Exercise Activity: determining the perceived barriers and physical benefits

Experiment conducted by Augustine A. Zavala and Donald Lowes

Transfer and freshman students were recruited and exposed to three different interventions intended to gauge the use of the Mavericks Activity Center at the University of Texas at Arlington. One group received information about the MAC through brochures; the second group received a brochure coupled with a tour of the MAC, and the final group viewed a demonstration of use of exercise equipment in the MAC in addition to brochures. Students answered questionnaires to measure perceived barriers to exercise and self-efficacy. The results indicated that there was no significant effect between the groups, and no significant effect within groups from Time 1 to Time 2. There was a small effect size of self-efficacy negatively with barriers in Time 1, and positively with benefits in Time 2. The hypothesis that groups receiving information about services available about the MAC will report fewer barriers to exercise than the non-intervention groups wasn’t supported. The hypothesis that groups receiving the interventions will also report greater benefits than the non-intervention groups weren’t supported either.

The topic of physical activity has been receiving a lot of attention in the media and the health research community. With the increased rates of obesity and physical ailments associated with lack of exercise, researchers have been looking into identifying the barriers to exercise. At the same time, researchers are hoping to find mechanisms to increase the frequency of and adherence to an exercise routine. The Maverick Activity Center on the campus of the University of Texas at Arlington has gone through a multi-million dollar renovation. The improvements have included more space for working out, a computer section, and more physical fitness activities for students to participate in. We have initiated a study to determine what factors affected the usage of the MAC (Maverick Activity Center) by incoming freshman or transfer students. The sample of students was divided into three groups to determine which level of intervention increased the level of participation in activity at the MAC. Students answered an online questionnaire asking questions about level of MAC usage and amount of physical activity. The hypotheses proposed that the groups that receive information about services available about the MAC would report fewer barriers to exercise than the non-intervention groups. The groups receiving the interventions will also report greater benefits than the non-intervention groups. One group received information about the MAC through brochures; the second group received a brochure coupled with a tour of the MAC, and the final group viewed a demonstration of use of exercise equipment in the MAC in addition to brochures.

The environment and distance to exercise facilities may be a factor in the level of physical activity. Cerin, Leslie, Vandelanotte and Merom (2008) researched the moderators of leisure time physical activity and self efficacy as a mediator to exercise. They hypothesized that environmental factors such as ease of access to recreational facilities, the ease of walking, running, or riding a bike in a neighborhood will have an impact on the leisure time physical activities of people. Participants were recruited through mass mailings that included an introduction letter and a postage paid return envelope. A lottery based incentive was used to increase response to the mailings. 2650 participants were recruited and were given self report questionnaires to answer. The questionnaires asked participants about home exercise equipment availability, perceived convenience of access to recreational facilities, and factors for neighborhood selection. A likert survey was used to measure self efficacy for moderate to vigorous physical activity. An International Physical Activity Questionnaire was used to assess leisure time physical activity. The results indicated that significant associations between recreational facilities and LTPA were a minor factor for residents’ selecting to live in a specific neighborhood because of access to facilities (p.131). Individuals who were within walking or riding distance of recreational facilities exercised more vigorously than individuals with no access to recreational facilities. People who didn’t have access to recreational facilities resorted to walking or running and exercised less vigorously. The findings also suggested that individuals who didn’t prioritize exercise may do so if facilities are close enough for them to use. Students who live on campus and near the MAC may be motivated to use it more often than those who live off campus.

In the previous paragraphs, distance and access to exercise facilities affected the use of recreational facilities. Motl, Dishman, Ward, Saunders, Dowda, Felton and Pate (2005) compared barriers to self efficacy and perceived behavioral control among adolescent girls over a 1 year period. Participants were eighth and ninth grade girls recruited from 24 schools with a total sample of 1038. The ethnicity of the girls was roughly split between Caucasian and African American. Barriers to behavioral control and self-efficacy were recorded using a likert type questionnaire. A 3 day physical activity recall questionnaire was used to measure the frequency and intensity of physical activity. The study found that perceived behavioral control is a viable mediator of behavior, and that targeted social-cognitive interventions increased the girls’ physical activity during early adolescence (p.109-110). The intervention that increases the participant’s perception that they are in control of their physical activity may also increase their level of exercise.

