This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Effective interventions are needed to reduce the chronic disease epidemic. The Internet has the potential to provide large populations with individual advice at relatively low cost.
The focus of the study was the Web-based tailored physical activity intervention Active-online. The main research questions were (1) How effective is Active-online, compared to a nontailored website, in increasing self-reported and objectively measured physical activity levels in the general population when delivered in a real-life setting? (2) Do respondents recruited for the randomized study differ from spontaneous users of Active-online, and how does effectiveness differ between these groups? (3) What is the impact of frequency and duration of use of Active-online on changes in physical activity behavior?
Volunteers recruited via different media channels completed a Web-based baseline survey and were randomized to Active-online (intervention group) or a nontailored website (control group). In addition, spontaneous users were recruited directly from the Active-online website. In a subgroup of participants, physical activity was measured objectively using accelerometers. Follow-up assessments took place 6 weeks (FU1), 6 months (FU2), and 13 months (FU3) after baseline.
A total of 1531 respondents completed the baseline questionnaire (intervention group n = 681, control group n = 688, spontaneous users n = 162); 133 individuals had valid accelerometer data at baseline. Mean age of the total sample was 43.7 years, and 1146 (74.9%) were women. Mixed linear models (adjusted for sex, age, BMI category, and stage of change) showed a significant increase in self-reported mean minutes spent in moderate- and vigorous-intensity activity from baseline to FU1 (coefficient = 0.14, P = .001) and to FU3 (coefficient = 0.19, P < .001) in all participants with no significant differences between groups. A significant increase in the proportion of individuals meeting the HEPA recommendations (self-reported) was observed in all participants between baseline and FU3 (OR = 1.47, P = .03), with a higher increase in spontaneous users compared to the randomized groups (interaction between FU3 and spontaneous users, OR = 2.95, P = .02). There were no increases in physical activity over time in any group for objectively measured physical activity. A significant relation was found between time spent on the tailored intervention and changes in self-reported physical activity between baseline and FU3 (coefficient = 1.13, P = .03, intervention group and spontaneous users combined). However, this association was no longer significant when adjusting for stage of change.
In a real-life setting, Active-online was not more effective than a nontailored website in increasing physical activity levels in volunteers from the general population. Further research may investigate ways of integrating Web-based physical activity interventions in a wider context, for example, primary care or workplace health promotion.
To reduce the burden of chronic disease and premature death due to an inactive lifestyle [
Computer-tailored interventions simulate a personal counseling situation by providing individual feedback based on the behavior, motivation, and attitudes of the user [
To date, studies investigating the effectiveness of second-generation Web-based tailored physical activity interventions have either been carried out in small confined populations [
Intensity of intervention use may be associated with induced physical activity changes [
The focus of the present study was a Web-based tailored physical activity intervention that is freely accessible on the Internet [
Participants for this Web-based study were recruited by advertisements in newspapers, in magazines, and on the Internet. They were invited to take part in a physical activity study and were given the link to the study website (with a domain name different from the one of the intervention). At the same time, spontaneous users were recruited directly from the Active-online website by redirecting them to the study website if they chose to participate in the study. The study was carried out in German, and recruitment lasted from May 1 to August 2, 2006. Based on sample size calculations assuming an increase in meeting the HEPA recommendations of 30% in the intervention group and 20% in the control group (alpha = .05, power = 0.8), 250 participants were required per group. Assuming a realistic loss-to-follow-up in a Web-based survey without face-to-face contact of about 50% over 1 year [
Interested individuals could access the study website from any computer with Internet access. Information regarding the study and all study questionnaires were provided there. Individuals completing the baseline questionnaire and leaving their email address were registered. Media-recruited participants were randomly allocated to either the intervention group (IG) or the control group (CG) and were forwarded to Active-online or the nontailored website, respectively. Spontaneous users (SU) were included as a separate study group but followed the same study protocol as the IG.
