Establishing an Evaluation Indicator System for User Satisfaction With Hypertension Management Apps: Combining User-Generated Content and Analytic Hierarchy Process.

BACKGROUND: Hypertension management apps (HMAs) can be effective in controlling blood pressure, but their actual impact is often suboptimal. Establishing a user satisfaction evaluation indicator system for HMAs can assist app developers in enhancing app design and functionality, while also helping users identify apps that best meet their needs. This approach aims to improve the overall effectiveness of app usage. OBJECTIVE: This study aims to systematically collect data on HMAs and their user reviews in the United States and China. It analyzes app usage patterns and functional characteristics, identifies factors influencing user satisfaction from existing research, and develops a satisfaction evaluation indicator system to provide more accurate recommendations for improving user satisfaction. METHODS: We conducted a descriptive statistical analysis to assess the development status of HMAs in both countries and applied the task-technology fit model to evaluate whether the app functionalities align with business needs. We separately summarized the factors influencing user satisfaction in both countries from previous research, utilized the analytic hierarchy process to develop an evaluation indicator system for HMA user satisfaction, and calculated satisfaction levels. Based on these findings, we propose improvements to enhance app functionality and user satisfaction. RESULTS: In terms of current development status, there were fewer HMAs and user reviews in China compared with the United States. Regarding app functional availability, fewer than 5% (4/91) of the apps achieved a demand fulfillment rate exceeding 80% (8/10). Overall, user satisfaction in both countries was low. CONCLUSIONS: In the United States, user satisfaction was lowest for advertising distribution, data synchronization, and reliability. By contrast, Chinese apps need improvements in cost efficiency and compatibility.

The Current Status and Trends of Research Related to Vestibular Disorders, Vertigo, and Cognitive Impairment in the Elderly Population: A Bibliometric Analysis.

Background: The vestibular system not only supports reflex function at the brainstem level, but is also associated with higher levels of cognitive function. Vertigo due to vestibular disorders may lead to or be associated with cognitive dysfunction. Patients with deficits of both vestibular as well as cognitive function may be at particularly high risk for events like falls or certain diseases, such as Alzheimer's. Objective: To analyze the current state of research and trends in the global research literature regarding the correlation between vestibular disorders, vertigo, and cognitive impairment. Methods: We utilized Bibliometrix package to search databases including PubMed, Web of Science, etc for search terms. Results: Databases were searched up to December 15, 2022, and a total of 2222 publications were retrieved. Ultimately, 53 studies were included. A total of 261 authors published in 38 journals and conferences with an overall increasing annual growth rate of 6.94%. The most-published journal was Frontiers in Neurology. The most-published country was the United States, followed by Italy and Brazil. The most-published institution was Johns Hopkins University with a total of 13 articles. On performing trend analysis, we found that the most frequent focus of research in this field include the testing of vestibular perception, activation of the brain-related cortex, and the influence of stimulus-triggered vestibular snail reflex on visual space. The potential focal points are the risk of falling and the ability to extract spatial memory information, and the focus of research in recent decades has revolved around balance, falling, and Alzheimer's disease. Conclusions: Vestibular impairment in older adults affects cognitive function, particularly immediate memory, visuospatial cognition, and attention, with spatial cognition being the most significantly affected. In the future, virtual reality-based vestibular rehabilitation techniques and caloric stimulation could be potential interventions for the treatment of cognitive impairment.

Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study.

