Participants, Usage, and Use Patterns of a Web-Based Intervention for the Prevention of Depression Within a Randomized Controlled Trial
Open accessAbstract
BACKGROUND: Although Web-based interventions have been shown to be effective, they are not widely implemented in regular care. Nonadherence (ie, participants not following the intervention protocol) is an issue. By studying the way Web-based interventions are used and whether there are differences between adherers (ie, participants that started all 9 lessons) and nonadherers, more insight can be gained into the process of adherence. OBJECTIVE: The aims of this study were to (1) describe the characteristics of participants and investigate their relationship with adherence, (2) investigate the utilization of the different features of the intervention and possible differences between adherers and nonadherers, and (3) identify what use patterns emerge and whether there are differences between adherers and nonadherers. METHODS: Data were used from 206 participants that used the Web-based intervention Living to the full, a Web-based intervention for the prevention of depression employing both a fully automated and human-supported format. Demographic and baseline characteristics of participants were collected by using an online survey. Log data were collected within the Web-based intervention itself. Both quantitative and qualitative analyses were performed. RESULTS: In all, 118 participants fully adhered to the intervention (ie, started all 9 lessons). Participants with an ethnicity other than Dutch were more often adherers (χ²₁=5.5, P=.02), and nonadherers used the Internet more hours per day on average (F₁,₂₀₃=3.918, P=.049). A logistic regression showed that being female (OR 2.02, 95% CI 1.01-4.04; P=.046) and having a higher need for cognition (OR 1.02; 95% CI 1.00-1.05; P=.02) increased the odds of adhering to the intervention. Overall, participants logged in an average of 4 times per lesson, but adherers logged in significantly more times per lesson than nonadherers (F₁,₂₀₄=20.710; P<.001). For use patterns, we saw that early nonadherers seemed to use fewer sessions and spend less time than late nonadherers and adherers, and fewer sessions to complete the lesson than adherers. Furthermore, late nonadherers seemed to have a shorter total duration of sessions than adherers. CONCLUSIONS: By using log data combined with baseline characteristics of participants, we extracted valuable lessons for redesign of this intervention and the design of Web-based interventions in general. First, although characteristics of respondents can significantly predict adherence, their predictive value is small. Second, it is important to design Web-based interventions to foster adherence and usage of all features in an intervention. TRIAL REGISTRATION: Dutch Trial Register Number: NTR3007; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3007 (Archived by WebCite at http://www.webcitation.org/6ILhI3rd8).
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Adherence to a Web-based intervention can be predicted on the usage patterns of an intervention.
“Recent studies have shown that adherence to a Web-based intervention can be predicted on the usage patterns of an intervention [8,9].”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The Results (Use Patterns; page unavailable) describe differences in usage between adherers and nonadherers, and the Discussion (Principal Results; page unavailable) proposes using those differences to identify participants at risk of nonadherence. But the paper did not establish a predictive model based on usage patterns; its logistic regression predicted adherence from baseline participant characteristics. The claim presents a proposed application as an established finding. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFor realizing effects, the uptake of all features of the intervention is important.
“The study found that the predictive value of characteristics of participants on adherence is very small and that for realizing effects the uptake of all features of the intervention is important.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The Conclusion says participant characteristics have small predictive value for adherence and recommends designing interventions to foster use of all features. The Discussion links greater feature use to improved outcomes only as an ideal goal; it does not establish that uptake of all features is necessary to realize effects. The indexed passages provide section labels but no page numbers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe number of logins in the first week of a treatment process or the number of active days in the first week can be used as predictors for adherence to the interventions.
“For example, the number of logins in the first week of a treatment process or the number of active days in the first week can be used as predictors for adherence to the interventions.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The Results (Usage; page unavailable) report that adherers logged in significantly more often per lesson than nonadherers. They do not establish that first-week logins or first-week active days predict later adherence; the reported adherence prediction analysis instead used baseline characteristics (Results, Adherence; page unavailable). (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe use of the intervention (adherence rate) fluctuated over time, with critical moments for dropout appearing in Lessons 3 and 6.
“The use of the intervention (adherence rate) fluctuated over time; critical moments for dropout appeared in Lessons 3 and 6 [9].”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
High active users can benefit more from the intervention.
“The aforementioned studies [7,9] found that high active users can benefit more from the intervention, but that more insight is needed in how use of a program of therapy impacts outcomes.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The decrease in logins might be a learning effect; from the parent study adherers needed fewer sessions to complete a lesson at the end than during the first lessons.
“The decrease in logins might be a learning effect; from the parent study [9] it appeared that adherers needed fewer sessions (logins) to complete a lesson at the end of the treatment than during the first lessons of the treatment program.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The predictive value of characteristics of participants on adherence is very small.
“The study found that the predictive value of characteristics of participants on adherence is very small and that for realizing effects the uptake of all features of the intervention is important.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The sample of participants that used the SMS coach and multimedia was reduced due to a fractional factorial design in the prior study.
“The other limitation we have is a reduced sample of participants that used the SMS coach and multimedia, due to a fractional factorial design in the prior study [9].”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
From prior study, adding a single feature to the intervention, except feedback, does not impact adherence or outcomes.
