Efficacy of Two Theory-Based 12-Week E-mail-Mediated Walking. Programs: A Pilot Study. Charles E. Sepers Jr., AA & R. Todd Bartee, PhD. Department of ...
Efficacy of Two Theory-Based 12-Week E-mail-Mediated Walking Programs: A Pilot Study Charles E. Sepers Jr., AA & R. Todd Bartee, PhD Department of HPERLS, University of Nebraska–Kearney Introduction
Results
► Many theory-based physical activity programs fail to consistently elicit change in mediating variables or produce irrelevant treatment effects resulting in inconsistent outcomes1. ► Enhancing theoretical fidelity across future programs is necessary to identify key process variables responsible for behavior change2,3. ► Internet-based, self-report, physical activity at behavior change mediating variables should the scope of future interventions designed to operational mastery of program components4.
Intervention Design
(PA) programs aimed be used to increase increase
► The purpose of this pilot study is to compare processes of 2 groups within a 12-week walking program: one that has a high fidelity framework and one that is theory-based but not directly focused on variable change.
the mediating to theoretical mediating
► A secondary rationale for this investigation is to develop a protocol for incorporating e-mail focused investigator contact, internet-based self-report, and automated, logarithmic feedback generation.
future studies
Mediating Variable High Fidelity Low Fidelity F (df) Walking self-efficacy Mean change 0.62 ± 0.32 0.43 ± 0.66 0.28 (1,8) Walking skills self-efficacy Mean change 0.12 ± 0.14 0.03 ± 0.22 0.60 (1,8) Positive outcome expectations Mean change 3.03 ± 4.47 - 1.05 ± 8.11 0.62 (1,6) Negative outcome expectations Mean change 0.79 ± 1.28 - 2.06 ± 2.96 2.40 (1,6) Enjoyment Mean change 1.00 ± 0.82 - 0.50 ± 1.53 3.08 (1,7) Goal Setting Mean change 1.18 ± 0.26 0.42 ± 0.90 2.55 (1,7) Planning and scheduling Mean change 1.05 ± 0.50 0.14 ± 0.64 5.41 (1,7) Family social support Mean change 0.48 ± 0.31 - 0.03 ± 0.44 3.87 (1,7) Friend social support Mean change 0.15 ± 0.54 - 0.05 ± 0.48 0.34 (1,7)
p
η2
.61
0.10
.46
0.07
.46
0.09
.17
0.29
.12
0.31
.27
0.28
.05
0.44
.09
0.36
.58
0.05
Note. Partial η2 effect sizes 0.01, 0.06, and 0.14 have been characterized as small, medium, and large, respectively (Cohen, 1988).
Outcome Variable High Fidelity Low Fidelity 1-mile walk test time, min Mean change - 2.57 ± 0.59 - 1.64 ± 1.81 Estimated VO2max mL ● kg-1 ● min-1 Mean change 3.92 ± 5.09 2.85 ± 6.08 1-mile walk test heart rate, bpm Mean change 29.75 ± 24.66 15.50 ± 19.95 Body mass index, kg/m2 Mean change - 0.39 ± 0.43 0.07 ± 0.56 NHIS walking, min/week Mean change 80.00 ± 47.83 51.00 ± 123.08 Total walking bouts Mean 53.00 ± 8.76 36.43 ± 9.41 Walking bouts lasting at least 30 min Mean 49.00 ± 11.02 27.00 ± 15.36 Average min per week (Week 7-12) Mean 131.37 ± 29.55 88.26 ± 30.09 Perceived program mastery Mean 4.25 ± 0.50 2.43 ± 1.51
0.60 (1,8)
.46
0.07
0.08 (1,8)
.78
0.01
0.92 (1,8)
.37
0.10
1.88 (1,8)
.21
0.19
0.19 (1,6)
.68
0.03
8.26 (1,9)
.02
0.48
6.23 (1,9)
.03
0.41
5.27 (1,9)
.05
0.37
5.26 (1,9)
.05
0.37
Note. Partial η2 effect sizes 0.01, 0.06, and 0.14 have been characterized as small, medium, and large, respectively (Cohen, 1988). The modified National Health Institute Survey is represented as NHIS walking.
