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

假设：有充分的证据表明睡眠和吸烟之间存在相互关系。例如，我们之前已经发现了强有力的证据，证明失眠与开始吸烟和吸烟量之间存在正相关的遗传关系。使用孟德尔随机化，我们还发现失眠与吸烟严重程度增加有因果关系（反方差加权固定效应荟萃分析：beta 1.21, 95% CI 0.20至2.22），并与戒烟可能性降低有因果关系（优势比0.80,95% CI 0.65至0.97）。这些数据表明，睡眠障碍可能在吸烟的持续中起着重要作用。此外，吸烟与睡眠障碍和睡眠结构改变有关，这表明睡眠障碍和吸烟之间可能存在难以打破的恶性循环。有几种经过验证的睡眠干预措施可以为吸烟者量身定制，我们假设，利用数字技术有针对性地向吸烟者提供这些干预措施，可能会减少吸烟的严重程度，提高戒烟率。目的：本博士项目将包括三个工作包，以指导该领域未来的博士后工作。这些将评估：(1)睡眠障碍或睡眠质量差可能影响吸烟行为和戒烟的机制，以便更好地定制干预措施；(2)机器学习和建模技术，用于预测戒断、吸烟冲动、戒烟自我效能、注意偏差和吸烟地形；(3)在现有睡眠干预措施的基础上，开发针对吸烟者的数字干预措施。实验研究：研究1将是一个有一个睡眠障碍因素（与正常睡眠相比）的受试者内设计。吸烟者将在一晚睡眠不安稳（通过夜间反复发送短信直到回复）和一晚正常睡眠（间隔至少一周）后完成与吸烟相关的一系列测试（自我报告尼古丁戒断、吸烟冲动、戒烟自我效能、注意力偏差和吸烟地貌）。一项调查戒烟研究中复发预测因素的研究（Nakamura et al. 2014）发现，在比较复发吸烟者（M = 0.6, SD 0.8）和未复发吸烟者（M = 0.4, SD 0.6）的吸烟冲动时，效应量为d = 0.2。为了在5%的α水平下观察到80%功率的可比效果，我们需要总样本量为140。由于我们将采用受试者内设计，我们将总共招募70名吸烟者。机器学习和建模：研究2将包括使用第三方（现有）应用程序技术开发触发算法。将开发机器学习模型作为预测工具，根据研究1的数据预测吸烟复发的高风险实例。共同设计：研究3采用了当前吸烟者和戒烟者的共同设计方法。这项研究将为数字干预的设计提供信息。它还将解决吸烟者获得高质量睡眠或实施睡眠干预的任何额外障碍，为吸烟者开发一种新型数字睡眠干预提供信息。数字干预的发展：睡眠干预的目的是总体上改善睡眠质量，但这并不能否定某些夜晚睡眠质量差的发生。这些情况很可能使戒烟者再次吸烟的风险增加。因此，我们建议针对吸烟的干预措施应包括触发干预（根据研究2提供的信息），即通过智能手表识别睡眠不佳。这反过来又启动JITAI。提供的支持将由研究1提供（该研究将确定哪些与吸烟相关的变量对睡眠障碍特别敏感）。干预的设计将由研究3中的协同设计过程产生。这些研究将共同支持未来的筹资投标，以评估开发的数字JITAI的可行性和可接受性。

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.