Defining Participant Exposure Measures in Web-Based Health Behavior Change Programs
Open accessAbstract
BACKGROUND: Published research on the use of Web-based behavior change programs is growing rapidly. One of the observations characterized as problematic in these studies is that participants often make relatively few website visits and spend only a brief time accessing the program. Properly structured websites permit the unobtrusive measurement of the ways in which participants access (are exposed to) program content. Research on participant exposure to Web-based programs is not merely of interest to technologists, but represents an important opportunity to better understand the broader theme of program engagement and to guide the development of more effective interventions. OBJECTIVES: The current paper seeks to provide working definitions and describe initial patterns of various measures of participant exposure to ChewFree.com, a large randomized controlled trial of a Web-based program for smokeless tobacco cessation. METHODS: We examined measures of participant exposure to either an Enhanced condition Web-based program (interactive, tailored, and rich-media program) or a Basic condition control website (static, text-based material). Specific measures focused on email prompting, participant visits (number, duration, and pattern of use over time), and Web page viewing (number of views, types of pages viewed, and Web forum postings). RESULTS: Participants in the ChewFree.com Enhanced condition made more visits and spent more time accessing their assigned website than did participants assigned to the Basic condition website. In addition, exposure data demonstrated that Basic condition users thoroughly accessed program content, indicating that the condition provided a meaningful, face-valid control to the Enhanced condition. CONCLUSIONS: We recommend that researchers conducting evaluations of Web-based interventions consider the collection and analysis of exposure measures in the broader context of program engagement in order to assess whether participants obtain sufficient exposure to relevant program content.
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Adherence reflects the degree to which participants receive the active ingredients of the program and was supported to be a significant mediator toward treatment outcomes.
“Adherence reflects the degree to which participants receive the “active ingredients” of the program (Danaher et al., 2006), and was supported to be a significant mediator toward treatment outcomes (Arndt et al., 2020).”
From Can We Boost Treatment Adherence to an Online Transdiagnostic Intervention by Adding Self-Enhancement Strategies? Results From a Randomized Controlled Non-inferiority Trial (2021) · cites this paper as Danaher et al., 2006
Early studies indicate that certain website elements matter.
“Although preliminary research has demonstrated the importance of some of these elements [24–26], additional work is necessary to determine the best use of each.”
From A Behavior Change Model for Internet Interventions (2009) · cites this paper as [25]
Initial results indicate a preference among users for audio, graphics, and interactivity in online interventions.
“Early findings suggest that users prefer audio, graphics, and interactivity as elements of Internet interventions [24–26].”
From A Behavior Change Model for Internet Interventions (2009) · cites this paper as [25]
Previous research has focused on the summative measurement of engagement, such as level of adherence or intervention exposure.
“Previous research has focused on the summative measurement of engagement, such as level of adherence [12,13] or intervention exposure [14-16].”
From Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data (2014) · cites this paper as [14]
It is difficult to sustain visitors' loyalty to an intervention over an extended period of time.
“It has been demonstrated that it is difficult to sustain visitors’ loyalty to an intervention over an extended period of time [12,13], which may result in premature attrition from a session or in non-use of follow-up sessions.”
The information structure has been found to be related to the use of information, with less structured websites tending to prematurely lose visitors.
“The information structure has been found to be related to the use of information, with less structured websites tending to prematurely lose visitors [13,22,23].”
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“The study of DHI engagement is a nascent science and developers of DHIs initially defined engagement simply as some form of usage (46).”
BackgroundComparing Two Web-Based Smoking Cessation Programs: Randomized Controlled Trial
McKay, H. G., Danaher, B. G., Seeley, J. R., Lichtenstein, E., & Gau, J. M. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.993 · Cited by 99 · see citing works
“The extent to which participants accessed their assigned Web-based program was measured unobtrusively using a combination of database tracking and Web server log analysis [45] to determine both number of visits (sessions) and duration of visits.”
