Using Smartwatches to Monitor Smoking in Real-life Situations

Official title

Use of Noninvasive Wearables Biomonitoring to Detect Pre-Smoking, Smoking, And Post-Smoking Stages: An Observational Laboratory Study

Overview

Background: It is important to quickly identify when people are at risk of smoking. Most current methods rely on people reporting when they smoke or what makes them want to smoke. But this can be hard for people to do and may not always be accurate. Other methods use special gadgets to identify smoking movements, but these may not always work well in real life. We want to see if we can use devices available on the market such as smartwatches to identify signs in the body and hand movements that might indicate when someone is about to smoke, is currently smoking, or has just smoked. Objective: To record body signs and hand movements before, during, and after smoking in real-life and in a lab to see how they change when someone is craving cigarettes, while a person is smoking, and after a person has smoked. Eligibility: People who are 21 years or older and smoke, have no college degree or be currently enrolled in college, and are low-income earners. To participate in the study, participants have to pass a breath test that shows they smoke cigarettes and, for women, a urine test to show that they are not currently pregnant. Design: Participants will complete an eligibility survey to see if they qualify to be in the study. If they qualify, they will answer a brief baseline survey that includes questions about themselves, their health, and their smoking behavior. Participants will get a smartwatch to wear for 3 days at home, log each time they are about to smoke and have finished smoking, and answer a 5-question health survey via the app. They will get instructions on how to set up and wear the smartwatch. They will download a mobile application on their phone. The app will collect data from the smartwatch. Participants will then come to the lab but will be asked not to smoke or drink alcohol for at least 12 hours. They will have to take a breath test to show they have not smoked or had alcohol. They will also give a blood sample. In the lab, they will sit in a room where they will be hooked to devices that monitor their vitals such as heart rate and blood pressure for one hour. They will also wear a smartwatch on each hand. While they are in the smoking room, they will go through 3 different phases: (a) pre-smoking where they will be asked to stay seated for about 25 minutes, (b) smoking where they will be asked to smoke as many cigarettes of their choice as they want for about 10 to 15 minutes, (c) post-smoking where they will be asked to stay seated, not smoking, for about 25 minutes. They will answer a brief 10-minute health survey before and after the session. Participation will last for 3 days of home monitoring and 2 visits to the research clinic that last about 2 hours.

Detailed 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.

Primary outcome measures

• The primary endpoint is to evaluate changes in biomarker measurements obtained from wearables, which include heart rate, heart rate variability, respiratory rate, blood oxygen levels & hand/arm movement, across three smoking stages. [Time frame: 7 days]

Eligibility criteria

• INCLUSION CRITERIA:

We will recruit up to 30 non-treatment seeking participants with low socioeconomic status who smoke. We will strive to have representation across racial and ethnic groups and biological sex. At least half of the participants must earn at or below 200% of the federal poverty guideline for 2024 corresponding to number of people in their household (US Department of Health and Human Services).

To be included in the study, participants must:

• be 21 years or older in compliance with Tobacco 21 laws
• have no college degree or be currently enrolled in college
• have smoked 100 cigarettes in their lifetime and 5+ cigarettes per day in the past year to establish daily smoking status
• willing to abstain from smoking for >=12 hours prior to the lab visit verifiable by a CO reading that is below 10 ppm and retrospectively by minimal nicotine in blood sample
• willing to abstain from using any nicotine products or devices for >=12 hours prior to the lab visit
• willing to abstain from alcohol use for >=12 hours prior to the lab session verifiable by breath alcohol concentration (BrAC = 0.000)
• willing to adhere to all study procedures including agreeing to a blood draw and bringing >=1cigarette for use during the study visit
• have a smartphone compatible with wearable device requirements for Bluetooth and operating system (iphone iOS 14 and higher, Android 9.0 and higher) and willing to download the study apps onto their phone
• speak English
• and willing and able to provide informed consent.

Exclusion criteria

Participants will be excluded if they:

• are not abstinent before the lab session through biochemical verification after two attempts
• have sought treatment for tobacco/nicotine use in the past 2 months (e.g., on pharmacotherapy for smoking cessation, enrolled in smoking cessation support programs or interventions)
• have past 3-month pulmonary or cardiovascular event, or those who have been hospitalized or visited the emergency room for seizure, stroke, or new heart problems,
• are pregnant or breastfeeding
• and are unable or unwilling to provide informed consent.

Vulnerable populations will not be enrolled in the study (i.e., adults unable to consent, individuals who are not yet adults, pregnant women, prisoners).

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Case-only

Study locations

United States · 1 center

• University of Kansas Medical Center — Kansas City

Publications

• Hojjatinia S, Daly ER, Hnat T, Hossain SM, Kumar S, Lagoa CM, Nahum-Shani I, Samiei SA, Spring B, Conroy DE. Dynamic models of stress-smoking responses based on high-frequency sensor data. NPJ Digit Med. 2021 Nov 23;4(1):162. doi: 10.1038/s41746-021-00532-2. PMID 34815538
• Hoeppner BB, Hoeppner SS, Seaboyer L, Schick MR, Wu GW, Bergman BG, Kelly JF. How Smart are Smartphone Apps for Smoking Cessation? A Content Analysis. Nicotine Tob Res. 2016 May;18(5):1025-31. doi: 10.1093/ntr/ntv117. Epub 2015 Jun 4. PMID 26045249
• Hoeppner BB, Hoeppner SS, Schick MR, Milligan CM, Helmuth E, Bergman BG, Abroms LC, Kelly JF. Using the text-messaging program SmokefreeTXT to support smoking cessation for nondaily smokers. Subst Use Misuse. 2019;54(8):1260-1271. doi: 10.1080/10826084.2018.1552300. Epub 2019 Apr 19. PMID 30999794
• Ho BK, Robinson JK. Color bar tool for skin type self-identification: A cross-sectional study. J Am Acad Dermatol. 2015 Aug;73(2):312-3.e1. doi: 10.1016/j.jaad.2015.05.024. No abstract available. PMID 26183973
• Henrich J, Heine SJ, Norenzayan A. Most people are not WEIRD. Nature. 2010 Jul 1;466(7302):29. doi: 10.1038/466029a. No abstract available. PMID 20595995
• Hebert ET, Stevens EM, Frank SG, Kendzor DE, Wetter DW, Zvolensky MJ, Buckner JD, Businelle MS. An ecological momentary intervention for smoking cessation: The associations of just-in-time, tailored messages with lapse risk factors. Addict Behav. 2018 Mar;78:30-35. doi: 10.1016/j.addbeh.2017.10.026. Epub 2017 Oct 28. PMID 29121530
• Hebert ET, Ra CK, Alexander AC, Helt A, Moisiuc R, Kendzor DE, Vidrine DJ, Funk-Lawler RK, Businelle MS. A Mobile Just-in-Time Adaptive Intervention for Smoking Cessation: Pilot Randomized Controlled Trial. J Med Internet Res. 2020 Mar 9;22(3):e16907. doi: 10.2196/16907. PMID 32149716
• Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991 Sep;86(9):1119-27. doi: 10.1111/j.1360-0443.1991.tb01879.x. PMID 1932883

Identifiers

NCT: NCT07067151 · 10002323 · 002323-MD

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