Healthcare reforms and policies in many countries have emphasized the need to support patients in taking a more active role in managing their illness.1,2 Realizing that patients often play the key role in providing care for themselves and their families,3 interactive health communication applications (IHCAs) that can educate, equip, and empower patients to better manage their illness are seen as important means to improve patient care, especially for people with chronic or long-term illnesses such as cancer. Patient-centered IHCAs have become high priority for nursing, medical informatics research, and health policy because they allow patients to access help and advice for their problems at the point of need, better understand and manage their illness and related symptoms, communicate with healthcare providers from home, become more active in their own care, and hopefully reduce the need for costly visits. Patient-centered care has been variously defined as the consideration of patients’ needs, perspectives, and experiences; opportunities for patients to participate in their own care; and enhancement of patient-clinician communication and partnership.4–6 Patient-centered IHCAs can be an important means for the redesign of health services from today’s disease- or institutional-centered models to patient-centered models of care. Nurses can play a crucial role in providing patient-centered care through IHCAs because they have a special focus on supporting patients in illness management and self-care.
A number of studies have demonstrated that IHCAs can successfully support patients on a wide range of health problems,7–10 including cancer,11 diabetes,12,13 asthma,14 arthritis,15 and heart disease16 and to assist in health behavior change such as improving diet and weight loss17 or smoking cessation.18 Interactive health communication applications have been defined as computer-based, usually Web-based, packages for patients that combine health information with at least one of the components of social support, decision support, or behavior change support.19 A Cochrane review of 24 randomized controlled trials (RCTs) that summarized the effects of different IHCAs for people with chronic diseases concluded that they have a significant effect on knowledge, perceived social support, health behaviors, clinical outcomes, and a greater likelihood than not to have a positive effect on self-efficacy.19 Interactive health communication applications that provide individually tailored support rather than standardized recommendations are more likely to be successful because information that is personally relevant to the individual is more likely to be used.20 So far, however, there has been little evidence of IHCAs’ effect on symptom distress. Because cancer patients experience their illness primarily through symptoms, reduced symptom distress is a crucial outcome for cancer patients and therefore a critical indicator of successful illness management support. Research on symptom management and the need for interventions that help reduce barriers to optimal symptom relief have been declared an understudied, high-priority area at the National Cancer Institute at the National Institutes of Health.21
Cancer patients may have particularly much to gain from IHCAs because they often experience multiple, and frequently severe, symptoms; impaired functional status; complex emotional, psychosocial, and existential issues; and substantial worries. Also, the adverse effects of treatment are often at their worst after the patient is discharged and sent home, leaving patients with considerable symptom distress without much assistance. Furthermore, up to one-third of cancer patients develop depression and/or an anxiety disorder that requires treatment.22–24 There is compelling evidence that cancer patients within traditional consultations often do not receive sufficient help with their symptoms and problems,25–27 calling for better and more patient-centered models of care delivery. Interactive health communication applications that can demonstrate to reduce symptom distress and can provide cancer patients with assistance when and where they need it will make an important contribution to the care of cancer patients.
WebChoice is an Internet-based IHCA based on patient-centered principles and designed to support cancer patients in self-managing their illness and to enhance patient-centered care. WebChoice, described in more detail below, is composed of a set of components to address patients’ needs, perspectives, and experiences; to enable patients to self-manage their illness; and to facilitate patient-provider communication and partnership. WebChoice allows patients to monitor their symptoms and health problems, currently and over time; provides them with individually tailored, just-in-time information and support to manage their symptoms and illness-related problems; contains self-management options that adapt instantaneously to patients’ self-reported problems; and offers personal mail communication with expert nurses in cancer care who answer questions and address concerns and an e-forum for group discussion with other cancer patients.28
The primary purpose of WebChoice is to help cancer patients reduce their symptom distress, improve health-related quality of life (HRQoL) and emotional well-being, and enhance self-efficacy and social support. Consistent with the literature showing that self-monitoring, knowledge, and individual tailoring promote health behavior change and self-efficacy,29–31 the self-monitoring and the information tools in WebChoice include knowledge about options for symptom relief, primarily developed to help patients self-manage and reduce symptoms. The e-communication tool that allows patients to stay connected with cancer nurses who can quickly answer questions and concerns, as well as provide emotional support, was designed to enhance emotional well-being and self-efficacy and reduce depression.32 Patients’ concerns have been linked to high levels of emotional distress.24,32 We recently reported, in this journal, on the analysis of the content of cancer patients’ messages sent to clinical nurse specialists through WebChoice. This study demonstrated that patients had many serious questions and concerns that created considerable uncertainty and anxiety that nurses with the help of WebChoice could address immediately.33,34 Lastly, the purpose of the fellowship with other patients that is created through the e-forum is to enhance social support that in turn may be associated with improved self-efficacy and mental health, including less depression.19,35 We therefore hypothesized that the combined components in WebChoice may reduce symptom distress and depression and increase self-efficacy, HRQoL, and social support.