MethodSelf-citationMerchant, G., Weibel, N., Patrick, K., Fowler, J. H., Norman, G. J., Gupta, A. E., Servetas, C., Calfas, K. J., Raste, K., Pina, L. R., et al. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3267 · Cited by 101 · see citing works
“The extent to which engagement with online interventions equates with behavior change and the mechanisms through which engagement changes behavior are not well understood [46,57,69,73].”
BackgroundWanner, M., Martin‐Diener, E., Bauer, G. F., Braun‐Fahrländer, C., & Martin, B. W. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1361 · Cited by 95 · see citing works
“Similar, although less pronounced, results have been reported for spontaneous users of a cognitive behavior therapy website compared with participants in an RCT through the same website [17].”
BackgroundInfluentialRandomised controlled trial evaluation of Tweet2Quit: a social network quit-smoking intervention
Pechmann, C., Delucchi, K., Lakon, C. M., & Prochaska, J. J. (2016) · Tobacco Control · doi:10.1136/tobaccocontrol-2015-052768 · Cited by 94 · see citing works
McClure, J. B., Shortreed, S. M., Bogart, A., Derry, H., Riggs, K., St John, J., Nair, V., & An, L. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2508 · Cited by 83 · see citing works
“This definition is consistent with the literature [5-7] and reflects the fact that engagement is multidimensional.”
Pechmann, C., Pan, L., Delucchi, K., Lakon, C. M., & Prochaska, J. J. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3772 · Cited by 88 · see citing works
“An overriding concern is that engagement or interactivity may be low [5-8].”
BackgroundWeymann, N., Dirmaier, J., von Wolff, A., Kriston, L., & Härter, M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3904 · Cited by 73 · see citing works
“A major body of evidence suggests that the effect of online interventions increases with the dose (longer stays, repeated website visits, total contact hours) [35,36], and the effectiveness is maximized if patients intensively work with the information offered [37-39] and return for repeated visits [40,41].”
Robroek, S. J., Lindeboom, D. E., & Burdorf, A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1788 · Cited by 79 · see citing works
“Furthermore, to assess whether participants obtain sufficient exposure to the intervention, program use as well as characteristics of program users and non-users need to be studied [2].”
Schneider, F., van Osch, L., & de Vries, H. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1863 · Cited by 75 · see citing works
Perski, O., BLANDFORD, A. E., Garnett, C. V., Crane, D., West, R., & Michie, S. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz039 · Cited by 70 · see citing works
An, L. C., Schillo, B. A., Saul, J. E., Wendling, A. H., Klatt, C. M., Berg, C. J., Ahulwalia, J. S., Kavanaugh, A. M., Christenson, M., & Luxenberg, M. G. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1018 · Cited by 70 · see citing works
Miller, S., Ainsworth, B., Yardley, L., Milton, A., Weal, M. J., Smith, P. W. F., & Morrison, L. G. (2018) · Journal of Medical Internet Research · doi:10.2196/10966 · Cited by 66 · see citing works
“The importance of the type of content viewed as well as the amount has also been acknowledged [20].”
The University of North Carolina at Chapel Hill University Libraries (2020) · PubMed · doi:10.17615/m4tg-fa10 · Cited by 74 · see citing works
Michie, S., Brown, J., Geraghty, A. W. A., Miller, S., Yardley, L., Gardner, B., Shahab, L., McEwen, A., Stapleton, J., & West, R. (2012) · Translational Behavioral Medicine · doi:10.1007/s13142-012-0135-6 · Cited by 65 · see citing works
White, I., Kalaitzaki, E., & Thompson, S. (2011) · Statistics in Medicine · doi:10.1002/sim.4360 · Cited by 66 · see citing works
“It is hard to define and measure website use [22]; in particular, although each page download was recorded, the length of time that participants spent actually using the website is unknown.”
BackgroundAnalyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data
Morrison, C., & Doherty, G. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3575 · Cited by 62 · see citing works
“Previous research has focused on the summative measurement of engagement, such as level of adherence [12,13] or intervention exposure [14-16].”
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 59 · see citing works
“Intensive engagement in an intervention session allows processing of the intervention content and involvement in its effective components and is therefore essential [22,27,28].”
