¿Influye la comunicación con el paciente sobre la kinesiofobia? Una revisión sistemática / Does patient communication influence kinesiophobia? A systematic review

El objetivo de la presente revisión sistemática consistió en determinar el efecto de la comunicación en el ámbito sanitario sobre la kinesiofobia. Para ello, se realizó una búsqueda bibliográfica en siete bases de datos entre noviembre de 2022 y febrero de 2023. La revisión se efectuó acorde a la declaración Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) y para el análisis de la calidad metodológica se utilizaron: la escala Physiotherapy Evidence Database (PEDro), los criterios de Van Tulder y el análisis del riesgo de sesgo de la Colaboración Cochrane. Se incluyeron un total de 13 artículos que presentaron una calidad metodológica media de 7,1 sobre 10. Se obtuvieron resultados significativos para al menos una variable (kinesiofobia, discapacidad o nivel de actividad física) en 12 trabajos. Existe evidencia sólida de que la comunicación puede influir sobre la kinesiofobia del sujeto. Es más probable que esta influencia ocurra en un sentido negativo o discapacitante, pero también puede actuar en sentido positivo disminuyendo la misma. (AU)

The aim of the present systematic review was to determine the effect of communication in the health care setting on kinesiophobia. To this end, a literature search was conducted in seven databases between November 2022 and February 2023. The review was carried out following the PRISMA statement and for the analysis of methodological quality we used: PEDro Scale, Van Tulder criteria and risk of bias analysis of the Cochrane Collaboration. A total of 13 articles were included with a mean methodological quality of 7.1 out of 10. Significant results were obtained for at least one variable (kinesiophobia, disability or level of physical activity) in 12 articles. There is strong evidence that communication can influence a subject's kinesiophobia. This influence is most likely to be in a negative or disabling sense, but it can also act in a positive sense by decreasing it. (AU)

Machine learning models using symptoms and clinical variables to predict coronary artery disease on coronary angiography.

Introduction: Coronary angiography (CAG) is invasive and expensive, while numbers of patients suspected of coronary artery disease (CAD) undergoing CAG results have no coronary lesions. Aim: To develop machine learning algorithms using symptoms and clinical variables to predict CAD. Material and methods: This study was conducted as a cross-sectional study of patients undergoing CAG. We randomly chose 2082 patients from 2602 patients suspected of CAD as the training set, and 520 patients as the test set. We utilized LASSO regression to do feature selection. The area under the receiver operating characteristic curve (AUC), confusion matrix of different thresholds, positive predictive value (PPV) and negative predictive value (NPV) were shown. Support vector machine algorithm performances in 10 folds were conducted in the training set for detecting severe CAD, while XGBoost algorithm performances were conducted in the test set for detecting severe CAD. Results: The algorithm of logistic regression achieved an average AUC of 0.77 in the training set during 10-fold validation and an AUC of 0.75 in the test set. When probability predicted by the model was less than 0.1, 11 patients in the test set (520 patients) were screened out, and NPV reached 90.9%. When probability predicted by the model was less than 0.2, 110 patients in the test set were screened out, and reached 83.6%. Meanwhile, when threshold was set to 0.9, PPV reached 97.4%. When the threshold was set to 0.8, PPV reached 91.5%. Conclusions: Machine learning algorithm using data from hospital information systems could assist in severe CAD exclusion and confirmation, and thus help patients avoid unnecessary CAG.

Implementing Community-Based Strategies for Improved Pneumonia Care in Children: Insights From a Pilot Study.

INTRODUCTION: Respiratory ailments, encompassing a spectrum of disorders, are a leading cause of mortality and morbidity in children, with pneumonia being particularly significant, accounting for 16% of child mortality. To ensure timely engagement with healthcare services, it is imperative to instill awareness through Information, Education, and Communication (IEC) initiatives targeting mothers of children under five. The primary objective of this pilot study is to assess the feasibility of a community-based intervention on health-seeking behaviour, knowledge, and practice measures concerning the management and prevention of pneumonia in children. METHODOLOGY: The pilot study mirrored the main study's procedures in two villages, Bhuvanahalli and Gavanahalli, each randomly assigned as either an experimental or a control group. We selected 12 mothers with children under the age of five who had community-acquired pneumonia, employing a straightforward random technique, with six mothers from each group. These mothers were interviewed using a structured questionnaire focusing on health-seeking behaviour, knowledge, and practices related to the management and prevention of pneumonia. Mothers in the experimental group received a community-based intervention, specifically an educational set focusing on health-seeking behaviour, knowledge, and practice measures concerning the management and prevention of pneumonia in children, while those in the control group continued with their routine practices. We collected post-test data from the mothers in both groups at the 2nd, 4th, and 6th months of the intervention. The data analysis was conducted using the IBM SPSS Statistics for Windows, Version 28 (Released 2021; IBM Corp., Armonk, New York) software. The Mann-Whitney test and Kruskal-Wallis analyses indicated a notable and statistically significant shift in health-seeking behaviour, knowledge, and practices pertaining to the management and prevention of pneumonia in children as a result of the community-based educational intervention implemented in the experimental group (P<0.05). CONCLUSION: Community-based intervention is crucial to preventing mortality and morbidity in children. The findings of the pilot study affirm its feasibility and lay a strong foundation for further investigation and implementation.

