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Unhealthy lifestyle behaviors often co-occur and are related to chronic diseases. One effective method to change multiple lifestyle behaviors is web-based computer tailoring. Dropout from Internet interventions, however, is rather high, and it is challenging to retain participants in web-based tailored programs, especially programs targeting multiple behaviors. To date, it is unknown how much information people can handle in one session while taking part in a multiple behavior change intervention, which could be presented either sequentially (one behavior at a time) or simultaneously (all behaviors at once).
The first objective was to compare dropout rates of 2 computer-tailored interventions: a sequential and a simultaneous strategy. The second objective was to assess which personal characteristics are associated with completion rates of the 2 interventions.
Using an RCT design, demographics, health status, physical activity, vegetable consumption, fruit consumption, alcohol intake, and smoking were self-assessed through web-based questionnaires among 3473 adults, recruited through Regional Health Authorities in the Netherlands in the autumn of 2009. First, a health risk appraisal was offered, indicating whether respondents were meeting the 5 national health guidelines. Second, psychosocial determinants of the lifestyle behaviors were assessed and personal advice was provided, about one or more lifestyle behaviors.
Our findings indicate a high non-completion rate for both types of intervention (71.0%; n = 2167), with more incompletes in the simultaneous intervention (77.1%; n = 1169) than in the sequential intervention (65.0%; n = 998). In both conditions, discontinuation was predicted by a lower age (sequential condition: OR = 1.04;
Possible reasons for the higher dropout rate in our simultaneous intervention may be the amount of time required and information overload. Strategies to optimize program completion as well as continued use of computer-tailored interventions should be studied.
Dutch Trial Register NTR2168
Unhealthy lifestyle behaviors, such as physical inactivity, insufficient fruit and vegetable intake, high alcohol consumption, and smoking, often co-occur [
One method to change lifestyle behavior is the use of computer tailoring. Positive aspects of web-based computer-tailored programs are that these can be administered in privacy and at a time that suits the respondent [
The public health impact of an intervention is defined by the program’s efficacy multiplied by its reach [
Various tailoring strategies can be used to address multiple behaviors with computer-tailored interventions, such as a sequential or a simultaneous strategy. A simultaneous strategy concurrently targets multiple behaviors for intervention, while a sequential strategy targets a single behavior at a time. The few studies that have investigated the effects on behavioral change of sequential versus simultaneous strategies to provide multiple health-behavior change interventions reported inconsistent findings [
In any case, behavioral change will be more likely when someone completes the whole intervention program [
In addition to the problem of dropout, another important aspect is that high-risk populations (such as the less-educated and people with many unhealthy behaviors) are often insufficiently reached [e.g. 41], and it is especially those with unhealthy behavior who should engage in online health interventions and spend enough time on the website [
In conclusion, computer-tailored technology addressing multiple behaviors is still in its infancy [
In this study, which was part of a randomized controlled trial (Dutch Trial Register NTR2168), tailored information was provided to two groups, one receiving a sequential behavior tailoring condition (referred to below as sequential condition) and one receiving a simultaneous behavior tailoring condition (simultaneous condition) [
In the autumn of 2009, several Dutch Regional Health Authorities in the provinces of North-Brabant and Zeeland conducted an
Screenshot of the homepage of the intervention program.
The aim of the intervention was to stimulate participants to improve their lifestyle, focusing on 5 health behaviors. Based on the respondents’ answers to the different questions, an expert system selected the appropriate feedback messages from a large database and presented these directly on the respondent’s computer screen [
The first part of the feedback consisted of a health risk appraisal. Based on their answers on the
Afterwards, in the second part of the program, personal advice was provided, based on additional questions about psychosocial determinants (ie, attitude, social influence, preparatory action plans, self-efficacy, and coping plans; see
Screenshot of the health risk appraisal.
Screenshot of items regarding the pros and cons of alcohol intake.
Screenshot of a personal advice regarding the pros of alcohol intake.
After receiving the health risk appraisal, individuals in the sequential condition were invited to choose one of the health behaviors for which they were currently failing to meet the guideline. Respondents were encouraged to select the behavior that they were most motivated to change. This was followed by a progressive scheme consisting of 4 steps, in which respondents received personal advice based on various psychosocial constructs: (1) attitude, (2) social influence, (3) preparatory plans, and (4) self-efficacy and coping plans regarding the lifestyle behavior that they had chosen. Personal advice was given after the questions about each psychosocial construct (ie, attitude questions were followed by personal feedback about these items).
