To identify Clinical Decision Support Software (CDSS) that have been implemented in hospital which aim to influence empirical antibiotic prescribing, and to establish their impact on antibiotic prescribing and patient outcomes.

Systematic review & meta-analysis.

MEDLINE, Cochrane Central Register of Controlled Trials and Embase were searched from their inception to February 2024.

Studies evaluating the impact of digital CDSS with the primary purpose of influencing initial empirical antibiotic prescribing for patients with acute infection in hospital.

Study characteristics, intervention characteristics and outcome data were extracted independently by two reviewers. Outcomes were grouped into four domains including clinical outcomes (mortality, length of stay, readmission rates), antibiotic appropriateness (guideline adherence, coverage of causative organism), antimicrobial stewardship and health economics. Risk of bias assessment was conducted using Risk of Bias In Non-randomised Studies - of Interventions for non-randomised studies and Cochrane Risk of Bias 2 for randomised studies. Outcome data with sufficient reporting and homogeneity were synthesised quantitatively using a random-effects meta-analysis; other outcomes were synthesised qualitatively.

15 full texts met the eligibility criteria after screening 7984 unique studies. Low-quality evidence suggested that implementation of CDSS was associated with lower mortality (OR 0.76, 95% CI 0.57 to 1.01) and improved adherence to antibiotic prescribing guidelines (OR 1.75, 95% CI 1.26 to 2.43). No change in length of stay or readmission rates were observed. Coverage of the causative organism was similar after CDSS implementation (OR 1.26, 95% CI 0.97 to 1.63). High-quality evidence supported the association between CDSS implementation and reduced broad-spectrum antibiotic prescribing.

CDSS can be used to reduce the unnecessary prescribing of broad-spectrum antibiotics. Further high-quality studies are required to establish whether their implementation also results in improvements in other outcomes.

CRD42024501185.

To develop and psychometrically evaluate a multidimensional Disaster Health Literacy Questionnaire (DHLQ) for diabetic patients in Iran, using advanced item response theory approaches. The questionnaire was designed in the Persian (Farsi) language.

A sequential mixed-methods study incorporating qualitative (scoping review and interviews) and quantitative (psychometric validation) phases.

Diabetes clinics and healthcare centres across Iran (2022–2023).

The study enrolled 570 patients with diabetes (56% female, mean age 45.57±16.33 years) for quantitative validation; 15 experts and 15 patients for qualitative validation.

The psychometric properties evaluated included content validity (using content validity ratio (CVR) and content validity index (CVI)), construct validity (assessed via multidimensional item response theory (MIRT)), and reliability (measured by Cronbach’s alpha and test-retest Kappa). Additionally, item parameters (multidimensional difficulty (MDIFF) and multidimensional discrimination (MDISC)) and model fit indices (RMSEA, CFI and TLI) were examined.

The final 30-item DHLQ demonstrated excellent content validity (scale-level CVI=1; item-level CVI>0.79; CVR>0.49). Cronbach’s alpha for the total scale was 0.606; test-retest reliability showed significant agreement (Kappa=0.35–1, p

The DHLQ is a rigorously validated tool for assessing disaster health literacy in diabetic populations. Its multidimensional structure and strong psychometric properties support its use in clinical and emergency planning contexts to identify literacy gaps and tailor interventions.

Telehealth has the potential to address challenges faced by the healthcare industry. To achieve the intended goals of telehealth programmes, stakeholders should engage with these services. Prior research demonstrates that perceived value influences stakeholder engagement in a system-based service. Therefore, this review aims to synthesise the value perceptions of telehealth stakeholders.

The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Articles published between 1 January 2013 and 31 December 2024 were identified through SCOPUS, PubMed and Association for Computing Machinery (ACM) digital library database search, and screening relevant article reference and citation lists.

Articles examining a single-specific telehealth application, proving evidence of post-use value by one or more stakeholder groups were selected.

Two independent reviewers used standardised methods to search, screen and code included studies. Information was recorded related to telehealth type, stakeholders, reported perceived value from the articles and codes were developed successively from specific perceived outcomes.

