Exploring the “Active Ingredients” of an Online Smoking Intervention: A Randomized Factorial Trial

McClure, Jennifer B.; Peterson, Daniel J.; Derry, Holly A.; Riggs, Karin; Saint-Johnson, Jackie; Nair, Vijayan N.; An, Lawrence C.; Shortreed, Susan M.

doi:10.1093/ntr/ntu057

Cited by 42 publications

(38 citation statements)

References 48 publications

(53 reference statements)

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“…As in our prior research with smokers who are not yet ready to quit [10-12], both phase 1 and phase 2 participants were interested in getting assistance in changing their smoking behavior, even though they were not ready to commit to quitting. Notably, 88% of phase 2 survey respondents expressed interest in an app to help them reduce their smoking and 91% expressed interest in an app to help them decide “if, when, or how” to quit, with nearly half of participants saying that learning how to quit was “very appealing.” This suggests that mHealth tools targeting this population should have a broader focus than cessation, even though the content should still help users understand the process of quitting for when they are ready.…”

Section: Discussionmentioning

confidence: 99%

“…In prior research, we found precontemplative and contemplative smokers were receptive to both counseling [10,11] and Internet-based programs [12] when these programs were designed to help them make informed decisions about their smoking behavior (as opposed to quitting, per se), and as a consequence, many ultimately quit smoking. Thus, we hypothesized that smokers who are not yet ready to quit could also be interested in using mHealth apps, if these programs are designed to address their needs and interests and marketed or distributed in a way to encourage their use when people are not actively seeking treatment.…”

Section: Introductionmentioning

confidence: 99%

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Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study

McClure

Heffner

Hohl

et al. 2017

JMIR Mhealth Uhealth

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BackgroundMobile health (mHealth) smoking cessation programs are typically designed for smokers who are ready to quit smoking. In contrast, most smokers want to quit someday but are not yet ready to quit. If mHealth apps were designed for these smokers, they could potentially encourage and assist more people to quit smoking. No prior studies have specifically examined the design considerations of mHealth apps targeting smokers who are not yet ready to quit.ObjectiveTo inform the user-centered design of mHealth apps for smokers who were not yet ready to quit by assessing (1) whether these smokers were interested in using mHealth tools to change their smoking behavior; (2) their preferred features, functionality, and content of mHealth programs addressing smoking; and (3) considerations for marketing or distributing these programs to promote their uptake.MethodsWe conducted a sequential exploratory, mixed-methods study. Qualitative interviews (phase 1, n=15) were completed with a demographically diverse group of smokers who were smartphone owners and wanted to quit smoking someday, but not yet. Findings informed a Web-based survey of smokers from across the United States (phase 2, n=116). Data were collected from April to September, 2016.ResultsFindings confirmed that although smokers not yet ready to quit are not actively seeking treatment or using cessation apps, most would be interested in using these programs to help them reduce or change their smoking behavior. Among phase 2 survey respondents, the app features, functions, and content rated most highly were (1) security of personal information; (2) the ability to track smoking, spending, and savings; (3) content that adaptively changes with one’s needs; (4) the ability to request support as needed; (5) the ability to earn and redeem awards for program use; (6) guidance on how to quit smoking; and (7) content specifically addressing management of nicotine withdrawal, stress, depression, and anxiety. Results generally did not vary by stage of change for quitting smoking (precontemplation vs contemplation). The least popular feature was the ability to share progress via social media. Relevant to future marketing or distribution considerations, smokers were price-sensitive and valued empirically validated programs. Program source, expert recommendations, and user ratings were also important considerations.ConclusionsSmokers who are not yet ready to quit represent an important target group for intervention. Study findings suggest that many of these individuals are receptive to using mHealth tools to reduce or quit smoking, despite not having made a commitment to quit yet. The preferences for specific mHealth intervention features, functionality, and content outlined in this paper can aid addiction treatment experts, design specialists, and software developers interested in creating engaging interventions for smokers who want to quit in the future but are not yet committed to this important health goal.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study

McClure

Heffner

Hohl

et al. 2017

JMIR Mhealth Uhealth

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“…Several studies have attempted to address self-selected treatment adherence through approaches such as modified intent-to treat analyses (19) in which only participants exposed to the intervention are included in outcome analyses (20, 21). Although this approach allows for a meaningful comparison between participants exposed and not exposed to an intervention, risks of this approach are that it can elevate rates of false positive findings due to the use of post-randomization exclusion criteria, fail to account for differential engagement by study arm, reduce the generalizability of conclusions, and increase study costs by requiring larger sample sizes.…”

mentioning

confidence: 99%

“…Our study builds on work by Strecher (23), McClure (20, 24), and others (25, 26) that has focused on the role of engagement in promoting abstinence. The first strategy in this trial was a theory-driven social network (SN) approach to increase the proportion of individuals that participate in an existing online social network for cessation.…”

mentioning

confidence: 99%

Improving Adherence to Smoking Cessation Treatment: Smoking Outcomes in a Web-based Randomized Trial

