Usage Metrics Research Articles

An eHealth intervention for patients with a low socioeconomic position during their waiting period preceding cardiac rehabilitation: a randomized feasibility study

Abstract Aims Cardiac rehabilitation (CR) shows lower effectiveness and higher dropouts among people with a low socioeconomic position (SEP) compared to those with a high SEP. This study evaluated an eHealth intervention aimed at supporting patients with a low SEP during their waiting period preceding CR. Methods and results Participants with a low SEP in their waiting period before CR were randomized into an intervention group, receiving guidance videos, patient narratives, and practical tips, or into a control group. We evaluated adherence (usage metrics), acceptance (modified Usefulness, Satisfaction, and Ease of use questionnaire), and changes in feelings of certainty and guidance between the waiting period’s start and end. Semi-structured interviews provided complementary insights. The study involved 41 participants [median interquartile range (IQR) age 62 (14) years; 33 males], with 21 participants allocated to the intervention group, using the eHealth intervention for a median (IQR) duration of 16 (10) days, using it on a median (IQR) of 100% (25) of these days, and viewing 88% of the available messages. Key adherence themes were daily routine compatibility and curiosity. Acceptance rates were 86% for usability, 67% for satisfaction, and 43% for usefulness. No significant effects on certainty and guidance were observed, but qualitative data suggested that the intervention helped to inform and set expectations. Conclusion The study found the eHealth intervention feasible for cardiac patients with a low SEP, with good adherence, usability, and satisfaction. However, it showed no effect on feelings of certainty and guidance. Through further optimization of its content, the intervention holds promise to improve emotional resilience during the waiting period. Registration This trial is registered as follows: ‘Evaluation of a Preparatory eHealth Intervention to Support Cardiac Patients During Their Waiting Period (PReCARE)’ at ClinicalTrials.gov (NCT05698121, https://clinicaltrials.gov/study/NCT05698121).

Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud Performance: An Experimental Study

Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools—EdgeSimPy in particular—to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools.

IOT-Enabled Smart Healthcare: Developing Predictive Models for Chronic Disease Management Using Wearable Sensors

Abstract: Real-time monitoring and prediction models will be used to assess the efficiency of the IoT-enabled smart healthcare solutions in managing chronic diseases. From this study, in addition to the quantitative data, including the performance of the model, patient engagement, and cost savings, qualitative information obtained from the patients and healthcare professionals will be employed using a mixed-methods research approach. This collected rich data from surveys, performance measurements, real-time monitoring data, and cost analyses with 50 health professionals and 200 chronic disease patients. The results of the study indicate that predictive models, more particularly the Neural Network, enhanced the ability to identify cases of chronic illness with an accuracy of 92.4%. Extremely high patient satisfaction rates: 9.0 out of 10. Real-time monitoring shows hospital admissions falling by as much as 50% among those suffering with respiratory disease. Cost-benefit analysis also documented financial sustainability, with overall annual cost reduction at 43.3%. Soon after implementation, usage metrics also revealed that the average user uses it three times a day, while patient-provider interactions increased by 150%. This study highlights how IoT technologies would benefit patients' clinical conditions, improve chronic disease management, and save healthcare expenses.

Navigating the complexity of User Engagement: need for normative evaluations in digital interventions

Abstract Digital behavioral change interventions (DBCI), leveraging digital technology to promote cognitive behavioral therapy or positive psychology techniques, can potentially revolutionize health promotion. With applications across a spectrum of health behaviors-from managing common mental health disorders to enhancing physical activity, addressing smoking cessation, alcohol use, and weight management-these interventions offer promise for both healthy and clinical populations. However, their effectiveness hinges significantly on user engagement. Digital interventions are prone to attrition due to their self-help and unguided nature, which impedes their translation from research to real-life settings. This talk discusses the findings of a scoping review to shed light on these complexities. Firstly, we explore the various methodologies used to evaluate engagement across studies, hindering effective comparison and understanding. Secondly, we address the lack of systematic assessment of engagement strategies, advocating for experimental research to fill this gap. Lastly, we emphasize the need to establish an optimal level of engagement for desired outcomes, noting that continuous engagement may not always be conducive to behavioral change interventions, which aim to instill lasting changes in individuals. This underscores the importance of comprehensive assessment using both objective and subjective measures. In conclusion, engagement with digital interventions encompasses both quantitative usage metrics and qualitative subjective experiences, highlighting the need for comprehensive assessment beyond the mere evaluation of frequency or duration. Contrary to common belief, increased engagement does not always lead to better outcomes. Too much engagement can lead to exhaustion, while too little can result in inefficiency. Therefore, designing a DBCI that encourages optimal use is essential, fostering a balance that maximizes effectiveness while minimizing adverse effects.

