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Online health communication has the potential to reach large audiences, with the additional advantages that it can be operational at all times and that the costs per visitor are low. Furthermore, research shows that Internet-delivered interventions can be effective in changing health behaviors. However, exposure to Internet-delivered health-communication programs is generally low. Research investigating predictors of exposure is needed to be able to effectively disseminate online interventions.
In the present study, the authors used a longitudinal design with the aim of identifying demographic, psychological, and behavioral predictors of visiting, using, and revisiting an online program promoting physical activity in the general population.
A webpage was created providing the public with information about health and healthy behavior. The website included a “physical activity check,” which consisted of a physical activity computer-tailoring expert system where visitors could check whether their physical activity levels were in line with recommendations. Visitors who consented to participate in the present study (n = 489) filled in a questionnaire that assessed demographics, mode of recruitment, current physical activity levels, and health motivation. Immediately after, participants received tailored feedback concerning their current physical activity levels and completed a questionnaire assessing affective and cognitive user experience, attitude toward being sufficiently physically active, and intention to be sufficiently physically active. Three months later, participants received an email inviting them once more to check whether their physical activity level had changed.
Analyses of visiting showed that more women (67.5%) than men (32.5%) visited the program. With regard to continued use, native Dutch participants (odds ratio [OR] = 2.81, 95% confidence interval [CI] = 1.16-6.81,
The results suggest that online interventions could specifically target men, young people, immigrant groups, people with a low education, and people with a weak health motivation to increase exposure to these interventions. Furthermore, eliciting positive feelings in visitors may contribute to higher usage rates.
Cardiovascular diseases and cancers are the main causes of mortality in many Western countries. Because these and many other diseases are largely the result of unhealthy behaviors [
Previous research has identified several factors that can influence exposure to Internet-delivered health-communication interventions. First, intervention factors such as ease of enrollment and ease of dropout have been shown to influence exposure rates [
In the present study, we aimed to investigate demographic, behavioral and psychological determinants of exposure to an online program promoting physical activity. We assessed demographic variables, participants’ current physical activity level and their motivation to pursue and maintain health [
In sum, the Internet offers vast possibilities for health-communication efforts. Unfortunately, online health-communication interventions are characterized by low exposure rates. The present study sought to investigate predictors of visiting, using, and revisiting online interventions to increase our knowledge of exposure to these interventions.
A webpage was created providing the public with information about health and healthy behaviour (www.health-alert.nl). (See
Homepage of the Health-Alert website
The present study used an observational longitudinal design. After giving informed consent to participate, participants were invited to complete a questionnaire that assessed participants’ demographics, mode of recruitment, health motivation, and current physical activity levels (Time 1). Immediately after, participants were provided with a short message about the Dutch recommendations for physical activity. After participants were informed about these recommendations, they received tailored feedback concerning their current physical activity level. This tailored feedback informed them whether or not they met the recommendations for physical activity and offered tips on how to increase their physical activity or maintain their (already sufficient) current level of physical activity. The feedback was tailored solely to current physical activity levels and was not tailored to demographic or psychosocial variables. After reading the tailored feedback, participants completed an additional questionnaire assessing affective and cognitive user experience, attitude, and intention (Time 2). Three months later, participants received an email inviting them to participate in an assessment of whether their physical activity level had changed (Time 3).
Flowchart of the study
We assessed gender, age, ethnicity (1 = native Dutch; 2 = nonnative Dutch) and education (1 = low education; 2 = medium education; 3 = high education). In the complex schooling system in the Netherlands, a low education level refers to primary or basic vocational school, a medium education level refers to secondary vocational school or high school, and a high education level refers to advanced vocational school or university. In addition, we asked participants to indicate how they learned about the physical activity check (1 = through a search engine, eg, Google; 2 = through a link on another website; 3 = through a newspaper; 4 = through family, friends or co-workers; 5 = through local television).
