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InShape Prevention Plus Wellness

InShape Prevention Plus Wellness

A brief prevention program to improve physical, mental, and spiritual well-being of college students by connecting positive health habits and images with the avoidance of risky alcohol, tobacco, marijuana and other drug use.

Fact Sheet

Program Outcomes

  • Alcohol
  • Illicit Drug Use

Program Type

  • Cognitive-Behavioral Training
  • Drug Prevention/Treatment
  • School - Individual Strategies

Program Setting

  • School

Continuum of Intervention

  • Universal Prevention

Age

  • Early Adulthood (19-22)

Gender

  • Both

Race/Ethnicity

  • All

Endorsements

Blueprints: Promising
SAMHSA: 2.4-2.7

Program Information Contact

Chudley Werch, Ph.D., President
PreventionPLUSWellness
3595 Forest Bend Terrace
Jacksonville, FL 32224
904-472-5022
cwerch@preventionpluswellness.com
preventionpluswellness.com

Program Developer/Owner

Chudley Werch, Ph.D.
PreventionPLUSWellness


Brief Description of the Program

InShape Prevention Plus Wellness is based on the Behavior-Image Model (BIM) which states that positive social images and future self-images can be used to link and simultaneously motivate multiple different health risk habits of college students. InShape emphasizes the positive image benefits of setting goals to increase physical activity and exercise, healthy eating, sleep, and stress management, while avoiding alcohol, cigarette and illicit drug use to achieve and maintain a fit and active lifestyle. The main program components include a self-administered behavior image survey, a brief talk about fitness and health with a designated Fitness Specialist, and a set of fitness recommendations and goal plan to improve fitness behaviors and future image. Although materials developed by the program designer are available in both a group and a one-on-one format, only the one-on-one version is certified by Blueprints as it is the version that was used in the evaluation that met Blueprints quality standards.

Outcomes

In a study with over 300 college students, 12 weeks after program initiation, In-Shape relative to a control group resulted in:

  • reduced frequency and heavy use of alcohol,
  • reduced driving after drinking,
  • reduced initiation, quantity, and heavy use of marijuana,
  • increased hours of sleep,
  • improved spiritual and social health,
  • no significant results on cigarette use, exercise, and nutrition behaviors.

Brief Evaluation Methodology

A total of 303 college students attending a southeastern university in the fall of 2006 who had visited the campus medical services center and who volunteered for participation were randomly assigned to: 1) a brief one-on-one tailored consultation with goal plan; or 2) standard care print material, with three-month post-intervention data collected. The posttest assessment was conducted at week 12 (3 months postintervention).

Study 1

Werch, C. E., Moore, M. J., Bian, H., DeClemente, C. C., Ames, S. C., Weiler, R. M., . . . Huang, I. (2008). Efficacy of a brief image-based multiple behavior intervention for college students. Ann Behav Med, 36(2), 149-157.


Risk Factors

Individual: Stress*

Protective Factors

Individual: Exercise, Perceived risk of drug use

See also: InShape Prevention Plus Wellness Logic Model (PDF)

InShape is aimed at college-aged young adults, 18-21.

Race/Ethnicity/Gender Details

The majority of the sample was Caucasian (71.6%), followed by African American (12.7%), and Hispanic (8.7%). There were no tests related to specificity of results by gender or ethnicity.

Attending a best practices training workshop or webinar is required prior to implementing the InShape program. Training workshops/webinars provide interventionists/teachers with critical information and suggestions on how to successfully implement and evaluate the program, as well as how to adapt program materials to specific settings and populations. Instructor/implementer training workshops are approximately four hours in length, while webinars are about two hours plus two hours of assigned practice implementing InShape. Upon completing the workshop or webinar training, participants are required to take and pass a brief exam. Each InShape program includes implementation support. Program users can call or email about any problems or questions they may have during their program implementation. Assistance and recommendations will be provided upon request to users regarding how to best implement and evaluate InShape for maximum success and cost-effectiveness.

NOTE: Webinar training was not used in the evaluations which certified InShape and has thus not been certified by Blueprints.