140 articles were included in the review. The selected studies assessed various types of telehealth applications with a balanced representation of the types of care, telehealth modality and service. The stakeholders were patients and/or healthcare providers; the majority (82.85%) focusing on patients’ view. The reported perceived value outcomes were diverse and categorised into six themes: access to care, care effectiveness and efficiency, quality of care, affective outcomes and human capital. None of the studies reported all these value dimensions and there wasn’t a single value dimension reported by all studies.

The review demonstrates the diversity and fragmentation in perceived value of telehealth. Within each theme, there were variances in how different stakeholders defined their meaning. These insights highlight the multi-dimensional and context-specific nature of perceived value. This comprehensive view of value can inform the design of telehealth programmes to motivate the engagement of all stakeholders.

Electronic health register's (eRegisters) use have recently gained popularity in Africa. eRegisters are used to capture real-time patient information on several encounters with a healthcare provider. Given poor maternal and child health outcomes in Lesotho, eRegisters provide a promising innovative means of enhancing health outcomes, especially those related to midwifery. eRegisters capture maternal and newborn care services provided at healthcare facilities. Such data are important for informing evidence-based midwifery practice. Lesotho, a landlocked, sub-Saharan African country, piloted use of an eRegister in 2018. However, factors promoting eRegister and data use have not been fully documented. Therefore, this study explored factors promoting eRegister and data use for midwifery practice in Lesotho.

The study used a descriptive qualitative approach with interviews and focus group discussions used to collect data. Descriptive content analysis as outlined by Erlingsson and Brysiewicz (2017) was followed during data analysis.

The study was conducted at three of the eRegister piloting facilities in Lesotho to examine eRegister implementation across different levels of care. Data collection occurred between December 2023 and March 2024.

Purposive sampling was used to recruit healthcare workers across the three facilities. Participants were selected to capture the range of relevant roles and experience with eRegister across each facility, and 7, 6 and 5 participants were recruited.

Five categories emerged as factors promoting eRegister and data use: system readiness, organisational environment, data value and utility in practice, human resource competency and digital literacy and governance and stakeholder engagement.

This study identified critical factors that promote the use of the eRegister and data in Lesotho. The findings suggest that while external funding and partner responsiveness have been pivotal in sustaining eRegister operations, long-term sustainability will require stronger national ownership, including domestic investment in infrastructure, technical support and digital health governance. Future studies should explore the effect of eRegister use on clinical outcomes and examine strategies for scaling up digital health interventions in resource-limited settings.

In China, a large number of health-related short videos are posted on video platforms, including educational videos about irritable bowel syndrome (IBS). This study aimed to evaluate the reliability and quality of IBS-related video content on TikTok, Kwai and BiliBili.

Using ‘irritable bowel syndrome’ as the Chinese keyword, a new account was registered on each platform. On 1 November 2023, searches were conducted on TikTok, Kwai and BiliBili, and the top 100 recommended videos from each platform were analysed. After those that met the predefined exclusion criteria were removed, 244 short videos were included and evaluated for their characteristics, content, reliability and quality. Information quality was assessed using the Journal of the American Medical Association (JAMA) criteria, Global Quality Scale (GQS) and the modified Designed Information System Containing Evaluations of Reliability and Need (DISCERN) tool. Correlation analysis was conducted to evaluate the relationship between video characteristics and video reliability and quality.

A total of 244 eligible short videos were included. BiliBili videos were longer than TikTok and Kwai videos (p

Short videos of IBS-related health information on TikTok, Kwai and BiliBili were of poor quality; however, videos uploaded by health professionals and science communicators were relatively more reliable and comprehensive. Thus, the public are recommended to learn about IBS-relevant information through videos uploaded by health professionals and science communicators.

