Using Text Messaging Surveys in General Practice Research to Engage With People From Low-Income Groups: Multi-Methods Study.

BACKGROUND: SMS text messages through mobile phones are a common means of interpersonal communication. SMS text message surveys are gaining traction in health care and research due to their feasibility and patient acceptability. However, challenges arise in implementing SMS text message surveys, especially when targeting marginalized populations, because of barriers to accessing phones and data as well as communication difficulties. In primary care, traditional surveys (paper-based and online) often face low response rates that are particularly pronounced among disadvantaged groups due to financial limitations, language barriers, and time constraints. OBJECTIVE: This study aimed to investigate the potential of SMS text message-based patient recruitment and surveys within general practices situated in lower socioeconomic areas. This study was nested within the Reducing Alcohol-Harm in General Practice project that aimed to reduce alcohol-related harm through screening in Australian general practice. METHODS: This study follows a 2-step SMS text message data collection process. An initial SMS text message with an online survey link was sent to patients, followed by subsequent surveys every 3 months for consenting participants. Interviews were conducted with the local primary health network organization staff, the participating practice staff, and the clinicians. The qualitative data were analyzed using constructs from the Consolidated Framework for Implementation Research. RESULTS: Out of 6 general practices, 4 were able to send SMS text messages to their patients. The initial SMS text message was sent to 8333 patients and 702 responses (8.2%) were received, most of which were not from a low-income group. This low initial response was in contrast to the improved response rate to the ongoing 3-month SMS text message surveys (55/107, 51.4% at 3 months; 29/67, 43.3% at 6 months; and 44/102, 43.1% at 9 months). We interviewed 4 general practitioners, 4 nurses, and 4 administrative staff from 5 of the different practices. Qualitative data uncovered barriers to engaging marginalized groups including limited smartphone access, limited financial capacity (telephone, internet, and Wi-Fi credit), language barriers, literacy issues, mental health conditions, and physical limitations such as manual dexterity and vision issues. Practice managers and clinicians suggested strategies to overcome these barriers, including using paper-based surveys in trusted spaces, offering assistance during survey completion, and offering honoraria to support participation. CONCLUSIONS: While SMS text message surveys for primary care research may be useful for the broader population, additional efforts are required to ensure the representation and involvement of marginalized groups. More intensive methods such as in-person data collection may be more appropriate to capture the voice of low-income groups in primary care research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3399/BJGPO.2021.0037.

NFDI4Health Local Data Hubs Implementing a Tailored Metadata Schema for Health Data.

INTRODUCTION: NFDI4Health is a consortium funded by the German Research Foundation to make structured health data findable and accessible internationally according to the FAIR principles. Its goal is bringing data users and Data Holding Organizations (DHOs) together. It mainly considers DHOs conducting epidemiological and public health studies or clinical trials. METHODS: Local data hubs (LDH) are provided for such DHOs to connect decentralized local research data management within their organizations with the option of publishing shareable metadata via centralized NFDI4Health services such as the German central Health Study Hub. The LDH platform is based on FAIRDOM SEEK and provides a complete and flexible, locally controlled data and information management platform for health research data. A tailored NFDI4Health metadata schema for studies and their corresponding resources has been developed which is fully supported by the LDH software, e.g. for metadata transfer to other NFDI4Health services. RESULTS: The SEEK platform has been technically enhanced to support extended metadata structures tailored to the needs of the user communities in addition to the existing metadata structuring of SEEK. CONCLUSION: With the LDH and the MDS, the NFDI4Health provides all DHOs with a standardized and free and open source research data management platform for the FAIR exchange of structured health data.

Perceived usefulness of digital self-tracking among people with multiple sclerosis.

Background and aim: 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. Methods: 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. Results: 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. Conclusion: 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.

[Transfemoral amputation due to uncontrollable infections after implantation of a total knee endoprosthesis]. / Transfemorale Amputation aufgrund nichtbeherrschbarer Infektionen nach Implantation einer Knietotalendprothese.

