245
Journal of Physical Activity and Health, 2007, 4, 245-260
© 2007 Human Kinetics, Inc.
The authors are with Central Queensland University, Rockhampton, Queensland, Australia. Steele and Mummery are with the School of Health and Human Performance; Dwyer is with the School of Nursing and Health Studies.
Using the Internet to Promote Physical
Activity: A Randomized Trial
of Intervention Delivery Modes
Rebekah Steele, W. Kerry Mummery, and Trudy DwyerBackground: A growing number of the population are using the Internet for health information, such as physical activity (PA). The aim of this study was to examine the effectiveness of delivery modes for a behavior change program targeting PA. Methods: A randomized trial was conducted with 192 subjects randomly allocated to either a face-to-face, Internet-mediated, or Internet-only arm of a 12-wk interven-tion. Subjects included inactive adults with Internet access. The primary outcome variable was self-reported PA, assessed at four time points. Results: The results showed no group × time interaction for PA F(6, 567) = 1.64, p > 0.05, and no main effect for group F(2, 189) = 1.58, p > 0.05. However, a main effect for time F(3, 567) = 75.7, p < 0.01 was observed for each group. All groups were statistically equivalent immediately post-intervention (p < 0.05), but not at the follow-up time points (p > 0.05). The Internet-mediated and Internet-only groups showed similar increases in PA to the face-to-face group immediately post-intervention. Conclu-sions: This study provides evidence in support of the Internet in the delivery of PA interventions and highlights avenues for future research.
Key Words: web-based, behavior modification, health promotion, inactive
The burden of physical inactivity is well documented.1 Over the last few decades
much financial investment and resource allocation towards various types of inter-ventions have been expended in the area of physical activity (PA) and population health.2 Recently, researchers have focused upon information technology
applica-tions such as telephone-linked communication,3 cell phone text messaging,4 and
the Internet,5 as a means of engaging populations and individuals in health-related
behavior change. Internet-based applications have been shown to have the potential to service large numbers of the population, and offer the advantages of convenience, flexibility, and cost savings.6 Information and behavioral change strategies on the
Internet can be tailored to the users’ needs and also have the added advantage of being able to provide instantaneous feedback.7 The Internet also addresses barriers
program implementation and evaluation.6, 7 A recent meta-analysis of web-based
interventions showed overall improvements in a number of health outcomes includ-ing weight loss, depression, increased exercise time, and increased health knowledge (e.g., nutrition and asthma).5
To date, only a few studies have examined the efficacy of the Internet in pro-ducing PA behavior change. One study reported limited PA change with only a significant decrease in sitting time observed, in subjects allocated to a PA-based website compared to a print material group.8 Another reported increased minutes
of PA and walking behavior for a 12-wk PA Internet-based program, compared to a wait-list control group.9 To our knowledge, there have been no studies reported that
have examined the effectiveness of the Internet for PA behavior change compared to traditional approaches, such as face-to-face program delivery.
This study reports the primary outcome findings of a randomized trial (RT) examining the effectiveness of delivery modes for a 12-wk pedometer-based behavior change program (Health-eSteps) targeting inactive adults. This study compared traditional face-to-face (FACE), Internet-mediated [combined Inter-net and face-to-face] (IM) and InterInter-net-only (IO) delivery of the Health-eSteps program. The primary hypothesis was that the FACE, IM, and IO groups would significantly increase their PA from baseline to immediately post-intervention (intervention phase), and from baseline to the 2-month and 5-month post-inter-vention follow-up (maintenance phase), with no differences between groups. In this context, we conceptualized an equivalence study design, which aimed to test the null hypothesis that a new treatment (Internet delivery mode) is “as good as” an established treatment (face-to-face).10 This study also reports process outcome
measures (as a fidelity check) in terms of program exposure, and pedometer usage across intervention groups.
