• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Prev Med. Author manuscript; available in PMC Jun 27, 2012.
Published in final edited form as:
PMCID: PMC3384542
NIHMSID: NIHMS329204

Initial Efficacy of MI, TTM Tailoring and HRI’s with Multiple Behaviors for Employee Health Promotion

Abstract

Objective

This study was designed to compare the initial efficacy of Motivational Interviewing (MI), Online Transtheoretical Model (TTM) Tailored communications and a brief Health Risk Intervention (HRI) on four health risk factors (inactivity, BMI, stress and smoking) in a worksite sample.

Method

A randomized clinical trial assigned employees to one of three recruitment strategies and one of the three treatments. The treatment protocol included an HRI session for everyone and in addition either a recommended three TTM online sessions or three MI in person or telephone sessions over 6 months. At the initial post-treatment assessment at six months, groups were compared on the percentage who had progressed from at-risk to taking effective action on each of the four risks.

Results

Compared to the HRI only group, the MI and TTM groups had significantly more participants in the Action stage for exercise and effective stress management and significantly fewer risk behaviors at 6 months. MI and TTM group outcomes were not different. Conclusion: This was the first study to demonstrate that MI and online TTM could produce significant multiple behavior changes. Future research will examine the long-term impacts of each treatment, their cost effectiveness, effects on productivity and quality of life, and process variables mediating outcomes.

Keywords: Multiple behavior change, Motivational Interviewing, TTM Tailoring, exercise, stress, smoking, BMI

Increasingly it is recognized that a major barrier to dissemination of evidence-based health promotion programs is that most programs were not designed or developed to reach at-risk populations (Glasgow et al., 2003). They were designed for and evaluated on self-selected samples of at-risk individuals. For example, in the United States Public Health Services 1996 Clinical Guidelines for the Treatment of Tobacco, over 3,000 studies on tobacco were identified. The Guidelines were able to recommend a broad range of evidence-based interventions for motivated smokers; i.e., those prepared to quit in the next month (Fiore et al., 1996). There were no evidence-based interventions recommended for unmotivated smokers, even though they make up more than 80% of all U.S. smokers (Velicer et al., 1995) and more than 90% of daily smokers (Wewers et al., 2003). In the second edition of the Guidelines there were over 6,000 studies identified (Fiore et al., 2000). These evidence-based treatments were not designed to reach the vast majority of unmotivated smokers.

When health care systems disseminate free, evidence-based smoking cessation clinics for motivated smokers, the percentage of smokers who participate nationally is 1% (Lichtenstein & Hollis, 1992). When states issue Requests For Proposals (RFPs) to deliver for free, evidence- based quit lines recommended by CDC, they typically budget for one quarter of one percent of smokers using the programs each year (our analysis of four RFPs). Our project applied health promotion interventions that were developed and evaluated with unmotivated as well as motivated populations, since these are the types of programs that can have the most impact.

MI was originally developed for addictions counseling in the 1980s and is described as a “directive [goal-oriented], client-centered counseling style for eliciting behavior change by helping clients to explore and resolve ambivalence” (Miller & Rollnick, 2002). This approach has been found to produce significant efficacy across a broad range of health risk behaviors including alcohol abuse, smoking, high-risk sexual behaviors, exercise, and diet (Burke et al, 2003; Hettema et al., 2005). MI-based health coaching has also been found to improve physical and mental health status in a worksite setting (Butterworth et al., 2006). In most MI studies, however, only a single risk behavior has been targeted at one time. The present study is the first to apply MI-based health coaching simultaneously to four health risks in a worksite setting.

TTM tailored print feedback has been found to be effective with a broad range of single health behaviors including smoking (e.g., Prochaska et al., 1993, 2001a&b), stress (Evers et al., 2006), medication adherence (Johnson et al., 2006) mammography (Rakowski et al., 1998), diet (Greene et al., in press), sun protection (Weinstock et al., 2002), exercise (Marcus et al., 1998; Velicer et al., 2006), and depression (Levesque et al., 2007). TTM tailored print feedback has also been found effective when treating: 1) three health behaviors in a population of parents whose teens were participating in health promotion at school (Prochaska et al., 2004); 2) four behaviors in a population of primary care patients (Prochaska et al., 2005); 3) three behaviors in a population of patients with diabetes (Jones et al., 2003) and 4) three behaviors in a population of overweight individuals (Johnson et al., in press). Comparable TTM tailored feedback has also been presented in a multimedia format (Redding et al., 1999), but not yet online.