Effects of a fearful emotional state on financial decisions in the presence of prior outcome information.

Negative emotions have been shown to influence financial risk-taking. However, how receiving salient information about prior outcomes interacts with a decision-maker's emotional state is not well known. In a laboratory experiment, we induced a fearful emotional state to investigate its effects on financial investment when outcome probabilities are unknown but decision-makers observe prior outcomes. The effects of fear on investment depended on whether the sequence of previous outcomes was favorable or unfavorable and contained weak or strong information. Our findings suggest that fear affected investment, at least in part, through changes in expectations of success.

Quality assessment of videos on social media platforms related to gestational diabetes mellitus in China: A cross-section study.

Purpose: This study aimed to systematically evaluate the quality of content and information in videos related to gestational diabetes mellitus on Chinese social media platforms. Methods: The videos on various platforms, TikTok, Bilibili, and Weibo, were searched with the keyword "gestational diabetes mellitus" in Chinese, and the first 50 videos with a comprehensive ranking on each platform were included for subsequent analysis. Characteristic information of video was collected, such as their duration, number of days online, number of likes, comments, and number of shares. DISCREN, JAMA (The Journal of the American Medical Association) Benchmark Criteria, and GQS (Global Quality Scores) were used to assess the quality of all videos. Finally, the correlation analysis was performed among video features, video sources, DISCERN scores, and JAMA scores. Results: Ultimately, 135 videos were included in this study. The mean DISCERN total score was 31.84 ± 7.85, the mean JAMA score was 2.33 ± 0.72, and the mean GQS was 2.00 ± 0.40. Most of the videos (52.6%) were uploaded by independent medical professionals, and videos uploaded by professionals had the shortest duration and time online (P < 0.001). The source of the video was associated with numbers of "likes", "comments", and "shares" for JAMA scores (P < 0.001), but there was no correlation with DISCERN scores. Generally, videos on TikTok with the shortest duration received the most numbers of "likes", "comments", and "shares", but the overall quality of videos on Weibo was higher. Conclusion: Although the majority of the videos were uploaded by independent medical professionals, the overall quality appeared to be poor. Therefore, more efforts and actions should be taken to improve the quality of videos related to gestational diabetes mellitus.

The Annual Wellness Visit Health Risk Assessment: Potential of Patient Portal-Based Completion and Patient-Oriented Education and Support.

Background and Objectives: Patient portals are secure online platforms that allow patients to perform electronic health management tasks and engage in bidirectional information exchange with their care team. Some health systems administer Medicare Annual Wellness Visit (AWV) health risk assessments through the patient portal. Scalable opportunities from portal-based administration of risk assessments are not well understood. Our objective is 2-fold-to understand who receives vs misses an AWV and health risk assessment and explore who might be missed with portal-based administration. Research Design and Methods: This is an observational study of electronic medical record and patient portal data (10/03/2021-10/02/2022) for 12 756 primary care patients 66+ years from a large academic health system. Results: Two-thirds (n = 8420) of older primary care patients incurred an AWV; 81.0% of whom were active portal users. Older adults who were active portal users were more likely to incur AWV than those who were not, though portal use was high in both groups (81.0% with AWV vs 76.8% without; p < .001). Frequently affirmative health risk assessment categories included falls/balance concerns (44.2%), lack of a documented advanced directive (42.3%), sedentary behaviors (39.9%), and incontinence (35.1%). Mean number of portal messages over the 12-month observation period varied from 7.2 among older adults affirmative responses to concerns about safety at home to 13.8 for older adults who reported difficulty completing activities of daily living. Portal messaging varied more than 2-fold across affirmative health risk categories and were marginally higher with greater number affirmative (mean = 13.8 messages/year no risks; 19.6 messages/year 10+ risks). Discussion and Implications: Most older adults were active portal users-a group more likely to have incurred a billed AWV. Efforts to integrate AWV risk assessments in the patient portal may streamline administration and scalability for dissemination of tailored electronically mediated preventive care but must attend to equity issues.

