Elsevier

Addictive Behaviors

Volume 31, Issue 2, February 2006, Pages 264-277
Addictive Behaviors

A review of computer and Internet-based interventions for smoking behavior

https://doi.org/10.1016/j.addbeh.2005.05.002Get rights and content

Abstract

This article reviews studies of computer and Internet-based interventions for smoking behavior, published between 1995 and August 2004. Following electronic and manual searches of the literature, 19 studies were identified that used automated systems for smoking prevention or cessation, and measured outcomes related to smoking behavior. Studies varied widely in methodology, intervention delivery, participant characteristics, follow-up period, and measurement of cessation. Of eligible studies, nine (47%) reported statistically significant or improved outcomes at the longest follow-up, relative to a comparison group. Few patterns emerged in terms of subject, design or intervention characteristics that led to positive outcomes. The “first generation” format, where participants were mailed computer-generated feedback reports, was the modal intervention format and the one most consistently associated with improved outcomes. Future studies will need to identify whether certain patients are more likely to benefit from such interventions, and which pharmacological and behavioral adjuncts can best promote cessation.

Introduction

The recent Institute of Medicine (2001) report paints a picture of an outdated healthcare system in need of innovative and cost-saving methods for improving health outcomes. In this process, smoking cessation programs will undoubtedly play a large role. Cigarette smoking is the leading cause of premature morbidity and mortality in the United States, and is responsible for nearly half a million deaths each year (Centers for Disease Control and Prevention, 2002, Centers for Disease Control and Prevention, 2003). Unfortunately, the U.S. Preventive Services Task Force has described tobacco cessation as one of the highest priority services with the lowest delivery rate (Coffield et al., 2001). Indeed, taking into account the number of current smokers and the number of people who start smoking each year, public health efforts are currently making very little headway in reducing the total number of smokers.

Among current smokers, an estimated 41% reported that they had stopped smoking for at least one day in the previous year because they were trying to quit (Centers for Disease Control and Prevention, 2004). However, rates of long-term cessation are significantly lower (U.S. Department of Health and Human Services, 2000b). Smoking cessation interventions, particularly those that combine behavioral and pharmacological methods, can produce rates well above the rates of smokers choosing to quit on their own (U.S. Department of Health and Human Services, 2000b), but these intensive programs also have the lowest rates of participation. For instance, free clinical interventions offered by health maintenance organizations (HMOs) may only enroll only about 1% of eligible persons (Lichtenstein & Hollis, 1992). Less intensive interventions, such as physician advice and self-help materials, may reach more eligible persons, but they typically result in lower cessation rates (Lancaster & Stead, 2004, Silagy & Stead, 2004).

Healthy People 2010 established a goal of reducing the rates of adult smoking from 23.3% to 12% by the year 2010 (U.S. Department of Health and Human Services, 2000a). If this is to be accomplished, there will be an increased need for interventions that can be disseminated to larger numbers of smokers at a relatively low cost. In moving toward this goal, one trend is toward interventions that can be delivered via mail, computer and the Internet. These new modes of delivery may be well suited for tailoring self-help materials to the individual, a strategy that is generally more effective than no intervention (Lancaster & Stead, 2004). In a typical format, smokers are surveyed via a computerized or paper assessment, and the results are tailored to some characteristic of the individual, such as gender, dependence level, perceived barriers to quitting, or stage of change. Based on a theoretical model of motivation and change (e.g., Transtheoretical Model; Prochaska, Norcross, & DiClemente, 1994), the algorithm library generates instructions for each possible survey response. The resultant feedback, information or advice is then presented on a computer screen or through printed materials. Indeed, this format has been widely utilized in health behavior areas such as nutrition education, weight loss, diabetes management, alcohol consumption, HIV risk reduction, and cancer support and counseling (e.g., Bessell et al., 2002, Brug et al., 1996, Cloud & Peacock, 2001, Firby et al., 1991, Green & Fost, 1997, Hester & Delaney, 1997, Kumar et al., 1993, Paperny, 1997, Tate et al., 2003, Tate et al., 2001).

Section snippets

Rationale for the present review

In an earlier review of ten randomized trials of computer-tailored smoking materials, Strecher (1999) found a significant impact in a majority of studies. Though few patterns emerged, the computer-tailored materials seemed to be more effective for those in the precontemplation stage of change. Studies that combined tailored materials with other behavioral or pharmacological interventions also showed promise. At the time of the Strecher (1999) review, however, computer interventions for smoking

Search strategy

Medline, CINAHL, and PsycInfo databases were used to locate English-language studies published between 1995 and August 2004. The bibliographies of retrieved articles were scanned for additional references. Key search terms included (computer or Internet or web) and (behavior change or intervention or treatment or therapy) and (smoking or tobacco). The search terms were intentionally broad to ensure, as much as possible, that all relevant articles would be captured.

Inclusion criteria

Review criteria included

Results

Literature searches yielded 199 unique references that met our search criteria. After reviewing articles for relevance, 19 were retained for this review. Four studies were focused on adolescent smoking prevention, while 15 targeted adult smokers. Table 1, Table 2 summarize the resulting studies. The number of study participants ranged from 65 to 8352. Participants ranged from 11 to 65 years old, and the percent of female participants varied from 40.5% to 100%. Follow-up periods ranged 1 to 24

Discussion

While computer-based smoking prevention and cessation programs show promise in influencing tobacco-related behaviors, published studies show mixed results in terms of translating the educational experience to real-world practice. Of the 19 automated, computer-based interventions that we reviewed, nine (47%) showed evidence of effectiveness at the longest follow-up.

This review should not be considered an exhaustive analysis. The scope of our investigations was limited to English-language

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