Training Certification Process

A passing score of 100% correct on a multiple item response test is needed to be certified to implement InShape.

Program Benefits (per individual): $451.00
Program Costs (per individual): $15.00
Net Present Value (Benefits minus Costs, per individual): $466.00
Measured Risk (odds of a positive Net Present Value): $47.00

Source: Washington State Institute for Public Policy All benefit-cost ratios are the most recent estimates published by The Washington State Institute for Public Policy for Blueprint programs implemented in Washington State. These ratios are based on a) meta-analysis estimates of effect size and b) monetized benefits and calculated costs for programs as delivered in the State of Washington. Caution is recommended in applying these estimates of the benefit-cost ratio to any other state or local area. They are provided as an illustration of the benefit-cost ratio found in one specific state. When feasible, local costs and monetized benefits should be used to calculate expected local benefit-cost ratios. The formula for this calculation can be found on the WSIPP website.

Program Costs

Start-Up Costs

Initial Training and Technical Assistance

InShape is provided to college students and young adults by staff from colleges, community groups and clinics. In-person program instructor/implementer training with program materials is available to a minimum of eight staff at workshops costing $5,984 plus travel. In-person Training of Trainers (TOT) workshops with program and training materials are available to a minimum of four staff costing $5,992 plus travel. Webinar training costs $499 per trainee and includes the cost of program materials; however, this form of training was not used in evaluations and thus is not Blueprints-certified.

Curriculum and Materials

Program materials including program manual, digital downloads of reproducible materials and PowerPoint slides are included in the cost of in-person training.

Licensing

Certification testing after training.

Other Start-Up Costs

None.

Intervention Implementation Costs

Ongoing Curriculum and Materials

None.

Staffing

No information is available

Other Implementation Costs

None.

Implementation Support and Fidelity Monitoring Costs

Ongoing Training and Technical Assistance

Program support by email and phone.

Fidelity Monitoring and Evaluation

Fidelity monitoring and outcome evaluation instruments included in program materials.

Ongoing License Fees

None.

Other Implementation Support and Fidelity Monitoring Costs

No information is available

Other Cost Considerations

No information is available

Year One Cost Example

As an example, a college with 8 staff in its student health clinic who see all incoming students could offer InShape.

Trainer travel $1,000
In-person training and program materials for 8 staff $5,984
Total Year One Cost $6,984

The total cost for the college to implement InShape in Year 1 would be $6,984. The per student expense would be largely dependent on the size of the student population and number of students accessing the clinic annually.

Funding Strategies

Funding Overview

Since InShape is implemented by the training of existing staff at colleges, universities, community groups and clinics, it is likely that the minimal cost would be absorbed by the implementing institutions as part of staff training. Therefore, most of the funding options commonly considered would not be relevant.

Funding Strategies

Improving the Use of Existing Public Funds

No information is available

Allocating State or Local General Funds

Public colleges and universities would implement InShape from existing training funds for their student health facilities.

Maximizing Federal Funds

No information is available

Foundation Grants and Public-Private Partnerships

Private colleges and universities could consider funds from donors to pay for the initial InShape training.

Debt Financing

No information is available

Generating New Revenue

No information is available

Data Sources

All information comes from the responses to a questionnaire submitted by the developer of InShape, Dr. Chudley Edward Werch, PhD, to Blueprints.

Evaluation Abstract

Program Developer/Owner

Chudley Werch, Ph.D. President PreventionPLUSWellness 3595 Forest Bend Terrace Jacksonville, FL 32224 (904) 472-5022 cwerch@preventionpluswellness.com preventionpluswellness.com

Program Outcomes

  • Alcohol
  • Illicit Drug Use

Program Specifics

Program Type

  • Cognitive-Behavioral Training
  • Drug Prevention/Treatment
  • School - Individual Strategies

Program Setting

  • School

Continuum of Intervention

  • Universal Prevention

Program Goals

A brief prevention program to improve physical, mental, and spiritual well-being of college students by connecting positive health habits and images with the avoidance of risky alcohol, tobacco, marijuana and other drug use.

Population Demographics

InShape is aimed at college-aged young adults, 18-21.