This project is the first time that TTM tailoring was delivered online to a population of adults. This project is also the first time that MI delivered in person and telephonically was compared to TTM tailoring.

This report is part of the first randomized population trial to be based on impact on multiple behaviors. The original impact equation was reach (percent participating) times efficacy (percent who change). Our first report compared three additive recruitment strategies to enhance reach: 1. persuasive communications; 2. persuasive communications plus small incentives; and 3. both of these plus person-to-person telephone outreach (Butterworth et al., 2007). This report compares the initial efficacy of three interventions (health risk assessment and intervention only (HRI), online HRI plus Transtheoretical Model- tailored communications (TTM), and online HRI plus Motivational Interviewing-based health coaching (MI)) targeting four health risks: stress, exercise, smoking and BMI.

Methods

Procedure

Prior to recruitment, a random sample of N=6000 employees at a major medical university were randomly assigned to one of three additive recruitment strategies (mail/email only, mail/email + incentive, and mail + incentive + phone prompt) and to one of three treatment groups (HRI, MI, TTM). Employees in recruitment group 1 received a persuasive letter and email inviting them to participate in the study. Employees in recruitment group 2 received the same letter and email plus a small incentive for each time that they participated in the study. Employees in recruitment group 3 received a phone call if they did not respond to the same initial letter, email and incentive offer. Participants were recruited into the specific treatment condition they had been randomized into to as would be the case in real world programs. Employees in all groups received an initial email inviting them to join the study. If an employee stated (by phone or email) that s/he did not want to participate, s/he was not approached again. If there was no response, two follow-up persuasive emails invited study participation and provided a link to the study website. The study website described the study, contained informed consent information and documentation, and provided links to the baseline survey. Eligible participants were at risk for at least one risk behavior targeted in the study (exercise, stress, BMI > 25, or smoking). More detail about baseline recruitment procedures and sample characteristics are reported elsewhere (Butterworth et al., 2007). All study procedures and communications were reviewed and approved by Institutional Review Boards at OHSU and URI.

Measures

Demographics

Included age, gender, race and ethnicity, and years of education completed. Self reported height and weight were also assessed, which were then used to compute BMI categories.

Stage of Change was assessed for all groups across all behavioral risks. For smoking cessation, a three item algorithm was used with precontemplation defined as not intending to quit smoking in the next six months; contemplation defined as intending to quit in the next 6 months; and preparation defined as intending to quit in the next month and having made a quit attempt in the past 12 months. This cessation algorithm has been found to have strong predictive validity over 6 months (DiClemente et al., 1991), 12, 18 and 24 months (Prochaska et al., 2004;Velicer et al., 2007). For exercise, the items and algorithm recommended by Sarkin and colleagues (2001) were adapted to reflect the 30 minutes/day X 5 days/week criterion. For dietary fat, the items and algorithm reported by Prochaska and colleagues (2004, 2005) were used. For stress management, the items and algorithm reported by Evers and colleagues (2006) were used.

Outcome Measures

The primary outcome measure for each of three risk behaviors (exercise, stress, smoking) was the percentage of those not at criterion (at risk) at baseline who were in action (at criterion) at the six month follow-up. The action criterion for smoking was point prevalence abstinence; for exercise the criterion was 30 minutes of moderate exercise at least five days a week; and for stress the criterion was effectively managing stress via relaxation, physical activity and/or social support. For BMI categories, the primary outcome measure was percent above or below a BMI of 25. These outcome measures reflect the percentage who progressed from at-risk for a particular behavioral risk at baseline to being at criteria at six months.

Interventions

All randomized groups received the HRI at baseline. The second group received additional MI- based health coaching and the third group received additional TTM tailored feedback.

HRI

The HRI combines a traditional HRA that provides feedback on what stages of change participants were in for each risk and the single most important step they could take to begin progressing. The HRI was provided by Pro-Change Behavior Systems.

