Using Smartwatches to Monitor Smoking in Real-life Situations

Description

Background:

The study is part of a systematic effort to develop and evaluate a smoking cessation intervention, Quit Journey, a smoking cessation intervention targeting individuals with low socioeconomic status. Specifically, the study will inform the development of just-in-time momentary support to preempt a lapse or prevent it from progressing to a relapse to users of Quit Journey. Additionally, the study fills a gap in wearable-based studies for smoking detection that have thus far relied exclusively on socially and economically advantaged populations by increasing the representation of underserved populations and more accurately developing algorithms for smoking detection that include input from minority populations and pave the way for its replication with a larger sample in real-world settings. Finally, the data collected from this laboratory-based study will serve as baseline parameters for detecting smoking events in free-living conditions. In a planned real-world study, we aim to finetune our smoking-detection algorithms with data collected in natural environments, potentially improving their robustness and generalizability. Insights from this study have real-world applications of smoking-detection algorithms in wearables and mobile applications, ultimately advancing technology-assisted smoking cessation and contributing to reduced smoking rates.

Objectives:

* The objective of this study is to identify wearables-based digital biomarkers that are associated with nicotine deprivation (i.e., pre-smoking) and satiation (i.e., post-smoking) and with smoking episodes (i.e., during smoking). Identifying signature digital biomarkers of smoking can help us to unobtrusively detect with great probability the proclivity to smoke or smoking behavior, which can inform the delivery of momentary support to preempt lapsing or progressing toward a relapse among people attempting to quit smoking.
* We hypothesize that, relative to pre-smoking, (a) blood oxygen saturation will be lower, (b) heart rate will be higher, (c) heart rate variability will be higher, and (d) respiratory rate will be lower during a smoking episode and post-smoking.

Endpoints:

* Primary endpoint: Changes in wearables-measured biomarker measures including heart rate, heart rate variability, respiratory rate, and blood oxygenation, in addition to hand/arm movement, across three smoking stages.
* Secondary endpoint: Not applicable.