Abstract
Objective: Digital Mental Twins (DMTs) - adaptive, data-driven models intended to represent individual’s mental health in real time - have been proposed as a basis for personalized mental health services and shared decision-making. However, empirical evidence on service users’ perspectives remains limited. This study aimed to investigate subjective relevance, acceptability, and implementation consideration of service users on the conceptual and methodological approach of DMTs. Methods: We conducted semi-structured interviews with N=17 in-patient service users (mean age 35,8; 47% female) with differing mental health conditions (e.g., major depressive disorder, schizophrenia) according to ICD-10. Two experts with lived experiences contributed as co-researchers. The interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Results: Four overarching themes were identified: (1) Perceived role of DMTs in mental health care: Participants described DMTs as potentially beneficial, especially by providing “neutral” feedback to support reflection. (2) Perceived risks, barriers and potential adverse effects: Concerns about data privacy were common, alongside anticipated unintended consequences of continuous feedback and recommendations, including guilt, pressure, and self-fulfilling prophecies. (3) Balancing data utility and privacy: Participants emphasized the importance of integrating diverse data sources (e.g. self-reports, passive sensing, biomarkers), while highlighting the need for transparent data collection and user control. (4) Perceived clinical value and implementation preferences: Participants valued low-threshold support and preferred implementation of DMTs within blended-care models. Conclusions: This qualitative study delineates opportunities and challenges of DMTs from service users’ perspective. Overall, participants viewed DMTs as potentially valuable adjuncts to mental health care, while emphasizing that acceptability will depend on user-centered design, data transparency, and integration into clinical workflows.