Digital innovations in healthcare can improve its quality and access, but evidence also reveals threats to health equity, conceptualized as a digital divide. This divide results from existing inequities in both access to and the ability to use digital technologies. Therefore, the purpose of this study is twofold: (1) to investigate temporal trends in the use of specific digital health applications (DHAs) and (2) to examine their use among different population groups. The analysis is based on two cross-sectional, online surveys conducted in Germany in 2020 (N = 1,570) and 2022 (N = 1,200). Participants were recruited by an external survey company. Commonly provided DHAs in Germany, like online services (online appointment booking or online request for medication), online video consultations, and insurer applications for personal data exchange, were included. Socioeconomic, sociodemographic, and health-related indicators were assessed. For data analysis, chi-squared tests, U-tests, and logistic regression analyses were used. DHA use increased from 2020 to 2022, with the use of online services growing the most. A digital divide was observed. Lower use was identified among older generations and those with low educational attainment and low subjective social status. Additionally, digital (health) literacy, and positive attitudes toward DHAs were associated with increased DHA use. The findings indicate the existence of a digital divide that persists over time. The analysis underscores the need for interventions to mitigate related inequities. Beyond improving access to DHAs, interventions to promote digital (health) literacy are of paramount importance.

Background: The COVID-19 pandemic highlighted the importance of effective health communication and reliable information for crisis management, particularly following the introduction of vaccinations. Varied attitudes toward COVID-19 vaccination and an overwhelming amount of online information complicated communication and pandemic management. Previous studies have often focused on general vaccination behavior and its correlation with vaccination attitudes, establishing a link between information-seeking and vaccination decisions. However, there is insufficient analysis distinguishing specific user groups based on their actual online information behavior regarding COVID-19 vaccination and examining its correlation with vaccination behavior. Objective: This study aims to fill this research gap by identifying user groups based on their information behavior and investigating its influence on vaccination uptake. Methods: As part of the "Internetnutzung zur COVID-19-Impfung" (INCOVI) study, 1000 individuals in Germany were surveyed online (November 26 to December 8, 2021) regarding their internet usage related to COVID-19 vaccination. A hierarchical cluster analysis was conducted to identify user groups. Logistic regression analyses were then used to explore correlations among the user groups and their demographic characteristics, readiness to vaccinate, knowledge of vaccination, and health literacy. Additionally, a logistic regression analysis was performed to identify the influence of user groups and other factors on vaccination behavior. Results: A total of 3 user groups were identified: frequent and critical information evaluators (454/778, 58.4%), who primarily relied on official information sources, exhibited a higher level of health literacy, and were older than the other groups; infrequent and passive recipients (222/778, 28.5%), who rarely sought information actively and were younger than the other groups; and frequent and multichannel, interaction-focused users (102/778, 13.1%), who actively searched across multiple channels and engaged in information exchange. Notably, the user groups did not significantly differ in knowledge or willingness to vaccinate. User group affiliation, knowledge, and health literacy did not significantly influencevaccination behavior. The strongest predictor of vaccination was preexisting willingness to vaccinate. Additionally, women were more likely to be vaccinated than men, and individuals with medium or higher education levels were 6-11 times more likely to be vaccinated compared to those with only a basic level of education. Conclusions:Segmenting the population into different user groups allows for moretargeted communication tailored to the specific needs and beliefs of each group. Because these groups stem from observable usage patterns, they constitute a transferable framework for other health topics. For frequent and critical information evaluators, providing well-founded and detailed information on public channels is important. Infrequent and passive recipients benefit from straightforward formats, such as short explanatory videos, while frequent and multichannel, interaction-focused users are better reached through interactive offerings on social media. By specifically targeting these groups, informed decision-making about vaccinations can be supported.