Methods
Study Design and SubjectsThis study used an RT approach, in which subjects were assigned to one of three groups; FACE, IM, and IO. A non-intervention arm or control group was not included, as the aim of the study was to conduct equivalency testing of delivery modes.
Subjects were recruited from the local community of Rockhampton, Queensland via the local newspaper. Eligibility criteria included 1) age ≥ 18 y; 2) functionally mobile ≥ 10 min; 3) inactive; 4) access to Internet; and 5) signed informed consent. Program enrolment occurred via a central telephone system in which two research assistants, trained by the research investigators, were responsible for establishing eligibility and enrolling subjects. Simple randomization procedures were used in which subjects were allocated to a list of computer-generated numbers ranging from 1 to 3, with each number representing an intervention group. Subjects, enrolment staff, and data collection assessors were not blinded to group allocation. Based upon an 80% power to detect a moderately large effect size for change in PA, we calculated a minimum of 50 subjects per group would be required for the final analysis.11 Assuming a 20% to 30% drop out rate,5 we aimed to recruit
Internet-Based Behavior Modification 247
approximately 70 subjects per group. This study was approved by the Central Queensland University Human Research Ethics Committee and was conducted between July 2004 and February 2005.
Intervention
The intervention content was developed based upon social cognitive theory (SCT)12
and used self-management skills.13 The self-management framework acted as a
generic model for the translation of SCT into practice,14, 15 focusing upon skills
such as problem solving, decision making, action planning, and self-tailoring. Core SCT constructs were targeted through a variety of strategies to enhance self-efficacy, outcome expectancies, social support, as well as perceived benefits and barriers to PA participation. The curriculum for the Health-eSteps program included a variety of topics focusing upon lifestyle PA, benefits and barriers, goal setting, self-monitoring, self-talk, self-reinforcement, time and stress management, relapse prevention, and social support. These topics are similar to those reported in other self-management and/or SCT-driven interventions as well as Internet-based interventions.16, 17
Subjects were also given a pedometer as a motivational tool to enable them to monitor incidental and structured PA. As a motivational tool, pedometers provide immediate feedback and in conjunction with log books, changes in PA can be easily recorded and used to self-assess PA levels. The Health-eSteps program advocated 30 min of moderate-intensity PA, as well as lifestyle PA such as parking the car further away and taking the stairs.18
Intervention Protocol
The intervention program was conducted over a 12-wk period, in which Weeks 1 and 12 included data collection. To ensure the internal validity of the program content, the same degree-qualified facilitator conducted all face-to-face sessions with the FACE and IM groups. Incentives (gift vouchers, water bottles, sports socks) were used throughout the duration of the program to enhance engagement and retention; all three intervention groups were given the same opportunities to receive incentives.
Face-to-Face. The FACE group was required to attend 1-h weekly group-based
sessions, which were held on weekdays outside of working hours on university grounds. During the face-to-face sessions, the facilitator worked through the weekly content/activities and the relevant behavioral/self-management strategies. Subjects also received a log book to record step-counts, and were encouraged to attend each weekly session.
Internet-Mediated. Subjects in the IM group were given access to the intervention
website (username and password required). Each weekly module was made available on the corresponding week of the program, (i.e., subjects could not access Week 4 content until Week 4 of the program). Subjects received weekly e-mails detailing the following week’s module/topic. Subjects were also given the opportunity to attend two additional face-to-face sessions (two 1-h sessions) over the intervention
phase (Weeks 5 and 9), which were held at the same location (not the same time) as the FACE group. Weekly interactive web-based activities were included in each module to facilitate subject engagement and adoption of the relevant behavioral/self-management strategies. The website also included an online logbook for subjects to record their step-counts. E-mail access to an online support person was also available via the website throughout the intervention phase. Subjects were strongly encouraged to access the website at least once a week.
Internet-Only. The IO group had access to the same website used by the IM
group. The protocol outline for the IM group was used, with the exclusion of the additional face-to-face sessions.