BACKGROUND: Hypertension significantly impacts the well-being and health of individuals globally. Hypertension management apps (HMAs) have been shown to assist patients in controlling blood pressure (BP), with their efficacy validated in clinical trials. However, the utilization of HMAs continues to be suboptimal. Presently, there is a dearth of real-world research based on big data and exploratory mining that compares Chinese and American HMAs. OBJECTIVE: This study aims to systematically gather HMAs and their user reviews from both China and the United States. Subsequently, using data mining techniques, the study aims to compare the user experience, satisfaction levels, influencing factors, and asymmetry between Chinese and American users of HMAs. In addition, the study seeks to assess the disparities in satisfaction and its determinants while delving into the asymmetry of these factors. METHODS: The study sourced HMAs and user reviews from 10 prominent Chinese and American app stores globally. Using the latent Dirichlet allocation (LDA) topic model, the research identified various topics within user reviews. Subsequently, the Tobit model was used to investigate the impact and distinctions of each topic on user satisfaction. The Wald test was applied to analyze differences in effects across various factors. RESULTS: We examined a total of 261 HMAs along with their associated user reviews, amounting to 116,686 reviews in total. In terms of quantity and overall satisfaction levels, Chinese HMAs (n=91) and corresponding reviews (n=16,561) were notably fewer compared with their American counterparts (n=220 HMAs and n=100,125 reviews). The overall satisfaction rate among HMA users was 75.22% (87,773/116,686), with Chinese HMAs demonstrating a higher satisfaction rate (13,866/16,561, 83.73%) compared with that for American HMAs (73,907/100,125, 73.81%). Chinese users primarily focus on reliability (2165/16,561, 13.07%) and measurement accuracy (2091/16,561, 12.63%) when considering HMAs, whereas American users prioritize BP tracking (17,285/100,125, 17.26%) and data synchronization (12,837/100,125, 12.82%). Seven factors (easy to use: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; heart rate detection function: P=.02; blood pressure tracking function: P<.001; and interface design: P=.01) significantly influenced the positive deviation (PD) of Chinese HMA user satisfaction, while 8 factors (easy to use: P<.001; reliability: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; interface design: P<.001; real-time: P<.001; and data privacy: P=.001) affected the negative deviation (ND). Notably, BP tracking had the greatest effect on PD (ß=.354, P<.001), while cost had the most significant impact on ND (ß=3.703, P<.001). All 12 factors (easy to use: P<.001; blood pressure tracking function: P<.001; data synchronization: P<.001; blood pressure management effect: P<.001; heart rate detection function: P<.001; data sharing: P<.001; reliability: P<.001; compatibility: P<.001; interface design: P<.001; advertisement distribution: P<.001; measurement accuracy: P<.001; and cost: P<.001) significantly influenced the PD and ND of American HMA user satisfaction. Notably, BP tracking had the greatest effect on PD (ß=0.312, P<.001), while data synchronization had the most significant impact on ND (ß=2.662, P<.001). In addition, the influencing factors of PD and ND in user satisfaction of HMA in China and the United States are different. CONCLUSIONS: User satisfaction factors varied significantly between different countries, showing considerable asymmetry. For Chinese HMA users, ease of use and interface design emerged as motivational factors, while factors such as cost, measurement accuracy, and compatibility primarily contributed to user dissatisfaction. For American HMA users, motivational factors were ease of use, BP tracking, BP management effect, interface design, measurement accuracy, and cost. Moreover, users expect features such as data sharing, synchronization, software reliability, compatibility, heart rate detection, and nonintrusive advertisement distribution. Tailored experience plans should be devised for different user groups in various countries to address these diverse preferences and requirements.

Microstructure and Biomechanical Properties in Selective Laser Melting of Porous Metal Implants.

Two kinds of porous structure design strategies, ring-support (RS) and column-support (CS), are proposed for human implants. The accurate design of porosity is realized by adjusting the pore characteristics, such as strut diameter, pore diameter, and unit size. Porous specimens with porosity of 50%, 60%, 70%, and 80% were prepared by selective laser melting. The three-dimensional pore structure is basically consistent with the design characteristics, and the measured porosity is slightly lower than design value. The microstructure, microhardness, and friction and wear properties of the samples were studied. The results show that the performance along the scanning orientation is slightly better than that along the forming orientation. The compression and dynamic elastic modulus of porous specimens with different structures and porosities were analyzed. The CS porous with 60-80% porosity has suitable compressive strength and elastic modulus, which is close to that of human tissue, and effectively avoids the stress shielding phenomenon.

Hereditary Hearing Loss: A Systematic Review of Potential Treatments and Interventions.