BackgroundChen, 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].”
BackgroundSchneider, 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 58 · see citing works
Long, A. C. J., & Palermo, T. M. (2008) · Journal of Pediatric Psychology · doi:10.1093/jpepsy/jsn082 · Cited by 58 · see citing works
Jahangiry, L., Montazeri, A., Najafi, M., Yaseri, M., & Farhangi, M. A. (2017) · Nutrition and Diabetes · doi:10.1038/nutd.2016.35 · Cited by 58 · see citing works
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 55 · see citing works
“Introduction Online interventions have been developed for a range of substance-use issues including alcohol reduction, tobacco and cannabis smoking cessation.(1) 3 Typically, they are delivered or supported via the internet and Addictions Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College…”
BackgroundSmith, A. “., Bamgboje‐Ayodele, A., Butow, P., Klein, B., Turner, J., Sharpe, L., Fardell, J., Beatty, L., Pearce, A., Thewes, B., et al. (2020) · Psycho-Oncology · doi:10.1002/pon.5218 · Cited by 54 · see citing works
“(c) Engagement was facilitated by incorporating interactive activities such as personal evaluation, credibility checks, reflective activities, and concept quizzes.(22)”
Backgroundvan den Berg, S. W., Peters, E. J., Kraaijeveld, J. F., Gielissen, M. F., & Prins, J. B. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2566 · Cited by 53 · see citing works
“However, practical guidelines are scarce with regard to which usage parameters are preferred to measure observed usage [14,17].”
BackgroundGraham, A. L., Papandonatos, G. D., Cha, S., Erar, B., Amato, M. S., Cobb, N. K., NIAURA, R. S., & Abrams, D. B. (2016) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntw282 · Cited by 52 · see citing works
Alkhaldi, G., Hamilton, F. L., Lau, R., Webster, R., Michie, S., & Murray, E. (2015) · JMIR Research Protocols · doi:10.2196/resprot.3990 · Cited by 46 · see citing works
“number, and type of pages viewed and visited [17,32,33].”
BackgroundMediators of a successful web‐based smokeless tobacco cessation program
Danaher, B. G., Smolkowski, K., Seeley, J. R., & Severson, H. H. (2008) · Addiction · doi:10.1111/j.1360-0443.2008.02295.x · Cited by 45 · see citing works
“Each participant’s username and password were obtained during their log-in which permitted unobtrusive tracking of the number and duration of each of visit to access program content [20].”
BackgroundMethodInfluentialSelf-citationWeb-Assisted Tobacco Interventions: Empowering Change in the Global Fight for the Public’s (e)Health
Norman, C. D., McIntosh, S. E., Selby, P. L., & Eysenbach, G. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1171 · Cited by 41 · see citing works
“WATI-focused research has received considerable attention within the eHealth field, including many publications in this journal over the past 10 years [8, 18-26].”
BackgroundAlkhaldi, 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 · see citing works
“However, the effect sizes demonstrated in the systematic reviews of DHIs are often small [4-6], and this may be related to low engagement, or in some cases, lack of engagement with these DHIs [7-9].”
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 40 · see citing works
“The RE-AIM framework stresses the importance of evaluating the reach and representativeness of program participants [9], and Eysenbach [6] and Danaher et al [10] have emphasized the need to address process measures in addition to the effectiveness of Internet-delivered programs.”
BackgroundCynthia Logsdon, M., Myers, J., Rushton, J., Gregg, J. L., Josephson, A. M., Davis, D. W., Brothers, K., Baisch, K., Carabello, A., Vogt, K., et al. (2018) · Archives of Women's Mental Health · doi:10.1007/s00737-017-0804-z · Cited by 40 · see citing works
“In developing the prototype website, we collaborated with our community partners to ensure that the site was engaging to adolescents in order to increase use (Danaher et al. 2006).”