To measure participants’ health motivation, a 4-item questionnaire was used (based on [
Physical activity levels were assessed using the short version of the International Physical Activity Questionnaire (IPAQ) [
Two items assessed positive affective reactions to the online content (ie, positive affective user experience); these items assessed the extent to which participants thought the content made them feel happy (1 = very happy to 7 = not at all happy) and relieved (1 = very relieved to 7 = not at all relieved). Scores were reversed and combined to create an average score. (Cronbach alpha = .75). Two items assessed negative affective reactions to the online content (ie, negative affective user experience) and assessing the extent to which participants thought the content made them feel sad (1 = very sad to 7 = not at all sad) and afraid (1 = very afraid to 7 = not at all afraid). Scores were reversed and combined to create an average score (Cronbach alpha = .83).
Five items assessed cognitive user experience by asking participants to indicate the extent to which they thought the online content was relevant (1 = very relevant to 7 = not at all relevant), interesting (1 = very interesting to 7 = not at all interesting), objective (1 = very objective to 7 = not at all objective), and exaggerated (1 = very exaggerated to 7 = not at all exaggerated). Furthermore, one item asked participants to indicate the extent to which participants agreed with the content (1 = I totally agree to 7 = I totally disagree). After we reversed the scores of all items except the exaggerated item, the scores on the five items were averaged (Cronbach alpha = .75).
Five items were used to assess attitude toward physical activity asking participants to indicate on semantic differentials the extent to which they rated engaging in at least thirty minutes of physical activity for at least five days of the week as: 1 = very good to 7 = very bad; 1 = very important to 7 = very unimportant; 1 = very sensible to 7 = not sensible at all; 1 = very nice to 7 = not at all nice; 1 = a lot of fun to 7 = no fun at all. After scores on the attitude items were reversed an average score was created (Cronbach alpha = .90).
Three items were used to assess intention to be physically active. Two items asked participants to indicate whether they planned to be physically active for at least thirty minutes a day on at least five days of the week and whether they considered being physically active for at least thirty minutes a day on at least five days of the week (1 = definitely not to 7 = definitely). One item asked participants: “How likely is it that you will be physically active for at least thirty minutes a day on at least five days of the week in the coming six months?” (1 = very unlikely to 7 = very likely). An average intention score was calculated (Cronbach alpha = .89).
During the three-month follow-up, physical activity levels were assessed using the same procedure as in the pretest questionnaire (ie, using the IPAQ).
The present study had three main outcome measures: visiting the website at Time 1, using the website at Time 1, and revisiting the website at Time 3. Determinants of visiting were investigated by comparing the demographics of the study sample with those of the general Dutch population. To assess continued use, we recorded which webpages were accessed by participants. A dichotomous variable was created that indicated whether participants had continued in the program up to the point of being exposed to the tailored health-promoting information (0 = dropped out before exposure to the information; 1 = continued use up to exposure to the information). Revisiting at Time 3 was assessed by means of a dichotomous variable indicating participation at Time 3 (0 = did not revisit at Time 3; 1 = did revisit at Time 3).
