bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictors of high blood pressure in children from 6 to 11 years: The PREDI cohort study
Authors: Mastroeni M , Moletta A , Corrêa C , Mastroeni S
Published: October 2024

Background

Hypertension is an important cardiovascular disease (CVD) in Brazil that affects approximately 30% of the adult population and accounts for nearly 50% of CVD-related deaths in the country. Identifying the main predictors of high blood pressure (BP) during childhood will help prevent the development of other CVDs in adulthood and deaths. This study aimed to estimate the effect of biological, demographic, and socioeconomic characteristics on the child’s BP at 6, 9 and 11 years of age.

Methods

Cohort study conducted with 181 children in Joinville city, southeastern Brazil. Individual data were collected during three follow-ups (2016, 2021, and 2023) in a private room of the family’s home. BP was measured by the auscultatory method. Poisson regression analysis adjusted for important covariates was used to estimate the relative risk (RR) of maternal and child weight status, demographic, and socioeconomic characteristics on the child’s BP at ages 6, 9 and 11.

Results

The prevalence of high BP in children at ages 6, 9 and 11 was 26.5%, 20.4% and 3.0%, respectively. Pre-pregnancy body mass index (BMI) and the child’s BMI were associated with high BP over the period investigated. The risk of children having high diastolic BP from 6 to 11 years of age was 6% higher (RR = 1.06; 95%CI 1.01, 1.11) when mothers had pre-pregnancy BMI ≥25 kg/m2, even after adjusting for child BMI. For each one kg/m2 increase in child BMI, the risk of children having high systolic BP pressure increased by 14% (RR = 1.14; 95%CI 1.05, 1.23), even after adjusting for pre-pregnancy BMI.

Conclusions

Pre-pregnancy excess body weight and increased child BMI were associated with high BP in children from 6 to 11 years of age. Addressing pre-pregnancy excess body weight and overweight in childhood, combined with BP monitoring from the age of six, are actions that can prevent the development of CVDs in adulthood, thus reducing a serious global public health problem.

Key messages

• Encouraging parents to monitor their children’s BP is essential to prevent associated chronic diseases throughout life.

• Identifying children with altered blood pressure is an important public health indicator.
Public views on open data health research in Switzerland: Implications for research and policy
Authors: Daniore P , Sedlakova J , Zavattaro F , Huber Z , Knieps M , von Wyl V , Benhamou Y , Gille F
Published: October 2024

Background

The increasing volume of publicly available data offers health researchers opportunities to explore public sentiment through open data research on issues such as vaccination hesitancy and fake news propagation. To ensure ethical conduct and maintain public trust, assessing public perceptions of open data research in healthcare is crucial.

Methods

We conducted 10 focus groups across the German, French, and Italian-speaking regions of Switzerland. We presented participants with real-world examples of open data research in healthcare and guided discussions using a semi-structured format. The topic areas were: (1) using Twitter data to assess public opinion on gene-editing technology, (2) assessment of health policy documents, (3) using Twitter data to predict individual health status and (4) using YouTube data to assess public sentiment on health policies. We assessed our findings using a thematic analysis approach.

Results

Gathering insights from 75 citizens, four major themes emerged: (1) Expectations towards actors and open data research, such as using research findings for societal benefit, and ensuring transparency in data use through informed consent; (2) Supportive measures including communication strategies to raise awareness and inform the public about the use of publicly available data for health research; (3) Concerns about data reuse for purposes beyond the study’s objectives, especially for financial gain, and about data validity and privacy, particularly for sensitive health data; and (4) Mitigative measures to minimize harm, such as through the involvement of external oversight committees.

Conclusions

Our findings indicate public support for open data research in healthcare, provided that specific expectations are met. Developing a guiding framework for legitimate open data research in healthcare is a valuable next step, with a focus on enhancing public awareness through communication campaigns and providing oversight measures to foster public trust.

Key messages

• Open data research in healthcare relies on public data use without explicit consent, which can disconnect the research community from society.

• We found that public support for open data research in healthcare is generally present, however it is also conditional on communication and policy efforts addressing their concerns.
How does family support mitigate the impact of childhood adversity on adolescent mental health?
Authors: Adjei N , Jonsson K R , Straatmann V S , Bellevia A , Yaya S , McGovern R , Kaner E , Wolfe I , Taylor-Robinson D C
Published: October 2024

Background

Childhood adversity is associated with increased risk of developing mental health problems over the life course. We investigated how family emotional support in adolescence may mitigate the impact of childhood adversity on adolescent mental health.

Methods

We used longitudinal data from the UK Millennium Cohort Study on 9,269 children followed to age 17 years. Individuals were assigned to exposure trajectories characterised using group-based trajectory models that included measures of poverty and family adversities (parental mental ill health, parental alcohol misuse and domestic violence) up to age 14 years. Using counterfactual causal mediation analysis and four-way decomposition modelling approach, we evaluated how perceived family emotional support at age 14 (measured using the three-item Short Social Provisions Scale) influences the association of childhood adversity trajectories on mental health at ages 14 and 17, assessing the relative contributions of mediation and interaction simultaneously.

