Baseline Stage Of Change Research Articles

Digital Health Coaching for Type 2 Diabetes: Randomized Controlled Trial of Healthy at Home.

Digital health coaching is an intervention for type 2 diabetes mellitus (T2DM) that has potential to improve the quality of care for patients. Previous research has established the efficacy of digital interventions for behavior change. This pilot study addresses a research gap in finding effective and accessible behavioral interventions for under-resourced individuals with T2DM. We examined the impact of Healthy at Home, a 12-week phone and SMS-based (short message service) digital health coaching program, on insulin resistance which is an upstream marker for T2DM progression. We compared this intervention to usual diabetic care in a family medicine residency clinic in a randomized controlled trial. Digital health coaching significantly improved participants' calculated Homeostatic Model Assessment for Insulin Resistance (HOMA2-IR) by −0.9 ± 0.4 compared with the control group (p = 0.029). This significance remained after controlling for years diagnosed with T2DM, enrollment in Medicaid, access to food, baseline stage of change, and race (p = 0.027). Increasing access to digital health coaching may lead to more effective control of diabetes for under-resourced patients. This study demonstrates the potential to implement a personalized, scalable, and effective digital health intervention to treat and manage T2DM through a lifestyle and behavioral approach to improve clinical outcomes (http://clinicaltrials.gov, NCT04872647).

790-P: Multicomponent Integrated Care Program Benefits on Diabetes-Related Grief and Stage of Change and Glycemic Control Association

Self-care behavior is the cornerstone in diabetes care. Strategies to promote behavior change and overcome grief process to improve diabetes outcomes have been poorly studied. We examined a quality initiative Multicomponent Integrated Care (MIC) program on Grief Stage (GS) and Stage of Change (SOC) allocation and its association with A1c in persons with type 2 diabetes (PwT2D) in a public primary care T2D clinic. DIABEMPIC is a 5-months empowerment-based program including: interdisciplinary case management team, diabetes self-management education, individual/group sessions, shared medical appointments, audit and feedback, and guaranteed supply of medications. We evaluated the effect of the intervention using a ’before and after’ design. Advanced stages were defined by higher than “anger” and higher than “contemplation” for GS and SOC, respectively. Total PwT2D were 451, the mean (SD) age was 54.6 (11) years, 66% were female, diabetes duration was 12 (8) years and baseline A1c was 9.5% (2.1). Baseline GS: denial 43.6%, anger 25.1%, bargaining 22.8%, depression 8.2% and acceptance 0.3%. Baseline SOC: precontemplation 55.4%, contemplation 40.6%, determination 2.5%, action 2.5% and maintenance 0%. GS and SOC improved after the intervention (83% and 68.7% presented advanced stages, respectively), p<0.001. A1c decreased 2.7% (2.2), p-value <0.001. Advanced GS and SOC were associated with an A1c ≦ 8% (OR 0.29, 95% CI, 0.16-0.52 and OR 0.21, 95% CI, 0.12-0.38, respectively). Also, these advanced stages had higher decrease of A1c (-0.8, -0.7%, respectively) than those who persist in earlier stages (p<0.001). We report a high prevalence of initial GS and SOC in PwT2D and poor glycemic control. MIC program participation was associated with improving GS and SOC. Advanced stages of GS and SOC at the end of the program were associated with better glycemic control. MIC strategies must be considered to promote behavior change and overcome grief process in PwT2D. Disclosure R. Silva-Tinoco: Speaker’s Bureau; Self; Novo Nordisk Inc. O. Salas-Martínez: None. V. delaTorre-Saldaña: None. E.B. Guzman: None. E.C. Cuatecontzi: None. D. Cabrera-Gerardo: None. L. Castillo-Martinez: None. H. Gallardo-Rincón: None. J. Serna: None. A. Gonzalez-Cantu: None. M. Romero-Ibarguengoitia: None.