PURPOSE: The purpose of this study was to systematically review the research literature with regards to treatments and intervention methods for hereditary hearing loss. Our goal was to provide reference guidelines for the rational use of medication and gene-targeted therapy for patients with hereditary hearing loss and discuss the future development of research in this area. METHOD: We searched two core databases, PubMed and Web of Science, for relevant literature relating to potential treatments and interventional methods for hereditary hearing loss. Then, we used Microsoft Excel to perform basic statistical analysis of the data, the R language to perform bibliometric analyses, and VOSviewer and CiteSpace to visualize data. In addition, we clustered and descriptively analyzed the data and identified the relative importance of each approach with regard to precise patient outcomes. RESULTS: In this study, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standardized screening process and identified a total of 103 research articles. The average annual growth rate of publications in this area was 12.73%. The country with the highest number of publications and citations was the United States; 80 of these publications (associated with 76.92% of funding) were supported by grants from 16 countries. Potential treatments and interventions were clustered according to the stage of research and showed that 8.74% remain in the research design stage, 59.22% are in the clinical validation stage, and 32.04% are being applied in the clinic. The main research focus in this field is cochlear implants and gene therapy. CONCLUSIONS: Hereditary hearing loss is in a critical period of transition from preventive to therapeutic research. Gene-targeted interventions represent one of the most promising and effective treatments. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24309193.

Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion.

BACKGROUND: The use of Electronic Health Records is the most important milestone in the digitization and intelligence of the entire medical industry. AI can effectively mine the immense medical information contained in EHRs, potentially assist doctors in reducing many medical errors. OBJECTIVE: This article aims to summarize the research status and trends in using AI to mine medical information from EHRs for the past thirteen years and investigate its information application. METHODS: A systematic search was carried out in 5 databases, including Web of Science Core Collection and PubMed, to identify research using AI to mine medical information from EHRs for the past thirteen years. Furthermore, bibliometric and content analysis were used to explore the research hotspots and trends, and systematically analyze the conversion rate of research resources in this field. RESULTS: A total of 631 articles were included and analyzed. The number of published articles has increased rapidly after 2017, with an average annual growth rate of 55.73%. The US (41.68%) and China (19.65%) publish the most articles, but there is a lack of international cooperation. The extraction of disease lesions is a hot topic at present, and the research topic is gradually shifting from disease risk grading to disease risk prediction. Classification (66%), and regress (15%) are the main implemented AI tasks. For AI algorithms, deep learning (31.70%), decision tree algorithms family (26.47%), and regression algorithms family (17.43%) are used most frequently. The funding rate for publications is 69.26%, and the input-output conversion rate is 21.05%. CONCLUSIONS: Over the past decade, the use of AI to mine medical information from EHRs has been developing rapidly. However, it is necessary to strengthen international cooperation, improve EHRs data availability, focus on interpretable AI algorithms, and improve the resource conversion rate in future research.

Current Status and Trends in mHealth-Based Research for Treatment and Intervention in Tinnitus: Bibliometric and Comparative Product Analysis.

BACKGROUND: As a global medical problem, tinnitus can seriously harm human health and is difficult to alleviate, ranking among the top 3 complex diseases in the otolaryngology field. Traditional cognitive behavioral therapy and sound therapy require offline face-to-face treatment with medical staff and have limited effectiveness. Mobile health (mHealth), which, in recent decades, has been greatly applied in the field of rehabilitation health care, improving access to health care resources and the quality of services, has potential research value in the adjunctive treatment of tinnitus. OBJECTIVE: This study aimed to understand the research trends, product characteristics, problems, and research transformation of tinnitus treatment software by analyzing the research progress of mHealth for tinnitus treatment based on the literature and related marketed apps. METHODS: Bibliometric methods were used to describe the characteristics of the relevant literature in terms of the number and topics of publications, authors, and institutions. We further compared the features and limitations of the currently available tinnitus treatment software. RESULTS: Data published until February 28, 2022, were collected. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standardized screening process, 75 papers were included. The country with the highest number of publications was Germany, followed by the United Kingdom and the United States, whereas China had only a single relevant study. The most frequently found journals were the American Journal of Audiology and the Journal of the American Academy of Audiology (18/75, 24%). With regard to publication topics, cognitive behavioral therapy started to become a hot topic in 2017, and research on mHealth apps has increased. In this study, 28 tinnitus treatment apps were obtained (n=24, 86% from product data and n=4, 14% from literature data); these apps were developed mainly in the United States (10/28, 36%) or China (9/28, 32%). The main treatment methods were sound therapy (10/28, 36%) and cognitive behavioral therapy (2/28, 7%). Of the 75 publications, 7 (9%) described apps in the market stage. Of the 28 apps, 22 (79%) lacked literature studies or evidence from professional bodies. CONCLUSIONS: We found that, as a whole, the use of mHealth for treatment and intervention in tinnitus was showing a rapid development, in which good progress had been made in studies around sound therapy and cognitive behavioral therapy, although most of the studies (50/75, 67%) focused on treatment effects. However, the field is poorly accepted in top medical journals, and the majority are in the research design phase, with a lack of translation of the literature results and clinical validation of the marketed apps. Furthermore, in the future, novel artificial intelligence techniques should be used to address the issue of staged monitoring of tinnitus.

