The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
Characteristics of visitors and revisitors to an Internet-delivered computer-tailored lifestyle intervention implemented for use by the general public
Open accessAbstract
The Internet has become important for the delivery of behavior change interventions. This observational study examines how many people visited, registered and revisited a web-based computer-tailored intervention promoting heart-healthy behaviors when it is implemented for use by the general public. Among registered visitors, the association between visitors' characteristics and initiating, completing and revisiting the website and/or its behavior-specific modules was analyzed. Server statistics showed that 285 146 visitors from unique IP addresses landed on the home page in a 36-month period; of these, >50% left the intervention website within 30 s. In total, 81 574 (28.6%) visitors completed the registration procedure and gained access to the intervention; 99% of registered visitors initiated one module, 91% completed at least one module and 6% revisited the intervention. The majority of the registered visitors were women, medium to highly educated, with a body mass index (BMI) <25. Women, visitors aged 40-50 years, visitors with a medium educational level and visitors with a BMI <25 were more likely to initiate and finish the modules. It is concluded that a heart-healthy computer-tailored Internet program can reach substantial numbers of people, but additional research is needed to develop promotional strategies that reach the high-risk population, i.e. men, older and lower educated persons.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The bounce rate of 19.37% was also substantially lower than that observed in other interventions (eg, 32.9% in an intervention promoting sexual health, and 56.3% in an intervention promoting heart-healthy behaviors) [21,22], indicating that the home page was effective at engaging users.”
From Breaking open the black box: isolating the most potent features of a web and mobile phone-based intervention for depression, anxiety, and stress (2015) · cites this paper as [22]
In the citing paper
“However, as is common in nontherapist-assisted eMental Health interventions [22,25,26], frequency of program use decreased over the course of the intervention, emphasizing the need to identify program features that promote more regular program engagement.”
From Breaking open the black box: isolating the most potent features of a web and mobile phone-based intervention for depression, anxiety, and stress (2015) · cites this paper as [22]
In the citing paper
“other studies reported that only a minority of participants visited an intervention more than once [4,23].”
From Which Intervention Characteristics are Related to More Exposure to Internet-Delivered Healthy Lifestyle Promotion Interventions? A Systematic Review (2011) · cites this paper as [4]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Evaluating the public health impact of health promotion interventions: the RE-AIM framework.
Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999) · American Journal of Public Health · doi:10.2105/ajph.89.9.1322 · Cited by 7,217
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
Wendel‐Vos, W. (2003) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(03)00220-8 · Cited by 1,417
Lynch, J. W., Kaplan, G. A., & Salonen, J. T. (1997) · Social Science & Medicine · doi:10.1016/s0277-9536(96)00191-8 · Cited by 1,150
Rice, R. E. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.07.032 · Cited by 721
Tate, D. F., Jackvony, E. H., & Wing, R. R. (2003) · JAMA · doi:10.1001/jama.289.14.1833 · Cited by 666
Tailoring Health Messages: Customizing Communication With Computer Technology
Farrell, D. W., Rimer, B. K., Olevitch, L. R., Brennan, L. K., & Kreuter, M. W. (1999) · Cited by 635
Kroeze, W., Werkman, A., & Brug, J. (2006) · Annals of Behavioral Medicine · doi:10.1207/s15324796abm3103_2 · Cited by 570
Website-Delivered Physical Activity Interventions
Vandelanotte, C., Spathonis, K. M., Eakin, E. G., & Owen, N. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.02.041 · Cited by 475
Research works printed but not linked: 3
- 2007, Health Education Research, 33, 385
- 2006, J ONLINE LEARN TEACH, 2, 73
- 2006, SEXUALITY RESEARCH SOCIAL POLICY A JOURNAL OF THE NSRC, 3, 52
Cited by
Sorted by citations
Persuasive System Design Does Matter: a Systematic Review of Adherence to Web-based Interventions
Kelders, S. M., Kok, R. N., Ossebaard, H. C., & Van Gemert-Pijnen, J. E. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2104 · Cited by 1,308
Perski, O., Blandford, A., West, R., & Michie, S. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0453-1 · Cited by 1,193
“This kind of conceptualisation has emerged due to the observation that while many download and try DBCIs, sustained usage is typically low [21 – 24].”