Effect of School-based Wellness Intervention in 7th Graders on Stage of Change for Lifestyle Behaviors: The MATCH Program

Objective: For the MATCH intervention, assess concurrent validity of a single item Stage of Change (SOC) measure and association with effectiveness. Methods: Observational pre, post intervention study in 17 schools in North and South Carolina. Subjects included 908 youth with all measures (908/1,468=62%) who participated in an interdisciplinary seventh grade wellness program called Motivating Adolescents with Technology to Choose Health (MATCH). Measures included: Pre and Post-MATCH self-reported SOC, frequency of fruit and vegetable intake (F/V) and Physical Activity (PA) and measured Body Mass Index z-score (zBMI). Concurrent validity was assessed using Wilcoxon rank tests (pre-action vs. action SOC category for F/V and PA at pre- and post-MATCH) and Spearman correlations (between change in SOC, F/V and PA). Change in SOC category distribution was assessed with McNemar’s test; association between change in SOC and change in zBMI was investigated with multiple regression. Results: Both PA and F/V were found to be statistically significantly higher for students in the action SOC category (p<0.0001). Greater increase in SOC was weakly associated with a greater increase in PA (correlation 0.1069, p=0.0013). Participation demonstrated a shift in SOC post-MATCH (% in “action” SOC 47% pre, 53% post; p=0.003). ZBMI decreased significantly from pre- to post-MATCH (p<0.0001). A significant association existed between baseline SOC and change in zBMI: students with higher baseline SOC tended to have more decrease/less increase in zBMI (all overweight subgroup, b=-0.040, p=0.0108). Limitations: lack of a control group, survey questions not validated for individual behavior change. Conclusion: The SOC measure appears to be valid. Participation in MATCH may improve SOC and baseline SOC may predict increased effectiveness; however this result must be considered cautiously in this observational study.

Does Motivation Matter? Analysis of a Randomized Trial of Proactive Outreach to VA Smokers.

BackgroundCurrent guidelines advise providers to assess smokers’ readiness to quit, then offer cessation therapies to smokers planning to quit and motivational interventions to smokers not planning to quit.ObjectivesWe examined the relationship between baseline stage of change (SOC), treatment utilization, and smoking cessation to determine whether the effect of a proactive smoking cessation intervention was dependent on smokers’ level of motivation to quit.DesignSecondary analysis of a multicenter randomized controlled trial.ParticipantsA total of 3006 current smokers, aged 18–80 years, at four Veterans Affairs (VA) medical centers.Interventions: Proactive care included proactive outreach (mailed invitation followed by telephone outreach), offer of smoking cessation services (telephone or face-to-face), and access to pharmacotherapy. Usual care participants had access to VA smoking cessation services and state telephone quitlines.Main MeasuresBaseline SOC measured with Readiness to Quit Ladder, and 6-month prolonged abstinence self-reported at 1 year.Key ResultsAt baseline, 35.8 % of smokers were in preparation, 38.2 % in contemplation, and 26.0 % in precontemplation. The overall interaction between SOC and treatment arm was not statistically significant (p = 0.30). Among smokers in preparation, 21.1 % of proactive care participants achieved 6-month prolonged abstinence, compared to 13.1 % of usual care participants (OR, 1.8 [95 % CI, 1.2–2.6]). Similarly, proactive care increased abstinence among smokers in contemplation (11.0 % vs. 6.5 %; OR, 1.8 [95 % CI, 1.1–2.8]). Smokers in precontemplation quit smoking at similar rates (5.3 % vs. 5.6 %; OR, 0.9 [95 % CI, 0.5–1.9]). Within each stage, uptake of smoking cessation treatments increased with higher SOC and with proactive care as compared with usual care.LimitationsMostly male participants limits generalizability. Randomization was not stratified by SOC.ConclusionsProactive care increased treatment uptake compared to usual care across all SOC. Proactive care increased smoking cessation among smokers in preparation and contemplation but not in precontemplation. Proactively offering cessation therapies to smokers at all SOC will increase treatment utilization and population-level smoking cessation.

Smoking cessation intervention on Facebook: Which content generates the best engagement?

