Exposure to a patient-centered, Web-based intervention for managing cancer symptom and quality of life issues: impact on symptom distress

Donna L Berry, Traci M Blonquist, Rupa A Patel, Barbara Halpenny, Justin McReynolds, Donna L Berry, Traci M Blonquist, Rupa A Patel, Barbara Halpenny, Justin McReynolds

Abstract

Background: Effective eHealth interventions can benefit a large number of patients with content intended to support self-care and management of both chronic and acute conditions. Even though usage statistics are easily logged in most eHealth interventions, usage or exposure has rarely been reported in trials, let alone studied in relationship to effectiveness.

Objective: The intent of the study was to evaluate use of a fully automated, Web-based program, the Electronic Self Report Assessment-Cancer (ESRA-C), and how delivery and total use of the intervention may have affected cancer symptom distress.

Methods: Patients at two cancer centers used ESRA-C to self-report symptom and quality of life (SxQOL) issues during therapy. Participants were randomized to ESRA-C assessment only (control) or the ESRA-C intervention delivered via the Internet to patients' homes or to a tablet at the clinic. The intervention enabled participants to self-monitor SxQOL and receive self-care education and customized coaching on how to report concerns to clinicians. Overall and voluntary intervention use were defined as having ≥2 exposures, and one non-prompted exposure to the intervention, respectively. Factors associated with intervention use were explored with Fisher's exact test. Propensity score matching was used to select a sample of control participants similar to intervention participants who used the intervention. Analysis of covariance (ANCOVA) was used to compare change in Symptom Distress Scale (SDS-15) scores from pre-treatment to end-of-study by groups in the matched sample.

Results: Radiation oncology participants used the intervention, overall and voluntarily, more than medical oncology and transplant participants. Participants who were working and had more than a high school education voluntarily used the intervention more. The SDS-15 score was reduced by an estimated 1.53 points (P=.01) in the intervention group users compared to the matched control group.

Conclusions: The intended effects of a Web-based, patient-centered intervention on cancer symptom distress were modified by intervention use frequency. Clinical and personal demographics influenced voluntary use.

Trial registration: Clinicaltrials.gov NCT00852852; https://ichgcp.net/clinical-trials-registry/NCT00852852 (Archived by WebCite at http://www.webcitation.org/6YwAfwWl7).

Keywords: Internet; cancer; eHealth; patient-centered technology; quality of life; symptoms.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Adapted Health Outcomes Model [10].
Figure 2
Figure 2
Exemplar screen shots from the ESRA-C intervention: a) pushed Teaching Tip, b) Teaching Tips tab, c) View My Reports tab.
Figure 3
Figure 3
Components of the ESRA-C intervention with calculation of total exposure.
Figure 4
Figure 4
Sample selection for the propensity score analysis. Note: EOS=end of study; SDS=Symptom Distress Scale; Demo=demographics.

