Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Prev Med. Author manuscript; available in PMC 2009 Mar 1.
Published in final edited form as:
PMCID: PMC2408758

Cancer Prevention in Primary Care: Predictors of Patient Counseling Across Four Risk Behaviors over 24 Months



Rates of preventive counseling remain below national guidelines. We explored physician and patient predictors of preventive counseling across multiple cancer risk behaviors in at-risk primary care patients.


We surveyed 3557 patients, with at least one of four cancer risk behaviors: smoking, diet, sun exposure, &/or mammography screening, at baseline and 24 months. Patients reported receipt of 4A’s (Ask, Advise, Assist, Arrange follow-up); responses were weighted and combined to reflect more thorough counseling (Ask=1, Advise=2, Assist=3, Arrange=4, score range 0–10) for each target behavior. A series of linear regression models, controlling for office clustering, examined patient, physician and other situational predictors at 24 months.


Risk behavior topics were brought up more often for mammography (90%) and smoking (79%) than diet (56%) and sun protection (30%). Assisting and Arranging follow-up were reported at low frequencies across all behaviors. More thorough counseling for all behaviors was associated with multiple visits and higher satisfaction with care. Prior counseling predicted further counseling on all behaviors except smoking, which was already at high levels. Other predictors varied by risk behavior.


More thorough risk behavior counseling can be delivered opportunistically across multiple visits; doing so is associated with more satisfaction with care.

Keywords: Primary Care, physician counseling, cancer prevention


Health risk behaviors account for approximately 40% of known causes of death (Mokdad et al, 2000). Visits to primary care physicians account for 63% of all visits, with most patients making multiple visits per year (Woodell & Cherry, 2004). Still, rates of preventive counseling remain well below national guidelines (Pronk et al, 2004). Preventive counseling has been characterized by the 4A approach (Ask, Advise, Assist, Arrange follow-up), first developed to guide smoking cessation counseling in primary care (Fiore et al, 1996). Variations of this approach have been applied to other risk behaviors, including physical activity (Pinto et al, 2001), diet (Ockene et al, 1996) and sun protection Mikkilineni et al, 2002). A more recent expansion to the 5A’s added Assess readiness to change and was applied to smoking (Fiore et al, 2000) and multiple risk behaviors (Dosh et al, 2005). The 5A model was modified further by incorporating Ask into the Assess step and adding Agree, and this model was recommended as a unifying approach for brief primary care interventions across multiple risk behaviors and as a framework for examining findings across studies (Whitlock et al, 2002). While our study was designed when the earlier 4-A model was in use, both the 4A and 5A approaches incorporate common behavior change theories, each “A” strategy has been validated in the literature (Whitlock et al, 2002), and primary care intervention research suggests that delivery of the A’s combination is more effective than advice alone (Whitlock et al, 2002; Goldstein et al, 2004). For example, in smoking cessation studies, increased counseling intensity (dose response effect from < 3 minutes to > 20 minutes) is associated with higher abstinence rates (Fiore et al, 2000).

Few studies have examined the implementation of A’s across multiple risk behaviors. Dosh and colleagues (2005) described delivery of 5A’s, as assessed from chart reviews on tobacco, diet, physical activity and alcohol interventions, and found limited penetration of the construct; documentation was more common for Ask, while Assist and Arrange were infrequently recorded. Knowledge of factors that influence delivery of counseling across multiple behaviors will inform strategies to improve effectiveness of risk behavior interventions. Previous efforts to identify predictors of preventive care delivery have typically focused on just the first 2 A’s, Asking about or Advising on behavioral risks. Patient demographic factors associated with receipt of physician advice have included higher income for diet and exercise (Tiara et al, 1997); higher education for diet and exercise (Honda, 2004); middle age for diet and exercise (Honda, 2004); or older age for smoking (Denny et al, 2003) and for multiple risks behaviors (i.e, smoking, overweight and physical inactivity) (Friedman et al, 1994); women for smoking (Denny et al, 2003) and multiple risks (Friedman et al, 1994); and poorer perceived health for diet (Honda, 2004). Asking about or screening for risk factors among persons reporting one or more behavioral risk factors (i.e., overweight, physical inactivity, smoking, risky drinking) was associated with higher income, higher education, being male, less than age 65, having four or more healthcare visits in the past year, and having a regular source of preventive care (Coup et al, 2004).