Respondents could volunteer to take part in accelerometer measurements via an additional Web page that they were routed to after the baseline questionnaire, depending on the availability of accelerometers. Volunteers were not forwarded directly to the intervention websites but were sent an accelerometer to obtain baseline measurements and had to return a separate written consent form. Only after the accelerometer was returned was an email sent out with a link to Active-online or the nontailored website.
Randomization was carried out using random numbers provided by the University of Geneva’s online service [
Email addresses were used to identify and contact participants at follow-up. All participants were followed up 6 weeks (FU1), 6 months (FU2), and 13 months (FU3) after the baseline assessment, receiving a maximum of three email invitations each time with a personal link referring them back to the study website. Those volunteers having participated in the accelerometer measures at baseline were asked to repeat accelerometer measures at each follow-up in addition to the online questionnaires. Individuals in the IG and SU additionally received three reminder emails with a personal link to Active-online between FU2 and FU3 at 9, 10, and 11 months after the baseline assessment, encouraging them to revisit Active-online. The study procedure is depicted in
Study procedure for each group
Active-online is an interactive, individually tailored physical activity program targeting individuals aged 30 to 60 years. It has been freely available on the Internet since 2003. The aim of the program is to increase physical activity levels in users by offering individually tailored counseling and motivational feedback. The program was developed in German by an interdisciplinary team of experts in public health, sport sciences, psychology, design, and computer science, and then translated and culturally adapted for French and Italian audiences. The theoretical framework of the program is the transtheoretical model of behavior change [
Users may visit Active-online without registering, or they may register with their email address to obtain a password. Registered users have the possibility of following changes in their physical activity behavior when revisiting the website. They also receive reminder emails encouraging them to revisit Active-online.
Screenshot of the tailored intervention
Participants in the CG were forwarded to a nontailored website with general information on physical activity and health with no additional reminder emails. This was a static website with some tips on how to include more physical activity in daily life and some information regarding positive health effects of physical activity.
Screenshot of the standard website for the CG
The online baseline questionnaire included questions on demographics, physical activity behavior, stage of change, self-efficacy, and general and mental health. Data are only presented for physical activity. Physical activity was assessed using a short questionnaire with four items on frequency and duration of moderate- and vigorous-intensity activity that is used in the official monitoring of physical activity in the Swiss population [
Accelerometers (Actigraph models AM7164 and GT1M, formerly Computer Science and Applications, now Manufacturing Technology Inc, Fort Walton Beach, FL, USA) were used for objective physical activity assessment. The accelerometers have been validated in earlier studies [
Data regarding the use of Active-online for the IG and SU were obtained from the Active-online user database. Each visit to the website was recorded, including start date and time, end date and time, number of pages viewed, etc. Participants were provided with a password to re-enter Active-online in order to track their repeated visits. Use of the nontailored website in the CG was not measured.
Minutes of physical activity were positively skewed and were log-transformed for analysis. Chi-square tests for categorical variables and t-tests for continuous variables were used to compare responders and nonresponders and to compare differences between IG and CG and between IG and SU at baseline. In a preliminary analysis, paired t-tests and McNemar tests were applied, respectively, to compare changes in total activity time and changes in the proportion meeting the HEPA recommendations between baseline and each FU and for each group separately. Mixed logistic and mixed linear models were used to simultaneously analyze the effects of time and group allocation on the proportion meeting the HEPA recommendations and on total activity time, respectively, including gender, age, BMI category, and stage of change at baseline as covariates in the adjusted model. Stage of change was included to account for baseline motivation to change. The inclusion of time-group interaction terms in mixed models allows identification of potential differences in changes between groups at any time point. Changes in total reported activity time were analyzed for all participants and separately for participants meeting and not meeting the HEPA recommendations at baseline, because the latter are those most in need of effective interventions to increase their physical activity behavior. Participants were analyzed as randomized.
The impact of the use of Active-online on changes in physical activity behavior in the IG and SU was analyzed with a linear regression model including the difference in total reported activity time between baseline and FU3 as the dependent variable and the minutes spent in the tailored intervention as the independent variable, including gender, age, BMI category, and stage of change at baseline as covariates in the adjusted model. STATA 9.2 (STATACorp LP, College Station, TX, USA) was used for all analyses.
