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Published on 03.06.15 in Vol 17, No 6 (2015): June

This paper is in the following e-collection/theme issue:

    Original Paper

    Exposure to a Patient-Centered, Web-Based Intervention for Managing Cancer Symptom and Quality of Life Issues: Impact on Symptom Distress

    1Phyllis F. Cantor Center, Dana-Farber Cancer Institute, Boston, MA, United States

    2Department of Medicine, Harvard Medical School, Boston, MA, United States

    3Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, United States

    4Usability Engineering, GoDaddy Group, Kirkland, WA, United States

    5Phyllis F. Cantor Center, Dana-Farber Cancer Institute, Boston, MA, United States

    6Biomedical and Health Informatics, University of Washington, Seattle, WA, United States

    *these authors contributed equally

    Corresponding Author:

    Donna L Berry, RN, PhD, FAAN

    Phyllis F Cantor Center

    Dana-Farber Cancer Institute

    450 Brookline Ave; LW 518

    Boston, MA, 02215

    United States

    Phone: 1 617 632 1909

    Fax:1 617 394 2877

    Email: donna_berry@dfci.harvard.edu


    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; http://clinicaltrials.gov/ct2/show/NCT00852852 (Archived by WebCite at http://www.webcitation.org/6YwAfwWl7).

    J Med Internet Res 2015;17(6):e136

    doi:10.2196/jmir.4190

    KEYWORDS



    Introduction

    Background

    Clinicians and researchers have developed eHealth solutions that supplement the limited time for patient report and communication within the confines of the ambulatory care, face-to-face visit [1]. Reported benefits of eHealth solutions for oncology care include improved patient well-being [2,3], better patient-clinician communication [2,4], and lower symptom distress [5]. Effective eHealth interventions can benefit a large number of patients with both generic and tailored content. 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. As reviewed by Donkin et al [6], the “dose” of eHealth solutions, comprehensive measures of intervention exposure or patient engagement, have been documented in few trials evaluating health promotion and mental health interventions. Furthermore, eHealth intervention delivery has been studied in only one cancer symptom and quality of life trial, in association with outcomes in breast cancer survivors [7]. The ability and efforts of patients in active cancer treatment to fully utilize such solutions are uncertain.

    The Electronic Self Report Assessment for Cancer (ESRA-C) is a patient-centered technology developed with rigorous participatory design methods [8] and evaluated in multi-site randomized trials [4,5]. ESRA-C, a Web-based intervention that supports patients with any cancer diagnosis during treatment, has been shown to significantly increase the frequency of patient-clinician communication about problematic issues [4], reduce symptom distress over the course of active therapy [5], and increase the patient’s unsolicited and specific description of symptoms and quality of life (SxQOL) concerns [9]. However, when we conducted a mediation analysis of the impact of the intervention group’s increased patient verbal reports at one clinic visit during the trial, we found no significant impact on the primary outcome of symptom distress [9]. In other words, another aspect of the intervention was responsible for the reduction of symptom distress.

    Objective

    The purpose of this analysis was to determine the impact of the ESRA-C intervention exposure on cancer symptom distress and describe frequency of intervention use by participants in the ESRA-C II trial.


    Methods

    Overview

    This analysis addresses one component of our program of research founded on the Quality Health Outcomes Model, a framework proposed by Mitchell and colleagues [10] to illustrate that patient outcomes are rarely explained only by specific interventions but also by health care system/provider factors and patient-specific factors. The extent of patients’ use of the intervention can be placed in the model (Figure 1) as a patient-specific factor that may influence the impact of the ESRA-C intervention on symptom distress.