This study is different from prior studies reported in the literature as it examines predictors of provider counseling for more than Ask and Advice. We focus on the combination of 4A’s, with Ask, Advise, Assist and Arrange follow-up, examining predictors over a two-year time frame. While many studies focus on behavioral risk for heart disease, we focused on persons at risk for one or more of four cancer risk behaviors: smoking, high fat diet, unprotected sun exposure, and/ risk of missed mammography screening. We chose to investigate potential predictors previously studied, including patient age, gender, education, marital status, employment, income, race and ethnicity, perceived health, perceived cancer risk, number of healthcare visits in past year. Since we are examining counseling for multiple risks, we investigated whether being at risk in other risk behaviors was associated with counseling in a target behavior, and whether the degree of risk, as measured on a validated behavior score, was associated with counseling in that behavior. We also examined some less studied practice related factors, including physician specialty, satisfaction with care at last visit, prior advice on the target behavior. Finally, while some intervention study outcomes are reported elsewhere (Prochaska et al, 2005), we investigated the whether random assignment into one of two treatment groups, expert-system mailed or office-based intervention, was associated with delivery of 4A counseling. The project received approval by human subjects review boards of participating institutions.



Subjects were recruited into an intervention study where practices were randomly assigned to either an office-based intervention versus standard-care, and patients were randomly assigned within practices to either a tailored expert-system intervention versus assessment-only. The design of the larger study fully crossed the office-based intervention with the home-based expert system intervention, resulting in a two-by-two design. Practices were eligible if at least one physician was enrolled with the collaborating health insurer; identified specialty as Family Medicine, Internal Medicine or Obstetrics/Gynecology; reported that at least 25% of their patients were seen for continuity care; not hospital-based; and not planning to retire or relocate in the 4-year study period. Recruitment targeted 361 physicians within 274 practices. The study goal, to enroll 80 practices, was reached after contacting 172 practices (40 offices were ineligible, 52 refused). One practice dropped out soon after enrollment, resulting in 79 practices randomized.

Following physician recruitment, health plan subscribers listing a study physician as their doctor were identified. The health plan mailed letters to these patients, describing the project, their doctor’s participation, and the option to refuse a recruitment telephone call. A total of 12,384 patients were contacted by phone: 3,820 patients (30.8%) refused (declined to accept call), giving a 69.2% recruitment rate. Eligibility criteria included age 18–75 and being at-risk in one more of targeted behaviors (smoking, diet, sun exposure, and mammography screening). Of 8,564 patients agreeing to participate, 3,157 (36.9%) were ineligible because they had none of the four behavioral risks, leaving a baseline sample of 5,407 (see Prochaska et al, 2005 for recruitment and retention details).

For exploration of provider counseling behaviors reported here, the analyses used a sub-sample of 3,557 patients reporting at least one medical visit in the prior year at the 24-month follow-up.


The two experimental interventions utilized the Transtheoretical Model of Change (TTM)(Prochaska et al, 1992) to conceptualize individual behavior change. The TTM incorporates stages of readiness to change (precontemplation, contemplation, preparation, action, maintenance), pros and cons of changing, self efficacy, and processes of change to transition between stages. Medical providers in the office intervention condition were trained to use TTM to provide patients with stage-matched counseling and stage-appropriate resources, incorporating TTM components. Providers were given suggested scripts to use with patients who were ready versus not ready to change in each targeted risk behavior; each script applied the 4A approach. Intervention practices also received training on office systems to support cancer prevention activities (e.g. staff involvement, reminders, flowsheets); the standard office condition received only a copy of Guide to Clinical Preventive Services, published by U.S. Preventive Services Task Force (USPSTF, 2003). The office intervention was based on a combination of explanatory models for the adoption of new medical practice patterns. Offices received eight educational visits by project staff over two years. Participants in the expert-system intervention received three tailored reports (at 0, 6, & 12 months) plus self-help manuals to facilitate adoption of cancer prevention behaviors; control condition individuals received assessment only (Prochaska et al, 2005).


