Table 1.

Tough Talks HIV status disclosure virtual reality program development and research methods

Study Phase:	Phase 1: Formative Research	Phase 2: Iterative Development and Usability Testing			Phase 3: Pilot Trial
		Round 1	Round 2	Round 3
Data Collection Methods:	Focus groups that included disclosure role plays and assessment of visual program assets	Usability sessions with initial program, semi-structured user-experience interview, prepost surveys			Pilot testing with refined program, semi-structured user experience interviews, prepost surveys
Total Participants: (n=58)	4 focus groups (7 HIV-negative MSM; 8 HIV-positive MSM)	15 HIV-positive MSM	15 HIV-positive MSM	2 HIV-positive MSM	11 HIV-positive MSM
Total Disclosure dialogues: (n=132)	6 in-person disclosure role play dialogues	44 program disclosure dialogues	42 program disclosure dialogues	8 program disclosure dialogues	32 program disclosure dialogues
Program visuals: virtual character, disclosure settings, virtual disclosure coach	- Sample photographs - Visual mock-ups of disclosure settings and program design - Sample virtual character designs	- Virtual character build (nonspeaking, immobile, neutral pose, refined details and features of face, skin, clothing) - Rig build (controls for facial expressions and lipsync)	- Audio utterance recordings - Speaking virtual character (lip automation) - Expressive virtual character (character face and body animation)	Coach feature programmed (speech bubbles with audio and verbal cues for end user)	Refined all built features for smooth integration with AI and NLP engines
Natural language processing: artificial intelligence	Initial virtual character utterance database (n=156 utterances)	- All virtual character responses manually selected or typed by clinician wizard - Refined virtual character utterance database (n=114 utterances)	AI program suggests virtual character responses, clinician wizard agrees or overrides with manual selection	- Neutral disclosure scenario: Virtual character responses fully automated using AI - Positive/negative disclosure scenarios: Program suggests virtual character responses, wizard agrees or overrides - Finalized virtual character utterance database (n=125 utterances)	- Neutral disclosure scenario: Virtual character responses fully automated using AI - Positive/negative disclosure scenarios: Program suggests virtual character responses, wizard agrees or overrides - Employed finalized virtual character utterance database (n=125 utterances)