Introduction Despite advances in tobacco dependence treatment in the past two decades, progress has been inconsistent and slow. This paper reviews pervasive methodological issues that may contribute to the lack of timely progress in tobacco treatment science including the lack of a dynamic model or framework of the cessation process, inefficient study designs, and the use of distal outcome measures that poorly index treatment effects. The authors then present a phase-based cessation framework that partitions the cessation process into four discrete phases based on current theories of cessation and empirical data. These phases include: (1) Motivation, (2) Precessation, (3) Cessation, and (4) Maintenance. Discussion Within this framework, it is possible to identify phase-specific challenges that a smoker would encounter while quitting smoking, intervention components that would address these phase-specific challenges, mechanisms via which such interventions would exert their effects, and optimal outcome measures linked to these phase-specific interventions. Investigation of phase-based interventions can be accelerated by using efficient study designs that would permit more timely development of an optimal smoking cessation treatment package. Keywords Tobacco dependence treatment . Motivation . Precessation . Cessation . Maintenance . Methodology At a Brookings Institute Conference, David Kessler, former FDA Commissioner, observed that the impressive progress made in AIDS research can be attributed, in part, to the development of "surrogate endpoints" such as HIV viral load that index treatment effects in randomized clinical trials far earlier than the obvious clinical endpoint of survival duration [1]. Kessler advocated the development of such surrogates for other diseases such as cancer: measures that would index effects that are powerfully linked with ultimate outcomes, but that can be ascertained relatively quickly and cheaply relative to such outcomes. As Kessler's observations suggest, standard operating procedures within a research field can profoundly influence progress, regardless of other factors such as innovative ideas, powerful theories, and level of financial support. The current paper identifies methods in the field of tobacco research that may be hobbling progress in discovering and validating effective interventions. It then recommends several complementary strategies that may improve the yield of tobacco dependence treatment research, some of which may be relevant to other areas of research as well. Considerable progress has been made in tobacco treatment research. The recent Public Health Service (PHS) clinical practice guideline Treating Tobacco Use and Dependence: 2008 Update [2], as well as multiple Cochrane Reports [3, 4] , offer ample evidence of this progress. For instance, the field has identified multiple types of effective cessation medications, established a strong dose-response relation between counseling intensity and outcome, shown that treatment can be delivered via diverse routes (in-person, via telephone, tailored messages), and has shown that these treatments work in different populations of smokers. Despite these findings, one could argue that progress has been disappointing. For instance, relatively few types of behav. med. (2011behav. med. ( ) 41:192-207 DOI 10.1007 behavioral and counseling interventions have been validated experimentally. The 2008 PHS Guideline identified only two counseling interventions as earning at least moderately strong research support: skill training and intra-treatment social support. These earned only a moderate strength of evidence ("B") rating, reflecting intrinsic limitations in the available evidence. In fact, comparison of the Clinical Practice Guidelines over the past 20 years (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) reveals little progress in identifying new, effective behavioral interventions. In sum, we know little about which counseling contents are most effective, and little progress has been made in this area over the past 20 years. We also have little understanding of how to best package interventions to maximize their net effects: e.g., via additive or interactive effects. For instance, we do not know which types of psychosocial interventions work best together, and which work best with pharmacotherapies. While new types of cessation medications have been introduced, we know little about how to use these optimally and how they exert their effects. Therefore, despite the introduction of multiple new cessation medications over the past 20 years, these medications have had only a modest impact on populationwide tobacco use [5] [6] [7] . Moreover, we know relatively little about the optimal timing of interventions. When should counseling or pharmacotherapy start, and end? Finally, relatively modest increases in absolute success rates of smoking cessation call into question our research progress. In general, cessation rates produced by treatments today seem little higher than those produced by treatments developed 20-30 years ago (cf. [2, [8] [9] [10] ), although extremely intensive treatments have shown higher success rates [11] . In sum, despite some areas of progress there is evidence that tobacco cessation research is in a "rut" [10, [12] [13] [14] [15] . While there may be many reasons for the relatively slow progress in intervention development, we will discuss one primary impediment. We believe that the field of smoking cessation research has not adopted a conceptual framework that encourages the engineering and testing of optimal packages of cessation elements. That is, the field has lacked a framework that organizes information about the challenges, treatment opportunities, and