Danaher, B. G., Tyler, M. S., Crowley, R. N., Brendryen, H., & Seeley, J. R. (2019) · Journal of Medical Internet Research · doi:10.2196/13290 · Cited by 40 · see citing works
“Participant Engagement (Use of Assigned Treatment) Both interventions unobtrusively tracked the overall number and duration of website visits from enrollment to the end of the 6-month follow-up assessment [37,43].”
Self-citationBrendryen, H., Lund, I. O., Johansen, A. B., Riksheim, M., Nesvåg, S., & Duckert, F. (2014) · Addiction · doi:10.1111/add.12383 · Cited by 39 · see citing works
Hasson, H., Brown, C., & Hasson, D. (2010) · Health Education Research · doi:10.1093/her/cyq005 · Cited by 39 · see citing works
Improving adherence to web-based cessation programs: a randomized controlled trial study protocol
Graham, A. L., Cha, S., Papandonatos, G. D., Cobb, N. K., Mushro, A., Fang, Y., NIAURA, R. S., & Abrams, D. B. (2013) · Trials · doi:10.1186/1745-6215-14-48 · Cited by 38 · see citing works
“Also referred to as engagement [16,34-38], persistence [39], non-usage attrition [40,41], and exposure [42,43] in a growing number of eHealth research studies, the problem of adherence has been observed across internet studies [28,41,44].”
BackgroundGraham, A. L., Chang, Y.-J., Ye, F., Cobb, N. K., Tinkelman, D. S., NIAURA, R. S., Abrams, D. B., & Mandelblatt, J. S. (2012) · Tobacco Control · doi:10.1136/tobaccocontrol-2012-050465 · Cited by 37 · see citing works
A Process Evaluation of a Web-Based Mental Health Portal (WalkAlong) Using Google Analytics
Song, M. J., Ward, J., Choi, F., Nikoo, M., Frank, A., Shams, F., Tabi, K., Vigo, D., & Krausz, M. R. (2018) · JMIR Mental Health · doi:10.2196/mental.8594 · Cited by 38 · see citing works
“…about sexual health [19], an internet-delivered genetics education resource developed for nurses [21], and a Web-based tool to encourage the proper use of antibiotics [16], as well as websites related to osteoporosis and fractures [22], smoking cessation [23], and knowledge translation [24].”
Engagement in “My Child's Asthma”, an interactive web-based pediatric asthma management intervention
Meischke, H. W., Lozano, P., Zhou, C., Garrison, M. M., & Christakis, D. (2011) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2011.08.002 · Cited by 35 · see citing works
“[13] Although some web-interventions are designed for a one-time interaction, others, particularly those dealing with a chronic disease or problem, are designed to track progress and provide feedback over time.”
BackgroundA Pilot Randomized Controlled Trial of an Internet-Based Alcohol Intervention in a Workplace Setting
Brendryen, H., Johansen, A. B., Duckert, F., & Nesvåg, S. M. (2017) · International Journal of Behavioral Medicine · doi:10.1007/s12529-017-9665-0 · Cited by 37 · see citing works
Information Architecture of Web-Based Interventions to Improve Health Outcomes: Systematic Review
Pugatch, J., Grenen, E., Surla, S., Schwarz, M., & Cole-Lewis, H. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.7867 · Cited by 34 · see citing works
“Sample sizes ranged from 561 [16] to 2523 [17] participants.”
InfluentialPositive Affect and Mood Management in Successful Smoking Cessation
Bränström, R. (2010) · American Journal of Health Behavior · doi:10.5993/ajhb.34.5.5 · Cited by 34 · see citing works
mHealth App recommendation based on the prediction of suitable behavior change techniques
Mao, X., Zhao, X., & Liu, Y. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113248 · Cited by 32 · see citing works
Alshurafa, N. I., Jain, J., Alharbi, R., Iakovlev, G., Spring, B. J., & Pfammatter, A. F. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287031 · Cited by 30 · see citing works
“Studies involving participant experiences with technology, not only in mHealth, but in most studies involving participant interaction with technology, generally agree that engagement with the technology is essential in generating health behavior change [7, 12, 16, 17, 28, 44, 51, 52, 60, 63, 64, 77].”