    Figure 1. Adapted Health Outcomes Model [10].
    View this figure

    Design, Sample, Intervention

    The ESRA-C II was a randomized trial conducted at two comprehensive cancer centers. Full details of the trial [5] and system development were reported elsewhere [11]. In brief, adult participants with any type or stage of cancer, and about to start a new anti-cancer therapy, used ESRA-C to self-report baseline SxQOL and then were randomized to receive usual education about SxQOL topics or usual education plus the opportunity to self-monitor when not in the clinic, tailored self-care instruction for SxQOL issues, and communication coaching on how to report each SxQOL to clinicians. While in the clinic, the intervention group participants were shown an overview of the ESRA-C intervention and voluntary remote use was encouraged. Participants in the intervention group could access the ESRA-C program from home or in clinic on a touch-screen computer at any time throughout the trial to electronically track SxQOL and view the intervention. Those intervention group participants without Internet access were encouraged to meet the research coordinator during any subsequent visit to the clinic and use ESRA-C on a study tablet. Participants in both groups were asked to report SxQOL using the ESRA-C system from home or clinic at three study time points (T2-T4) throughout the course of therapy, coinciding with clinic visits at which clinicians would receive a printed summary of the patient report for participants in both groups. Home user participants in both groups were prompted by email, 24 hours prior to a scheduled clinic visit, to use the SxQOL report feature of ESRA-C. Clinic users were notified to arrive about 45 minutes prior to scheduled clinic visits corresponding to each study time point in order to use the reporting feature and, if in the intervention group, components. Intervention group participants had access to the ESRA-C intervention Teaching Tips and View My Reports components.

    Following the SxQOL report in prompted T2-T4 sessions, the intervention group participants received pushed teaching tips for those SxQOL issues reported as moderate-to-severe. Within each pushed teaching tip was the option to expand linked text addressing (1) “Why does this happen?”, (2) “What can I do about this?”, and (3) “What do I tell my clinical team?” (Figure 2a). After the SxQOL report and pushed teaching tips, the participant could navigate to the Teaching Tips tab or the View My Reports tab within the intervention home page (Figure 2b-c) by clicking on the designated tab. A click on the (non-pushed) Teaching Tips tab displayed a dropdown list of all 26 SxQOL issues and the option to select and expand any issues. A click on the View My Reports tab displayed thumbnail line graphs tracking SxQOL reports over time.

    Intervention group participants were invited to access ESRA-C at any time between prompted sessions and clinic visits. These sessions were defined as any intervention use that was not prompted. During voluntary, non-prompted sessions, the participant did not receive pushed Teaching Tips, but did have the option to report SxQOL, and click the (non-pushed) Teaching Tips tab and the View My Reports tab.

    The ESRA-C intervention was considered delivered if the participant accessed the Teaching Tips and/or View My Reports. As a conservative measure of exposure to Teaching Tips, if at least one pushed tip was delivered during a prompted session, this was considered comparable to a single click on the non-pushed Teaching Tips. For example, a participant with at least one pushed teaching tip at each of three prompted sessions would have a total of three pushed teaching tips. Total exposure to the intervention consisted of three components: (1) the number of pushed teaching tips during prompted sessions, (2) the number of clicks on the non-pushed Teaching Tips tab during prompted and non-prompted sessions, and (3) the number of clicks on the View My Reports tab during prompted and non-prompted sessions. Voluntary exposure only occurred during non-prompted sessions and consisted of (1) the number of clicks on the (non-pushed) Teaching Tips tab, and (2) the number of clicks on the View My Reports tab.

    At prompted study time points, all participants were presented a set of SxQOL self-report assessments that included the Symptom Distress Scale-15 (SDS-15) [5], cancer quality of life questionnaires EORTC QLQ-C30 [12] and EORTC-CPIN20 [13], the Patient Health Questionnaire (PHQ-9) depression scale [14], a 0-10 pain intensity numerical scale, and a skin problems questionnaire [15]. At unprompted sessions, intervention group participants could choose to access any or all of the questionnaires. These procedures were fully described previously [5].

    A total of 752 participants were randomized in the parent trial: 374 intervention and 378 control. Of those, 523 (262 intervention, 261 control) had complete SDS-15 baseline and end-of-study scores. In the primary analysis of covariance (ANCOVA), the average SDS-15 score was reduced by an estimated 1.21 points (95% CI 0.23-2.20; P=.02) in the intervention group compared to the control group [5].

    Figure 2. Exemplar screen shots from the ESRA-C intervention: a) pushed Teaching Tip, b) Teaching Tips tab, c) View My Reports tab.
    View this figure

    Analytic Methods

    Total exposure was calculated as the sum of the number of clicks on pushed teaching tips, clicks on the non-pushed Teaching Tips tab, and the number of clicks on the View My Reports tab. Similarly, voluntary exposure was calculated as the sum of the number of clicks on the Teaching Tips tab and the number of clicks on the View My Reports tab during non-prompted sessions (Figure 3). Descriptive statistics were used to summarize the total and voluntary intervention exposures. The median of total exposure was calculated and used to indicate sufficient exposure to the intervention and defines intervention use.

