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BACKGROUND: There are more than 1.5 million children and young people in England with special educational needs (SEN), with over 160,000 young people in the United Kingdom attending a special school or alternative provision (AP) setting. Young people with SEN have been found to be at risk for poorer mental health and well-being than non-SEN peers. However, there is a range of both school-related and research challenges associated with identifying difficulties in a timely manner. OBJECTIVE: This Delphi study aims to determine a list of stakeholder priorities for improving school-based measurement of mental health and well-being among young people with SEN, at an aggregated level, within secondary special school or AP settings. A secondary objective is to inform the implementation of school-based well-being surveys, improve engagement in special schools or AP settings, and improve survey response rates among children and young people with SEN. METHODS: A mixed methods Delphi study will be conducted, including a scoping review and preliminary focus groups with school staff members and researchers to establish key issues. This will be followed by a 2-round Delphi survey to determine a list of stakeholder priorities for improving the measurement of mental health and well-being at an aggregate level within special schools and AP settings. A final stakeholder workshop will be held to discuss the findings. A list of recommendations will be drafted as a report for special schools and AP settings. RESULTS: The study has received ethical approval from the University College London Research Ethics Committee. The stage 1 scoping review has commenced. Recruitment for focus groups will begin in Autumn 2024. The first round of the Delphi survey will commence in early 2025, and the second round of the Delphi survey in the spring of 2025. The final workshop will commence in mid-2025 with final results expected in late 2025. CONCLUSIONS: There is a need for clear recommendations for special schools and AP settings on priorities for improving the measurement of mental health and well-being problems among young people with SEN. There is also a need for recommendations to researchers implementing school-based well-being surveys, including the #BeeWell program, to enable them to improve their engagement in special schools and AP settings and ensure surveys are accessible. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/58610.

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Increasingly, studies use social media to recruit, enroll, and collect data from participants. This introduces a threat to data integrity: efforts to produce fraudulent data to receive participant compensation, e.g., gift cards. MOMENT is an online symptom-monitoring and self-care study that implemented safeguards to protect data integrity. Facebook, Twitter, and patient organizations were used to recruit participants with chronic health conditions in four countries (USA, Italy, The Netherlands, Sweden). Links to the REDCap baseline survey were posted to social media accounts. The initial study launch, where participants completed the baseline survey and were automatically re-directed to the LifeData ecological momentary assessment app, was overwhelmed with fraudulent responses. In response, safeguards (e.g., reCAPTCHA, attention checks) were implemented and baseline data was manually inspected prior to LifeData enrollment. The initial launch resulted in 411 responses in 48 hours, 265 of which (64.5%) successfully registered for the LifeData app and were considered enrolled. Ninety-nine percent of these were determined to be fraudulent. Following implementation of safeguards, the re-launch yielded 147 completed baselines in 3.5 months. Eighteen cases (12.2%) were found fraudulent and not invited to enroll. Most fraudulent cases in the re-launch (15 of 18) were identified by a single attention check question. In total, 96.1% of fraudulent responses were to the USA-based survey. Data integrity safeguards are necessary for research studies that recruit online and should be reported in manuscripts. Three safeguard strategies were effective in preventing and removing most of the fraudulent data in the MOMENT study. Additional strategies were also used and may be necessary in other contexts.

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This paper reports on the growing issues experienced when conducting web-based-based research. Nongenuine participants, repeat responders, and misrepresentation are common issues in health research posing significant challenges to data integrity. A summary of existing data on the topic and the different impacts on studies is presented. Seven case studies experienced by different teams within our institutions are then reported, primarily focused on mental health research. Finally, strategies to combat these challenges are presented, including protocol development, transparent recruitment practices, and continuous data monitoring. These strategies and challenges impact the entire research cycle and need to be considered prior to, during, and post data collection. With a lack of current clear guidelines on this topic, this report attempts to highlight considerations to be taken to minimize the impact of such challenges on researchers, studies, and wider research. Researchers conducting web-based research must put mitigating strategies in place, and reporting on mitigation efforts should be mandatory in grant applications and publications to uphold the credibility of web-based research.

