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BackgroundArtificial intelligence (AI)-driven chatbots are increasingly being used in health care, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (eg, patients and caregivers) use chatbots for self-diagnosis purposes in real-world scenarios. ObjectiveThe aim of this research was to understand how health chatbots are used in a real-world context, what issues and barriers exist in their usage, and how the user experience of this novel technology can be improved. MethodsWe employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our data set consisted of 47,684 consultation sessions initiated by 16,519 users over 6 months. The log data included a variety of information, including users’ nonidentifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis on this heterogeneous data set. ResultsThe chatbot users spanned all age groups, including middle-aged and older adults. Users consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled 2 prominent issues in the use of the chatbot: (1) a considerable number of users dropped out in the middle of their consultation sessions, and (2) some users pretended to have health concerns and used the chatbot for nontherapeutic purposes. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestions. ConclusionsAlthough health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address the issues and user concerns to achieve the best uptake and utilization. We conclude the paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement.

BackgroundPeople undergoing mass home- and community-based quarantine are vulnerable to mental health disorders during outbreaks of coronavirus disease (COVID-19), but few studies have evaluated the associated psychosocial factors. ObjectiveThis study aimed to estimate the prevalence of anxiety and depressive symptoms and identify associated demographic and psychosocial factors in the general Chinese population during the COVID-19 pandemic quarantine period. MethodsParticipants aged 18 years or above were recruited in a cross-sectional online survey using snowball sampling from February 26-29, 2020. The survey included questions on demographics, family relationships, chronic diseases, quarantine conditions, lifestyle, COVID-19 infection, and anxiety and depressive symptoms. Logistic regression analyses were conducted to identify factors associated with elevated anxiety or depressive symptoms. ResultsOut of 2331 participants, 762 (32.7%) experienced elevated anxiety or depressive symptoms. Nine risk factors associated with anxiety or depressive symptoms included younger age, reduced income, having cancer or other chronic diseases, having family members living with cancer, concerns related to COVID-19 infection for themselves or family members, living alone, having family conflicts, having 8 hours of sedentary time per day, and worsened sleep quality. ConclusionsThe findings highlight an urgent need for psychological support for populations at high risk for elevated anxiety or depressive symptoms during the COVID-19 pandemic.

Background Stroke is an acute cerebrovascular disease in which blood fails to flow into the brain, resulting in damage to brain tissue, with high rates of disability, death, and recurrence, and most of the related studies in Ningxia region are clinical analyses of strokes, which lack trend analysis of stroke hospitalization in Ningxia in recent years. Objective To analyze the disease prevalence characteristics and distribution of stroke hospitalized patients in Ningxia, and provide a basis for the development of comprehensive prevention and treatment measures for stroke. Methods The case home page data of 55 hospitals of traditional Chinese medicine (TCM) and western medicine in Ningxia region were collected, and the data discharged from 2013 to 2019 with a primary diagnosis of stroke [International Classification of Diseases (ICD) -10 coded as I60-I63] were screened and analyzed. SPSS 24.0 software was applied to analyze the differences in gender and age using the chi-square test, and a geographic information system (GIS) was applied to analyze the distribution of stroke hospitalization rate data across Ningxia from 2013 to 2019 using global spatial auto correlation analysis. Results A total of 190 634 stroke patients were finally included. The hospitalization rate of stroke in Ningxia region showed an increasing trend year by year from 2013 to 2019 (hospitalization rates of 24.302/10 000, 30.045/10 000, 34.949/10 000, 39.397/10 000, 44.049/10 000, 47.617/10 000, and 52.944/10 000, respectively, χ2trend=5.982, P=0.014) , and the hospitalization rates were higher during the cold season of each year, with a significant peak in March and a slight decline thereafter. Among the stroke inpatients in Ningxia from 2013 to 2019, there were 167 194 cases of ischemic stroke and 23 440 cases of hemorrhagic stroke; there was significant difference between ischemic stroke and hemorrhagic stroke patients in gender and age (χ2=473.533, 7 518.543, P

C14H12Cl2N2O, orthorhombic, P212121 (no. 19), a = 8.5344(8) Å, b = 12.0204(16) Å, c = 13.4504(13) Å, V = 1379.8(3) Å3, Z = 4, R gt(F) = 0.0896, wR ref(F 2) = 0.2327, T = 293 K.

C14H13ClN2O, monoclinic, P21/c (no. 14), a = 23.904(2) Å, b = 4.6173(3) Å, c = 11.6906(13) Å, β = 96.895(9)°, V = 1281.0(2) Å3, Z = 4, R gt(F) = 0.0646, wR ref(F 2) = 0.1314, T = 293 K.