Background: Given that cognitive decline lacks effective treatment options and has severe implications for healthy aging, internet use may achieve nonpharmacological relief of cognitive decline through cognitive stimulation and social engagement. Objective: This longitudinal study aimed to investigate the relationship between the diversity, frequency, and type of internet use and cognitive decline, and to provide theoretical support and suggestions for mitigating cognitive decline in middle-aged and older adults. Methods: Data were obtained from a total of 10,532 survey respondents from the China Family Panel Studies database from wave 3 (2014) and wave 5 (2018) of the survey. Cognitive function was measured using vocabulary tests, and internet use was categorized into five aspects: study, work, socializing, entertainment, and commercial-related activities. Associations between the diversity, frequency, and type of internet use and cognitive decline were estimated by controlling for demographic variables and health status risk factors through fixed-effects models. Results: After controlling for demographic and health status risk factors, the type and frequency of internet use were found to be associated with cognitive functioning during the subsequent 4-year period, and different types of internet use had different effects on cognitive decline. Frequency of internet use of at least once a week for study (β=0.620, 95% CI 0.061 to 1.180; P=.04), work (β=0.896, 95% CI 0.271 to 1.520; P=.01), and entertainment (β=0.385, 95% CI –0.008 to 0.778; P=.06), as well as less than once a week for social purposes (β=0.860, 95% CI 0.074 to 1.650; P=.06), were associated with better cognitive function. Frequency of internet use of less than once a week for commercial-related activities (β=–0.906, 95% CI –1.480 to –0.337; P=.005) was associated with poorer cognitive function. Using the internet for more than one type of activity (β=0.458, 95% CI 0.065 to 0.850; P=.03) and at least once a week (β=0.436, 95% CI 0.066 to 0.806; P=.02) was associated with better cognitive function. Conclusions: This study shows that breadth and depth of internet use are positively associated with cognitive function and that different types of internet use have different roles in cognitive decline. The importance of the internet as a nonpharmacological intervention pathway for cognitive decline is emphasized. Future research could explore specific mechanisms of influence.

Background: Medical crowdfunding provides opportunities for individuals who lack financial resources to access the health services that they need. Despite the popularity of medical crowdfunding, the current understanding of the success of medical crowdfunding campaigns is fragmented and inadequate. Objective: We aimed to comprehensively investigate which factors lead to the success of medical crowdfunding campaigns. Methods: A search was conducted in PubMed, PsycINFO, Web of Science, ACM Digital Library, and ScienceDirect from 2010 to June 2020. Papers directly and indirectly related to the success of medical crowdfunding campaigns were included. Two reviewers independently extracted information on the success of medical crowdfunding campaigns. Results: Our search yielded 441 articles, of which 13 met the inclusion criteria. Medical crowdfunding is increasingly attracting academic attention, and most studies leverage text analysis as their research methods; however, there is a lack of consensus on the definition of medical crowdfunding among researchers. Four categories of factors that affect the success of medical crowdfunding were identified: platforms, raisers, donors, and campaigns. Conclusions: Although some limitations exist in our systematic review, our study captured and mapped literatures of the success of medical crowdfunding campaigns systematically, which can be used as the basis for future research on this topic.

Background: Verified and authentic information about coronavirus disease (COVID-19) on social networking sites (SNS) could help people make appropriate decisions to protect themselves. However, little is known about what factors influence people’s sharing of verified information about COVID-19. Thus, the purpose of this study was to explore the factors that influence people’s sharing of verified information about COVID-19 on social networking sites. Methods: Based on social exchange theory, we explore the factors that influence sharing of verified information about COVID-19 from two perspectives: benefits and costs. We employed the survey method to validate our hypothesized relationships. By using our developed measurement instruments, we collected 347 valid responses from SNS users and utilized the partial least squares method to analyze the data. Results: Among the benefits of sharing verified information about COVID-19, enjoyment in helping (β = 0.357, p = 0.000), altruism (β = 0.133, p = 0.029) and reputation (β = 0.202, p = 0.000) were significantly associated with verified information sharing about COVID-19. Regarding the costs of sharing verified information about COVID-19, both verification cost (β = −0.078, p = 0.046) and executional cost (β = −0.126, p = 0.011) also significantly affect verified information sharing about COVID-19. All the proposed hypotheses were supported. Conclusions: By exploring factors from both benefits and costs perspectives, we could understand users’ intention to share verified information about COVID-19 comprehensively. This study not only contributes to the literature on information sharing, but also has implications concerning users’ behaviors on SNS.

