OBJECTIVE: 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.

UNASSIGNED: With US Centers for Disease Control and Prevention funding, from 2018 to 2022, 4 large healthcare systems (n = 53 health centers across 7 states) serving people of reproductive age trained staff and provided implementation support for alcohol screening and brief intervention (SBI). This cross-site evaluation explores each healthcare system\'s implementation approach to implement SBI, reduce excessive alcohol use, and prevent prenatal alcohol exposure (PAE) and fetal alcohol spectrum disorders.
UNASSIGNED: The SBIRT (Screening, Brief Intervention, and Referral to Treatment) Program Matrix framed the multilevel strategies to implement alcohol SBI programs from 2018 to 2022. Qualitative and quantitative data sources examined outcomes, guided by one logic model, through systems-level process data and provider-level performance metrics. Data analyses utilized frequencies and means for quantitative data and themes for qualitative data according to an established framework.
UNASSIGNED: Successful approaches within systems included using electronic health records, flexible implementation and workflow protocols, customized training and technical assistance programs, quality assurance feedback loops, and stakeholder buy-in. Centralized management structures were efficient in standardizing implementation across health centers. Decentralized management structures used tailored approaches, enhancing provider/staff SBI acceptance. Across systems, 1259 staff (eg, clinicians, medical assistants) were trained to provide alcohol SBI services and reported pre-post training increases in self-efficacy in performing brief intervention; skills in PAE counseling; and confidence in screening. Fifty-three (48 providing data) health centers implemented alcohol SBI, screening 106 826 patients over the study period with most of the 10 087 patients who screened positive for excessive alcohol use receiving a BI.
UNASSIGNED: Maximizing the use of technology, employing flexibility in program delivery, and institutionalizing processes and protocols improved workflow, efficiency, and program reach. Ongoing partnership and stakeholder communication identify areas for ongoing improvement, engagement, and best practices for sustainability around substance use screening, which are essential with increases in substance use since the pandemic.

Qualitative research is a relatively new approach for conducting studies in health disciplines. The value of this research approach is to explore peoples\' experiences and gain a deeper understanding of the meaning of their experiences. Qualitative inquiries answer research questions about what, why and how by implementing various research designs such as qualitative descriptive, qualitative case study, ethnological, phenomenology, or grounded theory designs. Purposive and snowball sampling methods are commonly used to recruit participants followed by personal interviews or focus group discussions to collect data. Data analysis requires several coding procedures performed by the researcher or an alternative is using a coding software program. Preparing a manuscript for dissemination of the results can be challenging, although achievable.