Research on modelling geographical accessibility to healthcare services has witnessed rapid methodological advancement and refinement. One of the contributing factors is the increasing availability of big data detailing the link between the population in need of care and the health facility such as infrastructure, travel modes and speeds, traffic congestion and the quality of road network. This has allowed more granular computation of geographic access metrics, particularly in low-and-middle income countries where data are scarce. However, there are no reviews providing a comprehensive overview of the availability and use of big data for assessing geographical accessibility to healthcare. This protocol aims to describe a methodological approach that will be used to review the existing literature on the application of big data (past or potential) in evaluating geographical accessibility to healthcare.

To characterise the big data that can be used to model geographical accessibility to healthcare, a scoping review will be undertaken and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for Scoping Reviews guidelines. We will search seven scientific databases (PubMed, Scopus, Web of Science, EBSCOhost-CINAHL, Cochrane, Embase and MEDLINE via Ovid), grey literature, reference lists of identified publications and conference proceedings. Search engines will be used to identify relevant big data services not yet used in published academic literature. All literature published in English or French will be included, regardless of publication type, geographical location or year of publication provided it describes or mentions big data that may be useful for evaluating geographical accessibility to healthcare. Study selection and data extraction will be performed independently by two researchers with a third resolving any discrepancies. Analysis will be conducted to summarise big data providers, their characteristics and their usefulness in terms of types of spatial accessibility metrics that can be derived.

Formal ethical approval is not required, as primary data will not be collected in this review. Findings will be disseminated through peer-reviewed publication in a journal, conference presentation and condensed summaries for stakeholders through professional networks and social media summaries.

Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/S496F.

Equitable access to healthcare technology is a major public health issue. For adults with type 2 diabetes (T2D), continuous glucose monitoring (CGM) technology can improve diabetes self-management and clinical outcomes. Even though CGM is now recommended by professional guidelines for all patients with diabetes on insulin therapy, evidence suggests that CGM is underutilised and inequitably prescribed across health systems. As CGM is an emergent technology, it is vital to understand what approaches have been studied to overcome inequities in CGM access for adults with T2D, what aspects of equitable access have yet to be addressed and what are facilitators and barriers to CGM access at the individual, facility and health system levels.

We will use the Joanna Briggs Institute’s revised scoping review framework to conduct our analysis. The protocol is registered with Open Science Framework (https://osf.io/z2exn). We will search for peer-reviewed literature containing empirical evidence for the facilitators and barriers to equitable access to CGM technology for patients with T2D. Findings will be organised according to research objectives and the Framework for Digital Health Equity, and summarised using narrative synthesis of descriptive statistics for quantitative findings, and themes for qualitative findings. This review will be conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews.

The findings from this review will provide valuable information and support for future research into the equitable implementation and use of CGM for patients with T2D. We will disseminate findings at conferences and publish in a peer-reviewed journal.

https://osf.io/z2exn.

Systematic literature reviews (SLRs) are essential for synthesising research evidence and guiding informed decision-making. However, SLRs require significant resources and substantial efforts in terms of workload. The introduction of artificial intelligence (AI) tools can reduce this workload. This study aims to investigate the preferences in SLR screening, focusing on trade-offs related to tool attributes.

A discrete choice experiment (DCE) was performed in which participants completed 13 or 14 choice tasks featuring AI tools with varying attributes.

Data were collected via an online survey, where participants provided background on their education and experience.

Professionals who have published SLRs registered on Pubmed, or who were affiliated with a recent Health Economics and Outcomes Research conference were included as participants.

The use of a hypothetical AI tool in SLRs with different attributes was considered by the participants. Key attributes for AI tools were identified through a literature review and expert consultations. These attributes included the AI tool’s role in screening, required user proficiency, sensitivity, workload reduction and the investment needed for training. Primary outcome measures: The participants’ adoption of the AI tool, that is, the likelihood of preferring the AI tool in the choice experiment, considering different configurations of attribute levels, as captured through the DCE choice tasks. Statistical analysis was performed using conditional multinomial logit. An additional analysis was performed by including the demographic characteristics (such as education, experience with SLR publication and familiarity with AI) as interaction variables.