Although the use of digital health technologies (DHTs) offers health benefits, it can also exacerbate health inequities. This applies in particular to socially disadvantaged population groups, among whom DHT use is lower, limiting opportunities for good health. Additionally, attitudes are considered relevant predictors of their use, which has rarely been addressed in research, especially considering social and health-related characteristics. The purpose of this analysis was to investigate whether social and health-related characteristics influence individuals’ use of digital health applications (DHAs) and DHTs. We used data from an online cross-sectional survey (STELLAR) of 1200 participants conducted in March 2022. The quota sample was based on the 2021 Eurostat database. An exploratory factor analysis was conducted to obtain an assessment of attitudes toward DHAs. Binary logistic regressions were then used to determine the influence of social and health characteristics on attitudes toward DHAs and their use. DHT use was lower among older generations and among individuals with lower subjective social status. Moreover, DHT use was higher among those with positive attitudes toward DHAs and higher levels of digital health literacy. With regard to attitudes toward DHAs, an inconclusive picture emerged after stratifying for social and health-related characteristics. Although there was no clear evidence of a digital divide in attitudes toward DHAs, there were signs of a digital divide concerning DHT use. Thus, measures are needed to counteract this divide, such as by promoting digital health literacy.

IntroductionHospital digital transformation, including the integration of digital technologies into hospital supply chain management is critical for improving operational efficiency, workflow standardisation, and data quality in healthcare settings. This study evaluates the implementation of an internal supply chain project aimed at digitalising material documentation and request processes via an intelligent central hub and mobile barcode scanners in a hospital network.MethodsEmploying a logic model framework, we conducted an embedded explanatory mixed-methods evaluation of a hospital digital transformation initiative over five years at Klinikum Region Hannover, Germany. Data sources included pre- and post-implementation user surveys (n = 310 and n = 148), anonymised system log data (2021–2025), and qualitative interviews with project leaders. The logic model mapped inputs, activities, outputs, and outcomes to assess evidence related to the proposed pathways of implementation success.ResultsThe implementation of the digital system accompanied by organisational measures achieved a transition from paper-based to scanner-based digital workflows, with increasing adoption rates, measurable process satisfaction and efficiency gains. Positive correlations between high process throughput and scanner usage suggest that higher process volumes encourage adoption, providing evidence for the proposed logic model pathway to adoption. A significant positive association was observed between satisfaction with staff contributions to the project and overall process satisfaction, providing pathway-consistent evidence for the proposed logic model pathway to satisfaction. Technical and organisational inputs enabled process re-engineering and workflow standardisation as outputs and time savings as outcomes, providing pathway-consistent evidence for the proposed logic model pathway to efficiency. Implementation outcomes varied across ten hospital sites, reflecting heterogenous local implementation conditions.DiscussionThese findings confirm that comprehensive digital transformation of hospital supply chains can enhance satisfaction, workflow standardisation and efficiency but requires ongoing user engagement, continuous competence development, supportive organisational cultures and removal of technical barriers. The logic model proved valuable in revealing implementation patterns, implementation gaps, and areas for targeted improvement. Future research should prioritise the validation of the logic model and particularly of the proposed pathways.

Patientenzentrierung berücksichtigt individuelle Bedürfnisse der Patient*innen und fördert partizipative Entscheidungen. Damit dieses Konzept umgesetzt werden kann, muss es die Vielfalt gesellschaftlicher Lebensrealitäten anerkennen. Dem steht Diskriminierung, die auf Zuschreibungen und Ausgrenzungen basiert und im Gesundheitswesen auf personaler, struktureller und institutioneller Ebene wirkt, entgegen. Versorgungsungerechtigkeit wird durch Diskriminierung verhindert, zum Beispiel in Hinblick auf Geschlecht, sexuelle Orientierung, Sprachbarrieren, den rechtlichen Zugang oder sozialen Status. Eine individuell orientierte Patientenzentrierung muss ein intersektionales Verständnis für die Diskriminierungserfahrungen mitbringen, um Barrieren zu erkennen und diese in der Versorgung adressieren zu können. Diskriminierung ist zudem nicht nur ein ethisches Problem, sondern produziert vermeidbare Kosten. Internationale und nationale Ansätze fordern zunehmend, marginalisierte Gruppen in den Fokus der Patientenzentrierung zu rücken und (soziale) Ungleichheiten in der Versorgung zu berücksichtigen. Es gibt bereits zahlreiche Initiativen, die das Wissen und die Erfahrungen von marginalisierten Gruppen in die Konzeption, Methodik und Umsetzung gesundheitsbezogener Interventionen einbeziehen. Zudem ist es wichtig, das Fachpersonal und damit auch die strukturelle Kompetenz in den Institutionen zu professionalisieren. Die routinemäßige Erfassung von Daten zu gesundheitlichen Ungleichheiten und Diskriminierungserfahrungen unterstützen die Entwicklung und Umsetzung von konkreten Maßnahmen. Wissenschaft, Gesundheitsversorgung und Politik sind drei zentrale Handlungsfelder, an die konkrete Veränderungen zu knüpfen sind, um eine gleichberechtigte Patientenzentrierung zu ermöglichen.