After receiving the health risk appraisal, participants in the simultaneous condition received feedback on all behaviors for which they failed to adhere to the public health guidelines in a predefined order. At random, half of the respondents started with the modules addressing preventive health behaviors (ie, (1) physical activity, (2) vegetable consumption, (3) fruit consumption) and ended with the modules addressing addiction behaviors (ie, (4) alcohol intake, (5) smoking), whereas the other half passed through the modules in reversed order. Respondents were presented with additional questions concerning psychosocial constructs, as well as personal advice on all behaviors for which they failed to adhere to the lifestyle recommendations. The 4-step progressive scheme ((1) attitude, (2) social influence, (3) preparatory plans, and (4) self-efficacy and coping plans) was used for all relevant lifestyle behaviors. Again, questions and personal advice were presented alternately.
The following demographic variables were assessed: age, gender, educational level (no education, primary, or lower vocational school (low); secondary vocational school or high school (medium); or higher professional education or university (high)), income, current job status, marital status, number of persons in the household, and country of origin.
Quality of life was assessed using the SF-12 Health Survey [
Five lifestyle behaviors were assessed using validated questionnaires: (1) physical activity, (2) fruit consumption, (3) vegetable consumption, (4) alcohol intake, and (5) smoking.
Physical activity was measured by the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH) [
Fruit consumption was measured using a 4-item Food Frequency Questionnaire (FFQ) assessing weekly fruit and fruit juice intake [
Vegetable consumption was measured using a 4-item FFQ assessing the weekly consumption of boiled or baked vegetables, as well as salads or raw vegetables [
Alcohol intake was measured by the 5-item Dutch Quantity-Frequency-Variability (QFV) questionnaire [
Smoking was assessed by asking participants if they smoked, what they smoked (cigarettes, cigars, or pipe tobacco), and how much they smoked per day (cigarettes) or per week (cigars or pipe tobacco).
The following description of the psychosocial determinants that were assessed is presented here to provide an overview of the program; the data on these items were not included in the analysis. Based on earlier studies [
We counted the time respondents spent on the website during their first visit (ie, from logging in to the program until logging out or closing the website). Furthermore, we assessed the number of respondents who started with the first module and the number of respondents who filled out the program completely.
The data was analyzed using SPSS software, version 17.0. Descriptive statistics were used to describe the characteristics of the study sample and to calculate the dropout rates for the 2 tailoring conditions. In the sequential condition, a completer was defined as someone who filled in one module from start to finish (ie, including the final question) since the aim of the first visit was that respondents of this condition complete one module relating to a lifestyle behavior for which they failed to adhere to the guideline. In the simultaneous condition, a completer was defined as someone who completed all modules relating to the lifestyle behaviors for which they failed to adhere to the guidelines. The groups (ie, completers versus non-completers) were compared in terms of their demographics and lifestyle behaviors by means of Chi-square tests for discrete variables and independent-samples