Results

Compared with children experiencing low adversity and poverty, those exposed to childhood adversity were almost three times more likely to experience poor mental health (RR 2·99, 95% CI 2 ·41 to 3·57) at age 14 and age 17 (RR 2·58, 95% CI 2·09 to 3·06). Perceived emotional support mitigates up to 18% (95% CI: 9% to 26%) of this effect at age 14, and 13% (95% CI: 5% to 22%) at age 17, mainly due to interactive mechanisms.

Conclusions

Childhood adversity has a strong effect on mental health, which is partially mitigated by perceived family support in adolescence. Policies that support positive family functioning may be particularly beneficial for children who have experienced adversity.

Key messages

• Socio-economic – associated mental health problems in adolescence differed based on levels of family support.

• Approximately 20% of the social inequality in mental health outcomes in adolescence may be mitigated or reduced by improving levels of family support.
EvalDepApps: How to evaluate depression management based on evidence and end users
Authors: Robles N , Moretó S , Fuster A , Duarte A , Padilla M , Pérez A , Subirà S , Gelabert E , Gómez C , Carrion C
Published: October 2024

The use of apps is increasing in the field of mental health due to their ease of use and accessibility, although there is not enough evidence on their effectiveness and safety. EvalDepApps aims to develop an evaluation tool for depression management apps. A systematic review with meta-analysis (SRMA) was performed to evaluate the efficacy and safety of apps for depression, and to identify the evaluation criteria used. PRISMA methodology was followed. The MEDLINE, PsycINFO, and Embase databases were consulted. The risk of bias was assessed with the RoB2 scale. An online 2 rounds Delphi was carried out to prioritize the most relevant criteria identified. 44 people (26 professionals, 18 patients) were invited. They were asked to rate the importance of each criterion on a Likert scale (1 - 6). Those that obtained a high consensus were selected; those with a medium were submitted to the 2nd round. Empathization (6) and co-design (6) sessions were held with patients (23) and professionals (33) in Catalonia, the Canary Islands and Andalusia to identify what relevant aspects the tool should have. Twenty-nine studies were included in the SRMA (67% unclear bias), finding a significant effect of mHealth interventions in reducing depressive symptoms compared to non-active control (Hedges g = −0.62, 95% CI: −0.87 to − 0.37, I2 = 87%). In Round 1 of the Delphi (59% participation) 24 criteria obtained a high consensus, 20 a medium and 7 a low. In Round 2 (52% participation), 4 criteria reached high consensus. The empathy sessions showed that the actions most requested by patients were reduce anxiety, and information about their condition; for professionals, suicide prevention. Regarding co-design, it was proposed that the tool provide ranking of the apps, recommendation systems and a very visual format. The RSMA and Delphi guarantee that the tool will be based on scientific evidence and expert judgment, while empathization and co-design that it will fit with the needs of end users.

Key messages

• It is relevant to evaluate the quality of health apps, especially those addressed to vulnerable populations such as people with depression.

• It is crucial that the development of evaluation tools for digital health interventions will be developed based on evidence-based but also includes end users.
Innovative risk score to diagnose depression using data from the UAE Healthy Future Study
Authors: Al Balushi M , Guan J , AlBlooshi M , Al Balushi S , Ahamd A
Published: October 2024

Introduction

Depression has been recognized as a major mental health disorder. Diagnosing depression remains a challenge due to its complex multifactorial nature. The UAE Healthy Future Study is a long-term cohort study that focuses on understanding the factors that contribute to chronic disease among Emiratis. It involves answering a comprehensive questionnaire, undergoing physical measurements such as Body Mass Index (BMI), and biological samples for analysis. We analyized the eight-item depression screening instrument Patient Health Questionnaire (PHQ-8) for this project.

Methods

Out of 487 participants included in the PHQ-8, 205 participants (42.1%) were included in the statistical analysis after omitting missing values. A multivariate Least Absolute Selection Shrinkage Operator (LASSO) logistic regression model was performed using gender, age, BMI, waist circumference, hip circumference, body fat percentage, high-density lipoprotein (mg/dL), lowdensity lipoprotein (mg/dL), total cholesterol (mg/dL), diastolic blood pressure, systolic blood pressure, hemoglobin A1C, as predictors. The primary outcome was the binarized total PHQ-8 using a cutoff value of ten Tenfold cross-validation was applied.

Results

A cutoff value that yields an approximate sensitivity of 90% was selected to maximize the ability of the novel risk score to correctly identify subjects with depression symptoms. Sensitivity and specificity were estimated with a corresponding 95% confidence interval (95% CI). A novel depression risk score was computed as the linear predictor of the selected five variables in the LASSO regression analysis, as following: Risk score = -3.284 + 0.471*gender - 0.053*age + 0.049*BMI + 0.022*HDLmgdL - 0.072*HBA1c

Conclusions

We have shown that a novel depression risk score provides useful information for screening of depression. Further validation studies are needed to confirm the application of the risk score in daily practice.

