Reducing Health Information Avoidance by Mental Contrasting
Willa C. King
Department of Psychology, University of North Carolina at Chapel Hill Senior Honors Thesis in Psychology
Objective: Staying informed on one’s health is critical for positive health outcomes. However, the possibility of receiving bad news leads many people (via both deliberate and automatic processes) to opt out of learning personal health information. The self-regulation exercise of mental contrasting has helped people improve a vast range of behaviors, and this study is the first to test its effectiveness in helping people reduce health information avoidance. We hypothesized that participants who engaged in mental contrasting would be more open to learning their
personal melanoma risk than participants who completed a reflection activity. Method: A randomized controlled trial was administered on MTurk (n = 354). Participants completed a mental contrasting exercise or a reflection activity before filling out a melanoma risk calculator. Upon completion of the calculator, they chose whether and how much information they would like to learn about their melanoma risk. Results: Chi-square testing indicated that mental contrasting was effective in decreasing melanoma risk avoidance. Conclusion: As a brief and engaging method of reducing health information avoidance, mental contrasting may be useful in real health settings.
I would first and foremost like to thank my advisor, Dr. Paschal Sheeran, for his moral and practical support in bringing my honors thesis to fruition. I am grateful to Dr. Sheeran for teaching me the basics of health information avoidance and mental contrasting, and for providing resources as I delved further into these topics myself. In the year and a half that I have been part of his lab, I have observed his immense passion and dedication toward his work, which has been a huge inspiration for me as I prepare to pursue a career in psychology. I greatly appreciate the time and energy he dedicated to helping me with this research project from start to finish, as well as all of his enthusiasm and encouragement along the way.
I would like to express my gratitude to my honors thesis course professors, Dr. Beth Kurtz-Costes and Dr. Keely Muscatell, for their valuable advice on conducting a research project, and to our lab manager, Charlie Wright, for his extensive help in creating and administering the survey and conducting data analyses.
Reducing Health Information Avoidance by Mental Contrasting
People often fail to learn personal health information, even when it could be lifesaving. The most recent National Health Interview Survey (2015) revealed that millions of Americans forgo cancer screenings, despite the American Cancer Society’s clear guidelines and emphasis on the importance of screening to reduce cancer cases and deaths (ACS, 2019). Lung cancer is the leading cause of cancer deaths for men and women, and ACS suggests that annual low-dose computed tomography (LDCT) screening is greatly beneficial for current and former smokers at highest risk for the disease. However, the annual screening rate for those deemed eligible for LDCT has remained low and constant, from 3.3% in 2010 to 3.9% in 2015. Thus, the five-year survival rate for lung cancer is particularly low—16% for men and 22% for women—as a result of late detection. Colorectal cancer (CRC) is the second-leading cause of cancer deaths among American adults, and ACS recommends annual CRC screening for adults ages 45 and older both to prevent the disease and to detect tumors at earlier, more treatable stages. However, only 54% of this age group underwent CRC screening in 2015. Breast cancer is the most common cancer among women, and early detection via mammography screening has reduced deaths by 20%, and also allows for a greater range of and less invasive treatment options. Despite ACS’s
recommendation that women between the ages of 45 and 54 be screened annually, only 50% of women in this age group were screened in 2015 (ACS, 2019).
Research has identified several barriers to cancer screening, deriving from public policy, organizational systems, physician practices, and the patients themselves (ACS, 2019). Some of the resistance by patients stems from an unwillingness to know about personal health
researchers also found that this preference to avoid cancer risk information was associated with lower levels of CRC screening. In Howell et al.’s study (2014) on information avoidance, about 40% of their sample agreed or strongly agreed with statements like “I would avoid learning some things about my health” and “when it comes to my health, sometimes ignorance is bliss.”
The phenomenon that people opt not to know useful but potentially undesirable
arouse negative emotions (Sweeny et al., 2010). Thus, health information’s inherently threatening nature makes information avoidance a critical issue.