Security analysis and provision of authentication protocol, based on peer-to-peer structure in IOT platform

The Internet of Things technology allows you to transfer data to any asset through communication networks such as the Internet or intranet. One of the most important problems is security, which includes confidentiality, authenticity validation, and completeness. Among security problems, the most important ones are ensuring authenticity and protecting privacy. The elliptic curve encryption algorithm is famous protocols for maintaining privacy and verifying authenticity. Despite the capabilities they have and provide good security against network attacks, they face challenges such as response time, delay in the authentication and authentication process, as well as the amount of memory consumed. Two-factor authentication (2FA) is a security process where users provide two different authentication factors to authenticate themselves. It also provides a higher level of security than authentication methods that rely on single-factor authentication (SFA). This article presents two novel methods for 2FA that use encryption techniques, scramble architecture, and chord architecture. These methods allow for the authentication process to be initiated by both parties involved in the communication. Three scenarios and three metrics of reaction time, memory usage, and connection latency were used to assess the efficacy of the suggested approach. The average reaction time is improved and is 0.012 s when the lengths of the finger table in the chord structure are increased to 10, 15, and 20. The findings show that the suggested approach reduces computing complexity, reaction time, and communication latency. It has an average reaction time of 0.016 s, an average memory use of 30,360 kb, and an average connection latency of 0.042 s.

The FLASHFIT Interactive Device in Healthcare: Revolutionizing Patient Engagement and Monitoring

The study investigates the effectiveness of the FLASHFIT interactive device in enhancing patient engagement and monitoring within healthcare settings. As digital health solutions become increasingly vital, FLASHFIT aims to bridge the gap between patients and healthcare providers by promoting active participation in health management. This research employs a mixed-methods approach, combining quantitative data from patient usage metrics with qualitative feedback from users and healthcare professionals. Preliminary findings indicate that FLASHFIT significantly improves patient adherence to treatment plans and fosters a sense of autonomy in health management. Moreover, real-time monitoring capabilities facilitate timely interventions, enhancing overall patient outcomes. The study concludes that integrating interactive devices like FLASHFIT into healthcare practices empowers patients and streamlines communication between patients and providers, suggesting a transformative potential for future healthcare delivery models. Keywords: FLASHFIT interactive device, patient engagement and monitoring

Digital self-presentation and adolescent mental health: Cross-sectional and longitudinal insights from the “LifeOnSoMe”-study

BackgroundThe intensive use of social media among adolescents has caused concern about its impact on their mental health, but studies show that social media use is linked to both better and worse mental health. These seemingly contradictory findings may result from the diverse motivations, interactions, and experiences related to social media use, and studies investigating specific facets of social media use in relation to mental health and well-being, beyond general usage metrics, have been called for. Aspects of self-presentation on social media, such as feedback-seeking and upwards social comparison have been linked to worse mental health, however, there is a need for more studies exploring the relationship between self-presentation on social media and adolescent mental health over time.AimThe aim of this study was to explore the cross-sectional and longitudinal relationship between aspects of self-presentation and depression, anxiety, and well-being among adolescents.MethodsThis study utilised both cross-sectional and longitudinal datasets from the LifeOnSoMe-study, comprising 3,424 and 439 participants, respectively (OSF preregistration https://doi.org/10.17605/OSF.IO/BVPS8). Latent Class Analysis (LCA) was used to identify similar response patterns within the Self-Presentation and Upwards Social Comparison Inclination Scale (SPAUSCIS). Regression models and first differencing methods were applied to evaluate the cross-sectional and longitudinal associations between focus on self-presentation and mental health and well-being among adolescents.ResultsA strong emphasis on self-presentation was linked to increased levels of depression and anxiety in both males and females, and reduced well-being in females when compared to those with lower or intermediate self-presentation focus. The effect sizes ranged from small to medium. Furthermore, an escalation in self-presentation focus over time was associated with a slight increase in symptoms of anxiety and depression; however, the association with well-being did not reach statistical significance.ConclusionThe results of the present study suggest that a heightened focus on self-presentation, which includes behaviours such as seeking feedback, employing strategic self-presentation tactics, and engaging in upward social comparisons, is associated with an elevated risk of reduced mental health.