Background: Social media offer a great opportunity to deliver smoking cessation treatment to young adults, but previous online and social media interventions targeting health behavior change have struggled with low participant engagement. We examined engagement generated by content based on the Transtheoretical Model of Behavior Change (TTM) in a motivationally tailored smoking cessation intervention on Facebook. Objective: This study aimed to identify which intervention content based on the TTM (Decisional Balance and 10 processes of change) generated the highest engagement among participants in pre-action stages of change (Precontemplation, Contemplation, and Preparation). Methods: Participants (N=79, 20% female, mean age 20.8) were assessed for readiness to quit smoking and assigned to one of 7 secret Facebook groups tailored to their stage of change. Daily postings to the groups based on TTM Decisional Balance and the 10 processes of change were made by research staff over 3 months. Engagement was operationalized as the number of participant comments to each post. TTM content-based predictors of number of comments were analyzed and stratified by baseline stage of change, using negative binomial regression analyses with and without zero inflation. Results: A total of 512 TTM-based posts generated 630 individual comments. In Precontemplation and Contemplation groups, Decisional Balance posts generated above average engagement ( P =.01 and P <.001). In Contemplation groups, posts based on the TTM processes Dramatic Relief and Self-Liberation resulted in below average engagement ( P =.01 and P =.005). In Preparation groups, posts based on Consciousness Raising generated above average engagement ( P =.009). Participant engagement decreased over time and differed between groups within Precontemplation and Contemplation stages, but was independent of day of the week and time of day the content was posted to the groups. No participant baseline characteristics significantly predicted engagement. Conclusions: Participants not ready to quit in the next 30 days (in Precontemplation or Contemplation) engaged most when prompted to think about the pros and cons of behavior change, while those in the Preparation stage engaged most when posts increased awareness about smoking and smoking cessation. Findings support tailoring intervention content to readiness to quit and suggest intervention components that may be most effective in generating high participant engagement on social media. [J Med Internet Res 2015;17(11):e244]

A realist review of brief interventions for alcohol misuse delivered in emergency departments.

BackgroundBrief interventions (BIs) involve screening for alcohol misuse and providing feedback to patients about their use, with the aim of reducing alcohol consumption and related consequences. BIs have been implemented in various healthcare settings, including emergency departments (ED), where they have been found to contribute mixed results in their ability to address alcohol misuse among adults. Mechanisms through which BIs work and contextual factors impacting BI effectiveness are not clear. The purpose of this review was to understand how, for whom, and under what circumstances BIs work for adults misusing alcohol and who have been admitted to an ED. A realist review was chosen to answer these questions as realist reviews create context-mechanism-outcome configurations, leading to the development of comprehensive and detailed theories; in this case explaining how and for whom BIs work.MethodsDatabases including PsycINFO, Healthstar, CINAHL, Medline, and Nursing and Allied Health were searched for articles published until December 2013. The search strategy focused on studies examining BIs that targeted alcohol misuse among adults admitted into the ED. The search identified 145 relevant abstracts, of which 36 were included in the review. The literature was synthesized qualitatively (immersion/crystallization).ResultsFour mechanisms were found within reviewed studies, including engagement in/retention of BI materials, resolving ambivalence, increased awareness/insight into consequences of drinking, and increased self-efficacy/empowerment to use skills for change. The following contexts were found to impact mechanisms: emotional state, injury attributed to alcohol use, severity of alcohol use, and baseline stage of change.ConclusionsThis realist review provides advances in theories regarding which mechanisms to target during a BI and which contexts create the most favorable conditions for these mechanisms to occur, ultimately leading to optimal BI outcomes. These results can inform future clinical decision-making when delivering BIs in ED settings. Future research should conduct quantitative examination to confirm these findings.Systematic review registrationPROSPERO CRD42013006549.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-015-0024-4) contains supplementary material, which is available to authorized users.

Cognitive and affective predictors of smoking after a sentinel health event

Objective: This study examined how smoking-related causal attributions, perceived illness severity, and event-related emotions relate to both intentions to quit and subsequent smoking behavior after an acute medical problem (sentinel event). Methods: Three hundred and seventy-five patients were enrolled from 10 emergency departments (EDs) across the USA and followed for six months. Two saturated, manifest structural equation models were performed: one predicting quit attempts and the other predicting seven-day point prevalence abstinence at 14 days, three months, and six months after the index ED visit. Stage of change was regressed onto each of the other predictor variables (causal attribution, perceived illness severity, event-related emotions) and covariates, and tobacco cessation outcomes were regressed on all of the predictor variables and covariates. Results: Non-White race, baseline stage of change, and an interaction between causal attribution and event-related fear were the strongest predictors of quit attempt. In contrast, abstinence at six months was most strongly predicted by baseline stage of change and nicotine dependence. Conclusion: Predictors of smoking behavior after an acute medical illness are complex and dynamic. The relations vary depending on the outcome examined (quit attempts vs. abstinence), differ based on the time that has progressed since the event, and include significant interactions.