BACKGROUND: In the evaluation of an internal analysis of data on the increased effort for nursing during rehabilitation of patients with amputations in the Baumrainklinik of the HELIOS Rehabilitation Center Bad Berleberg, the number of patients with transfemoral amputations (TFA) due to uncontrollable multiple infections after implantation of a total knee endoprosthesis (total knee arthroplasty, TKA) was clearly emphasized.
OBJECTIVE: This article discusses the results of a retrospective, patient-controlled trial (PCT) and compares these with the data of the German Endoprosthesis Registry (EPRD). The study concentrated on patients who were admitted to rehabilitation after a TFA due to an uncontrollable infection after implantation of a knee TKA. The primary aims were the identification of patients who developed an uncontrollable infection after TKA with subsequent TFA and the comparison with national and international revision and amputation rates after TKA.
METHODS: An analysis of the medical history questionnaire was carried out for all 787 patients with amputation of the lower extremities who underwent rehabilitation in the time period from 1st January 2007 to 31st December 2015. The patient records were systematically analyzed based on the standardized documentation methods of the medical and nursing personnel using the Barthel index, the activity/function classes, phantom pain and length of stay, including demography, infection history and insurance company.
RESULTS: The analysis showed that 10 patients, 2.29% of all TFA, suffered the loss of a lower extremity due to an uncontrollable TKA infection. The revision rate 3 years after primary TKA in Germany is 3.0% (EPRD annual report 2023), whereas values of 1-4% are given in the international literature (status 2020). In the patient group of the EPRD, in 2022 revision surgery was necessary due to an infection in 15.0% of the cases. The current statistics of the EPRD (annual report 2023) show that 3 years after the initial revision surgery due to an infected TKA another revision was necessary in 23.5-30% of cases.
CONCLUSIONS: These numbers are alarming and should be critically evaluated and monitored. The future aim is to identify the causes of infections, systematic errors in the TKA and the pathogens that lead to infections after TKA and to correlate the associations.
UNASSIGNED: EINLEITUNG: Bei einer internen Analyse von Daten zum erhöhten Pflegeaufwand im Rahmen der Rehabilitation von amputierten Patienten im Helios Rehazentrums Bad Berleburg hob sich die Zahl der Patienten mit einer transfemoralen Amputation (TFA, Oberschenkelamputation) aufgrund nichtbeherrschbarer, multipler Infektionen nach Implantation einer Knietotalendoprothese (Knie-TEP) deutlich hervor.
UNASSIGNED: Die vorliegende Arbeit diskutiert die Ergebnisse einer retrospektiven, patientenkontrollierten Studie (PCT) und gleicht diese mit Daten des Endoprothesenregister Deutschland (EPRD) ab. Primäre Ziele sind die Identifikation von Patienten, die nach Implantation einer Knie-TEP eine nichtbeherrschbare Infektion mit konsekutiver TFA entwickelten, sowie der Vergleich mit nationalen und internationalen Registern.
METHODS: Auswertung der Anamnesebögen aller rehabilitierter 787 Patienten mit Amputationen an der unteren Extremität. Die Krankenakten wurden systematisch anhand der vom ärztlichen und Pflegepersonal dokumentierten standardisierten Methoden, des Barthel-Index, der Aktivitäts‑/Funktionsklassen, des Phantomschmerzes und der Aufenthaltsdauer ausgewertet und umfasste Demografie, Infektionsanamnese und Versicherungsträger.
UNASSIGNED: Zehn Patienten, 2,29 % aller TFA-Patienten, erlitten einen Verlust der unteren Extremität aufgrund einer unkontrollierbaren Knie-TEP-Infektion. Die Revisionsrate 3 Jahre nach primärer Knie-TEP beträgt in Deutschland 3,0 % (EPRD Jahresbericht 2023), während in der internationalen Literatur (Stand 2020) Werte zwischen 1 und 4 % angegeben werden.
UNASSIGNED: Diese Zahlen sind alarmierend und sollten kritisch evaluiert und gemonitored werden. Zukünftiges Ziel ist es, die Ursachen von Infektionen, systematische Fehler im Rahmen der Endoprothetik sowie die Erreger, die zu Infektionen nach Knie-TEP-Implantationen führen, zu identifizieren und Zusammenhänge zu korrelieren.

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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).
METHODS: Patients were randomized within hospitals to one of 3 modes (mail-only, phone-only, MM).
METHODS: 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).
CONCLUSIONS: 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.

UNASSIGNED: 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.

BACKGROUND: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.
OBJECTIVE: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.
METHODS: A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual\'s activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies.
RESULTS: The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score.
CONCLUSIONS: We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

UNASSIGNED: Self-care technologies may support patients with multiple sclerosis (MS) in their everyday disease management by enabling self-monitoring of various health indicators, such as symptom levels and physical activity levels. The aim of this study was to assess the usefulness of tracking self-selected MS- and health-related measures via a digital self-tracking tool for people with MS (PwMS) over a period of six weeks.
UNASSIGNED: An initial development phase was followed by a six-week testing phase with 58 test participants. The evaluation phase followed a sequential, exploratory mixed-methods design, consisting of 14 interviews with test participants during the testing phase, followed by a survey of all participants after the testing phase to confirm and elaborate on the interview findings. The interview data were analyzed through a five-step thematic analysis, and the survey data were analyzed descriptively.
UNASSIGNED: The results of the mixed-methods study can be summarized in the following findings: (1) Use of the self-tracking tool assisted users in clarifying patterns regarding their symptoms, physical activity, sleep quality and emotional well-being. (2) Tracking physical activity and, to some extent, sleep had a motivational effect on participants in relation to increasing activity and/or changing habits. (3) Data quality/accuracy constitutes an important criterion for considering the self-tracking tool relevant. (4) The self-tracking tool may support dialogue between patients and healthcare professionals, and/or it may potentially play a role in peer-to-peer support.
UNASSIGNED: The results of the present study indicate that the self-tracking of symptoms, sleep, physical activity and other measures may contribute positively to everyday self-management among PwMS. Professional support in interpreting and acting upon the data should be considered.

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.