“From our prior study [9], we know that adding a single feature to the intervention, except feedback, does not impact adherence or outcomes.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [9]
The Discussion > Principal Results says additional features were used less than features integral to the therapy, but it does not say whether adding a single feature affected adherence or outcomes. It also reports no significant follow-up effectiveness difference between human and automated feedback conditions. Searches for single-feature effects on adherence and outcomes found no passage establishing the claimed rule or its feedback exception. (LLM, conf=0.73)
Experimental — accuracy not yet measuredLog files will provide further insight into the suitability among groups and in the effectiveness of blended therapy from the perspective of what actually happens during therapy.
“This will provide further insight into the suitability among groups and in the effectiveness of blended therapy from a different perspective, namely from what actually happens during the therapy [40].”
A group of users dropped out from an intervention for reducing depressive complaints after a lesson that focused on applying newly acquired skills in practice, as doing so can be confrontational.
“Kelders et al who found that a group of users dropped out from an intervention for reducing depressive complaints after a lesson that focused on applying newly acquired skills in practice, as doing so can be confrontational [56].”
From Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence (2017) · cites this paper as [56]
Longer and more frequent exposure to an eHealth technology might indicate how well the system fits users' needs, but can also signify unfocused and/or non-strategic use or an inefficient system.
“For example, a longer and more frequent exposure to an eHealth technology might indicate how well the system fits the users’ needs, but it can also signify unfocused and/or non-strategic use or an inefficient system [9,21].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
Studies by Kelders et al and Van Gemert et al showed when users of a Web-based intervention for the early treatment of depressive symptoms were at risk of dropping out and might need additional support.
“For example, studies by Kelders et al [21] and Van Gemert et al [24] showed when users of a Web-based intervention for the early treatment of depressive symptoms (“Living to the Full”) were at risk of dropping out and might need additional support.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
Early non-adherers used fewer sessions to complete a lesson.
“Early non-adherers also used fewer sessions to complete a lesson.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
Late non-adherers had a shorter total duration of sessions than adherers.
“Furthermore, late non-adherers had a shorter total duration of sessions than the adherers.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
The 6th lesson is a hard lesson for participants because of the focus on observing themselves and learning new skills to accept suffering.
“It was revealed by counselors giving the course that this is indeed a hard lesson for participants because of the focus on observing themselves and learning new skills to accept suffering [21].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
Early non-adherers used fewer and shorter sessions than late non-adherers and adherers.
“A 1-way analyses of variance (ANOVA) and chi-square tests showed that early non-adherers used fewer and shorter sessions than late non-adherers and adherers.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
A fairly large group dropped out during the 6th lesson in the "Living to the Full" intervention.
“the log data analysis from the ”Living to the Full“ intervention showed that a fairly large group dropped out during the 6th lesson.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [21]
People with higher education may be more likely to use health apps in general, while women may be more likely to use self-care or self-management apps and tend to exhibit the highest adherence to digital interventions.
“People with higher education may be more likely to use health apps in general, while women may be more likely to use self-care or self-management apps and tend to exhibit the highest adherence to digital interventions [68].”
From Digital Self-Management in Support of Patients Living With Chronic Pain: Feasibility Pilot Study (2020) · cites this paper as [68]
Most Web-based interventions reach a limited group only, while aiming for a broad audience.
“On top of this, most interventions reach a limited group only, while aiming for a broad audience.”
From How to increase reach and adherence of web-based interventions: a design research viewpoint (2015) · cites this paper as [16]
The overrepresentation of higher-educated women in Web-based interventions was also found for alcohol abuse, depression, and smoking.
“However, the overrepresentation of this specific group was also found for areas like alcohol abuse [12], depression [16], and smoking [17]”
From How to increase reach and adherence of web-based interventions: a design research viewpoint (2015) · cites this paper as [16]
Current Web-based interventions often reach higher-educated women.
“However, several researchers have shown that current Web-based interventions often reach higher-educated women, as seen in several studies [13-17]”
From How to increase reach and adherence of web-based interventions: a design research viewpoint (2015) · cites this paper as [16]
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Cites
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population
Radloff, L. S. (1977) · Applied Psychological Measurement · doi:10.1177/014662167700100306 · Cited by 53,267 · see citing works
The Hospital Anxiety and Depression Scale
Zigmond, A. S., & Snaith, R. P. (1983) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.1983.tb09716.x · Cited by 47,139 · see citing works
The need to belong: Desire for interpersonal attachments as a fundamental human motivation.
