## 5 Results
### Case Study  
To illustrate the usage of the _[PERSON]_ chatbot, we selected conversations from one of the participants in the co-design procedure, which are in Brazilian Portuguese language. The first conversation shown in Figure 9a shows the user starting contact with _[PERSON]_ by sending a \"Qi\" (Hi) message, to which _[PERSON]_ responds using a greeting message. The user then informs the problem she is experiencing during breastfeeding: \"Fissura\" (fissure). _[PERSON]_, upon receiving the message from the user, asks two new questions to better understand which intent the reported problem falls into, namely \"Existe relato de febre?\" (Is there a fever report?) and \"O bico do peito esta ferido?\" (Is the breast nipple hurt?). Finally, based on the answers given by the user, _[PERSON]_ is able to identify the problem, and then presenting detailed textual information about it, its causes and tips on how to deal with the discomfort caused by it.  
Figure 8: NPS results in the last two rounds of the co-design procedure.  
Figure 9b displays a second conversation in which the chatbot guides the user on how to put the baby in the right shape to breastfeed. For this purpose, guidelines are transmitted using text and image messages. In the conversation, the chatbot asks whether the user would like to receive an illustrative image related to the topic addressed: \"Voce quer veruma imagem de como colocar o bebe?\" (Do you want to see an image of how to place the baby?). At the end of the conversation, _[PERSON]_ presents an image in which a mother is sitting with her baby in the correct breastfeeding position.  
Figure 10 shows _[PERSON]_ starting a conversation, i.e., the active notification mechanism, in which the chatbot itself starts the interaction without the need for a previous message from the user. In this interaction, _[PERSON]_ interacts by providing information about the importance of HM and its donation to the HMB: \"Voce sabia? Um frasco de 100 mL de leite materno doado pode alimentar ate 10 bebes internados na UTI!\" (Did you know? A 100 mL bottle of donated breast milk can feed up to 10 babies admitted to the ICU!). Health professionals (i.e., participants) believe that such a mechanism can encourage mothers to understand and practice HM donation. Finally, _[PERSON]_ concludes the conversation by informing that if the user is interested in learning more about HM donation, she can contact the HMB of the HU-UFMA via telephone number or go to the address of the HMB, both data forwarded in messages of the conversation.  
Figure 9: _[PERSON]_ guiding the user with texts and images; (**a**) Starting a conversation with _[PERSON]_; (**b**) Guidance using texts and images.  
Figure 11 shows an interaction in which _[PERSON]_ cannot understand the participant's message (i.e., a fallback trigger). It replies to the participant using a standard message (i.e., the fallback response) informing the user that their message was not understood, and then providing guidance for the conversation to resume: \"Desculpe, mas eu ainda estou aprendendo e por isso nao consegui te entender. Para continuaar, voce pode digitar novamente o que deseja, mas em poucas palavras. Ex: \"Dor nos seios\". (Sorry, but I'm still learning and that's why I couldn't understand you. To continue, you can retype what you want, but in a few words. E.g.: \"Pain in the breasts\").