Stage of Change and Other Predictors of Participant Retention in a Behavioral Weight Management Program in Primary Care

High attrition often limits the efficacy of weight management programs, particularly those that serve primary care patients. We investigated stage of change and other predictors of retention in a behavioral intervention program that enrolled adult obese patients at three primary care sites. The program included practice improvements and provider training, as well as individual lifestyle counseling and educational group classes for participants. We analyzed predictors of whether participants returned for counseling visits and whether they attended group classes. The 461 participants were mainly women (84%) and minorities (87%), and most of them were in the preparation stage for dietary and physical activity changes. A total of 134 (29%) participants returned for at least one follow-up visit with their counselor and 85 (18%) attended at least one class. Baseline stage of change was not significantly associated with either return visits or class attendance (p = .875 and .182, respectively). Men and participants with children in the household were less likely to return for subsequent counseling sessions (p = .012 and .027, respectively). Age and employment were associated with class attendance (p = .099 and .034, respectively). Focus groups with participants confirmed that reasons for dropout included physical limitations or health issues, family issues, stress, and lack of social support. We conclude that prescreening of patients for readiness to participate and attention to personal barriers related to family and work might improve program retention. More frequent contacts between visits and stronger provider engagement might also strengthen the intervention.

Process and outcomes of a skin protection intervention for young adults

Efforts to reduce skin cancer risk behaviors using appearance-oriented interventions (e.g. ultraviolet (UV) light photos showing skin damage) or motivational interviewing (MI) have shown promise in recent trials. In the study a randomized 2 (UV photo versus no UV photo) x 2 (MI versus no MI) factorial design with longitudinal follow up was used. Results showed that progression in stage of change (SOC) was significantly more likely in the photo than the education condition. Treatment credibility as rated by participants and counselor perceived positive therapeutic alliance predicted SOC progression. There was also preliminary evidence for differential intervention effectiveness by baseline SOC. The implications are discussed.

A 15-Minute Interactive, Computerized Condom Use Intervention With Biological Endpoints

Brief face-to-face-behavioral interventions have been shown to be efficacious, but are costly to sustain and to widely disseminate. This study evaluated the efficacy of a 15-minute theory-based behavioral intervention designed to increase condom use and reduce new cases of Neisseria gonorrhoeae and Chlamydia trachomatis. Participants were randomly assigned via the computer to the intervention or the comparison group stratified by gender and their baseline stage of change (motivational readiness) for using condoms consistently (100%) with their main partners. Behavioral data and biologic specimens for testing of Neisseria gonorrhoeae and Chlamydia trachomatis were obtained at baseline and at 6 months post intervention. The intervention was delivered via an audio, multimedia, computerized application that provided individualized interventions to patients based on their responses to assessment items; comparison patients interacted with a 15-minute, computerized, multiple health risk assessment with no intervention. The majority of the sample (N = 430) was black (88%); 54.5% women; with a mean age = 24.5. Assuming all participants who did not return to the clinic at 6 months were not using condoms consistently, 32% of the treatment group versus 23% in the comparison group reported consistent condom use (P = 0.03). The combined Neisseria gonorrhoeae and Chlamydia trachomatis incidence declined to 6% in the intervention group versus 13% in the comparison group (P = 0.04). Results from a regression analysis revealed that the only statically significant predictor of sexually transmitted diseases infection at the follow-up was group assignment (OR = 1.91, 95% confidence index = 1.09-3.34; P = 0.043). These findings suggest that brief, interactive, computer-delivered interventions provided at the evaluation visit increase condom use and reduce sexually transmitted diseases without putting additional burden on clinicians or staff.

