Investigating the mechanistic relationship between smoking and sleep to inform a tailored digital sleep intervention for smokers.

Hypothesis: There is good evidence of a reciprocal relationship between sleep and smoking. For example, we have previously found strong evidence for positive genetic correlations between insomnia and both smoking initiation and smoking heaviness. Using Mendelian randomization, we also found evidence that insomnia causally increases smoking heaviness (inverse-variance weighted fixed effects meta-analysis: beta 1.21, 95% CI 0.20 to 2.22) and causally decreases likelihood of smoking cessation (odds ratio 0.80, 95% CI 0.65 to 0.97). These data indicate that sleep disturbance may play an important role in the continuation of smoking. In addition, smoking has been linked to sleep disturbance and changes to sleep architecture, suggesting there may be a negative cycle of sleep disturbance and smoking that is difficult to break. Several well validated sleep interventions exist that could be tailored for smokers, and we hypothesise that delivering these in a targeted way to smokers utilising digital technology may reduce smoking heaviness and improve cessation rates.
Aims: This PhD project will involve three work packages to inform future post-doctoral work in this area. These will assess: (1) the mechanisms through which sleep disturbance or poor sleep quality may affect smoking behaviour and cessation in order to better tailor interventions; (2) machine learning and modelling techniques to predict withdrawal, smoking urges, abstinence self-efficacy, attentional bias and smoking topography ; (3) the development of a digital intervention tailored to smokers based on current sleep interventions.
Experimental Studies: Study 1 will be a within-subjects design with one factor of sleep disturbance (vs. normal sleep). Smokers will complete a smoking-related test battery (self-reported nicotine withdrawal, smoking urges, abstinence self-efficacy, attentional bias and smoking topography) after one night of disturbed sleep (achieved by text messages during the night that are repeated until responded to) and after one night of normal sleep (at least one week apart).
A study investigating predictors of relapse in a smoking cessation study (Nakamura et al. 2014) identified an effect size of d = 0.2 when comparing smoking urges in smokers who relapsed (M = 0.6, SD 0.8) and those who did not (M = 0.4, SD 0.6). To observe a comparable effect with 80% power at an alpha level of 5%, we would require a total sample size of 140. As we will use a within-subjects design, we will recruit 70 smokers in total.
Machine learning and modelling:
Study 2 would include the development of trigger algorithms using third party (existing) app technology. A machine learning model will be developed as a predictive tool to predict instances of high risk of smoking relapse based on data from study 1 .
Co design: Study 3 utilises a co -design approach with current and quitting smokers This study will inform the design of the digital intervention. It will also address any additional barriers to good quality sleep or implementing sleep interventions in smokers, to inform development of a novel digital sleep intervention for smokers.
Digital Intervention development: The aim of a sleep intervention is to improve sleep quality generally, but this does not negate the occurrence of poor sleep quality on some nights. These situations are likely to put quitting smokers at heightened risk of smoking relapse. We propose that the smoking-tailored intervention would therefore include a trigger intervention (informed by study 2), whereby poor sleep is identified by a smartwatch. This in turn initiates a JITAI. The support delivered would be informed by study 1 (which will identify what smoking-related variables are particularly sensitive to sleep disturbance). The design of the intervention will be generated by the co-design process in study 3. Together these studies would support a future funding bid to assess the feasibility and acceptability of the developed digital JITAI.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.