The COVID-19 pandemic highlighted the importance of effective health communica-tion and reliable information for crisis management, particularly following the intro-duction of vaccinations. Varied attitudes toward COVID-19 vaccination and an over-whelming amount of online information complicated communication and pandemic management. Previous studies have often focused on general vaccination behavior and its correlation with vaccination attitudes, establishing a link between information-seeking and vaccination decisions. However, there is insufficient analysis distinguish-ing specific user groups based on their actual online information behavior regarding COVID-19 vaccination and examining its correlation with vaccination behavior. This study aims to fill this research gap by identifying user groups based on their in-formation behavior and investigating its influence on vaccination uptake. As part of the INCOVI study, 1000 individuals were surveyed online (November 26th to December 8th, 2021) regarding their internet usage related to COVID-19 vaccina-tion. A hierarchical cluster analysis was conducted to identify user groups. Logistic regression analyses were then employed to explore correlations among the user groups and their demographic characteristics, willingness to vaccinate, knowledge of vaccina-tion, and health literacy. Additionally, a logistic regression analysis was performed to identify the influence of user groups and other factors on vaccination behavior. Three user groups were identified: Frequent and Critical Information Evaluators (58.4%), who primarily relied on official information sources, exhibited a higher level of health literacy, and were older than the other groups; Infrequent and Passive Recipi-ents (28.5%), who rarely sought information actively and were younger than the other groups; and Frequent and Multi-Channel, Interaction-Focused Users (13.1%), who actively searched across multiple channels and engaged in information exchange. No-tably, the user groups did not significantly differ in knowledge or willingness to vac-cinate. User group affiliation, knowledge, and health literacy did not significantly in-fluence vaccination behavior. The strongest predictor of vaccination was pre-existing willingness to vaccinate. Additionally, women were more likely to be vaccinated than men, and individuals with medium or higher education levels were 6 or 11 times more likely to be vaccinated compared to those with only a basic level of education. The results highlight the complexity of the decision-making process regarding vaccina-tions; merely providing access to information does not appear to be the sole determin-ing factor. Individual conviction about vaccination, risk perception, and willingness to adapt to social norms are crucial factors. Segmenting the population into different user groups allows for more targeted communication tailored to the specific needs and be-liefs of each group. For Frequent and Critical Information Evaluators, providing well-founded and detailed information on public channels is important. Infrequent and Pas-sive Recipients benefit from straightforward formats, such as short explanatory videos, while Frequent and Multi-Channel, Interaction-Focused Users are better reached through interactive offerings on social media.

BACKGROUND:As the potential of artificial intelligence (AI) in healthcare (HC) grows, so too does the number of potential risks, which has contributed to the development of regulations at national and international level that specifically address trust in and trustworthiness of AI. OBJECTIVE:Our research aims were 1) to assess the extent to which trust and trustworthiness are considered in the field of AI research in healthcare and 2) to show how the different scientific disciplines discuss both concepts. METHODS:We used a mixed-methods approach including a co-occurence analysis (19,940 publications) and a modified scoping review based on this, which included 30 publications. RESULTS:As shown in the co-occurrence network, trust and trustworthiness play a subordinate role in AI health-related research. 72 factors were identified that may have an influence on trust or trustworthiness. However, there is a lack of overlap in these factors across different disciplines, which is related to the heterogeneity of definitions and empirical approaches. CONCLUSION:The amount of research carried out to date regarding trust in and trustworthiness of AI systems in the HC sector appears to be significantly low and interdisciplinary approaches seem to be almost non-existent.