MI

In addition to the HRI described above, MI-based health coaching was provided face-to-face or telephonically according to the preference and convenience of each participant. The initial MI session was 30-45 minutes and the two follow-up sessions were 10-15 minutes. Health coaches were deemed proficient in MI per the Motivational Interviewing Skill Code (MISC) (Moyers, Martin & Catley, 2003). During each session they addressed the participant’s risk factor(s) and employed client-centered techniques such as empathic listening, collaboration, evocation, and affirmation in order to establish rapport, reduce resistance, support self-efficacy, and elicit “change talk” (one’s own reasons and arguments for change) (Hettema, Steele & Miller, 2005; Miller & Rollnick, 2002). The intended outcome of these MI sessions was for clients to resolve ambivalence (a central goal), move through the stages of change (Prochaska, 1979), and follow through on desirable lifestyle change, which would ideally result in reduced health risks and improved health outcomes. Health coaches were supervised by a MI specialist who is part of the Motivational Interviewing Network of Trainers (MINT).

TTM

In addition to the HRI described above, TTM assessments and tailored feedback on each construct (stage, pros, cons, efficacy, processes) relevant to the participant’s stage for a particular risk behavior were provided online using Pro-Change LifeStyle Management Programs (Evers et al., 2006; Johnson et al., in press). Participants could interact online with up to four tailored intervention programs (stress, exercise, smoking, weight management), depending on what they were at risk for at baseline (see Table 1). In the first session an algorithm generated normative feedback comparing participants’ efforts to peers who made the most progress for their current stage. Remaining sessions provided ipsative feedback on which variables the participants were improving and on which they needed to make better efforts.

Table 1
Baseline Sample Description by Treatment Group

Three sessions of TTM tailored feedback were recommended for each risk and future sessions could only occur after at least 30 days passed to allow some progress to be achieved. As in real world applications, participants could access as many sessions for each risk as they preferred over the six months of treatment.

Statistical Analyses and Power

For purposes of power calculation, analyses were assumed to be one-way analyses of variance. Two-tailed significance tests were employed for the overall ANOVA tests (α = .05). Significant ANOVA results were followed by two group ANOVAs to assess specific directional hypotheses (one-tailed α= .05). Under this set of assumptions, a sample size of 125 per group was sufficient for the follow-up procedures to detect as little as 1% of the outcome variance. With discrete outcome data, chi-square tests compared percentage at criteria across the three groups. Significant chi-square results were followed by the Levy (1975) test for pair-wise proportions.

Results

Participants

After random assignment to recruitment/treatment groups, the following sample was recruited. Approximately N=1730 employees responded to the letters/emails and N=1400 were eligible and completed the online baseline assessment. A total of 25% of eligible employees were recruited across the three additive recruitment strategies (Butterworth et al., 2007). A total of 23.3% (N=464) were recruited to the HRI only condition, 21.7% (N = 433) to the MI treatment, and 25.2% (N=504) to the online TTM tailored treatment.

Table 1 presents the percent at-risk for each of the four targeted health risks and the number of risk behaviors in the baseline sample. The sample of 1400 had a mean age of 41.6 years with 79% female and nearly 87% non-Hispanic Caucasian (Butterworth et al., 2007). Consistent with the University setting, less than 8% of this sample reported high school or fewer years of education.

Table 1 describes the distribution of risk behaviors across the sample and across treatment groups. No differences between treatment groups were observed on any sample characteristics. In order of decreasing prevalence, 71.1% were sedentary, 67.1% overweight or obese, 35.6% not managing stress effectively and 9.7% were current smokers. Table 1 demonstrates that nearly 80% of the sample were at risk for one or two behavioral risks (M=1.8), with 18% at risk for three behavioral risks, and a very small number at risk for all four risks. Number of risk behaviors at baseline did not vary significantly by treatment group (χ2(6)=6.075, p>.05).

Table 2 shows the baseline stage of change for each risk behavior for the total sample and for each treatment group. No significant differences were found between the treatment groups for baseline stage of change for any of the three risk behaviors. Across the three risks, about 50% to 85% of the participants would be considered as unmotivated as measured by not being prepared to take action.

Table 2
Baseline Stages of Change by Risk Behavior and Treatment Group

Initial Treatment Utilization

Of baseline participants enrolled in the MI treatment, 73% (N=316) called to set up an MI appointment. Of baseline participants enrolled in the TTM treatment, 83.7% (N=422) went online again to participate in at least one TTM-tailored treatment session.