Key messages

• A novel depression risk score provides useful information for screening of depression.

• If the risk score can be verified in a different population, then it can improve the screening methods for depression.
Digital Pathology: A Cost-Effective Solution For Healthcare Systems
Authors: Chng JJH , Chong CZY
Published: October 2024

Background

Digital pathology represents a transformative technology in healthcare, utilizing digital analysis and interpretation of pathology microscopy images. While traditional light microscopy remains the standard, digital pathology offers potential advantages: improved diagnostic accuracy, increased efficiency, and enhanced collaboration. However, cost-effectiveness remains a crucial consideration for widespread adoption. This systematic review aims to evaluate the cost-effectiveness of digital pathology in various healthcare settings.

Methods

A literature search was conducted in PubMed, Scopus and Cochrane Library, using keywords “digital pathology”, “telepathology” “cost”, and “effectiveness”. The search was limited to English-language studies discussing digital pathology and its cost effectiveness. Prior to inclusion, studies were evaluated for relevance and applicability. Included studies were analyzed for methodology, sample size, data collection methods, and key findings.

Results

Of 125 articles identified, 16 studies met inclusion criteria. Key findings included streamlined workflows allowing for smoother collaboration across institutions, enabling simultaneous evaluation of multiple slides, and utilizing image processing software to automate time-consuming tasks. Additionally, digital pathology reduces the need for physical slide transportation, reducing costs and enabling access to specialist expertise in remote or underserved areas. This combination of efficiency gains and potential cost savings suggests digital pathology’s potential for long-term cost-effectiveness in healthcare systems.

Conclusions

Digital pathology has the potential to increase cost-effectiveness and improve quality and efficiency of pathology services. Further research is necessary to determine optimal implementation strategies, but the evidence suggests digital pathology warrants continued investment and adoption by healthcare systems to improve public health outcomes.

Key messages

• Digital pathology demonstrates potential for cost-effectiveness.

• Despite the need to refine implementation, the efficiency and potential cost savings of digital pathology warrant further exploration.
Perspectives on intersectoral collaboration and citizen participation in community-based prevention
Authors: Sonneveld J M , Bruijning J E , Jerković-Ćosić K , de Wit N J
Published: October 2024

Background

Challenges in healthcare, such as rising healthcare costs, shortages of healthcare staff, and health disparities, have made many countries focus on intersectoral collaboration (IC) and citizen participation (CP) in community-based prevention (CBP) and health promotion (HP). Previous research on IC and CP has addressed the importance of exchanging perspectives to arrive at a shared vision and ambition. Our study is part of a long-term action research project conducted in two neighborhoods in the Netherlands. It aims to provide insight into stakeholders’ perspectives on IC and CP in CBP and HP to facilitate building partnerships.

Methods

Data collection takes place from March to June 2024. As part of a Q-study, we perform post-sort interviews. The Q-methodology is based on sorting statements concerning various aspects of IC and CP in CBP and HP (e.g., roles and tasks, involvement of citizens, communication, perceived barriers, etc.). We aim to include 40-50 participants from the aforementioned neighborhoods with diverse backgrounds. Participants sort 40 statements (Q-set) into a fixed-scaled framework ranging from -4 (disagree) to + 4 (agree). The interview is about individual sorting and perspectives on their own role within the collaboration.

Results

So far, the results of 10 participants with backgrounds in health care, social work, sports, local authority, volunteer work, and public health have been analyzed.Preliminary data synthesis shows six themes concerning shared purpose, collaboration partners, social collaboration competencies, personal motivation, conditions, and collaboration organization.

Conclusions

The preliminary themes overlap with findings from previous research. We will adapt existing models to reflect on our findings and enrich the literature with new themes or insights in the specific context. The interviews will help interpret the Q-sorts and develop follow-up actions in the action research projects in the participating neighborhoods.

Key messages

• Insights in stakeholders’ perspectives are valuable for building local collaborative partnerships.

• Shared purpose, collaboration partners, social collaboration competencies, personal motivation, conditions, and collaboration organization are themes of interest to intersectoral collaboration.
A new digital model for Italian Integrated Home Care: strengths, barriers, and future implications
Authors: Melnyk A , Gentili A , Beccia F , Causio F A , Battilomo S , Paone S , Borghini A , Bartolo M , Chiarolla E , Cascini F
Published: October 2024

Issue/Problem

After the COVID-19 pandemic, Italy’s National Health Service (NHS) is transforming by adopting telemedicine through its National Recovery and Resilience Plan, aiming to modernize healthcare delivery and ensure equity. In this plan Territorial Coordination Centers (COT) were designed to improve patient access and continuity of care. However, regulatory challenges hinder the widespread adoption of telemedicine. Our work describes the technical, structural, and operational aspects of the new Italian Integrated Home Care (IHC) digital model, addressing strengths, barriers, and future implications.

