BMC Public Health 2009, 9:101 http://www.biomedcentral.com/1471-2458/9/101 readiness to change, and to help patients increase readi- ness [6]. These changes in readiness are seen as short term goals on the way to decreased consumption [7,8]. Processes of change have been conceptualized in various ways; in the Transtheoretical model, Prochaska and DiClemente described the progression of individuals through stages of change [9]. Others have pointed out the role of importance of change for the patient [10], while Bandura emphasized the role of self efficacy, or confi- dence in ability to change [11]. Importance and confi- dence are considered components of readiness or readiness-related factors. For the purpose of this article we will refer to readiness, importance and confidence as 3 behavior change constructs, cognitive dimensions of behavior change, that comprise "readiness to change." These three constructs can facilitate clinical conversations about health behaviors, and may even have predictive value for later behavior change [12-14]. They can be assessed with visual analog scales (VAS), which, because of their brevity, can be widely used. Longer assessments such as the Readiness to Change Questionnaire (RTCQ) (developed to assess readiness per se, not importance or confidence specifically) allow classification according to stage of readiness to change in addition to the computa- tion of a continuous score [15]. The predictive value of cognitive dimensions of behavior change, however, has been mixed. Demmel et al. demon- strated that in alcohol dependent inpatients, readiness accounted for 9.4% of the variance in outcome [12]. Self- efficacy also appears to be a predictor of abstinence [16]. In other studies, however, readiness was not associated with subsequent consumption, and was predictive of more, not fewer consequences (or at least greater recogni- tion of consequences) [17,18]. Thus, how behavior change constructs relate to later behavior change is not well understood. Furthermore, how these constructs change over time and in response to brief counseling, particularly in general healthcare set- tings, is also not well known. The efficacy for BI in primary care for unhealthy alcohol use is modest, and interven- tions with different theoretical rationales (e.g. MATCH and COMBINE studies) lead to similar results [10,19,20], calling into question the roles played by behavior change versus other constructs. These concerns are also encoun- tered more broadly in psychotherapy research, and were named by Rosenzweig in 1936 after Alice in Wonderland as the "Dodo Bird effect" [21]. Rosenzweig proposed that "common" factors were responsible for the efficacy of psy- chotherapy. He used Lewis Carroll's Alice in Wonder- land's Dodo bird conclusion "everybody has won and all must have prizes." In most trials of alcohol BI, control (and intervention) groups decrease consumption over time (one way to understand natural history in these set- tings). This improvement has a number of possible expla- nations: regression to the mean, effects of assessments or contact with study staff, natural history, and selection of individuals more prone to change (since agreement to participate might indicate some desire to change) [22]. A better understanding of behavior change constructs, the natural history of behavior change, and how the con- structs relate to outcome might therefore inform the design of more efficacious interventions that achieve and sustain changes in drinking [23]. Therefore we studied improvements and predictors of improvements in 3 behavior change constructs (readiness, importance, confi- dence) and subsequent drinking after a primary care visit in a prospective cohort of patients. Methods We studied a prospective cohort of adults with unhealthy alcohol use visiting an urban academic primary care prac- tice who participated in a randomized trial of the impact of providing (or not providing) primary care physicians with patients' alcohol screening results [24]; physicians were the unit of randomization. Additional detail regard- ing the clinical trial has been published [24]. Patients were screened if they had a visit with an included physician. A trained staff researcher approached and interviewed enrolled subjects before and after their visits with a physi- cian (February 1998–August 1999) in the waiting room. Patients were told they were being asked (initial screen- ing) questions for research purposes. Eligible patients were told they were being asked to participate because they were primary care patients who reported drinking alcohol, and that the study might help physicians learn how to identify alcohol use by patients. Six months later, subjects were interviewed by telephone, after which they received compensation in the form of a voucher worth ten U.S. dollars. The screening was done using the CAGE questionnaire and the 3 questions published in the 1995 NIAAA guide to assess quantity and frequency of drinking [25,26]. The CAGE and the 3 alcohol questions were com- pleted by paper and pencil in the waiting room or by help of a research assistant when needed. The baseline assess- ment was done by face to face interview. Inclusion for the present study was based on drinking as assessed using the Timeline Followback method [27]. Subjects were asked how many drinks they had each day for the past 30 days using a calendar and chronologic cues (e.g. weekends, holidays, significant events during the time period) based on published instructions for this assessment. For the present study, eligible patients drank risky amounts (> 14 U.S. standard drinks [12 g each]/week or > 4 drinks/occasion for men and > 7 drinks/week or > 3 drinks/occasion for women in the past month), were flu- Page 2 of 9 (page number not for citation purposes)