BACKGROUND: 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. OBJECTIVE: 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? METHODS: 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. RESULTS: A total of 1,531 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 1,146 (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 = 0.001) and to FU3 (coefficient = 0.19, p < 0.001) 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 = 0.03), with a higher increase in spontaneous users compared to the randomized groups (interaction between FU3 and spontaneous users, OR 2.95, p = 0.02). 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 = 0.03, intervention group and spontaneous users combined). However, this association was no longer significant when adjusting for stage of change. CONCLUSIONS: 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.

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