Drs. Baker and Lewin Reply

Back to table of contents Previous article Next article Letters to the EditorFull AccessDrs. Baker and Lewin ReplyAMANDA BAKER Ph.D.TERRY J. LEWIN B.Com.(Psych)Hons.,AMANDA BAKER Ph.D.Search for more papers by this authorTERRY J. LEWIN B.Com.(Psych)Hons.Search for more papers by this author,Published Online:1 May 2007https://doi.org/10.1176/ajp.2007.164.5.828AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To The Editor: Drs. Lan, Chiu, Wu, Hung, and Hu raise the important issue of the role of stage of change in adherence to treatment and treatment outcome in our smoking cessation trial, although evidence in support of the stage of change model is generally lacking (1) . In our analysis of the baseline characteristics of our cohort (2) , we reported that precontemplators and contemplators had significantly fewer lifetime attempts to quit compared with those at the preparation stage of change. Among the 147 smokers with a psychotic disorder who were assigned to the treatment group, there were 14 precontemplators, 73 contemplators, and 60 at the preparation stage. On average, those at the precontemplation (mean=5.79 sessions) and contemplation (mean=5.74 sessions) stages attended one less treatment session than those at the preparation stage (mean=6.80 sessions) (F=4.15, df=2,144, p=0.02). One of the key outcomes in our article was the smoking reduction status (i.e., whether or not participants had reduced their daily consumption of cigarettes by 50% or more, including abstinence, relative to baseline). Baseline stage of change was not significantly associated with smoking reduction status at 3, 6, or 12 months (for the cohort as a whole, nor among those receiving treatment). For example, among the treatment group, 28.6% of precontemplators, 31.5% of contemplators, and 31.7% of those at the preparation stage met our smoking reduction criterion at 12 months (χ 2 =0.05, df=2, not significant). Moreover, all of the associations between treatment status and smoking reduction status reported in Table 1 of our article remained statistically significant after controlling for baseline stage of change. Although nicotine replacement therapy was only provided to participants during the 10-week intervention period, we assessed self-reported use of nicotine replacement therapy between the 6- and 12-month follow-up interviews (N=272). Likelihood of using nicotine replacement therapy during this period was associated with baseline stage of change (precontemplators, 12.1%; contemplators, 24.8%; preparation, 38.2% [χ 2 =10.04, df=2, p<0.01]). Additionally, there was no significant association between nicotine replacement therapy use during this period and intervention status (comparison group, 24.1%; attended fewer than five sessions, 31.3%; attended five to seven sessions, 35.9%; attended all sessions, 30.9% [χ 2 =2.65, df=3, not significant]). In summary, among our cohort of smokers with a psychotic disorder, we found the stage of change to be associated with the number of previous attempts to quit, the number of treatment sessions attended, and the subsequent use of nicotine replacement therapy, but not smoking reduction status. Thus, in our study, the stage of change does not account for the reduction in smoking reported, suggesting that the intervention (consisting of nicotine replacement therapy, motivational interviewing, and CBT) led to these changes. We propose that strategies to enhance engagement in treatment and the identification and delivery of effective treatments appear to be separate but important issues that need to be considered in parallel. Stage of change for smoking reduction may be a useful index of treatment preparedness, but it should not be the primary basis for initiating or changing treatment.Newcastle, AustraliaAuthor disclosures accompany the original article.