Measurements
Measurements were collected at baseline (Week 1), immediately post-intervention (Week 12), 2 months and 5 months post-intervention. Questionnaires were self-report, and self-administered during face-to-face sessions. The primary outcome measure was mean minutes of PA measured by the Active Australia Question-naire.19 The Active Australia instrument asks questions related to moderate and
vigorous activities performed for a period of at least 10 min. Subjects are asked to report the number of times and duration spent in the following activity categories; recreational/leisure walking, walking for transport, moderate- and/or vigorous-intensity activity. Minutes of activity in each category were summed (vigorous activity minutes were doubled) and truncated to a maximum of 840 min/wk and 4 h/d. Total activity (sum of all activity categories) was truncated at a maximum of 1680 min/wk.19 “Sufficient” PA was defined as participating in a minimum of 150
min/wk of activity as per the national PA recommendations.18, 19 Test-retest
reli-ability of the Active Australia instrument has been previously established, and it has been reported to have satisfactory convergent validity with previously established survey tools used in Australia.20, 21
Demographic variables included: age, gender, occupation, employment status, as well as height, and weight (using standard anthropometric techniques). Questions related to computer/Internet usage and Internet self-efficacy (ISE)22 were also asked
to control for any potential confounding between experienced and inexperienced users. The ISE questionnaire is based upon confidence in using the Internet; items are ranked from “not confident at all” to “very confident,” on a 5-point Likert scale. The PA Readiness Questionnaire (PAR-Q)23 was first used to screen subjects for
cardiac and other health problems prior to participation. Medical clearance from the subjects’ general practitioner was required if subjects answered yes to any of the PAR-Q questions.
Intervention Fidelity
An assessment of intervention fidelity was also conducted during the interven-tion phase. This included the assessment and recording of the following: program exposure (attendance at face-to-face sessions/website access); drop-outs; subject satisfaction with the program; program delivery preference; satisfaction with group allocation; use of behavioral strategies (e.g., self-monitoring, goal attainment); and self-reported pedometer usage. This article reports fidelity aspects of program
Internet-Based Behavior Modification 249
exposure and pedometer usage as manipulation checks for program delivery across intervention groups.
Program Exposure. Program exposure was defined as being exposed to either
the face-to-face content or website content each week. Exposure was tracked using weekly attendance rolls in the FACE group which were collated by the facilitator. A combination of attendance rolls (collated by the same facilitator) and a web-tracking system was used to track exposure across the IM group. Exposure in the IO group was also tracked using the same web tracking system (Advanced Web Statistics 5.9; AWStats; http://awstats.sourceforge.net/). AWStats is a web server logfile analyzer that provides user statistics including visits, unique visitors, pages, hits and rush hours.
Pedometer Use. Although used as a measurement tool, baseline and immediately
post-intervention step-count data were collected as a means to assess program fidel-ity. The Yamax Digiwalker model SW-700 was used.24 Subjects wore the pedometer
for a period of 4 d; the average of these 4 d was recorded for their “steps/day.” Data on pedometer usage immediately post-intervention was also collated to enable us to check that one group was not more influenced by the use of the pedometer than another. These results are reported as means (SD)/percentages (numbers) for each group, with chi-square analysis of group differences for pedometer usage.
Data Analysis
All statistical analysis was conducted using SPSS, version 11.5 (SPSS, Inc., Chi-cago, IL). PA was logarithmically transformed owing to its skewed distribution, all analysis was conducted using the transformed variables (raw means and SD are presented in tables). Descriptive statistics for total PA (min/wk) using the Active Australia Questionnaire and demographic characteristics were calculated. Changes in total minutes of PA were analyzed using a split plot repeated-measures analysis of variance (ANOVA) (3 group × 4 time ANOVA) to examine changes over time and across each intervention group. Separate repeated measures ANOVA for each intervention group were then calculated (1 group × 4 time), with post hoc Scheffé test to determine changes in mean minutes of PA within groups over time.