The study received responses from 187 participants with diverse experience in performing SLRs and AI use. The familiarity with AI was generally low, with 55.6% of participants being (very) unfamiliar with AI. In contrast, intermediate proficiency in AI tools is positively associated with adoption (p=0.030). Similarly, workload reduction is also strongly linked to adoption (p

The findings suggest that workload reduction is not the only consideration for SLR reviewers when using AI tools. The key to AI adoption in SLRs is creating reliable, workload-reducing tools that assist rather than replace human reviewers, with moderate proficiency requirements and high sensitivity.

There are many barriers to mental health services, including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited in part by the cyclical nature of psychiatric symptoms. The human voice might have the potential to serve as a valuable biomarker in the identification, early diagnosis or monitoring of psychiatric conditions. Therefore, this protocol presents a proposed scoping review with the aim of synthesising existing knowledge on the application of artificial intelligence (AI) or machine learning (ML) in the management of individuals at risk of/suffering from schizophrenia through audio samples as a biomarker.

Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and Arksey & O’Malley’s scoping review framework (with recent advancements), we systematically mapped the literature on the application of voice-based biomarkers in schizophrenia. Several databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Embase, Compendex, CINAHL, Scientific Information Database, Magiran, IranMedex and Barakat knowledge network system) will be systematically searched for relevant studies through 2025. All searches will be conducted for peer-reviewed articles/studies published in Persian and English between 1 January 2012 and 1 September 2025. Two researchers will independently carry out screening of the included studies and extraction of data. Any discrepancies will be resolved by consensus. In case no initial consensus is reached, a third researcher will be consulted to make a decision. Findings will be presented narratively in the form of text, summary tables, charts and figures for each research question.

This proposed scoping review is based on publicly available information and is also a review of primary studies, so ethics and publication ethics approval are not required because all data from this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at national or international congresses and conferences. Importantly, the initial results from this review will serve as a basis for the design and validation of an intelligent clinical decision support system based on acoustic biomarkers for patients with schizophrenia, using AI or ML techniques.

Not registered.

The rise of electronic nicotine delivery systems (ENDS) has introduced new challenges to tobacco control and regulation, particularly among young adults, raising questions about their safety. This umbrella review aimed to synthesise existing systematic reviews with or without meta-analyses to evaluate the health impacts of ENDS.

We conducted a systematic literature search via the PICO strategy across multiple databases, focusing on e-cigarettes, ENDS and e-liquids, while excluding non-nicotine e-cigarette and nicotine replacement therapies (NRTs). Health outcomes include a range of clinical diseases and physiological changes. Quality assessment was performed via assessing the methodoligcal quality of systematic reviews 2 (AMSTAR-2), and the findings were synthesised narratively and in tables, prioritising the highest-rated reviews. The meta-analyses used R software (V.4.3) random effects models, and evidence quality was assessed via the Grading of Recommendations, Assessment, Development and Evaluation criteria.

Of the 5055 records, 69 systematic reviews were included. Systematic reviews have indicated increased risks of cardiovascular and respiratory diseases, mental health issues and substance abuse with ENDS use, especially among adolescents. Cardiovascular risk factors included increased heart rate (mean difference (MD) 1.41, 95% CI 0.81 to 2.01, I²=91%) from 25 studies; increased blood pressure (MD for systolic blood pressure=0.51 mm Hg, 95% CI 0.26 to 0.75, I²=89%; MD for diastolic blood pressure=0.59 mm Hg, 95% CI 0.35 to 0.83, I²=82%) from 23 studies; endothelial dysfunction and increased platelet activity. Respiratory risk factors included reduced lung function and a higher incidence of asthma in nine studies (OR 1.30, 95% CI 1.1 to 1.55; I²=43%) and chronic obstructive pulmonary disease. Mental health concerns, such as depression and suicidality, were also prevalent among adolescent ENDS users. Nine studies reported a negative effect of ENDS on periodontal health. Evidence of carcinogens has been found in the urinary examinations of ENDS users in some studies. The adverse events reported in seven randomised controlled trials with 2611 participants were similar between ENDS and NRT (RR 1.13, 95% CI 0.83 to 1.54, I²=12%).

