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BackgroundWeb-based interventions are popular for promoting healthy lifestyles such as physical activity. However, little is known about user characteristics, adherence, attrition, and predictors of repeated participation on open access physical activity websites. ObjectiveThe focus of this study was Active-online, a Web-based individually tailored physical activity intervention. The aims were (1) to assess and compare user characteristics and adherence to the website (a) in the open access context over time from 2003 to 2009, and (b) between trial participants and open access users; and (2) to analyze attrition and predictors of repeated use among participants in a randomized controlled trial compared with registered open access users. MethodsData routinely recorded in the Active-online user database were used. Adherence was defined as: the number of pages viewed, the proportion of visits during which a tailored module was begun, the proportion of visits during which tailored feedback was received, and the time spent in the tailored modules. Adherence was analyzed according to six one-year periods (2003-2009) and according to the context (trial or open access) based on first visits and longest visits. Attrition and predictors of repeated participation were compared between trial participants and open access users. ResultsThe number of recorded visits per year on Active-online decreased from 42,626 in 2003-2004 to 8343 in 2008-2009 (each of six one-year time periods ran from April 23 to April 22 of the following year). The mean age of users was between 38.4 and 43.1 years in all time periods and both contexts. The proportion of women increased from 49.5% in 2003-2004 to 61.3% in 2008-2009 (P< .001). There were differences but no consistent time trends in adherence to Active-online. The mean age of trial participants was 43.1 years, and 74.9% were women. Comparing contexts, adherence was highest for registered open access users. For open access users, adherence was similar during the first and the longest visits; for trial participants, adherence was lower during the first visits and higher during the longest visits. Of registered open access users and trial participants, 25.8% and 67.3% respectively visited Active-online repeatedly (P< .001). Predictors of repeated use were male sex (odds ratio [OR] = 1.2, 95% confidence interval [CI] = 1.04-1.38) and increasing age category in registered open access users, and age 46-60 versus < 30 years (OR = 3.04, 95% CI = 1.25-7.38) and Swiss nationality (ORnonSwiss= 0.64, 95% CI = 0.41-1.00) in trial participants. Despite reminder emails, attrition was much higher in registered open access users compared with trial participants, with a median lifetime website usage of 0 days in open access users and 290 days in trial participants. ConclusionsAdherence, patterns of use, attrition, and repeated participation differed between trial participants and open access users. Reminder emails to encourage repeated participation were effective for trial participants but not for registered open access users. These issues are important when interpreting results of randomized controlled effectiveness trials.

BackgroundEffective 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. ObjectiveThe 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? MethodsVolunteers 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. ResultsA 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. ConclusionsIn 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.

Objectives: Exposure to various sources of radio frequency electromagnetic fields (RF EMF) is common in industrialized settings, for example from mobile phones or broadcast transmitters. We assessed exposure to RF EMF in volunteers to gain a better understanding of exposure levels and to quantify the contributions of different sources. Methods: Exposure was assessed using the EME SPY 120 personal exposure meter (Antennessa, Brest, France), which allows separate measurements of the following frequency bands of RF EMF: radio transmitters, television transmitters, TETRA (radio communication for authorities), mobile phone handsets (uplink), mobile phone base stations (downlink), cordless phones and wireless LAN. A total of 34 volunteers living in Basel (Switzerland) and surroundings carried a personal exposure meter during one week and completed a diary of daily activities at the following locations: "at home", "at the working place", "on the way" and "other". Nine persons living close to a mobile phone base station were specifically asked to participate in the study and treated as the highly exposed group, The reference group consisted of 25 unselected volunteers. Measurements were taken every 90 seconds. To allow for values below the detection limit of 0.05 V/m, mean values were calculated by means of the robust ROS method, which assumes that measurements below the detection limit follow a lognormal distribution. Results: Overall, the mean (range) exposure to RF EMF over the whole week was 0.20 V/m (0.09-0.40 V/m). In the highly exposed group the mean was 0.27 V/m compared to 0.17 V/m in the reference group (Wilcoxon rank-sum test: p 0.004). Radio transmitters contributed 2%, television transmitters 2%. TETRA 0%, mobile phone handsets 31%, mobile phone base stations 36%, cordless phones 24% and wireless LAN 6%. At home, exposure (mean 0.19) V/m) was mainly due to mobile phone base stations (48%) and cordless phones (36%), at work (mean 0.22 V/m) due to mobile phone handsets (46%) and wireless LAN (26%), and on the way (mean 0.32 V/m) due to mobile phone handsets (68%) and mobile phone base stations (20%). In the reference group, cordless phones were the most important source of exposure at home (60%) for people owning a cordless phone. In the highly exposed group, the mobile phone base stations contributed 65% to their exposure at home and 57% to their total exposure. Conclusions: The median total exposure (0.20 V/m) is well below the standard limits. Major sources include mobile phone base stations, handsets and cordless phones. This study will be extended to a sample of 200 volunteers to determine the most relevant predictors of total personal exposure, according to different lifestyles. The data will allow modelling of personal exposure in the large-scale QUALIFEX cohort study (see www.qualifex.ch), which will examine non specific symptoms of ill health and exposure to RF EMF. [ABSTRACT FROM AUTHOR]