► Program mastery is a function of self-monitoring, and feedback4. program
Method
Moderating Variables
► This study featured 2 simultaneously delivered, 12-week, e-mail-based walking programs comprised of a high theoretical fidelity treatment group and a low theoretical fidelity control group. ► Participants included 11 Caucasian women aged 30 to 65 years (Mage = 45.73 ±11.10 years) employed at a Midwestern university in rural Nebraska.
► Rockport 1-mile walk tests were administered at baseline and at the completion of the 12-week intervention. Estimated maximal oxygen consumption (VO2max ml • kg-1 • min-1) was calculated using a validated multiple regression equation5.
Rewards
► Equivalent procedures between groups included a weekly frequency goal of 5 weekly walking bouts lasting 30 minutes or more. Participants reported walking frequency each week using an online form. This information was used to provide individualized feedback goals, e.g., increase pace or walking frequency. Both groups received a weekly theory-based newsletter. ► Nonequivalent procedures between groups included providing hierarchal goal setting to the treatment group. The high fidelity group received a target step goal in addition to the frequency goal if the frequency goal was met for the previous week. The control group was simply told to increase walking pace.
SCT and includes modeling, goal setting, Significant results between groups regarding mastery suggests that the treatment condition resulted in marked success over the control condition. These data indicate theoretical fidelity should be considered in the design of future programs.
► A key delivery component of both programs included automated data collection and goal generation. This enabled an efficient method of program implementation that allowed this multifaceted intervention to be delivered by a single person.
Mediating Variables
► Participants were grouped with associates within groups and matched between groups with those of similar physical characteristics. The treatment condition was randomly assigned. Model HJ-113 Omron pedometers were given to both groups.
► Participants completed questionnaires that measured Social Cognitive Theory (SCT) variables and reported 2-week walking quantity4 at baseline and post, see Table 1. Process evaluations and exit surveys were administered post intervention.
η2
Discussion
Implementation
► Body mass index was determined from weight and height values measured with a Befour PS6600 ST model scale and a wall-mounted stadiometer. Heart rates were recorded using Polar T31 heart rate monitors.
p
F (df)
Outcomes
► This investigation did not see a significant difference between groups for decreased Rockport walk test time and goal setting, as seen in a previous study4, but instead, resulted in a significant difference in exercise planning and larger effects over all. Methodology may elucidate these differences.
1.
Baranowski, T., Anderson, C., & Carmack, C. (1998). Mediating variable framework in physical activity interventions: How are we doing? How might we do better? American Journal of Preventative Medicine, 15, 266-297.
I dedicate this work to my family and those that have lent support along the way. I thank you.
2.
Baranowski, T. & Jago, R. (2005). Understanding the mechanisms of change in children’s physical activity programs. Exercise and Sport Sciences Reviews, 33, 163-168.
This study was made possible by grant funding associated with the 2010 Undergraduate Summer Student Research Project.
3.
Bauman, A. E., Sallis, J. F., Dzewaltowski, D. A., & Owen, N. (2002). Toward a better understanding of the influences on physical activity: The role of determinants, correlates, causal variables, mediators, moderators, and confounders. American Journal of Preventative Medicine, 23, 5-14.
4.
Rovniak, L. S., Hovell, M. F. Wojcik, J. R. Winett, R. A., & Martinez-Donate, A. P. (2005). Enhancing theoretical fidelity: An e-mailbased walking program demonstration. American Journal of Health Promotion, 20, 85-96.
5.
Kline, G. M., Porcari, J. P., Hintermeister, R., Freedson, P. S., Ward, A., McCarron, R. F., Ross, J., & Rippe, J. M. (1987). Estimation of VO2max from a one-mile track walk, gender, age, and body weight. Medicine and Science in Sports and Exercise, 19, 253-259.
