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Behavioral Research

Table of Contents
1 Introduction
2 Self-Report of Cancer Behaviors
3 Self-Reports of Family History

Self-Reported Psychosocial Risk Factors among Cancer Patients


Application of Self-Report Measures in Cancer


Suggestions for Use of Self-Report for Cancer-Related Variables

7 Overall Conclusions
8 References
9 Published Examples

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Other Constructs



Dispositional Optimism




Illness Representations

  Implementation Intentions
  Intention, Expectation, and Willingness
  Normative Beliefs
  Optimistic Bias
  Perceived Benefits
  Perceived Control
  Perceived Severity
  Perceived Vulnerability
  Self-Reported Behavior
  Social Influence
  Social Support

Self-Report of Cancer-Related Behaviors
Joshua M. Smyth, Monica S. Webb, and Masanori Oikawa

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Self-Reported Psychosocial Risk Factors among Cancer Patients

Secondary and tertiary prevention research often assesses and intervenes on processes that affect the cancer experience. These include health-related quality of life and medical adherence and, because there are often no reliable biomedical markers for these factors, self-report is the primary assessment method. Although we describe both quality of life and medical adherence in more detail below, self-report can be more generally improved by including collateral reports from spouses or family members and/or utilizing structured clinical interviews (rather than "paper and pencil" self-report).

Quality of life. It is well established that many cancer patients experience reduced quality of life (QoL), and that QoL has become an important outcome measure in cancer research. Reduced QoL can be identified through specific limitations in physical, role, cognitive, emotional, and social functioning. QoL decrements have been well documented among multiple populations, such as in breast (Roth, Lowery, Davis, & Wilkins, 2005) and colorectal cancer patients (Arndt, Merx, Stegmaier, Ziegler, & Brenner, 2004), among many others. Researchers often create their own QoL self-report instruments, although several published measures are available (e.g., the SF-36; McHorney, Ware, Lu, & Sherbourne, 1994).

The Functional Assessment of Cancer Therapy (FACT-G; Cella et al., 1993; Fairclough & Cella, 1996) questionnaires are a validated set of general measures (n = 3), cancer specific measures (n = 18), cancer specific-symptom measures (n = 11), treatment specific measures (n = 4), and symptom specific measures (n = 12). Evidence suggests that the FACT-G has smaller coefficients of variations and larger effect sizes compared to other commonly used tools (Cheung, Goh, Thumboo, Khoo, & Wee, 2005). In addition to patient self-reports of QoL, physician evaluations may be valuable. In particular, some evidence suggests that physician reports are more sensitive in recognizing changes in patient physical or general health than are patient reports (Sneeuw et al., 1997). Caregiver reports may also be a complementary source of information, particularly when a patient report is judged to be less reliable (e.g., for social desirability reasons; Sneeuw et al., 1997).

Medical adherence. Most patients with cancer receive some form of treatment (oral, chemotherapy, or radiation) as part of their regimen. In these cases, medical adherence is essential to reduce morbidity and increase longevity. Multiple approaches are used to determine whether patients are taking their prescribed medication at the correct dosage and frequency (Atkins & Fallowfield, 2006), adhering to inpatient and outpatient treatments, and attending appointments. Treatment adherence can be assessed using both objective (e.g., drug levels in blood or urine) and subjective (e.g., diary, interview, or clinical interview self-report) methods.

Although evidence suggests that poor adherence is a barrier to effective treatment (Atkins & Fallowfield, 2006; Hoagland, Morrow, Bennett, & Carnrike, 1983; Miaskowski et al., 2001), treatment adherence for cancers is generally higher than for other medical conditions (e.g., diabetes, sleep disorders, pulmonary diseases; see DiMatteo, 2004; cf. Escalada & Griffiths, 2006). Factors related to non-adherence have also been studied. For example, among breast cancer patients, poorer adherence is related to more prescriptions at baseline (Lash, Fox, Westrup, Fink, & Silliman, 2006), treatment side effects or psychological distress (Demissie, Silliman, & Lash, 2001; Hoagland et al., 1983), low medication efficacy expectations (Fink, Gurwitz, Radowski, & Guadagnoli, & Silliman, 2004), positive node status (Demissie et al., 2001; Fink et al., 2004), better physical functioning (Demissie et al., 2001), and younger age (Partridge, Wang, Winer, & Avorn, 2003). However, the extant studies of treatment adherence (particularly outside of medication regimens) have generally relied on self-reports, with little research examining the validity of patients’ self-reported adherence to cancer treatment. A necessary first step appears to be the generation of standardized definitions and instruments for treatment adherence assessments in cancer patients (Escalada & Griffiths, 2006). In addition, researchers assessing medical compliance should include items that differentiate intentional (willful) versus non-intentional noncompliance (Atkins & Fallowfield, 2006).

In short, research among cancer populations includes multiple psychosocial factors that are measured using self-report. Evidence exists for the validity of existing tools to measure quality of life. Additional methods of assessing medical adherence are needed. We will see tremendous advancements as innovative methods are developed to measure these constructs.

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