Global research trends in benign paroxysmal positional vertigo: a bibliometric analysis.

Background: Benign paroxysmal positional vertigo is the most common disease in which vertigo is the main clinical manifestation, and it has become a global medical problem, affecting a wide range of areas and seriously affecting the quality of human life. Objective: This article presents an analysis of the current characteristics of BPPV-related research and summarizes the current hot topics and trends, with the goal of inspiring future research into the prevention and treatment of BPPV, thereby improving the differential diagnosis and prevention of peripheral vertigo. Methods: A bibliometric approach was used to collect 1,219 eligible studies on BPPV from four databases-PubMed, Embase, Scopus, and Web of Science-published between 1974 and 2022. The characteristics and status of the accumulated scientific output were processed using R and VOSviewer so that we could visualize any trends or hotspots. Results: The results showed a significant increase in the annual number of publications, with an average annual growth rate of 21.58%. A possible reason for the especially pronounced peak in 2021 was an increase in the prevalence of BPPV as a result of COVID-19. The new coronavirus became a focus of research in 2021. A total of 3,876 authors (of whom 1,097 were first authors) published articles in 307 different journals; 15.7% of the articles were published in Acta Oto-Larygologica, Otology and Neurotology, and Frontiers in Neurology. Acta Oto-Laryngologica was well ahead of the other journals in terms of growth rate and number of articles published. American scholars generated the largest number of articles overall, and the USA was involved in the greatest number of international collaborations, followed by Italy and China. The themes of the research centered around three topics, namely the treatment of BPPV, its influencing factors, and diagnosis. Conclusions: There has been a major increase in BPPV-related research over the last 50 years, leading to an increase in related articles and rapid development of the field. Key directions for future research include the improvement of individualized treatment for residual symptoms after initial treatment of BPPV among the elderly; effective control of comorbidities such as osteoporosis; and secondary inner ear disease, such as Ménière's disease.

Understanding the research on tracking, diagnosing, and intervening in sleep disorders using mHealth apps: Bibliometric analysis and systematic reviews.

Objectives: In solving the global challenge of sleep disorders, Mobile Health app is one of the important means to monitor, diagnose, and intervene in sleep disorders. This study aims to (1) summarize the status and trends of research in this field; (2) assess the production and usage of sleep mHealth apps; (3) calculate the conversion rate of grants that the proportion of newly developed apps from being funded and developed to published on application stores. Methods: Using bibliometric and content analysis methods, based on "Research Paper-Product Output-Product Application" chain and considering the "Research Grants" of articles, we conducted a systematic review of eight databases, to identify relevant studies over the last decade. Results: Over the past decade, 1399 authors published 313 papers in 182 journals and conferences. The number of publications increased with an average annual growth of 41.6%. The current focus area is research using cognitive behavioral therapy to intervene in sleep. Sleep-staging tracking is a shortcoming of this field. A total 368 sleep mHealth apps (233 newly developed and 135 existing) were examined in 313 papers; 323 grants supported 178 articles (56.9%). Only 12 of the newly developed apps are used in the real world, resulting in a 9% grant conversion rate. Conclusions: In the last decade, the field of tracking, diagnosing, and intervening in sleep disorders using mHealth apps has shown a trend of rapid development. However, the conversion rate of products from being funded and developed for use by end-users is low.

Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling.