The type of physical activity intervention may increase or decrease the rate of exercise among individuals. Napolitano et al. (2008) studied which interventions were the most effective for mediating physical activity behavior change. The three modes of intervention were print based feedback, telephone based feedback, and contact control. The sample studied was 239 healthy men and women whose physical activity was monitored over a 12 month period. The psychosocial variables that were measured were self efficacy, decisional balance and processes of change. The participants were recruited from newspaper ads requesting sedentary men and women from ages 18-65, worked out 90 minutes or less per week. The sample was randomly assigned to each of the three interventions. The process of change and self efficacy were assessed with a survey and questionnaire. Decision making related to exercise was measured using a 16 item decisional balance questionnaire. The results indicated that the print arm had an indirect effect of increasing physical activity over the control group, while the telephone arm produced an even larger benefit (p.415). Behavioral processes also mediated the intervention effect, while cognitive processes had a suppressor effect (p.417). Continual follow ups with the MAC users may be effective in increasing usage in the short term, but may not be economically feasible. Contacting activity center members via email on a quarterly basis may be effective.

Barriers to physical activity were researched by Salmon, Crawford, Owen, Bauman and Sallis (2003). They hypothesized that individuals who had high enjoyment and preference for exercise would report fewer barriers to physical activity. A group of 3,000 individuals were randomly selected with a sample of 1332 individuals responding to a survey to participate in the study. Leisure time and physical activity was recorded using a self report physical activity recall measure. The questionnaire asked about the frequency and duration of the physical activity. The leisure time sedentary behaviors were measured using a 1 week recall questionnaire. The participants listed their leisure activities, from watching T.V. to going for a leisurely drive. The barriers to exercise were rated on a 5 point Likert scale with 1 (not a barrier) to 5 (very much a barrier). Environmental barriers included the weather, air or noise pollution, and access to facilities. Personal barriers were injury, work commitments, family activities, exhaustion or lack of time. Enjoyment of physical activity and sedentary behavior was rated on a scale from 1 (no enjoyment) to 5 (very enjoyable). The final measurement assessed whether individuals preferred to participate in physical activities or sedentary activities. The results indicated that high barriers to physical activity were positively associated with a lack of exercise participation; individuals who were physically fit reported higher scores on the enjoyment scale than obese individuals (p.186). Sedentary behaviors were listed more often by overweight individuals, while physically active people preferred to engage in activities outside of the home. It may be that interventions that are addressing barriers to physical activity may increase the level of fitness of sedentary individuals. Instructional demonstrations on equipment use, access to nutritional counseling, and easy access to the facility may increase the level of enjoyment for exercise. This in turn could increase adherence to an exercise routine and increased MAC usage.

Understanding the patterns of sedentary behavior may give insight into designing an effective exercise intervention. Zabinski, Norman, Sallis, Calfas and Patrick (2007) studied the behaviors of adolescents. The participants, adolescents age 11 to 15, were recruited from 45 primary care providers within 6 clinics. The children in the study completed a web based questionnaire about the child’s home, neighborhood and family support for exercise. Parents of the children completed a pen and pencil survey reporting the child’s ethnicity and grades in school. The behaviors observed were watching T.V., working on the computer, talking on the phone, reading and doing homework. These were measured using a self report measure outlining time spent doing these activities. The child’s environment was measured by detailing the items found in the home that could facilitate physical activity, such as exercise equipment and access to a playground. The psychosocial variables like family support, self efficacy, and enjoyment of physical activity were measured using a Likert scale. The frequency of these activities was related to participant’s body weight as well as health enhancing behaviors. The results indicated that increasing the self efficacy of adolescents and parents limits the time spent on sedentary behaviors and this could be effective in increasing physical activity. An intervention that instructs students on how to design a diet and workout regimen for themselves could increase their level of self-efficacy. This could also reduce the amount of time spent on sedentary behaviors.

