Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention

Abstract

Background: Relapse to smoking is commonly triggered by stress, but behavioral interventions have shown only modest efficacy in preventing stress-related relapse. Continuous digital sensing to detect states of smoking risk and intervention receptivity may make it feasible to increase treatment efficacy by adapting intervention timing.

Objective: Aims are to investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects.

Study design: A micro-randomized trial will be implemented with 75 adult smokers who wear Autosense chest and wrist sensors and use the mCerebrum suite of smartphone apps to report and respond to ecological momentary assessment (EMA) questions about smoking and mood for 4 days before and 10 days after a quit attempt and to access a set of stress-management apps. Sensor data will be processed on the smartphone in real time using the cStress algorithm to classify minutes as probably stressed or probably not stressed. Stressed and non-stressed minutes will be micro-randomized to deliver either a prompt to perform a stress management exercise via one of the apps or no prompt (2.5-3 stress management prompts will be delivered daily). Sensor and self-report assessments of stress and smoking will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to prevent smoking relapse.

Significance: Sense2Stop will be the first digital trial using wearable sensors and micro-randomization to optimize a just-in-time adaptive stress management intervention for smoking relapse prevention.

Keywords: Digital intervention; Just-in-time adaptive intervention; Micro-randomized trial; Optimization; Smoking; Stress; mHealth.

Figure 1.

Sense2Stop Study Protocol Sequence. Shows periods of sensor wear and EMA assessments during 4-day pre-quit period and 10-day post-quit period. Laboratory visits and coaching calls also displayed.

Figure 2.

Availability for Randomization to Intervention Prompt versus no Prompt. Sense2Stop algorithms running on smartphone detect episodes of stress or no stress when fewer than 50% of the minutes between the start and midpoint of an episode have missing data or physical activity. Such minutes are available to be randomized in real time for the mobile phone to deliver an intervention prompt or no prompt if criteria are met for low burden, safety, and battery power. *The middle of an episode (also called the episode’s peak) is determined using a time-series method called Moving Average Convergence Divergence.^, **Causes of missing sensor data include sensor detachment and dropped WiFi connection.^,

Figure 3.

Design of the Sense2Stop Micro-Randomized Trial. Depicts assessment modalities (sensor, self-report) used to observe Sense2Stop proximal outcomes (smoking, stress) and confounders (physical activity). Algorithms operating on the mobile phone transmit episodes of continuous digital data for micro-randomization (R) to intervention or no intervention only when the participant is available and the episode is able to be classified as probably stressed or probably not stressed. Proximal outcomes (probabilities of stress and smoking) are assessed in the 2-hour window after each micro-randomization. Note that the 10-day distal outcome is not being directly assessed in either the primary or secondary analytic plans outlined in the current manuscript.