A telephone survey was conducted at baseline, 12-, and 24-months for all participants, while expert-system intervention participants received an additional assessment at 6-months to generate a tailored report. Surveyors were blind to group assignment. Demographic items consisted of gender, age, marital status, education level, income, race, and ethnicity. General perceived health was assessed using an adapted item of the Medical Outcome Survey (Ware, 1976) (“Would you say that your health in general is ‘poor’, ‘fair’, ‘good’, ‘very good’ or ‘excellent’?”), and perceived-risk for cancer (“Compared to others your same age and sex, how would you rate your risk of getting cancer within the next 10 years?” with 5-point response scale, 1=much lower than average to 5=much higher than average).

Participant behavioral risk for cancer was assessed for 1) smoking: self report of current daily smoking; 2) high-fat diet: estimated fat intake ≥ 30% calories and total score on the 22-item Dietary Behavior Questionnaire, consisting of items on substituting low- for high-fat foods, modifying food preparation, avoiding high-fat foods, and increasing fruits, vegetables and grains (Greene et al, 1996); 3) sun exposure: reporting sun exposure more than 15 minutes per day or inconsistently using SPF-15 or higher sunscreen, and total Sun Protection Behavior Scale score, with items on sunscreen use and sun avoidance (Rossi et al, 1995); 4) while all women over age 50 were considered “at risk” and eligible for mammography screening, we further defined relapse risk as having no screening in the past year, using standardized questions (Clark et al, 2002). Patients were asked to self-report height and weight for body mass index (BMI).

At baseline, patients reported recall of prior behavior change advice, “Have you ever been advised by your doctor or your doctor’s assistants to do any of the following?: 1) give up smoking, 2) reduce amount of fat in your diet, 3) increase amount of fiber in your diet, 4) avoid harmful effects of the sun”. At follow-up surveys, patients were asked about number of medical visits in the past 12 months. Satisfaction with care was asked at follow-up (Rubin et al, 1993). “Think about your last visit to your doctor’s office and rate your satisfaction with this visit overall”, rating 1=poor to 5=excellent. Physician specialty was obtained from participating physicians.

Also at follow-up, patients who reported having a medical visit in the past year were asked receipt of 4A’s (Ask, Advise, Assist, Arrange follow-up) for each target behavior, from anyone in the medical office. Ask was worded as, “In the past 12 months, did any of the following topics come up, either through talking with a health care provider, and/or filling out a form”, followed by the list of four target behaviors. Patients having any one of the target risk behaviors were then asked, “In the past 12 months, did your doctor or anyone in a medical office advise you”, followed by the four target behaviors: to make diet changes, to protect your skin from sun exposure, to quit smoking, and for women over age 50 only, to get a mammogram. Assist was defined as, “In the past 12 months, did your doctor or anyone in a medical office help you, for example by setting goals, providing written material, or referring you for help, in any of the following areas?” Lastly, patients were asked if follow-up was arranged on any of the behaviors, as described above for Advise. Response options were “yes” or “no”. Our dependent measure was a composite score for receipt of 4A’s in each target behavior. “Yes” responses were weighted and combined to reflect thorough counseling (Ask=1, Advise=2, Assist=3, Arrange=4, score range 0–10) for each target behavior. The rationale for weighting is based on the Public Health Service Smoking Cessation Guideline (Fiore et al, 2000), where meta-analysis showed a dose-response relationship for increased counseling intensity, as a function of encounter time. Use of specific components, such as problem-solving assistance, providing support, and arranging follow-up, were also found to have larger effect sizes (Fiore et al, 2000), suggestive of a higher weighting for these components.

Statistical analyses

The 4A weighted scores for each of the target behaviors were used as dependent measures in a series of linear-regression models to examine predictors at the 24-month follow-up. The sample (n=3,557) was divided into analysis models based on risk status, i.e., models on smoking counseling included only smokers, models on diet counseling included only persons at diet risk, etc.

There were three exploratory models for each behavior. The first model included demographic items as independent variables (Table 1). The second model focused on physician/practice variable (Table 2). The third modal focused on other patient variables (Table 3). A fourth final comprehensive model for each behavior was then constructed including all significant (p< .10) variables from each of the three earlier models. The analysis also included intervention group assignments for practices and patients. These assignments were explored as possible influences of counseling delivery in the context of other variables. We used hierarchical linear models with SAS Proc MIXED procedures for all analyses, to address nesting of patients within practices, and with office clustering effects controlled.