First, we investigated the demographic profile of the sample and the prevalence of the different modes of recruitment by means of descriptive analyses. Second, logistic regression analyses were performed to investigate whether continued use of the study could be predicted by gender, age, ethnicity, education (we created two dummy variables to be able to estimate the contribution of the three education groups), mode of recruitment (we created four dummy variables to be able to estimate the contribution of the five modes of recruitment groups), health motivation, and baseline physical activity. Third, logistic regression analyses were performed to investigate which variables could predict participation at Time 3. In step 1 of the logistic regression analyses, the Time 1 variables were entered (demographic variables, mode of recruitment, health motivation, physical activity), and in step 2, the Time 2 variables were entered in addition (affective user experience, cognitive user experience, attitude and intention). We used the statistical package SPSS 15.0 (SPSS Inc, Chicago, IL, USA) for the analyses. To calculate the statistical power of this study to reject false null hypotheses, we conducted a post-hoc statistical power test [
In total, 489 people participated in the study. The sample consisted of more women (n = 336; 67.5%) than men (n = 162; 32.5%). Age ranged from 18 to 86 years, with a mean age of 38.6 years (SD 15.0). Reflecting the general Dutch population, most of the participants were native Dutch. Approximately one-third of participants (n = 186) had a high education level, 48.6% (n = 242) had a medium education level, and 14.1% (n = 70) had a low education level. In the general population these percentages are 29.0%, 42.3%, and 28.6% respectively [
Demographic profile of participants
Variable | Percentage | |
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Male | 32.5 | |
Female | 67.5 | |
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Native | 81.5 | |
Nonnative | 18.5 | |
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Low | 14.1 | |
Medium | 48.6 | |
High | 37.3 | |
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Search engine | 28.6 | |
Hyperlink on related website | 48.7 | |
Advertisement in newspaper | 3.5 | |
Through family, friends, coworkers | 2.2 | |
Local television | 17.0 | |
|
||
0-15 | 21.3 | |
16-30 | 16.9 | |
31-45 | 15.7 | |
46-60 | 12.0 | |
61 or more | 34.1 |
The majority of participants indicated that they had learned about the physical activity check on related websites. Percentages for mode of recruitment, as well as additional demographics and physical activity levels, are presented in
Of the 489 participants who enrolled in the study, 276 (55.4%) continued in the study up to the point of being exposed to the tailored information. We conducted a logistic regression analysis to investigate whether demographics, mode of recruitment, or physical activity levels could predict continued use at the first measurement. Results of the logistic regression analyses showed that native Dutch participants completed more pages than nonnative participants. Furthermore, participants with a strong health motivation completed more pages than those with a weak health motivation (
Results of the logistic regression analysis with continued use as the dependent variable
Odds Ratio (OR) | 95% Confidence Interval (CI) | Wald χ2 |
|
|
Gender | 1.22 | 0.67-2.39 | 0.51 | .48 |
Age | 1.02 | 1.00-1.04 | 2.29 | .13 |
Medium educationb | 0.97 | 0.40-2.35 | 0.01 | .94 |
High educationb | 1.44 | 0.56-3.66 | 0.57 | .45 |
Ethnicity (0 = nonnative Dutch; 1 = native Dutch) |
|
|
|
|
Recruitment through other websitec | 1.43 | 0.73-2.83 | 1.08 | .30 |
Recruitment through newspaperc | 0.79 | 0.14-4.33 | 0.07 | .79 |
Recruitment through family, friends, coworkersc | 1.03 | 0.18-5.75 | 0.00 | .98 |
Recruitment through televisionc | 0.92 | 0.40-2.13 | 0.04 | .85 |
Health motivation |
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|
|
|
Physical activity | 1.00 | 1.00-1.00 | 0.02 | .88 |
a Significant effects (
b “Low education” as reference group
c “Search engine” as reference group
Of all 489 participants, 126 (25.3%) participated in the three-month follow-up assessment (Time 3), of which 117 (23.5%) completed all measures. We first conducted correlation analyses to investigate whether revisiting was associated with physical activity, health motivation, positive affective user experience, negative affective user experience, cognitive user experience, attitude, intention, and continued use. (We used Spearman’s ρ as a measure of all correlations involving continued use and revisiting and Pearson’s
