Evidence map›Paper›PMID 38133916›Full record

ArticleJMIR formative research2023

Developing Mood-Based Computer-Tailored Health Communication for Smoking Cessation: Feasibility Randomized Controlled Trial.

Donghee N Lee, Rajani S Sadasivam, Elise M Stevens

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Donghee N LeeDepartment of Population and Quantitative Health Sciences, Division of Preventive and Behavioral Medicine, UMass Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-9932-4376
Rajani S SadasivamDepartment of Population and Quantitative Health Sciences, Division of Health Informatics and Implementation Science, UMass Chan Medical School, Worchester, MA, United States.ORCID https://orcid.org/0000-0001-8406-6207
Elise M StevensDepartment of Population and Quantitative Health Sciences, Division of Preventive and Behavioral Medicine, UMass Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-0494-4362

Funding

iDAPT: Implementation and Informatics - Developing Adaptable Processes and Technologies for Cancer Control P50CA244693 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DRESSLER, EMILY VAN METER · 2019 to 2023
$4.1M
Prevention And Control of Cancer: Training for Change in Individuals and SystemsT32CA172009 · NCI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEMON, STEPHENIE C., OCKENE, JUDITH K · 2019 to 2023
$1.5M
Young Adults Responses to E-Cigarette Advertisement Features and the Effects of Restricting Features on Tobacco UseR00DA046563 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI STEVENS, ELISE MARIE · 2021 to 2023
$914k
NCI NIH HHS P50 CA244693NCI NIH HHS T32 CA172009NIDA NIH HHS R00 DA046563
6 · The paper itself

Abstract

backgroundComputer-tailored health communication (CTHC), a widely used strategy to increase the effectiveness of smoking cessation interventions, is focused on selecting the best messages for an individual. More recently, CTHC interventions have been tested using contextual information such as participants' current stress or location to adapt message selection. However, mood has not yet been used in CTCH interventions and may increase their effectiveness.

objectiveThis study aims to examine the association of mood and smoking cessation message effectiveness among adults who currently smoke cigarettes.

methodsIn January 2022, we recruited a web-based convenience sample of adults who smoke cigarettes (N=615; mean age 41.13 y). Participants were randomized to 1 of 3 mood conditions (positive, negative, or neutral) and viewed pictures selected from the International Affective Picture System to induce an emotional state within the assigned condition. Participants then viewed smoking cessation messages with topics covering five themes: (1) financial costs or rewards, (2) health, (3) quality of life, (4) challenges of quitting, and (5) motivation or reasons to quit. Following each message, participants completed questions on 3 constructs: message receptivity, perceived relevance, and their motivation to quit. The process was repeated 30 times. We used 1-way ANOVA to estimate the association of the mood condition on these constructs, controlling for demographics, cigarettes per day, and motivation to quit measured during the pretest. We also estimated the association between mood and outcomes for each of the 5 smoking message theme categories.

resultsThere was an overall statistically significant effect of the mood condition on the motivation to quit outcome (P=.02) but not on the message receptivity (P=.16) and perceived relevance (P=.86) outcomes. Participants in the positive mood condition reported significantly greater motivation to quit compared with those in the negative mood condition (P=.005). Participants in the positive mood condition reported higher motivation to quit after viewing smoking cessation messages in the financial (P=.03), health (P=.01), quality of life (P=.04), and challenges of quitting (P=.03) theme categories. We also compared each mood condition and found that participants in the positive mood condition reported significantly greater motivation to quit after seeing messages in the financial (P=.01), health (P=.003), quality of life (P=.01), and challenges of quitting (P=.01) theme categories than those in the negative mood condition.

conclusionsOur findings suggest that considering mood may be important for future CTHC interventions. Because those in the positive mood state at the time of message exposure were more likely to have greater quitting motivations, smoking cessation CTHC interventions may consider strategies to help improve participants' mood when delivering these messages. For those in neutral and negative mood states, focusing on certain message themes (health and motivation to quit) may be more effective than other message themes.

Indexed as

adultcessationcomputer-tailored health communicationdigital interventioneffectivenessinnovationmoodmotivationsmokingsmoking cessation messagestext mining

Identifiers

PMID38133916
PMCPMC10770788

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.