Which orthodontic articles are accessed online the most? Exploring article usage metrics along with citations and altmetrics.

To investigate the current state of article usage metrics in orthodontics. Out of all orthodontic journals listed in Journal Citation Reports 2022, the European Journal of Orthodontics and The Angle Orthodontist fulfilled the inclusion criteria. All journal issues published in 2021 were scrutinized for original research articles and systematic reviews/meta-analyses, and the following features were collected: article type, subject, title, number of words and authors, Altmetric Attention Score (AAS), X (formerly Twitter) posts, and Mendeley reads. Article citations, number of publications, and h-index of the last authors were retrieved from Web of Science (WoS). 181 articles were considered eligible. The median number of views and downloads of included articles was 1296 (range: 355-10,233) and 793 (range: 167-3629). Page views, downloads, and total views were significantly correlated with WoS citations (rho > 0.345; P < .001). There was no correlation between usage metrics, AAS, X posts, and Mendeley reads. Number of downloads were significantly higher in studies dealing with new technologies, and where the last author had 1-40 publications or an h-index of 0-30. Page views, downloads, and total views were positively correlated with WoS citations and, therefore, may serve as an early estimate of future citations. Significant variations in article downloads may be expected in relation to article subject, scientific productivity, and impact of the last authors.

The analysis of the models performance indicators by cloud and post-cloud computing paradigms

The work describes cloud and post-cloud computing paradigms, on the basis of which it is possible to model and build intelligent video surveillance systems. Based on the analysis of literary sources, it was concluded that performance indicators largely depend on the paradigm of the computational model, but the degree of importance of each performance indicator is different for different paradigms. Taking into account the peculiarities of different paradigms, the performance indicators characteristic of each of them are described. It has been found that the common indicators for evaluating all models developed on the basis of cloud and post-cloud computing paradigms (with varying degrees of importance) are: Throughput, Network Congestion, SLA Violation, Fault Tolerance, Statistical Analysis Measurements, The Number of Orchestration Decisions, The number of Contradictory Decisions, Time to Adaptation, Competition Ratio, Cache Hit Ratio, The number of Contradictory Actions, Oscillation Mitigation, Cost, Technique’s Overhead, Privacy. Based on the analysis of cloud and post-cloud computing simulators, it is established that the metrics of resource usage, energy, response time, and latency are those that can be used by researchers when conducting a comparative analysis of models built on the basis of the following paradigms: fog, edge, fog, and cloud computing. The practical significance of the conducted research is that, depending on the tasks that will be set before the future video surveillance system, researchers will be able to evaluate the effectiveness of using different architectures (Cloud, FOG, Mist, etc.) with the help of certain simulators. In the future, it is advisable to conduct a study of the degree of importance of common metrics for evaluating all models developed on the basis of cloud and post-cloud computing paradigms, and conduct their normalization for the possibility of conducting an adequate comparative analysis. It is also relevant in future research that it is advisable to conduct research using different types of sensor devices (surveillance cameras with different technical characteristics, unmanned aerial vehicles, etc.).

Barriers to the adoption of routine surgical video recording: a mixed-methods qualitative study of a real-world implementation of a video recording platform

