A Web-Based Questionnaire Builder to Facilitate form Management for the Electronic Data Capture with REDCap.

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.

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.

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.

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.

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.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

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.

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.

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.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

[Managing routine health information system data during a security crisis in the health district of Timbuktu, Mali in 2023]. / La gestion des données du système d'information sanitaire de routine en période de crise sécuritaire dans le district sanitaire de Tombouctou, au Mali en 2023.

Introduction: an effective health information system (HIS) ensures the production, analysis, dissemination and use of reliable and up-to-date information on the determinants of health. However, it can encounter obstacles that hinder its functioning, such as armed conflicts, which limit access and quality of healthcare services. The purpose of our study was to help improve data management for routine health information system in the health district of Timbuktu during a security crisis. Methods: we conducted a descriptive cross-sectional study, among health information management professionals in the Timbuktu Health District from 15 April to 08 September 2023. Data obtained from a survey questionnaire were analyzed using Epi Info version 7.2.2. and processed using Microsoft Word and Excel 2016. Results: a total of 6 health facilities were surveyed. Data collection, analysis and feedback were very poor. Data quality was 100% complete, 92.40% prompt and 68.11% accurate. The major constraints were: low involvement of health workers in the SIS (22.22%), insufficient training on the SISR (29.63%), supervision (47.06%), internet inaccessibility (66.67%), feeling of insecurity (37.04%) and fear (61.76%) in health facilities. Conclusion: our results show low-level processes, poor network coverage, shortage of qualified health information management professionals and increasing insecurity. A broader mixed-methods research would provide a better understanding.

Rapid Online Data Collection and Reporting to Local Health Departments during the Pandemic: The COVID-19 Outbreak Public Evaluation (COPE) Initiative SARS-CoV-2 Case Surveys.

OBJECTIVES: To develop and implement a pilot online data collection tool to help local health departments with their COVID-19 pandemic response efforts and inform health department actions. DESIGN: The COVID-19 Outbreak Public Evaluation (COPE) was an online survey and was distributed by participating sites to individuals who recently tested positive for SARS-CoV-2. Surveys recorded participant demographics and assessed recent infection risk behaviors (eg, mask use, air travel), vaccination status, sleep and exercise habits, social behaviors and beliefs, and physical and mental health. SETTING: Seven health departments participated in the initiative, which took place during May 1 to September 30, 2022. Identical items were administered to demographically representative samples of adults nationally in the United States within a similar timeframe. PARTICIPANTS: A total of 38 555 participants completed surveys. Responses from participants with recent SARS-CoV-2 infections were compared with respondents from the national surveys who did not have evidence or awareness of prior SARS-CoV-2 infections. MAIN OUTCOME MEASURE: To implement of a process that allows health departments to receive data from local cases and compare this information to national controls during the COVID-19 pandemic. RESULTS: Fifty-four biweekly reports were provided to public health departments between May and September 2022. Information and comparisons within the reports were updated in response to evolving public health priorities for the pandemic response. The initiative helped to guide public health response efforts during the COVID-19 pandemic. Moreover, the receptiveness by local health departments and participants provides evidence to support this data collection and reporting model as a component of the public health response to future emergencies. CONCLUSION: This project demonstrates the feasibility of a centralized, rapid, and adaptive data collection system for local health departments and provides evidence to advocate for data collection methods to help guide local health departments to respond in a timely and effective manner to future public health emergencies.

Indigenous governance, ethics and data collection in Australian clinical registries.

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.

Telephone-Based Psychological Care During the 2020 Lockdown in Spain and Protocol for Data Collection.

BACKGROUND: During the COVID-19 lockdown in 2020, the General Council of Psychology in Spain, together with the regional Official Colleges of Psychology, launched the Psychological Care Telephone Program (PCTP) to provide mental health services to the population. METHOD: The aim of the present study was to perform a descriptive analysis of the PCTP by analysing the data collected during the lockdown and at the 12-month follow-up, and to develop a brief protocol designed to standardise data collection procedures. RESULTS: A total of 10,119 inbound telephone calls were made to the PCTP from March to May 2020, and 337 follow-up calls at 12 months. The most common reasons for contacting the PCTP were to consult for symptoms of anxiety (66.8%), depression (30.5%), and/or family problems (13.9%). At the 12-month follow-up, many users experienced anxiety (38%), depressive (35%), and panic (34%) symptoms. More than half of users reported using psychopharmacological medicines. CONCLUSIONS: This study demonstrates the need to offer the population telephone-based mental health consultations during times of crisis. It also shows the importance of systematising intervention and data collection procedures for future crises. We propose a data collection protocol for use with emergency telephone psychological assistance programmes.

Trends in Sexual Orientation and Gender Identity Data Collection.

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.

Determining an Appropriate Sample Size for Qualitative Interviews to Achieve True and Near Code Saturation: Secondary Analysis of Data.

BACKGROUND: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals' perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews. OBJECTIVE: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation. METHODS: The analyses for this study were based on data from 5 Food and Drug Administration-funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached. RESULTS: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, "in general"), uncertainty or confusion (eg, "don't know"), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached. CONCLUSIONS: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

Evaluation of software technical quality for collecting data from patients under palliative care.

OBJECTIVES: to evaluate software technical quality for collecting data from patients under palliative care. METHODS: this is methodological technology evaluation research, according to the technical standard International Organization for Standardization/International Electrotechnical Commission 25040-2011, developed from August 2021 to August 2023. Eight nurses and eight information technology professionals participated as judges, who evaluated six quality characteristics and 23 subcharacteristics. Items that reached a percentage of agreement greater than 70% were considered suitable. RESULTS: the characteristics evaluated by nurses/information technology professionals received the following percentages of agreement, respectively: functional suitability (94%-84%); reliability (100-70%); usability (89.9-66.8%); performance efficiency (95.8%-86.1%); compatibility (95.8-79.6%); and safety (96%-83.4%). CONCLUSIONS: the software was considered suitable in quality evaluation to offer support to nurses in collecting patient data under palliative care, with the potential to operationalize the first Nursing Process stage.

Collecting Sociodemographic Data in Pediatric Emergency Research: A Working Group Consensus.

Understanding and addressing health care disparities relies on collecting and reporting accurate data in clinical care and research. Data regarding a child's race, ethnicity, and language; sexual orientation and gender identity; and socioeconomic and geographic characteristics are important to ensure equity in research practices and reported outcomes. Disparities are known to exist across these sociodemographic categories. More consistent, accurate data collection could improve understanding of study results and inform approaches to resolve disparities in child health. However, published guidance on standardized collection of these data in children is limited, and given the evolving nature of sociocultural identities, requires frequent updates. The Pediatric Emergency Care Applied Research Network, a multi-institutional network dedicated to pediatric emergency research, developed a Health Disparities Working Group in 2021 to support and advance equitable pediatric emergency research. The working group, which includes clinicians involved in pediatric emergency medical care and researchers with expertise in pediatric disparities and the conduct of pediatric research, prioritized creating a guide for approaches to collecting race, ethnicity, and language; sexual orientation and gender identity; and socioeconomic and geographic data during the conduct of research in pediatric emergency care settings. Our aims with this guide are to summarize existing barriers to sociodemographic data collection in pediatric emergency research, highlight approaches to support the consistent and reproducible collection of these data, and provide rationale for suggested approaches. These approaches may help investigators collect data through a process that is inclusive, consistent across studies, and better informs efforts to reduce disparities in child health.