This study will measure the levels of physical activity after informing the groups about the MAC by using three different types of interventions. One group will be informed about the services available by brochure, the next group by presentation of the MAC with brochures, and the final group with demonstration of the MAC with equipment and brochures. The hypotheses proposed will be that the groups that receive information about services available about the MAC will report fewer barriers to exercise than the non-intervention groups. More specifically, the groups that receive the presentation and brochures about the MAC will increase their level of physical activity and self-efficacy over the brochure only group. The group that receives the demonstration of the MAC with equipment and brochures will show the highest level of exercise and self-efficacy.

Methods

Participants

Participants were incoming freshman and transfer students attending classes at University of Texas at Arlington. The students were recruited from the psychological study participant pool using SONA. No monetary award was given for participating; however students received extra credit points at the completion of the study. The sample of 23 students selected were students attending a course in psychology. Demographically the participant pool was 6 male and 17 females with ages ranging from 18-39 with a SD=5.23. These individuals were randomly assigned to three groups: 1) Group will be informed about the services offered at MAC by brochure. 2) Group will be informed with a presentation of the MAC with brochures. 3) The final group with demonstration of the MAC with equipment and brochures. Each participant received an overview of the experiment that outlined the expectations and extra credit awarded for their participation. No monetary award was offered. They also were briefed on the IRB protocol stating that they would be treated ethically.

Measures

A total of 6 questionnaires were used to assess and measure the different conditions associated with physical activity at the MAC. The scales are listed below:

a. Perceived Barriers and Benefits

The scale for perceived barriers and benefits scale was developed using 4 different scales with the duplicates removed then randomized. There are 34 barriers and 34 benefits with the scores of both being added together and averaged. The barriers addressed included distance, time constraints, and social support. The perceived benefits questions asked about possible change in health status, changes in emotional well being due to exercise, and how increased physical activity could benefits ones health, as well as possible benefits to exercise. A total of 69 questions were asked. This scale will be used to test the hypothesis that individuals who have a low perceived barrier and high benefit assessment of exercise will use the MAC more often than those who have high barriers, low benefit to exercise, ( Chronbachs alpha 0.88; Arroyave, Clipp, Miller, Jones, Ward, Bonner, Rosoff , Snyder, & Denmark-Wahnefried 2008; Brown, Huber & Bergman 2006; Myers & Roth 1997; Sechrist, Walker & Pender 1987).

b. Self -Efficacy

To measure self efficacy the scores were added and averaged using the final total as the individual’s level of self-efficacy. Eighteen different situations were described that may impact the individuals attitude and decision to exercise. The scale, which ranged from a 0=can’t do at all to 100= certainly can do, rated the individual’s confidence to exercise depending on the specific situation described. This measure tested our hypothesis that those who score higher on the self-efficacy to regulate exercise will use the MAC more often than those who score lower, (Chronbachs alpha 0.92-0.96; Bandura 2006).

Design

The study is a randomized mixed design incorporating between and within group’s methods. There were three intervention groups: 1) Received brochure only. 2) Received brochure and presentation. 3) Received demonstration of equipment and brochures. The independent variables are the three interventions: brochure, brochure and presentation, demonstration of equipment and brochures. The dependent variable is the level of MAC use depending on the type of intervention. These were measured by the weekly self report SHEP questionnaire sent online.

Procedure

The participants were incoming freshman/transfer students taking an introduction to psychology class. The students who participated were briefed on the study and were told their participation was voluntary. No monetary award was given, however they would receive extra credit points towards their psychology course. After receiving the initial briefing and signing an acknowledgment stating they understood their expectations, they were given times to attend a meeting about the MAC. The participants were randomly assigned to the three experimental groups. Two groups were scheduled to receive the brochure and presentation and the demonstration of equipment at the MAC and brochures; the final group received brochure only. All groups answered an online SHEP questionnaire that was sent weekly. At the conclusion of the experiment the participants were debriefed.

Results

Table 1 shows reports the correlation between self-efficacy and perceived benefits/barriers for time 1&2. For time 1 self-efficacy benefits the results were not significant, with r (23) = .15, p > .05 vs. time 2 at r (19) = .21, p > .05. For time 1 self efficacy barriers the results were not significant, with r (23) = -0.21, p >.05 vs. time 2 at r (19) = -0.15, p > .05. Figure 1 shows the means barriers between SHEP groups at time 1 & 2. Participants in the SHEP 1 presentation condition reported the lowest mean barriers (M = 2.2, SE = 0.52). Figure 2 shows the mean benefits between SHEP groups at time 1 & 2. Participants in the SHEP 1 demonstration condition reported the highest mean benefits (M = 5.6, SE = 0.41).