BackgroundLatulippe, K., Hamel, C., & Giroux, D. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6731 · Cited by 458
Brouwer, W., Kroeze, W., Crutzen, R., de Nooijer, J., de Vries, N. K., Brug, J., & Oenema, A. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1639 · Cited by 455
“However, disappointing results regarding revisiting have been published, as website visits tend to decrease sharply after the initial weeks of participation [4,23,39].”
BackgroundInfluentialSelf-citationOnline Prevention Aimed at Lifestyle Behaviors: A Systematic Review of Reviews
Kohl, L. F., Crutzen, R., & de Vries, N. K. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2665 · Cited by 388
Self-citationBeatty, L., & Binnion, C. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9556-9 · Cited by 347
“This is consistent with research on adherence to other online health interventions [55, 56] and broader research on health behaviors indicating that women are more likely to engage in health-related behaviors than men [38, 57].”
ResultGuertler, D., Vandelanotte, C., Kirwan, M., & Duncan, M. J. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4339 · Cited by 147
“Besides characteristics of the intervention itself, personal characteristics of the users and the degree of engagement with the intervention may affect nonusage attrition [15,20,23].”
Schulz, D. N., Kremers, S., Vandelanotte, C., van Adrichem, M. J., Schneider, F., Candel, M. J. J. M., & de Vries, H. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3094 · Cited by 135
“This is in-line with the findings of Brouwer et al [72] who reported that more than half of their visitors left the website within 30 seconds.”
BackgroundResultKarekla, M., Kasinopoulos, O., Neto, D. D., Ebert, D. D., Van Daele, T., Nordgreen, T., Höfer, S., Oeverland, S., & Jensen, K. L. (2019) · European Psychologist · doi:10.1027/1016-9040/a000349 · Cited by 117
Crutzen, R., Roosjen, J. L., & Poelman, J. (2013) · Health Promotion International · doi:10.1093/heapro/das008 · Cited by 113
“…bounce rate is quite low in comparison with other Internet-delivered interventions [e.g. 56.3% in an intervention promoting heart-healthy behaviors (Brouwer et al., 2010)], it demonstrates the importance of the first impression which has a long-lasting effect on website use (Lindgaard et al.,…”
BackgroundInfluentialSelf-citationNeed satisfaction, motivational regulations and exercise: moderation and mediation effects
Josefsson, K. W., Lindwall, M., & Ivarsson, A. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0226-0 · Cited by 115
Wang, Y., Lin, Y., Chen, J., Wang, C., Hu, R., & Wu, Y. (2020) · Supportive Care in Cancer · doi:10.1007/s00520-020-05383-3 · Cited by 91
Crutzen, R., Cyr, D., & de Vries, N. K. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1922 · Cited by 91
“The objective was to prevent participants from thoroughly studying the website, and to mimic a real-life situation in which time and opportunity to invest in the website is limited [5].”
BackgroundSelf-citationRiet, J. V. ‘., Crutzen, R., & de Vries, H. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1345 · Cited by 93
“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 [46] for similar results).”
ResultSelf-citationRuland, C. M., Maffei, R. M., Børøsund, E., Krahn, A., Andersen, T., & Grimsbø, G. H. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2013.02.007 · Cited by 88
Beatty, L., Koczwara, B., & Wade, T. (2016) · Supportive Care in Cancer · doi:10.1007/s00520-015-2867-6 · Cited by 84
Weymann, N., Dirmaier, J., von Wolff, A., Kriston, L., & Härter, M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3904 · Cited by 73
“Still, the effectiveness of those online applications is limited by high attrition rates [30,31], and users often visit a health intervention website only once [32-34].”
Willems, R. A., Mesters, I., Lechner, L., Kanera, I. M., & Bolman, C. A. W. (2017) · Journal of Cancer Survivorship · doi:10.1007/s11764-017-0625-0 · Cited by 84
Compernolle, S., Vandelanotte, C., Cardon, G., De Bourdeaudhuij, I., & De Cocker, K. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3402 · Cited by 80
“Moreover, the recruitment through white-collar workplaces resulted in a selection bias with more highly educated people being involved in the study, which is in line with the outcomes of previous reviews that indicated that mainly higher educated people participate in online interventions [20,48].”
