Tuesday, March 4, 2014

Online Disease Specific Support: The Effects on Rare Cancer Types

Opening Statement

Since the beginning of the internet age, researchers have been interested in the psychosocial effects of online experiences. As a result of the growing evidence of the complex, but poorly understood, mind body relationship, the psychophysiological effects of online support as it effects disease have become a focus of interest (Juneau & Remolino, 2000). The growing field of psychneuroimmunology has provided ample empirical evidence that psychosocial experiences, play a salient, perhaps primary, role in immunological functioning, susceptibility to disease, disease progression, and overall survival (Goodwin et al., 2001; Herbert & Cohen, 1993). Huber et al. (2011) found online peer-to-peer interaction provided the same support as traditional face-to-face support groups, and online forums provided an ease of use in that direct personal contact was circumvented.

                                                            Background of the study

There is, however, a paucity of research on the effects of disease-specific online support for rare cancer types. This paucity is regrettable since the addition of disease-specific online support is readily available for many cancer patients, at least those in developed nations. As an adjunct therapy in the treatment of rare cancer types, oncologists can prescribe this effective addition to a new or established therapeutic treatment plan. Since social support, in the form of face-to-face support groups has been somewhat effective in the survival of cancer patients (Goodwin et al., 2001), it seems prudent to determine the role of disease-specific online support for rare cancer types. Depression is a common issue in patients undergoing cancer treatments (Seckin, 2009) and the emotional experience of a cancer diagnosis is known to contribute to depressive symptoms in cancer patients (Huber, 2011; Seckin, 2009; Sen, 2008). It is prudent, if not imperative to determine the most effective means of relieving depressive symptoms in cancer patients with rare cancer types, and to understand whether online, disease-specific support has a more effective influence on depressive symptoms than face-to-face support groups.

                                                                  Problem Statement

An abundance of research exists on the positive effects of social support for a diverse range of cancer patients of both genders (Goodwin et al., 2001). There is a handful of studies on the effects of online group support for cancer patients (Huber et al., 2011); however, far less on the psychosocial effects of disease-specific online support groups for patients with rare cancer types. If the addition of online support specifically related to the patient's cancer type is an effective addition to medical treatment, understanding the effects of this type of support may be a valuable tool for patients as well as their healthcare professionals. The benefits of online support have been documented for cancer patients (Goodwin et al., 2001; Huber et al., 2011; Sen, 2008; Seckin, 2009); however, patients with rare cancer diagnoses may experience challenges exclusive to their particular cancer type. For example, fewer specialists may be available locally, and online support enhances information exchange between patients who utilize specialized oncological care and those who do not have access to those resources. This information exchange is effective for patients seeking education about treatment options and general information specific to their disease (Huber et al., 2011; Seckin, 2009). Considering the unusual nature of rare cancers, disease-specific support groups may be more effective in providing pertinent information for patients.

                                                               Purpose Statement

This study will attempt to expand the knowledge of the effects of psychosocial support in rare cancer types by comparing the effects of disease-specific online support and face-to-face support for patients with rare cancer types. It will compare the relationship between participation in support groups and depressive symptoms in patients with rare cancer types. The hypothesis is that participation in online, disease-specific support groups is more effective at relieving depressive symptoms in patients with rare cancer types than face-to-face support groups. The independent variables are participation in online disease-specific support groups and participation in face-to-face support groups. The dependent variable is depressive symptoms.

Owen, Boxley, Goldstein, Lee, Breen, and Rowland (2010) suggested internet-based support has the potential to reach a greater number of individuals than face-to-face groups, and an increasing number of patients are utilizing online resources. The scientific evaluation of the contribution of online disease-specific support groups has the potential to contribute to health care providers' understanding of their patients needs, as well as for patients' effective management of the psychological and psychophysiological effects of rare cancer diagnoses (Owen et al., 2010; Tehrani, Farajzadegan, Rajabi, & Zamani, 2011).