Testing of statistical equivalency was conducted as per the protocol outlined by Rogers et al.,10 using the confidence interval approach. Equivalency testing is
conducted to determine if the difference between two groups lies within a predefined “equivalence interval.” A difference that lies within the equivalence interval is considered not clinically meaningful.10 For this study, the criterion was defined
as a percentage difference of 70 min of moderate-intensity activity per week (10 min/d) between groups. This represented a percentage difference of approximately ± 23% to 28% between groups.
Chi-square analysis was used to assess changes in the proportion of subjects meeting current PA recommendations (150 min of moderate-intensity PA). The results reported in this study are non-adjusted. However, to verify that our results were not affected by the higher ISE (and physical activity self-efficacy score at baseline [data not shown]) reported in the IM group (compared to the FACE group), we ran our analysis controlling these variables, and found no difference from our unadjusted results. Statistical significance was accepted at an alpha level of 0.05.
All analysis was completed using intention-to-treat25 in which subject baseline data
were carried forward to represent data lost to follow-up/drop-out. All subjects were analyzed as randomized.
Results
The progress of subjects through the study is presented in Figure 1. A total of 280 potential subjects responded to the recruitment strategy. Of those eligible, base-line measurements were obtained for 192 subjects who were randomly allocated to the FACE (n = 65), IM (n = 65), IO (n = 62) groups. The program attracted predominately white females (83%), age 38.7 (± 12) y with a BMI of 32.1 (± 7.5). Sixty-four percent reported using the Internet ≥ 3 y, and average ISE was 3.4 (± 0.88) on a 5-point Likert scale. The study had an overall drop-out rate of 17.2% immediately post-intervention. Data were collected from 80% (52/65), 72% (47/65), and 77% (48/62) for the FACE, IM, and IO groups respectively, at the 5 month post-intervention follow-up. The principal reason for loss to follow-up was voluntary withdrawal.
At baseline the IM group reported significantly higher ISE than the FACE group. Overall, mean minutes of PA per week was 79.6 (± 104.3) with approximately 83% (159/192) being classified as participating in less than 150 min of moderate PA per week. Participant characteristics and baseline PA are provided in Table 1.
Physical Activity
Table 2 shows changes in PA variables across the intervention and maintenance phase. A split plot ANOVA (4 time × 3 group) showed no group by time interaction
F(6, 567) = 1.64, p > 0.05, for PA, and no main effect for group F(2, 189) = 1.58,
p > 0.05). A main effect for time F(3, 567) = 75.7, p < 0.01 was observed. Mean minutes of PA participation increased by 270 min, 177 min, and 170 min from baseline to the 5 month follow-up for the FACE, IM, and IO groups, respectively. A total of 62.5% of subjects reported participating in at least 150 min of moderate-intensity PA immediately following the intervention, χ2(1, N = 92) = 11.4, p < 0.05;
a significant increase of 44%. Figure 2 shows the percentage of subjects reporting sufficient PA participation from baseline to the 5 month follow-up.
To examine the main effect for time, a series of repeated measures ANOVA for each intervention group were conducted. These results showed significant changes across time (FFACE(3, 192) = 2 4.9, p < 0.01, FIM(3, 192) = 26.4, p < 0.01, FIO(3, 183) = 2 7.4, p < 0.00). Post hocanalysis for the FACE group showed increases from baseline to each follow-up assessment, as well as an increase between the 2 month and 5 month follow-up (p < 0.05). The IM group also showed increases from baseline to each follow-up assessment (p < 0.01). Increases from baseline to each follow-up assessment were also observed for the IO group (p < 0.01); however, a decrease was seen between the 2 month and 5 month follow-up (p < 0.01).