References

    1. Dwamena Francesca, Holmes-Rovner Margaret, Gaulden Carolyn M, Jorgenson Sarah, Sadigh Gelareh, Sikorskii Alla, Lewin Simon, Smith Robert C, Coffey John, Olomu Adesuwa. Interventions for providers to promote a patient-centred approach in clinical consultations. Cochrane Database Syst Rev. 2012;12:CD003267. doi: 10.1002/14651858.CD003267.pub2.
    1. Velikova Galina, Booth Laura, Smith Adam B, Brown Paul M, Lynch Pamela, Brown Julia M, Selby Peter J. Measuring quality of life in routine oncology practice improves communication and patient well-being: a randomized controlled trial. J Clin Oncol. 2004 Feb 15;22(4):714–24. doi: 10.1200/JCO.2004.06.078.
    1. Ruland Cornelia M, Holte Harald H, Røislien Jo, Heaven Cathy, Hamilton Glenys A, Kristiansen Jørn, Sandbaek Heidi, Kvaløy Stein O, Hasund Line, Ellison Misoo C. Effects of a computer-supported interactive tailored patient assessment tool on patient care, symptom distress, and patients' need for symptom management support: a randomized clinical trial. J Am Med Inform Assoc. 2010;17(4):403–10. doi: 10.1136/jamia.2010.005660.
    1. Berry Donna L, Blumenstein Brent A, Halpenny Barbara, Wolpin Seth, Fann Jesse R, Austin-Seymour Mary, Bush Nigel, Karras Bryant T, Lober William B, McCorkle Ruth. Enhancing patient-provider communication with the electronic self-report assessment for cancer: a randomized trial. J Clin Oncol. 2011 Mar 10;29(8):1029–35. doi: 10.1200/JCO.2010.30.3909.
    1. Berry Donna L, Hong Fangxin, Halpenny Barbara, Partridge Ann H, Fann Jesse R, Wolpin Seth, Lober William B, Bush Nigel E, Parvathaneni Upendra, Back Anthony L, Amtmann Dagmar, Ford Rosemary. Electronic self-report assessment for cancer and self-care support: results of a multicenter randomized trial. J Clin Oncol. 2014 Jan 20;32(3):199–205. doi: 10.1200/JCO.2013.48.6662.
    1. Donkin Liesje, Christensen Helen, Naismith Sharon L, Neal Bruce, Hickie Ian B, Glozier Nick. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. 2011;13(3):e52. doi: 10.2196/jmir.1772.
    1. van den Berg Sanne W, Peters Esmee J, Kraaijeveld J Frank, Gielissen Marieke F M, Prins Judith B. Usage of a generic web-based self-management intervention for breast cancer survivors: substudy analysis of the BREATH trial. J Med Internet Res. 2013;15(8):e170. doi: 10.2196/jmir.2566.
    1. Wolpin S E, Halpenny B, Whitman G, McReynolds J, Stewart M, Lober W B, Berry D L. Development and usability testing of a web-based cancer symptom and quality-of-life support intervention. Health Informatics J. 2015 Mar;21(1):10–23. doi: 10.1177/1460458213495744.
    1. Berry Donna L, Hong Fangxin, Halpenny Barbara, Partridge Anne, Fox Erica, Fann Jesse R, Wolpin Seth, Lober William B, Bush Nigel, Parvathaneni Upendra, Amtmann Dagmar, Ford Rosemary. The electronic self report assessment and intervention for cancer: promoting patient verbal reporting of symptom and quality of life issues in a randomized controlled trial. BMC Cancer. 2014;14:513. doi: 10.1186/1471-2407-14-513.
    1. Mitchell P H, Ferketich S, Jennings B M. Quality health outcomes model. American Academy of Nursing Expert Panel on Quality Health Care. Image J Nurs Sch. 1998;30(1):43–6.
    1. Karras Bryant T, Wolpin Seth, Lober William B, Bush Nigel, Fann Jesse R, Berry Donna L. Electronic Self-report Assessment--Cancer (ESRA-C): Working towards an integrated survey system. Stud Health Technol Inform. 2006;122:514–8.
    1. Aaronson N K, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez N J, Filiberti A, Flechtner H, Fleishman S B, de Haes J C The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993 Mar 3;85(5):365–76.
    1. Postma T J, Aaronson N K, Heimans J J, Muller M J, Hildebrand J G, Delattre J Y, Hoang-Xuan K, Lantéri-Minet M, Grant R, Huddart R, Moynihan C, Maher J, Lucey R, EORTC Quality of Life Group The development of an EORTC quality of life questionnaire to assess chemotherapy-induced peripheral neuropathy: the QLQ-CIPN20. Eur J Cancer. 2005 May;41(8):1135–9. doi: 10.1016/j.ejca.2005.02.012.