Community readiness (CR) describes the degree to which a municipality is prepared to actively address a health problem. It is a central condition for successful and sustainable prevention efforts. The CR for childhood obesity prevention is low in German municipalities, which makes it difficult to implement effective measures.The aim of this study was to develop strategies to increase CR for the prevention of childhood obesity in municipalities. In Bavarian municipalities, workshops (n=5) were conducted with municipal stakeholders after an initial CR assessment. Their specific results were analysed and based on this, strategies were developed in a participatory manner.The municipal strategies focused on three areas: (1) optimising resources for prevention efforts, (2) supporting knowledge about childhood obesity and prevention and (3) strengthening prioritisation of childhood obesity.The strategies offer practical approaches to promote CR and provide a basis for further development and implementation of effective prevention efforts. An evaluation of effectiveness can be made through follow-up measurements after implementation of the strategies.

Hintergrund: Handlungsbereitschaft (Community Readiness, CR) beschreibt, in welchem Ausmaß eine Kommune bereit ist, ein Gesundheitsproblem aktiv anzugehen. Sie ist eine zentrale Voraussetzung für erfolgreiche und nachhaltige Präventionsmaßnahmen. Die CR zur Prävention von Kinderübergewicht ist in deutschen Kommunen niedrig, was die Umsetzung effektiver Maßnahmen erschwert. Methoden: Ziel dieser Studie war es, Strategien zur Steigerung der CR für die Prävention von Kinderübergewicht in Kommunen zu entwickeln. In bayerischen Kommunen wurden nach einer initialen CR-Ermittlung Workshops (N=5) mit kommunalen Akteur/-innen durchgeführt. Dabei wurden deren spezifische Ergebnisse analysiert und darauf aufbauend Strategien partizipativ erarbeitet. Ergebnisse: Die kommunalen Strategien umfassen drei Schwerpunkte: (1) Die Optimierung von Ressourcen für Präventionsbemühungen, (2) die Erhöhung des Wissens über Kinderübergewicht und die Prävention und (3) die Förderung der Prioritätensetzung im Bereich Kinderübergewicht. Schlussfolgerung: Die entwickelten Strategien bieten praxisorientierte Ansätze zur Förderung der CR und stellen eine Grundlage für die Weiterentwicklung und Implementierung effektiver Präventionsmaßnahmen dar. Eine Bewertung der Wirksamkeit kann durch Follow-up-Messungen nach Umsetzung der Strategien erfolgen. Background: Community readiness (CR) describes the degree to which a municipality is prepared to actively address a health problem. It is a central condition for successful and sustainable prevention efforts. The CR for childhood obesity prevention is low in German municipalities, which makes it difficult to implement effective measures. Methods: The aim of this study was to develop strategies to increase CR for the prevention of childhood obesity in municipalities. In Bavarian municipalities, workshops (N=5) were conducted with municipal stakeholders after an initial CR assessment. Their specific results were analysed and based on this, strategies were developed in a participatory manner. Results: The municipal strategies focus on three areas: (1) optimising resources for prevention efforts, (2) supporting knowledge about childhood obesity and prevention and (3) strengthening the prioritisation of childhood obesity. Conclusion: The strategies offer practical approaches to promote CR and provide a basis for the further development and implementation of effective prevention efforts. An evaluation of effectiveness can be made through follow-up measurements after implementation of the strategies.