Description of the problem

The implementation of the IHC digital model faces multiple challenges. Italy’s poor technical infrastructure, especially in rural areas, hampers access to critical services. Resistance to change among rural systems and the elderly, coupled with low digital literacy, inhibits telemedicine adoption. Fragmentation in the NHS leads to resource disparities, particularly disadvantaging southern regions. Italy also grapples with a severe shortage of healthcare professionals, posing a threat to the model’s sustainability.

Results

The National Digital Health Agency enacts the IHC model by overseeing telemedicine services through a cloud-based protocol. Each Region provides “Basic Telemedicine Services”: healthcare professionals’ and patient teleconsultation, telemonitoring, teleassistance. Communication between services, infrastructures, and local health authorities is facilitated by a modular interoperability layer, ensuring data standards and quality. A national telemedicine dissemination portal will contain data on all telemedicine solutions provided.

Lessons

Clear communication is essential for telemedicine acceptance. COTs enhance coordination and management. Bridging the technology gap for disadvantaged groups, supporting frontline employees and addressing adoption factors is vital to overcome the challenges faced.

Key messages

• The National Digital Health Agency drives telemedicine services under the IHC model. Each region offers Basic Telemedicine Services, ensuring data standards, quality and interoperability of data.

• Italy’s telemedicine adoption faces poor infrastructure, resistance and workforce shortages. Focusing on equity, healthcare workers support and bridging the technology gap will be key.
Exploring Empathy Dynamics in Virtual Patient Training
Authors: Petrova G , Kostadinov K , Lalova V , Ivanova S
Published: October 2024

Background

This study investigates the manifestation of empathy among nursing students and teachers during virtual patient training with VR glasses at the Medical Simulation Training Centre of Medical University - Plovdiv. The pilot training, conducted from March 22 to April 12, 2024, was part of the Erasmus+ project ‘Caring violent child safely in child psychiatric and residential units (Safe4Child) 2021-1-FI01-KA220-HED-000032106.’ Participants were divided into two focus groups: third-year nursing students and lecturers teaching specialized academic disciplines in nursing.

Results

A total of 34 participants partook in the study, comprising 16 instructors with basic nursing education and 16 third-year nursing students. Students exhibited considerable empathy towards the virtual child patient, expressing an inclination to provide assistance, acknowledging the necessity for personalized care, and displaying a curiosity about the patient’s experiences. They conveyed sentiments such as ‘I felt the need to help the child/patient’, ‘the child needs individual work with a nurse’ and ‘I want to understand the child’s experiences.’ In contrast, instructors demonstrated more reserved emotional responses, characterized by professionalism and a focus on offering comfort. They expressed, ‘Normal feelings...but you want to hug the child and comfort him.’

Conclusions

The study unveiled a marked difference in empathy expression between students and instructors, with students displaying stronger emotional reactions. These findings underscore the need to recognize and address individual variances in empathy expression within nursing education. Tailored strategies should be devised to bolster empathy development in both students and instructors during virtual patient training. Further investigation is warranted to explore effective methodologies for optimizing empathy development and fostering compassionate patient care in nursing education.

Key messages

• A Comparative Analysis of Nursing Students and Instructors Using VR Glasses.

• Erasmus+ project (Safe4Child) 2021-1-FI01-KA220-HED-000032106.
Labour migration and mental health of left-behind children in Georgia: a mixed methods study
Authors: Antia K , Račaitė J , Šurkienė G , Winkler V
Published: October 2024

Background

Global migration is on the rise, and as a result, millions of children are left in their home countries while their parents migrate abroad. Little is known about the mental health of left-behind children (LBC) in Eastern Europe. The study addresses this research gap in Georgia, a leading migrant-sending country in the region.

Methods

Applying convergent parallel design, this mixed methods research integrates a qualitative study with schoolteachers from six public schools in Samegrelo and Guria regions (6 focus group discussions and 6 in-depth interviews) (i); a school survey with 933 adolescents (ii) from 18 public schools, (12-18 age group, internalizing and externalizing problems assessed with Aschenbach’s “Youth Self-Report” questionnaire), and a qualitative study using life-narrative interviews with 39 adolescents (iii). We analysed qualitative data using reflexive thematic analyses and applied multiple linear regression to analyse quantitative data.

Results

The Quantitative and qualitative findings converge in some respects while diverging in others. Schoolteachers perceive migratory separation as a traumatizing life event, making children vulnerable to mental health issues. Similarly, schoolchildren perceive separation from parents as a stressful life event; however, they also acknowledge positive aspects of migrant labour, such as material benefits. From the perception of LBC, close family ties, friendships, school support, and optimism help with coping. This study also revealed a gender disparity, with boys exhibiting more rule-breaking behaviour and girls performing worse on most Youth Self- Reported syndrome scales. Regression analysis showed associations between adolescents’ problem behaviours and relationships with family members, friendships, and school environment.