outcomes (see [16] ) that are relevant to the dynamic process of smoking cessation. The lack of such a framework has discouraged researchers from building optimal interventions in which effective intervention components are delivered at strategically selected times. The field is faced with other key obstacles to progress. For instance, the effort to develop optimal packages of cessation interventions requires the screening of multiple interventions with regards to both type and timing, and these require highly efficient research methodologies. While randomized clinical trials (RCTs) can be effective in evaluating an individual intervention component, they are not efficient for examining the individual effects of multiple components or their interactions. It is clear that we need more efficient research methods that speed the testing and identification of effective smoking interventions, methods that complement a conceptual framework that encourages the synthesis of evidence across the dynamic process of smoking cessation. In addition, traditional RCTs performed under ideal (efficacy) conditions may translate poorly into real-world settings, and current strategies for bridging this translation "gap" in real-world effectiveness studies are costly and time consuming [17, 18] . In sum, the development of optimal packages of intervention components has been hampered by (1) the lack of a dynamic framework of the cessation process and the resultant use of insensitive outcome assays, (2) testing only isolated intervention components, (3) inefficient experimental designs that constrain the number of hypotheses that can be addressed both within and across studies, and (4) a practice of conducting formative evaluations of interventions in contexts that are unrepresentative of conditions of their intended use. The current paper addresses the first of these obstacles to progress, while the companion paper [19] addresses the remaining obstacles. Together, these two papers offer a conceptual framework and efficient research strategies that might accelerate the rate of progress in smoking cessation research. Specifically, we argue that superior progress will occur if we adopt new research strategies in several different areas: (1) use more sensitive and theoretically relevant outcome measures; (2) use a framework that promotes the optimal identification, testing, and integration of intervention components; (3) use more efficient experimental designs; and (4) conduct formative evaluation of interventions in conditions of realworld use. While all of these suggestions individually have merit [20] [21] [22] , in our view, their coordinated use has the potential to advance progress significantly in tobacco intervention research. Most studies of smoking cessation treatments, and grant proposals, focus on the ability of treatments to increase long-term cessation rates (e.g., 6-or 12-month pointprevalence abstinence; some studies do report short-term outcomes as well: [23, 24] ). Some studies and grant proposals may also focus on an outcome measure that spans the post-quit period (i.e., continuous abstinence or survival). These measures have important clinical and public health value as they reflect net or residual treatment effects across meaningful time periods. While research suggests that most relapse starts within the first 2 weeks of ann. behav. med. (2011) 41:192-207 193 a quit attempt [25, 26] , research also suggests that a considerable amount of relapse occurs after the 1-year follow-up mark (perhaps about 10% of the remaining abstainers per year), suggesting that even longer-term follow-up may be optimal for forecasting the duration and health benefits of sustained abstinence [27] ; although cf. [28]. Although the prognostication of long-term abstinence is one purpose of outcome assessment, it is also vital to detect therapeutic effects. Therefore, assessments may be good for one purpose, but not another. Specifically, long-term outcome measures may be fairly insensitive to treatment effects. This may occur for at least two reasons. First, these measures are temporally distant from treatment exposure, which may permit treatment effects to dissipate prior to assessment. For instance, to the extent that counseling treatments depend upon ongoing provision of social support from treatment personnel [2] , their effects would be expected to diminish once that support stops. That is, some treatment effects may, by their very nature, have steep decay gradients. Second, it is clear that many events affect long-term success, not just treatment. It is also clear that the likelihood that such events will occur increases with time. A wealth of research shows that long-term outcomes are affected by events such as stressors [29] [30] [31] , exposure to highly provocative smoking cues and smokers [32] [33] [34] [35] [36] , alcohol use [37, 38] , and smoking opportunities [39, 40] . These events may or may not be viewed as sources of error that obscure important treatment effects; it depends upon what the treatment is supposed to do and how it does it. For instance, if a goal of treatment is to inoculate the smoker to future episodic or severe stressors, or reduce the likelihood of such events, then it would certainly be appropriate to measure treatment success on the basis of ability to avoid or weather such challenges (although this specific treatment mechanism might be tested better with a treatment by stressor interaction term). In theory, skill training interventions might produce their effects via such routes. In addition, long-term outcomes would provide reasonable tests of maintenance or relapse prevention treatments. Thus, in some cases, long-term outcomes could be relevant to tests