CoChat
Sign Up
Cited by 23Smoking Behavior and CessationMobile Health and mHealth ApplicationsDigital Mental Health Interventions

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Open access
Nahum-Shani, Inbal, Potter, Lindsey N., Lam, Cho Y., Yap, Jamie, Moreno, Alexander, Stoffel, Rebecca, Wu, Zhenke, Wan, Neng +6 more·2021·Contemporary Clinical Trials
DOI 10.1016/j.cct.2021.106513PMC PMC8824313
Open this paper in CoChat — free account

Abstract

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cct.2021.106513

Cites

145 of 145 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A microrandomized trial has been successfully applied to evaluate dynamic and user-tailored eHealth interventions focused on weight management, smoking cessation, and mental health.

    “an MRT has been successfully applied to evaluate dynamic and user-tailored eHealth interventions focused on weight management [52], smoking cessation [53], and MH [54].”

    From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [53]

    The Abstract and Research Design sections describe a micro-randomized trial of a mobile intervention for smokers attempting to quit. But this is a design protocol: the Conclusions say the trial will be used, and Materials and methods say results will be published upon completion. The paper therefore supports the smoking-cessation example, not the claim that the trial had already been successfully applied to evaluate the intervention. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. [1]Ries LG,, eds. SEER Cancer Statistics Review, 1975–2001, National Cancer Institute, Bethesda, MD, 2004.
  2. [95]Group, W.A.W, The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility, Addiction 97 (9) (2002) 1183– 1194. 12199834
  3. [99]Ertin E, Stohs N, Kumar S,, AutoSense: Unobtrusively wearable sensor suite for inferencing of onset, causality, and consequences of stress in the field, in: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys), 2011, pp. 274– 287.
  4. [123]Power Calculation for the Mobile-Assistance for Regulating Smoking (MARS) Micro-Randomized Trial, Available from, https://d3lab.isr.umich.edu/software/. PMC8824313 34314855
  5. [132]G. Marlatt, D. Donovan (Eds.), Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors, 2nd ed., Guilford Press, New York, 2005.

Cited by

Sorted by citations

Related

Authors
Nahum-Shani, I., Potter, L. N., Lam, C. Y., Yap, J., Moreno, A., Stoffel, R., Wu, Z., Wan, N., Dempsey, W., Kumar, S., Ertin, E., Murphy, S. A., Rehg, J. M., & Wetter, D. W.
Affiliations
  • University of Michigan (US)
  • Huntsman Cancer Institute (US)
  • Georgia Institute of Technology (US)
  • University of Utah (US)
  • University of Memphis (US)
  • The Ohio State University (US)
  • Harvard University (US)
Corresponding author
Nahum-Shani, Inbal
ORCID
Venue
Contemporary Clinical Trials
Publisher
Elsevier BV
Published
2021-11
Volume
110
Pages
106513
ISSN
1551-7144 (ISSN-L)
DOI
10.1016/j.cct.2021.106513
PMC
PMC8824313
OpenAlex
W3186157710
Cited by
23
References
142
Citations by year
2026: 2 · 2025: 6 · 2024: 5 · 2023: 3 · 2022: 6 · 2021: 1
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Contemporary Clinical Trials (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • University of Michigan
  • Huntsman Cancer Foundation
  • National Institutes of Health (P30 CA042014, P41 EB028242, P50 DA039838, R01 AA023187, R01 DA039901, TL1 TR002540, U01 CA229437-03S1, U54 EB020404, UL1 TR002538)
Topics
  • Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
smoking cessation, self-regulation, mobile health intervention, micro-randomized trial, push notifications, craving, user engagement, intensive longitudinal data, sensor-based assessment
MeSH terms
Smokers, Humans, Motivation, Smoking, Randomized Controlled Trials as Topic, Smoking Cessation, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review