Study protocol: Using ecological momentary assessment and wearable sensors to examine mechanisms linking sleep and smoking cessation among adults who are socioeconomically disadvantaged

TL;DR

This study explores how sleep and smoking cessation are connected in socioeconomically disadvantaged adults using real-time data from wearable sensors and smartphone assessments.

Contribution

The study introduces a real-time, bidirectional analysis of sleep dysregulation and smoking cessation processes in lower SES adults using ecological momentary assessment and wearable sensors.

Findings

The study will capture bidirectional and temporal associations between daily sleep dysregulation and smoking cessation processes.

Findings will inform the development of a Just-in-Time-Adaptive Intervention to improve sleep health during smoking cessation.

The approach addresses methodological limitations by using real-time data and including lower SES adults.

Abstract

Cigarette smoking is highly concentrated among individuals with lower socioeconomic status (SES) who often lack access to smoking cessation services. Thus, smoking cessation in lower SES adults remains a critical public health concern that warrants further study and attention. Smokers attempting to quit are at the highest risk for lapse within the first weeks of their quit attempt, and an initial lapse is highly likely to lead to full relapse. It is essential to identify and understand behavioral factors that may increase or decrease the likelihood of successful smoking cessation among lower SES adults during a quit attempt (pre-and post-quit). Recently, sleep dysregulation, such as insufficient sleep duration, has been considered as a potential intervention target to address smoking behaviors (e.g., number of cigarettes smoked per day) and improve smoking cessation outcomes (e.g., abstinence). Recent studies have found that lower SES is associated with higher rates of poor sleep. Thus, SES should be accounted for when assessing sleep dysregulation during smoking cessation attempts. Although previous studies have examined the relationship between sleep dysregulation and smoking behavior and/or cessation outcomes, they have several methodological limitations, including the use of retrospective survey methods, use of cross-sectional study designs, relying solely on laboratory-based data collection, not assessing integrated sleep health dimensions (usually only sleep duration or quality is assessed), omitting lower SES adults who smoke, and focusing on a single pathway rather than bidirectional associations.

This study will use a real-time data capture approach among lower SES adults who are attempting to quit smoking. This approach will involve a granular examination of the bidirectional and temporal associations between daily sleep dysregulation and smoking cession processes (pre- and post-quit) using smartphone-based ecological momentary assessment (EMA) and wearable sensors. Specifically, we aim to identify bidirectional and temporal associations between daily smoking abstinence and sleep dysregulation via EMA and wrist-worn sensors during the first four weeks of a smoking cessation attempt.

Findings from this study will yield preliminary data that will be used to develop and implement a Just-in-Time-Adaptive Intervention (JITAI) that aims to improve sleep health during smoking cessation.

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