Baumeister, R. F., & Leary, M. R. (1995) · Psychological Bulletin · doi:10.1037//0033-2909.117.3.497 · Cited by 23,245 · see citing works
(2008) · Medical Entomology and Zoology · Cited by 7,477 · see citing works
(1999) · Cited by 5,934 · see citing works
The Efficient Assessment of Need for Cognition
Cacioppo, J. T., Petty, R. E., & Feng Kao, C. (1984) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4803_13 · Cited by 2,913 · see citing works
Wherever you go, there you are: Mindfulness meditation in everyday life
Kwee, M. G. T. (1995) · Behaviour Research and Therapy · doi:10.1016/0005-7967(95)90133-7 · Cited by 2,900 · see citing works
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524 · see citing works
SPINHOVEN, P., ORMEL, J., SLOEKERS, P. P. A., KEMPEN, G. I. J. M., SPECKENS, A. E. M., & HEMERT, A. M. V. (1997) · Psychological Medicine · doi:10.1017/s0033291796004382 · Cited by 2,158 · see citing works
Eysenbach, G., & CONSORT-EHEALTH Group (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1923 · Cited by 1,863 · see citing works
Persuasive Systems Design: Key Issues, Process Model, and System Features
Oinas-Kukkonen, H., & Harjumaa, M. (2009) · Communications of the Association for Information Systems · doi:10.17705/1cais.02428 · Cited by 1,663 · see citing works
The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview
Black, A. D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., McKinstry, B., Procter, R., Majeed, A., & Sheikh, A. (2011) · PLoS Medicine · doi:10.1371/journal.pmed.1000387 · Cited by 1,442 · see citing works
Internet-Based and Other Computerized Psychological Treatments for Adult Depression: A Meta-Analysis
Andersson, G., & Cuijpers, P. (2009) · Cognitive Behaviour Therapy · doi:10.1080/16506070903318960 · Cited by 1,368 · see citing works
Andrews, G., Cuijpers, P., Craske, M. G., McEvoy, P., & Titov, N. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013196 · Cited by 1,311 · see citing works
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 · see citing works
Self-citationBarak, A., Hen, L., Boniel-Nissim, M., & Shapira, N. (2008) · Journal of Technology in Human Services · doi:10.1080/15228830802094429 · Cited by 1,283 · see citing works
Adherence in Internet Interventions for Anxiety and Depression
Christensen, H., Griffiths, K. M., & Farrer, L. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1194 · Cited by 1,133 · see citing works
A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies
van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1672 · Cited by 1,126 · see citing works
Self-citationMohr, D. C., Cuijpers, P., & Lehman, K. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1602 · Cited by 1,097 · see citing works
A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies
Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1772 · Cited by 876 · see citing works
Andrews, G., Issakidis, C., Sanderson, K., Corry, J., & Lapsley, H. (2004) · British Journal of Psychiatry · doi:10.1192/bjp.184.6.526 · Cited by 560 · see citing works
Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012) · Psychological Science · doi:10.1177/0956797611436349 · Cited by 506 · see citing works
Preventing the Onset of Depressive Disorders: A Meta-Analytic Review of Psychological Interventions
Cuijpers, P., van Straten, A., Smit, F., Mihalopoulos, C., & Beekman, A. (2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.07091422 · Cited by 456 · cited 1× · see citing works
“Research shows that easily accessible interventions for indicated prevention (targeted at people at risk) are essential and can be cost-effective [1-3].”
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 · see citing works
Hollon, S. D., Muñoz, R. F., Barlow, D. H., Beardslee, W. R., Bell, C. C., Bernal, G., Clarke, G. N., Franciosi, L. P., Kazdin, A. E., Kohn, L. P., et al. (2002) · Biological Psychiatry · doi:10.1016/s0006-3223(02)01384-7 · Cited by 327 · see citing works
Haringsma, R., Engels, G. I., Beekman, A. T. F., & Spinhoven, P. (2004) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.1130 · Cited by 299 · cited 1× · see citing works
“Depressive symptoms were measured with the CES-D (20 items, score 0-60; higher=more depressive symptoms [37,46]), anxiety symptoms were measured with the Hospital Anxiety and Depression Scale (HADS-A; 7 items, score 0-21; higher scores=more anxiety symptoms [47,48]).”
Christensen, H., Griffiths, K. M., & Korten, A. (2002) · Journal of Medical Internet Research · doi:10.2196/jmir.4.1.e3 · Cited by 292 · cited 3× · see citing works
“Our results on the usage of the different features mirror the results of studies on the usage of freely available Web-based interventions in that participants do not use all the features that they can use [22-30].”
Tuten, T. L., & Bošnjak, M. (2001) · Social Behavior and Personality An International Journal · doi:10.2224/sbp.2001.29.4.391 · Cited by 249 · see citing works
Engagement and retention: measuring breadth and depth of participant use of an online intervention
Couper, M. P., Alexander, G. L., Zhang, N., Little, R. J., Maddy, N., Nowak, M. A., McClure, J. B., Calvi, J. J., Rolnick, S. J., Stopponi, M. A., et al. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1430 · Cited by 244 · see citing works
Bohlmeijer, E. T., Fledderus, M., Rokx, T. A. J. J., & Pieterse, M. E. (2011) · Behaviour Research and Therapy · doi:10.1016/j.brat.2010.10.003 · Cited by 232 · cited 1× · see citing works
“The intervention has been shown to be effective in reducing depressive and anxiety symptoms as a group course and as a self-help course with email support [42-44].”