Stanczyk, N. E., Bolman, C. A. W., van Adrichem, M. J., Candel, M. J. J. M., Muris, J. W. M., & de Vries, H. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3016 · Cited by 80
“Although Web-based computer-tailored smoking cessation interventions have been shown to be potentially effective [15,16], they often report problems in attracting, engaging, and retaining smokers and quitters [7,17-20].”
Børøsund, E., Cvancarova, M., Ekstedt, M., Moore, S. M., & Ruland, C. M. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2285 · Cited by 80
“Interestingly, we did not find any statistically significant association between age and gender and frequency of use; other studies have described younger people and women as the most frequent users of Web-based interventions [20,24-26,28,29].”
Peels, D. A., Bolman, C. A. W., Golsteijn, R. H. J., de Vries, H., Mudde, A. N., van Stralen, M. M., & Lechner, L. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2229 · Cited by 79
“Different demographic groups, based on age or SES for example, use and respond to Web-based interventions differently [7,11,12].”
BackgroundRobroek, S. J., Lindeboom, D. E., & Burdorf, A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1788 · Cited by 79
“It has been reported that women [10-14], and individuals with a medium or high educational level [10,12-13] are more likely to start participating.”
InfluentialSchneider, F., van Osch, L., & de Vries, H. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1863 · Cited by 75
Bidargaddi, N., Pituch, T., Maaieh, H., Short, C., & Strecher, V. (2018) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2018.07.004 · Cited by 77
“Age, gender and BMI were speci fi ed as covariates as women, people with lower BMI, higher education level and age are more likely to engage with online lifestyle interventions (Brouwer et al., 2010; Schneider et al., 2012).”
BackgroundLara, J., O’Brien, N., Godfrey, A., Heaven, B., Evans, E. H., Lloyd, S., Moffatt, S. M., Moynihan, P. J., Meyer, T. D., Rochester, L., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159703 · Cited by 76
van Genugten, L., van Empelen, P., Boon, B. J. F., Borsboom, G., Visscher, T. L. S., & Oenema, A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1901 · Cited by 79
“Even though multiple visits are associated with higher efficacy of (computer-tailored) interventions [48], it has also been well documented that attrition is high in online interventions with multiple sessions [49-51].”
Self-citationCheung, K. L., Durusu, D., Sui, X., & de Vries, H. (2019) · Digital Health · doi:10.1177/2055207618824727 · Cited by 71
“8,9 However, all these opportunities are tempered by the problem that these interventions are used suboptimally.”
BackgroundBringing Loyalty to E-health: Theory Validation Using Three Internet-Delivered Interventions
Crutzen, R., Cyr, D., & de Vries, N. K. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1837 · Cited by 69
“The reason behind this was to prevent participants from thoroughly studying the intervention website, and to mimic a real-life situation in which the time being exposed to and willing to investigate an intervention website is often limited [4].”
InfluentialSelf-citationSchulz, D. N., Candel, M. J. J. M., Kremers, S., Reinwand, D. A., Jander, A., & de Vries, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2568 · Cited by 70
“Although Internet-based programs have the potential to reach large numbers of people, various studies have pointed out that the actual use may be limited and that high rates of attrition are common [42-47].”
van der Krieke, L., Blaauw, F. J., Emerencia, A. C., Schenk, H., Slaets, J. P. J., Bos, E. H., de Jonge, P., & Jeronimus, B. F. (2016) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000378 · Cited by 73
Whitton, A. E., Proudfoot, J., Clarke, J., Birch, M.-R., Parker, G., Manicavasagar, V., & Hadzi-Pavlovic, D. (2015) · JMIR Mental Health · doi:10.2196/mental.3573 · Cited by 69
Leung, L. W. (2008) · CyberPsychology & Behavior · doi:10.1089/cpb.2007.0189 · Cited by 69
Peels, D. A., van Stralen, M. M., Bolman, C. A. W., Golsteijn, R. H. J., de Vries, H., Mudde, A. N., & Lechner, L. (2014) · Health Education Research · doi:10.1093/her/cyu039 · Cited by 72
Davies, C., Corry, K., Itallie, A. K. V., Vandelanotte, C., Caperchione, C. M., & Mummery, W. K. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1792 · Cited by 66
“However, it is often reported that these types of interventions have high attrition and limited or declining website engagement [4,10,13-19]; and it is shown that low intervention exposure results in lower overall effectiveness of the intervention [2,16,17,20].”