Equivalency testing showed that the FACE and IM, and the FACE and IO groups were equivalent immediately post-intervention (p < 0.05). Equivalency was also observed between the FACE and IM groups at 2 months (p < 0.05), but not between the FACE and IO groups (p > 0.05). Differences between the FACE and IM, and FACE and IO groups, were not statistically equivalent (p > 0.05) at
251
Figure 1—Participant flow, enrolment and allocation, baseline, follow-up, and analysis
Note. Reason for subject “lost to follow-up” at the second and fifth month assessment could not be determined for some subjects as they could not be contacted.
Table 1 Demographic Characteristics and Physical Activity Levels of Participants at Baseline
Variable (nOverall = 192) (nFACE = 65) (n = 65)IM (n = 62)IO
Gender Male Female 32 (16.7%) 160 (83.3%) 7 (10.8%) 58 (89.2%) 14 (21.5%) 51 (78.5%) 11 (17.7%) 51 (82.3%) Employment status Full-time Part-time Home duties Student Not working/retired 108 (56.3%) 38 (19.8%) 16 (8.3%) 17 (8.9%) 13 (6.8%) 36 (55.4%) 12 (18.5%) 5 (7.7%) 5 (7.7%) 7 (10.8%) 37 (56.9%) 13 (20%) 4 (6.2%) 9 (13.8%) 2 (3.1%) 35 (56.5%) 13 (21%) 7 (11.3%) 3 (4.8%) 4 (6.5%) History of Internet use
< 6 months 6–12 months 1–1.5 y > 2 y > 3 y 23 (12%) 9 (4.7%) 14 (7.3%) 23 (12%) 123 (64.1%) 11 (16.9%) 2 (3.1%) 7 (10.8%) 7 (10.8%) 38 (58.5%) 4 (6.2%) 4 (6.2%) 4 (6.2%) 9 (13.8%) 44 (67.7%) 8 (12.9%) 3 (4.8%) 3 (4.8%) 7 (11.3%) 41 (66.1%) Active status Inactive Active 157 (81.8%) 35 (18.2%) 52 (80%) 13 (20%) 53 (81.5%) 12 (18.5%) 52 (83.9%) 10 (16.1%) Age (y) Mean (± SD) 38.7 (12.0) 37.6 (12.4) 39 (13.0) 39.6 (10.5) Internet Self-efficacy#
Mean (± SD) 3.36 (0.88) 3.16 (0.9) 3.55 (0.74)* 3.37 (0.96) Body mass index (kg/m2)
Mean (± SD) 32.1 (7.53) 31.3 (7.63) 32.0 (7.53) 33.0 (7.74) Physical activity (min/wk)
Mean (± SD) 79.6 (104.3) 83.2 (108.5) 71.1 (89.9) 84.7 (114.5)
Note. Values are expressed as the number and percentage unless otherwise indicated; *p < 0.05 Post Hoc IM> FACE; # 5-Point Likert Scale
5 months. The results are presented in Figure 3 which shows the 90% and 95% confidence intervals around the equivalency criteria of 70 min of moderate-intensity PA per week, expressed as a percentage of the difference.
Program Exposure
Exposure was defined as attending face-to-face sessions or logging onto the website. The mean number of face-to-face sessions attended per person (exposure) by those in the FACE group was 6.1. The total number of logins for the website was 1310 and the mean number of logins (exposure), was 11.5 and 11.8 sessions for the IM and IO groups, respectively.
253 Table 2 Changes in Minutes of Self-report Physical Activity
(mean minutes per week)
Group
Baseline Post-InterventionImmediately Post-Intervention2 months Post-Intervention5 months η2
Mean
(SD) Mean(SD) DifferenceMean Mean(SD) DifferenceMean Mean(SD) DifferenceMean
FACE (n = 65) (108.5)83.2 (285.0)299.2 215.9* (274.9)268.5 185.3* (383.9)353.3 270.1* 0.28 IM (n = 65) (89.9)71.1 (232.3)243.5 172.4* (283.2)254.0 182.9* (260.4)248.1 177.0* 0.29 IO (n = 62) (114.5)84.7 (272.3)321.6 236.9* (364.2)364.5 279.8* (251.1)255.0 170.3* 0.31
Note. Data are presented as raw means (±SD), however, analysis was conducted on log transformed data owing to the skewness of the PA variable. FACE = Face-to-face, IM = Internet Mediated, IO= Internet Only. *Significant change from baseline within group (p < 0.05); η2 = Partial Eta Squared for the overall main effect for time for each intervention group; Effect size magnitude; 0.01 = small; 0.06 = medium and 0.14 = large.