Table 1
Demographic Profile of Analysis Samplea
Table 2
Physician practice variables in analysis samplea
Table 3
Other Patient variables in analysis samplea


The sample were mostly women (72%), well educated (65% > high school), married/living with a partner (73%), White (97%), and employed (70%)(Table 1). Patients’ physicians specialized in Internal Medicine (43%), Family Practice (33%), and Obstetrics/Gynecology (24%)(Table 2). While only 19% were smokers, 68% reported having a high-fat diet, 72% reported low use of sun protection behaviors; and, among women over age 50, 7% were off-schedule for mammography (Table 3).

Patients’ recall was higher for Asking and Advising than for Assisting and Arranging follow-up (Table 4). Rates for all smoking and mammography-related counseling steps were higher than for diet or sun protection. Except for counseling on Mammography screening, Assist and Arrange rates were underutilized across all risk behaviors.

Table 4
Percent of patients reporting 4 A’s at 24-months by Target Behaviora

Table 5 shows predictors for both exploratory and final multivariate models. All statistically significant variables from the exploratory models were included in one final model for each of the four risk behaviors. While not significantly associated with counseling in exploratory models where it was included, the physician treatment group was included in the final models, since risk behavior counseling was a focus of the office intervention.

Table 5
Significant predictors of more thorough counseling (more of 4-A’s) at 24 months among at-risk patients on four cancer risk behaviors

Patient factors most often varied by risk behavior. Overall statistical results for final multivariate models are described as follows. For smoking, significant factors were higher age (≥35)(F(5, 541)=4.51, p<.001) and less education (≤12 years)(F(3, 541)=2.71, p<.05). Predictors for diet were poorer perceived general health (fair or good) (F (4, 1953)=7.12, p<.0001) higher BMI (≥25) (F 2, 1953) =40.47, p<.0001), and higher total dietary score (poorer behavior)(F(1, 1953)=14.90, p<.0001). Predictors for sun protection were higher age (F (5, 2221)=3.64, p<.01), higher total sun protection score (more protective behavior)(F(1, 2221)=19.37, p<.0001), and not being assigned to expert-system condition (F(1, 2221)=39.33, p<.0001). Significant predictors for mammography counseling were being off-schedule for mammography at baseline (F(1, 582)=4.27, p<.05) and being at-risk for high-fat diet (F(1, 582)=9.64, p<.01).

Some predictors were found to have an influence on multiple health behaviors. Prior advice was a significant predictor for diet (F(1, 1953)=28.63, p<.0001), sun (F(1, 2185)=55.01, p<.0001), and mammography counseling (F(1, 582)=5.16, p<.05). More frequent health care visits was a significant predictor of counseling for all health behaviors: smoking (≥5 visits)(F(3, 541) = 3.53, p < .01); diet (≥5) (F(3, 1953) = 14.98, p< .0001), sun protection (≥5) (F (4, 2221)=4.39, p<01); and mammography (3–4 visits) (F(3, 582) = 7.27, p<.0001). Patient satisfaction with care was associated with higher counseling scores at follow-up for all behaviors—smoking (F (1, 541) =6.74, p<.01), diet (F(1, 1953)=12.93, p<001), sun (F(1, 2221)=14.84, p<.0001), and mammography counseling (F(1, 582)=10.49, p<.01). Family or Internal Medicine physicians were more likely to provide thorough counseling for diet than ObGyn physicians (F(2, 1953)=3.77, p< 05).


This study provided a unique perspective on multiple risk behaviors and provider 4A’s counseling across cancer risk behaviors in a large sample of insured primary care patients. This is the first study to investigate predictors of the combination of A’s in counseling, with at risk patients, using a composite weighted score to reflect more intensive counseling, from anyone in the primary care practice. Patients’ recall of 4A’s counseling showed more activity for smoking (mean combined 4A scores = 4.18) and mammography screening (6.39) than for diet (2.30) and sun protection (1.25). Smoking and mammography have received more attention in the past decade, with consistent evidence to support primary care intervention (USPTF, 2003). Encouragingly, provider counseling across all 4A’s increased compared to an early 1990’s random-digit-dial study of Rhode Islanders with a past-year medical visit, which showed 51% of smokers asked about smoking, 45.5% were advised, 14.9% assisted, and 3% had follow-up arranged (Goldstein et al, 1997). Counseling about mammography screening showed highest rates across the A’s (90% Asked, 86% Advised, 71% Assisted, 41% Arranged), suggesting this has become routine in primary care. Lower counseling rates for diet may reflect more complex messages needed to support dietary change. Sun protection received lowest rates, which may reflect the limited attention skin cancer prevention has received compared to other targets for health behavior counseling (Mikkilineni, et al, 2001).

