Immediate and short-term impact of a brief motivational smoking intervention using a biomedical risk assessment: The Get PHIT trial

McClure, Jennifer B.; Ludman, Evette; Grothaus, Louis C.; Pabiniak, Chester; Richards, Julie; Mohelnitzky, Amy

doi:10.1093/ntr/ntp004

Cited by 15 publications

(17 citation statements)

References 26 publications

(33 reference statements)

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“…Increased motivation would, in turn, lead to increased uptake of a free empirically-validated phone counseling program and higher long-term cessation rates in the long term, compared to persons who received generic smoking risk information and personalized advice regarding other health behaviors. The immediate and short-term impact of this intervention on motivation and perceived disease susceptibility has been previously reported 31. Longer-term effects on treatment utilization and cessation are reported here.…”

Section: Introductionsupporting

confidence: 56%

“…Some participants (n = 48) were also recruited through ads placed in local media, public clinics, and other local venues. Additional recruitment details are available elsewhere 31, 32. The study was presented as a health risk screening intervention study, as opposed to a smoking cessation trial, and people were informed that they did not have to want to quitting smoking to participate.…”

Section: Methodsmentioning

confidence: 99%

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Impact of a Brief Motivational Smoking Cessation Intervention

McClure

Ludman

Grothaus

et al. 2009

American Journal of Preventive Medicine

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Section: Introductionsupporting

confidence: 56%

Section: Methodsmentioning

confidence: 99%

Impact of a Brief Motivational Smoking Cessation Intervention

McClure

Ludman

Grothaus

et al. 2009

American Journal of Preventive Medicine

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“…The third RCT used another adaptation of the same Fletcher and Peto graph, 3 but only calculated lung age if FEV 1 percent predicted was below 80%, indicating possible impairment. 16,17 This RCT failed to show differences in the quit rate between their intervention and control groups. 16,17 In Japan, lung age has also been investigated in relation to smoking status and body mass index 18 and in detecting pulmonary function abnormality in pulmonary diseases.…”

Section: Introductionmentioning

confidence: 96%

“…16,17 This RCT failed to show differences in the quit rate between their intervention and control groups. 16,17 In Japan, lung age has also been investigated in relation to smoking status and body mass index 18 and in detecting pulmonary function abnormality in pulmonary diseases. 19 Despite mixed results, this flurry of recent publications indicates a renewed interest in the use of lung age to educate smokers about the deterioration of their lung function caused by smoking.…”

Section: Introductionmentioning

confidence: 96%

Exploring the need to update lung age equations

Newbury

Briggs

et al. 2010

Primary Care Respiratory Journal

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Aims: A renewed interest in lung age is evidenced by recent smoking cessation publications. This research compares the original Morris lung age equations (1985) with contemporary Australian lung age equations.Methods: Both lung age equations were applied to the spirometry results of two sub-groups (never-smokers n=340, and current smokers n=50) from an independent dataset. Means of both lung age estimates were compared to the mean of the chronological age of each group by paired Student's t-test.Results: The Morris lung age estimates were paradoxically lower (younger) than chronological age in both groups. The new Australian equation produced lung age estimates that were equivalent to chronological age in the never-smoker group and significantly higher (older) than chronological age in the current smoker group.Conclusions: These results strongly suggest that the Morris lung age equations are in need of review. The use of contemporary lung age equations may translate into greater success for smoking cessation programs. The new Australian equations seem to possess internal validity.

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“…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%

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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Immediate and short-term impact of a brief motivational smoking intervention using a biomedical risk assessment: The Get PHIT trial

Cited by 15 publications

References 26 publications

Impact of a Brief Motivational Smoking Cessation Intervention

Impact of a Brief Motivational Smoking Cessation Intervention

Exploring the need to update lung age equations

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

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