Graham

Papandonatos

Cha

et al. 2018

Annals of Behavioral Medicine

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Background Partial adherence in Internet smoking cessation interventions presents treatment and evaluation challenges. Increasing adherence may improve outcomes. Purpose To present smoking outcomes from an Internet randomized trial of two strategies to encourage adherence to tobacco dependence treatment components (i.e., skills training, social support, medication): 1) a social network (SN) strategy to integrate smokers into an online community, and 2) free nicotine replacement therapy (NRT). In addition to intent-to-treat analyses, we used novel statistical methods to distinguish the impact of treatment assignment from treatment utilization. Methods N=5,290 current smokers on a cessation website (WEB) were randomized to WEB, WEB+SN, WEB+NRT, or WEB+SN+NRT. The main outcome was 30-day point prevalence abstinence at 3 and 9 months post-randomization. Adherence measures included website metrics of skills training and community use, and self-reported medication use. Inverse Probability of Retention Weighting and Inverse Probability of Treatment Weighting jointly addressed dropout and treatment selection. Propensity weights were used to calculate Average Treatment effects on the Treated. Results Treatment assignment analyses showed no effects on abstinence for either adherence strategy. Treatment utilization analyses revealed that joint community and medication use yielded large percentage point increases in 3-month abstinence rates over skills training alone across arms: WEB=20.6 (95% CI=10.8, 30.4), WEB+SN=19.2 (95% CI=11.1, 27.3), WEB+NRT=13.1 (95% CI=4.1, 22.0), and WEB+SN+NRT=20.0 (95% CI=12.2, 27.7). Abstinence rates among participants that used all treatment components were 25.7%-32.2%. Conclusions Novel propensity weighting approaches can serve as a model for establishing efficacy of Internet interventions and yield important insights about mechanisms. Clinical Trials.gov NCT01544153

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“…Thus, associations between component use and outcomes would be highly confounded by self-selection. Factorial experiments that randomize individuals to mHealth components would be better suited to answering questions about what components produce treatment efficacy (Collins, Dziak, Kugler, & Trail, 2014; McClure et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders

Glass

Gustafson

Kornfield

et al. 2017

Journal of Substance Abuse Treatment

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Background We estimated the efficacy of the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) in increasing the use of services for addiction and examined the extent to which this use of services mediated the effects of A-CHESS on risky drinking days and abstinence from drinking. Methods We conducted secondary data analyses of the A-CHESS randomized controlled trial. Recruitment occurred in five residential treatment programs operated by two addiction treatment organizations. Participants were 349 adults with alcohol use disorders recruited two weeks before discharge from residential treatment. We provided intervention arm participants with a smartphone, the A-CHESS application, and an 8-month service plan. Control arm participants received treatment as usual. Telephone interviews at 4, 8, and 12-month follow-ups assessed past-month risky drinking days, past-month abstinence, and post-discharge service utilization (past-month outpatient addiction treatment and past-week mutual help including Alcoholics Anonymous and Narcotics Anonymous). Using mixed effects latent variable models, we estimated the indirect effects of A-CHESS on drinking outcomes, as mediated by post-discharge service utilization. Results Approximately 50.5% of participants reported outpatient addiction treatment and 75.5% reported mutual help at any follow-up interview in the year following randomization. Assignment to the A-CHESS intervention was associated with an increased odds of outpatient addiction treatment across follow-ups, but not mutual help. This use of outpatient addiction treatment mediated the effect of A-CHESS on risky drinking days, but not abstinence. The effect of A-CHESS through outpatient addiction treatment appeared to reduce the expected number of risky drinking days across follow-ups by 11%. Conclusions The mobile health (mHealth) intervention promoted the use of outpatient addiction treatment, which appeared to contribute to its efficacy in reducing risky drinking. Future research should investigate how mHealth interventions could link patients to needed treatment services and promote the sustained use of these services.

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Exploring the “Active Ingredients” of an Online Smoking Intervention: A Randomized Factorial Trial

Abstract: introduction: Research needs to systematically identify which components increase online intervention effectiveness (i.e., active ingredients). This study explores the effects of 4 potentially important design features in an Internet-based, populationlevel smoking intervention.

Cited by 42 publications

References 48 publications

Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study

Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study

Improving Adherence to Smoking Cessation Treatment: Smoking Outcomes in a Web-based Randomized Trial

Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders

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