It is also not surprising that Ask and Advise steps were more often reported by patients. Assist and Arrange require more time, skills, resources, and active orientation towards preventive counseling. Assist strategies address motivation, barriers to change, self-help skills, and/or help to access referral resources. Arrange strategies include arranging follow-up counseling at the next visit regardless of the patient’s motivational stage (Whitlock et al, 2001). Assisting and Arranging for mammography may be easier than for other behaviors since this may be addressed, at least in the patient’s view, by simply writing an order and developing a plan to provide results. These tasks are also more closely aligned with traditional clinician roles.

Family and Internal medicine doctors were more likely than Ob/Gyn doctors to provide dietary counseling. Ob/Gyn doctors may be less likely to view such counseling as part of their role. Prior advice on all behaviors except smoking was a significant predictor of counseling for the same behavior. This finding suggests that providers who do this counseling do it consistently. Prior advice was reported by 92% of smokers at baseline, which probably explains the lack of a relationship between prior smoking advice and smoking counseling.

There were more differences than similarities across the four risk behaviors on patient predictors. Older patients reported more counseling on smoking, consistent with Denny et al (2003) and sun protection, perhaps because these health effects appear more often with older patients, providing counseling cues. More counseling among smokers with lower education levels (high school or less) is consistent with the demographics of current smokers. Dietary counseling was more likely among patients with lower perceived health, consistent with Coup et al (2004), with a higher total diet behavior score (higher risk), and with higher BMI, which may have served as a cue to counseling. Conversely, patients who reported more sun-protection counseling also reported a higher baseline behavior score (lower risk), and prior sun-protection advice. Being off-schedule for mammography at baseline appeared to have a negative effect on counseling for mammography screening, but it is unclear if this reflects actual advice delivery or recall bias for these women. Interestingly, the presence of other risk behaviors (i.e. multiple risk factors within patients) did not generally predict counseling on a specific target behavior. The one exception was that having a high-fat diet as a risk factor was predictive of mammography screening counseling.

The number of medical visits was a predictor across all behaviors. This is consistent with the report by Coup et al (2004) on risk factor screening for persons with one or more risks. More frequent visits provide more opportunity for providers to intervene (Flocke & Stange, 2004). Moreover, the delivery of more thorough counseling may be spread over multiple visits, a distinct advantage when delivering counseling in the primary care setting. Flocke and colleagues (1998) found evidence for “opportunistic” preventive care, as counseling more likely occurred with higher-risk patients, while delivery of acute care and drug prescription during the visit decreased preventive services delivery.

Other studies have also reported an association between patient satisfaction and preventive care services delivery (Weingarten et al, 1995; Solberg et al, 2001; Stange et al, 1998). Recently, a primary care-based study of smoking cessation counseling showed that increased patient satisfaction was associated with receipt of each element of the 5As, independent of readiness to quit smoking; moreover, satisfaction with care increased as counseling intensity increased (Conroy et al, 2005). Our results do not indicate whether the delivery of counseling leads to greater satisfaction, or whether more satisfied patients elicit and/or report more counseling.

An unexpected finding for sun protection counseling was that those patients who received assessment only versus those receiving the expert-system report intervention were more likely to report physician counseling on sun protection. Perhaps completing assessments in the absence of an intervention, increased awareness and interest in sun protection, such that participants elicited more sun protection counseling from their doctors. Separate analyses on the expert-system intervention showed significant improvements across all risk behaviors (Prochaska et al, 2005). There was no evidence that the expert system intervention, delivered outside the context of primary care practice, increased demand for or otherwise stimulated clinicians to deliver preventive counseling.