Exposure to ENDS is harmful to various organ systems, especially cardiovascular and respiratory systems. Comprehensive regulatory measures and public health strategies are necessary to curb the use of ENDS, particularly among young people.

Structural MRI of the brain is routinely performed on patients referred to memory clinics; however, resulting radiology reports, including volumetric assessments, are conventionally stored as unstructured free text. We sought to use natural language processing (NLP) to extract text relating to intracranial volumetric assessment from brain MRI text reports to enhance routine data availability for research purposes.

Electronic records from a large mental healthcare provider serving a geographic catchment of 1.3 million residents in four boroughs of south London, UK.

A corpus of 4007 de-identified brain MRI reports from patients referred to memory assessment services. An NLP algorithm was developed, using a span categorisation approach, to extract six binary (presence/absence) categories from the text reports: (i) global volume loss, (ii) hippocampal/medial temporal lobe volume loss and (iii) other lobar/regional volume loss. Distributions of these categories were evaluated.

The overall F1 score for the six categories was 0.89 (precision 0.92, recall 0.86), with the following precision/recall for each category: presence of global volume loss 0.95/0.95, absence of global volume loss 0.94/0.77, presence of regional volume loss 0.80/0.58, absence of regional volume loss 0.91/0.93, presence of hippocampal volume loss 0.90/0.88, and absence of hippocampal volume loss 0.94/0.92.

These results support the feasibility and accuracy of using NLP techniques to extract volumetric assessments from radiology reports, and the potential for automated generation of novel meta-data from dementia assessments in electronic health records.

Digitalisation in healthcare has resulted in fragmented solutions and limited interoperability. The Fast Healthcare Interoperability Resources (FHIR) standard is increasingly adopted to enable standardised data exchange, yet its complexity creates usability challenges for clinicians and developers. To address these challenges, this study evaluates the usability of an enhanced FHIR Questionnaire Resource Editor designed to improve workflow efficiency, accessibility and user satisfaction in creating and managing healthcare questionnaires.

This mixed-methods usability evaluation will recruit 10 healthcare professionals and/or informatics experts via convenience sampling. The study will consist of four general phases: (1) an initial session to familiarise users with the tool; (2) a task analysis phase supported by eye-tracking to identify strengths and weaknesses; (3) retrospective think-aloud interviews to explore strategies used during tasks and (4) completion of a validated usability questionnaire, such as the System Usability Scale, to quantify user satisfaction. We will analyse quantitative data using descriptive and inferential statistics. Qualitative feedback will be examined through thematic analysis and affinity mapping. The primary outcome is to assess the editor’s usability and accessibility and to identify areas for improvement.

This study protocol has been reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Würzburg (ethikkommission@uni-wuerzburg.de) under approval number (24/24-sc). All participants will provide informed consent. Results will be disseminated through peer-reviewed journals, conferences and open-access platforms to inform future iterations of FHIR-based tools.

To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.

Cross-sectional non-inferiority validation study.

Tertiary academic hospital.

52 physicians from 8 specialties at a large Dutch academic hospital participated, either in writing summaries (n=42) or evaluating them (n=10).

Physician writers wrote summaries of 50 patient records. LLM-generated summaries were created for the same records using an EHR-integrated LLM. An independent, blinded panel of physician evaluators compared physician-written summaries to LLM-generated summaries.

Primary outcome measures were completeness, correctness and conciseness (on a 5-point Likert scale). Secondary outcomes were preference and trust, and time to generate either the physician-written or LLM-generated summary.

The completeness and correctness of LLM-generated summaries did not differ significantly from physician-written summaries. However, LLM summaries were less concise (3.0 vs 3.5, p=0.001). Overall evaluation scores were similar (3.4 vs 3.3, p=0.373), with 57% of evaluators preferring LLM-generated summaries. Trust in both summary types was comparable, and interobserver variability showed excellent reliability (intraclass correlation coefficient 0.975). Physicians took an average of 7 min per summary, while LLMs completed the same task in just 15.7 s.