BACKGROUND: Sleep disorders are a global challenge, affecting a quarter of the global population. Mobile health (mHealth) sleep apps are a potential solution, but 25% of users stop using them after a single use. User satisfaction had a significant impact on continued use intention. OBJECTIVE: This China-US comparison study aimed to mine the topics discussed in user-generated reviews of mHealth sleep apps, assess the effects of the topics on user satisfaction and dissatisfaction with these apps, and provide suggestions for improving users' intentions to continue using mHealth sleep apps. METHODS: An unsupervised clustering technique was used to identify the topics discussed in user reviews of mHealth sleep apps. On the basis of the two-factor theory, the Tobit model was used to explore the effect of each topic on user satisfaction and dissatisfaction, and differences in the effects were analyzed using the Wald test. RESULTS: A total of 488,071 user reviews of 10 mainstream sleep apps were collected, including 267,589 (54.8%) American user reviews and 220,482 (45.2%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58% vs the United States: 45.87%). We identified 14 topics in the user-generated reviews for each country. In the Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The 2 variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among these topics, the app's sound recording function (ß=1.026; P=.004) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app (ß=1.185; P<.001). In the American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among these, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect (ß=1.389; P<.001), whereas the app's sleep improvement effect (ß=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the PD and ND models of user satisfaction in both countries (all P<.05), indicating that the influencing factors of user satisfaction with mHealth sleep apps were asymmetrical. Using the China-US comparison, hygiene factors (ie, stability, compatibility, cost, and sleep monitoring function) and 2 motivation factors (ie, sleep suggestion function and sleep promotion effects) of sleep apps were identified. CONCLUSIONS: By distinguishing between the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted.

Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis.

The mobile health (mHealth) industry is an enormous global market; however, the dropout or continuance of mHealth is a major challenge that is affecting its positive outcomes. To date, the results of studies on the impact factors have been inconsistent. Consequently, research on the pooled effects of impact factors on the continuance intention of mHealth is limited. Therefore, this study aims to systematically analyze quantitative studies on the continuance intention of mHealth and explore the pooled effect of each direct and indirect impact factor. Until October 2021, eight literature databases were searched. Fifty-eight peer-reviewed studies on the impact factors and effects on continuance intention of mHealth were included. Out of the 19 direct impact factors of continuance intention, 15 are significant, with attitude (ß = 0.450; 95% CI: 0.135, 0.683), satisfaction (ß = 0.406; 95% CI: 0.292, 0.509), health empowerment (ß = 0.359; 95% CI: 0.204, 0.497), perceived usefulness (ß = 0.343; 95% CI: 0.280, 0.403), and perceived quality of health life (ß = 0.315, 95% CI: 0.211, 0.412) having the largest pooled effect coefficients on continuance intention. There is high heterogeneity between the studies; thus, we conducted a subgroup analysis to explore the moderating effect of different characteristics on the impact effects. The geographic region, user type, mHealth type, user age, and publication year significantly moderate influential relationships, such as trust and continuance intention. Thus, mHealth developers should develop personalized continuous use promotion strategies based on user characteristics.

Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: Retrospective, Cross-Sectional Study of Internet Hospitals.

BACKGROUND: Internet-based online virtual health services were originally an important way for the Chinese government to resolve unmet medical service needs due to inadequate medical institutions. Its initial development was not well received. Then, the unexpected COVID-19 pandemic produced a tremendous demand for telehealth in a short time, which stimulated the explosive development of internet hospitals. The Second Affiliated Hospital of Zhejiang University (SAHZU) has taken a leading role in the construction of internet hospitals in China. The pandemic triggered the hospital to develop unique research on health service capacity under strict quarantine policies and to predict long-term trends. OBJECTIVE: This study aims to provide policy enlightenment for the construction of internet-based health services to better fight against COVID-19 and to elucidate future directions through an in-depth analysis of 2 years of online health service data gleaned from SAHZU's experiences and lessons learned. METHODS: We collected data from SAHZU Internet Hospital from November 1, 2019, to September 16, 2021. Data from over 900,000 users were analyzed with respect to demographic characteristics, demands placed on departments by user needs, new registrations, and consultation behaviors. Interrupted time series (ITS) analysis was adopted to evaluate the impact of this momentous emergency event and its long-term trends. With theme analysis and a defined 2D model, 3 investigations were conducted synchronously to determine users' authentic demands on online hospitals. RESULTS: The general profile of internet hospital users is young or middle-aged women who live in Zhejiang and surrounding provinces. The ITS model indicated that, after the intervention (the strict quarantine policies) was implemented during the outbreak, the number of internet hospital users significantly increased (ß_2=105.736, P<.001). Further, long-term waves of COVID-19 led to an increasing number of users following the outbreak (ß_3=0.167, P<.001). In theme analysis, we summarized 8 major demands by users of the SAHZU internet hospital during the national shutdown period and afterwards. Online consultations and information services were persistent and universal demands, followed by concerns about medical safety and quality, time, and cost. Users' medical behavior patterns changed from onsite to online as internet hospital demands increased. CONCLUSIONS: The pandemic has spawned the explosive growth of telehealth; as a public tertiary internet hospital, the SAHZU internet hospital is partially and irreversibly integrated into the traditional medical system. As we shared the practical examples of 1 public internet hospital in China, we put forward suggestions about the future direction of telehealth. Vital experience in the construction of internet hospitals was provided in the normalization of COVID-19 prevention and control, which can be demonstrated as a model of internet hospital management practice for other medical institutions.

