Zhijun Yan

Purpose: Mental health applications (MH apps), offering round-the-clock access to mental health care, are increasingly being adopted to address the prevalence of depression. However, vulnerable users affected by depression might suffer from a potential loss of privacy because MH apps demand extensive personal information disclosure. Drawing on communication privacy management (CPM) theory and irrational beliefs from rational-emotive behavior therapy (REBT), our research model explains the mental process of MH app users affected by depression. Specifically, it examines how they manage perceived privacy and disclosure intentions via privacy risk and privacy control. The model accounts for the influence of MH apps' privacy policies, irrational beliefs, emotion dysregulation and privacy stress. Design/methodology/approach: We tested the proposed research model using partial least squares structural equation modeling, using online survey data from 346 US MH app users with mild to moderate-to-severe depression. Findings: MH app users' perceived effectiveness of privacy policies primarily enhances privacy control, while emotion dysregulation and privacy stress mostly amplify perceived privacy risk. The level of depression negatively moderates the relationship between emotion dysregulation and privacy risk. Practical implications: Developers should implement granular privacy controls and therapeutic UX designs to mitigate emotional distress. MH practitioners are encouraged to actively guide patients through privacy settings to reduce stress and facilitate sustained engagement with digital treatments. Originality/value: This is among the first wave of studies to provide empirical evidence for depressed users' privacy and disclosure management on MH apps.

Purpose: This paper investigates factors influencing initial and continuous user engagement with emotional AI chatbots for mental health, using technostress theory as the theoretical lens. We conceptualize human interaction, emotional competence and perceived pleasantness as challenge techno-stressors, while perceived risk and system quality defects are treated as threat techno-stressors. User appraisals of technostress, including self-efficacy, personal innovativeness and effort expectancy, are also examined. Design/methodology/approach: A two-step research design was adopted, combining user review analysis and semi-structured interviews with a survey tested through hierarchical regression. Both linear and curvilinear relationships were explored, with particular attention to potential U-shaped effects. Moderator analyses of gender, occupation and education were conducted to capture subgroup differences. Findings: Results indicate that human interaction, self-efficacy, personal innovativeness and perceived risk significantly influenced engagement, with U-shaped effects for human interaction and self-efficacy. Emotional competence and pleasantness showed no main effects but were conditionally important (e.g. students, highly educated users). Gender moderated personal innovativeness, occupation moderated pleasantness, effort expectancy and risk, while education moderated emotional competence, system quality and effort expectancy. Originality/value: We distinguish initial versus continuous engagement with emotional AI chatbots, extend technostress theory into an emotional health support context and reveal both curvilinear effects and subgroup-specific variations. These findings enrich theory by clarifying the dual roles of challenge and threat stressors and offer actionable guidance for tailoring chatbot design to diverse user groups.

Background: Depression affects over 95 million people in China (lifetime prevalence 6.8%), yet traditional assessments rely on episodic, retrospective evaluations missing dynamic symptom fluctuations. While digital phenotyping offers continuous monitoring potential, feasibility questions remain for psychiatric populations: patient retention, data completeness, and technical challenges. This study assesses the feasibility of intensive multimodal digital phenotyping in routine outpatient care and explores digital phenotypes in major depressive disorder (MDD). Methods and analysis: This is a prospective protocol for a 12-month observational cohort study at Peking University Sixth Hospital that recruited 202 MDD outpatients and 100 healthy adults (aged 18–60 years) from August 2023 to February 2025, with follow-up ongoing through February 2026. The study evaluates practicality and reliability of multimodal digital phenotyping in real-world clinical settings. Data collection integrates: (1) high-frequency clinician-administered assessments using the 17-item Hamilton Depression Rating Scale (HAMD-17) at seven time points for patients with MDD and at baseline for healthy participants; (2) daily ecological momentary assessments and biweekly self-report questionnaires; (3) passive smartphone sensor data (physical activity, GPS, screen usage , etc. ); (4) continuous wearable data (sleep, heart rate variability, step count , etc. ); and (5) environmental exposures (photoperiod, temperature, air pollution , etc. ) linked to GPS coordinates. Primary outcomes include recruitment and retention rates, data completeness across modalities, and platform-specific constraints. Secondary analyses—prospectively planned pending full data collection—will (1) characterize digital phenotypes and temporal dynamics; (2) compare MDD vs. healthy participants; (3) explore environment-symptom associations. Results: MDD patients (n=202) were younger (30.8 ± 10.1 vs 42.8 ± 11.4 years) and more often unmarried (42.1% vs 15.0%) than healthy participants (n=100). Clinical-assessment completion declined from 100% (baseline) to 68.3% (Week 8). Among Android users (n=214), adequate EMA completion was 31.6% (MDD) vs 84.0% (healthy). Wearable adherence was 29.2% vs 70.0% (n=302). Conclusion: This feasibility study reveals marked compliance disparities between MDD patients and healthy participants, highlighting implementation barriers for real-world digital phenotyping in psychiatric populations.