Conclusions

This research found that parental migration effects on children are complex and depend on individual and transnational family characteristics.

Key messages

• Left-behind children may need emotional and academic support.

• Strengthening family ties, supportive school environment and friendships contribute to adolescents’ coping with parental migration.
Population Health Management effectiveness in the primary healthcare setting: a systematic review
Authors: McNamara R , Nour N , Mannix M , Browne J , Donohue S
Published: October 2024

Background

Population Health Management (PHM) has shown promise as a tool in the improvement of health at scale. It works by; harnessing health-related datasets, allowing for population segmentation and risk stratification, and targeting interventions to yield positive health impact. Prior reviews on PHM, have sought to define PHM and assess feasibility. To date, there have been no systematic reviews that assess the effectiveness of PHM in achieving some or all of the quintuple aims of PHM (WHO 2023). The aim of this study is to determine the value and impact of a PHM approach in managing ambulatory care sensitive conditions (ACSCs) in the primary healthcare setting.

Methods

This systematic review is underpinned by a Population-Intervention-Comparator-Outcome (PICO) framework. The PROSPERO-registered study commenced in February 2024 for papers published since January 2000. Pubmed, Cinahl and Web of Science databases were used. Titles and abstracts of studies were reviewed using agreed eligibility criteria to select studies for inclusion and risk of bias assessment. Data abstraction and synthesis determined the effectiveness of PHM against agreed outcome measures.

Results

The search yielded 427 articles of which 19 were included. Studies addressed diabetes, hypertension, atrial fibrillation, asthma and comorbid conditions. Nearly all studies were based in the U.S.A. Almost 50% of studies showed a significant improvement in health outcomes following PHM. Study features that corresponded with positive health outcomes included; the use of population management tools, the employment of disease/population health managers and the mobilisation of medication reviews/education.

Conclusions

The challenges facing communities and health services require an alternative innovative approach to health such as a PHM approach. The results of this study will inform a larger body of work which seeks to establish a PHM approach to the delivery of services in the Mid-West region of Ireland.

Key messages

• Population health management (PHM) could be a viable mechanism for health services to serve increasing need and multi-morbid populations.

• This is the first systematic review of PHM against the quintuple aims of PHM, specifically in the management of ambulatory care sensitive conditions in primary healthcare.
Effects of a multidisciplinary program with physical exercise in pediatric obesity
Authors: Meneses C , Lopes A , Fonseca F , Eusébio A , Peixoto R , Vilaça-Alves J , Santos C , Moreira T
Published: October 2024

Background

Childhood obesity is a growing public health concern worldwide, with prevalence rates increasing in recent decades. This epidemic not only predisposes children to immediate health risks such as cardiovascular disease and musculoskeletal problems but also significantly increases their risk of obesity-related comorbidities in adulthood. Recognizing the multifactorial nature of pediatric obesity, interventions targeting lifestyle modifications are crucial to its control. Among these interventions, physical exercise emerges as a cornerstone in combating pediatric obesity.

Purpose

Our study examines the effectiveness of a twelve-week online physical exercise program on body composition and functional capacity in children and adolescents with obesity.

Methods

The sample consists of 30 participants (9 girls and 21 boys, aged 11-17 y) from the Pediatrics Department of Senhora da Oliveira Hospital in Guimarães (HSOG). Using standardized protocols, anthropometric measures (body weight and waist circumference) were measured. Functional capacity was assessed through squats, push-ups, and elbow plank tests (maximum repetitions in 60 seconds). All analyses were done using SPSS software, and the alpha level was 5%.

Results

Significant impacts of the exercise program were observed in body weight and waist circumference variables. Multivariate tests indicated a statistically significant interaction between the Moment*Groups factors (weight p = 0.040; waist circumference p = 0.007). Moreover, functional capacity was also influenced by the exercise program (squat p = 0.034; push-ups p = 0.013; and elbow plank p = 0.025).

Conclusions

Our results underscore the significant public health impact of integrating physical exercise into a multidisciplinary program for pediatric obesity. Innovative use of online interventions facilitated broader reach and accessibility, resulting in measurable improvements in body weight, waist circumference, and functional capacity among participants.

Key messages

• Integrating exercise into multidisciplinary programs improves pediatric obesity outcomes.

• Online interventions broaden accessibility and effectiveness in preventing childhood obesity.
Predicting diabetes prognosis using machine learning techniques in a Hungarian clinical database
Authors: Nagy A , Bata R , Ghanem A , Móré M
Published: October 2024

Background

Chronic conditions such as type 2 diabetes mellitus have great impact on patients’ quality of life. Although clinical databases provide a perfect base for research aimed at improving diabetes care, analyzing such databases requires extensive pre-processing, mainly due to the large amount of unstructured data. Our study aimed to present the steps of generating a dataset from a large clinical database, and to apply machine learning-based analytical techniques regarding.