The practice-based intervention did not have measurable impact on providers’ behavioral counseling, as reported by patients. To accommodate the complexity of intervening on multiple behavioral risks, we used a menu-driven intervention approach, where providers and their staff were given the choice of starting with one target behavior and one office system, based on perceived need and interest, and progressing to other risk factors and/or other systems, with guidance from our consultants. This flexible, practice-centered approach was meant to parallel a patient-centered approach to multiple risk factor counseling (Goldstein et al, 2004; Ockene et al, 1996). However, this menu-driven approach may have diluted the intervention impact on counseling delivery across risk behaviors. For example, we observed during our intervention visits that 25% of intervention practices chose to focus only on a single health behavior for their improvement efforts over two years, although we measured study outcomes across all counseling behaviors across all practices. Additionally, the intervention intensity was probably insufficient in the setting of competing office demands, limited office leadership buy-in, and minimal incentives for preventive counseling delivery. To address the possibility that we missed an early, but not sustained intervention effect, we applied the same analysis models at the end of the first intervention year, but findings were similar to those reported here. The lack of an office-based intervention effect on clinician counseling behaviors reflects the challenges reported by others on delivering interventions to independent community-based practices, with high variability in resources, motivation, and external influences (Solberg et al, 2000; Cohen et al, 2005).

Strengths and Limitations

Data on provider behaviors are based on patient reports, which may be inaccurate or subject to recall bias. Patients with multiple visits during the reporting period may be more likely to report that counseling occurred due to more accurate recall than because counseling occurred more often. Because our subjects all had health insurance and were predominately White and highly educated, these results may not generalize to practice settings with less insurance coverage and/or greater diversity. However, this sample of insured patients represents the majority of patients seen in primary practices and profile is consistent with other insured populations (Stevens et al, 2003). The emphasis on at-risk patients increases focus on predictors of counseling for patients who most need interventions and who are the target of national preventive care guidelines (USPTF, 2003). Since we designed our study and began initial assessments prior to the newer 5A model, we are unable to assess delivery of the expanded model, which was intended to provide a better fit across multiple behaviors. However, both the 4A and 5A models represent the value of the combination of A’s, as a more thorough approach to counseling. Therefore we believe this analysis adds to the literature examining delivery of brief interventions.


This study provided a unique description of the variation of primary care provider counseling across four cancer risk behaviors. Participating patients provided data about naturally occurring visits in which providers often opportunistically addressed preventive care. More frequent counseling for smoking and mammography screening, than for diet and sun protection, was not surprising, considering greater recent attention to these behaviors. Also, providers can more easily assess smoking and mammography screening status than dietary or sun exposure risk, which may require more extensive assessment. Higher rates of Asking and Advising than Assisting and Arranging follow-up may be explained by the greater time, skill, and resources needed for the latter. Prior counseling was predictive of further counseling on all behaviors expect smoking, which was already at high levels. Clearly, broad gaps remain between preventive care guidelines and recommendations for health behavior counseling in primary care (US Preventive Services Task Force, 2003; Whitlock et al., 2002) and actual rates of counseling in real world clinical practice settings. Increasing preventive counseling rates is a daunting task, as reflected by the lack of a practice-based intervention effect on clinician counseling behavior in this study. On the other hand, our results provide some guidance for future researchers and others interested in promoting primary care-based multiple risk factor counseling. More thorough counseling for all behaviors was reported when patients made more frequent visits and this counseling was associated with greater patient satisfaction with care, suggesting thorough counseling can be achieved with opportunistic interventions over time and also that patients appear to appreciate this effort. Our findings regarding the impact of prior receipt of counseling as well as cues for counseling (e.g., high BMI for diet; older age for sun protection counseling) suggest that strategies that remind or cue clinicians to intervene are likely to have considerable value. Results from this study and other efforts to promote health behavior counseling in primary care reflect a need for a systematic integrated approach to multiple health risk behavior interventions (Pronk et al, 2004; Glasgow et al, 2004; Curry, 2004; Cifuentes et al, 2005). Such an approach would include helping providers to learn the principles of 5A counseling, but also include system-based interventions (such as information technology systems to prompt and document , team-based approaches for delivering 5A counseling, and incentives to reward and reinforce counseling behavior) that support the delivery of planned, proactive preventive care (Pronket al, 2004; Glasgow et al, 2004; Curry, 2004; Cifuentes et al, 2005).