6 Month Outcomes

Table 3 presents the six-month outcomes for the percent at criteria by treatment group for each behavioral risk. There were significant differences between the three groups for both effective stress management (χ2(2) = 8.913, p < .05) and exercising moderately 30 mins./day for at least 5 days/wk (χ2(2) = 8.195, p < .05). Table 3 also shows that the MI and TTM groups had significantly (p < .01) greater proportions at criteria at 6 months for both stress and exercise compared to the HRI only group. There were no significant differences (p > .05) between the MI and TTM groups for stress and exercise. For smoking, where only 9.8% were smoking at baseline, we saw a nonsignificant trend favoring the MI and TTM groups, but lacked the sample size to find significant differences. There were no significant differences in percent at criteria for BMI (<25) by treatment groups at six months.

Table 3
Percent Reaching Criterion At 6 Months By Treatment Groups

An ANOVA on mean number of risk behaviors at six months was significantly different by treatment group (F(2, 975) = 4.086, p < .05). An ANCOVA on mean number of risks at six months by treatment group, including baseline mean number of risk behaviors as a covariate, retained statistical significance (F(2, 975) = 4.494, p < .05). At six months, the HRI group (M = 1.445) had a significantly greater (p < .05) mean number of risks than the MI (M = 1.254) and the TTM (M = 1.319) groups.

Discussion

This study generated a series of important results. It is the first study we could find demonstrating MI to produce significant efficacy on at least two behaviors when simultaneously treating multiple behaviors within a population. The results are particularly important given how widely MI has been disseminated and the fact that it has been found to be more effective with less ready and less motivated participants as well as those prepared to act. It is also worthy to note that there were only three coaching sessions that were time limited. These results suggest that MI counselors can be more ambitious and simultaneously target multiple and not just single behavior risks in a population.

Secondly, the results replicated the multiple behavior changes within a target population that have been generated by TTM tailored interventions (Johnson et al., in press; Jones et al., 2003; Prochaska et al., 2004, 2005). More innovatively, this is the first study with adults to demonstrate such results when the TTM tailoring was delivered online. In previous projects, the TTM assessments were done by mail or telephone and TTM print communications were generated by computer and mailed to participants. Given the much lower cost and higher convenience of online programs, these results are particularly promising in a worksite setting.

Informal comparisons across studies add to the importance of these results. The six month outcomes for effective stress management of 61.6% for the HRI minimal treatment replicates the 60% found at six months with TTM Tailoring delivered by mail and telephone with a national sample of 1,035 participants (Evers et al., 2006). The level of stress in the national sample would predict that this population would be highly likely to seek medical care in the next two weeks. Managing such stress effectively would clearly impact on health care costs. In the national population trial funded by NIH, there was a true no treatment control group that showed only 40% progressing to effective stress management at six months. Further, the outcomes for both the TTM tailoring and control groups were maintained at 12 and 18 months follow-up (Evers et al., 2006).

In this Oregon Worksite Wellness study, the TTM Tailoring (73.9%) and the MI Coaching (78.2%) had even greater efficacy than before (Evers et al., 2006). These results are important in their own right given the effects of stress on personal well-being, health, health care utilization and productivity. These results, if maintained as in the Evers et al. (2006) study, are also important for the maintenance of other health behavior changes, since stress is such a common cause of relapse.

The percentage (35.1%) of the HRI group reaching criterion for exercise was below what has been found in other studies with TTM Tailoring (40%, Johnson et al., in press). On the other hand, the percentages for the TTM Tailoring (45.2%) and MI Coaching (46%) were higher. The BMI results at six months are consistent with no significant effects at six months in a national Randomized Clinical Trial (RCT) with TTM tailoring for multiple behavior change for healthy weight management. At 24-months, however, significant effects were found for participants treated for exercise and/or diet (Johnson et al., in press).

While the smoking sample was too small to find statistical significance, the results are surprisingly similar to much larger samples. Across a growing range of population trials, the control groups attained abstinence rates of 16% to 19%, while the TTM Tailoring produced 22% to 26% abstinence at longer term follow-up (Prochaska et al., 1993, 2001a, 2001b, 2004, 2005). The abstinence rates with MI (34.6%) were higher than the 24% found with MI by Carpenter et al. (2004), but that study limited their sample only to unmotivated smokers in the precontemplation or contemplation stages.