Developing a Multiprofessional Mobile App to Enhance Health Habits in Older Adults: User-Centered Approach.

BACKGROUND: Although comprehensive lifestyle habits are crucial for healthy aging, their adherence tends to decline as individuals grow older. Sustaining a healthy life over time poses a motivational challenge. Some digital tools, such as smartphone apps aimed at promoting healthy habits, have been used to counteract this decline. However, a more profound investigation is necessary into the diverse experiences of users, particularly when it concerns older adults or those who are unfamiliar with information and communications technologies. OBJECTIVE: We aimed to develop a mobile app focused on promoting the health of older adults based on the principles of software engineering and a user-centered design. The project respected all ethical guidelines and involved the participation of older adults at various stages of the development of the app. METHODS: This study used a mixed methods approach, combining both quantitative and qualitative methodologies for data collection. The study was conducted in Ribeirão Prêto, São Paulo, Brazil, and involved 20 older adults of both genders who were aged ≥60 years and enrolled in the Physical Education Program for the Elderly at the University of São Paulo. The research unfolded in multiple phases, encompassing the development and refinement of the app with active engagement from the participants. RESULTS: A total of 20 participants used a mobile health app with an average age of 64.8 (SD 2.7) years. Most participants had a high school education, middle-class status, and varying health literacy (mean score 73.55, SD 26.70). Overall, 90% (18/20) of the participants owned smartphones. However, 20% (4/20) of the participants faced installation challenges and 30% (6/20) struggled with web-based searches. The focus groups assessed app usability and satisfaction. Adjustments increased satisfaction scores significantly (Suitability Assessment of Materials: 34.89% to 70.65%; System Usability Scale: 71.23 to 87.14). Participant feedback emphasized font size, navigation, visual feedback, and personalization, and suggestions included health device integration, social interaction, and in-app communication support. CONCLUSIONS: This study contributes to the development of health care technologies tailored to the older adult population, considering their specific needs. It is anticipated that the resulting app will serve as a valuable tool for promoting healthy habits and enhancing the quality of life for older adults.

Oligopeptide-based molecular labelling of (bio)degradable polyester biomaterials.

Nowadays, a very important motivation for the development of new functional materials for medical purposes is not only their performance but also whether they are environmentally friendly. In recent years, there has been a growing interest in the possibility of labelling (bio)degradable polymers, in particular those intended for specific applications, especially in the medical sector, and the potential of information storage in such polymers, making it possible, for example, to track the ultimate environmental fate of plastics. This article presents a straightforward green approach that combines both aspects using an oligopeptide, which is an integral part of polymer material, to store binary information in a physical mixture of polymer and oligopeptide. In the proposed procedure the year of production of polymer films made of poly(l-lactide) (PLLA) and a blend of poly(1,4-butylene adipate-co-1,4-butylene terephthalate) and polylactide (PBAT/PLA) were encoded as the sequence of the appropriate amino acids in the oligopeptide (PEP) added to these polymers. The decoding of the recorded information was carried out using mass spectrometry technique as a new method of decoding, which enabled the successful retrieval and reading of the stored information. Furthermore, the properties of labelled (bio)degradable polymer films and stability during biodegradation of PLLA/PEP film under industrial composting conditions have been investigated. The labelled films exhibited good oligopeptide stability, allowing the recorded information to be retrieved from a green polymer/oligopeptide system before and after biodegradation. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay) study of the PLLA and PLLA/PBAT using the MRC-5 mammalian fibroblasts was presented for the first time.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale and modular design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs composed of simple molecular structures. This strategy is based on a universal core unit with signal selection capability, and a timeadjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal crosscatalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for largescale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Morphometric analysis of Halda River basin, Bangladesh, using GIS and remote sensing techniques.

