How user characteristics affect use patterns in web-based illness management support for patients with breast and prostate cancer

Elin Børøsund, Milada Cvancarova, Mirjam Ekstedt, Shirley M Moore, Cornelia M Ruland, Elin Børøsund, Milada Cvancarova, Mirjam Ekstedt, Shirley M Moore, Cornelia M Ruland

Abstract

Background: Frequently eHealth applications are not used as intended and they have high attrition rates; therefore, a better understanding of patients' need for support is warranted. Specifically, more research is needed to identify which system components target different patient groups and under what conditions.

Objective: To explore user characteristics associated with the use of different system components of a Web-based illness management support system for cancer patients (WebChoice).

Methods: For this secondary post hoc analysis of a large randomized controlled trial (RCT), in which WebChoice was tested among 325 breast cancer and prostate cancer patients who were followed with repeated measures for 1 year, usage patterns of 162 cancer patients in the intervention arm with access to WebChoice were extracted from the user log. Logistic regression was performed to identify patterns of associations between system use and patient characteristics. Latent class analyses (LCA) were performed to identify associations among the use of different system components and levels of social support, symptom distress, depression, self-efficacy, and health-related quality of life.

Results: Approximately two-thirds (103/162, 63.6%) of the patients logged on to WebChoice more than once, and were defined as users. A high level of computer experience (odds ratio [OR] 3.77, 95% CI 1.20-11.91) and not having other illnesses in addition to cancer (OR 2.10, 95% CI 1.02-4.34) increased the overall probability of using WebChoice. LCA showed that both men with prostate cancer and women with breast cancer who had low scores on social support accompanied with high levels of symptom distress and high levels of depression were more likely to use the e-message component. For men with prostate cancer, these variables were also associated with high use of the self-management advice component. We found important differences between men with prostate cancer and women with breast cancer when associations between WebChoice use and each user characteristic were analyzed separately. High use of all components was associated with low levels of social support among women with breast cancer, but not among men with prostate cancer. High use of e-messages, advice, and the discussion forum were associated with high levels of depression among women with breast cancer, but not among men with prostate cancer. For men with prostate cancer (but not women with breast cancer), high use of symptom assessments, advice, and the discussion forum were associated with high levels of symptom distress. However, it is unclear whether these findings can be attributed to differences related to diagnosis, gender, or both.

Conclusions: This study provides evidence that different user characteristics are associated with different use patterns. Such information is crucial to target Web-based support systems to different patient groups. LCA is a useful technique to identify subgroups of users. In our study, e-messages and self-management advice were highly used components for patients who had low levels of social support and high illness burden, suggesting that patients with these characteristics may find such tools particularly useful.

Trial registration: ClinicalTrials.gov NCT00710658; https://ichgcp.net/clinical-trials-registry/NCT00710658 (Archived by WebCite at http://www.webcitation.org/6EmEWZiwz).

Conflict of interest statement

Conflicts of Interest: CMR is the developer of the system, but has no ownership rights to the application.

Figures

Figure 1
Figure 1
Screenshot of the WebChoice overview page.
Figure 2
Figure 2
Screenshot of the results of an assessment and the associated advice/interventions.
Figure 3
Figure 3
Screenshot showing an example of content and layout in the advice component.