INTRODUCTION: 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. METHOD: 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. CONCLUSION: 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.

Cross-Site Evaluation of Alcohol Screening and Brief Intervention Implementation Programs in Healthcare Systems Serving Individuals of Reproductive Age.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Burden and Inattentive Responding in a 12-Month Intensive Longitudinal Study: Interview Study Among Young Adults.

BACKGROUND: Intensive longitudinal data (ILD) collection methods have gained popularity in social and behavioral research as a tool to better understand behavior and experiences over time with reduced recall bias. Engaging participants in these studies over multiple months and ensuring high data quality are crucial but challenging due to the potential burden of repeated measurements. It is suspected that participants may engage in inattentive responding (IR) behavior to combat burden, but the processes underlying this behavior are unclear as previous studies have focused on the barriers to compliance rather than the barriers to providing high-quality data. OBJECTIVE: This study aims to broaden researchers' knowledge about IR during ILD studies using qualitative analysis and uncover the underlying IR processes to aid future hypothesis generation. METHODS: We explored the process of IR by conducting semistructured qualitative exit interviews with 31 young adult participants (aged 18-29 years) who completed a 12-month ILD health behavior study with daily evening smartphone-based ecological momentary assessment (EMA) surveys and 4-day waves of hourly EMA surveys. The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions (ACQs) to understand participants' response patterns and potential factors leading to IR. RESULTS: Thematic analysis revealed 5 overarching themes on factors that influence participant engagement: (1) friends and family also had to tolerate the frequent surveys, (2) participants tried to respond to surveys quickly, (3) the repetitive nature of surveys led to neutral responses, (4) ACQs within the surveys helped to combat overly consistent response patterns, and (5) different motivations for answering the surveys may have led to different levels of data quality. CONCLUSIONS: This study aimed to examine participants' perceptions of the quality of data provided in an ILD study to contribute to the field's understanding of engagement. These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified 5 factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, the consideration of dynamic motivations, and the potential benefit of including ACQs as a technique to reduce IR and leveraging the intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multimonth ILD studies through better data collection protocols. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/36666.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.

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.

Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data.

Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.

Applying the FAO surveillance evaluation tool (SET) to assess the fish farming disease surveillance system in Spain.

EU Member States should ensure that they implement adequate health surveillance schemes in all aquaculture farming areas, as appropriate for the type of production. This study presents the results of applying the FAO's Surveillance Evaluation Tool (SET) to assess the Spanish disease surveillance system for farmed fish species, which although applied previously in livestock production, is applied here to aquaculture for the first time. Overall, there were important score differences between trout and marine fish (seabass and seabream) surveillance, which were higher for trout in the following areas: Institutional (70.8% versus 50.0%), Laboratory (91.7% versus 47.2%), and Surveillance activities (75.3% versus 61.3%). For other categories, the values were lower and no significant differences were found. However, most surveillance efforts focused only on trout, for which there are EU and WOAH listed (notifiable) diseases. In contrast, for seabream and seabass, for which there are no listed diseases, it was considered that surveillance efforts should, nevertheless, be in place and should focus on the identification of abnormal mortalities and emerging diseases, for which there are as yet no standardized harmonised methodologies.

Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

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

Design of the 18-year follow-up of the Danish COPSAC2000 birth cohort.

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

A Multistakeholder Qualitative Study to Inform Sexual Orientation and Gender Identity Data Collection in the Cancer Care Setting.