A total of 3473 individuals participated in the present study. The mean age of the participants was 44 years. Slightly more men than women took part. With regard to the participants’ lifestyle, 17.4% (n = 608) failed to meet the physical activity guidelines, 67.4% (n = 2323) did not eat enough vegetables, 54.6% (n = 1873) did not eat enough fruit, 28.2% (n = 978) drank too much alcohol, and 19.0% (n = 660) reported that they smoked. Almost two-thirds did not adhere to two or more health behavior guidelines (n = 2106; 61.7%). The characteristics of the total sample are listed in
Demographics, health status and lifestyle of the study sample (N = 3473)
Variable | Total group | |
|
43.61 (19-64; SD = 12.60) | |
|
||
Male | 1849 (53.2%) | |
Female | 1624 (46.8%) | |
|
||
Low | 367 (10.6%) | |
Medium | 1607 (46.5%) | |
High | 1483 (42.9%) | |
|
||
< € 1000 | 226 (6.5%) | |
€ 1001 - € 1350 | 228 (6.6%) | |
€ 1351 - € 1750 | 373 (10.8%) | |
€ 1750 - € 3050 | 1177 (33.9%) | |
> € 3051 | 976 (28.1%) | |
“I don’t want to say” | 488 (14.1%) | |
|
||
Employed | 2655 (76.6%) | |
Studying | 229 (6.6%) | |
Homemaker | 176 (5.1%) | |
Not currently in employment | 407 (11.7%) | |
|
||
Married | 2092 (60.5%) | |
Living together | 528 (15.3%) | |
Unmarried | 639 (18.5%) | |
Divorced | 170 (4.9%) | |
Widowed | 28 (0.8%) | |
# persons in household n = 3473 | 2.91 (1-20; SD = 1.42) | |
|
||
The Netherlands | 3300 (95.1%) | |
Other | 171 (4.9%) | |
|
25.17 (15.03-58.11; SD = 3.96) | |
|
40.11 (16-48; SD = 5.15) | |
|
44.78 (12-50; SD = 5.70) | |
|
||
0 | 25 (0.7%) | |
1 | 226 (6.6%) | |
2 | 681 (20.0%) | |
3 | 1174 (34.4%) | |
4 | 947 (27.8%) | |
5 | 358 (10.5%) | |
|
||
Compliance | 2865 (82.5%) | |
Non-compliance | 608 (17.4%) | |
|
||
Compliance | 1123 (32.6%) | |
Non-compliance | 2323 (67.4%) | |
|
||
Compliance | 1560 (45.4%) | |
Non-compliance | 1873 (54.6%) | |
|
||
Compliance | 2495 (71.8%) | |
Non-compliance | 978 (28.2%) | |
|
||
Compliance | 2813 (81.0%) | |
Non-compliance | 660 (19.0%) |
As shown in
On average, respondents in the sequential condition spent 10 minutes and 8 seconds on the web-based tailored program, while respondents in the simultaneous condition spent an average of 9 minutes and 47 seconds. In the sequential condition, respondents completed the program on average within 18 minutes and 10 seconds, while non-completers spent an average of 6 minutes and 20 seconds on the program. In the simultaneous condition, respondents completed the program within 20 minutes and 52 seconds, while non-completers left the program on average after 6 minutes and 16 seconds.
The completion rate generally decreased as the number of guidelines that the respondents failed to meet increased (see
The 2 tailoring groups did not differ in terms of their demographics, health status or lifestyle behaviors, indicating that randomization had been successful. A comparison of respondents who filled in the entire program (ie, completers) with respondents who prematurely left the site (ie, non-completers) showed that the two groups differed on all variables, except for income, native country, K10 and alcohol intake (see
Attrition diagram.
Number of guidelines respondents failed to adhere to against the number of completers in the sequential condition (n = 1536) and the simultaneous condition (n = 1517).
Differences in demographics, health status and lifestyle between non-completers and completers (N = 3053)
Variable | Non-completer |
Completers |
|
|
|
|||
|
41.85 (SD = 12.64) | 47.21 (SD = 11.96) | 11.07 ( |
0.43 | ||||
|
||||||||