Use of Machine Learning to Mine User-Generated Content From Mobile Health Apps for Weight Loss to Assess Factors Correlated With User Satisfaction.

Importance: The effectiveness of mobile health (mHealth) apps for reducing obesity is not ideal in daily life. Therefore, it would be useful to explore factors associated with user satisfaction with weight loss apps. Currently, research on these factors from the perspective of user-generated content is lacking. Objective: To mine the themes and topics frequently discussed in user-generated content in mHealth apps for weight loss, explore correlations of the topics with user satisfaction and dissatisfaction, and assess whether these correlations were asymmetric. Design, Setting, and Participants: In this population-based cross-sectional study, unsupervised machine learning was used to identify themes and topics in online discussions generated between January 1, 2019, and May 20, 2021, by Chinese users of mHealth apps for weight loss. Main Outcomes and Measures: Based on the 2-factor theory, a tobit regression model was used to explore the correlation of various app discussion topics with user satisfaction and dissatisfaction. Differences of the coefficients in models of positive rating deviation (PD) and negative rating deviation (ND), defined as the difference between the users' rating of the app and the app's comprehensive rating in the app stores, were analyzed by the Wald test. Results: In total, 191 619 reviews and ratings from unique usernames were collected for 2139 weight loss apps; 86 423 reviews (45.1%) from 339 apps (15.8%) were included in the study. Most users (65 249 [75.5%]) were satisfied with the mHealth app. Eighteen topics were identified and summarized into 9 themes. Nine topics had significant positive correlations with the PD of user satisfaction, and 6 had significant negative correlations. The factor with the strongest positive correlation with the PD was celebrity effect (ß = 0.307; 95% CI, 0.290-0.323), and the factor with the weakest correlation was economic cost (ß = -0.426; 95% CI, -0.447 to -0.406). Nine topics had significant positive correlations with the ND of user satisfaction, whereas 7 topics had significant negative correlations. The factor with the strongest positive correlation with the ND was fitness effect (ß = 1.369; 95% CI, 1.283-1.455), and the factor with the strongest negative correlation was economic cost (ß = -2.813; 95% CI, -2.875 to -2.751). There were significant differences in the PD and ND of user satisfaction. Nine motivation factors (ie, value-added attributes) and 7 hygiene factors (ie, user-expected attributes) for mHealth apps were identified. Conclusions and Relevance: In this cross-sectional study, 16 factors had asymmetric correlations with user satisfaction and dissatisfaction with weight loss apps; 7 were related to basic expected attributes of the apps and 9 to value-added attributes. By distinguishing between expected and value-added factors, the use of weight loss apps may be improved.

Current Status of the Health Information Technology Industry in China from the China Hospital Information Network Conference: Cross-sectional Study of Participating Companies.