GIS and remote sensing techniques were effectively used to analyse the morphometric parameters including linear, geometric, basin texture (aerial) and relief aspects of the Halda River Basin, Bangladesh. Along with measuring the morphometric parameters using predetermined formulas, advanced geo-computing tools of spatial analysis, cartography, math, geoprocessing and geometric analysis were employed to carry out the spatial distribution of selected parameters, especially aerial parameters. The linear aspect indicates the basin is six-order and oval-shaped. The bifurcation ratio (4.03) and relevant parameters indicate the moderate effect of geology and structural control is evident. The mean stream length (1.27) and Rho value (ranges between 0.11 and 0.20) indicate high runoff in steep areas and hydrologic storage capacity in flat areas. The stream frequency (0.83), drainage density (1.22), drainage intensity (0.68), infiltration ratio (1.02), length of the overland flow (0.41), and constant of channel maintenance (0.82) indicate the presence of moderate hard rock, less structural disturbances and moderate to high surface runoff in the basin. Basin relief (489 m), relative relief (2.02), ruggedness number (400), Melton's ruggedness number (12.43), and mean slope (9.33%) indicate the potential of high erosion and material transfer. The spatial distribution of selected aerial aspects significantly correlated to elevation and slope. The hierarchical pattern and spatial distribution of the morphometric parameters indicate areas with high slopes and lower-order streams have a high potential to be affected by soil erosion, landslides and flash floods, elsewhere, the areas with low slopes are prone to short-duration riverine floods. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morphohydrological, geological and climatological research will be beneficiary.

Evaluating online health information utilisation and its psychosocial implications among breast cancer survivors: Qualitative explorations.

Background: This study investigated the online information-seeking behaviours of breast cancer patients at Jordan University Hospital, focusing on their dissatisfaction with available online health resources and its impact on their well-being and anxiety levels. Methods: Employing descriptive phenomenology and convenience sampling, we conducted five Skype-based focus groups with 4-6 breast cancer survivors each, from March to July 2020. Data analysis was performed using NVivo, following Braun and Clark's inductive thematic analysis framework. Results: The thematic analysis revealed critical insights into survivors' interactions with online cancer resources, identifying key subthemes such as the quality of online information, cyberchondriasis, health literacy and search strategies, the distress caused by counterproductive searches, and the tendency to avoid internet searches. Conclusion: The study underscores the challenges breast cancer survivors face in accessing online health information, especially in Arabic. It highlights the need to improve the quality and accessibility of these resources. Enhancing the cultural relevance of online materials and educating patients on effective information evaluation are crucial. These measures can significantly boost health literacy, mitigate anxiety, and provide better support for breast cancer survivors.

Online health information seeking behavior, healthcare access, and health status during exceptional times.

Online health information seeking behavior (e-HISB) is becoming increasingly common and the trend has accelerated as a result of the COVID-19 pandemic when individuals strongly relied upon the Internet to stay informed by becoming exposed to a wider array of health information. Despite e-HISB having become a global trend, very few empirical investigations have analyzed its potential influence on healthcare access and individuals' health status. In this paper, we try to fill this gap. We use data from the second SHARE Corona Survey, supplemented with data from the previous 8th wave of SHARE, and estimate a recursive model of e-HISB, healthcare access, and individuals' health status that accounts for individuals' unobserved heterogeneity. Our findings suggest that e-HISB can empower individuals to better understand health concerns, facilitating improved health condition management. However, e-HISB can also trigger a chain reaction, as navigating vast amonts of online health information can heighten fear and anxiety. This increased anxiety may lead to higher utilization of medical services, adversely affecting individuals' perceptions of their health.

Public awareness and understanding of stem cell treatments available in Saudi Arabia and their trust in hospitals and research centers involved in stem cell research-a cross sectional study.

Introduction: Although stem cell research and therapeutic applications hold great promise for medical advancements, and have rapidly progressed globally, there remains a lack of genuine public awareness of the status of this subject in Saudi Arabia. Successful integration of stem cell therapy into healthcare relies on public awareness, understanding, and trust. Therefore, we aimed in this cross-sectional study to assess the public's knowledge, awareness, trust, support, participation, and confidence in stem cell treatments and centers involved in it. Materials and methods: A voluntary questionnaire of 20 questions was distributed randomly via social media outlets. Results: Three thousand five hundred eighty four individuals participated in the survey, with approximately half of them falling within the age range of 35-50 years (46.71%). Majority of the participants, 90.71%, would like to know more about stem cell therapy and more than half of the participants (56.94%) were unfamiliar with the idea, and a comparable proportion (50.41%) expressed concerns about the safety of stem cell therapy. A lower level of awareness, indicated by a score of 5, was evenly distributed across all age groups and genders. However, regardless of gender, older participants-especially those 50 years of age or older-tended to report higher levels of confidence, trust, and support than participants in other age groups. Moreover, trust, support, participation, and confidence score for those attained high school or less was statistically significantly lower than those attained master's or PhD degree. Of the participants, 33.57% had either received stem cell therapy themselves or known someone who had; about 24.07% of them reported that it was a cosmetic type of treatment. Conclusion: The study emphasizes the persistent need for awareness and educational initiatives to minimize the lack of public awareness and understanding of approved stem cell treatments in Saudi Arabia. It advocates for increased education, transparency, and communication to bridge knowledge gaps and enhance public trust to ensure the understanding of successful treatment.