Methodologic and Design Issues in Patient-Centered e-Health Research
Resnicow, K., Strecher, V., Couper, M., Chua, H., Little, R., Nair, V., Polk, T. A., & Atienza, A. A. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2009.09.034 · Cited by 30 · see citing works
Poduval, S., Ahmed, S., Marston, L., Hamilton, F. L., & Murray, E. (2018) · JMIR Diabetes · doi:10.2196/10925 · Cited by 30 · see citing works
“These data were chosen as measures of use following best practice [46].”
MethodNguyen, M. H., Bol, N., & Lustria, M. L. A. (2020) · Journal of Health Communication · doi:10.1080/10810730.2020.1743797 · Cited by 27 · see citing works
“M. H. Nguyen et al. research showed that tailored websites (vs. standard websites) are visited longer, revisited more often, and evaluated more positively by users (Crutzen et al., 2011; Danaher, Boles, Akers, Gordon, & Severson, 2006; Severson, Gordon, Danaher, & Akers, 2008).”
BackgroundMeasuring participant rurality in Web-based interventions
Danaher, B. G., Hart, L. G., McKay, H. G., & Severson, H. H. (2007) · BMC Public Health · doi:10.1186/1471-2458-7-228 · Cited by 28 · see citing works
“In addition, selective use of video when combined with other forms of interactivity may well encourage longer and more frequent visits (building participant engagement and exposure to helpful program content) [20].”
BackgroundMethodSelf-citationLifeGuide: A platform for performing web-based behavioural interventions
Hare, J. S., Osmond, A., Yang, Y., Wills, G. B., Weal, M. J., Roure, D. D., Joseph, J. A., & Yardley, L. (2009) · ePrints Soton (University of Southampton) · Cited by 26 · see citing works
Graham, A. L., Jacobs, M. A., Cohn, A. M., Cha, S., Abroms, L. C., Papandonatos, G. D., & Whittaker, R. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010687 · Cited by 26 · see citing works
Yeager, C. M., Shoji, K., Łuszczyńska, A., & Benight, C. C. (2018) · JMIR Mental Health · doi:10.2196/mental.9449 · Cited by 27 · see citing works
“Objective measures consisted of automatically recorded data that quantified the frequency (number of pages visited) and duration (total number of minutes logged in) of intervention usage [18,52].”
Kim, A. E., Hansen, H. M., Duke, J. C., Davis, K., Alexander, R. L., Rowland, A., & Mitchko, J. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4299 · Cited by 25 · see citing works
“Prior studies [32-36] have relied on self-reported survey data, the use of cookies, session identifiers, online ad campaign tracking (eg, Google AdWords), or website analytics tools (eg, Google Analytics), which have limitations such as recall bias or users deleting cookies that affect the accuracy of measuring campai…”
MethodThe Law of Attrition Revisited – Author’s Reply
Eysenbach, G. (2006) · Journal of Medical Internet Research · doi:10.2196/jmir.8.3.e21 · Cited by 25 · see citing works
Crenshaw, K. M., Curry, W. A., Salanitro, A. H., Safford, M. M., Houston, T. K., Allison, J. J., & Estrada, C. A. (2010) · Academic Medicine · doi:10.1097/acm.0b013e3181eac036 · Cited by 25 · see citing works
Men's Use of an Internet-Based Decision Aid for Prostate Cancer Screening
Kassan, E. C., Williams, R. M., Kelly, S. P., Barry, S., Penek, S., Fishman, M. B., Cole, C. A., Miller, E. M., & Taylor, K. L. (2011) · Journal of Health Communication · doi:10.1080/10810730.2011.579688 · Cited by 25 · see citing works
“Perhaps requiring the user to respond in some way generates more engagement than simply reading or listening to the information (Danaher et al., 2006).”
