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

Battalio, Samuel L.; Conroy, David E.; Dempsey, Walter; Liao, Peng; Menictas, Marianne; Murphy, Susan; Nahum‐Shani, Inbal; Qian, Tianchen; Kumar, Santosh; Spring, Bonnie

doi:10.1016/j.cct.2021.106534

Cited by 45 publications

(30 citation statements)

References 52 publications

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“…This project will be the first known implementation of an MRT protocol to optimize a JITAI on the proximal outcome of eating behavior. Across the field of health behavior change, MRTs remain novel; there are several MRTs in the process of optimizing JITAIs in the areas of physical activity [ 85 , 86 ], smoking cessation [ 87 , 88 ], stress management [ 88 ], mood [ 86 ], medication adherence [ 89 ], and substance use [ 90 , 91 ]. A recently completed MRT to increase self-monitoring in a commercial wellness app for lifestyle behaviors found that sending prompts with tailored suggestions (vs tailored feedback) significantly increased the odds of self-monitoring and that the frequency of engagement with the app moderated this effect (eg, as frequency of self-monitoring increased and sending prompts with suggestions reduced the odds of engagement) [ 92 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

Goldstein¹,

Zhang²,

Klasnja³

et al. 2021

JMIR Res Protoc

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Background Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. Objective The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. Methods Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. Results The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. Conclusions This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). Trial Registration ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585 International Registered Report Identifier (IRRID) DERR1-10.2196/33568

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Section: Discussionmentioning

confidence: 99%

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

Goldstein¹,

Zhang²,

Klasnja³

et al. 2021

JMIR Res Protoc

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“…Participants were compensated if they completed lab visits, responded to mCerebrum-triggered ecological momentary assessments (with bonus for wearing the sensors), and received an overall bonus incentive for wearing the devices more than 70% of the requested wear time during the study period (i.e., 11.2 h/day); however, due to either battery issues or incorrect device wear, the overall wear time was less than that for many participants. Full procedures for the Sense2Stop trial are available from Battalio et al 37 .…”

Section: Methodsmentioning

confidence: 99%

“…Data for the current research were drawn from the parent Sense2Stop clinical trial, a micro-randomized trial of 75 smokers that used wearable sensors to optimize a just-in-time-adaptive stress-management intervention for smoking relapse prevention 37 . The Sense2Stop study, described elsewhere 37 , used wearable devices to collect intensive longitudinal data and applied algorithms to estimate the probability of stress (cStress) at every minute and detect smoking events (puffMarker) 38 , 39 . These two algorithms have been used to evaluate stress and smoking lapses in another study using independent data conducted by some of the current paper’s authors 40 .…”

mentioning

confidence: 99%

Dynamic models of stress-smoking responses based on high-frequency sensor data

et al. 2021

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Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

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“…Although advances in mobile and wireless technology offer tremendous potential for delivering JITAIs, researchers often need more empirical evidence to inform the selection and adaptation of components in a JITAI, and these knowledge gaps can motivate scientific questions for randomized studies [e.g., (47)(48)(49)]. Consider Example D discussed above and suppose there is not yet sufficient evidence to determine (a) whether delivering a prompt that recommends a brief self-regulatory strategy is beneficial on average in preventing a lapse in the next 3 h, when individuals are not driving; and (b) what is the level of urge at which the prompt would be most beneficial.…”

Section: Rapidly: Components Address Conditions That Change Rapidlymentioning

confidence: 99%

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

Nahum‐Shani

Dziak

Wetter

2022

Front. Digit. Health

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Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

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

Cited by 45 publications

References 52 publications

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

Dynamic models of stress-smoking responses based on high-frequency sensor data

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

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