BACKGROUND: The China Hospital Information Network Conference (CHINC) is one of the most influential academic and technical exchange activities in medical informatics and medical informatization in China. It collects frontier ideas in medical information and has an important reference value for the analysis of China's medical information industry development. OBJECTIVE: This study summarizes the current situation and future development of China's medical information industry and provides a future reference for China and abroad in the future by analyzing the characteristics of CHINC exhibitors in 2021. METHODS: The list of enterprises and participating keywords were obtained from the official website of CHINC. Basic characteristics of the enterprises, industrial fields, applied technologies, company concepts, and other information were collected from the TianYanCha website and the VBDATA company library. Descriptive analysis was used to analyze the collected data, and we summarized the future development directions. RESULTS: A total of 205 enterprises officially participated in the exhibition. Most of the enterprises were newly founded, of which 61.9% (127/205) were founded in the past 10 years. The majority of these enterprises were from first-tier cities, and 79.02% (162/205) were from Beijing, Zhejiang, Guangdong, Shanghai, and Jiangsu Provinces. The median registered capital is 16.67 million RMB (about US $2.61 million), and there are 35 (72.2%) enterprises with a registered capital of more than 100 million RMB (about US $15.68 million), 17 (8.3%) of which are already listed. A total of 126 enterprises were found in the VBDATA company library, of which 39 (30.9%) are information technology vendors and 57 (45.2%) are application technology vendors. In addition, 16 of the 57 (28%) use artificial intelligence technology. Smart medicine and internet hospitals were the focus of the enterprises participating in this conference. CONCLUSIONS: China's tertiary hospital informatization has basically completed the construction of the primary stage. The average grade of hospital electronic medical records exceeds grade 3, and 78.13% of the provinces have reached grade 3 or above. The characteristics are as follows: On the one hand, China's medical information industry is focusing on the construction of smart hospitals, including intelligent systems supporting doctors' scientific research, diagnosis-related group intelligent operation systems, and office automation systems supporting hospital management, single-disease clinical decision support systems assisting doctors' clinical care, and intelligent internet of things for logistics. On the other hand, the construction of a compact county medical community is becoming a new focus of enterprises under the guidance of practical needs and national policies to improve the quality of grassroots health services. In addition, whole-course management and digital therapy will also become a new hotspot in the future.

The Alteration of Brain Interstitial Fluid Drainage with Myelination Development.

The integrity of myelination is crucial for maintaining brain interstitial fluid (ISF) drainage in adults; however, the mechanism of ISF drainage with immature myelin in the developing brain remains unknown. In the present study, the ISF drainage from the caudate nucleus (Cn) to the ipsilateral cortex was studied at different developmental stages of the rat brain (P 10, 20, 30, 40, 60, 80, 10-80). The results show that the traced ISF drained to the cortex from Cn and to the thalamus in an opposite direction before P30. From P40, we found impeded drainage to the thalamus due to myelin maturation. This altered drainage was accompanied by enhanced cognitive and social functions, which were consistent with those in the adult rats. A significant difference in diffusion parameters was also demonstrated between the extracellular space (ECS) before and after P30. The present study revealed the alteration of ISF drainage regulated by myelin at different stages during development, indicating that a regional ISF homeostasis may be essential for mature psychological and cognitive functions.

The Impact of Gamification-Induced Users' Feelings on the Continued Use of mHealth Apps: A Structural Equation Model With the Self-Determination Theory Approach.

BACKGROUND: Continued use of mHealth apps can achieve better effects in health management. Gamification is an important factor in promoting users' intention to continue using mHealth apps. Past research has rarely explored the factors underlying the continued use of mobile health (mHealth) apps and gamification's impact mechanism or path on continued use. OBJECTIVE: This study aimed to explore the factors influencing mHealth app users' intention to continue using mHealth apps and the impact mechanism and path of users' feelings induced by gamification on continued mHealth app use. METHODS: First, based on the expectation confirmation model of information system continuance, we built a theoretical model for continued use of mHealth apps based on users' feelings toward gamification. We used self-determination theory to analyze gamification's impact on user perceptions and set the resulting feelings (competence, autonomy, and relatedness) as constructs in the model. Second, we used the survey method to validate the research model, and we used partial least squares to analyze the data. RESULTS: A total of 2988 responses were collected from mHealth app users, and 307 responses were included in the structural equation model after passing the acceptance criteria. The intrinsic motivation for using mHealth apps is significantly affected by autonomy (ß=.312; P<.001), competence (ß=.346; P<.001), and relatedness (ß=.165; P=.004) induced by gamification. The intrinsic motivation for using mHealth apps has a significant impact on satisfaction (ß=.311, P<.001) and continuance intention (ß=.142; P=.045); furthermore, satisfaction impacts continuance intention significantly (ß=.415; P<.001). Confirmation has a significant impact on perceived usefulness (ß=.859; P<.001) and satisfaction (ß=.391; P<.001), and perceived usefulness has a significant impact on satisfaction (ß=.269; P<.001) and continuance intention (ß=.273; P=.001). The mediating effect analysis showed that in the impact path of the intrinsic motivation for using the mHealth apps on continuance intention, satisfaction plays a partial mediating role (ß=.129; P<.001), with a variance accounted for of 0.466. CONCLUSIONS: This study explored the impact path of users' feelings induced by gamification on the intention of continued mHealth app use. We confirmed that perceived usefulness, confirmation, and satisfaction in the classical continued use theory for nonmedical information systems positively affect continuance intention. We also found that the path and mechanism of users' feelings regarding autonomy, competence, and relatedness generated during interactions with different gamification elements promote the continued use of mHealth apps.