Kanera, I. M., Willems, R. A., Bolman, C. A. W., Mesters, I., Zambon, V., Gijsen, B. C., & Lechner, L. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5975 · Cited by 59
“Prior research confirms that the possibility to choose within multiple behavior interventions may prevent high attrition rates and could improve intervention outcomes [31,32,62].”
Schneider, F., van Osch, L., Schulz, D. N., Kremers, S., & de Vries, H. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1939 · Cited by 60
“Previous studies have pointed out that Internet interventions tend to reach women [40-43] and older people [44].”
BackgroundResultInfluentialSchneider, F., de Vries, H., Candel, M. J. J. M., van de Kar, A., & van Osch, L. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2151 · Cited by 60
Stanczyk, N. E., Smit, E. S., Schulz, D. N., de Vries, H., Bolman, C. A. W., Muris, J. W. M., & Evers, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110117 · Cited by 58
Comparing usage of a web and app stress management intervention: An observational study
Morrison, L. G., Geraghty, A. W. A., Lloyd, S., Goodman, N., Michaelides, D. T., Hargood, C., Weal, M., & Yardley, L. (2018) · Internet Interventions · doi:10.1016/j.invent.2018.03.006 · Cited by 57
“Other work has sought to analyse user characteristics associated with greater usage of or exposure to the intervention content (e.g. Brouwer et al., 2009; Van't Riet et al., 2010) or compare usage across di ff erent interventions that are focused on a particular health condition or behaviour (Nelson…”
BackgroundLet's Get Technical! Gaming and Technology for Weight Control and Health Promotion in Children
Baranowski, T., & Frankel, L. A. (2011) · Childhood Obesity · doi:10.1089/chi.2011.0103 · Cited by 57
“17 AVG, also called exergames, are video games designed primarily to make a profit, but they also happen to require/ facilitate physical activity by players. examples of this genre include Dance, Dance Revolution, 18 and Wii.”
What makes online substance-use interventions engaging? A systematic review and narrative synthesis
Milward, J., Drummond, C., Fincham-Campbell, S., & Deluca, P. (2018) · Digital Health · doi:10.1177/2055207617743354 · Cited by 58
Yardley, L., Miller, S., Schlotz, W., & Little, P. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1963 · Cited by 55
Schulz, D. N., Schneider, F., de Vries, H., van Osch, L., van Nierop, P. W., & Kremers, S. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1968 · Cited by 54
Turnbull, S., Cabral, C., Hay, A. D., & Lucas, P. J. (2020) · Journal of Medical Internet Research · doi:10.2196/17849 · Cited by 56
“People from lower socioeconomic groups and older adults are less likely to seek health information on the web and have problems using the web-based information available [31-38].”
BackgroundPaul, C. L., Carey, M. L., Sanson-Fisher, R. W., Houlcroft, L. E., & Turon, H. E. (2013) · Health Education Research · doi:10.1093/her/cyt053 · Cited by 51
“Given previous findings that patients of lower socioeconomic status were less likely to engage with web-based cancer support groups [87] and that high risk, lower educated groups were less likely to engage with Internet-delivered lifestyle advice [88], sociodemographic mediators of effectiveness are worthy of explorat…”
BackgroundCheng, C. C., Elsworth, G. R., & Osborne, R. H. (2021) · Journal of Medical Internet Research · doi:10.2196/30243 · Cited by 51
“The study by Brouwer et al [52] also found that women recorded a higher participation rate for a web-based health intervention than men, and women were more likely to engage in preventive activities related to health than men [49].”
Stanczyk, N. E., de Vries, H., Candel, M. J. J. M., Muris, J. W. M., & Bolman, C. A. W. (2015) · Preventive Medicine · doi:10.1016/j.ypmed.2015.11.002 · Cited by 49
Wahle, F., Bollhalder, L., Kowatsch, T., & Fleisch, E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7381 · Cited by 48
“From a system component perspective, there is a strong need to counteract the decrease of program usage over the course of the intervention that is typically found in unguided technology-mediated interventions for mental health [118,141,142].”