The lack of significant outcome differences at six months between MI and TTM Tailoring is also important, since this study is the first to compare these two widely used treatment modalities. Longer term follow-ups will be essential, including comparisons of impacts on multiple behaviors, cost-effectiveness, effects on productivity and quality of life, and process to outcome evaluations.

Limitations

This study is limited by targeting a single large employer, and a sample that was highly educated, Caucasian and non-representative. The sample was also limited by the recruitment (25%) and retention (70%) rates. These limitations are mitigated somewhat by the fact that the stage distributions of the present sample were comparable to what is typically found in more general populations for smoking and physical activity. The stage distribution for effective stress management, however, indicates that a more prepared or motivated sample was recruited for this behavior. These limitations are also mitigated somewhat by the fact that the study was an RCT with both the three treatments and three recruitment strategies being randomly applied. Still greater generalization will require further replications with more diverse and representative employee populations with higher recruitment and retention rates.

This report is limited to the initial efficacy at six months, and long-term follow-ups will need to demonstrate the treatment trajectories over time. To date, the two most common trajectories with TTM tailoring have been either stable (e.g., with stress effects at six months remaining stable at 12 and 18 months, (Evers et al., 2006)) or increasing (e.g., with smoking cessation in the treatment group increasing over 18 months compared to the assessment only condition (Prochaska et al., 2001a, 2001b, 2004, 2005).

Another limitation is the reliance on self-reports. This limitation is mitigated some by findings that in population trials, self-reports for behaviors like smoking cessation have been highly valid (Velicer et al., 1992). It would be better if more objective measures could be used for risks like exercise and BMI. With population trials, however, the physical demands of objective measures add serious complications to proactive recruitment and retention.

Another limitation is that when participants were randomized to the three recruitment strategies, they were informed of which treatment they were being recruited to. While this represents a real-world approach where people are not recruited blindly to programs, it does raise concerns that participants might have differentially self-selected into a preferred treatment. If so, the treatment assignment would not have been fully random. This concern is mitigated somewhat by the fact that there were no significant differences on any of the baseline demographics and baseline behavioral risk variables. The lack of significant differences suggests that the treatments were sampled from the same subpopulations. If there were no sampling differences, then informing people about which treatment they were being recruited to may have been unnecessary.

Finally, these results may lead some to conclude that more treatment is better, since both MI and TTM treatment groups had better 6 month outcomes than the HRI only condition. This concern is mitigated somewhat by studies that did not find a dose-response relationship for TTM tailored print interventions (Velicer et al., 1999) and by studies where more treatment was clearly not better (Prochaska et al., 2001b). Nevertheless, future studies should assess components of both MI and TTM-tailored treatments, including contact time.

Future Research

Future reports will include analyses of outcomes over longer-term follow-ups. These reports will include outcomes based on recent advances in assessing impact for multiple behavior change (Prochaska, Judith et al., 2007). Such impact analyses take into account recruitment, participation and efficacy rates summed across all treated behaviors. Such impact analyses are best applied over longer term follow-ups to take into account treatment trajectories on different risk behaviors that may be increasing, decreasing or remaining stable.

Future studies will examine process to outcome analyses to identify moderating and mediating variables, including the number of TTM and MI sessions completed, the time spent in each session and the use of change variables like processes of change in the TTM treatments. The present outcome results indicated significant treatment effects of MI and TTM tailoring, but future studies will need to assess what treatment variables were producing significant outcomes.

Future research will also focus on dissemination factors that include not only recruitment but retention or program adoption as well. Such research will be based on the RE-AIM model (Glasgow et al., 2003) that requires at a minimum adequate recruitment and initial efficacy. The present report demonstrates initial efficacy and future studies will analyze impacts and other factors related to dissemination from research to practice.

Acknowledgement

This study was supported by CDC Grant 1 RO1 DP000103 to Susan Butterworth, Ph.D.

Footnotes

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.

References

  • Burke BL, Arkowitz H, Menchola M. The efficacy of Motivational Interviewing: A Meta-Analysis of Controlled Clinical Trials. Journal of Consulting and Clinical Psychology. 2003;71(5):843–861. [PubMed]
  • Butterworth S, Linden A, McClay W, Leo M. The Effect of Motivational Interviewing-based Health Coaching on Employees’ Physical and Mental Health Status. J Occup Healt Psych. 2006;11(4):358–365. [PubMed]
  • Butterworth S, et al. 2007. [unpublished raw data]. Grant # RO1 DP000103.
  • Carpenter MJ, Hughes JR, Solomon LJ, Callas PW. Both smoking reduction with nicotine replacement therapy and motivational advice increase future cessation among smokers unmotivated to quit. Journal of Consulting and Clinical Psychology. 2004;72:371–381. [PubMed]
  • Evers KE, Prochaska JO, Johnson JL, Mauriello LM, Padula JA, Prochaska JM. A randomized clinical trial of a population- and transtheoretical model-based stress-management intervention. Health Psychology. 2006;25(4):521–529. [PubMed]
  • Fiore MC, Bailey WC, Cohen SJ, Dorfman SF, Goldstein MG, Gritz ER. Smoking Cessation, Clinical Practice Guideline 18 (No. 96-0692) US Dept of Health & Human Svcs, Agency for Health Care Policy and Research; Rockville: 1996.
  • Fiore MC, Bailey WC, Cohen SJ, Dorfman SF, Goldstein MG, Gritz ER. Treating tobacco use and dependence, Clinical practice guideline. US Dept of Health & Human Svcs, Public Health Svc; Rockville: 2000.
  • Glasgow RE, Lichtenstein E, Marcus AC. Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. American Journal of Public Health. 2003;93(8):1261–1267. [PMC free article] [PubMed]
  • Greene GW, Fey-Yensan N, Padula C, Rossi SR, Rossi JS, Clark PG. Change in fruit and vegetable Intake over 24 months in older adults: results of the senior project intervention. The Gerontologist. in press. [PubMed]
  • Hettema J, Steele J, Miller WR. Motivational interviewing. Annual Review of Clinical Psychology. 2005;1:91–111. [PubMed]
  • Johnson SS, Driskell M-M, Johnson JL, Prochaska JM, Zwick W, Prochaska JO. Efficacy of a transtheoretical model-based expert system for antihypertensive adherence. Disease Management. 2006;9(5):291–301. [PubMed]
  • Johnson SS, Driskell MM, Johnson JL, Dyment SJ, Prochaska JO, Prochaska JM, et al. Transtheoretical model intervention for adherence to lipid-lowering drugs. Disease Management. 2006;9(2):102–114. [PubMed]
  • Johnson SS, Paiva AL, Cummins C, Johnson JL, Dyment S, Wright JA, Prochaska JO, Prochaska JM, Sherman K. Evidence-based Multiple Behavior Intervention for Weight Management: Effectiveness on a Population Basis. Preventive Medicine. in press. [PMC free article] [PubMed]
  • Jones H, Edwards L, Vallis TM, Ruggiero L, Rossi SR, Rossi JS, et al. Changes in diabetes self-care behaviors make a difference in glycemic control: the Diabetes Stages of Change (DiSC) study. Diabetes Care. 2003;26(3):732–737. [PubMed]
  • Levesque DA, VanMarter d.F. An Expert System to Reduce Depression in Primary Care (Final Report to NIMH: Grant No. R44 MH60522) Pro-Change Behavior Systems, Inc.; Kingston, RI: 2007.
  • Levy KJ. Large-sample pair-wise comparisons involving correlations, proportions, or variances. Psychological Bulletin. 1975;82:174–176.
  • Lichtenstein E, Hollis J. Patient referral to a smoking cessation program: who follows through? Journal of Family Practice. 1992;34(6):739–744. [PubMed]
  • Marcus BH, Bock BC, Pinto BM, Forsyth LH, Roberts MB, Traficante RM. Efficacy of an individualized, motivationally-tailored physical activity intervention. Annals of Behavioral Medicine. 1998;20:174–180. [PubMed]
  • Miller W, Rollnick SR. Motivational interviewing:Preparing people to change. 2nd ed. The Guilford Press; New York: 2002.
