## 3 Materials and Methods
#### 3.1.4 Measures and Data Analysis  
In this study, we used a mixed methods approach based on quantitative and qualitative feedback. Quantitative data were measured using the SCM and WEAR scales, complemented by two seven-point Likert items assessing social acceptance and aesthetic design expectations. These data were analyzed through descriptive and inferential statistics. Additionally, qualitative feedback derived from participants' free-text responses was assessed using thematic analysis. The methods and data analysis procedures are described in detail in the following paragraphs. Quantitative: Stereotype Content Model (SCM)  
The SCM questionnaire by [PERSON] et al. [43] was used to assess stereotypes and social perceptions of various foot orthoses based on two perceived dimensions: warmth and competence. This standardized questionnaire consists of nine items rated on a 5-point scale ranging from _not at all_ (1) to _extremely_ (5). The competence dimension was evaluated using five items (_competent_, _confident_, _independent_, _competitive_, and _intelligent_), each assessed by the question _\"As viewed by society, how...are members of this group?\"_. Additionally, the same question was used to assess the warmth dimension, consisting of four items: _tolerant_, _warm_, _good-natured_, and _sincere_. For the data analysis, a two-factorial repeated measures (RM) ANOVA was used. Quantitative: Wearable Acceptability Range (WEAR)  
To measure the social acceptability of wearable technology, the WEAR scale by [PERSON] [44] was used. The WEAR scale (version 3) consists of 14 items, each rated on a 6-point scale ranging from _strongly disagree_ (1) to _strongly agree_ (6). Five of these 14 items are reverse-scored. The total score was divided by 14 to obtain an average score, which can be graded from _extremely low social acceptance_ (1) to _extremely high acceptance_ (6). An RM ANOVA was used for data analysis, similar to the procedure used for the SCM scale. Quantitative: 7-Point Likert Scales  
As described previously in Section 3.1.3, two questions were used to rate statements about social acceptance and aesthetic design expectations on 7-point Likert scales. For the evaluation, a non-parametric Aligned Rank Transform (ART) RM-ANOVA was applied. Qualitative Feedback  
Qualitative results from the free text entries were assessed using inductive thematic analysis [69]. Anonymized data were transcribed, coded, and analyzed paragraph-wise to identify categories and main themes. Two researchers were involved in the data analysis process: one researcher transcribed and coded the data set independently, while the second researcher reviewed the results for consistency. Study 2: The Impact of GPT Customization and Prompt Keywords on the Social Acceptability of AI-Generated Orthotic Designs  
Based on our first study, which showed that AI-generated designs can increase the social acceptance of orthopedic foot wearables, this study further explored the role of prompt customization in shaping user perception. In particular, the first study revealed that high-leg orthoses received the lowest acceptance ratings. Therefore, we examined which factors in AI image generation could positively influence the acceptance of this device category. While the initial study confirmed the potential of generative AI for orthotic design, it also raised questions regarding the influence of specific keywords in the prompts used and the application of customized GPTs. Furthermore, it remained unclear how the inclusion of keywords related to user preferences would affect the perceived acceptability of designs. To address this, our second study investigated the impact of various keywords and the use of a personalized GPT tailored to specific patient and product requirements. Previous research has demonstrated the influence of prompt structures and provided recommendations for different user roles [22].