Figure 2—Percentage change in meeting recommendations for sufficient physical activity (150 min of moderate-intensity physical activity per week) at baseline, immediately post-intervention, 2 months post-post-intervention, and 5 months post-intervention.
254 F ig u re 3 — Te st o f e qu iv al en cy w ith th e 90 % a nd 9 5% c on fid en ce in te rv al s ar ou nd p hy si ca l a ct iv ity g ro up d if fe re nc es im m ed ia te ly p os t-in te rv en tio 2 months post-interv
ention and 5 months post-interv
ention for each interv
ention group. Note . Outer bar limits reflect 95% CI. Inner bar limits reflect 90% CI. FA CE = Face-to-F ace; IM = Internet Mediated; IO = Internet Only . If on visual inspection the interv al falls within the equi valence interv al, it can be concluded there is equi valence with a 5% risk of Type I error . If on visual inspection the 95% interv al excludes zero (0%), the traditional hypothesis test of no dif ference may be rejected with a 5% risk of a Type I error . (Rogers JL, Ho w ard KI, V esse y JT . Using significance to ev aluate equi valence between tw o experimental groups. 1993; 113:553-565). * The actual equi valenc y interv al for the FA CE-IO immediately post-interv ention 23%, and ± 26% at 2 months (to represent a percentage dif ference of 70 min/wk of moderate-intensity ph ysical acti vity). Immediately post-interv ention, the inner CI bars for the FA CE-IO are within ± 23% (Not Dif ferent and Equi valent); at 2 months the inner 90% CI bars are outside ± 26% (Not Dif ferent and Not Equi valent).
For all other time points the equi
valenc
y interv
255
Tab
le 3
P
edometer Use During Inter
vention Phase
(fidelity c
hec
k—data collected immediatel
y post-inter vention) Variab le Overall (n = 192) FA CE ( n = 65) IM ( n = 65) IO ( n = 62) Ho
w often do you wear your pedometer? Everyday Few times a week Few times per month Don’
t use it an ymore Missing/didn’ t complete follo w-up* 48 (25.0%) 59 (30.7%) 27 (14.1%) 20 (10.4%) 38 (19.8%) 13 (20.0%) 24 (36.9%) 7 (10.8 %) 6 (9.2 %) 15 (23.1%) 18 (27.7%) 13 (20%) 10 (15.4%) 9 (13.8%) 15 (23.1%) 17 (27.4 %) 22 (35.5 %) 10 (16.1 %) 5 (8.1 %) 8 (12.9%) Do you k
eep track of your daily steps?
Y es No Missing 105 (54.7 %) 49 (25.5 %) 38 (19.8%) 36 (55.4 %) 13 (20.0 %) 16 (24.6%) 33 (50.8 %) 17 (26.2 %) 15 (23.0%) 36 (58.1 %) 17 (30.6 %) 7 (11.3%) Baseline (steps/d) #* Mean ( SD ) 6209 (2504) a 6419 (1997) b 6345 (2929) c 5866 (2497) d Post-interv ention (steps/d) #* Mean ( SD ) 9143 (3102) a 9218 (2476) b 9294 (3456) c 8917 (3344) d Note . V alues are expressed as the number and (percentage) unless otherwise indicated. #Participant numbers with baseline and follo w-up pedometer data a = total ( n = 144); b = FA CE ( n =47); c = IM ( n = 49); d = IO ( n = 48). *Step data are missing due to incomplete data or did not complete the immediately post-interv ention follo w-up.