Target Population

Age

  • Early Adulthood (19-22)

Gender

  • Both

Race/Ethnicity

  • All

Race/Ethnicity/Gender Details

The majority of the sample was Caucasian (71.6%), followed by African American (12.7%), and Hispanic (8.7%). There were no tests related to specificity of results by gender or ethnicity.

Risk/Protective Factor Domain

  • Individual

Risk/Protective Factors

Risk Factors

Individual: Stress*

Protective Factors

Individual: Exercise, Perceived risk of drug use

See also: InShape Prevention Plus Wellness Logic Model (PDF)

Brief Description of the Program

InShape Prevention Plus Wellness is based on the Behavior-Image Model (BIM) which states that positive social images and future self-images can be used to link and simultaneously motivate multiple different health risk habits of college students. InShape emphasizes the positive image benefits of setting goals to increase physical activity and exercise, healthy eating, sleep, and stress management, while avoiding alcohol, cigarette and illicit drug use to achieve and maintain a fit and active lifestyle. The main program components include a self-administered behavior image survey, a brief talk about fitness and health with a designated Fitness Specialist, and a set of fitness recommendations and goal plan to improve fitness behaviors and future image. Although materials developed by the program designer are available in both a group and a one-on-one format, only the one-on-one version is certified by Blueprints as it is the version that was used in the evaluation that met Blueprints quality standards.

Description of the Program

Fitness Behavior Image Screen - College-aged young adults, ages 18-21, first complete the Fitness Behavior Screen, a nine-item instrument on selected health behaviors addressed in the consultation and goal plan. The items ask participants about their physical activity, exercise, diet, sleep, stress management habits, gender, and their alcohol and cigarette use, as well as their desire to achieve selected images, using primarily yes and no response items. Responses are used to tailor consultation messages to each participant's specific health habits.

Consultation and Goal Plan - After participants complete the Fitness Behavior Screen, they are provided with scripted messages by the fitness specialist using a consultation protocol. Consultations last approximately 25 minutes. The consultation is based on the Behavior-Image Model, emerging paradigm for planning multiple behavior interventions. The Model uses 'gain' framed messages to illustrate how health promoting behaviors promote salient social and self-images, and 'loss' framed messages to show how health risk behaviors interfere with image outcomes and achievement of health promoting habits. Image-based gain and loss framed messages are thought to activate prototypes and future self-images, thereby coupling and motivating multiple behavior change within single, brief interventions. The consultation protocol provides tailored content addressing each of the health behaviors in the screen and their relation to salient image achievement. PowerPoint slides are shown at designated points in the consultation to reinforce key images and health behaviors using colorful text and illustrations.

At the conclusion of the consult, the fitness specialist provides participants with a one-page goal plan. The goal plan is also based on the Behavior-Image Model as well as research indicating that the selection of self-concordant goals reflecting one's image or aspirations facilitates behavior change. The plan includes fitness recommendations which reiterate the key points of the consultation, and couples salient images to target behaviors. For example, one recommendation is to participate in moderate physical activity for at least 30 minutes on most days of the week if one wants to be a more physically active young adult. Then, participants are asked to select at least one goal from each of four behavior groups to improve in the next week, including: 1) increase physical activity & exercise, 2) decrease alcohol use, 3) decrease cigarette use, or 4) increase other fitness behaviors (i.e., nutrition, stress management, other drug use, and sleep).

Theoretical Rationale

InShape Prevention Plus Wellness is based on the Behavior-Image Model (BIM) which asserts that positive social images and future self-images can be used to link and simultaneously motivate multiple divergent health risk habits among young adults and adolescents. BIM is also founded on self-regulation theory of health indicating that programs provide feedback on behaviors and self-images to increase commitment to setting concrete goals for change across multiple health habits.

Theoretical Orientation

  • Cognitive Behavioral

Brief Evaluation Methodology

A total of 303 college students attending a southeastern university in the fall of 2006 who had visited the campus medical services center and who volunteered for participation were randomly assigned to: 1) a brief one-on-one tailored consultation with goal plan; or 2) standard care print material, with three-month post-intervention data collected. The posttest assessment was conducted at week 12 (3 months postintervention).

Outcomes (Brief, over all studies)

The results indicate that a brief multiple behavior intervention consisting of a screening survey, one-on-one consult tailored to targeted health behaviors, and behavioral goal plan appears to have decreased marijuana and alcohol consumption and driving after drinking, increased hours of sleep, and improved spiritual and social health-related quality of life, compared to students receiving standard health care information. In addition, effect sizes were typically two to four times larger for brief intervention young adults than control participants on measures found to improve over time for both treatment groups. These effects were found 12 weeks after initiation.

Outcomes

In a study with over 300 college students, 12 weeks after program initiation, In-Shape relative to a control group resulted in:

  • reduced frequency and heavy use of alcohol,
  • reduced driving after drinking,
  • reduced initiation, quantity, and heavy use of marijuana,
  • increased hours of sleep,
  • improved spiritual and social health,
  • no significant results on cigarette use, exercise, and nutrition behaviors.

Mediating Effects

None reported.

Effect Size

Effect sizes were invariably weak. For the 9 significant program effects (out of 29 outcomes tested), the average effect size was. 28 (ranging from .19 to .38).

Generalizability

This study is generalizable to similar college populations.

Limitations

Students volunteered to participate in the program, with all participants having visited the campus medical center, which may have resulted in a selection bias with students already interested in improving their health volunteering. There is also concern that the information in the materials provided to the control group was similar to the information given to those in the intervention condition, which could speak to why several desired outcomes were observed in both groups. Only a three-month follow-up was utilized. The study was conducted at a single college site. There was no report of differential attrition between the groups, however, attrition rates were low with 6 lost from intervention and 12 lost from the Control group.

Endorsements

Blueprints: Promising
SAMHSA: 2.4-2.7

Program Information Contact

Chudley Werch, Ph.D., President
PreventionPLUSWellness
3595 Forest Bend Terrace
Jacksonville, FL 32224
904-472-5022
cwerch@preventionpluswellness.com
preventionpluswellness.com

References

Study 1

Certified Werch, C. E., Moore, M. J., Bian, H., DeClemente, C. C., Ames, S. C., Weiler, R. M., . . . Huang, I. (2008). Efficacy of a brief image-based multiple behavior intervention for college students. Ann Behav Med, 36(2), 149-157.

Werch, C. E., Moore, M. J., Bian, H., DeClemente, C. C., Ames, S. C., Weiler, R. M., . . . Huang, I. (2008). Efficacy of a brief image-based multiple behavior intervention for college students. Ann Behav Med, 36(2), 149-157.

Study 1

Evaluation Methodology

Design: Students aged 18-21 years who were currently enrolled at the target university and who visited the campus medical services center were eligible for this trial conducted in the fall of 2006. Students attending the medical center were recruited to participate in a study evaluating a new health promotion program titled Project Fitness. Posters and flyers were placed in the center announcing the new study. Students were asked to complete a registration sheet so that research staff could call them to schedule an appointment to provide a complete description of the study purpose and risks. Additional announcements were made on the university's weekly student update email, and by distributing flyers in selected undergraduate health courses and common areas throughout campus. Students were paid $20 for participating in each of two data collections.

Participants were randomly assigned to receive either a brief tailored consultation and fitness goal plan, or standard care print materials as they presented for appointments with a fitness specialist (i.e., trained bachelor's level research staff). All fitness specialists received a two-day training that included demonstrations, role-playing with other research personnel, feedback from research staff, and take-home practice on how to implement the consultation and goal plan. The quality of consultation and goal plan implementation was ensured by using a standardized implementation protocol, with randomly selected intervention sessions audio-taped to monitor implementation quality across interventionists.

After providing written consent, all students completed a brief paper-and-pencil health behavior screen, and then the baseline survey via a secure online computer program in a quiet office on campus. Immediately after the collection of baseline data, participants were provided with one of the two interventions, and then completed an online feedback questionnaire on the acceptability of the interventions. Participants were contacted 11 weeks after their initial appointment in order to schedule the follow-up survey at week 12 (three months post-intervention). A total of 283 students completed the post-intervention data collection for a response rate of 93%.

Participants in both groups were first asked to complete the Fitness Behavior Screen, a nine-item instrument designed to elicit responses on selected health behaviors addressed in the consultation and goal plan. Responses were used to tailor consultation messages to each participant's specific health habits. After participants completed the screen and baseline survey, those assigned to receive the one-on-one consultation (intervention group) were provided with scripted messages by the fitness specialist using a consultation protocol. Consultations lasted approximately 25 minutes. At the conclusion of the consult, the fitness specialist provided participants with a one-page goal plan. The control consisted of a commercial brochure titled “Fitness.” The brochure included information about the benefits of being fit including characteristics of people who are physically fit, the three components of fitness, the FIT method (Frequency, Intensity, Time), and an action plan and commitment form to identify habits to start, stop, and keep. Participants assigned to this condition were asked to take time to read the brochure in the quiet, private office. After reading the brochure, students completed the online feedback questionnaire.

Sample: A total of 303 college students attending a mid-sized southeastern university were recruited throughout the fall of 2006 to participate in this trial. During baseline data collection, a computer error resulted in the loss of four participants' data, yielding 299 usable surveys. The majority of participating students were female (59.5%), with a mean age of 19.2 years old (SD=1.12). The majority of the sample was Caucasian (71.6%), followed by African American (12.7%). Nine percent (8.7%) reported being Hispanic. Most participants lived in a co-ed residence hall (44.8%), or off-campus housing (38.5%).

Measures: The updated Fitness & Health Survey was used to collect data on alcohol, cigarette, and marijuana consumption, alcohol and drug problems, driving after drinking, exercise behaviors, nutrition habits, sleep quantity, frequency of using stress management techniques, and five areas of health quality of life. Health risk behaviors measured included alcohol, cigarette, and marijuana use items adopted from standard youth substance use instruments and research, including four measures of length of use, 30-day frequency, 30-day quantity, and 30-day heavy use for alcohol, cigarettes, and marijuana. Heavy use for alcohol was defined as 5 or more drinks in a row if a male and 4 or more drinks in a row if female, whereas heavy use for smoking was a pack or more of cigarettes, and heavy use for marijuana was getting really high or stoned from marijuana. An 18-item measure of alcohol and drug problems experienced during the past 30-days was included. In addition, a single measure of driving after drinking alcohol was adopted from prior epidemiologic studies.

Health promoting behaviors measured included exercise, nutrition habits, sleep habits, and use of stress management techniques. Five exercise behavior measures were adopted from past research, and included length of exercising, 30-day vigorous exercise, 30-day moderate exercise, 7-day strenuous exercise, and 7-day moderate exercise. Three measures of nutrition habits were based on dietary guidelines from the U.S. Department of Health and Human Services and U.S. Department of Agriculture, and included past 30-day servings of fruits and vegetables, numbers of times eating foods containing healthy carbohydrates, and numbers of times eating foods containing healthy fats. Sleep was measured with one item of the number of hours usually slept each night during the past 30 days, taken from prior research on sleep patterns. Frequency of five techniques used to relieve stress in the past 30-days was adopted from a health promotion scale for adolescents. Health-related quality of life was measured using five items. These assessed the number of days during the past 30-days that physical health, mental health, spiritual health, and social health was not good, and the number of days that poor health of any kind kept one from doing their usual activities. These measures were adopted from research on health-related quality of life among adolescents.

Analysis: Repeated measures MANOVAs and ANOVAs were used to test intervention effects over time. Repeated measures MANOVAs were performed to more efficiently address the multiple health behaviors targeted by the intervention. This approach creates a new dependent variable maximizing group differences, while controlling for Type I error resulting from performing individual tests on multiple dependent variables. Repeated measures ANOVAs were used to examine temporal effects on single behavior health measures. Effect sizes were calculated using Cohen's d statistic based on standard deviations of baseline and post-intervention scores within treatment groups.

Outcomes

Baseline Equivalence and Attrition Analyses: No significant differences were found on any of the socio-demographic, substance use, or other health behavior measures between groups. Sixteen participants were lost to attrition (5%), with no differences in attrition between treatment groups. Significantly more students who dropped out of the study received mostly B grades (rather than A grades) on their last report card, reported a family alcohol or drug problem, and used marijuana in the past 30 days, than those who did not drop out.

Response and Intervention Implementation Fidelity: To determine the likelihood of participants responding to questions on the outcome survey in a socially desirable manner, students were asked about their willingness to provide honest answers to questions about their alcohol and drug use and other health habits. At baseline, 92.6% strongly agreed and 7.4% agreed that they were willing to give honest answers to questions on the survey, with none disagreeing or strongly disagreeing, indicating little probable influence of social desirability. In addition, to estimate the extent to which responses may have been unreliable due to participant lying or other factors, a bogus/fake drug (i.e., Zanatel) was included among the list of substances that students were asked whether they used in the past 30-days. No one reported using the bogus drug, suggesting that widespread error due to lying or sloppy completion of the data collection instrument was unlikely.

To assess implementation fidelity, feedback was collected from participants immediately after administration of each intervention using a computer based, self-administered questionnaire. These data showed that participants who received the consultation and goal plan rated the intervention significantly better than those who received the standard care control on eight of nine measures of acceptability and potential efficacy.

Posttest:

Omnibus repeated measures MANOVAs were performed for six groupings of health behavior measures. These analyses were significant for group by time interaction on alcohol consumption, marijuana use, and health-related quality of life. Univariate repeated measures tests showed college students exposed to the brief intervention drank alcohol less frequently, as well as drank heavily less often, whereas students receiving the standard care control increased their alcohol use frequency and heavy use over time. The intervention group also used marijuana for a shorter length of time, used less quantity of marijuana, and used marijuana heavily less often, while the control group showed increases in these three measures of marijuana use over time. In addition, brief intervention participants experienced fewer days in which their spiritual health was not good, and fewer days in which their social health was not good, compared to control participants. While no omnibus group by time interactions were found for exercise, a univariate group by time interaction was found for 30-day moderate exercise, with brief intervention participants showing an increase and control participants a decrease in moderate exercise in the past 30 days. In addition, ANOVAs were performed for another four single measure health behaviors. Participants in the intervention group got more sleep and drove less after drinking alcohol, than those in the control group.

MANOVAs also indicated significant time effects for exercise, nutrition, and health-related quality of life. Univariate tests showed increases in 30-day vigorous exercise for participants in both groups, and increases in the consumption of healthy carbohydrates, but just among control participants, and healthy fats, but primarily among those participants receiving the intervention. In addition, students in both treatment groups had fewer days in which their physical health was not good, fewer days in which their mental health was not good, and fewer days in which poor health kept them from conducting their usual activities. Finally, ANOVAs for single measure behaviors indicated significant time effects with fewer alcohol/drug problems among participants in both groups, increased number of hours of sleep each night for participants in both groups, and increased use of stress management techniques for participants in both groups.

Effect sizes were calculated for univariate tests within treatment groups. These effect sizes were generally small, with some approaching medium size. Small effects were found for the brief intervention on alcohol and marijuana behaviors, with reductions on alcohol and marijuana among brief intervention participants that paralleled equal size increases in consumption in the control group. Small effects were also found for the intervention group on reduced driving after drinking, and increased vigorous and moderate exercise. Small effect sizes were found for brief intervention participants on increasing two nutrition habits (i.e., eating healthy carbohydrates and fats), and improving all five measures of health-related quality of life, with the greatest improvements on spiritual, social, and mental health. Larger effects were also found for the intervention group on reductions in alcohol/drug problems, with effects approaching medium size on increases in sleep and stress management. On measures shown to significantly improve over time for both treatment groups, effect sizes were generally two to four times larger for brief intervention participants than for control students.

Contact

Blueprints for Healthy Youth Development
University of Colorado Boulder
Institute of Behavioral Science
UCB 483, Boulder, CO 80309

Email: blueprints@colorado.edu

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