In total, 1919 respondents recruited via different media channels and 220 respondents recruited via the Active-online website started the baseline survey on our study website; 1401 and 168, respectively, finished the survey and were registered as participants (
Participant flow: recruitment channels, randomization, baseline, and follow-up assessments
Characteristics of participants at baseline according to groupa
Self-Reported Measures | Total (n = 1531) | CG (n = 688) | IG (n = 681) | P(IG-CG) | SU (n = 162) | P(IG-SU) |
|
||||||
Female (%) | 74.9 | 75.9 | 74.7 | .63 | 71.0 | .33 |
Age, years | 43.7 ± 13.1 | 44.2 ± 12.8 | 44.2 ± 13.3 | .99 | 38.8 ± 13.0 | < .001 |
Age groups (%) | .32 | < .001 | ||||
< 30 years | 16.2 | 13.8 | 15.3 | 30.3 | ||
30-60 years | 72.9 | 75.6 | 72.1 | 64.8 | ||
> 60 years | 10.9 | 10.6 | 12.6 | 4.9 | ||
Living with a partner (%) | 70.0 | 70.4 | 70.8 | .86 | 65.4 | .18 |
Living with children (%) | 53.2 | 56.3 | 53.5 | .30 | 38.9 | .001 |
Swiss nationality (%) | 87.3 | 86.2 | 88.6 | .19 | 86.4 | .45 |
University degree (%) | 24.9 | 25.2 | 24.1 | .65 | 27.2 | .41 |
|
||||||
Smokers (%) | 13.1 | 10.9 | 12.8 | .28 | 23.5 | .001 |
BMI, kg/m2 | 24.6 ± 4.6 | 24.5 ± 4.5 | 24.8 ± 4.6 | .38 | 24.5 ± 4.6 | .57 |
Overweight and obese (%) | 39.3 | 38.3 | 41.1 | .30 | 36.4 | .28 |
|
||||||
Meeting HEPA recommendations (%) | 40.8 | 40.4 | 40.9 | .84 | 42.2 | .75 |
Total reported activity time, minutes/week | 277 ± 253 | 276 ± 256 | 276 ± 258 | .99 | 283 ± 222 | .76 |
Objective Measures | Total (n = 133) | CG (n = 52) | IG (n = 62) | P(IG-CG) | SU (n = 19) | P(IG-SU) |
|
||||||
Mean counts per minute | 451 ± 186 | 450 ± 176 | 457 ± 196 | .85 | 436 ± 193 | .69 |
Total accelerometry activity time, |
377 ± 214 | 383 ± 211 | 383 ± 227 | .99 | 341 ± 183 | .47 |
aValues are mean ± SD unless otherwise noted.
There were significant differences in some variables between participants who responded to each follow-up (responders) and those who did not respond to at least one follow-up (nonresponders). Nonresponders were slightly younger, less likely to be Swiss, more likely to be smokers at baseline, more likely to be overweight or obese, and less likely to meet the HEPA recommendations at baseline. The subgroup of participants with accelerometers (n = 144) were slightly older, more likely to live with children, and more likely to be overweight or obese than those not participating in the accelerometer part of the study.