Methods

Data of the Clinical Center of University of Debrecen was used. To structure the unstructured data, regular expressions and natural language processing methods were used. The main machine learning models were as follows: Gradient Boosting Machines to predict the risk of complication development; Long Short-Term Memory Networks to forecast future health outcomes. All analysis and procedures were done using Python.

Results

The database contains approximately 1600 tables, with a total size of 1.9 terabytes, where the largest table is 21.07 gigabytes with approximately 44.64 million rows. The final dataset consisted of 40,332 patients. Most variables originate from the unstructured data, including complications and comorbidities of diabetes, as well as physical and laboratory parameters. Related to laboratory parameters, the number of measurements and the median value for every half-year were available. The diagnosis time of the complications’ occurrence is also presented. Machine learning methods were more accurate compared with traditional statistical methods in predicting the prognosis (p < 0.05).

Discussion

Our research highlights the importance of clinical data in chronic disease management. There are challenges in pre-processing and managing datasets, but machine learning-based methods are very efficient not only in extracting useful information from unstructured data but also in predicting the prognosis and identifying potential intervention points for better care.

Key messages

• Natural language processing can be used to obtain useful information from unstructured clinical data.

• Using machine learning techniques on clinical data could improve diabates care.
Integrating telemedicine in European healthcare settings: Key enablers for healthcare professionals
Authors: Schmitt T , Habimana K , Gottlob A , Habl C , Rosińska M , Sønderskov Frydensberg M , Anker Gents S , Leclercq V , Delnord M , Van den Bulcke M
Published: October 2024

Background

Conducted as part of WP4 (Sustainability) of the eCAN Joint Action (JA), we aimed to understand the facilitating factors for the use of telemedicine services in healthcare organisations (HCOs) involved in cancer care, such as comprehensive cancer centres and hospitals. eCAN JA runs pilot studies in 18 HCOs across 10 EU Member States (MS) to test the integration of telemedicine into the daily practice of healthcare professionals.

Methods

Based on a literature review, we identified 26 facilitators to implement telemedicine services at the organisational level; these were integrated to an online survey and sent to healthcare professionals working in cancer care. Next, they were invited to a foresight workshop to complement the quantitative survey data with qualitative insights.

Results

24 healthcare professionals across 12 EU MS, predominantly from the pilot sites of the eCAN JA, responded to the survey. They advocate for trainings on telemedicine, emphasising the importance of user-friendly and interoperable platforms, alongside sufficient technological support, time, and financial resources to effectively implement telemedicine in HCOs. Trial periods for both healthcare professionals and patients should offer first-hand experience, while harmonising telemedicine protocols ensure consistency. In addition, promoting digital literacy among physicians and providing IT education from an early age could facilitate adoption. These strategies, coupled with awareness campaigns and infrastructure investments in HCOs, can pave the way for telemedicine adoption.

Conclusions

Based on the quantitative and qualitative outcomes, it can be concluded that policies should establish telemedicine training for healthcare professionals, support interoperability and user-friendliness of these services in HCOs. They should also address hospital workload challenges, create harmonised protocols, provide incentives, and invest in digital infrastructure in HCOs for telemedicine adoption.

Key messages

• Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility, and addressing resource constraints in HCOs for success.

• Financial and infrastructural support to HCOs is crucial for the seamless integration of telemedicine services into the daily workflows of healthcare professionals, enhancing health service delivery.
Attitudes, fears and experiences of medical scientists towards using AI tools in their work routine
Authors: Fichtner U A , Knaus J , Graf E , Stelzer D , Sahlmann J , Wolkewitz M , Binder H , Weber S
Published: October 2024

Background

Artificial intelligence (AI) has experienced an explosive growth in the healthcare sector and the scientific field. Large Language Models (LLMs) are investigated as potential tool to be used in medical treatment, by e.g. producing doctor’s reports, supporting personalized medicine or educating patients. However, limitations are broadly discussed addressing patient safety and ethical concerns due to false and misleading content. Therefore, LLMs face criticism among users not only towards their integration in the patient-provider-interaction, but also in its use in medical research and health care education. In the ongoing EXPOLS (Exploring the Potential of LLMs in Statistical Consulting) study, we address the question ‘What are the attitudes, fears and experiences regarding the integration of AI tools in the clinical/scientific work routine?’

Methods

We implemented an online survey that will be accessible in 07/2024. All employees at the University and Medical Center Freiburg will be asked to participate in the survey via online media. We expect a sample size of n = 600-1,200. The survey combines published measurements (e.g. TRI 2.0, result expectation measure), as well as a self-designed instrument with 15 items aiming to measure fears and attitudes towards AI. Latter was developed within a multidisciplinary team making use of ChatGPT as item generator. We will investigate its psychometric properties using exploratory factor analysis and describe distribution characteristics.