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BACKGROUND: There are different modes and ways to assess patient-reported outcomes (PROs) in clinical trials. However, there is little systematic information on how often different modes of assessment (MOA) are used in cancer clinical trials and how exactly assessments are conducted. The goal of this scoping review is to gain an understanding of the MOA and data management of PROs in cancer randomised controlled trials (RCTs) and the reporting quality thereof. METHODS AND ANALYSIS: This scoping review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Relevant trials will be identified via their indexed publications. We will search PubMed for RCTs conducted in cancer populations that evaluate a biomedical treatment with a PRO endpoint. Trials with publications published between January 2019 and November 2023 will be included. Two independent reviewers will review the references for both the abstract and full-text screening. We will extract data from the publications from a trial and the trial protocol if a protocol can be traced. Data will be summarised at the trial level. We will focus on a descriptive analysis of the MOA of PROs and on the relative frequencies of the different MOA. We will also evaluate the quality of reporting for the relevant SPIRIT and CONSORT guidelines that refer to the assessment of PROs in trials. Due to the scoping nature of our review, we will not perform a dedicated quality assessment of all trials. ETHICS AND DISSEMINATION: The proposed review is based on secondary, published data. Hence, no ethics review is necessary. The review is part of an ongoing project on the use of electronic data capture methods in cancer clinical trials. The findings from the review will support the project and contribute to synthesising guidance to ultimately improve the (electronic) measurement of patient-reported outcome measures in clinical trials.

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BACKGROUND: SATURN (Systematic Accumulation of Treatment practices and Utilisation, Real world evidence, and Natural history data) for the rare condition osteogenesis imperfecta (OI) has the objective to create a common core dataset by utilising existing, well-established data sources to meet the needs of the various stakeholders (physicians, registry/dataset owners, patients and patient associations, OI community leaders, European [EU] policymakers, regulators, health technology assessments [HTA]s, and healthcare systems including payers). This paper describes the steps taken to assess the feasibility of one existing OI registry (i.e., the Registry of OI [ROI]) as a candidate for SATURN. The same methodology will be applied to other existing OI registries in the future and this same concept could be utilised for other rare disease registries. METHODS: The approach to assessing the feasibility of the ROI registry consisted of three steps: (1) an assessment of the registry characteristics using the Registry Evaluation and Quality Standards Tool (REQueST); (2) a gap analysis comparing SATURN required Core Variables to those being captured in the registry's Case Report Form (CRF); and (3) a compliance check on the data exchange process following the Title 21 of Code of Federal Regulations (CFR) Part 11/EudraLex Annex 11 Compliance Checklist. The first registry that SATURN has assessed is the ROI database at the Istituto Ortopedico Rizzoli (IOR) in Italy. RESULTS: The results from the ROI REQueST have demonstrated satisfactory complete responses in terms of methodology, essential standards, interpretability, and interoperability-readiness for data linkage, data sources, and ethics to meet the needs of data customers. However, the ROI data is from a tertiary referral centre which may limit the ability to understand the full patient journey. The gap analysis has revealed that an exact or logical match between SATURN requested variables and the ROI current variables exists for the following items: patient characteristics, treatment of OI (medical and surgical) and treatment of pain (with the exception of frequency of treatment and reasons for discontinuation), fracture history and bone density. However, data on safety was missing. The compliance check has implied that the ROI implemented appropriate controls for the web-based platform (i.e., Genotype-phenotype Data Integration Platform [GeDI]) that is involved in processing the electronic patient data, and GeDI is a validated/compliant application that follows relevant 21 CFR Part 11/EudraLex Annex 11 regulations. CONCLUSIONS: This robust feasibility process highlights potential limitations and opportunities to develop and to refine the collaboration with the ROI as the SATURN programme moves forward. It also ensures that the existing datasets in the rare condition OI are being maximised to respond to the needs of patients, data customers and decision-makers. This feasibility process has allowed SATURN to build a compliant methodology that aligns with the requirements from the European Medicines Agency (EMA) and HTAs. More data variables will continue to be developed and refined along the way with more registries participating in SATURN. As a result, SATURN will become a meaningful and truly collaborative core dataset, which will also contribute to advancing understanding of OI diagnosis, treatment, and care.