Purpose: Sexual and gender minoritized (SGM) populations face health disparities along the cancer care continuum, although attempts to define these disparities are limited by a lack of comprehensive sexual orientation and gender identity (SOGI) data collection. The objective of this study was to interview a diverse group of stakeholders to understand attitudes, barriers, and facilitators to inform data collection approaches in a cancer care setting. Methods: This was a qualitative study conducted from March to July 2023 with paired surveys of stakeholders including patients, caregivers, providers, and cancer registry staff. Twenty participants across these categories, including half who identified as SGM, completed surveys and interviews. Qualitative data were reduced to themes with exemplar quotations using rapid qualitative analysis methods and compared to survey data. Results: Themes revealed general support for SOGI data collection as part of holistic cancer care, and all participants acknowledged that specific SOGI-related information, particularly correct pronoun usage, was essential to inform patient-centered care. Themes revealed tensions around optimal SOGI data collection methods, mixed opinions on the relevance of sexual orientation, experiences of discrimination and discomfort related to SOGI, and limited acknowledgment of population benefits of SOGI data collection. Conclusion: Themes demonstrated overall support for SOGI data collection but also revealed several barriers, such as a lack of recognition of population benefits and experiences of discrimination and discomfort, that will need to be addressed to comprehensively collect these data. Based on diverse preferences and limitations of all methods of collection, a multimodal approach may be needed to optimize completion.

Method for Using Voicemail and Email for Qualitative Data Collection Among Nurses.

BACKGROUND: Memorializing nurses' experiences during the COVID-19 pandemic had the potential to allow scientists and policymakers to learn about the impact on the nursing profession and health care systems. Yet, nurses are considered a difficult population to recruit for research. OBJECTIVE: To describe an innovative qualitative data collection method for capturing current practice experiences among nurses working during the COVID-19 pandemic. METHODS: Guerilla theory served as the theoretical framework. Utilizing a qualitative descriptive design, a telephone voicemail messaging system was developed to capture nurses' experiences. RESULTS: Nurses were recruited with convenience and snowball sampling via social media and state listservs. The telephone voicemail messaging system, Twilio, was used. After listening to the recording of the consent form, the participants shared their experiences by leaving a voice message where they answered the prompt, "Tell us about your experiences working during the COVID-19 pandemic." Seventy voicemails were included, and the voicemails were transcribed. After a nurse shared their experience via an email sent to the research team, emails were added to the data collection; 16 emails were received. Transcripts and emails were uploaded to the qualitative data analysis software program, Dedoose, and coded by 2 researchers using content analysis. Main themes were derived and discussed among the research team. CONCLUSION: Allowing participants multiple modes of expressing their experiences promote inclusivity in data collection. Further development and standardization of this method is needed for future research.

Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review.

BACKGROUND: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice. OBJECTIVE: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice. METHODS: A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs). RESULTS: The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk. CONCLUSIONS: This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine. TRIAL REGISTRATION: PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

Transport of critically ill children to paediatric intensive care units in the UK and Ireland: 2013-2022.

OBJECTIVE: To explore the trends and changes in the transport of children to paediatric intensive care units (PICUs) between 2013 and 2022. DESIGN: Retrospective analysis of routinely collected data. PATIENTS: Children transported for care in a PICU in the UK and Ireland aged<16 years. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 43 058 transports to a PICU involving 36 438 children from 2013 to 2022 with the majority of children requiring only one transport. The number of transports increased from 4131 (2013) to 4792 (2022). Over the study period the percentage of children aged under 1 year who were transported decreased from 50.2% to 45.2% and similarly, the percentage who were invasively ventilated also decreased from 81.1% to 70.2%. Conversely, the use of non-invasive ventilation during transports increased slightly from 4.0% to 7.0%. The percentage of transports where a parent was able to accompany the child increased over time (2013: 66.2% to 2019: 74.9%), although there were reductions due to the COVID-19 pandemic and requirements for social distancing (2020: 52.4%). CONCLUSIONS: We have demonstrated an increased use of specialist paediatric transport services and changes in the PICU population over time. Routine data collection from the transport services provide a means to measure improvements and changes over time in the service provided to critically ill children and young people who need transport to the PICU.

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.

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).

Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep Learning-Based Applications.

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

An End-to-End Deep Learning Framework for Fault Detection in Marine Machinery.

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

Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education.

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