Results

At the time of the conference, the project will be completed and both psychometric insights and content-related results will be reported. The results could have important public health implications, since attitudes towards AI will steer its integration in the medical and clinical routine.

Conclusions

The EXPOLS study makes a current inventory among medical scientists in Freiburg following a modern approach in instrument development by including ChatGPT as collegial advisor.

Key messages

• This study explores attitudes, fears and experiences towards the integration of AI tools in the work routine from medical and scientific staff at the University of Freiburg.

• The findings might be crucial in determining the integration of AI based tools in the medical and clinical work routine.
What to expect when generating data through the use of a mobile health application
Authors: Schwab I , Ohnhäuser T , Scholten N
Published: October 2024

Background

Digitalization has significantly expanded the possibilities for collecting health data. Besides options such as online surveys, data can be collected through the use of digital applications (apps). Once users have given their consent, contact information from the app registration can be accessed to invite the relevant target group for further surveys. To generate a sufficient amount of data, a high level of willingness to participate and long-term involvement are required. This study aims to illustrate the willingness to share data and what can be expected from it by means of the Neo-MILK app, which supports mothers during lactation.

Methods

The willingness to share data and the data resulting from the use of the app is shown by the frequency of consent to the different data use options and the users’ participation.

Results

While over 68% of the n = 1079 registered app users consented to the use of their app data, 47% agreed to be contacted again for scientific purposes. Data from n = 421 active users were available for analysis. From n = 220 mothers, milk pumping data was available. In 36% of cases, little (<10) or no information on pumping was provided. Of the users who agreed to be contacted again for scientific purposes, n = 300 were invited to participate in another survey and 23% (n = 69) participated.

Conclusions

In this example, the general willingness to share data and participate in further studies shows great potential for data collection. However, the data collection methods differ primarily in the motivation to participate. When collecting data through app usage, ensuring that the design of the app meets the needs of the user is essential to optimize the user experience and thereby generating complete data. If the use of contact information has been agreed to, incentives may be considered to increase the willingness to participate. Required ethics approvals and data protection regulations should also be ensured for app-based data collection.

Key messages

• Generating data via app usage as well as the collection of data through the use of users’ contact information shows great potential for health research.

• App-based data collection should be considered when planning future health research projects.
Record linkage as a vital key player for the COVID-19 syndemic
Authors: Doetsch J N , Kajantie E , Dias V , Indredavik M S , Devold R K , Teixeira R , Reittu J , Barros H
Published: October 2024

Chronicity, comorbidities, and social context influence COVID-19 risk and prognosis, drawing attention to a syndemic dimension. We discuss how linking routinely collected data and data collected through observational population-based cohorts can contribute to advance knowledge on the association between COVID-19 and chronic diseases and social indicators. Record linkage as a multidimensional tool may ultimately enable defining and optimizing of integrated strategies, which may create multilevel public health measures to foster solidarity on health in all policies when ethical and legal barriers under prerequisite data protection and privacy can be overcome. Legal discrepancies have been proving detrimental to research in member states, including those which already had established a margin for research for the common good without explicit consent. We hence call for further harmonization of data protection requirements for scientific research activities in the EU/EEA, focusing in particular on health-related research. A proposed framework demonstrates the role of record linkage as a trailblazing key player in research optimization due to its multidimensional possibilities calling for harmonization.

Key messages

• Further harmonization of data protection requirements for scientific research may create multilevel public health measures.

• As a multidimensional tool, it optimizes integrated strategies and fosters solidarity on Health in All Policies (HiAP).
Impact of AI on public health emergency communication in Europe and Central Asia: A Delphi study
Authors: Mahl D , Schäfer M S , Voinea S A , Adib K , Duncan B , Salvi C , Ortiz D
Published: October 2024

Background

Artificial Intelligence (AI) holds the potential to fundamentally transform public health communication - presenting promising opportunities alongside profound challenges and risks. This two-wave Delphi study therefore invited practitioners and scholars to assess the potential impact of AI on the risk communication, community engagement, and infodemic management (RCCE-IM) of public health authorities in European and Central Asian countries.

Methods

In two successive online surveys, 54 experts - both RCCE-IM practitioners and scholars with expertise in (digital) health communication, risk communication, and AI - from 28 countries provided their assessment of the following analytical dimensions: (1) opportunities, challenges, and risks of AI, (2) conditions for the responsible use of AI, (3) potential future scenarios, and (4) prioritized actions. The first Delphi wave followed an open and exploratory approach, while the second wave sought to prioritize and rank key findings from the previous round. The survey instruments included open-ended and closed-ended questions that were analyzed through statistical analysis, inductive and deductive coding.

Results

Results showed that AI holds various opportunities for the RCCE-IM of public health communicators. Challenges and risks, such as algorithmic bias or concerns about inclusivity, equally affect RCCE-IM efforts. Experts identified seven principles for the responsible use of AI (e.g., fair, human-centered) and predicted optimistic (e.g., optimized workflows) and pessimistic (e.g., flood of (mis)information) scenarios for the future impact of AI. Prioritized actions range from regulation, resource allocation, and feedback to capacity building, public trust, and education.