The stress and coping social support theory explains the buffering effects of social support (Cohen & Willis, 1985; Thoits, 1986). These buffering effects are a result of influencing thought processes; in effect, modifying an individual's appraisal of the stressor. In the case of social support for cancer, the stress buffering effect insulates the individual from negative health effects of stress, provides support for the effective adaptation to the circumstances, and enhances the individual's ability to cope (Cohen & Willis, 1985; Thoits, 1986). In addition to mitigating the negative effects of stress, an abundance of research has identified biopsychosocial pathways that link social support and health (Uchino, 2009). For example, social support is associated with better immune function and lower levels of inflammation (Uchino, 2006), lower cortisol levels (Turner-Cobb, Sephton, Koopman, Blake-Mortimer, & Spiegel, 2000), and benefits to the cardiovascular system (Uchino, 2006). The maintenance and support of these physiological functions may be valuable, even critical in cancer patients.

Seckin (2009) as well as Sen (2008) and Owen et al. (2010) found patient participation in online support groups perceived their participation as a salient component of their ability to manage the stress associated with a cancer diagnosis, and it instilled optimism and a sense of control. Further, participation increased their knowledge about the disease, and it helped them find meaning in coping (Seckin, 2009; Sen, 2008). If online support participation increases these benefits, then the possibility exists that online support specific to an individual's cancer type has the potential to provide disease information and psychosocial support more specific to the patient's needs. If these benefits have a significant effect on patients with rare cancer diagnoses, disease-specific online support participation may be an effective adjunct component of disease treatment.

                                                                 The Research Design

Quasi-experimental designs are advantageous when researchers seek information on real-life environments, although they lack adequate control over alternative explanations for the observations made in the study (Frankfort-Nachmias & Nachmias, 2008). They also lack the ability to determine direction of causation, and the resulting inferences are theoretical or logical.

However, quasi-experimental designs are superior to experimental designs when the utilization of randomization is unethical, unfeasible, or impossible (Morgan, 2000). Although the lack of random assignment causes a weakening of internal validity, a quasi-experimental design may provide a process by which researchers can study social phenomenon that may otherwise be inaccessible. The deficiency in internal validity in quasi-experiments points to a lack of control over confounding variables, and may not account for alternative explanations for the results made in the comparisons between groups. The lack of random assignment in the quasi-experimental design method may allow studies to be more feasible, but this also poses many challenges for the investigator in terms of internal validity (Trochim, 2006).

This quantitative analysis is a quasi-experimental design because my goal was to determine the difference between the effects of face-to-face support groups and online, disease-specific support groups for rare cancer types. In addition, participants were not randomly assigned to either of the groups. The quasi-experimental design will support the use of the intact groups, which are individuals diagnosed with rare cancer types. For my study, I will choose twenty-five individuals with rare cancer types who participate in face-to-face support groups and twenty-five individuals who participate in online disease-specific support groups.

Participants will be selected according to several criteria including participating in either a face-to-face support group or an online, disease specific support group. They must have been diagnosed at least six months prior to their selection for participation and they cannot be under hospice care or have been told they have less than six months to live. Participants must have participated in their type of support group for at least three months. Each participant will be assessed with the Beck Depression Inventory Fast Screen for Medical patients. Comparisons of the results will be made to determine if patients with rare cancer types who participate in online disease specific support groups experience lower levels of depression than patients with rare cancer types who participate in face-to-face support groups.

When researchers make group comparisons utilizing quasi-experimental designs, they are creating the comparison groups through non-random processes. Quasi-experimental design is an alternative to experimental design, which always utilizes a random assignment to groups. Because quasi-experimental design does not utilize a random selection process for assigning participants to comparison groups, the researcher must be aware of how the non-random selection process might affect the results of the study (Shannon, Goldenhar, & Hale, 2001). For example, the researcher might ask whether there are inherent differences between participants in the different groups because of age, gender, marital status, socio-economic status, level of education, or other undeterminable differences. In my study, some of these differences may include length of time since cancer diagnosis, age, geographical location, the particular cancer diagnosis, presence or severity of depression, and other factors that may be difficult to determine. It will be necessary to note these differences because they have the potential to affect how participants respond to the support group to which they are affiliated (Shannon et al., 2001). In addition, it will be important to describe and list information on these differences and account for them in the statistical analysis (Shannon et al., 2001).