This work was funded by a grant, “Promoting Cancer Prevention in Primary Care”, with the National Cancer Institute, Michael G. Goldstein, Principal Investigator.


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Précis At risk patients (n=3557) who receive more thorough counseling—more of the 4As—also report more satisfaction with their care. Yet counseling rates are still low across cancer risk behaviors.


  • Clark MA, Rakowski W, Ehrich B, Rimer BK, Velicer WF, Dube CE, Pearlman DN, Peterson MA, Goldstein M. The effect of a stage-matched and tailored intervention on repeat mammography. American Journal of Preventive Medicine. 2002;22:1–7. [PubMed]
  • Cohen DJ, Tallia AF, Crabtree BF, Young DM. Implementing health behavior change in primary care: lessons learned from prescription for health. Annals of Family Medicine. 2005;3:S12–S19. [PMC free article] [PubMed]
  • Conroy MB, Majchrzak NE, Silverman CB, Chang Y, Regan S, Schneider LI, Rigotti NA. Measuring provider adherence to tobacco treatment guidelines: a comparison of electronic medical record review, patient survey, and provider survey. Nicotine and Tobacco Research. 2005;7 Suppl 1:S29–S34. [PubMed]
  • Denny CH, Serdula MK, Holtzman D, Nelson DE. Physician advice about smoking and drinking: are U.S. adults being informed? 2003. American Journal of Preventive Medicine. 2003;24:71–74. [PubMed]
  • Dosh SA, Holtrop JS, Torres T, Arnold AK, Baumann J, White LI. Changing organizational constructs into functional tools: an assessment of the 5 A’s in primary care practices. Annals of Family Medicine. 2005;3:S50–S52. [PMC free article] [PubMed]
  • Fiore MC, Bailey WC, Cohen SJ, et al. AHCPR Publication No. 96-0692. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research; 1996. Smoking Cessation Clinical Practice Guideline No 18.
  • Fiore MC, Bailey WC, Cohen SJ, et al. Treating Tobacco Use and Dependence: Clinical Practice Guideline. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service; 2000. Jun, Available at: www.surgeongeneral.gov/tobacco.
  • Flocke SA, Stange KC, Goodwin MA. Patient and visit characteristics associated with opportunistic preventive services delivery. J Fam Pract. 1998;47:202–208. [PubMed]
  • Flocke SA, Stange KC. Direct observation and patient recall of health behavior advice. Preventive Med. 2004;38:343–349. [PubMed]
  • Friedman C, Brownson RC, Peterson DE, Wilkerson JC. Physician advice to reduce chronic disease risk factors. American Journal of Preventive Medicine. 1994;10:367–371. [PubMed]
  • Goldstein MG, Niaura R, Willey-Lessne C, DePue J, Eaton C, Rakowski W, Dubé C. Physicians counseling smokers: a population-based survey of patients’ perceptions of healthcare provider-delivered smoking cessation interventions. Archives of Internal Med. 1997;157:1313–1319. [PubMed]
  • Goldstein MG, Whitlock EP, DePue JD. planning committee of the Addressing Multiple Risk Factors in Primary Care Project. Multiple behavioral risk factor interventions in primary care: summary of research evidence. Am J Prev Med. 2004;(2S):61–79. [PubMed]
  • Greene G, Rossi S, Fava J, Velicer W, Laforge R, Willey C, Rossi J. The relationship between a dietary behavior change questionnaire and dietary intake; Presented at the 4th International Congress of Behavioral Medicine; Washington, DC. 1996.
  • Honda K. Factors underlying variation in receipt of physician advice on diet and exercise: applications of the behavioral model of health care utilization. American Journal of Health Promotion. 2004;18:370–377. [PubMed]
  • Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291:1238–1245. [PubMed]
  • Mikkilineni R, Weinstock MA, Goldstein MG, Dube CE, Rossi JS. Impact of the basic skin cancer triage curriculum on provider's skin cancer control practices. J Gen Intern Med. 2001;16:302–307. [PMC free article] [PubMed]