of specific treatment actions. However, we believe that a single-minded focus on longterm outcomes thwarts one of the primary goals of cessation research-identifying and quantifying the magnitude of treatment effects-because, in many cases, longterm outcomes are more likely to obscure treatment effects than detect them. This is because (1) some treatment elements are not intended to produce their primary effects via blunting or avoidance of environmental threatsespecially ones that occur many months after treatment termination; (2) the occurrence and severity of environmental threats will be somewhat random and unrelated to treatment, thereby increasing error; and (3) a considerable body of research suggests that most treatment effects occur while treatment is being applied: e.g., most treatments do not result in a "hardened" quitter-one who is more likely to weather future threats. There is evidence that treatment effects are most evident early in the course of treatment, and steadily dissipate thereafter [41] . In keeping with these arguments, prediction studies tend to show that treatment is a potent predictor of outcomes at early follow-up time points, but not later ones, where contextual and dispositional factors loom large [42] One piece of evidence that has been used to argue for diminished treatment effects over time is the convergence of survival curves (indexing abstinence success) as the follow-up interval increases (e.g., [41] ). Hughes and Callas [43] recently distinguished between additive and multiplicative effect sizes that can be derived from abstinence over the post-quit period. Additive effect sizes are those that reflect the absolute difference in abstinence rates between the control and the experimental group as a function of total size of each group (e.g., 60% vs. 40% abstinence rates out of the total groups treated). Multiplicative effect sizes are those that reflect the ratio of abstinence rates in the experimental vs. the control groups (e.g., an odds ratio). Hughes and Callas point out that plotted abstinence rates may converge as the follow-up interval lengthens, but that this does not mean that the multiplicative effect size diminishes. That is, the absolute difference between two groups may shrink, but their ratio may not, as the abstinence rate in the control group shrinks (e.g., as would happen if the experimental group achieved abstinence rates of 60% and 20% at 1 and 6 months, while the control group achieved abstinence rates of 30% and 10%). Hughes and Callas [43] are certainly correct that converging abstinence curves do not necessarily reflect diminishing multiplicative effect sizes. However, we believe that both substantive considerations and evidence suggest that treatment effects do tend to diminish with time, even when multiplicative effect sizes are considered, and that long-term outcomes tend to be relatively insensitive indices of treatment effects. As noted earlier, there are many influences on lapse/relapse events that are likely independent of treatment (e.g., fortuitous exposures to major stressors), and that could inflate error in detecting treatment effects. Another source of error that tends to increase with time is loss of subject contact/ascertainment. This leads to uncertainty regarding outcome, often for a significant number of individuals by late follow-up intervals. Further, there is little evidence that we have identified treatments that reduce the likelihood of relapse once the treatment is discontinued; in fact, there is considerable evidence, even using multiplicative indices, that lapses and relapses 194 ann. behav. med. (2011) 41:192-207 increase once an active treatment is discontinued and that treatment effects diminish thereafter [44] . Research on outcome milestones [45] also suggests relatively larger effects on early vs. late outcomes. In recent research on five different cessation medication interventions [46] , the results showed that the odds ratios (a multiplicative effect size index) were consistently and meaningfully greater for all medication interventions for achievement of initial abstinence than for achievement of point-prevalence abstinence at 6 months. Continuous, proximal measures are especially likely to be more sensitive than are distal, binary ones. It is important to note that we are not saying that treatments do not produce significant long-term effects, but such effects are largely residues of more robust early effects. Thus, long-term treatment effects typically reflect the residue of earlier effects that may have been eroded or obscured by fortuitous events. Certainly, these effects are germane to any intervention that is expressly designed to extend initial abstinence, but here the researcher might ensure that only initial abstainers are included in analyses [45] . Also, if a major hypothesized mechanism of treatment is to inoculate the smoker to future threats, then it is certainly important to use threat-related lapsing/relapsing as an outcome. However, much evidence suggests that this is not, in fact, how or why cessation treatments work, meaning that long-term outcomes will not sensitively index those effects (of course, some treatments could, in theory, produce such effects and they are worth searching for). If occurrence of environmental threats is not affected by treatment receipt (e.g., your dog is equally likely to die, or your marriage fail, whether or not you receive intratreatment support), and active treatment does not blunt them, then such threats should damage long-term outcomes more for successful treatments than for unsuccessful treatments or control conditions (to the extent that the latter two produce lower initial cessation). This is simply a reflection of the