References

    1. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res. 2004 Nov 10;6(4):e40. doi: 10.2196/jmir.6.4.e40.
    1. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive Health Communication Applications for people with chronic disease. Cochrane Database Syst Rev. 2005 Oct;(4):CD004274. doi: 10.1002/14651858.CD004274.pub4.
    1. Samoocha D, Bruinvels DJ, Elbers NA, Anema JR, van der Beek AJ. Effectiveness of web-based interventions on patient empowerment: a systematic review and meta-analysis. J Med Internet Res. 2010 Jun;12(2):e23. doi: 10.2196/jmir.1286.
    1. Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care. 2006 Nov;44(11):964–71. doi: 10.1097/01.mlr.0000233678.80203.c1.
    1. Lorig KR, Ritter PL, Dost A, Plant K, Laurent DD, McNeil I. The Expert Patients Programme online, a 1-year study of an Internet-based self-management programme for people with long-term conditions. Chronic Illn. 2008 Dec;4(4):247–56. doi: 10.1177/1742395308098886.
    1. Solomon M, Wagner SL, Goes J. Effects of a Web-based intervention for adults with chronic conditions on patient activation: online randomized controlled trial. J Med Internet Res. 2012;14(1):e32. doi: 10.2196/jmir.1924.
    1. Ruland CM, Andersen T, Jeneson A, Moore S, Grimsbø GH, Børøsund E, Ellison MC. Effects of an internet support system to assist cancer patients in reducing symptom distress: a randomized controlled trial. Cancer Nurs. 2013;36(1):6–17. doi: 10.1097/NCC.0b013e31824d90d4.
    1. Gustafson DH, Hawkins R, McTavish F, Pingree S, Chen WC, Volrathongchai K, Stengle W, Stewart JA, Serlin RC. Internet-based interactive support for cancer patients: are integrated systems better? J Commun. 2008 Jun;58(2):238–257. doi: 10.1111/j.1460-2466.2008.00383.x.
    1. Yun YH, Lee KS, Kim YW, Park SY, Lee ES, Noh DY, Kim S, Oh JH, Jung SY, Chung KW, Lee YJ, Jeong SY, Park KJ, Shim YM, Zo JI, Park JW, Kim YA, Shon EJ, Park S. Web-based tailored education program for disease-free cancer survivors with cancer-related fatigue: a randomized controlled trial. J Clin Oncol. 2012 Apr 20;30(12):1296–303. doi: 10.1200/JCO.2011.37.2979.
    1. van der Meer V, Bakker MJ, van den Hout WB, Rabe KF, Sterk PJ, Kievit J, Assendelft WJ, Sont JK, SMASHING (Self-Management in Asthma Supported by Hospitals‚ ICT‚ Nurses and General Practitioners) Study Group Internet-based self-management plus education compared with usual care in asthma: a randomized trial. Ann Intern Med. 2009 Jul 21;151(2):110–20.
    1. Lorig KR, Ritter PL, Laurent DD, Plant K. The internet-based arthritis self-management program: a one-year randomized trial for patients with arthritis or fibromyalgia. Arthritis Rheum. 2008 Jul 15;59(7):1009–17. doi: 10.1002/art.23817.
    1. Moore SM, Primm T. Designing and testing telehealth interventions to improve outcomes for cardiovascular patients. J Cardiovasc Nurs. 2007;22(1):43–50.
    1. Moore TJ, Alsabeeh N, Apovian CM, Murphy MC, Coffman GA, Cullum-Dugan D, Jenkins M, Cabral H. Weight, blood pressure, and dietary benefits after 12 months of a Web-based Nutrition Education Program (DASH for health): longitudinal observational study. J Med Internet Res. 2008 Dec;10(4):e52. doi: 10.2196/jmir.1114.
    1. Lieber SB, Redberg RF, Blumenthal RS, Gandhi A, Robb KJ, Mora S. A national interactive web-based physical activity intervention in women, evaluation of the american heart association choose to move program 2006-2007. Am J Cardiol. 2012 Jun 15;109(12):1754–60. doi: 10.1016/j.amjcard.2012.02.017.
    1. Myung SK, McDonnell DD, Kazinets G, Seo HG, Moskowitz JM. Effects of Web- and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Arch Intern Med. 2009 May 25;169(10):929–37. doi: 10.1001/archinternmed.2009.109.
    1. Kreuter MW, Wray RJ. Tailored and targeted health communication: strategies for enhancing information relevance. Am J Health Behav. 2003 Nov;27 Suppl 3:S227–32.
    1. Johnson JD. Cancer-Related Information Seeking (Health Communication) Cresskill, NJ: Hampton Press; 1997.
    1. Han JY, Wise M, Kim E, Pingree R, Hawkins RP, Pingree S, McTavish F, Gustafson DH. Factors associated with use of interactive cancer communication system: an application of the Comprehensive Model of Information Seeking. J Comput Mediat Commun. 2010 Apr;15(3):367–388. doi: 10.1111/j.1083-6101.2010.01508.x.
    1. Han JY, Kim JH, Yoon HJ, Shim M, McTavish FM, Gustafson DH. Social and psychological determinants of levels of engagement with an online breast cancer support group: posters, lurkers, and nonusers. J Health Commun. 2012 Mar;17(3):356–71. doi: 10.1080/10810730.2011.585696.