A better understanding of evidence-based nursing (EBN), which builds on the principles of evidence-based medicine (EBM), could serve as a good preparation for handling Clinical Decision Support Systems (CDSS) in nursing and improving EBN practice. This study builds upon previous research that explored the dimensions of EBN, identifying and operationalising the three dimensions: "Knowledge", "Trust" and "Practice". It therefore aimed at validating these dimensions in a binational and multicentred setting. To this end, answers of 402 nurses from Germany and Switzerland in a previously developed EBN questionnaire were analysed by factor analysis. This study revealed a factor structure that was identical to the three dimensions "Trust", coupled with motivation, importance, attitude towards quality of care, and use, "Knowledge", coupled with attitude and behaviour and "Practice", coupled with team, patient, and knowledge orientation. It explained 52.9% of the variance. Factor scores did not differ between the two countries. The consistency of results across the different institutions underscores the robustness of this factor structure. The identified factors match the findings reported in other studies, however, their operationalisation differs noticeably. Future studies should aim to compare measurement instruments and develop a unified tool that enables standardised assessment of EBN to improve its practice and leverage CDSS use.

Background Clinical decision-making is shaped by healthcare provider-related factors such as experience, qualification and cognitive skills. AI-based Clinical Decision Support Systems (CDSS) promise to enhance diagnostic accuracy but may also introduce risks, particularly through automation bias. The relative impact of correct and incorrect AI recommendations compared to human factors remains poorly understood. Methods A simulated diagnostic intervention study was conducted with 223 physicians and nurses, who generated 1,338 decisions when assessing wound maceration from images combined with AI recommendations. Participants first completed a baseline assessment of diagnostic performance without AI support, followed by a second phase including AI recommendations (correct or incorrect, based on a CNN). Diagnostic decisions were analysed using a generalised linear mixed model (GLMM) to examine the influence of AI recommendation correctness and healthcare provider-related factors (diagnostic performance, qualification, experience, trust in AI, gender, profession, age, healthcare sector) on decision accuracy. Results AI recommendations had a strong and bidirectional influence on diagnostic accuracy. Participants were ten times more likely to make correct decisions when receiving a correct AI recommendation (OR = 10.0, p < 0.001), but their accuracy decreased reciprocally when the AI recommendation was incorrect. Among provider-related factors, high baseline diagnostic performance (OR = 2.44, p = 0.019), pertinent formal qualifications (OR = 1.40, p = 0.049), longer work experience (OR = 1.89, p = 0.018), and female gender (OR = 1.55, p = 0.008) were associated with higher diagnostic accuracy. Trust in AI, age, profession, and healthcare sector showed no significant effects in the multivariate model. The overall effect of introducing AI was equivocal compared to baseline, however, there was a differential effect whether the recommendation was correct or wrong. Conclusions AI recommendations can exert a stronger influence on diagnostic decisions than healthcare provider-related factors. While AI support improved accuracy when correct, it reduced accuracy when incorrect, indicating overreliance on the system and posing a substantial safety risk. These findings highlight the dual nature of AI in clinical decision support and underscore the imperative for systems with consistently high quality in clinical practice. Equally important, clinicians must receive training and support to critically assess AI recommendations when making clinical decisions.

Artificial intelligence (AI) is increasingly integrated into healthcare, changing processes and structures, and thus the practice of healthcare professionals and potentially the role of patients and the healthcare professional-patient relationship. Beyond high-precision AI algorithms, knowledge of how to evaluate and use AI-based results in everyday healthcare is crucial for high-quality and safe care, and a prerequisite for trust. Therefore, this qualitative study aims to explore 1) the general perception of trust in AI used in healthcare and specifically in wound care, 2) the prerequisites for building trust in AI, and 3) the impact of AI on treatment and healthcare professional-patient relationship, all from the perspective of healthcare professionals and patients. Interviews were conducted in 2022/2023 with healthcare professionals specializing in wound care (N = 12) and in 2023 with patients with chronic wounds (N = 10). The interview guide included questions about digitalization in general and AI in particular, as well as trust and the healthcare professional-patient relationship. Our data revealed a limited understanding of AI principles and evaluation of AI-generated outcomes in both groups. Healthcare professionals recognized the potential of AI to provide data-driven suggestions for diagnosis and therapy, acting as a supportive "second opinion". Patients, on the contrary, expressed a preference for their physicians to incorporate AI-generated results into their care, thereby placing their trust in the physician's ability to apply them correctly. Neither group expected significant changes in the healthcare professional-patient relationship. Trust in AI was linked to general trust in digitalization, and healthcare professionals showed greater trust in AI results that were aligned with their existing expertise and were transparently explained. These findings suggest that AI can be a valuable tool for high-quality healthcare, but in-formed use requires meeting key prerequisites, including Explainable AI (XAI) principles and ongoing training.