fact that environmental threats can spur relapse only among abstainers, and there are more of the former among those who received an effective cessation treatment. In other words, long-term outcomes may actually penalize treatments to the extent that they boost initial cessation (especially with additive effect sizes). It is important to recognize some of the problems engendered by the field's fixation on long-term abstinence as a primary index of treatment outcome. One problem is that the use of an insensitive outcome indicator means that cessation treatment studies are typically inefficient. In the experience of the authors, grant proposals that target cessation treatments tend to be rejected out of hand if they are not adequately powered to detect a reasonable treatment effect at 6-or 12-months post-quit, even if such outcomes are not terribly relevant to theoretically based treatment hypotheses. This means that researchers use larger groups than are necessary, which, no doubt, decreases the number of studies and experimental conditions that can be run for a given amount of research support. In addition, the use of an insensitive index means that some effective treatments are rejected as ineffective because error masks their impact. Finally, a reflexive use of long-term cessation outcomes may discourage researchers from formulating more specific hypotheses about particular effects of treatment, and when such effects should occur. That is, a formulaic approach to treatment assessment may distract researchers from thinking deeply about specific change processes, which outcomes should be most reflective of such processes, and when the effects should be manifest. A lack of tailoring measures to the theoretically implicated goals of the treatment may produce insensitive treatment evaluations. What is the alternative to the use of a generic, long-term outcome? Identifying appropriate outcome measures demands that investigators generate causal models of treatment; what is the treatment intended to do, when will it do it, and how and when will the effects be manifested? Because smokers face different challenges, goals, and opportunities over the course of cessation, it may be advisable to use such knowledge in the generation of a phase-based model of cessation treatment (similar to the transtheoretical model; [48, 49] ). Thus, we believe that an informed selection of outcomes demands that the outcome assess whether the treatment is providing the individual what s/he needs, when s/he needs it. Below we discuss how data and theory might be used to identify sensitive outcome measures that themselves can be used to identify effective treatments. The phase-based framework that we are proposing is designed to provide an integrative context that allows the investigator to see how and when different interventions should be applied, and their effects assessed, based upon an appraisal of the challenges and opportunities presented by each phase of the cessation process. The proposed phase-based framework is intended to integrate existing research on smoking motivation and cessation to address four core questions that are critical to selecting and evaluating effective intervention components: 1 There is evidence that as abstinence continues there may be intrinsic processes that thwart lapsing or relapsing and that this results in a point at which relapse becomes relatively unlikely [28] . Thus, it is possible that with very long interventions, treatment will take the majority of individuals past this point and result in very durable effect sizes (e.g., [11, 47] ). ann. behav. med. (2011) 41:192-207 195 (1) What are the challenges and intervention opportunities that occur across the smoking cessation attempt? (2) What interventions are likely to address those challenges successfully? (3) What measures will sensitively index the therapeutic benefits of treatment? And (4) What measures will index mechanisms or mediators of treatment benefit? In other words, the intent of this model is to identify promising interventions and ways to measure their effects and mechanisms of action accurately. Several overarching principles should be considered when addressing these four core questions. One derives from the phase-based nature of cessation. The model assumes the existence of different phases in the process of smoking cessation, with phases being defined on pragmatic and empiric bases, with the assumption that the challenges that smokers face, and the optimal interventions and measures, differ across the phases. The model does not assume that a particular challenge, intervention, or measure is relevant to only one phase of cessation; rather, it assumes that phase-based relevance is by degree. A second principle is the notion that the challenges that smokers face, and opportunities for intervention, should influence the treatments that are tried. The assumption is that these will change across the cessation attempt, and that treatments selected for evaluation should be appropriate for the phasebased challenges and opportunities. This means that the proximal effects of treatments should redress hypothesized challenges and mediate clinical benefit (e.g., increased abstinence rates). This underscores the third principle that guides the application of the framework: the evaluation of mediational relations will index the extent to which a treatment is performing consistent with framework-based hypotheses. In other words, such analyses will reveal whether a treatment's hypothesized mechanisms of action address the targeted phase-based challenges. Thus, the framework emphasizes the importance of using sensitive, theoretically appropriate outcome measures and conducting mediational research that tests