    1. Im EO, Chee W, Liu Y, Lim HJ, Guevara E, Tsai HM, Clark M, Bender M, Shin H, Kim KS, Kim YH. Characteristics of cancer patients in internet cancer support groups. Comput Inform Nurs. 2007;25(6):334–43. doi: 10.1097/01.NCN.0000299655.21401.9d.
    1. Funk KL, Stevens VJ, Appel LJ, Bauck A, Brantley PJ, Champagne CM, Coughlin J, Dalcin AT, Harvey-Berino J, Hollis JF, Jerome GJ, Kennedy BM, Lien LF, Myers VH, Samuel-Hodge C, Svetkey LP, Vollmer WM. Associations of internet website use with weight change in a long-term weight loss maintenance program. J Med Internet Res. 2010 Jul;12(3):e29. doi: 10.2196/jmir.1504.
    1. Kelders SM, Van Gemert-Pijnen JE, Werkman A, Nijland N, Seydel ER. Effectiveness of a Web-based intervention aimed at healthy dietary and physical activity behavior: a randomized controlled trial about users and usage. J Med Internet Res. 2011 Apr;13(2):e32. doi: 10.2196/jmir.1624.
    1. Kerr C, Murray E, Noble L, Morris R, Bottomley C, Stevenson F, Patterson D, Peacock R, Turner I, Jackson K, Nazareth I. The potential of Web-based interventions for heart disease self-management: a mixed methods investigation. J Med Internet Res. 2010 Dec;12(4):e56. doi: 10.2196/jmir.1438.
    1. Owen JE, Goldstein MS, Lee JH, Breen N, Rowland JH. Use of health-related and cancer-specific support groups among adult cancer survivors. Cancer. 2007 Jun 15;109(12):2580–9. doi: 10.1002/cncr.22719.
    1. Brouwer W, Oenema A, Raat H, Crutzen R, de Nooijer J, de Vries NK, Brug J. Characteristics of visitors and revisitors to an Internet-delivered computer-tailored lifestyle intervention implemented for use by the general public. Health Educ Res. 2010 Aug;25(4):585–95. doi: 10.1093/her/cyp063.
    1. Eysenbach G. The impact of the Internet on cancer outcomes. CA Cancer J Clin. 2003;53(6):356–71.
    1. Fogel J, Albert SM, Schnabel F, Ditkoff BA, Neugut AI. Use of the Internet by women with breast cancer. J Med Internet Res. 2002 Nov;4(2):E9. doi: 10.2196/jmir.4.2.e9.
    1. Andreassen HK, Bujnowska-Fedak MM, Chronaki CE, Dumitru RC, Pudule I, Santana S, Voss H, Wynn R. European citizens' use of E-health services: a study of seven countries. BMC Public Health. 2007 Apr;7:53. doi: 10.1186/1471-2458-7-53.
    1. Høybye MT, Dalton SO, Christensen J, Ross L, Kuhn KG, Johansen C. Social and psychological determinants of participation in internet-based cancer support groups. Support Care Cancer. 2010 May;18(5):553–60. doi: 10.1007/s00520-009-0683-6.
    1. Or CK, Karsh BT. A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc. 2009 Aug;16(4):550–60. doi: 10.1197/jamia.M2888.
    1. Glasgow RE, Christiansen SM, Kurz D, King DK, Woolley T, Faber AJ, Estabrooks PA, Strycker L, Toobert D, Dickman J. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res. 2011 Jan;13(1):e9. doi: 10.2196/jmir.1391.
    1. Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. 2012 Jan;14(1):e19. doi: 10.2196/jmir.1619.
    1. Shaw BR, Hawkins R, Arora N, McTavish F, Pingree S, Gustafson DH. An exploratory study of predictors of participation in a computer support group for women with breast cancer. Comput Inform Nurs. 2006 Feb;24(1):18–27.
    1. Shaw BR, Dubenske LL, Han JY, Cofta-Woerpel L, Bush N, Gustafson DH, McTavish F. Antecedent characteristics of online cancer information seeking among rural breast cancer patients: an application of the Cognitive-Social Health Information Processing (C-SHIP) model. J Health Commun. 2008 Jun;13(4):389–408. doi: 10.1080/10810730802063546.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar;7(1):e11. doi: 10.2196/jmir.7.1.e11.
    1. Nijland N, van Gemert-Pijnen JE, Kelders SM, Brandenburg BJ, Seydel ER. Factors influencing the use of a Web-based application for supporting the self-care of patients with type 2 diabetes: a longitudinal study. J Med Internet Res. 2011;13(3):e71. doi: 10.2196/jmir.1603.
    1. Williamson K. Where one size does not fit all: understanding the needs of potential users of a portal to breast cancer knowledge online. J Health Commun. 2005 Sep;10(6):567–80. doi: 10.1080/10810730500228961.
    1. CommuniCare Tools. [2012-05-31]. WebChoice .
    1. Ruland CM, Jeneson A, Andersen T, Andersen R, Slaughter L, Bente-Schjødt-Osmo. Moore SM. Designing tailored Internet support to assist cancer patients in illness management. AMIA Annu Symp Proc. 2007:635–9.
