Comparative effectiveness of intervention components for producing long‐term abstinence from smoking: a factorial screening experiment

Abstract

Aims To identify promising intervention components that help smokers attain and maintain abstinence during a quit attempt. Design A 2×2×2×2×2 randomized factorial experiment. Setting Eleven primary care clinics in Wisconsin, USA. Participants 544 smokers (59% women, 86% White) recruited during primary care visits and motivated to quit. Interventions Five intervention components designed to help smokers attain and maintain abstinence: 1) extended medication (26 vs. 8 weeks of nicotine patch + nicotine gum); 2) maintenance (phone) counseling versus none; 3) medication adherence counseling versus none; 4) automated (medication) adherence calls versus none; and 5) electronic medication monitoring with feedback and counseling versus electronic medication monitoring alone. Measurements The primary outcome was 7-day self-reported point-prevalence abstinence 1 year after the target quit day. Findings Only extended medication produced a main effect. Twenty-six versus eight weeks of medication improved point-prevalence abstinence rates (43% vs. 34% at 6 months; 34% versus 27% at 1 year; p =.01 for both). There were four interaction effects at 1 year, showing that an intervention component’s effectiveness depended upon the components with which it was combined. Conclusions Twenty-six weeks of nicotine patch + nicotine gum (versus 8 weeks) and maintenance counseling provided by phone are promising intervention components for the cessation and maintenance phases of smoking treatment.

Cognitive behavioral interventions for alcohol and drug use disorders: Through the stage model and back again.

“…Briefly, MOST approaches utilize factorial (and fractional factorial) designs to efficiently evaluate individual components of an intervention and their contribution to producing outcome. MOST designs have been successfully implemented in smoking research to refine multicomponent interventions for smoking (Piper et al, 2016; Schlam et al, 2016). Given that attrition remains high in most clinician- and technology-delivered interventions and few individuals actually complete a full course of the intervention, MOST strategies for refining interventions and delivering highly parsimonious and effective components to the largest possible samples have clear promise, as the majority of substance users remain in treatment for only a matter of weeks and thus it is imperative we deliver our most potent and effective interventions from the outset.…”

Implementing Clinical Research Using Factorial Designs: A Primer

“…In a full factorial experiment, factors are completely crossed; that is, the factors and their levels are combined so that the design comprises every possible combination of the factor levels. For example, a recent factorial experiment (Schlam et al, 2016) crossed 5 2-level factors, resulting in 32 combinations of factor levels (see Table 1). In this case, each of the 32 unique combinations of factor levels could be viewed as constituting a different treatment or treatment condition .…”

“…Note that in the Schlam et al (2016) experiment (Table 1), all participants in the experiment received one level of each factor. Thus, some participants would receive an “on” or “Hi” level of every factor (an active intervention component); other participants would receive the “off” or “Low” levels of every factor (the “control” levels); and other participants would receive a mixture of the two levels of the various factors.…”

“…However, conducting an RCT that comprises ICs whose joint effects are unknown, poses clear risks. This is because research shows that the effectiveness of a IC can be substantially modulated by the other ICs with which it is used (Cook et al, 2016; Fraser et al, 2014; Schlam et al, 2016); i.e., they may interact. Thus, in an RCT designed to evaluate a medication, the effect of the medication might be significantly altered by features of the psychosocial intervention that accompanies it (e.g., by the type or intensity of counseling, the number of treatment delivery modalities, and so forth).…”

“…In addition, because they typically experimentally evaluate relatively discrete intervention components, they have the potential to yield especially informative data on the change mechanisms activated by specific treatments. Several recent research studies using factorial designs have now appeared and these attest to the value of such designs (Cook et al, 2016; Fraser et al, 2014; McClure et al, 2014; Schlam et al, 2016). …”