When including those participants with complete data for all four time points (n = 736), significant increases in the proportion of participants meeting the HEPA recommendations were observed in SU between baseline and FU1 (P = .045) and FU3 (P = .002). Nonsignificant increases between baseline and FU3 were seen in the IG and CG. Changes in total reported activity time per week between baseline and FU3 are depicted in
Changes in total reported activity time (minutes/week) between baseline and FU3 according to group, for all participants with complete data (n = 736) and separately for those meeting (n = 336) and not meeting (n = 400) the HEPA recommendations at baseline
Percent changes in self-reported physical activity between baseline and each follow-up according to groupa
Baseline to FU1 |
|
Baseline to FU2 |
|
Baseline to FU3 |
|
|
|
||||||
CG | +2.2% | .27 | −0.8% | .71 | +3.8% | .12 |
IG | +2.3% | .27 | −1.7% | .45 | +4.0% | .21 |
SU | +7.4% | .11 | −2.0% | .69 | +18.3% | .005 |
|
||||||
CG | +4.8% | .13 | −3.7% | .27 | +4.5% | .25 |
IG | +4.7% | .14 | −2.6% | .52 | +3.4% | .51 |
SU | +9.6% | .11 | +4.9% | .48 | +15.4% | .19 |
aResults are based on participants with complete data at two time points (see
Time and group parameters for changes in physical activity, based on mixed logistic and mixed linear modelsa
Meeting HEPA Recommendations | Total Reported Activity Time (minutes/week) | |||||||
Unadjusted | Adjusted | Unadjusted | Adjusted | |||||
OR | 95% CI | OR | 95% CI | Coeff | 95% CI | Coeff | 95% CI | |
|
||||||||
IG | 1.04 | 0.68-1.57 | 1.02 | 0.72-1.45 | 0.02 | −0.10, 0.15 | 0.02 | −0.08, 0.13 |
SU | 1.15 | 0.59-2.24 | 1.08 | 0.62-1.89 | 0.16 | −0.04, 0.35 | 0.17 | 0.000-0.35 |
|
||||||||
FU1 (6 weeks) | 1.34 | 0.96-1.85 | 1.30 | 0.93-1.82 | 0.15 | 0.06-0.23 | 0.14 | 0.05-0.22 |
FU2 (6 months) | 1.04 | 0.75-1.46 | 1.01 | 0.71-1.42 | 0.02 | −0.06, 0.11 | 0.02 | −0.07, 0.10 |
FU3 (13 months) | 1.49 | 1.05-2.11 | 1.47 | 1.03-2.09 | 0.19 | 0.10-0.28 | 0.19 | 0.10-0.28 |
aBasic unit is the CG at baseline. Adjusted models include gender, age, BMI category, and stage of change at baseline.
At baseline, 144 individuals (56 in CG, 68 in IG, and 20 in SU) wore an accelerometer, resulting in valid data for 133 individuals (92.4%). Valid accelerometer data were available for 117 individuals (88.0% of those with valid data at baseline) at FU1, for 114 individuals (85.7%) at FU2, and for 105 individuals (78.9%) at FU3; 93 participants (69.9%) had complete accelerometer data. There were no differences between groups.
Percent changes in objective physical activity between baseline and each follow-up according to groupa
Baseline to FU1 |
|
Baseline to FU2 |
|
Baseline to FU3 |
|
|
|
||||||
CG | −4.8% | .19 | −11.6% | .004 | −8.1% | .03 |
IG | +2.5% | .52 | −8.0% | .06 | −1.2% | .83 |
SU | +1.5% | .83 | −16.2% | .04 | −2.1% | .81 |
|
||||||
CG | −6.2% | .29 | −17.5% | < .001 | −10.3% | .03 |
IG | −1.5% | .82 | −5.4% | .29 | −1.2% | .87 |
SU | −5.1% | .72 | −29.1% | .045 | −16.0% | .16 |
aResults are based on participants with complete data at two time points.
In total, 2112 visits of IG and SU study participants (n = 843) were counted on Active-online, with a mean number of 2.5 (± 1.6) visits per person. The number of visits was described by a positively skewed distribution representing 50% with two or less visits on Active-online during the study period between baseline and FU3. On average, 46 pages were viewed per person, with a median of 31 pages.
In 1226 of all visits (58.0%), one of the two tailored modules was started. These 1226 visits can be attributed to 628 individuals (74.5% of all participants in IG and SU). The mean number of visits within a tailored module for these individuals was 1.9 (± 1.2). The mean and median time spent in the modules for participants who started a tailored module was 12 minutes and 9 minutes per visit, respectively, and 23 minutes and 15 minutes during the whole study period, respectively.
In 962 of all visits (45.5%), at least one tailored feedback was obtained in the module on everyday activities and endurance training, and in 460 of all visits (21.8%), at least one tailored feedback was obtained in the module on strength and flexibility training. There was no difference in the use of Active-online between the IG and SU.