BackgroundMéjean, C., de Edelenyi, F. S., Touvier, M., Kesse‐Guyot, E., Julia, C., Andreeva, V. A., & Herçberg, S. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3161 · Cited by 47
“In Web-based intervention studies with repeated measurements, women, older participants, nonsmokers, heavy alcohol consumers, and overweight participants were more likely to remain enrolled in Internet-delivered behavior change programs [17-22].”
Schroé, H., Crombez, G., Bourdeaudhuij, I. D., & Dyck, D. V. (2021) · JMIR mhealth and uhealth · doi:10.2196/30583 · Cited by 49
“4%), which has also been the case in other digital health intervention studies [64].”
Dyck, D. V., Plaete, J., Cardon, G., Crombez, G., & Bourdeaudhuij, I. D. (2016) · Health Education Research · doi:10.1093/her/cyw036 · Cited by 47
Gerber, M., Cheval, B., Cody, R., Colledge, F., Hohberg, V., Klimentidis, Y. C., Lang, C., Looser, V. N., Ludyga, S., Stults‐Kolehmainen, M., et al. (2025) · Physiological Reviews · doi:10.1152/physrev.00021.2024 · Cited by 59
Alkhaldi, G., Modrow, K., Hamilton, F. L., Pal, K., Ross, J., & Murray, E. (2017) · Interactive Journal of Medical Research · doi:10.2196/ijmr.6952 · Cited by 39
“…demographic information, namely their age, sex, highest level of education achieved, how long they had had diabetes for, and how they rated their expertise with computers (basic, intermediate, or advanced), as these characteristics may influence participants’ perceptions of email prompts [26-28].”
BackgroundRobroek, S. J., Brouwer, W., Lindeboom, D., Oenema, A., & Alex Burdorf, A. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1402 · Cited by 41
“However, there are also studies indicating that Internet-delivered programs have attracted individuals who would benefit most from them, that is, participants who are overweight [8,13,16].”
BackgroundSelf-citationArsanjani, J. J., & Bakillah, M. (2014) · International Journal of Digital Earth · doi:10.1080/17538947.2014.951081 · Cited by 41
“…Avelar et al. 2009; Drummond et al. 2012; Jokar Arsanjani, Helbich, Kainz et al. 2013; Li, Zhou, and Ouyang 2013), number of Internet users (e.g. Brouwer et al. 2010; Hernández, Jiménez, and José Martín 2011; Schneider, van Osch, and H. de Vries 2012), disease disorder (Généreux, Bruneau, and…”
BackgroundBrowne, S., Kechadi, T., O’Donnell, S., Dow, M., Tully, L., Doyle, G., & O’Malley, G. (2020) · JMIR mhealth and uhealth · doi:10.2196/16925 · Cited by 42
“…depend on a range of factors known to be influential in clinical care (eg, school absenteeism, dissatisfaction with care components, demographic factors) [44-46], in addition to factors influencing dropout from eHealth-specific interventions (eg, registration requirements and male gender) [47-49].”
Smit, E. S., Hoving, C., Cox, V. C. M., & de Vries, H. (2012) · Health Education Research · doi:10.1093/her/cyr099 · Cited by 35
“Although online advertising has been suggested to be able to achieve relatively high recruitment rates [11], it is not yet clear whether all segments of the smoking population, such as smokers with a low socio-economic status [12-14], can be successfully reached via the Internet.”
BackgroundTait, R. J., McKetin, R., KAY‐LAMBKIN, F. J., Carron‐Arthur, B., Bennett, A., Bennett, K., Christensen, H. M., & Griffiths, K. M. (2014) · JMIR Mental Health · doi:10.2196/mental.3278 · Cited by 38
“It uses all available data without requiring substitution or estimation of missing independent variables to avoid the exclusion of cases with noncomplete data and does not assume homogeneity of correlations over waves of measurement [30,31].”
Broekhuizen, K., Jelsma, J. G. M., van Poppel, M. N. M., Lando Koppes, L. J., Brug, J., & van Mechelen, W. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-348 · Cited by 35
“reported a registration rate of 29% and found 91% of the registered users actually finishing at least one module [47].”
BackgroundSelf-citationSchweier, R., Romppel, M., Richter, C., Hoberg, E., Hahmann, H., Scherwinski, I., Kosmützky, G., & Grande, G. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3434 · Cited by 32
“The problem of low usage rates is well known and an often cited problem in trials on Web-based interventions [32-35].”