  • Moyers T, Martin T, Catley D, Harris K, Ahluwalia JS. Assessing the integrity of motivational interviewing interventions: Reliability of the motivational interviewing skills code. Behavioural and Cognitive Psychotherapy. 2003;31:177–184.
  • Prochaska, Judith J, Velicer WF, Prochaska JO. Methods of measuring change in multiple risk-factor interventions. Annals of Behavioral Medicine. 2007;33:S152.
  • Prochaska JO. Systems of psychotherapy: A transtheoretical analysis. Dorsey Press; Homewood, IL: 1979.
  • Prochaska JO, DiClemente CC, Velicer WF, Rossi JS. Standardized, individualized, interactive, and personalized self-help programs for smoking cessation. Health Psychology. 1993;12(5):399–405. [PubMed]
  • Prochaska JO, Velicer WF, Fava JL, Rossi JS, Tsoh JY. Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation. Addictive Behaviors. 2001a;26(4):583–602. [PubMed]
  • Prochaska JO, Velicer WF, Fava JL, Ruggiero L, Laforge RG, Rossi JS, et al. Counselor and stimulus control enhancements of a stage-matched expert system intervention for smokers in a managed care setting. Preventive Medicine. 2001b;32(1):23–32. [PubMed]
  • Prochaska JO, Velicer WF, Redding CA, Rossi JS, Goldstein M, DePue J, et al. 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(2):406–416. [PubMed]
  • Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, et al. Stages of change and decisional balance for 12 problem behaviors. Health Psychology. 1994;13(1):39–46. [PubMed]
  • Prochaska JO, Velicer WF, Rossi JS, Redding CA, Greene GW, Rossi SR, et al. Multiple risk expert systems interventions: impact of simultaneous stage-matched expert system interventions for smoking, high-fat diet, and sun exposure in a population of parents. Health Psychology. 2004;23(5):503–516. [PubMed]
  • Rakowski WR, Ehrich B, Goldstein MG, Rimer BK, Pearlman DN, Clark MA, Velicer WF, Woolverton H. Increasing mammography among women aged 40-74 by use of a stage-matched, tailored intervention. Preventive Medicine. 1998;27:748–756. [PubMed]
  • Redding CA, Prochaska JO, Pallonen UE, Rossi JS, Velicer WF, Rossi SR, et al. Transtheoretical individualized multimedia expert systems targeting adolescents’ health behaviors. Cognitive & Behavioral Practice. 1999;6(2):144–153.
  • Sarkin JA, Johnson SS, Prochaska JO, Prochaska JM. Applying the transtheoretical model to regular moderate exercise in an overweight population: Validation of a stages of change measure. Preventive Medicine. 2001;33:462–469. [PubMed]
  • Velicer WF, Fava JL, Prochaska JO, Abrams DB, Emmons KM, Pierce JP. Distribution of smokers by stage in three representative samples. Preventive Medicine. 1995;24(4):401–411. [PubMed]
  • Velicer WF, Friedman R, Redding C, Migneault J, Hoeppner BB. Project HEALTH: Comparing three computer-based multiple risk factor interventions. International Journal of Behavioral Medicine. 2006;13(S 1):188. Abstract.
  • Velicer WF, Prochaska JO, Fava JL, Laforge RG, Rossi JS. Interactive versus non- interactive interventions and dose-response relationships for stage matched smoking cessation programs in a managed care setting. Health Psychology. 1999;18:21–28. [PubMed]
  • Velicer WF, Prochaska JO, Rossi JS, Snow M. Assessing outcome in smoking cessation studies. Psychological Bulletin. 1992;111:23–41. [PubMed]
  • Velicer WF, Redding CA, Sun X, Prochaska JO. Demographic Variables, Smoking Variables, and Outcome Across Five studies. Health Psychology. 2007;26(3):278–287. [PubMed]
  • Weinstock MA, Rossi JS, Redding CA, Maddock JE. Randomized controlled community trial of the efficacy of a multicomponent stage-matched intervention to increase sun protection among beachgoers. Preventive Medicine. 2002;35:584–592. [PubMed]
  • Wewers ME, Stillman FA, Hartman AM, Shopland DR. Distribution of daily smokers by stage of change: Current Population Survey results. Preventive Medicine. 2003;36(6):710–720. [PubMed]
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...