Establishing Priorities for Improving Data Collection and Measurement of Mental Health and Well-Being of Adolescents With Special Educational Needs Within Nonmainstream Schools: Protocol for a Delphi Study.

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.

Experiences and prospects for national survey of terrestrial wildlife resources in China.

Wildlife resources are strategic resources of a country, and the investigation of which is a key task for effective management in protection and utilization. Since the 1990s, two national surveys of terrestrial wildlife resources have been carried out in China, and the situation of wildlife resources has been known to a certain extent. Due to the complexity and difficulty of national wildlife survey, we are still not able to grasp the background and dynamics of wildlife resources as a whole promptly and effectively. The results and effectiveness of wildlife resources investigation will directly affect the decision-making related in wildlife protection. According to Law of the People's Republic of China on the Protection of Wildlife and Regulations of the People's Republic of China for the Implementation of the Protection of Terrestrial Wildlife, it is imperative to carry out the third national survey of terrestrial wildlife resources, and to be integrated with the national strategy of ecological civilization construction. The aims of this review were to summarize the earlier experiences in time, to further improve the investigation scheme and technical methods, to serve the third national survey of terrestrial wildlife resources, in addition to obtain more comprehensive and reliable data of wildlife resources, grasp the development trend of domestic wildlife resources, and provide more effective supports for the wildlife conservation in China.

Lessons learned from the MOMENT study on how to recruit and retain a target population online, across borders, and with automated remote data collection.

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.

Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge.

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.

Development of an IoT-Based Device for Data Collection on Sheep and Goat Herding in Silvopastoral Systems.

To evaluate the ecosystem services of silvopastoral systems through grazing activities, an advanced Internet of Things (IoT) framework is introduced for capturing extensive data on the spatial dynamics of sheep and goat grazing. The methodology employed an innovative IoT system, integrating a Global Navigation Satellite System (GNSS) tracker and environmental sensors mounted on the animals to accurately monitor the extent, intensity, and frequency of grazing. The experimental results demonstrated the high performance and robustness of the IoT system, with minimal data loss and significant battery efficiency, validating its suitability for long-term field evaluations. Long Range (LoRa) technology ensured consistent communication over long distances, covering the entire grazing zone and a range of 6 km in open areas. The superior battery performance, enhanced by a solar panel, allowed uninterrupted operation for up to 37 days with 5-min interval acquisitions. The GNSS module provided high-resolution data on movement patterns, with an accuracy of up to 10 m after firmware adjustments. The two-part division of the device ensured it did not rotate on the animals' necks. The system demonstrated adaptability and resilience in various terrains and animal conditions, confirming the viability of IoT-based systems for pasture monitoring and highlighting their potential to improve silvopastoral management, promoting sustainable practices and conservation strategies. This work uniquely focuses on documenting the shepherd's role in the ecosystem, providing a low-cost solution that distinguishes itself from commercial alternatives aimed primarily at real-time flock tracking.

Data collection methods for patient-reported outcome measures in cancer randomised controlled trials: a protocol for a rapid scoping review.

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.

SATURN: assessing the feasibility of utilising existing registries for real-world evidence data collection to meet patients, regulatory, health technology assessment and payer requirements.

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.

Musculoskeletal Disorder (MSD) Health Data Collection, Personalized Management and Exchange Using Fast Healthcare Interoperability Resources (FHIR).

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).

Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection.

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.

Facilitators and barriers to monitoring and evaluation at syringe service programs.

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.

Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.

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.

Mixed Mode Substantially Increases Hospital Consumer Assessment of Healthcare Providers and Systems Response Rates Relative to Single-Mode Protocols.

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.

Landscape analysis of environmental data sources for linkage with SEER cancer patients database.

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.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

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.

Is it time to stop counting on saturation in pharmacy research? Ideas for a new way forward.

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.

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system.

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.