Pedometer Use
Post-intervention step-counts increased by 2934 steps/d from baseline (Table 3). Pedometer use across intervention groups is displayed in Table 3. Overall, there were no significant differences in how often subjects wore their pedometers across groups χ2(10, N = 155) = 11.3, p > 0.05. Twenty-five percent were still wearing
their pedometers daily, immediately post-intervention, with a further 30.7% wear-ing them a few times per week. Over half of the subjects reported keepwear-ing track of their daily step counts, with similar rates observed across groups, χ2(6, N = 56)
= 5.09, p > 0.05.
Discussion
This study found no difference between groups for mean minutes of PA and statis-tical equivalency for the immediately post-intervention follow-up. It showed that changes in PA were similar across intervention delivery modes, suggesting that each group increased their PA behavior comparatively during the intervention period; and that the Internet mode of delivery was as effective as traditional face-to-face deliv-ery. This study provided preliminary support for the effectiveness of Internet-based PA interventions. This study also demonstrated that changes in PA were sustained from the intervention period to the maintenance period irrespective of interven-tion delivery mode, unlike other interveninterven-tions that report a significant decrease in primary outcomes for short- and long-term follow-up.9, 26 Overall, Health-eSteps
participation rates in terms of face-to-face exposure by FACE subjects, and website exposure by the IM and IO groups, were also comparable.
An important finding of this study was that the magnitude of PA change over time produced in the Internet groups was similar to the large effect size found for the face-to-face group. Further, these effect sizes are at least equal to, or greater than, those reported in other Internet-based interventions.5 However, differences in
mean minutes of PA between the IO and FACE groups should be acknowledged. This study found differences of approximately 100 mean-minutes, with the IO group reporting more PA at the 2 month follow-up than the FACE group. Conversely, the FACE group reported more PA at the 5 month follow-up than the IM and IO groups. No significant group × time interaction was observed, but the results were statistically not equivalent at the follow-up time points.
The noted differences may therefore be due to limited statistical power to detect differences between these two time points. The study was powered to detect an overall change in PA. The effect size observed for the 2 month and 5 month follow-up was smaller than that observed for the overall study. The results plausibly reflect the notion that the FACE group was able to maintain long-term PA changes compared to the IM and IO groups. Long-term follow-ups (e.g., 12 months post-intervention), are required to examine the efficacy of Internet-based interventions over time. Differences between groups which are classified as “not equivalent” suggest that there is insufficient evidence to conclude a sizable differ-ence.10 We therefore cannot conclude that the groups were comparable in terms of
PA change at the 2 and 5 month follow-up, only that similar changes were shown from baseline to immediately post-intervention.
Internet-Based Behavior Modification 257
Previous studies have also used Internet delivery to improve nutrition,27 diabetes
management,28 and increase PA.8 Collectively, the authors of these studies suggest
that additional in-person contact and/or telephone support may assist with subject engagement and subsequent behavior change. Similar to Tate et al.,29 we found no
further enhanced outcome effect related to the extra in-person contact received by the IM group in this study. Although we were unable to show differences between groups, there are many advantages to the website that may have influenced the results observed, and produced similar effects immediately post-intervention to the FACE group in this RT.
The website included the use of reminder e-mails, interactive features (quiz-zes, entry and storage of personal information), self-assessment, online log book, and a weekly updated “What’s New at Health-eSteps” section. These features were included in the website based upon previous research9, 30 and an extensive formative
evaluation process during the website development phase. Further, compared to commercial/information-only websites, Health-eSteps used a theoretical approach to provide subjects with behavioral change skills. The content was delivered on a week-by-week basis that allowed for controlled program delivery. For example, the entire website could not be accessed in a single sitting, which has been previously identified as a barrier in Internet-based user engagement.8
Exploring avenues for enhancing the use of online support mechanisms (e-mail, discussion boards, etc.) may therefore be one way to increase Internet engagement. Interventions incorporating telephone technology have been reported as being as effective as structured face-to-face contacts, and have been found to increase adherence, and some long-term maintenance.31 As suggested by others,32 an avenue
of future research is to investigate the inclusion of telephone support during the intervention phase and/or maintenance phase, particularly in light of the decrease this study found for the IO group between the 2 and 5 month follow-up. For example
a combination of an interactive website with Telephone-Linked Communication3
or SMS messaging, may result in improved outcomes, enhanced user engagement, and/or prevent decreases in outcomes during the maintenance phase.