Furthermore, we conducted a logistic regression analysis to investigate which variables could predict revisiting at Time 3. Results of step 1 of the logistic regression analyses showed that older participants were more likely to participate at Time 3. In addition, highly educated participants were more likely to participate than participants with a low education level (
Correlations, means, and standard deviations for continued use, revisiting, and other variables
Variablesa | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Mean | SD | ||
(1) Physical activity | 68.71 | 85.06 | ||||||||||
(2) Health motivation | .04 | 5.96 | 0.77 | |||||||||
|
.49 | |||||||||||
(3) Positive affect |
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|
4.40 | 1.09 | ||||||||
|
< .01 | < .01 | ||||||||||
(4) Negative affect | -.10 | -.07 |
|
2.43 | 1.34 | |||||||
|
.10 | .23 | < .01 | |||||||||
(5) Cognitive user experience | .03 | .10 |
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|
5.18 | 0.88 | ||||||
|
.61 | .14 | < .01 | .02 | ||||||||
(6) Attitude |
|
|
|
|
|
5.92 | 0.85 | |||||
|
< .01 | < .01 | < .01 | .01 | < .01 | |||||||
(7) Intention |
|
|
|
|
|
|
5.49 | 1.34 | ||||
|
< .01 | < .01 | < .01 | < .01 | .02 | < .01 | ||||||
(8) Continued use | .00 | .04 | - | - | - | - | - | 0.55 | 0.50 | |||
|
.97 | .43 | ||||||||||
(9) Revisiting | .08 |
|
|
|
|
.03 | .09 |
|
0.25 | 0.44 | ||
|
.13 | .04 | < .01 | < .01 | .049 | .60 | .16 | < .01 |
a Significant correlations (
Results of step 1 of the logistic regression analysis with participation at Time 3 as the dependent variable
Variablesa | OR | 95% CI | Wald χ2 |
|
Gender | 1.03 | 0.58-1.85 | 0.01 | .91 |
Age |
|
|
|
|
Medium educationb | 2.15 | 0.85-5.44 | 2.61 | .11 |
High educationb |
|
|
|
|
Ethnicity (0 = nonnative Dutch; 1 = native Dutch) | 1.14 | 0.58-2.21 | 0.14 | .71 |
Recruitment through other websitec | 1.33 | 0.69-2.59 | 0.72 | .40 |
Recruitment through newspaperc | 1.36 | 0.38-4.93 | 0.22 | .64 |
Recruitment through family, friends, coworkersc | 1.37 | 0.23-8.27 | 0.12 | .73 |
Recruitment through televisionc | 2.09 | 0.95-4.62 | 3.34 | .07 |
Health motivation | 1.24 | 0.86-1.77 | 1.33 | .25 |
Physical activity | 1.00 | 1.00-1.01 | 0.98 | .32 |
a Significant effects (
b “Low education” as reference group
c “Search engine” as reference group
Results of step 2 of the logistic regression analysis with participation at Time 3 as the dependent variable
Variablesa | OR | 95% CI | Wald χ2 |
|
Gender | 0.90 | 0.44-1.83 | 0.09 | .77 |
Age |
|
|
|
|
Medium educationb | 2.84 | 0.92-8.78 | 3.28 | .07 |
High educationb |
|
|
|
|
Ethnicity (0 = non-native Dutch; 1 = native Dutch) | 0.96 | 0.45-2.05 | 0.01 | .92 |
Recruitment through other websitec | 1.07 | 0.46-2.48 | 0.03 | .87 |
Recruitment through newspaperc | 1.66 | 0.34-8.22 | 0.39 | .53 |
Recruitment through family, friends, co-workersc | 1.44 | 0.19-11.20 | 0.12 | .73 |
Recruitment through televisionc | 1.49 | 0.55-4.00 | 0.62 | .43 |
Health motivation | 1.13 | 0.69-1.85 | 0.22 | .64 |
Physical activity | 1.00 | 1.00-1.01 | 0.25 | .62 |
Positive affective user experience |
|
|
|
|
Negative affective user experience | 0.88 | 0.67-1.15 | 0.91 | .34 |
Cognitive user experience | 1.06 | 0.71-1.59 | 0.09 | .77 |
Attitude | 0.90 | 0.54-1.52 | 0.15 | .70 |
Intention | 0.97 | 0.70-1.34 | 0.03 | .86 |
a Significant effects (
b “Low education” as reference group
c “Other” as reference group
The aim of the present study was to identify demographic, psychological and behavioral determinants of exposure to an online health-communication program advocating physical activity.
The results concerning visiting the website revealed that most participants were women and that, in comparison to the total Dutch population, our sample was highly educated. Almost half of our participants were recruited through links on related websites, suggesting that for online health-communication interventions, the Internet can be a valuable place for recruitment but that additional methods may be needed to attract more men and lower educated adults.