LLM-generated summaries are comparable to physician-written summaries in completeness and correctness, although slightly less concise. With a clear time-saving benefit, LLMs could help reduce clinicians’ administrative burden without compromising summary quality.

This study aimed to explore orthopaedic patients’ and families’ experiences with artificial intelligence (AI)-driven chatbots for perioperative health information, focusing on usability, effectiveness and perceptions.

A descriptive qualitative design was employed.

This study was conducted at a regional care centre for orthopaedics.

We recruited 13 participants (patients undergoing orthopaedic surgeries and family members) through purposive sampling. Face-to-face semistructured interviews were conducted to capture participants’ experiences and insights. Data collection was concluded when data saturation was achieved. All interviews were audio recorded and transcribed verbatim within 24 hours. Transcripts were verified and analysed using the Colaizzi’s data analysis method.

Four themes emerged from interviews, including: (1) preference of AI chatbots over search engines; (2) improved accessibility and quality of information; (3) preference of AI over human interactions and (4) importance of effective prompting.

AI-driven chatbots offer a promising adjunct to perioperative patient education by delivering immediate, tailored guidance that overcomes the limitations of conventional search engines and busy clinical settings. Study participants valued chatbots’ efficient, context-sensitive retrieval, professional-level advice and non-judgmental interactions, which fostered trust and reduced anxiety. Effective prompting emerged as a key user skill, directly shaping response relevance and accuracy. Chatbot-generated health information should be regularly reviewed for accuracy. Structured tutorials may be offered for user capacity building.

To engage individuals with lived experiences of dyspareunia in a co-design process to identify strategies for reducing stigma on digital health platforms.

Three virtual co-design workshops were conducted with 14 participants with lived experiences of dyspareunia. Data collection occurred in two phases. In phase 1, participants created individual prototypes of stigma-alleviating website designs. In phase 2, participants came together to collaboratively create a final design prototype using the individual designs as a guide. Participants then explained their reasons for selecting specific design elements and how these choices addressed stigma. The co-design workshops were recorded, transcribed verbatim and then analysed thematically.

The data revealed four overarching themes for developing destigmatising online platforms. These include providing extensive information on dyspareunia, designing for inclusivity, protecting users’ identities, and offering interactive features to support information access and community connection.

This study offers patient-led strategies for mitigating stigma through online platforms. The findings may inform the design of digital health resources for individuals seeking sexual health services online, particularly those from stigmatised populations who use web-based platforms to navigate or supplement their healthcare needs.

The aim of this study is to investigate the use and effectiveness of equity tools in current practices of patient safety incident analyses via a scoping review of the literature.

Scoping review of the literature using the two main search term concepts "health equity" AND "safety review". The PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist was used to report in this paper.

Databases including but not limited to MEDLINE, Embase and PubMed were searched from inception to 16 January 2023.

Studies that included an equity tool in patient safety reviews were included. There were no restrictions on language or setting for included studies. Review articles were excluded.

Two independent reviewers used standardised methods to search and screen included articles. Data from included studies was extracted and compiled.

Five studies out of 5026 screened studies were included in the final analysis, 4 were conducted in the USA and 1 in Norway. While all studies identified equity domains to guide their approach to the provision of more equitable care, only three proposed change ideas and one implemented their framework to evaluate the role of social determinants and bias in adverse events. Communication was the most common theme found across four of the five studies. Access to healthcare services and bias were included as equity domains in two of the five studies. Implicit bias training was one of the identified change ideas. Other change ideas included improving access and communication, for example, through increasing the use and availability of interpreter services. One of the studies piloted the implementation of their equity checklist and found adverse event causes rooted in equity in 50% of the cases.

This scoping review demonstrates that there is a gap in current patient safety incident analyses, specifically lacking the consideration of equity domains. The development of a comprehensive health equity tool is necessary to promote equitable and safe care.