whether an intervention is working as hypothesized. In sum, the framework should suggest what interventions to use, when to use them, and how to evaluate them. The above framework clearly assumes the need for two primary types of outcome measures. Investigators must identify measures that allow them to select treatments or intervention components that produce clinically meaningful effects (i.e., treatment selection measures). In addition, investigators should include measures of treatment mechanisms (measures that index mediators of clinical benefit: treatment mechanism measures). In sum, to address the four core questions critical to selecting and evaluating effective intervention components, this framework suggests the following: (1) different cessation phases present specific challenges and opportunities; (2) treatments applied and evaluated should be ones whose mechanisms of action address the phase-appropriate challenges and take advantage of the phase-specific opportunities (treatments should be "phase tuned"); (3) measures of mechanism should determine whether the tested treatments produce significant effects consistent with hypothesized mechanisms of action and whether these effects translate into significant clinical benefit; and (4) selection measures should be used to index clinically meaningful benefit, and such measures should be the primary basis of treatment selection for further evaluation or implementation. It is important to note that both the measures of mechanism and treatment selection should be appropriate for the challenges and opportunities presented by the phase in which they are used. Since the mediational models will relate treatment condition with both mechanism and selection measures, experiments guided by a phase-based strategy will test both the interventions and theories upon which they are based. Comprehensive tests of phase-based treatment hypotheses via mediational models will reveal whether (1) a treatment affects a mechanism, (2) variance in the mechanism is related to a clinically meaningful outcome (i.e., the treatment selection measure), (3) the treatment affects a clinically meaningful outcome (i.e., again, the treatment selection measure), (4) the treatment's effect on the mechanism accounts for its effects on the clinically meaningful outcome, and (5) a clinically meaningful outcome is affected by hypothesized mechanisms that are not significantly influenced by treatment (i.e., analyses can identify the causes of relapse that are unaffected by the tested treatments). Such information can be used to guide the future development and tests of treatments. Stage-or phase-based models of smoking are not new. For instance, the transtheoretical model [48] [49] [50] shares features with the phase-based model. The transtheoretical model has generated valuable research on tobacco interventions. The transtheoretical model is a theory of behavior change; it attempts to identify particular processes that account for change across both different stages and across different types of behaviors or problems. The proposed phase-based framework that we are advocating is not a theory. Rather, it is a strategy for using data and theories to identify impediments to change, generate hypotheses about possible timely interventions, and organize tests of both theory and interventions. Therefore, the proposed framework is not a transtheoretical theory or model of behavior change, but rather it is a conceptual and methodologic strategy that can be used with any theories that seem appropriate. In addition, the goal of the framework is not intended to be relevant to all types of addictive disorders, but rather is intended specifically to be relevant to smoking cessation (although this sort of approach could be modified for use with other types of disorders or behavior problems). Therefore, the framework is 196 ann. behav. med. (2011) 41:192-207 appropriate for smoking-specific risks or change processes (e. g., home or work smoking policies, withdrawal-induced anhedonia). In addition, it is not focused on synthetic factors or meta-variables to account for change over time (e.g., the "processes of change" that were selected to be at a "mid-level of abstraction" in the transtheoretical model; [51] ). Rather, the framework is appropriate for variables at any level of analysis: e.g., conditioned responses, electrophysiological responses indicative of motivational processing, and selfreport of attitudes. Such variables may reflect relevant change processes at only a single phase of the cessation process. The phase-based framework can be used in conjunction with different and specific models of associative learning, motivational models of addiction, and neuropharmacologic evidence, and so on. Therefore, another difference between this framework and the transtheoretical model is that it does not reflect an attempt to measure or integrate change processes that are common to many different theories or models of change (cf. [51]). What we are recommending is a broad application of a phase-based framework (partly inspired by the transtheoretical model) that can be used to test a diverse range of hypotheses, theories, assessments, and interventions. While it comprises a framework similar to that of the transtheoretical model, it attempts to organize the application of theory, but does not constitute a theory. In surveying changes in challenges, opportunities, and goals over the course of cessation, we suggest dividing the cessation process into four phases (see Fig. 1 ). We label the first phase the Motivation Phase, which persists until the smoker is ready to make a quit attempt. The second phase, the Precessation Phase, is defined somewhat pragmatically as the period of several weeks prior to the actual quit attempt; i.e., this phase is relevant to the person who has made a commitment to a quit attempt. 