First, with regard to continued use, our results showed that native Dutch participants completed more pages than nonnative participants. It is unclear why nonnative visitors were less likely to use the program than native Dutch visitors. More research is needed to identify the needs of specific ethnic populations and the potential reasons for limited usage of health-promoting programs in this group. Ethnic targeting (see for instance [
Second, participants who were highly motivated to live a healthy lifestyle were more likely to use the program. This suggests that when people are sufficiently motivated to live a healthy lifestyle, they will be more likely to search the Internet for specific health-related information such as computer-tailored advice. To reach individuals with a weak health motivation, it is conceivable that other types of content that relates to the interests of these individuals but does not necessarily relate to health may be used to attract this target group. If participants’ attention can be attracted with non health-related content, this might make it easier to engage people who are not intrinsically motivated to live a healthy lifestyle. Social marketing strategies may contribute to the development of appealing health-communication websites because, in marketing, much effort is expended to understand the needs of target groups and to create an exchange in which these needs can be fulfilled [
Our analyses of revisiting at Time 3 showed that age predicted participation at the three-month follow-up: older participants were more likely to participate at Time 3. These results suggest that it can be especially difficult to obtain high exposure rates when targeting online health-promoting programs at young people. Since young people mainly use the Internet for getting in touch with friends and potential friends and chatting [
Our last finding might offer an additional answer to this question. We found that participants were more likely to revisit the program when an earlier visit had resulted in positive feelings whereas there was no significant effect of cognitive user experience on revisiting. This underlines the importance of user experience [
A strength of the present study is the fact that we obtained a sample of participants from the general population and observed them in a real-life health-communication context, which contributes greatly to the ecological validity of our findings. Furthermore, our main outcome measures, visiting, using, and revisiting, did not depend on self-report measures but were objectively assessed.
One limitation of our study was the fact that our sample was predominantly female and modest in size. A more representative and larger sample could have contributed to greater validity and could have provided us with more certainty with regard to whether the results can be generalized. However, the fact that the online intervention investigated in the study attracted more women than men can in itself be an interesting result, suggesting that women are more interested in online health-promoting programs than men (see [
A second limitation of our study was the fact that the intervention that we used was relatively simple, consisting of questionnaires and tailored advice at two points in time. Future studies could offer visitors a much wider range of possibilities, varying from watching videos to participating in chat-boxes. By objectively tracking visitors in such interventions, researchers can obtain more sophisticated information on the determinants of exposure.
A further issue concerns our investigation of continued use. Several questions from the Time 1 questionnaire (ie, demographic variables, mode of recruitment, and health motivation) were included solely for the sake of the study and were not used in the tailored feedback. It could be argued that a lack of interest in answering these questions could have caused participants to drop out of the study. This would then not be an accurate reflection of poor continued use of a health website. Future studies could employ shorter questionnaires or limit questionnaires to contain only questions that are relevant to the tailored feedback. We note, however, that for any tailored intervention, it is essential that visitors continue the program long enough to be able to finish the necessary questions. Even though in the present study not all questions were used for the tailored feedback, we argue that our measure of continued use served as a useful proxy for continued use of online tailored health-promoting programs.
Another potential limitation may have been the fact that a reminder was sent to participants after three months. It is unclear whether participants revisited the intervention because they remembered the intervention or because of the reminder. The reminder email thus constituted a confounding factor. Yet, many online health-promoting programs make use of prompts or reminders by email [
A final limitation was the fact that we used an observational design. Research using experimental manipulations aimed to influence exposure rates can offer stronger grounds for the causality of the effects. In a recent study, for instance, Albarracín and colleagues [
The Internet offers vast possibilities for health-communication efforts. Unfortunately, online health-communication interventions are characterized by low exposure rates. The present study sought to investigate predictors of visiting, using, and revisiting to increase our knowledge of exposure to online interventions. The results showed that women were more likely to visit the website than were men. Furthermore, native Dutch participants and participants with a strong health motivation were most likely to continue usage of the website. Older participants, highly educated participants, and participants who reported high levels of positive affective user experience were most likely to revisit the website. Online health-communication interventions could be specifically targeted at men, young people, immigrants, and people with low education levels. Engaging non health-related content might be used to attract the attention of those people who are not intrinsically motivated to live a healthy lifestyle. Furthermore, it is important that interventions offer participants sufficient opportunities for enjoyment.
This research was funded by ZonMW, the Netherlands Organisation for Health Research and Development (6100.0005). The authors would like to thank Robert AC Ruiter for detailed comments on an earlier version of this manuscript and Michelle Stoel for assistance with recruitment.
None declared
International Physical Activity Questionnaire