Background: Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical services and enhanced accessibility. However, increasing concerns about personal information (PI) and strict legal compliance requirements necessitate privacy assessments for these platforms. Guided by the theory of contextual integrity, this study investigates the regulatory compliance of privacy policies for internet hospital apps in the mainland of China.
Objective: In this paper, we aim to evaluate the regulatory compliance of privacy policies of internet hospital apps in the mainland of China and offer recommendations for improvement.
Methods: We obtained 59 internet hospital apps on November 7, 2023, and reviewed 52 privacy policies available between November 8 and 23, 2023. We developed a 3-level indicator scale based on the information processing activities, as stipulated in relevant regulations. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 70 level-3 indicators.
Results: The mean compliance score of the 52 assessed apps was 73/100 (SD 22.4%), revealing a varied spectrum of compliance. Sensitive PI protection compliance (mean 73.9%, SD 24.2%) lagged behind general PI protection (mean 90.4%, SD 14.7%), with only 12 apps requiring separate consent for processing sensitive PI (mean 73.9%, SD 24.2%). Although most apps (n=41, 79%) committed to supervising subcontractors, only a quarter (n=13, 25%) required users' explicit consent for subcontracting activities. Concerning PI storage security (mean 71.2%, SD 29.3%) and incident management (mean 71.8%, SD 36.6%), half of the assessed apps (n=27, 52%) committed to bear corresponding legal responsibility, whereas fewer than half (n=24, 46%) specified the security level obtained. Most privacy policies stated the PI retention period (n=40, 77%) and instances of PI deletion or anonymization (n=41, 79%), but fewer (n=20, 38.5%) committed to prompt third-party PI deletion. Most apps delineated various individual rights, but only a fraction addressed the rights to obtain copies (n=22, 42%) or to refuse advertisement based on automated decision-making (n=13, 25%). Significant deficiencies remained in regular compliance audits (mean 11.5%, SD 37.8%), impact assessments (mean 13.5%, SD 15.2%), and PI officer disclosure (mean 48.1%, SD 49.3%).
Conclusions: Our analysis revealed both strengths and significant shortcomings in the compliance of internet hospital apps' privacy policies with relevant regulations. As China continues to implement internet hospital apps, it should ensure the informed consent of users for PI processing activities, enhance compliance levels of relevant privacy policies, and fortify PI protection enforcement across the information processing stages.
Background: Mobile health (mHealth) interventions that promote healthy behaviors or mindsets are a promising avenue to reach vulnerable or at-risk groups. In designing such mHealth interventions, authentic representation of intended participants is essential. The COVID-19 pandemic served as a catalyst for innovation in remote user-centered research methods. The capability of such research methods to effectively engage with vulnerable participants requires inquiry into practice to determine the suitability and appropriateness of these methods.
Objective: In this study, we aimed to explore opportunities and considerations that emerged from involving vulnerable user groups remotely when designing mHealth interventions. Implications and recommendations are presented for researchers and practitioners conducting remote user-centered research with vulnerable populations.
Methods: Remote user-centered research practices from 2 projects involving vulnerable populations in Norway and Australia were examined retrospectively using visual mapping and a reflection-on-action approach. The projects engaged low-income and unemployed groups during the COVID-19 pandemic in user-based evaluation and testing of interactive, web-based mHealth interventions.
Results: Opportunities and considerations were identified as (1) reduced barriers to research inclusion; (2) digital literacy transition; (3) contextualized insights: a window into people's lives; (4) seamless enactment of roles; and (5) increased flexibility for researchers and participants.
Conclusions: Our findings support the capability and suitability of remote user methods to engage with users from vulnerable groups. Remote methods facilitate recruitment, ease the burden of research participation, level out power imbalances, and provide a rich and relevant environment for user-centered evaluation of mHealth interventions. There is a potential for a much more agile research practice. Future research should consider the privacy impacts of increased access to participants' environment via webcams and screen share and how technology mediates participants' action in terms of privacy. The development of support procedures and tools for remote testing of mHealth apps with user participants will be crucial to capitalize on efficiency gains and better protect participants' privacy.