Drs. Baker and Lewin Reply

Back to table of contents Previous article Next article Letters to the EditorFull AccessDrs. Baker and Lewin ReplyAMANDA BAKER Ph.D.TERRY J. LEWIN B.Com.(Psych)Hons.,AMANDA BAKER Ph.D.Search for more papers by this authorTERRY J. LEWIN B.Com.(Psych)Hons.Search for more papers by this author,Published Online:1 May 2007https://doi.org/10.1176/ajp.2007.164.5.828AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To The Editor: Drs. Lan, Chiu, Wu, Hung, and Hu raise the important issue of the role of stage of change in adherence to treatment and treatment outcome in our smoking cessation trial, although evidence in support of the stage of change model is generally lacking (1) . In our analysis of the baseline characteristics of our cohort (2) , we reported that precontemplators and contemplators had significantly fewer lifetime attempts to quit compared with those at the preparation stage of change. Among the 147 smokers with a psychotic disorder who were assigned to the treatment group, there were 14 precontemplators, 73 contemplators, and 60 at the preparation stage. On average, those at the precontemplation (mean=5.79 sessions) and contemplation (mean=5.74 sessions) stages attended one less treatment session than those at the preparation stage (mean=6.80 sessions) (F=4.15, df=2,144, p=0.02). One of the key outcomes in our article was the smoking reduction status (i.e., whether or not participants had reduced their daily consumption of cigarettes by 50% or more, including abstinence, relative to baseline). Baseline stage of change was not significantly associated with smoking reduction status at 3, 6, or 12 months (for the cohort as a whole, nor among those receiving treatment). For example, among the treatment group, 28.6% of precontemplators, 31.5% of contemplators, and 31.7% of those at the preparation stage met our smoking reduction criterion at 12 months (χ 2 =0.05, df=2, not significant). Moreover, all of the associations between treatment status and smoking reduction status reported in Table 1 of our article remained statistically significant after controlling for baseline stage of change. Although nicotine replacement therapy was only provided to participants during the 10-week intervention period, we assessed self-reported use of nicotine replacement therapy between the 6- and 12-month follow-up interviews (N=272). Likelihood of using nicotine replacement therapy during this period was associated with baseline stage of change (precontemplators, 12.1%; contemplators, 24.8%; preparation, 38.2% [χ 2 =10.04, df=2, p<0.01]). Additionally, there was no significant association between nicotine replacement therapy use during this period and intervention status (comparison group, 24.1%; attended fewer than five sessions, 31.3%; attended five to seven sessions, 35.9%; attended all sessions, 30.9% [χ 2 =2.65, df=3, not significant]). In summary, among our cohort of smokers with a psychotic disorder, we found the stage of change to be associated with the number of previous attempts to quit, the number of treatment sessions attended, and the subsequent use of nicotine replacement therapy, but not smoking reduction status. Thus, in our study, the stage of change does not account for the reduction in smoking reported, suggesting that the intervention (consisting of nicotine replacement therapy, motivational interviewing, and CBT) led to these changes. We propose that strategies to enhance engagement in treatment and the identification and delivery of effective treatments appear to be separate but important issues that need to be considered in parallel. Stage of change for smoking reduction may be a useful index of treatment preparedness, but it should not be the primary basis for initiating or changing treatment.Newcastle, AustraliaAuthor disclosures accompany the original article.

Predictors of calcium intake patterns: A longitudinal analysis.

The study was guided by the precaution adoption process model; the author examined factors associated with (a) stage of change with respect to calcium intake at 4 time points and (b) calcium intake patterns across time. Data were collected by mailed questionnaire at baseline and at 3 follow-up assessments over 1 year. The primary outcome measures were calcium intake, stage of change with respect to calcium intake, and pattern of calcium intake across follow-up. Three patterns were identified: successful maintenance (adequate calcium intake at all 3 follow-ups), vacillation (adequate calcium consumption at 1 or 2 follow-ups), and inertia (inadequate calcium consumption at all 3 follow-ups). Five hundred six women completed baseline questionnaires, and 346 completed 3 follow-up assessments. Even after baseline stage of change was controlled for, longitudinal analyses revealed 3 variables that predicted different patterns of behavior across time. Isolated instances of adequate calcium intake were predicted by higher levels of knowledge and perceived benefits, whereas long-term maintenance was predicted by lower levels of perceived difficulty. These findings provide support for a central premise of the model, that different factors are important at different points in the behavior change process.

Predictors of Improvement in Women's Physical Activity

ABSTRACT This study identified factors that predicted improved levels of physical activity in working women. Data were from a prospective, controlled trial. Sedentary women (N = 287) were recruited during an employer-sponsored health screening. The experimental group received a brief, tailored physical activity intervention as well as a booster phone call 2 weeks later. Participants were evaluated by comparing pre-test/post-test measures at 6-week follow-up. It was hypothesized that variables in the Transtheoretical Model (baseline stage of change, perceived pros, cons, and self-efficacy), as well as demographic variables and physical characteristics would predict improved physical activity. In addition, the study tested whether increased physical activity changed the variables of the Transtheoretical Model. Factors that correlated to improved physical activity were entered into regression models to determine predictors of improved physical activity. Being assigned to the intervention group was a significant predictor in all of the regression models. Women who increased their stage of change of physical activity had greater improvement in self-efficacy (p = .013). Minority women showed greater improvement in stage of change of physical activity (p = .001) as well as week-end physical activity (p = .042). Women with less education were more likely to increase total minutes walked per week (p = .038). The research suggests that these cognitive and demographic variables are important for improving levels of physical activity. The Trans-theoretical Model was partially supported by the analyses.

