Development of Conversational AI for Sleep Coaching Programme (2021)

Abstract: Almost 30% of the adult population in the world is experiencing or has experience insomnia. Cognitive Behaviour Therapy for insomnia (CBT-I) is one of the most effective treatment, but it has limitations on accessibility and availability. Utilising technology is one of the possible solutions, but existing methods neglect conversational aspects, which plays a critical role in sleep therapy. To address this issue, we propose a PhD project exploring potentials of developing conversational artificial intelligence (AI) for a sleep coaching programme, which is motivated by CBT-I treatment. This PhD project aims to develop natural language processing (NLP) algorithms to allow the system to interact naturally with a user and provide automated analytic system to support human experts. In this paper, we introduce research questions lying under three phases of the sleep coaching programme: triaging, monitoring the progress, and providing coaching. We expect this research project’s outcomes could contribute to the research domains of NLP and AI but also the healthcare field by providing a more accessible and affordable sleep treatment solution and an automated analytic system to lessen the burden of human experts.

## introduction insomnia is one of the most common sleep disorders with a high prevalence. approximately onethird of adults experience one or more of the symptoms of insomnia @xcite . the consequences of insomnia include not only individual problems but also societal issues, such as daytime fatigue, low energy level, which can cause depression, and even increased risk of accidents @xcite . the cost associated with insomnia, including direct and indirect costs, in the us is around 92.5 to 107.5 billion usd per year @xcite . cognitive behaviour therapy for insomnia (cbt-i) is one of the most effective solutions to treat insomnia. during cbt-i process, a person who is suffering from symptoms of insomnia (patient) will consult with a cbt-i provider (therapist) who provides support to identify behaviours, thoughts, and feelings that are related to the symptoms. since its goal is to change the potential causes, both behavioural and cognitive factors, it produces longlasting improvement in the condition of insomnia, compared to the medication treatment @xcite . despite its effectiveness, cbt-i treatment has a limitation. from a patient's perspective, the treatment cost is high and, from clinician's perspectives, the number of patients that potentially can be treated is significantly large @xcite . consequently, many researchers and engineers have been working on developing more accessible and affordable therapy solutions that can also lessen the burden of clinicians, such as internet-or mobile-based computerised therapy tools @xcite @xcite . these studies explored opportunities for applying technologies to automate treatment process. however, the conversational aspect, which is the core of the in-person treatment, has been neglected. in this study, we will explore the possibilities of developing conversational ai (artificial intelligence) to make a computerised sleep therapy tool to be more close to in-person therapy. since this research field is still in its infancy, we consider a sleep coaching programme targeting healthy people who would like to optimise their sleep, rather than a sleep therapy for patients with the chronic sleep disorder. also, the goal of this study is to provide a user-friendly interface for users and to support human experts, rather than to replace or eliminate human-in-the-loop. therefore, we mainly focus on two things: 1) adding a conversational feature that allows users to provide inputs to enable a natural conversation between human and the system; and 2) adding an analytic feature that automates processing user inputs to support decision making. the main research questions of this project lie under three processes of the sleep coaching programme: triage, monitor the progress, and provide coaching. overview of research questions and overlapped components are illustrated in figure 1 . we start our research by analysing in-person sleep treatment and existing automated tools and identifying missing gaps to decide research questions (section 2). then we revisit the research questions and explain the methodology to address each question (section 3). as the first step, we introduce a pilot study and present a preliminary result to discuss and provide next steps (section 4). finally, we conclude this paper by summarising research questions and research plan of this phd project (section 5). ## related work in this section, we will first provide a brief overview of sleep treatment of cbt-i made by a human expert. secondly, we will review the existing methods of automated sleep treatment tools. lastly, we will identify missing gaps and introduce research questions. ## pilot study we ran a pilot experiment to examine our assumptions for rq1. the main goal of this pilot study as follows: 1) to build a model that classifies freetext user inputs. 2) to implement bn to select a follow-up question. following subsections describes details of the experiments and results. ## conclusion in this paper, we propose a phd project exploring potentials of developing conversational ai for a sleep coaching programme, which is motivated by cbt-i treatment, targeting healthy people who would like to optimise their sleep. the main goal of this phd project is to develop nlp algorithms for conversational ai to allow the system to interact naturally with a user and provide automated analytic. to this end, we identified three research questions lying under three phases of the sleep coaching programme: triage, monitor, and support. we expect this research project's outcomes could contribute to the research domains of nlp and ai but also the healthcare field by providing a more accessible and affordable sleep treatment solution and an automated analytic system to lessen the burden of human experts.