Self-citationFarvolden, P., Denisoff, E., Selby, P., Bagby, R. M., & Rudy, L. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e7 · Cited by 223 · cited 2× · see citing works
“Our results on the usage of the different features mirror the results of studies on the usage of freely available Web-based interventions in that participants do not use all the features that they can use [22-30].”
Engagement in a Diabetes Self-management Website: Usage Patterns and Generalizability of Program Use
Glasgow, R. E., Christiansen, S. M., Kurz, D., King, D. K., Woolley, T., Faber, A. J., Estabrooks, P. A., Strycker, L., Toobert, D., & Dickman, J. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1391 · Cited by 219 · see citing works
Fledderus, M., Bohlmeijer, E. T., Pieterse, M. E., & Schreurs, K. M. G. (2012) · Psychological Medicine · doi:10.1017/s0033291711001206 · Cited by 209 · cited 1× · see citing works
“The intervention has been shown to be effective in reducing depressive and anxiety symptoms as a group course and as a self-help course with email support [42-44].”
Self-citationFledderus, M., Bohlmeijer, E. T., Smit, F., & Westerhof, G. J. (2010) · American Journal of Public Health · doi:10.2105/ajph.2010.196196 · Cited by 197 · cited 1× · see citing works
“The intervention has been shown to be effective in reducing depressive and anxiety symptoms as a group course and as a self-help course with email support [42-44].”
Self-citationNijland, N., van Gemert-Pijnen, J. E., Kelders, S. M., Brandenburg, B. J., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1603 · Cited by 175 · see citing works
Self-citationKelders, S. M., Van Gemert-Pijnen, J. E. W. C., Werkman, A., Nijland, N., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1624 · Cited by 156 · see citing works
Self-citationChronic Health Conditions and Internet Behavioral Interventions
Schubart, J. R., Stuckey, H. L., GANESHAMOORTHY, A., & Sciamanna, C. (2011) · CIN Computers Informatics Nursing · doi:10.1097/ncn.0b013e3182155274 · Cited by 154 · see citing works
Linke, S., Murray, E., Butler, C., & Wallace, P. (2007) · Journal of Medical Internet Research · doi:10.2196/jmir.9.2.e10 · Cited by 127 · see citing works
Lustria, M. L. A. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20557 · Cited by 126 · see citing works
Crutzen, R., Roosjen, J. L., & Poelman, J. (2013) · Health Promotion International · doi:10.1093/heapro/das008 · Cited by 113 · see citing works
Cuijpers, P., van Straten, A., Warmerdam, L., & van Rooy, M. J. (2010) · BMC Health Services Research · doi:10.1186/1472-6963-10-181 · Cited by 106 · see citing works
Internet-Based Weight Control: The Relationship Between Web Features and Weight Loss
Krukowski, R. A., Harvey‐Berino, J., Ashikaga, T., Thomas, C. S., & Micco, N. (2008) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2007.0132 · Cited by 106 · see citing works
Selecting Effective Means to Any End: Futures and Ethics of Persuasion Profiling
Kaptein, M. C., & Eckles, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13226-1_10 · Cited by 88 · see citing works
Zbikowski, S. M., Hapgood, J., Smucker Barnwell, S., & McAfee, T. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.999 · Cited by 87 · see citing works
Nijland, N., van Gemert-Pijnen, J. E. W. C., Boer, H., Steehouder, M. F., & Seydel, E. R. (2009) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2009.06.002 · Cited by 84 · see citing works
Development of a web-based intervention for the indicated prevention of depression
Kelders, S. M., Pots, W. T., Oskam, M. J., Bohlmeijer, E. T., & van Gemert-Pijnen, J. E. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-26 · Cited by 80 · cited 2× · see citing works
“The intervention was developed employing methods from the CeHRes Roadmap for eHealth development [10] and this process is described elsewhere [45].”
Self-citationDo adherence variables predict outcome in an online program for the prevention of eating disorders?
Manwaring, J. L., Bryson, S. W., Goldschmidt, A. B., Winzelberg, A. J., Luce, K. H., Cunning, D., Wilfley, D. E., & Taylor, C. B. (2008) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.76.2.341 · Cited by 73 · see citing works
Christensen, H. M., Reynolds, J., & Griffiths, K. M. (2011) · Early Intervention in Psychiatry · doi:10.1111/j.1751-7893.2010.00242.x · Cited by 70 · see citing works
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 · see citing works
Patterns of Use of an Automated Interactive Personalized Coaching Program for Smoking Cessation
Balmford, J., Borland, R., & Benda, P. J. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1016 · Cited by 61 · see citing works
Preventing depression in high-risk groups
Beekman, A. T. F., Smit, F., Stek, M. L., Reynolds, C. F., & Cuijpers, P. J. (2009) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e328333e17f · Cited by 57 · see citing works
Utilization patterns and user characteristics of an ad libitum Internet weight loss program
Binks, M., & van Mierlo, T. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1347 · Cited by 52 · see citing works
Internet-Based Depression Prevention over the Life Course: A Call for Behavioral Vaccines
Voorhees, B. W. V., Mahoney, N., Mazo, R., Barrera, A. Z., Siemer, C. P., Gladstone, T. R. G., & Muñoz, R. F. (2011) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2010.11.002 · Cited by 42 · see citing works
Web Usage Data as a Means of Evaluating Public Health Messaging and Outreach
Tian, H., Brimmer, D. J., Lin, J.-M. S., Tumpey, A. J., & Reeves, W. C. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1278 · Cited by 40 · see citing works
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- [36]Kelders SM University of Twente 2012 2013-08-05 Enschede, The Netherlands University of Twente Understanding adherence to Web-based interventions [thesis] http://doc.utwente.nl/81967/
- [55]Fogg BJ Persuasive Technology: Using Computers to Change What We Think and Do 2003 Boston Morgan Kaufmann
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Self-citationvan der Vaart, R., Witting, M., Riper, H., Kooistra, L., Bohlmeijer, E. T., & van Gemert-Pijnen, L. J. (2014) · BMC Psychiatry · doi:10.1186/s12888-014-0355-z · Cited by 273 · see citing works
“This will provide further insight into the suitability among groups and in the effectiveness of blended therapy from a different perspective, namely from what actually happens during the therapy [40].”