Background: Depression acts as a significant obstacle to the overall well-being of individuals. Given the significant consequences, timely recognition and proactive steps to manage symptoms of depression become essential. Such actions not only reduce personal distress but also play a crucial role in reducing its far-reaching impact on society as a whole.
Objective: In response to this concern, the objective of this study was to explore the use of mobile-based interventions as a possible remedy. More specifically, this study aimed to investigate the effectiveness of 2 types of physical activity (PA), progressive and fixed, within a mobile-based app on depression, perceived stress, anxiety, physical health, and psychological health, aiming to contribute to the optimization of mental health benefits.
Methods: Participants (N=60; mean age 25.29, SD 6.10 years) were recruited using a combination of web-based and offline methods, and the study lasted for 8 weeks. The baseline and posttest questionnaires were administered to all participants. The participants were randomly assigned to 1 of the 3 groups: progressive group (n=20; performing mobile-based progressive PA), fixed group (n=20; performing mobile-based fixed intensity PA), and control group C (n=20). Data analysis involved comparing scores between the experimental and control groups using a one-way ANOVA, paired sample t tests (2-tailed), and repeated measures ANOVA with a 3 (group)×2 (time) design.
Results: The findings revealed significant improvements in mental health indicators among participants engaged in both fixed and progressive PA groups compared with the control group. However, the fixed PA group demonstrated more significant reductions in symptoms. Specifically, the progressive PA group showed significant reductions in depression (F1,36=6.941; P=.01; ηp2=0.16) and perceived stress (F1,36=5.47; P=.03; ηp2=0.13), while the fixed PA group exhibited significant reductions in depression (F1,37=5.36; P=.03; ηp2=0.12), perceived stress (F1,37=7.81; P=.008; ηp2=0.17), and general anxiety disorder (F1,37=5.45; P=.03; ηp2=0.13) compared with the control group.
Conclusions: This study underscores the potential of mobile-based PA in improving mental health outcomes. The findings offer significant insights for mental health professionals and researchers aiming to optimize mental well-being through innovative mobile therapies.
Trial registration: Clinical Research Information Service KCT0009100; https://tinyurl.com/mr33fmur.
Background: Different kinds of mobile apps are used to promote communications between patients and doctors. Studies have investigated patients' mobile app adoption behavior; however, they offer limited insights into doctors' personal preferences among a variety of choices of mobile apps.
Objective: This study aimed to investigate the nuanced adoption behaviors among doctors in China, which has a robust adoption of mobile apps in health care, and to explore the constraints influencing their selection of specific mobile apps. This paper addressed 3 research questions: (1) Which doctors opt to adopt mobile apps to communicate with patients? (2) What types of mobile apps do they choose? (3) To what degree do they exercise personal choice in adopting specific mobile apps?
Methods: We used thematic content analysis of qualitative data gathered from semistructured interviews with 11 doctors in Hangzhou, which has been recognized for its advanced adoption of mobile technology in social services, including health care services. The selection of participants was purposive, encompassing diverse departments and hospitals.
Results: In total, 5 themes emerged from the data analysis. First, the interviewees had a variety of options for communicating with patients via mobile apps, with the predominant ones being social networking apps (eg, WeChat) and medical platforms (eg, Haodf). Second, all interviewees used WeChat to facilitate communication with patients, although their willingness to share personal accounts varied (they are more likely to share with trusty intermediaries). Third, fewer than half of the doctors adopted medical platforms, and they were all from tertiary hospitals. Fourth, the preferences for in-person, WeChat, or medical platform communication reflected the interviewees' perceptions of different patient cohorts. Lastly, the selection of a particular kind of mobile app was significantly influenced by the doctors' affiliation with hospitals, driven by their professional obligations to fulfill multiple tasks assigned by the hospitals or the necessity of maintaining social connections with their colleagues.
Conclusions: Our findings contribute to a nuanced understanding of doctors' adoption behavior regarding specific types of mobile apps for patient communication, instead of addressing such adoption behavior of a wide range of mobile apps as equal. Their choices of a particular kind of app were positioned within a social context where health care policies (eg, limited funding for public hospitals, dominance of public health care institutions, and absence of robust referral systems) and traditional culture (eg, trust based on social connections) largely shape their behavioral patterns.
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