2 While this duration is admittedly somewhat arbitrary, the current definition is based on the length of prequit interventions that have demonstrated efficacy [2] , and also on our clinical experience that suggests that the smoker will lose motivation if the preparatory intervention is too long. It is clear though, that the suggested phase lengths are somewhat arbitrary and should be servants to hypotheses and the data available, and not vice versa. The Cessation Phase is defined as the 2 weeks immediately following the quit attempt, when both withdrawal severity and the likelihood of initial lapses are highest. The Maintenance Phase is an open-ended period of time following the Cessation Phase when clinicians wish to apply and study treatments designed to extend or reinstate abstinence following a quit attempt. Clearly, those smokers who fail to quit become appropriate for a motivational or precessation treatment at some future point in time. The phase-based framework as we propose it does not assume that phases are wholly distinct from one another. Rather, it assumes that phases differ by degree, with some processes and events being more relevant to some phases than others (e.g., while withdrawal peaks immediately postcessation, individuals experience withdrawal before quitting and months afterwards; [55] [56] [57] ). The proposed framework acquires value to the extent that it organizes evidence and hypotheses about the challenges and opportunities that vary across the course of cessation and that these suggest different treatments and treatment goals (see discussion below). There is ample evidence to support the view that challenges differ across times demarcated by the four proposed phases. For instance, before smokers quit smoking their withdrawal symptoms and craving are somewhat suppressed by their on-going smoking, and smokers differ from one another relatively little in terms of withdrawal severity [58] . However, once smokers stop smoking, withdrawal and craving typically jump precipitously on the quit day and remain somewhat elevated over the first 1-2 weeks post-cessation [58] [59] [60] . Thereafter, smokers show highly heterogeneous withdrawal patterns or trajectories with some showing steady decreases, others chronic elevations, and still others, highly volatile patterns [55] [56] [57] . While severity of withdrawal and craving at diverse time points can predict cessation success [56] , there is mounting evidence that the immediate jump in symptoms on the quit day is an especially potent determinant of such success [58, 61, 62] . Therefore, the evidence suggests that withdrawal and craving differ in severity, course, and relation with ultimate cessation success over the suggested phases. In theory, such information could be used to engineer treatments so that the timing and nature of interventions meets the nature of the phase-based challenges. A phase-based approach might also clarify which phases and challenges have been most refractory to treatment, and this might inform future research efforts. Therefore, while the phases are based on clear clinical demarcations (not willing to quit, willing to quit, occurrence of a quit attempt), their primary utility is their ability to organize data and hypotheses that are relevant to phasespecific interventions and assessment (e.g., see Table 1 ). Not only do challenges differ over phases but clinical goals do as well. As Prochaska, DiClemente, and others have noted, the majority of smokers are not ready to make a quit attempt at any given point in time (e.g., [63] ), and so 2 Some smokers make quit attempts in an apparently spontaneous manner. They do not select a quit day nor formally state an intention to quit [52] [53] [54] . The current model is designed to organize and assess interventions and intervention evaluations. Obviously, there would be little opportunity to organize Precessation interventions for the person who makes an unplanned quit attempt, and the framework would not, by itself, indicate the motivational factors that resulted in such a quit attempt. A theory such as the transtheoretical model would be more appropriate for that purpose. ann. behav. med. (2011) 41:192-207 197 for many clinicians the chief goal is to motivate quit attempts. However, once the smoker has decided to try to quit, the question becomes one of how to prepare the smoker for cessation when s/he initially faces the need to attain abstinence, and then later, the goal becomes the maintenance or reestablishment of abstinence. Some of the clinical implications of the phases are obvious; individuals who are not yet ready to make a quit attempt clearly need to be motivated to make one (e.g., [51] ). A Precessation phase may be conceptualized as an opportunity to train motivated individuals who are not yet struggling with withdrawal-induced distress and frequent urges. The immediate Postcessation phase is a time where many individuals experience a dramatic increase in withdrawal-which presents an opportunity for treatments to suppress withdrawal symptoms, and so on. So, there is a fairly compelling reason to demarcate phases in the cessation process because they seem to have clear relevance for treatment.
Baker et al. REF presented an effective phase-based framework that divides the cessation procedure into four major phases.
New methods for tobacco dependence treatment research.
Annals of behavioral medicine : a publication of the Society of Behavioral Medicine
Annals of behavioral medicine : a publication of the Society of Behavioral Medicine
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