Male | 1202 (55.5%) | 450 (50.8%) | ||||||
Female | 965 (44.5%) | 435 (49.2%) | 6.58 ( |
0.09 | ||||
|
||||||||
Low | 212 (9.8%) | 120 (13.6%) | ||||||
Medium | 1007 (46.7%) | 424 (48.0%) | ||||||
High | 938 (43.5%) | 339 (38.4%) | 13.35 ( |
0.13 | ||||
|
||||||||
< € 1750 | 504 (23.3%) | 221 (25.0%) | ||||||
€ 1751 - € 3050 | 1040 (48.0%) | 437 (49.5%) | ||||||
> € 3051 | 621 (28.7%) | 225 (25.5%) | 4.46 ( |
0.08 | ||||
|
||||||||
Job | 1852 (85.6%) | 686 (77.7%) | ||||||
No job | 311 (14.4%) | 197 (22.3%) | 33.34 ( |
0.21 | ||||
|
||||||||
Single | 557 (25.8%) | 191 (21.8%) | ||||||
In relationship | 1602 (74.2%) | 687 (78.2%) | 8.56 ( |
0.11 | ||||
|
2.99 (SD = 1.45) | 2.74 (SD = 1.25) | -4.86 ( |
0.18 | ||||
|
||||||||
The Netherlands | 2075 (95.8%) | 838 (94.7%) | ||||||
Other | 91 (4.2%) | 46 (5.3%) | 1.52 ( |
0.05 | ||||
|
25.00 (SD = 3.93) | 25.54 (SD = 3.84) | 3.47 ( |
0.14 | ||||
|
40.19 (SD = 5.06) | 39.39 (SD = 5.64) | -3.64 ( |
0.15 | ||||
|
44.71 (SD = 5.75) | 44.41 (SD = 6.12) | -1.27 ( |
0.05 | ||||
|
2.83 (SD = .96) | 3.12 (SD = .88) | 8.22 ( |
0.31 | ||||
|
||||||||
Compliance | 1698 (78.4%) | 761 (86.0%) | ||||||
Non-compliance | 469 (21.6%) | 124 (14.0%) | 23.62 ( |
0.18 | ||||
|
||||||||
Compliance | 501 (23.1%) | 253 (28.6%) | ||||||
Non-compliance | 1666 (76.9%) | 632 (71.4%) | 10.43 ( |
0.12 | ||||
|
||||||||
Compliance | 801 (37.0%) | 388 (43.8%) | ||||||
Non-compliance | 1366 (63.0%) | 497 (56.2%) | 13.14 ( |
0.13 | ||||
|
||||||||
Compliance | 1459 (67.3%) | 632 (71.4%) | ||||||
Non-compliance | 708 (32.7%) | 253 (28.6%) | 5.32 ( |
0.08 | ||||
|
||||||||
Compliance | 1671 (77.1%) | 731 (82.5%) | ||||||
Non-compliance | 496 (22.9%) | 154 (17.4%) | 11.56 ( |
0.12 | ||||
|
||||||||
Sequential | 998 (46.1%) | 537 (60.7%) | ||||||
Simultaneous | 1169 (53.9%) | 348 (39.3%) | 54.74 ( |
0.27 |
a Note: Respondents who did not want to report their income were classified in the category “€ 1751 - € 3050”
We performed a logistic regression analysis to identify predictors of program completion. After the various interaction terms had been added, the interaction term ‘tailoring condition*non-adherence to guidelines’ emerged as statistically significant (B = -.620;
In model 1 of both conditions, the factors significantly associated with non-completion were a lower age and being male. In the simultaneous condition, Dutch nationality was also significantly associated with dropout. In model 2, the effect of age remained significant in both conditions. In the sequential condition, being male continued to make a significant contribution, whereas in the simultaneous condition, the gender and native country variables became non-significant. In both conditions, discontinuation of the program was predicted by the number of guidelines respondents failed to adhere to (in addition to a younger age). This means that people with a less healthy lifestyle were more likely to drop out than those with a healthier lifestyle. The second model of the sequential condition explained 8.2% of the total variance for program completion, whereas the second model of the simultaneous condition explained 15.1% of the total variance.
Results of logistic regression analyses (Enter method) among the sequential condition on demographics and health status (model 1) and number of guidelines respondents failed to adhere to (model 2), with completion status (non-completers = 0; completers = 1) as dependent variable (N = 1496)
Model 1 | Model 2 | ||||||||
Variable | OR |
|
CI | OR |
|
CI | |||
|
|||||||||
|
1.04 | < .001 | 1.02-1.05 | 1.04 | < .001 | 1.02-1.05 | |||
|
|||||||||
Male (ref.) | 1.00 | 1.00 | |||||||