Sleep-related breathing disorders have become a significant public health concern due to their negative impact on the population’s quality of life and overall health. Despite being underdiagnosed, their prevalence has increased in recent years, particularly in cases of obstructive sleep apnoea (OSA). Early diagnosis and detection of OSA are essential for timely treatment to mitigate the physical and health consequences. While polysomnography remains the gold standard for diagnosis, its limitations have led to the adoption of nocturnal polygraphy as an alternative for diagnosis. The scientific community is seeking devices that enable continuous monitoring of sleep status and other relevant parameters in this population. This study aims to analyse a wearable device as a complementary tool for monitoring health status and daily activity in people with potential OSA.

This observational and cross-sectional study will be conducted at the Sleep Respiratory Disorders and Home Ventilation Unit of a Hospital Álvaro Cunqueiro in Vigo. The aim is to recruit 246 participants who meet the inclusion criteria. Specific statistical methods will be employed to evaluate the accuracy and quality of the data collected by the Xiaomi Mi Smart Band 9.

This protocol study has been approved by the Pontevedra-Vigo Ourense Research Ethics Committee (process number 2024/260). All participants will sign a statement of informed consent. Study results will be disseminated in peer-reviewed journal articles.

NCT06606691.

This study aimed to assess the proportion of nutrition-related information-seeking behaviour and its associated factors among pregnant women attending antenatal care at public hospitals in Bahir Dar City, northwest Ethiopia, 2023.

A cross-sectional quantitative supplemented with qualitative study design was conducted from March to April 2023 among 406 pregnant women. Pre-tested structured interviewer-administered and semistructured open-ended questionnaires were used to collect quantitative and qualitative data, respectively. Data were collected using the Kobo toolbox, and SPSS V.25 was used for analysis. Descriptive statistics were used to describe study subjects, and multivariate logistic regression analysis was employed to investigate the associated factors. The strength of associations was described using the OR with the corresponding 95% CI.

The study included 406 pregnant mothers with a median age of 28 with an IQR of 8 and 212 (52.2%) from rural settings. Of the total respondents, 173 (42.6%; 95% CI 37.7 to 47.6) of pregnant mothers were nutrition-related information seekers. Educational status, residence, monthly income and nutrition information literacy were significantly associated with nutrition-related information-seeking behaviour.

The proportion of nutrition information seeking among pregnant mothers in Bahir Dar City public hospitals was low. Lower educational status, low nutrition information literacy level, being from a rural residence and low monthly income are significantly associated factors.

Awareness creation for pregnant mothers from rural areas and with low educational status and improving nutrition information literacy of pregnant mothers are important activities to improve their nutrition information-seeking behaviour.

To synthesise current evidence on physiotherapists’ use of electronic health records (EHRs), with a focus on the determinants of adoption, implementation processes and associated implementation outcomes.

A systematic review employing a narrative synthesis approach.

PubMed, Cochrane, Scopus and Web of Science, covering all records from the inception of each database to 10 May 2024.

Studies conducted in physiotherapy clinical settings and using the International Classification of Functioning, Disability and Health (ICF).

Two authors independently screened articles and assessed methodological quality. Risk of bias was assessed using the Critical Appraisal Skills Programme tool for qualitative and for cohort studies, the Mixed Methods Appraisal Tool for mixed-methods studies and the JBI Critical Appraisal Checklist for analytical cross-sectional studies.

From 3820 records screened, 9 observational studies met inclusion criteria. Key factors influencing EHR adoption included organisational readiness, perceived usefulness, managerial support and training availability. Implementation patterns clustered into three domains: recorded content, ICF framework integration and record quality. Reported outcomes focused on care quality metrics and evidence of clinical effectiveness.

Persistent challenges in physiotherapy EHR use were identified, notably in data quality, completeness and alignment with the ICF framework. Improving EHR practices is crucial to improve clinical assessment and support digital health integration. However, limited evidence and methodological heterogeneity remain key limitations.

CRD42023420267.

Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.

Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.

GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.

This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.