BackgroundRoutine surgical video recording has multiple benefits. Video acts as an objective record of the operative record, allows video-based coaching and is integral to the development of digital technologies. Despite these benefits, adoption is not widespread. To date, only questionnaire studies have explored this failure in adoption. This study aims to determine the barriers and provide recommendations for the implementation of routine surgical video recording.Materials and methodsA pre- and post-pilot questionnaire surrounding a real-world implementation of a C-SATS©, an educational recording and surgical analytics platform, was conducted in a university teaching hospital trust. Usage metrics from the pilot study and descriptive analyses of questionnaire responses were used with the non-adoption, abandonment, scale-up, spread, sustainability (NASSS) framework to create topic guides for semi-structured interviews. Transcripts of interviews were evaluated in an inductive thematic analysis.ResultsEngagement with the C-SATS© platform failed to reach consistent levels with only 57 videos uploaded. Three attending surgeons, four surgical residents, one scrub nurse, three patients, one lawyer, and one industry representative were interviewed, all of which perceived value in recording. Barriers of ‘change,’ ‘resource,’ and ‘governance,’ were identified as the main themes. Resistance was centred on patient misinterpretation of videos. Participants believed availability of infrastructure would facilitate adoption but integration into surgical workflow is required. Regulatory uncertainty was centred around anonymity and data ownership.ConclusionBarriers to the adoption of routine surgical video recording exist beyond technological barriers alone. Priorities for implementation include integration recording into the patient record, engaging all stakeholders to ensure buy-in, and formalising consent processes to establish patient trust.

Cycling into the workshop: e-bike and m-bike mobility patterns for predictive maintenance in Barcelona’s bike-sharing system

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

The Impact of Digital Financial Inclusion on Banking Sector Stability: Evidence from Developing Countries

The research explores the transformative impact of Digital Financial Inclusion on banking sector stability in developing countries, where advanced technologies reshape financial services. With a focus on FinTech, E-wallets, and digital transactions, the study addresses a critical gap in the existing literature by examining the impact of digital Financial Inclusion indicators, such as ATMs and mobile money accounts, on developing countries banking stability. This study contributes valuable knowledge to policymakers and financial professionals in a rapidly evolving digital era. Employing data from 36 developing nations and covering the period of 2011 to 2017, the research establishes a link between digital Financial Inclusion and enhanced banking stability. Z-score is used to measure financial stability, and ATMs and mobile money accounts are used to measure digital financial inclusion, covering the outreach and usage metrics. Macroeconomic variables like gross domestic product and inflation are included to capture broader economic influences on banking stability. A panel regression was used to analyse the data. The study found that digital Financial Inclusion proxies significantly impact the banking sector's stability. The attention for enhancing digital financial services in improving and maintaining the banking sector stability is reconfirmed from this study based on a larger data set of developing countries.

Study of the impact of introducing a multimedia learning tool in podiatric medical courses

AbstractBackgroundMedical students face the challenge of learning vast amounts of complex information. Existing research suggests improved learning outcomes using multimedia resources but reports on their impact on podiatric education are scarce. To explore the potential of multimedia‐based learning tools in enriching medical education, this study examined the impact of Osmosis, a platform featuring interactive videos, flashcards, and self‐assessment quizzes on podiatric medical student outcomes.MethodsThis quasi‐experimental study examined the impact of Osmosis, a multimedia learning platform with videos, flashcards, and quizzes, on podiatric medical students' learning outcomes. Two cohorts (T = Osmosis access, N = 86; C = no access, N = 87) took Pharmacology and Podiatric Medicine courses consecutively. Final exam scores, final course grades, platform usage metrics (median weekly videos watched, flashcards, and quizzes), and student experience surveys were analyzed.ResultsAnalyses revealed no statistically significant differences in final exam scores between the groups in Pharmacology and Podiatric Medicine. While the treatment group exhibited a slight upward trend, further research is required for conclusive evidence. Student perceptions of Osmosis were overwhelmingly positive, with 90.2% of students agreeing that it facilitated concept learning and understanding compared to 54.9% for the textbook. Similarly, 80.4% of the treatment group felt that Osmosis enhanced their test performance, exceeding the 54.9% recorded for the textbook. Correlation analysis indicates a plausible connection between platform usage and academic success, as reflected by moderate positive correlations (r = [0.14, 0.28]) with final grades. Logistic regression analysis revealed that students with Osmosis access were 2.88 times more likely to score 90% or higher on the Pharmacology final exam (p &lt; 0.05) and exhibited increased odds of achieving high (90%+) final course grades in Podiatric Medicine (OR = 2.71).ConclusionsThese findings suggest that Osmosis holds promise as a tool to support podiatric medical student learning. While the lack of statistically significant differences in final exam scores warrants further investigation, the positive student perceptions, high engagement rates, and increased odds of high scores in specific areas indicate the potential for Osmosis to positively impact academic outcomes. Therefore, a multimedia‐based resource like Osmosis appears to show promise as a tool to support podiatric medical education. The limitations inherent in the quasi‐experimental design necessitate further studies to confirm its effectiveness and long‐term impact on podiatric medical education.

Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite

The rapid increase of Earth science data from remote sensing, models, and ground-based observations highlights an urgent need for effective data management practices. Data repositories track provenance and usage metrics which are crucial for ensuring data integrity and scientific reproducibility. Although the introduction of Digital Object Identifiers (DOIs) for datasets in the late 1990s has significantly aided in crediting creators and enhancing dataset discoverability (akin to traditional research citations), considerable challenges persist in establishing linkage of datasets used with scholarly documents. This study evaluates the citation coverage of datasets from NASA’s Earth Observing System Data and Information System (EOSDIS) across several major bibliographic sources ‒ namely Google Scholar (GS), Web of Science (WoS), Scopus, Crossref, and DataCite—which helps data managers in making informed decisions when selecting bibliographic sources. We provide a robust and comprehensive understanding of the citation landscape, crucial for advancing data management practices and advancing open science. Our study searched and analyzed temporal trends across the bibliographic sources for publications that cite approximately 11,000 DOIs associated with EOSDIS datasets, yielding 17,000 unique journal and conference articles, reports, and book records linked to 3,000 dataset DOIs. GS emerged as the most comprehensive source while Crossref lagged significantly behind the other major sources. Crossref’s record references revealed that the absence of dataset DOIs and shortcomings in the Crossref Event data interface likely contributed to its underperformance. Scopus initially outperformed WoS until 2020, after which WoS began to show superior performance. Overall, our study underscores the necessity of utilizing multiple bibliographic sources for citation analysis, particularly for exploring dataset-to-document connections.

Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.

Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.

Baby Vaccination Web Application

The Baby Vaccination System project aims to develop a comprehensive platform for managing child vaccination schedules efficiently. It incorporates frontend and backend development to provide users with tools for vaccine registration, tracking, and locating nearby vaccine centres. By targeting efficient management of vaccination schedules, the system contributes to public health initiatives and ensures timely immunisation for children. The objectives of the study include assessing the effectiveness of the system, evaluating user satisfaction, and determining the impact on vaccination completion rates. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative feedback collection. Participants include parents or guardians of children eligible for vaccination, healthcare providers, and system administrators. Data collection methods include system usage metrics, surveys, and interviews. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and adhering to relevant ethical guidelines. The study findings will provide insights into the usability and effectiveness of the Baby Vaccination System in improving vaccination management.In today’s life style due to the heavy burden and pressure of work it is difficult to remember vaccine period on new born baby as well as small once. Keywords: Blockchain Vaccination System, Healthcare, Immunization, Public Health, Technology Integration

Design and Implementation of Reconfigurable Array Adaptive Optoelectronic Hybrid Interconnect Shunting Network

Addressing challenges regarding Hybrid Optoelectronic Network-on-Chip systems, such as congestion control, their limited adaptability, and their inability to facilitate optoelectronic co-simulation, this study introduces an adaptive hybrid optoelectronic interconnection shunt structure tailored for reconfigurable array processors. Within this framework, an adaptive shunt routing algorithm and a low-loss non-blocking five-port optical router are developed. Furthermore, an adaptive hybrid optoelectronic interconnection simulation model and a performance statistical model, established using SystemVerilog and Verilog, complement these designs. The experimental results showcase promising enhancements: the designed routing algorithm demonstrates an average 17.5% improvement in mitigating congestion at network edge nodes; substantial reductions in the required number of cross waveguides and micro-ring resonators for optical routers lead to an average path insertion loss of only 0.522 dB. Moreover, the hybrid optoelectronic interconnection performance statistical model supports the design of routing strategies and topology structures, enabling resource usage, power consumption, insertion loss, and other performance metrics to be accurately assessed.

Real-World Effectiveness of the Gla-300 + Cap + App Program in Adult Users Living with Type2 Diabetes in Taiwan.