Purpose: The difficulty of medical waste management lies in data management. Digital technology can improve the digital management ability of medical waste, and data are the core. But what are the key data of medical waste itself and its business processes, such as generation, collection, classification, weighing, packaging, storage and transportation? How to integrate digital technology with business data to drive the digital management of hospital medical waste? These are the main research purposes. Design/methodology/approach: Business Process Reengineering and Data-Driven Decision-Making are the theoretical method bases. The business process of medical waste recycling and classification was restructured and divided into four business links. The attribute characteristics of medical waste and the key data indicators of each business link were determined. Meanwhile, the composition of business data flow is analyzed, and how to deeply integrate Radio Frequency Identification (RFID), sensors and other digital technologies with data flow to optimize business process is discussed. Findings: The improvement of the digital management level of medical waste, on the one hand, requires the use of RFID, sensors and other digital technologies to reconstruct business processes to achieve the automation and intelligence of recycling and classification. On the other hand, the key business data types and index characteristics need to be refined to form data sets and transform them into data assets to provide support for medical waste management decisions. Originality/value: This study constructed a dual-driven mode of “Digital technology and Business data”, which provided technical application scenarios and theoretical methods for digital management of medical waste.

Purpose: Peer information can serve as an effective intervention to enhance individual fitness behaviors, offering potential benefits for health promotion and disease prevention. Extending prior literature on the influence of peer information on individual behaviors from the viewpoint of peer outcome information, this study aims to investigate the effect of peer progress information on targeted fitness and non-targeted sharing behaviors of users, as well as the moderating roles of users’ past performance and perceived peer pressure. Design/methodology/approach: The authors develop hypotheses based on the theories of social comparison and social identity. Leveraging a quasi-natural experiment on a popular online fitness platform, this study conducts several difference-in-differences models to test the causal effect of peer progress information. Findings: The results reveal several robust and interesting findings. First, peer progress information significantly increases both exercise engagement and information sharing. Second, users with poor past performance or strong peer pressure exhibit greater responsiveness to peer progress information. Furthermore, the positive effects on fitness behavior are driven by upward social comparison rather than downward comparison, suggesting upward comparison may serve as an underlying mechanism. Practical implications: This study provides actionable guidance for platform designers aiming to foster sustainable engagement through progress-based mechanisms. Originality/value: The findings contribute to the literature on peer information by analyzing the effect of peer progress on user engagement and the corresponding heterogeneity. In particular, the findings offer an effective means to promote user retention for digital platforms.

Purpose: Multi-goal setting has complex effects on individual behavior, which may be due to the changes in self-efficacy in the process of multi-goal pursuit. Most research focuses on the pursuit of a single goal, and a few discuss the static effects of multi-goal setting on behavior change. However, self-efficacy dynamically changes when individuals pursue diverse goals, which is a dynamic process. This study takes an online fitness platform as an example to explore how multi-goal pursuit dynamically influences users’ self-efficacy and then changes their fitness behavior.Design/methodology/approach: We collect detailed goal pursuit and fitness information of 1,387 unique users from a popular online fitness platform. Hidden Markov model is used to reveal the real-time self-efficacy state of users and capture the varying effects of multiple goals across users and over time.Findings: We demonstrate that multiple goals have a positive effect on the transition to higher self-efficacy. Furthermore, the difficulty of goals and the tenure of users play different roles in the effect of physical and mental goals. Higher-difficulty goals only boost the relationship between physical goals and maintaining high self-efficacy. However, the moderation effect of user tenure is completely opposite.Originality/value: This is the first study using Hidden Markov model to explain the underlying mechanism between multiple goals and fitness behavior in online context. We also find heterogeneous effects of multiple goals for different goal difficulties, which extends the research on goal difficulty in goal-setting theory. Our study provides insights to managers on gearing interventions toward users in specific states.