Conclusions

To responsibly navigate the opportunities, challenges, and risks of AI for public health emergency communication, clear guiding principles, ongoing critical evaluation, and multi-sectoral collaboration are needed.

Key messages

• AI is likely to transform public health emergency communication.

• A set of agreed-upon principles will support public health authorities in the responsible management of the opportunities, challenges, and risks of AI.
Usability of an AI-based independent pedestrian navigation device designed for blind people
Authors: Dascal M , Blaga O , Padure M , Amariei C
Published: October 2024

Background

2.2 billion people live with visual impairment (VI) worldwide and face orientation and mobility challenges in various aspects of their lives. Despite technological advancements, the most used mobility solutions for VI are still the walking cane and the guide dog. The objective of this research is to assess the system usability of an AI-based independent pedestrian navigation device (.lumen glasses), designed for blind people.

Methods

During 31st of October and 2nd of November 2023, 27 visually impaired people living in Bucharest, Romania, tested the .lumen glasses for 1-3 hours during an outdoor navigation task. The glasses use AI to recognize the user’s environment and haptic and audio feedback to guide them. The System Usability Scale (SUS), a 10-item instrument, was used to evaluate the level of usability the participants ascribe to the .lumen glasses. Qualitative feedback was collected as part of the post-test survey and analyzed using thematic analysis. Data were triangulated to identify usability issues.

Results

The mean age of the participants was 30.3 years old (SD = 12.07, min=21 max =56, range: 48), 55.6% (N = 15) of the participants were male, 96,3% (N = 26) were completely blind, and 81.5% were using a white cane for orientation and mobility. Our findings reveal a mean SUS score of 83.65 (SD = 15.23, range 47.5, min=52.5, max=100) indicating high usability. The thematic analysis revealed 5 themes: (1) auditive and (2) haptic feedback in navigation, (3) hardware (adjustment system & buttons), (4) tutorial & presentation of how the device works and (5) other aspects (usage in combination with other assistive technologies etc.). The participants declared that the glasses are intuitive to use and promote the feeling of safety and independence.

Conclusions

Data suggest that the .lumen glasses area promising assistive technology for the mobility of VI. Findings are currently used to steer the development and implementation of the AI-based system.

Key messages

• According to participants’ feedback, .lumen glasses are a scalable AI-based mobility solution that can make navigation and orientation easier for visually impaired individuals.

• .lumen glasses designed for the visually impaired individuals are one of the current and limited assistive technology improving the mobility and orientation.
Digital health and some thematic shifts in bioethics in academic publications after the pandemic
Authors: Mirchev M , Mircheva I
Published: October 2024

Background

Digital health (DH) presents vast opportunities, but also dynamic shift in viewpoints, which is reflected in academic works. Since DH concerns patient data, a major issue is complying with the bioethical principles, and ensuring patients’ privacy and security. The aim of this research is to explore avenues, in which ethics and privacy are reflected in DH, especially in terms of research, legislation, artificial intelligence (AI) and blockchain applications in medicine and public health.

Methods

Literature search for full text publications in PubMed, Scopus and ScienceDirect focused on ethics and privacy in DH in the period 2019-2023 found 325 articles in English authored by academicians. Six thematic categories were identified: ethics (fundamental principles; ethics in using novel DH tools); privacy (in use of apps/wearables; as a challenge/barrier to DH interventions/technologies); ethics/privacy in: DH research (data gathering, use, sharing); policy and legislation (for DH applications); AI and blockchains (in medicine and public health).

Results

Articles were from 32 countries: USA-24.3%; UK-13.5%; Germany-7.7%; Canada-7.4%; Australia-6.7%; Netherlands, Italy, Switzerland-4.7% each; China, India-3.0% each and 20.3% from 22 other countries; 86.5% in medical/DH journals. Only 10.2% were published in 2019; 17.8% - 2020; 19.4% - 2021; 27.1% - 2022 and 25.5% - 2023. Privacy was discussed in 26.5%; ethics-23.7%; research-19.4%; AI-15.1%; policy and legislation-9.5% and blockchains-5.5% of the articles. The publication activity of all six categories increased after the unfold of the pandemic in 2020. In 2023 publications for ethics (13%), privacy (5.8%) and research (6.3%) decreased; for legislation (12.9%), AI (14.3%) and blockchains in healthcare (16.8%) increased.

Conclusions

The pandemic and its aftermath present a change in academic interest. Traditional ethical fundaments in DH slightly lose position in favor of top notch technologies and apt legislation.

Key messages

• The pandemic and modern technologies enforced new digital health applications. Further research will reveal the needs of AI and blockchain applications respecting privacy and the ethical principles.

• Besides existing legislation and regulations, in order to catch up with the dynamic developments in DH, the legal base needs to become more flexible. Technologies change, privacy importance does not.