## 4 Discussion  
Digital interventions offer several advantages that can enhance the effectiveness and sustainability of behavior change [1]. However, effective digital interventions should be holistic, integrating various components to address the physical, emotional, and social aspects of health. Moreover, personalization, engagement, and accessibility are critical to ensuring these interventions are impactful and sustainable for children and adolescents. To achieve this goal, it is essential to incorporate user perspectives during the development process [5]. Personas are instrumental to support such a user-centered development process leading to long-term sustainability digital interventions [8,9]. By focusing on relevant user groups with their needs and values, such approaches can ensure that interventions are well-targeted, user-friendly, and effective, thereby enhancing the user experience and increasing engagement rates [22,26,30]. However, the most prominent method of Persona development, the qualitative approach, which creates the most representative Personas, presents some challenges, such as being time-consuming and subjective, potentially misrepresenting relevant user groups, and being slow to adapt to changing user needs [14]. With advancements in AI, particularly LLMs, more efficient methods for generating Personas have been proposed and explored [8,42,54,55,56]. The current study aimed to investigate how experts in the field perceive and evaluate Persona descriptions generated by three different LLMs and qualitatively developed Persona descriptions. The study focused on this exploration in two contexts, i.e., clinical and educational, and more broadly in the context of obesity research. In each context, three Personas were defined, and their descriptions were generated using both traditional, qualitative methods and three LLMs (i.e., Bard, Llama, and ChatGPT), resulting in a total of 12 Persona descriptions per context. These descriptions were then evaluated using the Persona Perception Scale by experts in the respective fields. Previous studies have found that LLMs can generate responses that are highly similar to human responses [42,57] and that LLM-generated Personas have been assessed as being of good quality [17]. The current study extends this work by comparing the descriptions generated by various LLMs among themselves and with qualitatively developed Persona descriptions. This approach builds upon and adds to the findings of related studies, such as those by [PERSON] and [PERSON] [54] and [PERSON] et al. [56], which did not evaluate the perceptions of LLM-generated Personas; a study by [PERSON] et al. [55], which did not include  
Figure 2: Means and standard deviations of the Personas’ perception dimensions with significant differences in pairwise comparisons. expert perspectives on the Personas; and the work of [PERSON] et al. [17], which is most closely related to the present study but explored Personas generated by one LLM. The results of this study showed that in many comparisons, perceptions of Persona descriptions did not significantly differ among LLM-generated Personas and qualitatively developed Personas, supporting the finding of [PERSON] et al. [17] that \"LLMs can generate consistent Personas that are perceived as believable, relatable, and informative for design/.../as perceived by the human evaluators\". However, post-hoc comparisons of significant ANOVA analyses revealed some differences that are worth exploring. In general, where these comparisons were significant, in most cases the descriptions generated using Bard model were evaluated most favorably, which is especially evident in evaluations of the Empathy, Likability, and Clarity of Personas' descriptions (i.e., in Patient and Principal Personas, and the Likability dimension also in the Teacher Persona). In these cases, Bard was evaluated significantly better than ChatGPT- and Llama-generated descriptions, as well as qualitatively developed Personas that were in many cases evaluated with the lowest or second-to-lowest scores.