Development of Stage of Readiness and Decisional Balance Instruments: Tools to Enhance Clinical Decision-Making for Adherence to Antiretroviral Therapy

This study presents the development of Stage of Readiness (SOR) and decisional balance instruments based on the Transtheoretical Model of Behavior Change (TTM) to improve adherence to antiretroviral therapy (ART). These instruments were tested on HIV positive women who enrolled in an adherence support study at a women's HIV clinic at a mid-western medical school. The decisional balance instrument was analyzed and 8 of 11 items were retained. These items were validated by follow-up administration of the instrument. Baseline stage of change and decisional balance scores prospectively predicted 1-year viral load level, thus identifying participants in need of adherence support interventions. Use of these instruments can give a provider added objective data on which to base a decision to either prescribe ART immediately or to first implement an intervention tailored to enhance this patient's readiness to adhere.

Effects of stage-matched repeated individual counseling on smoking cessation: A randomized controlled trial for the high-risk strategy by lifestyle modification (HISLIM) study.

The purpose of this study was to evaluate the effects of stage-matched repeated individual behavioral counseling as an intervention for the cessation of smoking. We conducted a multisite randomized controlled trial that enrolled smokers unselected for their readiness to quit. There were 979 smokers with hypertension or hypercholesterolemia recruited from 72 study sites and randomly allocated to the intervention or control group. Smokers in the intervention group received stage-matched individual counseling consisting of a 40 minute initial session and four 20-30 minute follow-up sessions. Smokers in the control group received individual behavioral counseling for hypertension or hypercholesterolemia. The point prevalence abstinence rate at 6 months, validated by carbon monoxide testing, in the intervention group (13.6%) was 5.4 times higher (p<0.001) than that in the control group (2.5%). When the data were analyzed based on the baseline stage of change, there were significant differences in the abstinence rates at 6 months in smokers versus controls with each stage of change except in immotives. The odds ratio was 6.4 (p<0.001) in precontemplators, 6.7 (p<0.001) in contemplators, and 6.2 (p<0.01) in preparators. There was a positive, consistent effect of the intervention regardless of study site (worksite or community) or the presence of hypertension or hypercholesterolemia. We showed the effects of an intervention with repeated individual behavioral counseling on the cessation of smoking in smokers unselected for their readiness to quit. This result suggests that stage-matched individual counseling, based on the transtheoretical model, is effective in smokers with a lower motivation to quit as well as those ready to quit.

Dynamic typology clustering within the stages of change for smoking cessation

Dynamic typology clustering was employed to find homogeneous subgroups of smokers within each of three early stages of change (precontemplation, contemplation, preparation) in a representative sample of smokers. Individual change profiles were created by coding the stage of change for each subject at five consecutive assessment points over a 2-year period (baseline and 6, 12, 18, and 24 months). A total of 446 unique change profiles were found in the sample of 2,088 smokers who had complete data for all five time points. The sample was initially split into three groups determined by baseline stage of change. Within each initial stage, subgroups that shared similar patterns of change were interpreted by examining the shape and elevation of the change profiles. Four major types of profiles emerged: a stable profile, a progressing profile, a vacillating profile, and a regressing profile. External validation revealed significant differences among the dynamic typology subtypes on key transtheoretical model constructs (processes of change, decisional balance, situational temptations). These results both support and complement key construct relationships within the transtheoretical model and can provide important predictive information to direct and enhance treatment interventions for smoking cessation.

Stages of Change, Self-Efficacy, and the Adoption of Vigorous Exercise: A Prospective Analysis

Components of the transtheoretical model of change were examined in a prospective study of the adoption of vigorous exercise in adults. Respondents to a random mail survey were resurveyed 2 years later. Those who reported no vigorous exercise at baseline were classified as either contemplators (n = 213) or precontemplators (n = 188). Contemplators had higher baseline self-efficacy scores than precontemplators (p &lt; .001). In multivariate analyses, baseline stage of change was a significant predictor (p &lt; .0005) of later adoption of vigorous exercise, even after controlling for differences in age, gender, and self-efficacy. During the first 6 months postbaseline, contemplators were nearly twice as likely as precontemplators to progress to the stage of action (46% vs. 24%), and four times more likely to progress to the stage of maintenance (25% vs. 6%). Use of the transtheoretical model in the study of exercise was supported in this prospective examination of exercise in a community sample.