BackgroundEngagement with a Social Networking Intervention for Cancer-Related Distress
Owen, J. E., Bantum, E. O., Gorlick, A., & Stanton, A. L. (2014) · Annals of Behavioral Medicine · doi:10.1007/s12160-014-9643-6 · Cited by 24 · see citing works
“Consistent with previous literature [64], exposures to specific pages that lasted longer than 30 min were assumed to be periods of inactivity and were not included in totals of time spent using the intervention, and time data were verified against the period of time during which the chat component of the intervention…”
MethodDanaher, B. G., Severson, H. H., Andrews, J. A., Tyler, M. S., Lichtenstein, E., Woolley, T. G., & Seeley, J. R. (2013) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntt006 · Cited by 23 · see citing works
“Because the frequency and duration of Web site visits tend to be positively skewed, program engagement was measured using the composite measure defined as the mean of the Z-score transformations of visits (number) and duration (minutes) (Danaher et al., 2006).”
MethodSelf-citationWeymann, N., Härter, M. C., Petrak, & Dirmaier, J. (2013) · Patient Preference and Adherence · doi:10.2147/ppa.s46924 · Cited by 22 · see citing works
Gill, S. K., Contreras, O., Muñoz, R. F., & Leykin, Y. (2014) · Internet Interventions · doi:10.1016/j.invent.2014.02.003 · Cited by 22 · see citing works
“…concern about poor retention has dampened the enthusiasm about Internet-based interventions (Christensen et al., 2005; Farvolden et al., 2005; Danaher et al., 2006; Eysenbach, 2005), with some researchers suggesting that high attrition may be inherent in eHealth trials (Eysenbach, 2005);…”
BackgroundFinlay, A., Evans, H. E. L., Vincent, A. D., Wittert, G., Vandelanotte, C., & Short, C. E. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17217920 · Cited by 22 · see citing works
“Two of the studies compared a tunnelled tailored condition to a non-tailored control [25,26].”
ResultYardley, L., Osmond, A., Hare, J. S., Wills, G. B., Weal, M. J., Roure, D. D., & Michie, S. (2009) · ePrints Soton (University of Southampton) · Cited by 21 · see citing works
Giacobbi, P. R., Hingle, M. D., Johnson, T. M., Cunningham, J. K., Armin, J. S., & Gordon, J. S. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5126 · Cited by 20 · see citing works
Self-citationRebar, A. L., Boles, C., Burton, N. W., Duncan, M. J., Short, C. E., Happell, B., Kolt, G. S., Caperchione, C. M., Rosenkranz, R. R., & Vandelanotte, C. (2016) · Mental health and physical activity · doi:10.1016/j.mhpa.2016.08.001 · Cited by 19 · see citing works
Improving Adherence to Smoking Cessation Treatment: Smoking Outcomes in a Web-based Randomized Trial
Graham, A. L., Papandonatos, G. D., Cha, S., Erar, B., & Amato, M. S. (2018) · Annals of Behavioral Medicine · doi:10.1093/abm/kax023 · Cited by 18 · see citing works
“Adherence to online interventions is operationalized as engagement with the website through which the intervention is delivered [5, 6], typically measured as the number of logins, time spent online, number of pages viewed, and the number of features used.”
BackgroundDanaher, B. G., Lichtenstein, E., McKay, H. G., & Seeley, J. R. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1172 · Cited by 18 · see citing works
“The extent to which participants accessed their assigned Web-based program was measured unobtrusively using a combination of database tracking and Web-server log analysis [14] to determine both number and duration of visits (sessions).”
MethodSelf-citationWeymann, N., Härter, M. C., & Dirmaier, J. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-24 · Cited by 18 · see citing works
“Since the effect of online interventions increases with dose (longer stays, repeated website visits, total contact hours) [15,16], effectiveness is maximized if patients work intensively with the information offered [17,18] and return for repeated visits [19,20].”
BackgroundPócs, D., Adamovits, O., Watti, J., Kovács-Huber, R., & Kelemen, O. (2021) · Journal of Medical Internet Research · doi:10.2196/27853 · Cited by 17 · see citing works
“In previous behavioral science studies, “engagement” of digital behavior change interventions was interpreted as the extent of usage [30,31].”