BACKGROUND:Although the COVID-19 pandemic has demonstrably led to an increase in health inequities, only a few studies have analyzed their underlying mechanisms by taking into account socioeconomic status and sociodemographic differences at the same time. Similarly, only few studies have explored the impact of COVID-19 containment measures on inequities in living conditions, health-related risks, and coping resources. This study aims to address these gaps by exploring the complex associations of socioeconomic and sociodemographic factors with changes in life circumstances, pandemic-related experiences, self-rated health, and well-being among adults living in Germany. METHODS:A total of 2,123 adults (women: 49.8%, men: 50.2%) living in Germany participated in the cross-sectional online study ExCo:Well between July and August 2022. The survey included questions on socioeconomic status, sociodemographic factors, social circumstances, resources and burdens, as well as health outcomes. The data were analyzed using bivariate and multivariable logistic regression analyses. RESULTS:Our results show significant disparities in self-rated health and mental well-being based on socioeconomic status. For sociodemographic differences, the results are mixed, with only women consistently showing worse health outcomes than men. Immigration status played a limited role. Although measures to contain the COVID-19 pandemic more commonly affected the life and work conditions of more privileged participants, socioeconomically disadvantaged participants experienced higher burdens and had fewer coping resources. Logistic regression analyses showed that health inequities decreased when resources and burdens were considered. CONCLUSIONS:By covering the whole period of the COVID-19 pandemic, our data allow for an overall assessment of this critical time as well as a better understanding of mechanisms underlying health inequities. Our findings suggest that more important than the number of government-induced social changes is their quality and their potential to negatively impact material and social livelihoods in the long run. To improve health equity, tailored social security and health promotion interventions need to be systematically integrated in pandemic or crisis response plans.

Introduction: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention. This study therefore aims to identify factors influencing automation bias in a diagnostic task. Methods: A quantitative intervention study with participants from different backgrounds (n = 210) was conducted, employing regression analysis to analyze potential factors. Automation bias was measured as the agreement rate with wrong AI-enabled recommendations. Results and Discussion: Diagnostic performance, certified wound care training, physician profession, and female gender significantly reduced false agreement rates. Higher perceived benefit of the system was significantly associated with promoting false agreement. Strategies like comprehensive diagnostic training are pivotal in the prevention of automation bias when implementing CDSS. Conclusion: Considering factors influencing automation bias when introducing a CDSS is critical to fully leverage the benefits of such a system. This study highlights that non-specialists, who stand to gain the most from CDSS, are also the most susceptible to automation bias, emphasizing the need for specialized training to mitigate this risk and ensure diagnostic accuracy and patient safety.

Problem-based learning (PBL) is currently a well-established and widely used teaching/learning method that follows a distinct structure, such as a seven-step approach. There is growing interest in providing PBL digitally. This scoping review aims to investigate whether the principles of PBL can be implemented in digital formats and whether comparable results in students’ competence development can be achieved. A systematic literature search was conducted from January 2017 to March 2022 in accordance with the Joanna Briggs Institute (JBI) guidance for scoping reviews. The search yielded 1,007 studies, of which 7 were included in the review. The results demonstrated that traditional PBL can be implemented in both blended and fully online formats following its rationale. Most of the identified courses followed the seven-step approach, thereby providing a clear structure for alternating between the group learning and self-learning phases and related tasks. The results showed that blended or fully online PBL not only achieved the desired competence development but also promoted additional competencies such as communication skills through the digital learning context. These formats expand the possibilities of using PBL in health-related courses, effectively combining the benefits of analogue and digital worlds. However, appropriate resources in terms of both technical infrastructure and trained staff are required. In the future, the implementation of blended and fully online PBL should be described in more detail to evaluate their specific requirements.