The population used for the BDI-FastScreen were medical patients from a variety of clinical settings. The BDI-FastScreen was normed on four groups (Whiston & Eder, 2003). The first was a group of 50 patients who had been referred to psychiatrists for consultation after hospitalization for a medical condition. The second group consisted of 94 patients referred from a family practice setting, and the third group was made up of 100 pediatric patients that ranged in age from 12 to 17 who were scheduled for routine medical appointments, and the fourth group consisted of 120 patients from a university clinic. This instrument was specifically designed for screening depressive symptoms related to medical circumstances (Hennessey & Pallone, 2003; Whiston & Eder, 2003). The BDI-FastScreen provides information for a unique population in which symptoms of depression from medical conditions must be differentiated from depressive symptoms from other sources.

The population in this study is cancer patients between the ages of 40 and 60 who have been diagnosed with a rare cancer at least six months prior to becoming a participant in this study, and have participated in a face-to-face support group, or an online disease-specific online support forum for at least three months. A rare cancer type is defined as a cancer that makes up less than 1% of all cancer diagnoses. It is challenging to determine an official consensus on the number of patients living with these cancer diagnoses.


Sampling Strategy

My study is a quasi-experimental design, which calls for the utilization of two intact groups; one, whose members have participated in face-to-face support for at least three months, and a second, whose members have participated in online, disease-specific support forums for at least three months. My sampling strategy will be to obtain a non-probability sample. It is a non-probability sample because it is not chosen randomly. Probability sampling is based on probability theory which increases the chance that each member of the population will be included in the sample and reduces the chance of a non-representative sample (Monette, Sullivan, & DeJong, 2011). The probability sample is obtained randomly (Frankfort-Nachmias & Nachmias, 2008). In my study, it is not possible to obtain a random sample; I must utilize self-selected individuals from each of the intact groups. Making a selection from the general population would not be feasible or advantageous, since participants must have been diagnosed with cancer at least six months prior to the sample selection process.

Depending on the number of responses, and after eliminating individuals who have not been diagnosed for at least six months, are in late stages of their cancer, or had been diagnosed with clinical depression for six or more months prior to receiving a cancer diagnosis, I will utilize systematic sampling, determine an interval and select every nth response according to the time and date the individual's response was received. The value n is the integer value of the ratio of the size of the population to the size of the sample. For example, if I receive 400 responses and I intend to use 45 participants for my sample, I would take the number of responses and divide it by the number of participants I need for my sample, which, in this case is 400/45, which equals 8.8. I would select every 8th person, after randomly choosing the starting point. This is called a 1-in-8 systematic sample. This selection method will decrease threats to validity that could be caused by selection bias.

Selection for Each Group

For the group that participates in face-to-face support, I will advertise for participation in various local cancer support groups, and support groups located in one eastern, one southern, one mid-western, and one west coast support groups. For the group that participates in online, disease specific support forums, I will post a call for participation in at least five disease-specific support forums provided by the Association of Cancer Online Resources (ACOR.org). ACOR is an online cancer community that provides forums for over 80 types of cancer as well as a forum for rare cancers. Responses to these notices will be randomly selected as previously described.