Self-citationHow to increase reach and adherence of web-based interventions: a design research viewpoint
Ludden, G. D., van Rompay, T. J., Kelders, S. M., & van Gemert-Pijnen, J. E. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4201 · Cited by 185 · see citing works
Self-citationMoberg, C., Niles, A., & Beermann, D. (2019) · Journal of Medical Internet Research · doi:10.2196/12556 · Cited by 157 · see citing works
Gamification and Adherence to Web-Based Mental Health Interventions: A Systematic Review
Brown, M., O'Neill, N., van Woerden, H., Eslambolchilar, P., Jones, M., & John, A. (2016) · JMIR Mental Health · doi:10.2196/mental.5710 · Cited by 178 · see citing works
Fischer, A., Schröder, J., Vettorazzi, E., Wolf, O. T., Pöttgen, J., Lau, S., Heesen, C., Moritz, S., & Gold, S. M. (2015) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)00049-2 · Cited by 143 · see citing works
Alfonsson, S., Olsson, E., & Hursti, T. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5352 · Cited by 118 · see citing works
Chien, I., Enrique, A., Palacios, J., Regan, T., Keegan, D., Carter, D., Tschiatschek, S., Nori, A., Thieme, A., Richards, D., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.10791 · Cited by 111 · see citing works
Kelders, S. M., Bohlmeijer, E. T., Pots, W. T. M., & van Gemert-Pijnen, J. E. W. C. (2015) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.06.014 · Cited by 105 · see citing works
Self-citationVan Gemert-Pijnen, J. E., Kelders, S. M., & Bohlmeijer, E. T. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2991 · Cited by 84 · see citing works
Self-citationForbes, A., Keleher, M. R., Venditto, M., & DiBiasi, F. (2023) · Journal of Medical Internet Research · doi:10.2196/43727 · Cited by 94 · see citing works
Enrique, A., Palacios, J. E., Ryan, H., & Richards, D. (2019) · Journal of Medical Internet Research · doi:10.2196/12775 · Cited by 81 · see citing works
Rigabert, A., Motrico, E., Moreno-Peral, P., Resurrección, D. M., Conejo-Cerón, S., Cuijpers, P., Martín-Gómez, C., López-Del-Hoyo, Y., & Bellón, J. Á. (2020) · Clinical Psychology Review · doi:10.1016/j.cpr.2020.101931 · Cited by 92 · see citing works
Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data
Sieverink, F., Kelders, S., Poel, M., & van Gemert-Pijnen, L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6452 · Cited by 88 · see citing works
Self-citationZarski, A.-C., Berking, M., Reis, D., Lehr, D., Buntrock, C., Schwarzer, R., & Ebert, D. D. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8814 · Cited by 87 · see citing works
“The findings of previous studies can, however, explain the variance in adherence only to some extent [9-12], with findings of, inter alia, 11% [13].”
eHealth to Enhance Treatment Adherence Among Youth Living with HIV
Mulawa, M. I., LeGrand, S., & Hightow‐Weidman, L. (2018) · Current HIV/AIDS Reports · doi:10.1007/s11904-018-0407-y · Cited by 70 · see citing works
Hightow‐Weidman, L., & Bauermeister, J. A. (2020) · mHealth · doi:10.21037/mhealth.2019.10.01 · Cited by 64 · see citing works
Eikey, E. V., Reddy, M. C., & Kuziemsky, C. E. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.08.006 · Cited by 63 · see citing works
Chen, A. T., Wu, S., Tomasino, K. N., Lattie, E. G., & Mohr, D. C. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103187 · Cited by 58 · see citing works
“Usage data is also often conceptualized as a measure of adherence[19,20] or exposure to intervention content[14,21].”