    1. Portenoy RK, Thaler HT, Kornblith AB, Lepore JM, Friedlander-Klar H, Kiyasu E, Sobel K, Coyle N, Kemeny N, Norton L. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer. 1994;30A(9):1326–36.
    1. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32(6):705–14.
    1. Radloff LS, Teri L. Use of the Center for Epidemiological Studies-Depression Scale with older adults. WCLI. 1986 Nov 1986;5(1):119–136. doi: 10.1300/J018v05n01_06.
    1. Merluzzi TV, Nairn RC, Hegde K, Martinez Sanchez MA, Dunn L. Self-efficacy for coping with cancer: revision of the Cancer Behavior Inventory (version 2.0) Psychooncology. 2001 Jun;10(3):206–17.
    1. Sintonen H. The 15D instrument of health-related quality of life: properties and applications. Ann Med. 2001 Jul;33(5):328–36.
    1. Collins LM, Lanza ST. Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Hoboken, NJ: Wiley; 2010.
    1. Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974 Dec 1974;19(6):716–723. doi: 10.1109/TAC.1974.1100705.
    1. Schwarz G. Estimating the dimension of a model. Ann Statist. 1978 Mar 1978;6(2):461–464. doi: 10.1214/aos/1176344136.
    1. Lanza ST, Collins LM, Lemmon DR, Schafer JL. PROC LCA: A SAS Procedure for Latent Class Analysis. Struct Equ Modeling. 2007;14(4):671–694.
    1. Baker TB, Hawkins R, Pingree S, Roberts LJ, McDowell HE, Shaw BR, Serlin R, Dillenburg L, Swoboda CM, Han JY, Stewart JA, Carmack-Taylor CL, Salner A, Schlam TR, McTavish F, Gustafson DH. Optimizing eHealth breast cancer interventions: which types of eHealth services are effective? Transl Behav Med. 2011 Mar;1(1):134–145.
    1. Grimsbø GH, Finset A, Ruland CM. Left hanging in the air: experiences of living with cancer as expressed through e-mail communications with oncology nurses. Cancer Nurs. 2011 Apr;34(2):107–16. doi: 10.1097/NCC.0b013e3181eff008.
    1. Grimsbø GH, Engelsrud GH, Ruland CM, Finset A. Cancer patients' experiences of using an Interactive Health Communication Application (IHCA) Int J Qual Stud Health Well-being. 2012;7 doi: 10.3402/qhw.v7i0.15511.
    1. Mo PK, Malik SH, Coulson NS. Gender differences in computer-mediated communication: a systematic literature review of online health-related support groups. Patient Educ Couns. 2009 Apr;75(1):16–24. doi: 10.1016/j.pec.2008.08.029.
    1. Blank TO, Schmidt SD, Vangsness SA, Monteiro AK, Santagata PV. Differences among breast and prostate cancer online support groups. Computers in Human Behavior. 2010 Nov 2010;26(6):1400–1404. doi: 10.1016/j.chb.2010.04.016.
    1. Seale C, Ziebland S, Charteris-Black J. Gender, cancer experience and internet use: a comparative keyword analysis of interviews and online cancer support groups. Soc Sci Med. 2006 May;62(10):2577–90. doi: 10.1016/j.socscimed.2005.11.016.
    1. Verheijden MW, Jans MP, Hildebrandt VH, Hopman-Rock M. Rates and determinants of repeated participation in a web-based behavior change program for healthy body weight and healthy lifestyle. J Med Internet Res. 2007 Jan;9(1):e1. doi: 10.2196/jmir.9.1.e1.
    1. Brouwer W, Kroeze W, Crutzen R, de Nooijer J, de Vries NK, Brug J, Oenema A. Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review. J Med Internet Res. 2011 Jan;13(1):e2. doi: 10.2196/jmir.1639.
    1. Statistics Norway. [2012-05-31]. The information society
    1. Nijland N, van Gemert-Pijnen JE, Boer H, Steehouder MF, Seydel ER. Increasing the use of e-consultation in primary care: results of an online survey among non-users of e-consultation. Int J Med Inform. 2009 Oct;78(10):688–703. doi: 10.1016/j.ijmedinf.2009.06.002.
    1. Couper MP, Alexander GL, Zhang N, Little RJ, Maddy N, Nowak MA, McClure JB, Calvi JJ, Rolnick SJ, Stopponi MA, Cole Johnson C. Engagement and retention: measuring breadth and depth of participant use of an online intervention. J Med Internet Res. 2010;12(4):e52. doi: 10.2196/jmir.1430.
    1. Hernes E, Kyrdalen A, Kvåle R, Hem E, Klepp O, Axcrona K, Fosså SD. Initial management of prostate cancer: first year experience with the Norwegian National Prostate Cancer Registry. BJU Int. 2010 Mar;105(6):805–11; discussion 811. doi: 10.1111/j.1464-410X.2009.08834.x.
    1. Dahl AA, Nesvold IL, Reinertsen KV, Fosså SD. Arm/shoulder problems and insomnia symptoms in breast cancer survivors: cross-sectional, controlled and longitudinal observations. Sleep Med. 2011 Jun;12(6):584–90. doi: 10.1016/j.sleep.2011.01.011.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel