Background: There is a wide variation in the quality of information available to patients on the treatment of the diseases afflicting them. To help patients find clear and accessible information, many scales have been designed to evaluate the quality of health information, including the Patient Education Materials Assessment Tool; the Suitability Assessment of Materials for evaluation of health-related information for adults; and DISCERN, an instrument for judging the quality of written consumer health information on treatment choices. These instruments are primarily in English. Few of them have been translated and adapted into simplified Chinese tools for health information assessment in China.Objective: This study aimed to translate and adapt DISCERN into the first simplified Chinese version and validate the psychometric properties of this newly developed scale for judging the quality of patient-oriented health information on treatment choices.Methods: First, we translated DISCERN into simplified Chinese using rigorous guidelines for translation and validation studies. We tested the translation equivalence and measured the content validity index. We then presented the simplified Chinese instrument to 3 health educators and asked them to use it to assess the quality of 15 lung cancer–related materials. We calculated the Cohen κ coefficient and Cronbach α for all items and for the entire scale to determine the reliability of the new tool.Results: We decided on the simplified Chinese version of the DISCERN instrument (C-DISCERN) after resolving all problems in translation, adaptation, and content validation. The C-DISCERN was valid and reliable: the content validity index was 0.98 (47/48, 98% of the items) for clarity and 0.94 (45/48, 94% of the items) for relevance, the Cronbach α for internal consistency was .93 (95% CI 0.699-1.428) for the whole translated scale, and the Cohen κ coefficient for internal consistency was 0.53 (95% CI 0.417-0.698).Conclusions: C-DISCERN is the first simplified Chinese version of the DISCERN instrument. Its validity and reliability have been attested to assess the quality of patient-targeted information for treatment choices.

Background: Mental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution. Objective: This study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers. Methods: We searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC. Results: From the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps. Conclusions: This is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide.

Background: Over 30% of university students from 8 countries were afflicted with mental distress according to a World Health Organization survey. Undergraduate students in increasing numbers in China have also been reported to suffer from different mental problems. Various psychological distresses significantly impact their academic and daily life, thereby causing role impairments and unsatisfactory academic achievements. While the prevalence of, diverse underlying factors for, and interventions of social support in college students’ mental health have extensively been investigated in China, there is no study exclusively focusing on the impact of interventions on their psychological well-being. Objective: The aim of this review was to identify and synthesize the interventions in the mental health concerns of Chinese undergraduate students studying in China reported in the literature to inform educational authorities, college and university management, students’ affairs counselors, and mental health providers. Methods: We performed a systematic review and reported the research findings of previous studies according to the protocol of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. First, based on the predefined search strategy, keyword searches were performed in the PubMed and ProQuest databases to retrieve relevant studies. Subsequently, we screened the candidate articles based on predefined inclusion and exclusion criteria. Finally, we analyzed the included papers for qualitative synthesis. Results: We retrieved a total of 675 studies from the PubMed and ProQuest databases using the search strategy on March 15, 2022. Among these candidate studies, 15 that were not written in English, 76 duplicates, and 149 studies of other document types were removed before screening. An additional 313 studies were excluded in the screening process, with 73 articles ruled out for being not relevant to interventions, not related to mental health, or not focused on undergraduate students in the full-text review. As a result, 49 papers were eligible and included in this systematic review. In the qualitative synthesis, we divided the interventions reported in the selected studies into two categories: (1) social support from government authorities, university authorities, students’ affairs counselors and teachers, family members, health care authorities and professionals, and the media (various online platforms), and (2) various coping strategies adopted by undergraduate students themselves. We identified further research on mental health interventions that may be delivered by digital medical platforms, conversational agents (eg, chatbots), and researchers. Conclusions: This was the first systematic review of interventions to address the mental health concerns of Chinese undergraduate students studying in China. The categorization of reported interventions and the identification of new intervention channels can effectively inform stakeholders. Interventions for undergraduate students’ mental health is a research topic worth further investigation.

Background: The usability of mobile health (mHealth) apps needs to be effectively evaluated before they are officially approved to be used to deliver health interventions. To this end, the mHealth App Usability Questionnaire (MAUQ) has been designed and proved valid and reliable in assessing the usability of mHealth apps. However, this English questionnaire needs to be translated into other languages, adapted, and validated before being utilized to evaluate the usability of mHealth apps. Objective: This study aims to improve, further adapt, and validate the Chinese version of the MAUQ (C-MAUQ; interactive for patients) on Left-handed Doctor, one of the most popular “reaching out to patients” interactive mHealth apps with chatbot function in China, to test the reliability and cross-cultural adaptability of the questionnaire. Methods: The MAUQ (interactive for patients) has been translated into Chinese and validated for its reliability on Good Doctor, one of the most influential “reaching out to patients” mHealth apps without chatbot function in China. After asking for the researchers’ approval to use this Chinese version, we adjusted and further adapted the C-MAUQ by checking it against the original English version and improving its comprehensibility, readability, idiomaticity, and cross-cultural adaptability. Following a trial survey completed by 50 respondents on wenjuanxing, the most popular online questionnaire platform in China, the improved version of the C-MAUQ (I-C-MAUQ) was finally used to evaluate the usability of Left-handed Doctor through an online questionnaire survey (answered by 322 participants) on wenjuanxing, to test its internal consistency, reliability, and validity. Results: The I-C-MAUQ still retained the 21 items and 3 dimensions of the original MAUQ: 8 items for usability and satisfaction, 6 items for system information arrangement, and 7 items for efficiency. The translation problems in the C-MAUQ, including (1) redundancy, (2) incompleteness, (3) misuse of parts of speech, (4) choice of inappropriate words, (5) incomprehensibility, and (6) cultural difference–induced improper translation, were improved. As shown in the analysis of data obtained through the online survey, the I-C-MAUQ had a better internal consistency (ie, the correlation coefficient between the score of each item and the total score of the questionnaire determined within the range of 0.861-0.938; P<.01), reliability (Cronbach α=.988), and validity (Kaiser–Meyer–Olkin=0.973), compared with the C-MAUQ. It was effectively used to test the usability of Left-handed Doctor, eliciting over 80% of informants’ positive attitudes toward this mHealth app. Conclusions: The I-C-MAUQ is highly reliable and valid for Left-handed Doctor, and suitable for testing the usability of interactive mHealth apps used by patients in China. This finding further confirms the cross-cultural validity, reliability, and adaptability of the MAUQ. We identified certain factors influencing the perceived usability of mHealth apps, including users’ age, gender, education, profession, and possibly previous experience with mHealth apps and the chatbot function of such apps. Most notably, we found a wider acceptance of this new technology among young Chinese female college students who were more engaged in the interaction with health care chatbots. The age-, gender-, and profession-induced preference for new digital health interventions in China aligns with the findings in other similar studies in America and Malaysia. This preference identifies areas for further research on the social, cultural, and gender adaptation of health technologies.

Background: Providing people with understandable and actionable health information can considerably promote healthy behaviors and outcomes. To this end, some valid and reliable scales assessing the patient-friendliness of health education materials, like the PEMAT-P (Patient Education Materials Assessment Tool for printable materials), have been well developed in English-speaking countries. However, the English version of the PEMAT-P has not been translated and adapted into simplified Chinese and validated in mainland China. Objective: This study sought to translate the PEMAT-P tool into a simplified Chinese (Mandarin) version (C-PEMAT-P, a Chinese version of the Patient Education Materials Assessment Tool for printable materials) and verify its validity and reliability for assessing the comprehensibility and actionability of health education resources written in simplified Chinese. As a result, the validated C-PEMAT-P could be used to guide health researchers and educators to design more comprehensible and actionable materials for more tailored and targeted health education and interventions. Methods: We translated the PEMAT-P into simplified Chinese in the following three steps: (1) forward-translating the PEMAT-P into simplified Chinese, (2) back-translating the simplified Chinese version into English, and (3) testing translation equivalence linguistically and culturally by examining the original English version of the PEMAT-P and the back-translated English version of the tool. Any discrepancies between the original English tool and the back-translated English tool were resolved through a panel discussion among the research team of all authors to produce a revised forward-translated Chinese version (C-PEMAT-P). We then evaluated the clarity of construction and wording as well as the content relevance of the C-PEMAT-P using a 4-point ordinal scale to determine its content validity. After that, 2 native Chinese speakers (health educators) used the C-PEMAT-P to rate 15 health education handouts concerning air pollution and health to validate their reliability. We calculated the Cohen coefficient and Cronbach α to determine the interrater agreement and internal consistency of the C-PEMAT-P, respectively. Results: We finalized the translated Chinese tool after discussing the differences between the 2 English versions (original and back-translated) of the PEMAT-P, producing the final Chinese version of the PEMAT-P (C-PEMAT-P). The content validity index of the C-PEMAT-P version was 0.969, the Cohen coefficient for the interrater scoring agreement was 0.928, and the Cronbach α for internal consistency was .897. These values indicated the high validity and reliability of the C-PEMAT-P. Conclusions: The C-PEMAT-P has been proven valid and reliable. It is the first Chinese scale for assessing the comprehensibility and actionability of Chinese health education materials. It can be used as an assessment tool to evaluate health education materials currently available and a guide to help health researchers and educators design more comprehensible and actionable materials for more tailored and targeted health education and interventions.

Background: Given the growing significance of conversational agents (CAs), researchers have conducted a plethora of relevant studies on various technology- and usability-oriented issues. However, few investigations focus on language use in CA-based health communication to examine its influence on the user perception of CAs and their role in delivering health care services. Objective: This review aims to present the language use of CAs in health care to identify the achievements made and breakthroughs to be realized to inform researchers and more specifically CA designers. Methods: This review was conducted by following the protocols of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. We first designed the search strategy according to the research aim and then performed the keyword searches in PubMed and ProQuest databases for retrieving relevant publications (n=179). Subsequently, 3 researchers screened and reviewed the publications independently to select studies meeting the predefined selection criteria. Finally, we synthesized and analyzed the eligible articles (N=11) through thematic synthesis. Results: Among the 11 included publications, 6 deal exclusively with the language use of the CAs studied, and the remaining 5 are only partly related to this topic. The language use of the CAs in these studies can be roughly classified into six themes: (1) personal pronouns, (2) responses to health and lifestyle prompts, (3) strategic wording and rich linguistic resources, (4) a 3-staged conversation framework, (5) human-like well-manipulated conversations, and (6) symbols and images coupled with phrases. These derived themes effectively engaged users in health communication. Meanwhile, we identified substantial room for improvement based on the inconsistent responses of some CAs and their inability to present large volumes of information on safety-critical health and lifestyle prompts. Conclusions: This is the first systematic review of language use in CA-based health communication. The results and limitations identified in the 11 included papers can give fresh insights into the design and development, popularization, and research of CA applications. This review can provide practical implications for incorporating positive language use into the design of health CAs and improving their effective language output in health communication. In this way, upgraded CAs will be more capable of handling various health problems particularly in the context of nationwide and even worldwide public health crises.