BackgroundStanczyk, N. E., Crutzen, R., Bolman, C. A. W., Muris, J. W. M., & de Vries, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2153 · Cited by 31
“Moreover, since our less educated smokers often show higher addiction rates, fewer quit attempts, and more relapses [14,15], approaching them via Internet may have added potential.”
BackgroundSelf-citationWeb-based interventions for comorbid depression and chronic illness: a systematic review
Charova, E., Dorstyn, D., Tully, P., & Mittag, O. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15571997 · Cited by 30
“It may also be that the delivery of psychological services in a technological environment introduces a selection bias – potentially excluding those sub-groups with reduced web literacy such as the elderly and those from disadvantaged socio-economic backgrounds.(54) Study evaluation identified the need to consider alte…”
BackgroundHealth Education and Health Promotion: Key Concepts and Exemplary Evidence to Support Them
de Vries, H., Kremers, S., & Lippke, S. (2018) · Springer eBooks · doi:10.1007/978-0-387-93826-4_17 · Cited by 39
Understanding adherence to web-based interventions
Kelders, S. M. (2012) · doi:10.3990/1.9789036534178 · Cited by 29
O’Connor, E. J., Farrow, M., & Hatherly, C. (2014) · JMIR Mental Health · doi:10.2196/mental.3654 · Cited by 28
“It has been proposed that the primary difficulty for eHealth interventions is to engage the community for long enough so that they obtain exposure to at least the most important aspects of the program [30].”
BackgroundHohberg, V., Fuchs, R., Gerber, M., Künzler, D., Paganini, S., & Faude, O. (2022) · Sports Medicine - Open · doi:10.1186/s40798-022-00489-w · Cited by 26
Backman, A., Mellblom, A., Norman-Claesson, E., Keith-Bodros, G., Frostvittra, M., Bölte, S., & Hirvikoski, T. (2018) · Research in Autism · doi:10.1016/j.rasd.2018.07.001 · Cited by 28
Psychenet.de: development and process evaluation of an e-mental health portal
Dirmaier, J., Liebherz, S., Sänger, S., Härter, M. C., & Tlach, L. (2015) · Informatics for Health and Social Care · doi:10.3109/17538157.2015.1008486 · Cited by 27
“Still, the effectiveness of e-mental health services is limited by high attrition rates (24), and few users visit a health intervention website more than once (25,26).”
BackgroundResultStanczyk, N. E., Bolman, C. A. W., Smit, E. S., Candel, M. J. J. M., Muris, J. W. M., & de Vries, H. (2013) · Health Education Research · doi:10.1093/her/cyt104 · Cited by 25
“make use of web-based lifestyle interventions [19–21].”
Vereecken, C. A., Covents, M., Maes, L., & Moyson, T. (2013) · Journal of Human Nutrition and Dietetics · doi:10.1111/j.1365-277x.2012.01290.x · Cited by 26
Walthouwer, M. J. L., Oenema, A., Lechner, L., & de Vries, H. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4496 · Cited by 26
Self-citationSchneider, F., Schulz, D. N., Pouwels, L. H., de Vries, H., & van Osch, L. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-721 · Cited by 22
“However, a multi-risk lifestyle behavior intervention offered to a comparable sample reported levels of reach around 3% of the eligible participants [54].”
BackgroundResultInfluentialPlaete, J., Bourdeaudhuij, I. D., Verloigne, M., & Crombez, G. (2016) · PeerJ · doi:10.7717/peerj.1666 · Cited by 24
“Previous studies observed the ‘if-then’ format to yield better 410 behaviour outcomes (Armitage & Arden 2010; Chapman et al. 2009).”
ResultAcceptance of the German e-mental health portal www.psychenet.de: an online survey
Tlach, L., Thiel, J., Härter, M. C., Liebherz, S., & Dirmaier, J. (2016) · PeerJ · doi:10.7717/peerj.2093 · Cited by 24
Gräfe, V., Moritz, S., & Greiner, W. (2020) · Health Economics Review · doi:10.1186/s13561-020-00273-0 · Cited by 22
“The higher educational level of participants within this trial could be explained by a higher demand for internet interventions by such people, which was shown for users of a web-based computer-tailored intervention promoting heart-healthy behaviors [48].”