[Artificial intelligence based Chinese clinical trials eligibility criteria classification].

Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro F1 score of 0.81 by using a pre-trained language model, i.e. bidirectional encoder representations from transformers (BERT) and ensemble modeling. With the error analysis we found out that from the point of data processing steps the data pre-processing and post-processing were very important for classification, while from the point of data volume these categories with less data volume showed lower classification performance. Finally, we hope that this study could provide a valuable dataset and state-of-the-art result for the research of Chinese medical short text classification.

Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences.

BACKGROUND: The adoption rate of electronic health records (EHRs) in hospitals has become a main index to measure digitalization in medicine in each country. OBJECTIVE: This study summarizes and shares the experiences with EHR adoption in China and in the United States. METHODS: Using the 2007-2018 annual hospital survey data from the Chinese Health Information Management Association (CHIMA) and the 2008-2017 United States American Hospital Association Information Technology Supplement survey data, we compared the trends in EHR adoption rates in China and the United States. We then used the Bass model to fit these data and to analyze the modes of diffusion of EHRs in these 2 countries. Finally, using the 2007, 2010, and 2014 CHIMA and Healthcare Information and Management Systems Services survey data, we analyzed the major challenges faced by hospitals in China and the United States in developing health information technology. RESULTS: From 2007 to 2018, the average adoption rates of the sampled hospitals in China increased from 18.6% to 85.3%, compared to the increase from 9.4% to 96% in US hospitals from 2008 to 2017. The annual average adoption rates in Chinese and US hospitals were 6.1% and 9.6%, respectively. However, the annual average number of hospitals adopting EHRs was 1500 in China and 534 in the US, indicating that the former might require more effort. Both countries faced similar major challenges for hospital digitalization. CONCLUSIONS: The adoption rates of hospital EHRs in China and the United States have both increased significantly in the past 10 years. The number of hospitals that adopted EHRs in China exceeded 16,000, which was 3.3 times that of the 4814 nonfederal US hospitals. This faster adoption outcome may have been a benefit of top-level design and government-led policies, particularly the inclusion of EHR adoption as an important indicator for performance evaluation and the appointment of public hospitals.

Correction: A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model.

[This corrects the article DOI: 10.2196/23578.].

Exploration of text matching methods in Chinese disease Q&A systems: A method using ensemble based on BERT and boosted tree models.

BACKGROUND: Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and medical texts, text matching has greater application and research value in the medical field. In 2019, at the China Health Information Processing Conference (CHIP), 30,000 sets of real disease Q&A data in Chinese on diabetes, hypertension, hepatitis B, AIDS, and breast cancer were released for public evaluation. A total of 90 teams participated in the evaluation. PURPOSE: To explore the best method of text matching of Chinese medical Q&A data by participating in an evaluation competition. METHOD: After analyzing the Chinese medical Q&A data provided by the competition, we used the bidirectional encoder representations from transformers (BERT) model and a boosted tree model to compare the effects. At the same time, we analyzed the importance of the features extracted through feature engineering. Finally, we integrated the BERT and boosted tree models, and proved the effectiveness of the ensemble through a correlation analysis. RESULTS: The final F1 score of the ensemble model is 0.90825, ranking first among the 90 participating teams. The highest F1 score of the single BERT model is 0.87443, whereas the highest F1 score of the boosted tree single model is only 0.86915. The F1 score of the BERT multi-model ensemble is 0.87473 (an average increase of 0.756% compared to the single model), and the F1 score of the boosted tree multi-model ensemble is 0.86720 (an average decrease of 0.03% compared to the single model). In the feature importance experiment, the out-degree and in-degree of the Q&A sentence are of utmost importance. In the correlation experiment, the correlation coefficients between models of the same type are all as high as 0.9, which shows a high similarity. The correlation coefficient between different types of models is approximately 0.7, which shows a certain degree of discrimination. With the ensemble of the two types of models, the F1 score reached 0.90825, which is 3.88% higher than that of the optimal single model. CONCLUSION: In our study, the proposed model ensemble method was shown to effectively improve the performance of a single model. It achieves good results in Chinese medical Q&A tasks and has a good generalization property.