  • Ockene IS, Hebert JR, Ockene JK, Merriam PA, Hurley TG, Saperia GM. Effect of training and a structured office practice on physician-delivered nutrition counseling: the Worcester-Area Trial for Counseling in Hyperlipidemia (WATCH) Am J Prev Med. 1996;12:252–258. [PubMed]
  • Pinto B, Lynn H, Marcus B, DePue J, Goldstein M. Physician-based activity counseling: Intervention effects on mediators of motivational readiness for exercise. Annals of Behavioral Medicine. 2001;23:2–10. [PubMed]
  • Pronk NP, Peek CJ, Goldstein MG. Addressing multiple behavioral risk factors in primary care. Am J Prev Med. 2004;27(2S):4–17. [PubMed]
  • Prochaska JO, Velicer WF, Norcross JC. In search of how people change. American Psychologist. 1992;47:1102–1114. [PubMed]
  • Prochaska JO, Velicer WF, Redding C, Rossi JS, Goldstein M, DePue J, Greene GW, Rossi SR, Sun X, Fava JL, Laforge R, Rakowski W, Plummer BA. Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer and receive regular mammograms. Preventive Medicine. 2005;41:406–416. [PubMed]
  • Rossi JS, Blais LM, Redding CA, Weinstock MA. Behavior change for reducing sun and ultraviolet light exposure: Implications for interventions. Dermatologic Clinics. 1995;13:613–622. [PubMed]
  • Rubin HR, et al. Patients’ ratings of outpatient visits in different practice settings: results from the Medical Outcomes Study. JAMA. 1993;270:835–840. [PubMed]
  • Solberg LI, Brekke ML, Fazio CJ, Fowles J, Jacobsen DN, Ktooke TE, Mosser G, O’Connor PJ, Ohnsorg KA, Rolnick SJ. Lessons from experienced guideline implementers: attend to many factors and use multiple strategies. Jt Comm J Qual Improv. 2000;26:171–188. [PubMed]
  • Solberg LI, Boyle RG, Davidson G, Magnan SJ, Carlson CL. Patient satisfaction and discussion of smoking cessation during clinical visits. Mayo Clin Proc. 2001;76:138–143. [PubMed]
  • Stange KC, Flocke SA, Goodwin MA. Opportunistic preventive services delivery. Are time limitations and patient satisfaction barriers? J Fam Pract. 1998;46:419–424. [PubMed]
  • Stevens VJ, Glasgow RE, Toobert DJ, Karanja N, Smith S. One-year results from a brief, computer-assisted intervention to decrease consumption of fruits and vegetables. Preventive Medicine. 2003;36:594–600. [PubMed]
  • Taira DA, Safran DG, Seto TB, Rogers WH, Tarlov AR. The relationship between patient income and physician discussion of health risk behaviors. Journal of American Medical Association. 1997;278:12–17. [PubMed]
  • Ware JE. Scales for measuring general health perceptions. Health Serv Res. 1976;11:396–415. [PMC free article] [PubMed]
  • Weingarten SR, Stone E, Green E, Pelter M, Nessim S, Huang H, Kristopaitis R. A study of patient satisfaction and adherence to preventive care practice guidelines. Am J Med. 1995;99:590–596. [PubMed]
  • Whitlock EP, Orleans CT, Pender N, Allan J. Evaluating primary care behavioral counseling interventions, an evidence-based approach. Am J Prev Med. 2002;22:267–284. [PubMed]
  • U.S. Preventive Services Task Force. Summary of recommendations. 2003. [accessed Oct. 8, 2006]. Available at: www.Ahrq.gov/clinic/uspstf.htm. [PubMed]
  • Woodell DA, Cherry KD. Advance Data from Vital and Health Statistics; No. 346. Hyattsville, MD: National Center for Health Statistics; 2004. [accessed Mar. 20, 2006]. National Ambulatory Medical Care Survey: 2002 Summary. Available at: www.cdc.gov/nchs.
PubReader format: click here to try


Save items

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • PubMed
    PubMed citations for these articles
  • Taxonomy
    Taxonomy records associated with the current articles through taxonomic information on related molecular database records (Nucleotide, Protein, Gene, SNP, Structure).
  • Taxonomy Tree
    Taxonomy Tree

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...