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In recent years, the integration of game-like elements into non-gaming contexts has shown promise in enhancing user engagement and motivation. This study assesses the impact of gamification elements on data collection efficacy in m-health applications. An ad-hoc mobile application was developed and used in a randomized two-arm pilot study. Participants interacted either with the gamified meal-logging application or with its non-gamified version for ten days. The results from this study emphasize the benefits of incorporating gamification techniques into health applications embedded in digital platforms. While both versions were well-received, reaching high System Usability Scale (SUS) scores (91 and 93.5) and generally positive feedback, the gamified app demonstrated a distinct advantage in promoting user engagement and consistent data logging. This highlights the importance of gamification in health research, suggesting its potential to ensure thorough and consistent data collection, which is essential for producing reliable research outcomes.

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Unlabelled: The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device's application programmable interface and the mobile app's adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations.

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BACKGROUND: Low response rates (RRs) can affect hospitals' data collection costs for patient experience surveys and value-based purchasing eligibility. Most hospitals use single-mode approaches, even though sequential mixed mode (MM) yields higher RRs and perhaps better patient representativeness. Some hospitals may be reluctant to incur MM's potential additional cost and complexity without knowing how much RRs would increase. OBJECTIVE: The aim of this study was to estimate the differences in RR and patient representation between MM and single-mode approaches and to identify hospital characteristics associated with the largest RR differences from MM of single-mode protocols (mail-only, phone-only). RESEARCH DESIGN: Patients were randomized within hospitals to one of 3 modes (mail-only, phone-only, MM). SUBJECTS: A total of 17,415 patients from the 51 nationally representative US hospitals participating in a randomized HCAHPS mode experiment. RESULTS: Mail-only RRs were lowest for ages 18-24 (7%) and highest for ages 65+ (31%-35%). Phone-only RRs were 24% for ages 18-24, increasing to 37%-40% by ages 55+. MM RRs were 28% for ages 18-24, increasing to 50%-60% by ages 65-84. Lower hospital-level mail-only RRs strongly predicted greater gains from MM. For example, a hospital with a 15% mail-only RR has a predicted MM RR >40% (with >25% occurring in telephone follow-up). CONCLUSION: MM increased representation of hard-to-reach (especially young adult) patients and hospital RRs in all mode experiment hospitals, especially in hospitals with low mail-only RRs.

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One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

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BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

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The concept of saturation is commonly mentioned in pharmacy research, but there has been recent debate among the applied qualitative research community that challenges the appropriateness of this construct for many qualitative research efforts. This begins by describing the origins of saturation as a grounded theory construct and discusses how saturation is currently being used. Three challenges are discussed related to the use of saturation in pharmacy related to the epistemological, methodological, and practical use of saturation by pharmacy researchers and how they relate to the goals and reporting quality of pharmacy practice research The commentary describes how the concept of information power and established guidance on analysis quality can better justify sample size inform decisions about when to cease further data collection, hopefully increasing the transparency of reporting and supporting rigorous and coherent analyses.

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Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

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REDCap, a popular platform for building surveys for electronic data capture, offers two methods for creating questionnaires: an interactive web interface to modify single questions and an upload method to import entire questionnaires. Both methods present limitations in terms of usability and time needed for different tasks. We propose a browser-based web application to design and manage REDCap questionnaires using a What-You-See-Is-What-You-Get approach. The application provides a user-friendly interface for a comprehensive overview of all imported questionnaires, and three distinct views cater to different aspects of the questionnaire design process. The questionnaires can be imported and exported through the REDCap CSV format and thus integrate seamlessly into its environment. REDCapQB represents a significant advancement in questionnaire design and management, offering researchers a powerful and user-friendly tool for electronic data capture in translational research studies within the REDCap ecosystem.

