The ubiquity of smartphones has led to an increase in on demand healthcare
being supplied. For example, people can share their illness-related experiences
with others similar to themselves, and healthcare experts can offer advice for
better treatment and care for remediable, terminal and mental illnesses. As
well as this human-to-human communication, there has been an increased use of
human-to-computer digital health messaging, such as chatbots. These can prove
advantageous as they offer synchronous and anonymous feedback without the need
for a human conversational partner. However, there are many subtleties involved
in human conversation that a computer agent may not properly exhibit. For
example, there are various conversational styles, etiquettes, politeness
strategies or empathic responses that need to be chosen appropriately for the
conversation. Encouragingly, computers are social actors (CASA) posits that
people apply the same social norms to computers as they would do to people. On
from this, previous studies have focused on applying conversational strategies
to computer agents to make them embody more favourable human characteristics.
However, if a computer agent fails in this regard it can lead to negative
reactions from users. Therefore, in this dissertation we describe a series of
studies we carried out to lead to more effective human-to-computer digital
health messaging.
In our first study, we use the crowd [...]
Our second study investigates the effect of a health chatbot's conversational
style [...]
In our final study, we investigate the format used by a chatbot when [...]
In summary, we have researched how to create more effective digital health
interventions starting from generating health messages, to choosing an
appropriate formality of messaging, and finally to formatting messages which
reference a user's previous utterances.

我们研究如何创建更有效的数字化医疗干预，从生成健康信息，选择适当的信息形式，到格式化参考用户先前的言述。