    Factors associated with using the intervention, both overall and voluntarily, were explored with Fisher’s exact test. Factors of interest included: age (≥50 years, <50 years), work status, frequent computer user, gender, married/partnered, education (>high school, ≤high school), and service (medical oncology, radiation oncology, and stem cell transplant). Stage of disease was not considered for two reasons: (1) the highly associated relationship of stage and working status, a phenomenon documented in our prior work [16], and (2) the study sample contained participants with hematologic cancers in which the solid tumor staging system was inappropriate. The propensity score [17-19] was used to match a subset of the control group to the exposed intervention group and was defined as the probability of using the intervention given baseline participant characteristics. The following factors, shown in prior work [5] to contribute to outcomes, were used to compute the propensity score: baseline SDS-15 score categories (15-19, 20-23, 24-28, >28), service, gender, frequent computer use, married/coupled, education, minority status, age category, and working status. The outcome, change in continuous SDS-15 scores from pre-treatment to end of study, was compared by intervention use and selected by propensity score matching using an ANCOVA approach.

    Propensity score matching was performed with the complete data method in which no missing information could exist in the covariates used to compute the propensity score. A sensitivity analysis was conducted with the goal to balance the missingness within the two most common missing factors, work status and minority status. The propensity score matched sample was obtained using the R package “MatchIt” [20] and nearest neighbor matching. All analyses were performed in SAS version 9.3 and R version 2.15.2. All tests were two-sided and considered significant at the .05 level.

    Figure 3. Components of the ESRA-C intervention with calculation of total exposure.
    View this figure

    Results

    Intervention Group Analysis

    Total and voluntary exposure to the intervention components can only be calculated within the intervention group. The median total exposure was 2 (range 0-29). Intervention use was defined as at least two exposures. Of the 374 intervention participants, 233 (62.3%) used the intervention. A software error precluded exposure to pushed teaching tips for any intervention participants with an appointment date from June 30, 2010 to May 12, 2011. Of the 141 not receiving pushed teaching tips in the intervention, this error precluded three possible exposures in 48 (34.0%), two exposures in 21 (14.9%), and 1 exposure in 15 (10.6%) intervention group participants. There were 55 participants on the intervention arm that required a clinic/assisted point of access; their median total exposure was 2 (range 0-13) and 16 (29%) viewed the intervention voluntarily in the clinic. There were 319 participants on the intervention arm that indicated home/independent access; the median total exposure for these remote users was 3 (range 0-29) and 111 (34.8%) viewed the intervention voluntarily.

    There were no statistically significant differences in the proportion of participants using versus not using the intervention based on characteristics, with the exception of clinical service (P=.02, Table 1). A total of 70.4% (88/125) of radiation oncology participants, followed by 60.2% (127/211) of medical oncology participants, used the intervention, whereas only 47% (18/38) of transplant participants used the intervention. The median voluntary exposure to the intervention was 0 (range 0-16). Voluntary use was defined as at least one voluntary exposure. Of the 374 participants randomized to the intervention group, 127 (34.0%) voluntarily used the intervention. There were marginally significant differences in the proportion of participants voluntarily using the intervention by work status (P=.06) and education (P=.05). Participants that used the intervention were working and had more than a high school education. Additionally, there was a significant difference in the proportion of participants voluntarily using the intervention by service (P=.001). More radiation oncology participants 58/125 (46.4%) voluntarily used the intervention compared to medical oncology 61/211 (28.9%) and transplant 8/38 (21%) participants (Table 1).

    Table 1. Number and frequency of total exposures and voluntary exposures by selected participant characteristic.
    View this table

    Propensity Score Analysis

    Figure 4 outlines the sample selection from the parent trial for the propensity score analysis. Of the 262 participants randomized to the intervention group with a baseline and end-of-study SDS-15 score, 188 (71.8%) used the intervention. Complete demographic data were available for 167 (88.8%) of the 188 who used the intervention and 218 (83.5%) of the 261 control participants with baseline and end-of-study SDS-15 score. Using the propensity score and nearest neighbor matching, 167 control participants were selected from the possible 218 as the matched control group. Covariates were confirmed to be balanced (data not shown). Participants who used the intervention had lower symptom distress; mean change in the SDS-15 score was 1.07 (SD 6.55) in the matched control group (higher distress) and −0.57 (SD 5.68) in the intervention group (lower distress). In the ANCOVA analysis, SDS-15 score was reduced by an estimated 1.53 points (95% CI 0.32-2.75; P=.01) in the intervention group compared to the matched control group. The sensitivity analysis that balanced the missingness within the work status and minority status factors produced similar results as the complete data analysis (data not shown).

    Figure 4. Sample selection for the propensity score analysis. Note: EOS=end of study; SDS=Symptom Distress Scale; Demo=demographics.
    View this figure

    Discussion

    Principal Findings

    The results of this propensity analysis suggested that reduction of cancer symptom distress, the primary outcome of our randomized trial, was associated with use of the ESRA-C intervention components. More than half of participants in the intervention group were exposed to pushed Teaching Tips, accessed non-pushed tips, and viewed reports of SxQOL outcomes graphed over time. About a third voluntarily accessed the intervention in between clinic visits. We discovered that use of the intervention significantly reduced the estimated symptom distress score when compared to participants who did not use the intervention. The magnitude of the estimate (1.53) was larger than in the primary outcome analysis when we compared study groups (1.21) [5], indicating that actual use promotes the impact of the intervention. While this may seem intuitive, actual use of psycho-educational or self-administered interventions is not always known to investigators without objective monitoring capability. Our findings are consistent with the Quality Health Outcomes Model [10], illustrating the influence of patients’ characteristics.

    Comparisons With Prior Work

    Use of, and adherence to, Web-based health care interventions have primarily been evaluated in health promotion and chronic disease self-management settings [21]. Few studies have associated changes in physical and/or psychosocial symptom distress with use of an eHealth intervention by patients with cancer and none for patients undergoing active cancer therapies or for those in the United States. Borosund et al [22] analyzed usage of a Web-based, symptom distress self-management system by prostate and breast cancer survivors in Norway over one year post-enrollment. Similar to our analysis, the Norwegian group defined “use” as at least two intervention sessions, but did not analyze symptom outcomes based on use. Van der Berg and colleagues [7] analyzed usage statistics of a Web-based self-management intervention for breast cancer survivors in the Netherlands. Participants were prompted by email to access the intervention. The survivors did not monitor or report symptoms, but were encouraged to read and/or view new educational material provided every 4 weeks over 16 weeks. Active usage was defined by Ven den Berg's group as a log-in to each of the four modules, and was observed in 44% of the 70 women in the trial. Our unprompted voluntary use percentage was lower at 34% of 374 intervention group participants. The explanation for a lower voluntary use percentage may be related to no systematic prompting for voluntary use or the fact that, unlike the group of Dutch survivors, our participants already were receiving the intervention at three time points prior to clinic visits throughout active cancer therapy.

    Not surprisingly, ESRA-C was accessed remotely and voluntarily more frequently by those with higher education, who may have been likely to use personal computers or tablets on a regular basis. This is consistent with the finding, while of borderline statistical significance, that working individuals also used ESRA-C more often in between clinic visits. Working when beginning cancer therapy has been shown as a significant variable in two of our earlier analyses, predicting a lower rate of emergency department and unplanned admissions [23], yet predicting depression in participants receiving stem cell transplant [24]. How the fact that a patient is working full- or part-time influences outcomes is not well understood. In this case however, participants who were working when about to start cancer therapy may have had the type of job that enabled easy access to the Internet.

    Participants who enrolled in the trial as they were about to undergo radiation treatments also accessed ESRA-C voluntarily significantly more often than those enrolled when beginning medical cancer therapies or stem cell transplant. We are not aware of differences in usual care symptom support between modality services at the cancer centers; yet, if differences existed, patients may have turned to ESRA-C more often in radiation. Alternatively, these participants were reminded of ESRA-C almost every day of the week as they entered the radiation setting where each had consented to the trial.

    Limitations

    Our findings are limited by the fact that about a third of the intervention group participants never received pushed teaching tips in the assessments prior to on-study clinic visits. Thus, the effect of the intervention may have been different if all had the opportunity to see the tips. Our participant sample was less diverse with regard to race and ethnicity than the rates of cancer diagnoses in minority groups in the United States [25] and all were patients at comprehensive cancer centers, precluding generalization of our findings beyond these parameters.

    Implications for Future Research

    First, propensity analyses could be replicated in other eHealth trials as a method to investigate the relationship of usage to health outcomes. Although our participants’ raw exposure to the intervention was not high in an absolute sense, we were able to study the association of symptom distress with usage rather than report raw usage. Second, investigators could evaluate whether usage was related to various characteristics, and whether they are characteristics of the intervention, of the user, or of the condition addressed by the intervention. We provided some rationale for the mechanism that triggered usage by certain demographic groups, but this could have been a combination of aspects of the intervention itself in addition to participant demographics. Although some investigators found that educated, older, employed women were the most active users of Web-based, chronic disease [26] and health promotion [27] interventions, other studies have revealed conflicting results with regard to demographic variables [28-29].

    Implications for Clinical Practice

    Our findings have implications for the many patients treated at institutions that have deployed a patient portal as a component of an electronic medical record system. There may be patients at risk for failed symptom and distress screening and/or failed symptom support delivery if such systems are available only to those Web-savvy, educated patients who regularly use email.

    Finally, implications for both future research and practice using patient-centered, Web-based technologies include improved communication of study design and workflow between the design and technical implementation teams and more rigorous quality checks on intervention integrity. Communication of research goals may be facilitated via improved use of shared artifacts such as models of study data and workflows [30]. Software unit-testing and continuous integration goals should be oriented toward detailed research data deliverables [31,32]. Methods to improve quality checks include improved training of software quality assurance staff and making descriptive study data available to the investigators early in the data collection period for interim review.

    Conclusions

    The intended effects of a Web-based, self-care education, monitoring, and communication coaching intervention on cancer symptom distress were modified by intervention use frequency. The voluntary, remote use of ESRA-C was most frequent in working participants with higher education levels and those receiving radiation therapy.

    Acknowledgments

    All authors were funded by a grant (R01 NR008726) from the National Institute of Nursing Research (NINR), National Institutes of Health, for contributions to the study. No author was paid specifically for manuscript preparation. The NINR did not make any publication decisions. The authors are grateful to the patients, family members, and clinicians who gave time and effort to participate in this trial. The work of many talented research staff and students in collecting and coding data is greatly appreciated.

    Conflicts of Interest

    None declared.

    References

    1. Dwamena F, Holmes-Rovner M, Gaulden CM, Jorgenson S, Sadigh G, Sikorskii A, et al. Interventions for providers to promote a patient-centred approach in clinical consultations. Cochrane Database Syst Rev 2012;12:CD003267. [CrossRef] [Medline]
    2. Velikova G, Booth L, Smith AB, Brown PM, Lynch P, Brown JM, et al. 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):714-724 [FREE Full text] [CrossRef] [Medline]
    3. Ruland CM, Holte HH, Røislien J, Heaven C, Hamilton GA, Kristiansen J, et al. 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):403-410 [FREE Full text] [CrossRef] [Medline]
    4. Berry DL, Blumenstein BA, Halpenny B, Wolpin S, Fann JR, Austin-Seymour M, et al. Enhancing patient-provider communication with the electronic self-report assessment for cancer: a randomized trial. J Clin Oncol 2011 Mar 10;29(8):1029-1035 [FREE Full text] [CrossRef] [Medline]
    5. Berry DL, Hong F, Halpenny B, Partridge AH, Fann JR, Wolpin S, et al. 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 [FREE Full text] [CrossRef] [Medline]
    6. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res 2011;13(3):e52 [FREE Full text] [CrossRef] [Medline]
    7. van den Berg SW, Peters EJ, Kraaijeveld JF, Gielissen MFM, Prins JB. 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 [FREE Full text] [CrossRef] [Medline]
    8. Wolpin SE, Halpenny B, Whitman G, McReynolds J, Stewart M, Lober WB, et al. 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. [CrossRef] [Medline]
    9. Berry DL, Hong F, Halpenny B, Partridge A, Fox E, Fann JR, et al. 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 [FREE Full text] [CrossRef] [Medline]
    10. Mitchell PH, Ferketich S, Jennings BM. Quality health outcomes model. American Academy of Nursing Expert Panel on Quality Health Care. Image J Nurs Sch 1998;30(1):43-46. [Medline]
    11. Karras BT, Wolpin S, Lober WB, Bush N, Fann JR, Berry DL. Electronic Self-report Assessment--Cancer (ESRA-C): Working towards an integrated survey system. Stud Health Technol Inform 2006;122:514-518. [Medline]
    12. Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, 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):365-376. [Medline]
    13. Postma TJ, Aaronson NK, Heimans JJ, Muller MJ, Hildebrand JG, Delattre JY, 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):1135-1139. [CrossRef] [Medline]
    14. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [Medline]
    15. Ryan JL, Bole C, Hickok JT, Figueroa-Moseley C, Colman L, Khanna RC, et al. 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 [FREE Full text] [CrossRef] [Medline]
    16. 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).
    17. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res 2011 May;46(3):399-424 [FREE Full text] [CrossRef] [Medline]
    18. 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):516-524. [CrossRef]
    19. Jo B, Stuart EA, Mackinnon DP, Vinokur AD. The Use of Propensity Scores in Mediation Analysis. Multivariate Behav Res 2011 May;46(3):425-452 [FREE Full text] [CrossRef] [Medline]
    20. Ho D, Imai K, King G, Stuart E. MatchIT: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Software 2011;42(8):1-28 [FREE Full text]
    21. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JEWC. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res 2012;14(6):e152 [FREE Full text] [CrossRef] [Medline]
    22. Børøsund E, Cvancarova M, Ekstedt M, Moore SM, Ruland CM. 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 [FREE Full text] [CrossRef] [Medline]
    23. 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. In: J Clin Oncol. 2013 Presented at: American Society of Clinical Oncology Annual Meeting; 31 May-4 June, 2013; Chicago p. e20552.
    24. Artherholt SB, Hong F, Berry DL, Fann JR. Risk factors for depression in patients undergoing hematopoietic cell transplantation. Biol Blood Marrow Transplant 2014 Jul;20(7):946-950. [CrossRef] [Medline]
    25. American Cancer Society. Cancer Facts & Figures 2014.   URL: http:/​/www.​cancer.org/​acs/​groups/​content/​@epidemiologysurveilance/​documents/​document/​acspc-036845.​pdf [accessed 2014-12-29] [WebCite Cache]
    26. Wangberg SC, Bergmo TS, Johnsen JAK. Adherence in Internet-based interventions. Patient Prefer Adherence 2008;2:57-65 [FREE Full text] [Medline]
    27. Strecher VJ, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, et al. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial. J Med Internet Res 2008;10(5):e36 [FREE Full text] [CrossRef] [Medline]
    28. Couper MP, Alexander GL, Zhang N, Little RJA, Maddy N, Nowak MA, et al. Engagement and retention: measuring breadth and depth of participant use of an online intervention. J Med Internet Res 2010;12(4):e52 [FREE Full text] [CrossRef] [Medline]
    29. Glasgow RE, Christiansen SM, Kurz D, King DK, Woolley T, Faber AJ, et al. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res 2011;13(1):e9 [FREE Full text] [CrossRef] [Medline]
    30. Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, Guy RT, et al. Best practices for scientific computing. PLoS Biol 2014 Jan;12(1):e1001745 [FREE Full text] [CrossRef] [Medline]
    31. 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. [CrossRef]
    32. 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. [CrossRef]


    Abbreviations

    ANCOVA: analysis of covariance
    ESRA-C: Electronic Self Report Assessment for Cancer
    SDS-15: Symptom Distress Scale
    SxQOL: symptoms and quality of life


    Edited by G Eysenbach; submitted 30.12.14; peer-reviewed by E Børøsund, G Juarez; comments to author 14.03.15; revised version received 23.03.15; accepted 07.04.15; published 03.06.15

    ©Donna L Berry, Traci M Blonquist, Rupa A Patel, Barbara Halpenny, Justin McReynolds. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.06.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.