While Sweeny et al. (2010) consider information avoidance a deliberative decision based on analysis of threat, Howell et al. (2016) revealed that automatic processes may also play a role in health information avoidance. Across three studies, participants who had implicit unfavorable opinions about learning health information, as measured by reaction time tasks, were more likely to avoid learning their risk for a fictitious disease, melanoma skin cancer, and heart disease. In one of these studies, implicit preference for avoiding health information predicted higher information avoidance even when explicit information-avoidance preference was low. This suggests there are implicit (automatic) and explicit (deliberate) components of people’s inclinations to avoid health information, and both may be important in determining avoidance behavior.
Previous Research on Reducing Health Information Avoidance
The majority of research targeting health information avoidance employs self-affirmation strategies. Self-affirmation theory states that people are motivated to preserve a global sense of self-worth, and they will be defensive toward information that threatens that sense. If feelings of self-worth are bolstered through self-affirmation interventions, people’s overall self-worth needs will be met, thus leaving them more receptive to potentially threatening health information. Self-affirmation interventions involve exercises that invite people to consider their positive aspects (Howell & Shepperd, 2012). Across three studies, Howell and Shepperd found that self-affirmation was successful in reducing participants’ avoidance of medical screening information, even when the information was altered to seem more threatening to participants’ beliefs,
needs are less satisfied are more likely to use defensive information processing when exposed to threatening health messages, thus making them unlikely to make necessary behavioral changes. After exposing subjects to a threatening message about alcohol consumption, those in the self-affirmation condition drank significantly less alcohol throughout the course of a four-week post-intervention period than subjects who were not self-affirmed.
While self-affirmation exercises may reduce information avoidance and defensive information processing during laboratory experiments, their success may be limited in real settings. A meta-meta-analysis of the effectiveness of interventions to increase patient adherence to medical treatments demonstrates the advantages of simplicity. The more complicated and time-consuming tasks are, the less likely people are to adhere to instructions or engage with the materials in the way they are supposed to (van Dulmen et al., 2007). The process of
self-affirmation is complex and involved because it requires participants to recall past events and define personal qualities, often in the form of short essays. Howell and Shepperd’s (2012) affirmation exercise instructed participants to (1) list traits they considered central to their self-concept, (2) identify the most important trait, and (3) write a short essay about a time they successfully demonstrated that trait. Armitage et al.’s intervention (2011) consisted of 10 questions that encouraged participants to recall and give examples of past acts of kindness. For example, one question was “Have you ever forgiven another person when they have hurt
Educational interventions may be more appropriate in medical and health-care settings because they clearly relate to the situation at hand; the goal of such interventions is to provide people with relevant information and directly help them manage their conditions. Van Dulmen et al. (2007) found educational interventions to be some of the most effective methods for
increasing patient adherence to medical treatment, including treatment for chronic diseases that require long-lasting adherence, such as diabetes, hypertension, and asthma. Self-regulation models are educational interventions that allow patients themselves to be the problem-solvers. Rather than just transferring knowledge to patients about why they should engage in certain behaviors, self-regulation interventions guide patients in bridging the gaps themselves between their current situations and their goal states. Self-regulation models regard emotions and
subjective experiences as large influences on behavior and thus incorporate them as key elements in behavior-change plans (van Dulmen et al., 2007). Mental contrasting (MC) is a brief self-regulation strategy that requires minimal effort but has been consistently successful in inducing behavior change, making it a promising remedy for health information avoidance (Oettingen, 2012).
Mental contrasting is the mental act of juxtaposing future with reality in order to generate active goal pursuit. It involves thoroughly imagining a future state, and subsequently pinpointing aspects of reality that keep one from arriving at that future state. There is ample support for mental contrasting’s capacity to help individuals translate their desired futures into reality. In such an exercise, individuals imagine the desired future and then identify current obstacles
their present obstacles (such as craving junk food) subsequently engaged in more weight-loss-oriented behaviors than control groups (Johannessen et al., 2011). In a study aimed at increasing physical activity rates among overweight, middle aged, low-SES fishermen, participants who were told to mentally elaborate on a future in which they had attained their physical activity goals and then identify obstacles (such as not having time to exercise) reported higher physical activity rates than the control group at both one-month and seven-month follow-ups (Sheeran et al., 2013). Mental contrasting has also been shown to reduce unhealthy snacking habits
(Adriaanse et al., 2010), increase self-discipline and academic performance in students
(Duckworth et al., 2011, 2013; Gollwitzer et al., 2011), help health-care workers provide better support to patients and their families (Oettingen, Stephens, et al., 2010), and more (review by Oettingen, 2012).
increased goal-related behaviors for those who expected to be successful at arriving at or evading the future in question (Oettingen, Mayer, & Thorpe, 2010).
