## 2 Materials and Methods
### Materials  
Participants were presented with three Personas in each context, namely Doctor (Pediatric), Dietitian, and Child patient in the clinical context, and Teacher, Principal, and Student in the educational context. Four versions of each Persona were presented, one developed by experts and three developed by LLMs, specifically Bard, ChatGPT 3.5, and Llama2 70B. Qualitative Personas were developed in combination with a quantitative approach. In the first step, experts answered open-ended questions, asking about the motivations, needs of potential users and their goals, the expected benefits, and the digital solutions they seek in relation to the respective platform. Then, the key characteristics of each Persona were further refined in a mapping exercise with experts. Following this, we sought additional input from experts in several sessions to refine the Persona list and create the Persona descriptions. The second step was the quantitative evaluation of Personas with the Persona Perception Scale (PPS) [34], which was similar to the method described below. The prompts were constructed to seek comprehensive, narrative-driven answers to key questions about the Persona's background, goals, needs, motivations, benefits from the platform, and sought-after digital solutions. At the same time, the prompts were quite generic to ensure generalizability of the approach, without a specific expert knowledge about LLMs required by the researchers. Namely, each prompt begins with a clear directive to create a structured representation of a Persona, with a specific role (e.g., Dietitian, Pediatric, Child, Patient, Teacher, Principal). Limited additional context, such as workplace (e.g., primary care, clinic, school) and target group (e.g., adolescents suffering from obesity), is also defined. The prompts also list specific questions to structure the Persona profile, i.e., background description of the Persona, the Persona's goals related to obesity, the Persona's needs, the Persona's motivations that drive the engagement, the digital solutions sought, and the expected benefits from the digital solutions/platform. In this way, we ensure that the Personas are detailed, relatable, and actionable for designing effective digital interventions for obesity prevention and treatment. Specific prompts used for generating the Personas via LLMs and all the Personas' descriptions are available in the Supplementary Materials. Participants completed a questionnaire on their perception of different Personas' descriptions and questions on their socio-demographic characteristics. The perception of Personas' descriptions was assessed via the PPS [34]. The PPS consists of eight subscales; however, in this study, the following six were used: Credibility (three items, e.g., The Persona seems like a real person), Consistency (four items, e.g., The Persona information seems consistent), Completeness (four items, e.g., The Persona profile is not missing vital information), Clarity (three items, e.g., The information in the persona profile is easy to understand), Empathy (three items, e.g., I feel like I understand this persona), and Likability (four items, e.g., I could be friends with this Persona). Most subscales showed acceptable to great internal consistency (Credibility: \(\alpha\) = 0.72-0.89, Completeness: \(\alpha\) = 0.76-0.91, Empathy: \(\alpha\) = 0.72-0.87, Likability: \(\alpha\) = 0.81-0.93). We modified one item of the Completeness subscale (i.e., The Persona profile is detailed enough to make decisions about the consumers it describes) by replacing the term 'consumers' with 'people' to align better with the Personas used in this study, and omitted one item from the Consistency subscale due to its reference to parts of the Persona description that we did not include in the Personas used in this study. The scale, therefore, consisted of 20 items answered on a 7-point scale (1--\"Strongly disagree\", 7--\"Strongly agree\"), with the additional \"Not applicable\" option, and it was completed for each of the Persona presented.