Key Decision Points for Personalization and Customization in Self-Guided Digital Suicide Prevention: Qualitative Interview Study

Background: Young adults in the United States experience high rates of suicidal thoughts and behaviors (STB) but low engagement with traditional care, highlighting the need for accessible and scalable digital interventions. Self-guided digital safety planning interventions (SPIs) can expand access to support but introduce important decisions about what content to deliver, when systems should intervene, and how users retain control over system actions. Personalization, defined as system-initiated adaptation, and customization, defined as user-initiated control, may offer complementary approaches to these decisions. However, how they should be coordinated to preserve autonomy and reduce burden during periods of distress remains unclear. Objective: This study aims to identify key decision points in self-guided digital SPIs and characterize what, when, how, and by whom (system or user) safety planning functions should be controlled. We further examined how personalization and customization can jointly support autonomy, reduce burden, and promote safety. Methods: Nine young adults (ages 18-24) with lived experience of suicidal thoughts and behaviors were recruited from an asynchronous remote community comprising individuals who had engaged with the Mental Health America (MHA) website. Participants completed semistructured individual interviews via Zoom. Three coders analyzed interview transcripts using codebook thematic analysis. One coder open-coded all transcripts to develop a preliminary codebook. Two additional coders then applied the codebook to overlapping transcripts, after which all coders revised the codebook and resolved discrepancies by consensus before coding the remaining transcripts. Themes were interpreted using the 5W1H (Who, What, Where, When, Why, and How) framework as an organizing lens for identifying and structuring decision points. Results: Participants identified 3 classes of decision points for self-guided digital SPIs: (1) system-level decisions about what content and actions are automatically provided; (2) user-level decisions about when and at what pace safety planning unfolds; and (3) shared decisions requiring coordination between users and the system. Participants favored system personalization of decision aids, coping suggestions, brainstorming prompts, example messages for contacting trusted others, affirmations, and relevant resources. In contrast, participants wanted control over the timing and pacing of safety planning and the representation and organization of their safety plans. Participants also emphasized that systems should obtain explicit permission before taking safety-relevant actions, such as contacting trusted others or connecting users with crisis counselors in response to increases in detected risk. Conclusions: Personalization and customization can function as complementary mechanisms for allocating decision authority within self-guided digital SPIs. The findings support a model of permissioned automation in which systems reduce cognitive and emotional burden through adaptive support while users retain control over safety-relevant actions. This decision-point framework provides a foundation for designing and evaluating self-guided suicide prevention technologies that balance adaptation, autonomy, and safety.