The changes in PA observed in this study across all intervention groups may also have been attributed to the use of pedometers as a motivational/self-monitor-ing tool. Several studies have been published to date that advocate their use in PA interventions. For example, Croteau33 reported that a minimal contact self-managed
pedometer intervention was successful in increasing PA. Health-eSteps advocated the accumulation of 30 min of moderate intensity, and incidental PA, in line with national PA recommendations.18 This study found an overall increase of 208 min/wk
which approximately equates to an additional 30 min/d of PA.
As a fidelity or manipulation check, we showed that average steps per day increased by 2934 from baseline to immediately post-intervention. Similarly, Wilde34 showed that by adding a 30 min walk to their daily routine, sedentary
women increased their step-count by approximately 2810 steps per day. Further, pedometer usage patterns were also similar across intervention delivery modes. Unfortunately, this study could not distinguish between the influence of the pedometer and behavior change strategies used. However, building upon the intervention reported in this study, an avenue of research is to examine the use of new electronic pedometers that permit users to upload step-count data directly to
the Internet. This may enhance the interaction and experience between users and the website, therefore increasing engagement.
A few limitations of this study should be noted and the interpretation of these results should be made with the following in mind. First, a “true” non-intervention control group was not used. In this study, a standard group (FACE) was compared to a novel group (IM and IO), however, without the inclusion of a control group it cannot be guaranteed that the intervention effects were a result of the Health-eSteps program alone. The inclusion of a non-intervention control group may assist in identifying mechanisms and mediators of behavior change in the program. Second, the findings of this study were based on self-report information and no objective measures of PA were used. Replication of this study using objective measures (such as accelerometry) of PA is warranted.
Third, the sample population was predominantly female, and the study was not powered to detect differences in gender. The subjects required Internet access and were located in a rural area of Queensland, Australia, therefore the results are not generalizeable to the population as a whole. Differences in program engage-ment and outcomes across delivery models for rural and non-rural populations also warrants further examination, as does the examination of intervention settings such as workplaces versus community. Fourth, subjects allocated to the IO group still received face-to-face contact for data collection. Future studies using the Internet should investigate the efficacy of PA change with no face-to-face contact, and examine the use of Internet-based data collection/online recruitment, which may increase the number and reach of the target population. Fifth, the study sample was self-selected and may be more motivated to change their behavior than the general population.
Despite these limitations, this study had several methodological strengths. The study used a RT design, with baseline observation carried forward intention to treat analysis.25 The PA questionnaire was shown to be valid and reliable,35 the
intervention materials used a theoretically sound approach and importantly, this study filled a gap in current literature concerning the effectiveness of an Internet-based PA program compared with traditional face-to-face delivery.
Conclusion
The results of this study showed that a PA behavior change intervention that included; access to an Internet-based program, with structured weekly modules, weekly e-mail reminders, interactive features (± additional in-person contact), and the use of pedometers as a motivational tool; produced similar changes in behavioral outcomes as a traditional face-to-face program immediately post-intervention. The Internet appears to be a viable method for the delivery of a structured behavior change PA program. However, further research is required to identify avenues for the maintenance of these changes.
Acknowledgments
This study was funded by the Research Training Scheme at Central Queensland Uni-versity. The authors would like to thank Dr. Evie Leslie for providing valuable insight in the conceptualization of this research project.
Internet-Based Behavior Modification 259
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