BackgroundCan interest and enjoyment help to increase use of Internet-delivered interventions?
Crutzen, R., Ruiter, R. A. C., & de Vries, N. K. (2014) · Psychology and Health · doi:10.1080/08870446.2014.921300 · Cited by 27
“6% completed the registration procedure and gained access to the intervention (Brouwer et al., 2010).”
BackgroundInfluentialSelf-citationWeymann, N., Härter, M. C., Petrak, & Dirmaier, J. (2013) · Patient Preference and Adherence · doi:10.2147/ppa.s46924 · Cited by 22
“Still, the effectiveness of online applications is limited by high attrition rates, and few users visit a health intervention website more than once.(23,24) Since the effect of online interventions increases with dose,(5,24) effectiveness is increased if users work intensively with the provided content(21,25) and retu…”
BackgroundArmbruster, C., Knaub, M., Farin‐Glattacker, E., & von der Warth, R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192013689 · Cited by 23
Beatty, L., Binnion, C., Kemp, E., & Koczwara, B. (2017) · Supportive Care in Cancer · doi:10.1007/s00520-017-3663-2 · Cited by 20
“This finding is consistent with the literature demonstrating gender differences in both the uptake and usage of online interventions [40] and suggests that online interventions may have less appeal to males.”
ResultSpäth, C., Hapke, U., Maske, U., Schröder, J., Moritz, S., Berger, T., Meyer, B., Rose, M., Nolte, S., & Klein, J. P. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.05.003 · Cited by 22
“It could be argued that the demand for Internet interventions is higher for people with a higher educational level which was shown for user of an in-ternet-delivered lifestyle intervention (Brouwer et al., 2010).”
User perceptions of a dementia risk reduction website and its promotion of behavior change
Farrow, M. (2013) · JMIR Research Protocols · doi:10.2196/resprot.2372 · Cited by 19
“Perhaps future research and development of Web-based health resources could address promotion strategies aimed specifically at men [27,39].”
BackgroundResultJosefsson, K. W., Johnson, U., & Lindwall, M. (2018) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2018.1462706 · Cited by 19
“Yet, the distribution of gender reflects a common situation, because women seem to be more prone to join web-based PA and exercise interventions (see e.g. Brouwer et al., 2010; Dawson et al., 2008).”
BackgroundResultSchweier, R., Romppel, M., Richter, C., & Grande, G. (2016) · Health Education Research · doi:10.1093/her/cyw019 · Cited by 21
Plaete, J., Crombez, G., der Mispel, C. V., Verloigne, M., Stappen, V. V., & Bourdeaudhuij, I. D. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5252 · Cited by 20
“Participants who complete health interventions tend to be female, middle-aged, and higher educated [17,37-39].”
Weymann, N., Härter, M. C., & Dirmaier, J. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-24 · Cited by 18
“Still, the effectiveness of those online applications is limited by high attrition rates [12,13], and few users visit a health intervention website more than once [14,15].”
BackgroundUrbanoski, K. A., van Mierlo, T., & Cunningham, J. A. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9591-6 · Cited by 18
Schneider, J., Matheson, E. L., Tinoco, A., Gentili, C., White, P., Boucher, C., Silva-Breen, H. L., Goorevich, A., Diedrichs, P. C., & LaVoi, N. M. (2023) · Journal of Applied Sport Psychology · doi:10.1080/10413200.2023.2212023 · Cited by 19
van Genugten, L., van Empelen, P., & Oenema, A. (2014) · JMIR Research Protocols · doi:10.2196/resprot.2599 · Cited by 15
“This contradicts the findings of other studies [12,13,51] that found that more motivated participants were more likely to revisit.”