Duman-Lubberding, S., van Uden‐Kraan, C. F., Peek, N. B., Cuijpers, P. J., Leemans, C. R., & Leeuw, I. M. V. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4870 · Cited by 53 · see citing works
Coley, N., Andre, L., Hoevenaar‐Blom, M. P., Ngandu, T., Beishuizen, C. R. L., Barbera, M., van Wanrooij, L. L., Kivipelto, M., Soininen, H. S., van Gool, W. A., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/32006 · Cited by 52 · see citing works
Stormshak, E. A., Seeley, J. R., Caruthers, A. S., Cardenas, L. E., Moore, K. J., Tyler, M. S., Fleming, C. M., Gau, J. M., & Danaher, B. G. (2019) · Development and Psychopathology · doi:10.1017/s0954579419000907 · Cited by 50 · see citing works
Felder, J. N., Segal, Z. V., Beck, A. L., Sherwood, N. E., Goodman, S. H., Boggs, J. M., Lemon, E. L., & Dimidjian, S. (2016) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2016.02.002 · Cited by 47 · see citing works
Addressing engagement in technology-based behavioural HIV interventions through paradata metrics
Bauermeister, J. A., Golinkoff, J., Muessig, K. E., Horvath, K. J., & Hightow‐Weidman, L. (2017) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0000000000000396 · Cited by 46 · see citing works
Kingston, D., Janes-Kelley, S., Tyrrell, J., Clark, L., Hamza, D., Holmes, P., Parkes, C., Moyo, N., McDonald, S., & Austin, M.-P. (2015) · JMIR Research Protocols · doi:10.2196/resprot.4037 · Cited by 42 · see citing works
Wilansky, P., Eklund, J., Milner, T., Kreindler, D. M., Cheung, A., Kovacs, T., Shooshtari, S., Astell, A. J., Ohinmaa, A. E., Henderson, J. L., et al. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5841 · Cited by 42 · see citing works
Digital Self-Management in Support of Patients Living With Chronic Pain: Feasibility Pilot Study
Bostrøm, K., Børøsund, E., Varsi, C., Eide, H., Flakk Nordang, E., Schreurs, K. M., Waxenberg, L. B., Weiss, K. E., Morrison, E. J., Cvancarova Småstuen, M., et al. (2020) · JMIR Formative Research · doi:10.2196/23893 · Cited by 34 · see citing works
Breitenstein, S. M., Brager, J., Ocampo, E. V., & Fogg, L. (2017) · Child Maltreatment · doi:10.1177/1077559517725402 · Cited by 38 · see citing works
Westerhof, G. J., Lamers, S. M. A., Postel, M. G., & Bohlmeijer, E. T. (2017) · The Gerontologist · doi:10.1093/geront/gnx140 · Cited by 38 · see citing works
Self-citationTalboom-Kamp, E. P. W. A., Verdijk, N. A., Kasteleyn, M. J., Harmans, L. M., Talboom, I. J. S. H., Numans, M. E., & Chavannes, N. H. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7037 · Cited by 33 · see citing works
Rathbone, A. L., & Prescott, J. (2018) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.16406 · Cited by 36 · see citing works
Health Behavior Change Support Systems as a research discipline; A viewpoint
Kelders, S. M., Oinas-Kukkonen, H., Oörni, A., & van Gemert-Pijnen, J. E. W. C. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.06.022 · Cited by 32 · see citing works
Self-citationLeong, Q. Y., Sridhar, S., Blasiak, A., Tadeo, X., Yeo, G., Remus, A., & Ho, D. (2021) · Journal of Medical Internet Research · doi:10.2196/27388 · Cited by 34 · see citing works
van der Hout, A., van Uden-Kraan, C. F., Holtmaat, K., Jansen, F., Lissenberg-Witte, B. I., Nieuwenhuijzen, G. A. P., Hardillo, J. A., Baatenburg de Jong, R. J., Tiren-Verbeet, N. L., Sommeijer, D. W., et al. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100429 · Cited by 29 · see citing works
Sieverink, F., Kelders, S. M., Braakman-Jansen, L. M. A., & van Gemert-Pijnen, J. E. W. C. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814525696 · Cited by 30 · see citing works
Self-citationDigital Interventions to Enhance Readiness for Psychological Therapy: Scoping Review
Jardine, J., Bowman, R., & Doherty, G. J. (2022) · Journal of Medical Internet Research · doi:10.2196/37851 · Cited by 28 · see citing works
Involvement as a Working Mechanism for Persuasive Technology
Kelders, S. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20306-5_1 · Cited by 25 · see citing works
Self-citationZerna, J., Strobel, A., & Strobel, A. (2024) · Collabra Psychology · doi:10.1525/collabra.92885 · Cited by 22 · see citing works
Jardine, J., Earley, C., Richards, D., Timulák, L., Palacios, J. E., Duffy, D. J., Tierney, K., & Doherty, G. J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376254 · Cited by 22 · see citing works
Kelders, S. M., Kip, H., Jong, N. B.-D., & Köhle, N. (2024) · Digital Health · doi:10.1177/20552076241283530 · Cited by 22 · see citing works
