external funding frequently forces changes to M&E systems; and strong M&E systems are often a necessary precursor for accessing funding.
CONCLUSIONS: Our findings highlight that SSPs are not resistant to data collection and M&E, but face substantial barriers to implementation, including lack of funding and disjointed data reporting requirements. There is a need to expand M&E-focused funding opportunities, harmonize quantitative indicators collected across funders, and minimize data collection to essential data points for SSPs.

UNASSIGNED: Household air pollution (HAP) is a leading environmental risk factor accounting for about 1.6 million premature deaths mainly in low- and middle-income countries (LMICs). However, no multicounty randomized controlled trials have assessed the effect of liquefied petroleum gas (LPG) stove intervention on HAP and maternal and child health outcomes. The Household Air Pollution Intervention Network (HAPIN) was the first to assess this by implementing a common protocol in four LMICs.
UNASSIGNED: This manuscript describes the implementation of the HAPIN data management protocol via Research Electronic Data Capture (REDCap) used to collect over 50 million data points in more than 4000 variables from 80 case report forms (CRFs).
UNASSIGNED: We recruited 800 pregnant women in each study country (Guatemala, India, Peru, and Rwanda) who used biomass fuels in their households. Households were randomly assigned to receive LPG stoves and 18 months of free LPG supply (intervention) or to continue using biomass fuels (control). Households were followed for 18 months and assessed for primary health outcomes: low birth weight, severe pneumonia, and stunting. The HAPIN Data Management Core (DMC) implemented identical REDCap projects for each study site using shared variable names and timelines in local languages. Field staff collected data offline using tablets on the REDCap Mobile Application.
UNASSIGNED: Utilizing the REDCap application allowed the HAPIN DMC to collect and store data securely, access data (near real-time), create reports, perform quality control, update questionnaires, and provide timely feedback to local data management teams. Additional REDCap functionalities (e.g. scheduling, data validation, and barcode scanning) supported the study.
UNASSIGNED: While the HAPIN trial experienced some challenges, REDCap effectively met HAPIN study goals, including quality data collection and timely reporting and analysis on this important global health trial, and supported more than 40 peer-reviewed scientific publications to date.

BACKGROUND: Patient-reported outcomes (PROs) can be defined as any report of a patient\'s health taken directly from the patient. Routine collection of PRO data has been shown to offer potential benefits to patient-doctor communication. Electronic forms of PRO measures (PROMs) could be more beneficial in comparison to traditional PROMs in obtaining PROs from patients. However, it is currently unclear whether the routine collection of electronic PRO data could result in better outcomes for patients undergoing laparoscopic cholecystectomy (LC).
OBJECTIVE: This study aims to explore the perspectives of patients and surgeons on the use of electronic PROMs. Based on prior research, technical skill and experience level of the surgeon, long-term quality of life, patient involvement in decision-making, communication skills of the surgeon, cleanliness of the ward environment, and standards of nursing care are identified to be the most important factors for the patients.
METHODS: This is a mixed methods prospective study that will collect both quantitative (survey) and qualitative (interview) data. The study has two components. The first involves the distribution of an electronic presurvey to patients who received elective LC within 48 hours of their surgery (n=80). This survey will explore the perspective of patients regarding the procedure, hospital experience, long-term outcomes, and the perceived value of using PROMs. These patients will then be followed up after 1 year and given another survey. The second component involves the distribution of the same survey and the completion of structured interviews with general surgeons (n=10). The survey will ascertain what PROs from the participants are most useful for the surgeons and the interviews will focus on how the surgeons view routine PRO collection. A convenience sampling approach will be used. Surveys will be distributed through Qualtrics and interviews will be completed on Microsoft Teams.
RESULTS: Data collection began on February 14, 2023. As of February 12, 2024, 71 of 80 recruited patients have been given the presurvey. The follow-up with the patients and the general surgeon components of the study have not begun. The expected completion date of this study is in April 2025.
CONCLUSIONS: Overall, this study will investigate the potential of electronic PRO collection to offer value for patients and general surgeons. This approach will ensure that patient care is investigated in a multifaceted way, offering patient-centric guidance to surgeons in their approach to care.
UNASSIGNED: DERR1-10.2196/57344.