Female | 1.30 | .02 | 1.04-1.63 | 1.27 | .04 | 1.01-1.59 | |||
|
|||||||||
Low | 1.42 | .99 | 0.98-2.05 | 1.03 | .90 | 0.69-1.53 | |||
Medium | 1.13 | .36 | 0.86-1.50 | 1.14 | .30 | 0.89-1.45 | |||
High (ref.) | 1.00 | 1.00 | |||||||
|
|||||||||
< € 1750 | 1.42 | .06 | 0.98-2.05 | 1.42 | .07 | 0.98-2.05 | |||
€ 1751 - € 3050 | 1.13 | .38 | 0.86-1.50 | 1.13 | .40 | 0.84-1.49 | |||
> € 3051 (ref.) | 1.00 | 1.00 | |||||||
|
|||||||||
Job (ref.) | 1.00 | 1.00 | |||||||
No job | 1.01 | .96 | 0.74-1.38 | 1.01 | .97 | 0.73-1.38 | |||
|
|||||||||
In relationship (ref.) | 1.00 | 1.00 | |||||||
Single | 0.94 | .71 | 0.68-1.30 | 0.97 | .83 | 0.70-1.33 | |||
|
0.94 | .18 | 0.87-1.03 | 0.94 | .16 | 0.87-1.02 | |||
|
|||||||||
The Netherlands (ref.) | 1.00 | 1.00 | |||||||
Other | 0.99 | .93 | 0.57-1.66 | 0.99 | .96 | 0.58-1.68 | |||
|
|||||||||
|
1.01 | .57 | 0.98-1.04 | 1.01 | .62 | 0.98-1.04 | |||
|
0.97 | .07 | 0.94-1.00 | 0.97 | .05 | 0.94-1.00 | |||
|
1.00 | .75 | 0.98-1.03 | 1.00 | .78 | 0.98-1.04 | |||
|
|||||||||
|
.86 | .01 | 0.76-0.97 | ||||||
Nagelkerke’s R2 | .076 | .082 |
a Note: Respondents who did not want to report their income were classified in the category “€ 1751 - € 3050”
Results of logistic regression analyses (Enter method) among the simultaneous condition on demographics and health status (model 1) and number of guidelines respondents failed to adhere to (model 2), with completion status (non-completers = 0; completers = 1) as dependent variable (N = 1473)
Model 1 | Model 2 | ||||||||
Variable | OR |
|
CI | OR |
|
CI | |||
|
|||||||||
|
1.04 | < .001 | 1.02-1.05 | 1.04 | < .001 | 1.02-1.05 | |||
|
|||||||||
Male (ref.) | 1.00 | 1.00 | |||||||
Female | 1.35 | .03 | 1.04-1.74 | 1.13 | .36 | 0.87-1.48 | |||
|
|||||||||
Low | 1.30 | .22 | 0.86-1.95 | 1.41 | .11 | 0.92-2.16 | |||
Medium | 1.02 | .88 | 0.77-1.35 | 1.09 | .55 | 0.82-1.46 | |||
High (ref.) | 1.00 | 1.00 | |||||||
|
|||||||||
< € 1750 | 0.91 | .65 | 0.60-1.38 | 0.87 | .51 | 0.56-1.33 | |||
€ 1751 - € 3050 | 0.89 | .44 | 0.65-1.21 | 0.90 | .51 | 0.65-1.24 | |||
> € 3051 (ref.) | 1.00 | 1.00 | |||||||
|
|||||||||
Job (ref.) | 1.00 | 1.00 | |||||||
No job | 1.18 | .35 | 0.84-1.66 | 1.11 | .56 | 0.78-1.58 | |||
|
|||||||||
In relationship (ref.) | 1.00 | 1.00 | |||||||
Single | 1.10 | .63 | 0.76-1.59 | 1.12 | .55 | 0.77-1.65 | |||
|
0.92 | .11 | 0.82-1.02 | 0.90 | .06 | 0.81-1.01 | |||
|
|||||||||
The Netherlands (ref.) | 1.00 | 1.00 | |||||||
Other | 1.78 | .04 | 1.03-3.08 | 1.54 | .14 | 0.87-2.70 | |||
|
|||||||||
|
1.00 | .79 | 0.96-1.03 | 1.00 | .82 | 0.96-1.03 | |||
|
0.98 | .35 | 0.95-1.02 | 0.98 | .18 | 0.94-1.01 | |||
|
1.00 | .82 | 0.97-1.04 | 1.00 | .87 | 0.96-1.03 | |||
|
|||||||||
|
0.49 | < .001 | 0.42-0.58 | ||||||
Nagelkerke’s R2 | .073 | .151 |
a Note: Respondents who did not want to report their income were classified in the category “€ 1751 - € 3050”
In view of the high number of people with an unhealthy lifestyle, there is a widely recognized need for interventions to change multiple behaviors. However, the best strategy to deliver such web-based interventions remains unclear. Addressing multiple health behaviors in one intervention leads to more extensive programs, which require more time and effort from the respondents [eg, 31]. We compared dropout rates of a sequential and a simultaneous version of a computer-tailored intervention regarding physical activity, fruit consumption, vegetable consumption, alcohol intake, and smoking, and investigated the predictive value of personal characteristics and lifestyle behaviors on completion and dropout rates for the 2 strategies.