Choice of Sample Size

Because of the lack of consensus on the exact population of people living with rare types of cancer, and because of the seminal nature of my research and few representative studies, I utilized Cohen's d and a t test for two independent samples. Using a power or .80 and a medium to small effect size of .70 (described as between .50 and .80), the necessary sample size should include 34 participants in each group. Using an inversion of Fort-Nachmias and Nachmias' (2008) formula (SE= s/√n), n=s2/SE2 I obtain a recommendation of 28 individuals for each group. Using G Power software program (Faul, Erdfelder, Lang, & Buchner, 2007), the recommendation was for 34 participants in each group as well.

On the other hand, I found two studies that related somewhat to my population and variable. Applying the formula for Cohen's d, I subtract the means (5.6-3.5 = 2.1) and average the standard deviations (3.5 + 2.7/2 = 3.1). Then I divided the means by the average of the standard deviations (2.1/3.1 = .68). Using .68 or approximately .70 as an average effect size, and the tables provided by Burkholder (n.d.), I found that for comparing two groups, I need two samples with approximately 34 individuals in each group. Since two methods of calculating my sample size resulted in recommending 34 individuals for each group, I am confident that by utilizing this number of participants, I can ensure my samples are an adequate size.

                               Ensuring Content, Empirical, and Construct Validity

Content Validity

Consulting the literature is an important component to choosing and assessing an instrument that will actually measure what the researcher intends to measure (Brockopp & Hastings-Tolsma, 2003). The contemporary knowledge base can provide examples of the successful use of instruments that have been of value in similar settings. A researcher's assessment of content validity will rely on prior research as well as the expert opinions of other scientists. If an instrument has not been utilized effectively for purposes similar to the researcher's study, it will be difficult to determine its content validity (Brockopp & Hastings-Tolsma, 2003).

In my study, I am utilizing the Beck Depression Inventory Fast Screen for Medical Patients (BDI-FastScreen), which has been utilized by healthcare professionals in medical and clinical settings, to assess behavioral and somatic symptoms associated with medical issues (Scheinthal, Steer, Giffin, & Beck, 2001). It is a reliable and valid information source when used to determine and differentiate depressive symptoms as a result of medical conditions from depressive symptoms from other sources (Hennessey & Pallone, 2003; Whiston & Eder, 2003).

Empirical Validity

Empirical or predictive validity is the extent to which scores on one assessment correspond to the same behaviors measured with other assessment instruments in other contexts. For an assessment to be empirically valid, statistical evidence must suggest the instrument measures what it is meant to measure (Trochim, 2006). Validity of an assessment is the degree to which tests correlate, or measure the same constructs similarly. The BDI-FastScreen was correlated with two other assessment instruments that measure symptoms of depression and also with the diagnostic criteria for depression in the Diagnostic and Statistical Manual of Mental Disorders IV-TR (DSM-IV-TR). The correlations were r = .62 with the Hospital Anxiety and Depression Scale and r = .86, when correlated with the Beck Anxiety Inventory for Primary Care. Correlation with the DSM-IV-TR was r = .69 (Hennessey & Pallone, 2003). Although the correlation to the less recent DSM may not continue to be relevant, the validity of this instrument is based on these correlations.

Construct Validity

Construct Validity is a primary concern for researchers (Schotte, Maes, Cluydts, De Doncker, & Cosyns, 1997) Assessing the validity of how well an instrument measures what it is supposed to measure is a critical component to the success of the researcher and the research (Cronbach, & Meehl, 1955). If the assessment measures something other than the construct of focus, the results may be misleading or meaningless. When assessing construct validity, it is critical to ensure the instrument measuring the variable is actually measuring the variable intended for measurement and not some other construct (Cronbach, & Meehl, 1955). For example, if the goal is to measure depression, it would not be prudent to measure fear.

Since the BDI-FastScreen instrument was correlated with other assessments designed to measure depressive symptoms, I am confident with the construct validity demonstrated by this assessment. The BDI-FastScreen has been utilized to measure depression in a variety of medical conditions (Whiston & Eder, 2003) such as in patients with multiple sclerosis (Benedict, Fishman, McClellan, Bakshi, & Weinstock-Guttman, 2003), geriatric patients (Scheinthal, Steer, Giffin, & Beck, 2001), patients with chronic pain (Poole, Bramwell, & Murphy (2009) and cancer patients (Alacacıoğlu, Öztop, & Yılmaz, 2012).

