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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.
Z et al. (Tue,) studied this question.