The detrimental effects of air pollutants on human health have prompted
increasing concerns regarding indoor air quality (IAQ). The emergence of
digital health interventions and citizen science initiatives has provided new
avenues for raising awareness, improving IAQ, and promoting behavioural
changes. The Technology Acceptance Model (TAM) offers a theoretical framework
to understand user acceptance and adoption of IAQ technology. This paper
presents a case study using the COM-B model and Internet of Things (IoT)
technology to design a human-centred digital visualisation platform, leading to
behavioural changes and improved IAQ. The study also investigates users'
acceptance and adoption of the technology, focusing on their experiences,
expectations, and the impact on IAQ. Integrating IAQ sensing, digital
health-related interventions, citizen science, and the TAM model offers
opportunities to address IAQ challenges, enhance public health, and foster
sustainable indoor environments. The analytical results show that factors such
as human behaviour, indoor activities, and awareness play crucial roles in
shaping IAQ.

通过使用 COM-B 模型和物联网技术设计以人为中心的数字可视化平台，该研究调查了用户对室内空气质量技术的接受和采用情况，关注它们的体验、期望和对室内空气质量的影响，进而导致行为变化和改善室内空气质量。

数字健康与室内空气质量：基于物联网的人性化可视化平台促进行为改变和技术接受

Digital Health and Indoor Air Quality: An IoT-Driven Human-Centred  Visualisation Platform for Behavioural Change and Technology Acceptance

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。