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PURPOSE: There has been rapid growth in the variety and number of real-world data (RWD) sources, as well as the number of regulatory documents that provide guidance for assessing the suitability of RWD sources for pharmacoepidemiology studies. This study aims to assess differences in RWD guidance and variability in current practice for identifying and assessing RWD for studies with regulatory purpose. METHODS: Key criteria for feasibility assessment were mapped against relevant regulatory guidance documents across US, EU, and Asia-Pacific regions. An online survey was designed and deployed to International Society for Pharmacoepidemiology members to understand current practice. Findings were summarized and used to inform key considerations and recommendations. RESULTS: Eleven RWD guidance documents were identified and mapped against 14 RWD assessment criteria. Variability was seen across these documents in guidance for these criteria. Between December 2022 and January 2023, 37 survey respondents reported having used RWD for post-marketing commitments (34, 92%) and/or background epidemiology (28, 76%). RWD were mostly identified through literature (33, 89%) and data landscaping (26, 70%); guidance documents referenced included: Food and Drug Administration (20, 54%), European Network for Centres for Pharmacoepidemiology and Pharmacovigilance (17, 46%), European Medical Agency (16, 43%), and Structured Process to Identify Fit-For-Purpose Data (11, 30%). Challenges for conducting feasibility assessments included RWD accessibility, ability to complete validation, and RWD provider responsiveness. CONCLUSIONS: Existing guidelines are used extensively by researchers, but key criteria for RWD identification and feasibility assessment are not reflected consistently and challenges remain. Recommendations have been made reflecting study findings.

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PURPOSE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

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OBJECTIVE: The aim of this study was to determine response patterns to sexual orientation and gender identity (SOGI) questions in the Behavioral Risk Factor Surveillance System (BRFSS) over time and to assess nonresponse and indeterminate responses by demographic characteristics. METHODS: This is a secondary data analysis of the SOGI module of the BRFSS. We used data from 46 states and Guam that implemented SOGI questions between 2014 and 2022. We used weighted analyses that accounted for the sampling design, determined SOGI response patterns by year, and assessed nonresponse and indeterminate responses by demographic characteristics. RESULTS: Over time, increasing numbers self-reported as sexual and gender minority respondents, while heterosexual identity declined. Sexual orientation nonresponse and indeterminate responses increased with time, while respondents' reports of not knowing gender identity declined. Hispanic, older, respondents, those with lower education, and those who completed the questionnaire in Spanish had higher SOGI nonresponse and indeterminate responses. CONCLUSIONS: The low amount of SOGI nonresponse and indeterminate responses in the BRFSS can be instructive for the implementation of SOGI questions in medical settings. SOGI data collection in all settings requires improving procedures for the groups that have been shown to have elevated nonresponse and indeterminate response.

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OBJECTIVES: To examine Indigenous Governance of Data processes in Australian clinical registries. DESIGN, SETTING, PARTICIPANTS: Audit (via desktop review and interviews) of registries in the Australian Register of Clinical Registries from 17 January 2022 to 30 April 2023. MAIN OUTCOME MEASURES: The number of clinical registries collecting ethnicity data, reporting Aboriginal and/or Torres Strait Islander representation on registry governance or steering committees, and reporting human research ethics committee approval. RESULTS: A total of 107 clinical registries were reviewed. Of these registries, 65 (61%) collected ethnicity data; when these were grouped by geographical coverage, those most likely to collect ethnicity data were binational (24/40 [60%]), national (19/26 [73%]) or state based (19/26 [73%]). Of the registries that collected ethnicity data, 29 (45%) classified their ethnicity item as Aboriginal and/or Torres Strait Islander. Only eight clinical registries (7%) reported Aboriginal and/or Torres Strait Islander representation on their governance or steering committees. Human research ethics approval was reported in 94 registries (88%), with only 11 (12%) having Aboriginal human research ethics committee approval. CONCLUSION: Significant variability is evident in clinical registry recording of Indigenous governance of data, meaning that Aboriginal and Torres Strait Islander communities remain invisible in data which is used to inform policy, clinical models of care, health services and initiatives. Radical change is required to facilitate meaningful change in quality indicators for clinical registries nationally.

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