Self-citationvan Gemert-Pijnen, L. (. E. W. C. )., Kelders, S. M., Jong, N. B.-D., & B, H. O.-K. A. (2018) · Routledge eBooks · doi:10.4324/9781315385907-11 · Cited by 23 · see citing works
Self-citationDonker, T., van Klaveren, C., Cornelisz, I., Kok, R. N., & van Gelder, J. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061614 · Cited by 22 · see citing works
Zainal, N. H., & Newman, M. G. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.10.001 · Cited by 21 · see citing works
Studying internet addiction profile of university students with latent class analysis
Hussaın, İ., Çakır, Ö., & Özdemir, B. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10203-6 · Cited by 21 · see citing works
Roelofsen, Y., Hendriks, S. H., Sieverink, F., van Vugt, M., van Hateren, K. J., Snoek, F. J., de Wit, M., Gans, R. O. B., Groenier, K. H., van Gemert‐Pijnen, J. E. W. C., et al. (2014) · BMC Endocrine Disorders · doi:10.1186/1472-6823-14-22 · Cited by 17 · see citing works
Aerts, J. E. M., & Dam, A. V. (2018) · Psychology · doi:10.4236/psych.2018.910139 · Cited by 16 · see citing works
Becker, D. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-03402-3_8 · Cited by 16 · see citing works
Peiris-John, R. J., Dizon, L., Sutcliffe, K., Kang, K., & Fleming, T. M. (2020) · Digital Health · doi:10.1177/2055207620947962 · Cited by 15 · see citing works
Eccles, H., Nannarone, M., Lashewicz, B., Attridge, M. D., Marchand, A., Aiken, A. B., Ho, K., & Wang, J. (2020) · Journal of Medical Internet Research · doi:10.2196/16961 · Cited by 17 · see citing works
What do humble doctors do? A conceptualization of clinician humility from the patient’s perspective
Huynh, H. P., Dicke-Bohmann, A. K., & Nelson, C. (2021) · Patient Education and Counseling · doi:10.1016/j.pec.2021.04.028 · Cited by 14 · see citing works
Beintner, I., Görlich, D., Berger, T., Ebert, D. D., Zeiler, M. D., Herrero, R., Waldherr, K., & Jacobi, C. (2018) · Internet Interventions · doi:10.1016/j.invent.2018.05.001 · Cited by 15 · see citing works
Brown, M., Hooper, N., James, P., Scott, D., Bodger, O. G., & John, A. M. (2020) · JMIR Formative Research · doi:10.2196/18586 · Cited by 12 · see citing works
SAWYER, M. G., Reece, C. E., Bowering, K., Jeffs, D., Sawyer, A. C. P., Peters, J. D., Mpundu‐Kaambwa, C., Clark, J. J., McDonald, D., Mittinty, M., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-009967 · Cited by 12 · see citing works
The Application of User Event Log Data for Mental Health and Wellbeing Analysis
Turkington, R., Mulvenna, M. D., Bond, R., O.Neill, S., & Armour, C. (2018) · Electronic workshops in computing · doi:10.14236/ewic/hci2018.4 · Cited by 12 · see citing works
Social Cognitive Theories and Electronic Health Design: Scoping Review
Grace-Farfaglia, P. (2019) · JMIR Human Factors · doi:10.2196/11544 · Cited by 11 · see citing works
Lau, E. Y., Lau, P. W. C., Cai, B., & Archer, E. (2015) · Journal of Health Communication · doi:10.1080/10810730.2015.1018580 · Cited by 9 · see citing works
Tönis, K. J. M., Kraiss, J. T., Drossaert, C. H. C., Karreman, J., Oostinga, M. S. D., Kloos, N., Austin, J., & Bohlmeijer, E. T. (2024) · Journal of Happiness Studies · doi:10.1007/s10902-024-00793-0 · Cited by 9 · see citing works
Self-citationLee, J. M., Kim, S., & Min, H. Y. (2017) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2017.04.012 · Cited by 9 · see citing works
El‐Haj‐Mohamad, R., Stein, J., Stammel, N., Nesterko, Y., Wagner, B., Böttche, M., & Knaevelsrud, C. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.09.080 · Cited by 8 · see citing works
Badaghi, N., van Kruijsbergen, M., Speckens, A. E. M., Vilé, J., Prins, J. B., Kelders, S. M., & Kwakkenbos, L. (2024) · JMIR Formative Research · doi:10.2196/52338 · Cited by 9 · see citing works
Self-citationFreund, J., Smit, F., Lehr, D., Zarski, A., Berking, M., Riper, H. M., Funk, B., Ebert, D. D., & Buntrock, C. (2024) · Journal of Medical Internet Research · doi:10.2196/48481 · Cited by 7 · see citing works
Liptáková, S., Světlák, M., Matis, J., Slezáčková, A., & Rastislav, R. (2022) · Ceskoslovenska psychologie · doi:10.51561/cspsych.66.3.233 · Cited by 6 · see citing works
Wentzel, M. J., Jong, D. N., Nijdam, L. C., Drie-Pierik, V. R., & Gemert-Pijnen, V. J. E. W. C. (2014) · University of Twente Research Information · Cited by 7 · see citing works
Peznola, S. J., Gauthier, L. V., Claypool, M. C., Roop, B. W., & Lammert, A. C. (2022) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2022.10.007 · Cited by 6 · see citing works