Background: Prior studies on health disparity have shown that socioeconomic status is critical to inequality of health outcomes such as depression. However, two questions await further investigation: whether disparity in depression correlated with socioeconomic status will become larger when depression becomes severer, and whether digital technology will reduce the disparity in depression correlated with socioeconomic status. Our study aims to answer the above two questions. Methods: By using the dataset from China Health and Retirement Longitudinal Study 2015, we use quantile regression models to examine the association between socioeconomic status and depression across different quantiles, and test the moderating effect of digital technology. Results: Our study obtains four key findings. First, the negative effects of socioeconomic status on depression present an increasing trend at high quantiles. Second, Internet usage exacerbates the disparity in depression associated with education level on average, but reduces this disparity associated with education level at high quantiles. Third, Internet usage reduces the disparity in depression associated with income on average and at high quantiles. Fourth, mobile phone ownership has almost no moderating effect on the relationship between socioeconomic status and depression. Conclusions: Our findings suggest the potential use of digital technology in reducing disparity in depression correlated with socioeconomic status among middle-aged and aged individuals in developing countries.

Background: Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such information are complex, time-consuming, and prone to errors. The challenge of quickly and accurately extracting patient characteristics has become a common issue in the study of chronic disease comorbidities. Objective: Our objective was to establish a comprehensive framework for extracting demographic and disease characteristics of patients with multimorbidity. This framework leverages large language models (LLMs) to extract feature information from unstructured and semistructured electronic health records pertaining to these patients. We investigated the model’s proficiency in extracting feature information across 7 dimensions: basic information, disease details, lifestyle habits, family medical history, symptom history, medication recommendations, and dietary advice. In addition, we demonstrated the strengths and limitations of this framework. Methods: We used data sourced from a grassroots community health service center in China. We developed a multifaceted feature extraction framework tailored for patients with multimorbidity, which consists of several integral components: feasibility testing, preprocessing, the determination of feature extraction, prompt modeling based on LLMs, postprocessing, and midterm evaluation. Within this framework, 7 types of feature information were extracted as straightforward features, and three types of features were identified as intricate features. On the basis of the straightforward features, we calculated patients’ age, BMI, and 12 disease risk factors. Rigorous manual verification experiments were conducted 100 times for straightforward features and 200 times for intricate features, followed by comprehensive quantitative and qualitative assessments of the experimental outcomes. Results: The framework achieved an overall F1-score of 99.6% for the 7 straightforward feature extractions, with the highest F1-score of 100% for basic information. In addition, the framework demonstrated an overall F1-score of 94.4% for the 3 intricate feature extractions. Our analysis of the results revealed that accurate information content extraction is a substantially advantage of this framework, whereas ensuring consistency in the format of extracted information remains one of its challenges. Conclusions: The framework incorporates electronic health record information from 1225 patients with multimorbidity, covering a diverse range of 41 chronic diseases, and can seamlessly accommodate the inclusion of additional diseases. This underscores its scalability and adaptability as a method for extracting patient-specific characteristics, effectively addressing the challenges associated with information extraction in the context of multidisease research. Research and medical policy personnel can extract feature information by setting corresponding goals based on the research objectives and directly using the LLM for zero-sample target feature extraction. This approach greatly improves research efficiency and reduces labor requirements; moreover, due to the framework’s high accuracy, it can increase study reliability.