Ensuring Reliability

Reliability is an estimation of the extent to which a measurement instrument will yield the same results upon reassessment (Trochim, 2006). An assessment can be reliable without being valid, however, if it is not reliable, it cannot be valid (Whiston, 2009). Assessment instrument must be consistent, replicable, and dependable, and the reliability and validity of an instrument create a level of usability for empirical research (Whiston, 2009). Reliability refers to the replicability and stability of a measurement and whether it will result in the same assessment in the same individuals when repeated. When determining the reliability of an assessment, a reliability coefficient of at least .80 indicates a trustworthy level of reliability (Trochim, 2006). In effect, the reliability of an instrument estimates the extent to which variance in response is real variance rather than an error in the implementation of the instrument. If an assessment is reliable, its results are stable and relatively true (Whiston, 2009). Utilizing the BDI-FastScreen will provide a true and stable assessment of depression in the participants.

                                  Strengths and Limitations of the BDI-FastScreen


The BDI-FastScreen is a user-friendly assessment for screening depressive symptoms in adolescents and adults. One critical benefit is that the BDI-FastScreen determines these symptoms as they relate to medical issues (Hennessey & Pallone, 2003; Whiston & Eder, 2003). In addition, this instrument is a pencil and paper self-report that is easily scored, and when utilized along with a complete patient evaluation, it is a reliable resource for evaluating depressive symptoms in medical patients (Whiston & Eder, 2003).


Whiston and Eder (2003) suggested the high correlation between the Beck Anxiety Inventory for Primary Care and the BDI-FastScreen may mean that both instruments evaluate anxiety rather than depression. If this were the case, measuring depression accurately would not be accomplished by the use of this assessment instrument. More clarity is necessary for understanding whether the BDI-FastScreen measures depression or anxiety. In addition, some discussion as to whether the samples utilized were not generalizeable since all participants were from the greater Philadelphia area, and the 268 participants randomly chosen were from four medical settings. Until additional studies are completed with representative samples this assessment instrument may not be representative of wider populations. In addition, no test-retest analysis was reported for the BDI-FastScreen and that deficiency renders this assessment's reliability questionable (Hennessey & Pallone, 2003). For my study, it is essential that the depressive symptoms identified in the data collection are related to a medical condition rather than to other life experiences. The BDI- FastScreen was chosen for the purpose of identifying depressive symptoms as they relate to medical conditions. If the data collected does not represent levels of depression related to a cancer diagnosis, my research may be misleading or meaningless.

                                                                Data Analysis

For data analysis, I will conduct an independent t-test, which is also called the two sample t-test or student's t-test. The independent samples t-test is an inferential statistical test that will determine whether a statistically significant difference exists in levels of depression between the two groups in my study. This statistical analysis evaluates the difference between the means of two independent groups (Green & Salkind, 2014). It will evaluate both population means to determine whether the population mean of the test variable for one of the group is different from the mean of the second group (Green & Salkind, 2014). The three assumptions underlying the independent-samples t test are that the test variable has a normal distribution in both populations of study; both populations have approximately equal variances; and both samples are randomly selected from the population and the observations in each sample are independent of the other (each has no influence on the other) (Green & Salkind, 2014). The variables and the populations for my study fulfill these assumptions.


One potential weakness of the study is its sampling methodology; specifically that participants were not chosen randomly. In addition, samples were constructed from three types of rare cancer, so samples drawn from other populations of rare cancer patients may have different responses and other potential differences whether they participate in face-to-face support groups or online support groups. Another potential weakness is the selection of participants in face-to-face support groups from four locations around the United States. The geographical location may have an unforeseen influence on depression as well as on their resources for support.