BackgroundStecher, C. D., Berardi, V., Fowers, R., Christ, J., Chung, Y., & Huberty, J. (2021) · Journal of Medical Internet Research · doi:10.2196/27282 · Cited by 15 · see citing works
“Statistical Analysis To characterize app use before and after completing the survey, we calculated the number of total sessions used, number of unique sessions used, duration of use, start time, and whether any app use occurred on a given day [27-31].”
Agachi, E., Bijmolt, T. H. A., van Ittersum, K., & Mierau, J. O. (2023) · JMIR mhealth and uhealth · doi:10.2196/43033 · Cited by 15 · see citing works
“Participant engagement has been defined as “the extent of usage of the digital behavior change intervention” [10], being separated into temporal patterns—frequency and duration, and depth—specific intervention content use [30,31].”
Estrada, C. A., Krishnamoorthy, P., Smith, A., Staton, L. J., Korf, M. J., Allison, J. J., & Houston, T. K. (2011) · Journal of Continuing Education in the Health Professions · doi:10.1002/chp.20097 · Cited by 15 · see citing works
Danaher, B. G., Severson, H. H., Crowley, R. N., van Meter, N., Tyler, M. S., Widdop, C., Lichtenstein, E., & Ebbert, J. O. (2014) · Internet Interventions · doi:10.1016/j.invent.2014.12.004 · Cited by 15 · see citing works
“Website program engagement was measured using the composite measure defined as the mean of the z score transformations of visits (number) and duration (minutes) (Danaher et al., 2006).”
BackgroundMethodSelf-citationIsbăşoiu, A. B., Tulbure, B. T., Rusu, A., & Sava, F. A. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.752249 · Cited by 14 · see citing works
“Adherence reflects the degree to which participants receive the “active ingredients” of the program (Danaher et al., 2006), and was supported to be a significant mediator toward treatment outcomes (Arndt et al.”
BackgroundAlfonsson, S., Olsson, E. M. G., & Hursti, T. J. (2015) · Internet Interventions · doi:10.1016/j.invent.2015.07.005 · Cited by 14 · see citing works
“Similarly, using presentation techniques that followpedagogical and learning principles and incorporate multimedia content may also improve treatment effects though this is somewhat less clear (Danaher et al., 2006).”
MethodCandy, S., Reeve, J., Dobson, R., Whittaker, R., Garrett, J., Warren, J., Calder, A. J., Tane, T., Robertson, T., Rashid, U., et al. (2023) · International Journal of COPD · doi:10.2147/copd.s408423 · Cited by 13 · see citing works
Perski, O., Watson, N. L., Mull, K. E., & Bricker, J. B. (2021) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntab008 · Cited by 12 · see citing works
“8% (10) Self-reported drug use, % (n) 2.”
Klimis, H., Nothman, J., Lu, D., Sun, C., Cheung, N. W., Redfern, J., Thiagalingam, A., & Chow, C. K. (2021) · JMIR mhealth and uhealth · doi:10.2196/27779 · Cited by 12 · see citing works
“Features enabling remote contact with a health care professional can positively influence engagement with DHIs [12,29,30], as can interactivity [31].”
Background
- Authors
- Danaher, B. G., Boles, S. M., Akers, L., Gordon, J. S., & Severson, H. H.
- Affiliations
- Oregon Research Institute (US)
- Corresponding author
- Danaher, Brian G
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2006-08-30
- Volume
- 8
- Issue
- 3
- Pages
- e15
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC1761946
- OpenAlex
- W2099073806
- Cited by
- 145
- References
- 39
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 7 · 2018: 16 · 2017: 8 · 2016: 10 · 2015: 9 · 2014: 7 · 2013: 12 · 2012: 7
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R01 CA 84225, R01CA084225)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- program engagement, website visits, personalized interventions
- MeSH terms
- Adult, Behavior Therapy, Humans, Health Knowledge, Attitudes, Practice, Online Systems, Patient Participation, United States, User-Computer Interface, Program Evaluation, Health Behavior, Telemedicine, Tobacco Use Cessation, Internet
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 ›
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