The success of mental contrasting can be explained by Fantasy Realization Theory (FRT), which is concerned with how people think about the future and the present in terms of their desires and goals. FRT discerns among three modes of thought: (1) fantasizing about a future state, (2) dwelling upon a present reality, and (3) mentally contrasting fantasy with reality.
Fantasizing involves envisioning the future—either a positive desired future or a negative undesired future. Dwelling involves reflecting on the present—either on the obstacles that inhibit a desired future or on the assets that are endangered by a feared future. Engaging in fantasizing or dwelling alone hinders goal pursuit because people are too wrapped up in either wishes or fears about the future or the obstacles or assets of the present to effectively pursue a goal.
However, mentally contrasting a desired future with present obstacles or an undesired future with current endangered assets helps individuals identify which features of reality are interfering with the future in question. FRT states that it is this revealed discrepancy between future and present that gives people the direction and energy needed to create and implement goals toward arriving at or evading that future. Goals to arrive at a desired future are termed approach or promotion
goals, whereas goals to evade an undesired future are termed avoidance or prevention goals (Oettingen, 2012; Oettingen & Reininger, 2016; Sheeran et al., 2013).
goal-directed behavior (Kappes & Oettingen, 2011; Kappes et al., in press; Sheeran et al., 2013). Mental contrasting also produces energization because it prompts people to endorse their reality as interfering with the future in question, which induces goal commitment. This motivational effect has been measured by self-report (Oettingen et al., 2009) as well as by systolic blood pressure (Kappes & Oettingen, 2011). Additionally, there is neurological evidence that mental contrasting is highly engaging and creates strong intention formation, whereas resting and indulging in fantasies do not (Achtziger et al., 2009).
MC for Health Information Avoidance
Additionally, MC is brief and simple enough that it could realistically be used in a doctor’s office or clinic. It is less arduous than many self-affirmation exercises because it requires minimal writing and recollection of past experiences, and thus patients would be more likely engage with it. The act of mental contrasting itself is highly engaging because patients get to indulge in fantasies and then juxtapose them with reality, and neurological evidence supports its engaging nature (Achtziger et al., 2009). Additionally, patients in a doctor’s office or clinic may be more inclined to participate in MC because there is a clear connection between the exercise and their medical visit. People may take MC more seriously than a self-affirmation exercise because it is not trying to induce particular cognitive states such as positive attitudes or self-efficacy feelings, which they may find bothersome or unnecessary (Sheeran et al., 2013). Current Study
The present study offers the first test of MC’s effectiveness in reducing health
information avoidance. It is also only the second test utilizing negative-future mental contrasting (Oettingen, 2012). We administered an online survey investigating whether MC was successful in encouraging participants to learn their melanoma risk. We chose melanoma as the health information because skin cancer is the most common cancer in the United States, and melanoma is the deadliest skin cancer, and therefore inherently threatening (“Melanoma Statistics”). The study disclosed to participants the daunting statistics about skin cancer and melanoma and emphasized the everyday activities that increase people’s risk, such as spending time outdoors without sunscreen. This was intended to increase the perceived threat around receiving
condition, had two levels: (1) an MC condition and (2) an active control condition. Those in the active control group reflected on reasons why they should or should not learn their melanoma risk and indicated how influential those reasons would be for their decision to learn their risk. This active control condition was used to ensure that all participants had some designated time to consider receiving their melanoma risk prior to filling out a melanoma risk calculator and making a choice about learning their risk. The dependent variable, choice of information to receive, had four levels: (1) submit without learning my risk, (2) submit and learn my overall risk (default), (3) submit and learn my overall risk and more information about melanoma, and (4) submit and learn my overall risk, more information about melanoma, and what I can do to reduce my risk. We expected that participants who engaged in MC before facing the decision about learning their melanoma risk would be more open to receiving that information than participants who did the reflection activity. Our primary hypothesis was that a greater proportion of control participants would avoid their risk information (by choosing option 1 as opposed to options 2-4) than MC participants. Our secondary hypothesis was that, among those who accepted their risk (by choosing options 2-4), a greater proportion of MC participants would seek additional health information (by choosing options 3-4 as opposed to option 2) than control participants.