Although the sample sizes are reasonable for this study, the sample size of 45 individuals in each group may not be representative of all cancer patients with rare cancer types. Because of the seminal nature of this research, it is hoped that other research will be undertaken to determine the best possible support for cancer patients with unusual and rare cancers. It is recommended that in the future, larger scale studies will be undertaken to determine the effects of online, disease specific support groups on patients with rare cancer types. In addition, it is essential to determine how different types of support affect newly diagnosed patients as well as patients in later stages of their disease.

Ethical Concerns

Each participant will be informed of the purpose of the study and will be offered information regarding the study's findings (U.S. Department of Health and Human Services, 1998). Each participant will be informed of their legal rights, especially that they can discontinue their participation in the study at any time. They will be informed that the study involves research and the purposes of the research will be explained. In addition, they will be notified of foreseeable risks or benefits of the research. If a benefit from being a member of one group or another becomes apparent during the study, the participants will be notified immediately (U.S. Department of Health and Human Services, 1998). Contact information will be provided to participants for questions regarding the research, their rights as a participant, and in the event they must discontinue participation. As part of informed consent, each participant will receive a statement that explains that their participation is voluntary, and they may discontinue their participation at will, and at any time, without penalty or loss of any benefits that will be extended to continuing participants (U.S. Department of Health and Human Services, 1998).

In addition to the above-mentioned information on consent, it will be my ethical responsibility to advise or otherwise provide resources for individuals who score low on the depression evaluation, or who appear depressed, discuss a plan to hurt themselves or others, or otherwise present as suicidal. At all times during the selection process, and throughout the study, a trained clinical psychologist will be available to evaluate participants and make recommendations and provide resources for individuals who appear or allude to being depressed or otherwise in need of mental health assistance.

                                                      Significance of the Study

Huber et al., (2011) suggested the scientific evaluation of online support groups is essential cancer support group because it contributes to increasing health care providers' understanding of their patients' needs. By exploring the perceived benefits of cancer patients and their experiences of disease-specific online participation, the psychological sciences gain an increased understanding of the effects of online support during illness and disease. Furthermore, it may encourage oncologists to recommend participation in disease-specific online support groups to their patients. If cancer patients benefit from such participation, oncologists have gained a new capacity to increase positive outcomes for their patients. Although social support during illness and disease is well-established in its ability to palliate pain and suffering and support positive outcomes, there is a deficiency of empirically derived research on disease specific online support groups for rare cancer types (Huber et al., 2011). The goal of this research is to identify and explain benefits to cancer patients that may be exclusive to disease-specific online support groups.

This study was designed to explore the differences in depression as evaluated by the BDI-FastScreen between patients who participate in online support and those who participate in face-to-face support. Understanding if and to what various supports influence depression in cancer patients is of value to cancer patients, especially those with rare cancer types. In addition, this information is valuable to oncologists and other health care professionals to understand patients' needs as well as how best to mitigate depression, which is common in cancer patients (Huber et al., 2011). Understanding the patients' most beneficial resources enables health care professionals to provide direction toward effective emotional support and the opportunity to increase disease-related knowledge.

                                                   Implications for Social Change

Determining the most effective means of social support for cancer patients is important for all cancer patients, as well as health care professionals, and families of cancer patients, especially those of patients with rare cancer types. Understanding how best to mitigate the stress of a rare cancer diagnosis, provide appropriate and pertinent information to those patients, and inform health care professionals how best to provide treatment for these patients has the potential to change the lives and experience of disease for cancer patients. Contemporary thought on social support for cancer patients includes consideration of the psychological well-being of the patient. It may be that the most appropriate and comprehensive treatment plan for cancer should include a prescribed social support plan. It is my hope that understanding and implementing online, disease-specific support will become an important adjunct therapy that will play a role in slowing disease progression and overall survival in rare cancers. It may have the potential to change the comprehensive treatment of cancer and other diseases.


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