Thus, the purpose of this study was to evaluate the effects of WebChoice in a 2-group prospective, repeated-measures RCT with breast and prostate cancer patients. We hypothesized that breast and prostate cancer patients who received the WebChoice intervention would have better outcomes over the 1-year study period on the primary outcomes of symptom distress compared with a control group that received links to publicly available, cancer-related Web sites. Furthermore, we hypothesized that WebChoice would also have a positive effect on the secondary outcomes of depression, self-efficacy, HRQoL, and social support.
WebChoice (www.communicaretools.org) was developed with extensive end-user participation and is described in more detail elsewhere.28 The content of WebChoice is based on a comprehensive review of the breast and prostate cancer–related scientific literature, and the information content is regularly updated. WebChoice contains the following:
- An assessment component that allows patients to monitor and report their symptoms, problems, and priorities for support along physical, functional, and psychosocial dimensions, currently and over time. Patients can use this information in many useful ways, for example, to monitor improvement/deterioration of their condition to know when to alert healthcare providers; to prepare for a hospital/doctor consultation to improve patient-provider communication and to prepare for discussions about treatment/care; or to obtain immediate access to tailored self-management support as described below.
- Tailored symptom self-management support. Patients’ self-reported symptoms trigger the display of the appropriate subset of self-management activities from which patients can choose. Each message contains an explanation of what the activity is, how to perform it, potential risks, adverse effects, contraindications, when to contact a physician, level of evidence, references to the source of information from where the evidence was obtained, and links to other relevant, reliable Web sites that contain related information. This information can be printed out for further reading or can be used to create an individualized self-management plan that includes selected activities tailored to the patient’s individual symptoms and problems.
- An information section where patients have access to other reliable, relevant Web resources, such as information about specific tests, treatments and potential adverse effects, lifestyle suggestions, information about patients’ rights, and links to support groups. All information complies with the HON Code of Conduct for medical and health Web sites that promote the highest principles for privacy, security, credibility, and reliability of information on Internet health sites.36
- A communication section where patients can share their experiences with other patients and obtain professional support. It includes (a) an unrestricted support forum for group discussion, allowing users to post messages anonymously, and (b) a question-and-answer area where patients, in private, can ask questions to expert nurses in cancer care. In our study, the expert nurses accessed the communication section daily on weekdays and participated in the group discussions when appropriate. The communication component gives patients an opportunity to ask difficult questions anonymously and to learn how others deal with similar problems—in their own homes and with confidentiality.
- In addition, patients have access to a diary, where they can keep personal notes.
All pages in WebChoice are based on language-independent templates that are expanded at run time by separating the content from the system into separate files. The system is generic; content adjustments, for example, to fit it to patients with different diagnoses, can be dynamically performed without making any changes to the system. Strong security measures are implemented to protect patients’ submitted information. Patients are authenticated using a smart card–based public key (PKI) solution. All data are submitted to a secure server through an encrypted connection. All procedures comply with the Norwegian Personal Data Act (equivalent of Health Insurance Portability and Accountability Act in the United States). More information about WebChoice is available at www.communicaretools.org.
Inclusion criteria were as follows: being diagnosed with, and undergoing treatment for, breast cancer (surgery plus additional treatment of either radiation, chemotherapy, hormone therapy, or a combination of those), or being diagnosed with, and undergoing treatment for, prostate cancer; older than 18 years; able to speak Norwegian; Internet access at home; and no radiation on the brain as this may influence a patient’s ability to reliably report symptoms and to fill out questionnaires.
Sample Size Calculation
The estimated sample size was based on an expected effect size of 0.2 in the difference of the mean changes in the primary outcome of symptom distress between the intervention and control group from baseline through the 1-year study period, divided by its SD. Because no previous data were available from which we could calculate an expected effect size, we chose a small effect size because we expected large SDs due to large variations in stage of disease among study participants. Based on a repeated-measures analysis of variance via a mixed-model approach, we needed to enroll 113 patients per group to achieve 85% power at a 2-sided 5% significance level with 5 repeated measurements.37,38 To account for an up to 30% dropout rate, estimated from a similar 1-year follow up study,39 we needed to enroll 161 patients per group.
The study received approval from the Regional Ethical Review Committee (institutional review board) of the Health Region South and the Data Security Inspectorate in Norway. Written informed consent was obtained from all study participants. After pilot testing the intervention and procedures, patients were recruited into this study from May 2006 through July 2007 and followed up for 1 year.
Invitations to participate in this study were disseminated through advertisements in national newspapers, in weekly magazines, on the Norwegian Cancer Society’s Web site, as well as through information pamphlets mailed to patients from the Norwegian National Cancer Registry. The advertisements contained information about the purpose of the study, what it involved, study duration, inclusion criteria, the name of the principal investigator, the responsible institution, and a contact number to call. Those who were interested in participating or wanted more information were invited to call. When calling, they were screened for inclusion criteria by a trained research assistant using a screening script. Callers who did not meet the inclusion criteria were thanked for their time and interest. Those who met the inclusion criteria were asked for their mailing address and sent a letter containing information about the study on a consent form and baseline questionnaires, along with instructions for completion and 2 self-addressed, stamped return envelopes. Participants were asked to return the consent form and baseline questionnaires in 2 separate envelopes.
Upon return of participants’ signed consent forms and completed baseline questionnaires, they were randomized into either the experimental or control group, using a computerized minimization algorithm to balance covariates.40 This algorithm simultaneously capitalizes on the benefits of random assignment and, at the same time, equalizes the proportion of cases on covariates, so that differences between treatment groups on covariates do not emerge based on the “randomness” of random assignment. In this study, participants were stratified on cancer diagnosis and stage of disease (primary, recurrence, metastases).
Participants who were included were informed about their group assignment by receiving a letter that also included information about follow-up study procedures. All participants received questionnaires by mail at 3, 6, 9, and 12 months of the study. Participants were not paid for study participation, but received at the 6-month data collection point a scratch-off lottery ticket worth Norwegian Kroner 25 (approximately US $4) as a token of appreciation for their continued participation.
EXPERIMENTAL GROUP PROTOCOL
Because of the nature of the study, intervention participants were not blinded. Participants who were randomly assigned to the experimental group received a letter informing them of their group assignment, a user manual with instructions to use WebChoice, and a contact address and phone number for technical support. Participants were told that they could use WebChoice and any of its components as they liked during their 1-year study participation and that the use of the system was entirely voluntary. Participants were instructed not to identify themselves by their real names when using the communication components in WebChoice.
CONTROL GROUP PROTOCOL
In addition to the letter informing them of their group assignment, participants who were assigned to the control group received an information sheet with suggestions for publicly available, cancer-relevant Internet sites that could be useful to them.
The primary study outcome was symptom distress. Secondary outcomes were depression, self-efficacy, HRQoL, and social support. Symptom distress was measured 5 times: at baseline and at 3, 6, 9, and 12 months. Secondary outcomes were measured only at baseline and at 3, 6, and 12 months because of institutional review board’s concerns about response burden.
Because of the subjective nature of the experience of symptoms, self-reports are today considered the criterion standard for symptom assessment. Consistent with this approach, symptom distress was measured using the Memorial Symptom Assessment Scale–Short Form (MSAS-SF).41 A physical symptom subscale, a psychological symptom subscale, and a global distress index (GDI) that is considered to be a measure of overall symptom distress can be derived from the MSAS-SF.42 Respondents are asked to indicate how bothersome each of the symptoms are, on a 5-point rating scale from “not at all” (0) to “very much” (4). Higher scores indicate greater symptom distress. Cronbach α as a measure of reliability for our sample at baseline was .91.
Depression was measured using the Center for Epidemiological Studies–Depression Scale,43 a 20-item instrument where respondents answer brief questions by responding to 5-point rating scale statements ranging from “rarely or none of the time” to “most or all of the time.” Higher scores indicate greater depression. Cronbach α for our sample at baseline was .74.
Self-efficacy was measured using the Cancer Behavior Inventory version 2.0,44 a 33-item scale measuring self-efficacy for coping with cancer-related stress that comprises 7 dimensions: maintenance of activity and independence, seeking and understanding medical information, stress management, coping with treatment-related adverse effects, accepting cancer and maintaining a positive attitude, affective regulation, and seeking support. Respondents answer to 9-point rating scale questions from “not at all confident” to “totally confident.” Higher scores indicate greater self-efficacy. Cronbach α for our sample at baseline was .96.
HEALTH-RELATED QUALITY OF LIFE
Health-related quality of life was measured using the 15D HRQoL instrument, a generic, comprehensive, 15-dimensional, self-administered instrument measuring HRQoL among adults. It combines the advantages of a profile and a preference-based, single-index measure. Respondents respond to 5 ordinal levels on each dimension, by which more or less of the attribute is distinguished. The respondent chooses from each dimension the level that best describes his/her present health status. Higher scores indicate greater HRQoL.45 Cronbach α for our sample at baseline was .70.
Social support was measured using the Medical Outcomes Study Social Support Survey, a 20-item instrument with 2 subscales addressing instrumental and emotional support.46 Respondents respond on a 5-point rating scale ranging from “none of the time” to “all of the time.” Higher scores indicate more social support. Cronbach α for our sample at baseline was .81.
Analyses were performed using intention to treat, regardless of whether intervention group participants had logged on to WebChoice or not. A linear mixed-effects model methodology,37,38,47 which accounts for both the correlation between the repeated measurements across times within each subject and the variability between the subjects, was applied to compare groups on slopes over time on primary and secondary outcomes. Assuming that missing data were randomly distributed and because sufficient data were available, all included participants were kept in the final analyses. Because “time since diagnosis” showed close-to-significant group differences (P = .07) at baseline (Table 1), and there were large variations among patients, we included this variable as a covariate in the analyses and controlled it statistically. We performed all statistical analyses using SAS release 9.1.3 (SAS Institute, Inc, Cary, North Carolina).
Four hundred forty-five persons who called met the inclusion criteria and expressed continued interest to participate and therefore received baseline questionnaires and consent forms. Three hundred twenty-five of those (73%) returned completed consent forms and questionnaires and were enrolled into the study; 162 (96 breast cancer and 66 prostate cancer patients) were randomized into the experimental group and 163 participants (93 breast cancer and 70 prostate cancer patients) into the control group (Figure 1).
One hundred twenty-five of the study participants assigned to the experimental group (77%) logged onto WebChoice at least once. Twenty-three percent never logged on. The only difference between users and nonusers on any demographic or other variables was that users had slightly more previous computer experience, although this difference was only borderline significant (P = .07). Those of the 103 study participants (64%) who used WebChoice more than once used it on average 60 times over the 1-year study period; however, there were large individual variations (range, 2–892). The e-forum and e-communication with expert nurses were most frequently used. Sixty-two patients wrote personal messages to the nurse (total, 385; range, 1–49; average, 6.2), and 50 patients posted messages to the forum (total, 506; range, 1–58; average, 10.15). However, patients visited the forum and messaging service many times more to read information without posting messages. On average, nurses spent 15 minutes to answer a message. More details on usage patterns are provided elsewhere.48
To ensure that the 2 study groups did not differ at baseline, we compared baseline characteristics of age, time since diagnosis, socioeconomic status, treatment, gender, stage of disease, and comorbidity. As seen in Table 1, there were no statisticall significant group differences at baseline on these variables. However, because time since diagnosis showed close-to-significant group differences (P = .07), and there were large variations among patients, we included this variable as a covariate in the analyses. Baseline data are displayed in Table 1.
Primary Outcome: Symptom Distress
To test the hypothesis that patients in the experimental group would have significantly less symptom distress over time, we compared within- and between-group differences on the MSAS-SF total score and subscales in trends over time and controlled for time since diagnosis. Within- and between-group variances and slopes were compared between the 2 groups using the linear mixed-effects model.
Results are displayed in Table 2. Figure 2 depicts the findings graphically, displaying the slopes for changes over time in symptom distress scores from the baseline data collection point over the 12-month study period for the intervention group compared with the control group.
Between-group differences were statistically significant for the GDI only (slope estimate, −0.052; 95% confidence interval [CI], −0.101 to −0.004; t = 4.42; P = .037). There were no significant within- or between-group differences on the other MSAS-SF subscales or the total score. Therefore, the hypothesis that WebChoice would reduce the primary outcome of symptom distress was only partially supported. However, Figure 2 shows a downward trend, although not significant, toward less symptom distress in the WebChoice group on all subscales and the MSAS-SF total score, whereas the control group showed a trend in the opposite direction toward increased symptom distress.
Secondary Outcomes: Depression, Self-efficacy, HRQoL, and Social Support
To test the hypothesis that the experimental group would have significantly better scores over time on depression, self-efficacy, HRQoL, and social support, we again compared within- and between-group variances and slopes on the Center for Epidemiological Studies–Depression Scale, Cancer Behavior Inventory, 15D, and Medical Outcomes Study Social Support Survey, using the linear mixed-effects model.
As seen in Table 2, there were no significant between-group differences on any secondary outcomes. So the hypothesis that WebChoice would improve secondary outcomes was not supported. However, participants in the experimental group showed significant within-group improvements in depression (slope estimate, −0.41; 95% CI, −0.71 to −0.11; t = −2.71; P = .007) during the study period that were not observed in the control group. Furthermore, the control group worsened their within-group self-efficacy (slope estimate, −3.77; 95% CI, −6.38 to −1.15; t = −2.82; P = .005) and HRQoL scores significantly (slope estimate, −0.01; 95% CI, −0.01 to −0.00; t = −2.77; P = .006), but the experimental group did not. There were no within- or between-group differences in social support (Figure 3).
Additional Exploratory Analyses
Because there were large variations in participants’ time since diagnosis, a variable that could potentially influence symptoms as well as patients’ need for support, we wanted to explore if the data could tell us something about whether WebChoice may work differently for patients at different stages of their illness trajectory. We therefore analyzed primary and secondary outcomes separately for patients who at the time of enrollment had been diagnosed within the last 12 months and for patients who had been diagnosed for more than 2 years. Because the sample size for the 2 groups did not leave us with enough power, our additional analyses are purely exploratory.
There was a statistically significant difference between patients newly diagnosed (<12 months) and those who were diagnosed for more than 24 months in terms of cancer recurrence and metastases (P < .001) but not in frequency of other illnesses (P = .24). However, when we performed the separate subgroup analyses on group differences for patients newly diagnosed compared with those diagnosed for more than 2 years, these differences were no longer statistically significant, meaning that the group differences described below were not confounded by stage of disease.
When patients were diagnosed within 1 year (n = 174), there were significant within-group reductions in MSAS-SF total scores (slope estimate, −0.11; 95% CI, −0.20 to −0.01; t = −2.27; P = .024), the global symptom distress (slope estimate, −0.13; 95% CI, −0.22 to −0.05; t = −3.07; P = .003), the physical symptoms subscale (slope estimate, −0.12; 95% CI, −0.22 to −0.03; t = −2.51; P = .013), and close to significant reductions in the psychological symptoms subscale (slope estimate, −0.10; 95% CI, −0.20 to 0.00; t = −1.9; P = .06) in the experimental group, but not in the control group. Also, the WebChoice group had significant within-group improvements in self-efficacy (slope estimate, 5.97; t = 2.36; P = .02), not observed in the control group.
For patients who were diagnosed for more than 2 years (n = 74), there were no significant within- or between-group differences on any subscales measuring symptoms or global symptom distress. However, the control group significantly worsened their self-efficacy scores over the study period (slope estimate, −3.46; 95% CI, −6.84 to −0.08; t = −2.01; P = .04), and there was a downward trend in HRQoL (slope estimate, −0.01; 95% CI, −0.01 to 0.00; t = −1.76; P = .08) and depression (slope estimate, 0.37; 95% CI, −0.06 to 0.79; t = 1.7; P = .09), but not in the WebChoice group.
Although group differences were statistically significant only for the global symptom distress subscale (GDI) on the MSAS-SF, all trends point in the same direction: better scores in the intervention group compared with the control group. Notably, the GDI is a composite measure of the most prevalent physical and psychological symptoms experienced by cancer patients. Because patients experience their illness primarily through symptoms, reducing distress of the most prevalent symptoms is an important and promising finding. In addition, it is noteworthy that the WebChoice group, but not the control group, showed a significant within-group reduction in depression over the study period, a debilitating symptom often found in cancer patients.22–24
The fact that differences in outcome variables, with the exception of the GDI, did not reach significance requires caution when making conclusions about WebChoice’s effectiveness. Nevertheless, the positive trends in the intervention group not found in the control group hold promise that WebChoice can be helpful to cancer patients in managing their illness. This is also supported by the analysis of patients’ e-mail communications with cancer nurses reported earlier in this journal where WebChoice provided patients with space to raise questions and concerns related to symptom experiences and fear of relapse and uncertainty and provided them with answers to otherwise unmet questions.34
Several factors may have influenced the lack of statistically significant group differences on secondary outcome variables. The large variability in patients and time since diagnosis could have made a difference in secondary outcomes, which is an important opportunity for future research. Furthermore, our study used an intention-to-treat analysis. Twenty-three percent of the participants who were randomly assigned to the experimental group never logged onto WebChoice, and only 64% logged on more than once (our definition of a WebChoice user), a finding that is consistent with other studies on the use of Internet interventions.7,19,48 These participants could therefore not reap any benefits from the intervention, which may have diluted the results. Although it would have been interesting to do additional subgroup analyses on only those participants who did use WebChoice actively, with the limited sample size we did not have enough power to do so. Also, although there were no significant group differences between study groups at baseline, the sample was very heterogeneous in terms of age, time since diagnosis, and type and stage of treatment, and participants came from urban as well as rural areas all over Norway where there are considerable practice variations. This variability could also have influenced study results. Although studying nursing informatics interventions under natural conditions is recommended,49,50 real-world implementation studies typically require larger sample sizes than strictly controlled RCTs to allow monitoring and controlling for potential confounders and heterogeneity.51 This study should therefore be repeated with a larger sample size and a more intensive data collection protocol that allows statistical control of contextual variables.
Although the finding that only about two-thirds of participants actively used WebChoice is consistent with other studies on the use of IHCAs, it raises the question about what may motivate patients whether to use interventions such as WebChoice. Very few studies so far have investigated patients’ reasons for using or not using IHCAs, and previous studies on user experiences have primarily included active users only. Therefore, our team has recently conducted interviews with high users, medium users, and nonusers of WebChoice to gain more insights into patients’ reasons and motivations for use or nonuse. Although the results of these interviews are not published yet, preliminary findings suggest that different patients perceived the usefulness of WebChoice quite differently. Whereas some perceived it very helpful (typically high users), others (the nonusers) expressed as one reason for nonuse that they wished to get on with their lives, not wanting to assume a “sick” role, or being reminded of having cancer when using WebChoice. This suggests that there is no “one size fits all” and that patients have different coping styles and therefore different needs for support. More research is needed to better understand how to tailor support interventions that fit patients’ personality types, coping styles, and individual preferences for support.
Our analyses of whether time since diagnosis would make a difference on patient outcomes lacked sufficient statistical power and must therefore be considered purely exploratory. Yet the analyses point toward new interesting research questions: When diagnosed with cancer within 1 year, WebChoice patients showed significant within-group improvements on the MSAS total scores and all its subscales as well as on self-efficacy, but not the control group. On the other hand, when patients had been diagnosed with cancer for more than 2 years, the control group showed a significant decrease in self-efficacy not observed in the WebChoice group. This suggests that interventions such as WebChoice may work differently for patients at different stages of the disease and treatment. If we had allowed a less strict significance level of .1—that could have been justified because of the exploratory nature of these analyses—we would have found that the control group, when diagnosed for more than 2 years, significantly worsened their self-efficacy, depression, and HRQoL scores, but not the WebChoice group. The findings that there are increased benefits to survivors within 1 year of treatment call for a potentially new model of survivorship intervention for those within the first year and those beyond the 2 years of treatment.
We have not found any earlier studies that have compared the effectiveness of Internet interventions to support cancer patients at different stages of illness and treatment. Research studies designed to better understand for whom, why, under which circumstances, and how interventions such as WebChoice work could provide important insights into how to improve and specifically tailor such interventions to meet the needs of individual patients in different phases of their illness. The questions raised by our additional analyses support that this line of research is important to pursue.
Because WebChoice consists of several components, more research is also needed to determine which particular components of the application are more or less effective. For example, we do not know whether the information or communication components, such as being able to ask questions to cancer nurses, or a combination of several components is the most helpful. Interactive health communication applications such as WebChoice represent new forms of interaction and information sharing between patients and care providers by which patients can seamlessly access communication and information from wherever and whenever they need it. There is still not much known about the best ways to deliver IHCAs, the role nurses could play in their delivery, and their impact on clinician-patient relationships and communication.
More research is also needed to evaluate the effects of interventions such as WebChoice on healthcare utilization and costs. To address this important issue as well as add to knowledge on IHCA interventions, our team is currently conducting a 3-group RCT with more statistical power and control of contextual variables to test the effects of the next-generation WebChoice intervention in breast cancer patients on healthcare utilization and costs. This study also will allow us to compare the effects of different components of WebChoice.
The current study adds to the research on IHCAs and their effects on behavioral and health outcomes.7–19 So far, however, there have been very few IHCAs to support patient-centered symptom management, and there is little evidence that they can reduce symptom distress. Only 1 previous study was found with cancer patients that investigated the effects of an Internet support system for young breast cancer patients in an RCT and found significant differences in favor of the intervention group in social support and information competence, but not on symptoms.11 Therefore, our study adds to an understudied area of cancer research.21
This study has several limitations. The sample size was too small to account for the larger-than-expected variation in the data, which may explain the nonsignificant results on the majority of outcomes. We did not adjust for multiple testing in order to not lose additional power and increase the risk of type II error.
Because participants were at different illness stages, and we had no access to their medical records, we were not able to standardize the administration of outcome measures, particularly the MSAS, in relation to treatment. Some patients may have just completed an intensive chemotherapy course with significant distress of many symptoms; some may have received hormonal treatment with a different set of issues, whereas others may have completed their treatment weeks or months ago without experiencing physical symptoms. This may have contributed to some of the nonsignificant results.
Also, participants consisted of a self-selected sample, which limits generalizability. One might suspect that participants who took the initiative to enroll in a study by calling themselves may be more active and have higher levels of commitment and self-efficacy than cancer patients in general. Participants in our study had higher income and education levels than average, suggesting that they were not representative of the general population. Seeking health information on the Internet has been found to be associated with education, gender, and age.52 Although more than 80% of households in Norway were reported to have Internet access at the time of the study, being required to have Internet access at home may have favored those with higher socioeconomic status.
The strengths of this study are its real-world setting, intention-to-treat analysis, its relevance for cancer care, and its focus on symptom distress, which is highly important to patients’ well-being. This study suggests that WebChoice to some extent can meet patients’ need for advice and information and thus be an important healthcare supplement. As nurses in their professional role have a special focus on illness management and self-care, nurses might be particularly qualified to help patients through Internet support with problems related to the impact of cancer on their daily life. As questions and worries often are the reason why patients schedule a doctor’s appointment that may take weeks to arrive, a nurse-supported support system such as WebChoice may reduce patients’ needs for office visits and the time needed for rehabilitation and recovery. As cost concerns and shortages of health professionals continue, this could become a viable healthcare supplement and means to improve care quality for many patients, which is an important area for nursing research. Thus, nurses should become actively engaged in the opportunity to provide online support to patients that can effectively help them in managing their illness.
In summary, although the study hypotheses were only partially supported, this study shows that WebChoice is a promising tool to help cancer patients better manage their illness and reduce symptom distress. Despite that the secondary outcome measures did not show significant differences between study groups with respect to depression, self-efficacy, HRQoL, and social support, the benefits of WebChoice were still quite respectable. If these findings can be further supported with additional research, WebChoice may be the type of patient-centered support system highly needed to educate, equip, and empower patients to better manage their illness and reduce needs for costly specialist care.1,2,53
1. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academies Press; 2001.
2. Epstein R, Fiscella K, Lesser CS, Stange K. Why the nation needs a policy push on patient-centered health care. Health Aff. 2010; 29: 1489–1495.
3. Lorig KR, Sobel DS, Ritter PL, Laurent D, Hobbs M. Effect of a self-management program on patients with chronic disease. Eff Clin Pract. 2001; 4: 256–262.
4. Perocchia RS, Hodorowski JK, Williams LA, Kornfeld J, Davis NL, Monroe M, . Patient-centered communication in cancer care: the role of the NCI’s Cancer Information Service. J Cancer Educ. 2011; 26 (1): 36–43.
5. 5. Epstein RM Jr. Patient-Centered Communication in Cancer Care: Promoting Healing and Reducing Suffering
. Bethesda, MD: National Cancer Institute; 2007. NIH Publication 07-6225.
6. Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff. 2001; 20: 64.
7. Gustafson DH, Hawkins RP, Boberg EW, et al.. CHESS: 10 years of research and development in consumer health informatics for broad populations, including the underserved. Int J Med Inform. 2002; 65: 169–177.
8. 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; 12: e23.
9. Griffiths KM, Farrer L, Christensen H. The efficacy of Internet interventions for depression and anxiety disorders: a review of randomised controlled trials. Med J Aust. 2010; 192: 4–11.
10. Wantland D, 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; 6: e40.
11. Gustafson D, Hawkins R, Pingree S, et al.. Effect of computer support on younger women with breast cancer. J Gen Intern Med. 2001; 16: 435–445.
12. Shea S, Weinstock RS, Teresi JA, et al.. A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5 year results of the IDEATel study. J Am Med Inform Assoc. 2009; 16: 446–456.
13. Lorig K, Ritter PL, Laurent DD, et al.. Online diabetes self-management program: a randomized study. Diabetes Care. 2010; 33: 1275–1281.
14. van der Meer V, Bakker MJ, van den Hout WB, et al.. Internet-based self-management plus education compared with usual care in asthma: a randomized trial. Ann Intern Med. 2009; 151: 110–120.
15. Stinson JN, McGrath PJ, Hodnett ED, et al.. An Internet-based self-management program with telephone support for adolescents with arthritis: a pilot randomized controlled trial. J Rheumatol. 2010; 37: 1944–1952.
16. Moore SM, Primm T. Designing and testing telehealth interventions to improve outcomes for cardiovascular patients. J Cardiovasc Nurs. 2007; 22: 43–50.
17. Moore TJ, Alsabeeh N, Apovian CM, et al.. 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; 10: e52.
18. 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. 2010; 169: 929–937.
19. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev. 2005; CD004274.
20. Kreuter MW, Wray RJ. Tailored and targeted health communication: strategies for enhancing information relevance. Am J Health Behav. 2003; 27 (suppl 3): S227–S232.
21. 21. Department of Health and Human Services. Centers of Excellence in Symptom Management
Research or Centers of Excellence in Health Promotion/Disease Prevention (P30), RFA-NR 09-002. 2008 [cited September 6, 2010]. http://grants.nih.gov/grants/guide/rfa-files/rfa-nr-09-002.html#PartI
; Accessed March 4, 2012.
22. Derogatis LR, Morrow GR, Fetting J, et al.. The prevalence of psychiatric disorders among cancer patients. JAMA. 1983; 249: 751–757.
23. Parle M, Jones B, Maguire P. Maladaptive coping and affective disorders among cancer patients. Psychol Med. 1996; 26: 735–744.
24. Harrison J, Maguire P. Predictors of psychiatric morbidity in cancer patients. Br J Psychiatry. 1994; 165: 593–598.
25. Chang VT, Hwang SS, Feuerman M, Kasimis BS. Symptom and quality of life survey of medical oncology patients at a Veterans Affairs medical center: a role for symptom assessment. Cancer. 2000; 88: 1175–1183.
26. Newell S, Sanson-Fisher RW, Girgis A, Bonaventura A. How well do medical oncologists’ perceptions reflect their patients’ reported physical and psychosocial problems? Data from a survey of five oncologists. Cancer. 1998; 83: 1640–1651.
27. Butow PN, Brown RF, Cogar S, Tattersall MH, Dunn SM. Oncologists’ reactions to cancer patients’ verbal cues. Psychooncology. 2002; 11: 47–58.
28. Ruland CM, Jeneson A, Andersen T, et al.. Designing tailored Internet support to assist cancer patients in illness management. AMIA Annu Symp Proc. 2007; 635–639.
29. Poolsup N, Suksomboon N, Rattanasookchit S. Meta-analysis of the benefits of self-monitoring of blood glucose on glycemic control in type 2 diabetes patients: an update. Diabetes Technol Ther. 2009; 11: 775–784.
30. van Stralen MM, de VH, Mudde AN, Bolman C, Lechner L. Efficacy of two tailored interventions promoting physical activity in older adults. Am J Prev Med. 2009; 37: 405–417.
31. Kukafka R, Lussier YA, Eng P, Patel VL, Cimino JJ. Web-based tailoring and its effect on self-efficacy: results from the MI-HEART randomized controlled trial. Proc AMIA Symp. 2002: 410–414.
32. Heaven CM, Maguire P. The relationship between patients’ concerns and psychological distress in a hospice setting. Psychooncology. 1998; 7: 502–507.
33. Andersen T, Ruland CM. Cancer patients’ questions and concerns expressed in an online nurse-delivered mail service: preliminary results. Stud Health Tech Inform. 2009; 146: 149–153.
34. Hjelmeland Grimsbø G, 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; 34 (2): 107–116.
35. Griffiths KM, Calear AL, Banfield M. Systematic review on Internet support groups (ISGs) and depression (1): do ISGs reduce depressive symptoms? J Med Internet Res. 2009; 11: (3) e40.
36. 36. Health On the Net Foundation. The HON Code of Conduct for medical and health Web sites (HONcode). 2010 [cited September 7, 2010]. http://www.hon.ch/HONcode/Conduct.html
. Accessed March 4, 2012.
37. Littell RC, Milliken GA, Stroup WW, Wolfinger RD. SAS System for Mixed Models. Cary, NC: SAS Institute Inc; 1996.
38. Diggle PJ, Liang KY, Zeger SL. Analysis of Longitudinal Data. New York: Oxford University Press; 1994.
39. Ruland CM, Holte HH, Røislien 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.
40. Zeller R, Good M, Anderson GC, Zeller DL. Strengthening experimental design by balancing potentially confounding variables across treatment groups. Nurs Res. 1997; 46: 345–349.
41. Portenoy R, Thaler H, Kornblith A, et al.. The Memorial Symptom Assessment Scale: an instrument for evaluation of symptom prevalence, characteristics, and distress. Eur J Cancer. 1994; 30A: 1326–1336.
42. Chang V, Hwang S, Feuerman M, Kasmis B, Thaler H. The Memorial Symptom Assessment Scale Short Form (MSAS-SF) validity and reliability. Cancer. 2000; 89: 1162–1171.
43. Radloff LS, Teri L. Use of the Center for Epidemiological Studies–Depression Scale with older adults. Clin Gerontol. 1986; 5: 119–136.
44. Merluzzi T, Nairn R, Hegde K, Sanchez M, Dunn L. Self-efficacy for coping with cancer: revision of the Cancer Behavior Inventory (version 2.0). Psychooncology. 2001; 10: 206–217.
45. Sintonen H. The 15D instrument of health-related quality of life: properties and applications. Ann Med. 2001; 33: 328–336.
46. Sherbourne CD, Stewart AL. The MOS Social Support survey. Soc Sci Med. 1991; 32: 705–714.
47. Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med. 1997; 16: 2349–2380.
48. Ruland CM, Andersen T, Krahn A. Cancer patients’ patterns of use of an Internet system to support them in illness management. AMIA Annu Symp Proc. 2009: 247.
49. Woolf SH. The meaning of translational research and why it matters. JAMA. 2008; 299: 211–213.
50. 50. National Institutes of Health. Re-engineering the Clinical Research Enterprise: Translational Research. 2009 [cited September 8, 2010]. http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp
. Accessed March 4, 2012.
51. Horn SD, Gassaway J. Practice-based evidence study design for comparative effectiveness research. Med Care. 2007; 45: 50–57.
52. Tate DF, Finkelstein EA, Khavjou O, Gustafson A. Cost effectiveness of Internet interventions: review and recommendations. Ann Behav Med. 2009; 38: 40–45.
53. Wagner TH, Jimison HB. Computerized health information and the demand for medical care. Value Health. 2003; 6: 29–39.