    1. Kroenke K, Spitzer R L, Williams J B. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–13.
    1. Ryan J L, Bole C, Hickok J T, Figueroa-Moseley C, Colman L, Khanna R C, Pentland A P, Morrow G R. Post-treatment skin reactions reported by cancer patients differ by race, not by treatment or expectations. Br J Cancer. 2007 Jul 2;97(1):14–21. doi: 10.1038/sj.bjc.6603842.
    1. Siefert M, Blonquist T, Berry DL, Hong F. Symptom-related emergency department visits and hospital admissions during ambulatory cancer treatment. J Comm Supp Oncol. 2015:In–Press. (forthcoming)(forthcoming)
    1. Austin Peter C. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011 May;46(3):399–424. doi: 10.1080/00273171.2011.568786.
    1. Rosenbaum P, Rubin D. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score. Journal of the American Statistical Association. 1984 Sep;79(387):516–24. doi: 10.2307/2288398.
    1. Jo Booil, Stuart Elizabeth A, Mackinnon David P, Vinokur Amiram D. The Use of Propensity Scores in Mediation Analysis. Multivariate Behav Res. 2011 May;46(3):425–452. doi: 10.1080/00273171.2011.576624.
    1. Ho D, Imai K, King G, Stuart E. MatchIT: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Software. 2011;42(8):1–28.
    1. Kelders Saskia M, Kok Robin N, Ossebaard Hans C, Van Gemert-Pijnen Julia E W C. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. 2012;14(6):e152. doi: 10.2196/jmir.2104.
    1. Børøsund Elin, Cvancarova Milada, Ekstedt Mirjam, Moore Shirley M, Ruland Cornelia M. How user characteristics affect use patterns in web-based illness management support for patients with breast and prostate cancer. J Med Internet Res. 2013;15(3):e34. doi: 10.2196/jmir.2285.
    1. Berry DL, Hong F, Blonquist T, Halpenny B, Siefert M, Partridge A. Self report assessment and support for cancer symptoms: Impact on hospital admissions and emergency department visits. J Clin Oncol; American Society of Clinical Oncology Annual Meeting; 31 May-4 June, 2013; Chicago. 2013. p. e20552.
    1. Artherholt Samantha B, Hong Fangxin, Berry Donna L, Fann Jesse R. Risk factors for depression in patients undergoing hematopoietic cell transplantation. Biol Blood Marrow Transplant. 2014 Jul;20(7):946–50. doi: 10.1016/j.bbmt.2014.03.010.
    1. American Cancer Society Cancer Facts & Figures 2014. [2014-12-29]. .
    1. Wangberg Silje C, Bergmo Trine S, Johnsen Jan-Are K. Adherence in Internet-based interventions. Patient Prefer Adherence. 2008;2:57–65.
    1. Strecher Victor J, McClure Jennifer, Alexander Gwen, Chakraborty Bibhas, Nair Vijay, Konkel Janine, Greene Sarah, Couper Mick, Carlier Carola, Wiese Cheryl, Little Roderick, Pomerleau Cynthia, Pomerleau Ovide. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial. J Med Internet Res. 2008;10(5):e36. doi: 10.2196/jmir.1002.
    1. Couper Mick P, Alexander Gwen L, Zhang Nanhua, Little Roderick J A, Maddy Noel, Nowak Michael A, McClure Jennifer B, Calvi Josephine J, Rolnick Sharon J, Stopponi Melanie A, Cole Johnson Christine. 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. Glasgow Russell E, Christiansen Steven M, Kurz Deanna, King Diane K, Woolley Tim, Faber Andrew J, Estabrooks Paul A, Strycker Lisa, Toobert Deborah, Dickman Jennifer. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res. 2011;13(1):e9. doi: 10.2196/jmir.1391.
    1. Wilson Greg, Aruliah D A, Brown C Titus, Chue Hong Neil P, Davis Matt, Guy Richard T, Haddock Steven H D, Huff Kathryn D, Mitchell Ian M, Plumbley Mark D, Waugh Ben, White Ethan P, Wilson Paul. Best practices for scientific computing. PLoS Biol. 2014 Jan;12(1):e1001745. doi: 10.1371/journal.pbio.1001745.
    1. Remmel H, Paech B, Bastian P, Engwer C. System Testing a Scientific Framework Using a Regression-Test Environment. Comput. Sci. Eng. 2012 Mar;14(2):38–45. doi: 10.1109/MCSE.2011.115.
    1. Ackroyd KS, Kinder SH, Mant GR, Miller MC, Ramsdale CA, Stephenson PC. Scientific Software Development at a Research Facility. IEEE Softw. 2008 Jul;25(4):44–51. doi: 10.1109/MS.2008.93.

Source: PubMed

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