Discussion

The results indicated that there was no significant effect between the groups, and no significant effect within groups from Time 1 to Time 2. There was a small negatively effect size of self-efficacy with barriers in Time 1, and positively with benefits in Time 2. Although the results weren’t significant, the comparison of the means indicated that there were some subtle differences between interventions during SHEP one and two. There were many confounds that impacted our study. The IRB did not approve the protocol for our study until three weeks after the fall 2008 semester started. This impaired our ability to recruit more students for the study, with a sample of only 23 students. The SHEP questionnaire was very detailed, long, and had to be completed weekly. This could cause the participants to have a response bias, answering the questions using the same answer for most or all of the questions.

Questionnaires were used to measure changes in physical activity, self-efficacy and barriers to exercise. Another way to determine if the participants are using the MAC is to monitor their MAVS card swipe at the facility. Barring any possible issues with privacy or IRB protocol, the experimenters could combine the questionnaire scores with the number of reported visits to the MAC. This could add some validity to the results. People who perceive fewer barriers to exercise, increased self-efficacy should use the MAC more often, and card swipes could be another indicator of that. Another facet that could be added to the exercise demonstration is to have participants actually use the equipment after the demonstration with the trainer present. This could have a positive effect in self-efficacy and a reduction of barriers towards exercise. Although we didn’t find any statistical significance within our study, some of our observations led us to conclude that there should be further research into the topic.

References

Arroyave, W., Clipp, E., Miller, P., Jones, L., Ward, D., Bonner, M., Rosoff, P., Snyder, D., & Denmark-Wahnefried, W. (2008). Childhood cancer survivors’ perceived barriers to improving exercise and dietary behaviors. Oncology Nursing Forum, 35 (1), 121-130.

Bandura, A. (2006). Guide for constructing self-efficacy scales. In T. Urdan & F. Pajares (Eds.) Self-Efficacy Beliefs of Adolescents (pp. 307-337).

Brown, S. A., Huber, D., & Bergman, A. (2006). A Perceived Benefits and Barriers Scale for Strenuous Physical Activity in College Students. American Journal of Health Promotion, 21 (2), 137-140.

Cerin, E., Leslie, E., Vandelanotte, C., Merom, D. (2008). Recreational facilities and leisure-time physical activity: An analysis of moderators and self-efficacy as a mediator. Health Psychology, 27 (2), 131.

Motl, R.W., Dishman, R.K., Saunders, R.P., Dowda, M., Felton, G., Pate, R.R., Ward, D.S. (2005). Comparison of barriers self-efficacy and perceived behavioral control for explaining physical activity across 1 year among adolescent girls. Health Psychology, 24 (1), 109-110.

Myers, R., S., & Roth, D., L. (1997). Perceived benefits of and barriers to exercise and stage of exercise adoption in young adults. Health Psychology, 16 (3), 277-283.

Napolitano, M.A., Lewis, B.A., Williams, D.M., Bock, B.C., Pinto, B., Papandonatos, G.D., Whiteley, J.A., King, A.C., Marcus, B.H. (2008). Mediators of physical activity change: A multivariate approach. Health Psychology, 27 (4), 415-417.

Salmon,J., Crawford, D., Owen, N., Bauman, A., Sallis, J.F. (2003). Physical activity and sedentary behavior: A population based study of barriers, enjoyment, and preference. Health Psychology, 22 (2), 185-186.

Sechrist, K. R., Walker, S. N., & Pender, N.J. (1987). Development and psychometric evaluation of the Exercise Benefits/Barriers Scale. Research in Nursing & Health, 10 , 357-365.

Zabinski, M.F., Norman, G.J., Sallis, J.F., Calfas, K.J., Patrick, P. (2007). Patterns of Sedentary behavior among adolescents. Health Psychology 26 (1), 118.

©2010 Augustine A. Zavala