male and ranged in age from 19 to 80 (M = 36.92, SD = 11.76). The sample was 74.60% Non-Hispanic/Latino White, 7.10% Non-Hispanic/Latino White, 9.30% Black or African American, 0.80% Native Hawaiian or other Pacific Islander, 6.20% Asian, 0.60% American Indian or Alaska Native, and 1.40% other or mixed. Thirty-nine percent of the participants were married, and 41.50% had children. Forty-nine percent of participants had completed a bachelor’s degree or even higher level of education. Participants were asked about their annual household incomes: 35.30% had incomes under $40,000; 43.20% had incomes between $40,00 and $79,999; 15.00% had incomes between $80,000 and $119,999; and 6.50% had incomes of $120,000 or higher. The vast majority of participants had health coverage (98.60%). Participants received $0.60 for completing the study. All participants provided informed consent and were told that they could withdraw from the study at any time.
This study used a between-groups experimental design. We created a survey on Qualtrics (https://www.qualtrics.com) that we published on MTurk, a website in which users get paid small amounts to participate in studies. There has been persistent concern about the quality of MTurk data, but research suggests that MTurk participants are just as attentive as participants recruited offline, and they buy into interactive experiments as much as in-lab participants. Further, rigorous exclusion methods can improve data quality (Thomas & Clifford, 2017). Using the Qualtrics randomization feature, participants were randomly assigned to a mental contrasting condition (n = 183) or an active control condition (n = 171). Participants were unaware of condition assignments.
Procedure and Materials
three-item risk-perception scale, completed a two-block MC or reflection activity, read a short paragraph relaying facts about melanoma, filled out a 24-item melanoma risk calculator, and, lastly, made a decision about learning their melanoma risk.
or two sentences. During the second block, the MC group was presented with the following question: “What could you lose if you ignore your risk of melanoma and information about this disease?” They were told to take time to picture what they could lose in their minds, then write down what they were thinking and feeling in one or two sentences.
During the control group’s first block, participants were instructed to list three reasons they or somebody like them should learn their risk of melanoma. They were then told to rate the extent to which each of the reasons they listed would be important in deciding to find out their risk on a 7-point Likert-type scale, 1 indicating “not at all important” and 7 indicating “extremely important.” During their second block, the control group was instructed to list three reasons they or somebody like them should not learn their risk of melanoma. They were then told to rate the extent to which each of the reasons they listed would be important in deciding to find out their risk on a 7-point Likert-type scale, 1 indicating “not at all important” and 7 indicating “extremely important.”
All participants were then presented with the following paragraph about the increasingly high prevalence of skin cancer and the deadliness of melanoma:
Skin cancer is the most common cancer in the United States, with more than 3 million new cases diagnosed per year—that is more than all other annual cancer diagnoses combined. One in five Americans develop skin cancer in their lifetime. Rates of skin cancer are also increasing. By 2030, the number of newly diagnosed cases is expected to more than double. Melanoma is the deadliest form of skin cancer, and 20 Americans die from Melanoma every day (“Melanoma Statistics”).
sentence informing participants that they would soon be asked questions from the National Cancer Institute (NCI)’s Melanoma Risk Calculator, a tool used to assess individuals’ personal risk of melanoma.
Participants proceeded to fill out the melanoma risk calculator. While many items on this calculator were actually pulled from the NCI’s Melanoma Risk Assessment Tool (“Melanoma Risk”), we included additional questions that were designed to increase participants’ suspicions that they may be at risk, such as, “When outside for more than 10 minutes on a day that is hot and sunny, how often do you cover your arms and legs?” Our aim was to lead participants to think that their melanoma risk is higher than it actually is in order to increase the perceived threat of their melanoma risk score.