In the CG, 62 of 453 participants responding to FU3 (13.7%) stated that they had heard about Active-online and had used it at least once during the preceding year.
Linear regression showed a weak but significant relation between total minutes spent within one of the tailored modules (IG participants and SU combined) and changes in total reported activity time between baseline and FU3 in the unadjusted model (coefficient = 1.13, 95% CI 0.09 - 2.17, P = .03), and a borderline significant relation in the model adjusted for age, gender, and BMI category (coefficient = 1.07, 95% CI 0.004 - 2.13, P = .049). When adding stage of change to the model, the relation was attenuated and no longer significant (coefficient = 0.58, 95% CI −0.43 to 1.59, P = .26), indicating that stage of change was associated with both changes in total reported activity time as well as time spent in the tailored modules. There was no interaction between stage of change and time spent in the tailored modules.
In the present study, there were significant increases in self-reported physical activity levels between baseline and the last follow-up after 13 months in all participants, but there were no significant differences between the randomized groups. More pronounced increases were found in SU of Active-online. However, these individuals were not randomized and thus cannot be directly compared with the randomized groups. Furthermore, SU willing to participate in the study may not be representative of all Active-online users since they were a self-selected sample and only represented 7.4% of all visits on Active-online during the recruitment period.
Self-reported changes in physical activity levels were not confirmed by objective measures. Differences between self-reported and objective measures may be due to the possibility that study participation influenced the perception of physical activity behavior and thus reporting of physical activity levels. A seasonal pattern [
Results of other computer-tailored [
As per the real-life setting, study participants were free to start and stop the intervention. In addition, the anonymous nature of the Internet and the wealth of available information may make it difficult to achieve sufficient levels of intervention use. On average, individuals in the IG and SU started a tailored module less than twice during the study period, accumulating a mean of 23 minutes in the tailored modules in total (12 minutes per visit). In a study assessing user attitudes toward a physical activity website, an average time of 7.1 minutes spent on the tailored intervention per visit and a total average of 356 minutes over 1 year was reported [
Because of the challenges that we face with stand-alone Web-based interventions that are freely accessible on the Internet, it may be more promising to embed a program like Active-online in a wider context of health promotion. Possibilities for better utilization of Active-online may be its application in a workplace setting, the “prescription” of Active-online to patients in primary care, or the inclusion of Active-online in a larger health promotion packet targeting different health issues, for example, in a community setting. Two studies that have evaluated Web-based tailored interventions in a primary care setting have reported increases in physical activity levels after 1 month [
A strength of the study was the delivery of the Web-based intervention under real-life conditions, not in a controlled setting. There were no face-to-face contacts or other factors that may increase compliance, because they do not represent realistic conditions for open-access Web-based interventions. Furthermore, objective physical activity assessment was used in a subsample of participants in addition to the questionnaires. We included SU of Active-online as an additional study arm. Frequency and duration of use of Active-online were monitored using objective data from the Active-online user database, making it possible to look at the relation between use of Active-online and physical activity changes. Other strengths are the long-term follow-up and the large number of participants included in the randomized study.
Several reasons may be responsible for the limited effectiveness of Active-online. The website was tested in 2003 and acceptability was generally good; participants especially liked the individual counseling, the pleasant tone, and the simple structure and design [
This study has several limitations. A rather low overall response of around 50%, as observed in other studies [
The present study showed limited effectiveness of Active-online in a randomized sample of volunteers from the general adult population when offered as a stand-alone intervention delivered online under real-life conditions. Further research may investigate the potential of Web-based physical activity interventions integrated in a wider context, for example, primary care or workplace health promotion.
This study was funded by the Swiss Federal Council of Sports. The authors would like to thank Dr. Christian Schindler for his support with the statistical analyses and the colleagues from the University of Basel who have critically reviewed the manuscript.
None declared.
Application for funding
body mass index
control group
confidence interval
follow-up
health-enhancing physical activity
intervention group
odds ratio
spontaneous users