The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students' evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.

The acceptance and use of digital technologies depend on the trustworthiness attributed to them. Experts were interviewed about how they assign trust to digital technologies or AI (N=12). The data were analyzed applying the focused qualitative content analysis. All of the experts have experience with digital technologies, but only seven with AI. The majority of experts generally trust digital technologies, but only five experts expressed a general trust in AI. Similar reasons contributing to trust building were given for digital technologies and AI. The results show the complexity of the trust building process and the construct of trust itself. The development of explainable AI and professional training are prerequisites to support a critical and safe use of these technologies.

Aim Health literacy is necessary to access, understand, assess, and apply information on COVID-19. Studies have shown that health literacy is unequally distributed across social groups. This study aimed to analyze the differences in COVID-19-related health literacy (hereinafter referred to as “COV-19-HL”), knowledge about COVID-19, and the assessment of the measures taken regarding the sociodemographic characteristics as well as the influence of COV-19-HL on knowledge and assessments. Subject and methods The study used the data obtained from the cross-sectional online survey “Digital divide in relation to health literacy during the COVID-19 pandemic.” The data covers 1570 participants aged ≥18 years in Germany between April 29, 2020 and May 8, 2020. To analyze the differences by way of sociodemographic variables, t-tests and analyses of variance were carried out. Multivariate logistic regression models were used to determine the effect of COV-19-HL on knowledge and the assessment of measures. Results The overall COV-19-HL was high with an average value of 37.4 (with 50 representing the highest COV-19-HL). COV-19-HL and knowledge about COVID-19 were slightly lower in men, migrants, people with low subjective social status, and with low education. Government requirements and recommendations were rated as more effective by women, older people, and individuals with a chronic illness. The chance of better knowledge about COVID-19 and rating measures as effective increased with higher COV-19-HL. Conclusion The findings of this study show that COV-19-HL and knowledge about the virus are unequally distributed in Germany. Health communication should strengthen pandemic-related health literacy that is tailored to specific target groups.

The World Health Organization has identified childhood obesity as one of the most serious public health problems of the 21st century. Understanding a municipality's readiness to address it is crucial to achieve successful interventions. However, the preparedness of German municipalities to address childhood obesity has not yet been investigated. This study is the first in Germany to apply the community readiness model (CRM) in this context. The purpose was to determine readiness of five municipalities for childhood obesity prevention and to identify factors that influence their readiness. Therefore, 27 semi-structured key informant interviews were conducted. First, the interviews were analysed following the CRM protocol to categorize the municipalities into a readiness level between one and nine. In a second step, a content analysis was carried out for an in-depth interpretation of the readiness scores. The municipalities achieved an average readiness of 3.84, corresponding to the 'Vague Awareness' stage. A lack of prioritization and leadership support, insufficient low-threshold efforts, a lack of knowledge and problem awareness as well as a lack of structures and resources were identified as factors that can determine municipal readiness to prevent childhood obesity. This study not only extends the application of the CRM to childhood obesity in German municipalities but also offers practical implications for professionals in assessing readiness.

Einleitung Gesundheitsinformationen im Internet, bspw. zur COVID-19-Impfung, sollen den Bürger:innen dabei helfen, informierte Entscheidungen treffen zu können. Qualitätskriterien für gute Gesundheitsinformationen liegen vor, jedoch spiegeln sie nicht zwangsläufig die Wünsche der Nutzer:innen in Pandemiezeiten wider. Daher wurde untersucht, welche Anforderungen an Online-Informationen zur COVID-19-Impfung gestellt werden.