Health2Sync (H2S) is a digital health technology platform that provides coaching and titration support to patients with diabetes. The Mallya cap converts a conventional insulin pen into a smart connected device that can automatically synchronize dose values and associated timestamps (upon injection) to the H2S platform. This single-arm real-world study evaluated the effectiveness of insulin glargine 300U/mL (Gla-300) combined with H2S and Mallya cap (Gla-300 + Cap + App program) on clinical outcomes among users with type 2 diabetes (T2D) in Taiwan. Adults (aged ≥ 20years) with T2D who were registered H2S users and initiated Mallya cap for a new/existing Gla-300 regimen (identification period May1, 2021-May31, 2022) were included in this retrospective cohort study. Follow-up data from H2S were collected for 90days. Glycated hemoglobin (HbA1c) change (baseline to follow-up) and HbA1c goal attainment were primary outcomes. Hypoglycemia incidence and usage metrics of Mallya cap were secondary outcomes. Of 83 participants, 38.6% were new Gla-300 users. HbA1c was reduced in both new (- 2.4 [2.7] %, - 26.2 [29.5] mmol/mol) and previous Gla-300 users (- 0.5 [1.6] %, - 5.5 [17.5] mmol/mol). Reduction in HbA1c was significant (p < 0.05) in both groups. At follow-up, 43.4% of users had a reduction of > 0.5%. Mean HbA1c reductions increased numerically with higher baseline HbA1c and with longer duration of Mallya cap usage. Use of digital technology within a connected ecosystem such as Gla-300 + Cap + App program could help people with type2 diabetes to improve their glycemic condition.

Recommendations for Use of p16/Ki67 Dual Stain for Management of Individuals Testing Positive for Human Papillomavirus.

The Enduring Consensus Cervical Cancer Screening and Management Guidelines Committee developed recommendations for dual stain (DS) testing with CINtec PLUS Cytology for use of DS to triage high-risk human papillomavirus (HPV)-positive results. Risks of cervical intraepithelial neoplasia grade 3 or worse were calculated according to DS results among individuals testing HPV-positive using data from the Kaiser Permanente Northern California cohort and the STudying Risk to Improve DisparitiES study in Mississippi. Management recommendations were based on clinical action thresholds developed for the 2019 American Society for Colposcopy and Cervical Pathology Risk-Based Management Consensus Guidelines. Resource usage metrics were calculated to support decision-making. Risk estimates in relation to clinical action thresholds were reviewed and used as the basis for draft recommendations. After an open comment period, recommendations were finalized and ratified through a vote by the Consensus Stakeholder Group. For triage of positive HPV results from screening with primary HPV testing (with or without genotyping) or with cytology cotesting, colposcopy is recommended for individuals testing DS-positive. One-year follow-up with HPV-based testing is recommended for individuals testing DS-negative, except for HPV16- and HPV18-positive results, or high-grade cytology in cotesting, where immediate colposcopy referral is recommended. Risk estimates were similar between the Kaiser Permanente Northern California and STudying Risk to Improve DisparitiES populations. In general, resource usage metrics suggest that compared with cytology, DS requires fewer colposcopies and detects cervical intraepithelial neoplasia grade 3 or worse earlier. Dual stain testing with CINtec PLUS Cytology is acceptable for triage of HPV-positive test results. Risk estimates are portable across different populations.

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP). Dual wrist-worn accelerometry data were collected from three females and seven males with unilateral CP (mean age = 10 years 2 months [SD 3 years]) while performing hand tasks during video-recorded play sessions. Video observers labelled instances of functional and non-functional hand use. Machine learning was compared to the conventional activity count approach for identifying unilateral hand movements as functional or non-functional. Correlation and agreement analyses compared the functional usage metrics derived from each method. The best-performing machine learning approach had high precision and recall when trained on an individual basis (F1 = 0.896 [SD 0.043]). On an individual basis, the best-performing classifier showed a significant correlation (r = 0.990, p < 0.001) and strong agreement (bias = 0.57%, 95% confidence interval = -4.98 to 6.13) with video observations. When validated in a leave-one-subject-out scenario, performance decreased significantly (F1 = 0.584 [SD 0.076]). The activity count approach failed to detect significant differences in non-functional or functional hand activity and showed no significant correlation or agreement with the video observations. With further development, wearable accelerometry combined with machine learning may enable quantitative monitoring of everyday functional hand use in children with unilateral CP.