InfluentialSelf-citationSocial presence and use of Internet-delivered interventions: a multi-method approach
Crutzen, R., Cyr, D., Larios, H., Ruiter, R. A. C., & de Vries, N. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057067 · Cited by 12
Self-citationBedrosian, R. C., Striegel‐Moore, R. H., & Wang, C. (2010) · International Journal of Eating Disorders · doi:10.1002/eat.20844 · Cited by 15
Gräfe, V., Berger, T., Hautzinger, M., Hohagen, F., Lutz, W., Meyer, B., Moritz, S., Rose, M., Schröder, J., Späth, C., et al. (2019) · Health Economics Review · doi:10.1186/s13561-019-0233-y · Cited by 14
Cremers, H., Mercken, L. A. G., Crutzen, R., Willems, P., de Vries, H., & Oenema, A. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3069 · Cited by 13
Self-citationBuchner, U. G., Koytek, A., Wodarz, N., & Wolstein, J. R. (2018) · International Gambling Studies · doi:10.1080/14459795.2018.1515974 · Cited by 13
Tailored eHealth Lifestyle Promotion: Which Behavioral Modules Do Users Prefer?
Schulz, D. N., Kremers, S., & de Vries, H. (2015) · Journal of Health Communication · doi:10.1080/10810730.2015.1012243 · Cited by 12
“Yet, few studies have described which behaviors people like to start with and focus on (Brouwer et al., 2010; Campbell et al., 2000).”
BackgroundResultInfluentialHealey, B. J., Griffiths, K. M., & Bennett, K. (2017) · DIGITAL HEALTH · doi:10.1177/2055207617729937 · Cited by 11
“For instance, one study of an intervention promoting heart-healthy behaviours found that of 285,146 visitors over a 36-month period, only 81,574 (29%) completed the registration procedure.(11) Low visit-to-registration rates may represent a missed opportunity to maximise the reach of internet interventions.”
BackgroundVoncken-Brewster, V., Amoureus, M., de Vries, H., Nagykaldi, Z. J., Winkens, B., van der Weijden, T., & Tange, H. J. (2017) · JMIR Formative Research · doi:10.2196//formative.6585 · Cited by 11
“Studies focusing on online health promotion applications in other target populations show mixed results [28-30].”
BackgroundChong, C., Smekal, M. D., Hemmelgarn, B. R., Elliott, M. J., Allu, S. O., Wick, J., McBrien, K., Jackson, W., Bello, A. K., Jindal, K. K., et al. (2022) · Canadian Journal of Kidney Health and Disease · doi:10.1177/20543581221097456 · Cited by 10
“23 23-26 Understanding the dissemination methods that effectively reach the target audience may be a greater driver for success in sustaining overall Web site traffic than the number of activities disseminated.”
BackgroundCoumans, J. M. J., Bolman, C. A. W., Oenema, A., & Lechner, L. (2020) · Journal of Medical Internet Research · doi:10.2196/15024 · Cited by 10
“This is somewhat in contrast to our expectations and previous findings in which older people generally participated more in eHealth interventions in terms of start and (re)visit [14,15,17,19].”
BackgroundResultInfluentialSelf-citationCocker, K. A. D., Cardon, G., Bennie, J. A., Kolbe-Alexander, T. L., Meester, F. D., & Vandelanotte, C. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15051049 · Cited by 9
“The current bounce rate of 51% can be considered average and is comparable to that of a Dutch Internet-delivered, computer-tailored lifestyle intervention (56%) implemented for use by the general public [35].”
Result
Metadata
- Authors
- Brouwer, W., Oenema, A., Raat, H., Crutzen, R., de Nooijer, J., de Vries, N. K., & Brug, J.
- Affiliations
- Erasmus MC (NL)
- Maastricht University (NL)
- Amsterdam Public Health (NL)
- Corresponding author
- Brouwer, W.
- Venue
- Health Education Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-11-06
- Volume
- 25
- Issue
- 4
- Pages
- 585-595
- ISSN
- 0268-1153 (ISSN-L)
- 1465-3648
- OpenAlex
- W2111665408
- Cited by
- 147
- References
- 49
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 5 · 2020: 8 · 2019: 5 · 2018: 11 · 2017: 8 · 2016: 16 · 2015: 17 · 2014: 18 · 2013: 14 · 2012: 16
- FWCI
- 16.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Health Education Research (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- computer-tailored intervention, online intervention, heart-healthy lifestyle, visitor characteristics, website engagement, intervention completion, user registration, health promotion targeting, vulnerable populations
- MeSH terms
- Smoking Prevention, Adult, Diet, Female, Health Promotion, Humans, Life Style, Male, Middle Aged, Socioeconomic Factors, Health Behavior, Exercise, Body Mass Index, Internet, Overweight
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