Our first finding was that there were more non-completers in the simultaneous intervention than in the sequential intervention. The most important factor explaining the difference in dropout rate between these two conditions may be the difference in the length of the questionnaires and the computer-tailored advice that respondents received after the initial health risk appraisal. For example, if a respondent failed to adhere to 2 guidelines, the sequential intervention consisted of approximately 25 questions (average 10 minutes completion time), whereas the simultaneous intervention in such cases consisted of 50 questions, with an average completion time of 20 minutes. The advice also became twice as long, since the respondent had to fill in 2 modules in this case. Earlier research has also shown that the length of the program may be a primary reason to leave a website prematurely [
Although the dropout rate was higher in the simultaneous intervention than in the sequential intervention, our findings revealed a high rate of non-completion in both types of intervention. One possible reason might be the recruitment strategy used. Completing the health risk appraisal took approximately 5 minutes in both conditions. The health risk appraisal was based on the
In terms of personal characteristics that were predictive of completion or non-completion of the program, significant influences were found of age and gender. Older people and women were more likely to complete the program, which is in line with earlier findings [
Our findings – with dropout rates being higher in the simultaneous condition than in the sequential condition – suggest that a sequential tailoring strategy might be able to reach the largest group of participants. However, since approximately 60-70% of the population fails to adhere to multiple public health guidelines, people may need information about more than one lifestyle behavior. The sequential strategy used in our intervention may therefore be insufficient to meet the needs of a large part of the population, especially those of people who are interested in several health behaviors and who are motivated to change multiple lifestyle behaviors. In our sequential intervention, respondents received the health risk appraisal, including information about the 5 health behaviors. Yet respondents were limited to one single module in the second part of the program at their first visit. In the long term, this approach can be regarded as a multiple behavior change intervention using a sequential strategy, but in the short term, detailed information is made available about one behavior only. Since the dropout rate at the very first visit was high, future research should first concentrate on prolonged use (ie, continuing the intervention for a substantial period of time) and possible information overload. To date, it seems to be a challenge to hold respondents’ attention in online interventions. Since the dropout rate even in the sequential condition is rather high, the number of psychosocial constructs as well as the tailored texts could be shortened, spread over time or delivered in different forms. Including more interactive elements, such as videos or games, may improve the attractiveness of eHealth programs, which in turn may result in longer visits [
The simultaneous tailoring strategy has advantages as well, insofar as people may receive tailored feedback on more than one lifestyle behavior at once. However, it may be better not to offer the modules in a predefined order. A study by Brouwer et al. [
Another option to explore is a mixture of both tailoring strategies, called preference-based tailoring [
To our knowledge, this is the first study to compare sequential and simultaneous interventions addressing the 5 lifestyle behaviors of physical activity, fruit consumption, vegetable consumption, alcohol intake, and smoking, in terms of dropout rates. The study has yielded new information about predictors of completion of the 2 intervention types.
The findings of this study should be interpreted keeping several limitations in mind. Our findings were based on self-reports, which could have led to recall bias (e.g., the high proportion of people who reported to meet the physical activity guideline may represent an overestimation of their actual level of physical activity); and the amounts of variance explained by our regression models were relatively low, indicating that other factors might play a role in determining program completion. Future research is necessary to identify additional relevant factors, for example, motivation to change, available time, interest in the topic, program evaluation (in terms of, eg, user-friendliness and attractiveness), and expectations from the program.
The present study provides initial evidence for higher attrition rates in the simultaneous intervention strategy. Although this is likely to result in lower effectiveness of this intervention, future studies need to address the relative efficacy and effectiveness of simultaneous versus sequential tailoring. Hence, re-visiting rates for the two types of interventions should be compared, and the differences in effectiveness in terms of successful behavior change should be tested. It is imaginable that despite the higher dropout in the simultaneous condition, more respondents in this condition received all relevant information compared to those in the single/sequential condition who possibly only read information about the most preferred behavior module and/or never return to the intervention program. More research remains to be done to study in which condition more modules are opened and/or completed by the respondents during the duration of the project.
Our findings indicate a high rate of non-completion in both types of intervention, with more incompletes in the simultaneous intervention and among respondents with unhealthier lifestyles. In both conditions, discontinuation of the program was related to a younger age of the respondent, and in the sequential condition, being male was also associated with non-completion of the program. The results of this study suggest opportunities for optimizing online tailored lifestyle interventions: such programs should be tailored to all individual users; their efficiency should be improved; their attractiveness should be enhanced by integrating interactive elements; and their content and length or duration should be balanced.
CONSORT EHEALTH checklist V1.6 [
This study was funded by ZonMw, the Netherlands Organization for Health Research and Development (grant number: 120610012). Intervention development and implementation took place at Maastricht University. Data collection and data analysis were done in collaboration with the Regional Health Authorities of the Dutch provinces of North-Brabant (
Hein de Vries is the scientific director of Vision2Health, a company that licenses evidence-based innovative computer-tailored health communication tools. No other authors reported conflicts of interest.