Moore, B. A., Buono, F. D., Printz, D. M. B., Lloyd, D. P., Fiellin, D. A., Cutter, C. J., Schottenfeld, R. S., & Barry, D. T. (2017) · Experimental and Clinical Psychopharmacology · doi:10.1037/pha0000149 · Cited by 6 · see citing works
Keeping an eye on the context: participatory development of eHealth to support clinical practice
Wentzel, J. (2015) · doi:10.3990/1.9789036539784 · Cited by 6 · see citing works
Dejonghe, L. A. L., Rudolf, K., Becker, J., Stassen, G., Froboese, I., & Schaller, A. (2020) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-019-0154-4 · Cited by 5 · see citing works
Scholten, M. R., Kelders, S. M., & van Gemert‐Pijnen, J. E. W. C. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06509 · Cited by 5 · see citing works
Self-citationApolinário-Hagen, J., & Salewski, C. (2018) · Springer eBooks · doi:10.1007/978-981-10-6577-4_22 · Cited by 5 · see citing works
Sieverink, F. (2017) · doi:10.3990/1.9789036544177 · Cited by 5 · see citing works
Bruin, E. K.-D., Muntingh, A., Bourguignon, E., Hoogendoorn, A., Maarsingh, O., van Balkom, A. V., Batelaan, N., van Straten, A., & van Meijel, B. (2022) · JMIR Mental Health · doi:10.2196/25441 · Cited by 4 · see citing works
Yu, L. Q., Amato, M. S., Papandonatos, G. D., Cha, S., & Graham, A. L. (2024) · Journal of Medical Internet Research · doi:10.2196/54248 · Cited by 2 · see citing works
Stavric, V., Saywell, N. L., & Kayes, N. M. (2025) · JMIR mhealth and uhealth · doi:10.2196/66678 · Cited by 1 · see citing works
Schmidt, U., Rickstal, R. V., Goethem, V. V., Bakker, E. M., Pilch, M., Connolly, M., D’Alton, P., Jordan, C., Larkin, P. J., May, P., et al. (2026) · Palliative Medicine · doi:10.1177/02692163261424464 · Cited by 1 · see citing works
Bruin, E. K.-D., Geerlings, J. A., Muntingh, A., Scholten, W., Maarsingh, O. R., van Straten, A., Batelaan, N. M., & van Meijel, B. (2021) · JMIR Formative Research · doi:10.2196/23200 · Cited by 1 · see citing works
EHealth Interventions for People Suffering From Sleep Problems and Depressive Symptoms
Langrial, S. U., & Lappalainen, P. (2017) · Advances in psychology, mental health, and behavioral studies (APMHBS) book series · doi:10.4018/978-1-5225-3241-5.ch005
Digital interventions to enhance readiness for psychological therapy: A scoping review (Preprint)Preprint
Jardine, J., Bowman, R., & Doherty, G. J. (2022) · doi:10.2196/preprints.37851
Tong, Y. A., & Newman, M. G. (2021) · doi:10.31234/osf.io/2qn6b
EHealth Interventions for People Suffering From Sleep Problems and Depressive Symptoms
Langrial, S. U., & Lappalainen, P. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8544-3.ch065
Jong, N. B.-D., van Gemert-Pijnen, L. (. E. W. C. )., B, H. O.-K. A., & Kelders, S. M. (2024) · Routledge eBooks · doi:10.4324/9781003302049-14
Self-citationJung, G., Lim, H., Han, K., Kim, H., & Lee, U. (2025) · JMIR Human Factors · doi:10.2196/64967 · Cited by 1 · see citing works
Sunzeri, E., & Sunzeri, F. (2026) · Springer eBooks · doi:10.1007/978-3-032-14465-2_17
Schoemann, N., Hügel, J. C., Vogel, L., Deliens, T., Löchner, J., Lux, U., Opitz, A., Seiferth, C., Wolstein, J. R., & Henning, C. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100968
Bartelt, S., Werner, T., von Boros, L., Schäfer, S. K., Koehler, F., Wirth, B. E., Lieb, K., Tuescher, O., & Wessa, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-67088-7
- Authors
- Kelders, S. M., Bohlmeijer, E. T., & Van Gemert-Pijnen, J. E.
- Affiliations
- National Institute for Public Health and the Environment (NL)
- University of Twente (NL)
- Corresponding author
- Kelders, Saskia M
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2013-08-20
- Volume
- 15
- Issue
- 8
- Pages
- e172
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3757912
- OpenAlex
- W2027534121
- Cited by
- 88
- References
- 55
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 8 · 2023: 1 · 2022: 6 · 2021: 11 · 2020: 11 · 2019: 5 · 2018: 9 · 2017: 11 · 2016: 6 · 2015: 9 · 2014: 5
- FWCI
- 10.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Rivm (National Institute for Public Health and the Environment) (submittedVersion)
- University of Twente Research Information (submittedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- online intervention, depression prevention, intervention adherence, usage patterns, logs, need for cognition, gender differences, ethnicity, internet use, participant characteristics
- MeSH terms
- Adult, Depression, Female, Humans, Male, Middle Aged, Internet
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