A Framework for Neonatal Prematurity Information System Development Based on a Systematic Review on Current Registries: An Original Research.

Background: Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies. Objective: This study aimed to probe the studies, including premature infants' registries, adapt the needed minimum data set, and provide an offered framework for premature infants' registries. Material and Methods: For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed. Results: Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure. Conclusion: To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory.

Purpose: How individuals engage with social media can significantly impact their psychological well-being. This study examines the impact of social media interactions on mental health, grounded in the frameworks of the Elaboration Likelihood Model and Schema Activation Theory. It aims to uncover behavioral differences in information sharing between the general population and individuals with depression, while also elucidating the psychological mechanisms underlying these disparities. Methods: A pre-experiment (N=30) and three experiments (Experiment 1a N=200, Experiment 1b N=180, Experiment 2 N=128) were executed online. These experiments investigated the joint effects of information quality, content valence, self-referential processing, and depression level on the intention to share information. The research design incorporated within-subject and between-subject methods, utilizing SPSS and SPSS Process to conduct independent sample t-tests, two-factor ANOVA analyses, mediation analyses, and moderated mediation analyses to test our hypotheses. Results: Information quality and content valence significantly influence sharing intention. In scenarios involving low-quality information, individuals with depression are more inclined to share negative emotional content compared to the general population, and this tendency intensifies with the severity of depression. Moreover, self-referential processing acts as a mediator between emotional content and intention to share, yet this mediation effect weakens as the severity of depression rises. Conclusion: Our study highlights the importance of promoting viewpoint diversity and breaking the echo chamber effect in social media to improve the mental health of individuals with depression. To achieve this goal, tailoring emotional content on social media could be a practical starting point for practice.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

BACKGROUND: Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. OBJECTIVE: This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). METHODS: We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. RESULTS: A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. CONCLUSIONS: This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Information density as a predictor of communication dynamics.

In a recent paper, Aceves and Evans computed information and semantic density measures for hundreds of languages, and showed that these measures predict the pace and breadth of ideas in communication. Here, we summarize their key findings and situate them in a broader debate about the adaptive nature of language.

Suggestive answers strategy in human-chatbot interaction: a route to engaged critical decision making.

In this study, we proposed a novel chatbot interaction strategy based on the suggestive ending of answers. This strategy is inspired by the cliffhanger ending narrative technique, which ends a story without specifying conclusions to spark readers' curiosity as to what will happen next and is often used in television series. Common chatbots provide relevant and comprehensive answers to users' questions. In contrast, chatbots with our proposed strategy end their answers with hints potentially interest-triggering users. The suggestive ending strategy aims to stimulate users' inquisition for critical decision-making, relating to a psychological phenomenon where humans are often urged to finish the uncompleted tasks they have initiated. We demonstrated the implication of our strategy by conducting an online user study involving 300 participants, where they used chatbots to perform three decision-making tasks. We adopted a between-subjects factorial experimental design and compared between the following UIs: (1) plain chatbot-it provides a generated answer when participants issue a question; (2) expositive chatbot-it provides a generated answer for a question, adding short summaries of a positive and negative person's opinion for the answer; (3) suggestive chatbot-it provides a generated answer for a question, which ends with a suggestion of a positive and negative person for the answer. We found that users of the suggestive chatbot were inclined to ask more questions to the bot, engage in prolonged decision-making and information-seeking actions, and formulate their opinions from various perspectives. These findings vary with the users' experience with plain and expositive chatbots.

Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study.

BACKGROUND: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process. OBJECTIVE: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability. METHODS: The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained using a structured electronic template incorporating the differential points of skilled pediatricians. An extreme gradient boosting-based machine learning model was developed using the data of 4174 patients aged ≤24 months who underwent RSV rapid antigen testing. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, and December 31, 2015. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve. The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity. RESULTS: Our model demonstrated an area under the receiver operating characteristic curve of 0.811 (95% CI 0.784-0.833) with good calibration and 0.746 (95% CI 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% (95% CI 5.4%-8.5%) of patients in the entire cohort would be positive and 68.7% (95% CI 65.4%-71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients. CONCLUSIONS: Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.