Upon completing the calculator, participants were given the following four options: (1) “submit without learning my risk,” (2) “submit and learn my overall risk,” (3) “submit and learn my overall risk and more information about melanoma,” or (4) “submit and learn my overall risk, more information about melanoma, and what I can do to reduce my risk.” Option 2, the default, was indicative of information acceptance. Any movement away from the default revealed an active choice to avoid or seek information. Option 1 was indicative of active information avoidance, while options 3-4 indicated increasing levels of active information seeking. The study assessed the relationship between the condition that participants were assigned to (independent variable) and their choice of information to receive (dependent variable).
fictitious in order to ease any psychological distress the study may have engendered. Participants were told the true purpose of the study and given contact information so they could reach out to the researchers with additional questions if they so chose.
Results Randomization Check
Statistical analysis revealed no differences between the MC and control groups on demographic variables or risk perception (see Table 1). Thus, successful randomization allowed for fair comparison of the two groups.
Table 1: Randomization Check
Variable MC Control X2/ t p
Age 36.76(12.04) 37.08(11.48) t(348.19) = 1.49 .138
Gender (% male) 56.80% 57.90% χ²(1,353) = 0.04 .840
Race (% non-white) 29.00% 21.60% χ²(1,353) = 2.50 .114
Education (elementary school = 1; junior high or middle school = 2; some high school = 3; high school graduate = 4; some college = 5; associate’s degree = 6; bachelor’s degree = 7; some graduate school = 8; master’s degree = 9; M.D., Ph.D., or other advanced degree = 10)
6.25(1.39) 6.20(1.47) t(352) = -0.27 .787
Income ($0-$19,999 = 1; $20,000-$39,999 = 2; $40,000-$59,999 = 3; $60,000-$79,999 = 4; $80,000-$99,999 = 5; $100,000-$119,999 = 6; $120,000-$139,999 = 7; $140,000-$159,999 = 8; $160,000-$179,999 = 9; $180,000-$199,999 = 10; $200,000+ = 11)
3.24(1.74) 3.53(1.72) t(348.19) = 1.49 .138
Marital Status (% married) 35.00% 42.70% χ²(1,353) = 2.22 .136
Parental Status (% with kids) 38.80% 44.40% χ²(1,353) = 1.16 .281
Risk Perception (1 = low risk perception, 7 = high risk perception)
2.93(1.45) 3.18(1.53) t(352.00) = 1.57 .118
Note. Values are percentages or means (standard deviations). Primary Analyses
can do to reduce my risk) differed between the MC (n = 183) and control (n = 171) groups, we conducted a Chi-Square Test of Independence. Results of the 2-sided Chi-Square Test indicated that there were differences between the MC and control groups on choices of information, χ²(3,
N = 354) = 8.98, p = .030. See Figure 1. Figure 1: Information Choice by Condition
Submit and learn my overall risk, more information about melanoma, and what I can do to reduce risk
Submit and learn my overall risk and more information about melanoma Submit and learn my overall risk (default) Submit without learning risk
0 10 20 30 40 50 60 70 80 90 100 8.7
Information Choice by Condition
Reflection Mental Contrasting Percentage of Condition
Follow-up analyses were conducted to determine whether MC participants were (a) less likely to avoid their risk and/or (b) more likely to seek additional information than control participants. To test whether the MC group was less likely to avoid their risk than the control group, we combined the three acceptance options (options 2-4) into one category and compared that with the one avoidance option (option 1). Results of the 2-sided Chi-Square Test indicated that a significantly smaller percentage of those in the MC group (12.00%) avoided their
melanoma risk than those in the control group (23.40%), χ²(1, N = 354) = 7.91, p = 0.005. See
options (options 2-4). Of the MC participants who accepted their risk information (n = 161), 86.34% chose option 2, 3.73% chose option 3, and 9.93% chose option 4. Of the control
participants who accepted their risk information (n = 131), 83.20% chose option 2, 3.05% chose option 3, and 13.74% chose option 4. Results of the 2-sided Chi-Square Test indicated that among participants who chose to accept melanoma risk information there was no significant difference between the MC and control groups in how much information they accepted, χ²(2, N
= 292) = 1.08, p = 0.584. Thus, while mental contrasting prompted participants to learn their personal melanoma risk, it did not push them to seek extra information about melanoma.
The results supported our primary hypothesis that engaging in mental contrasting reduces health information avoidance. Participants in the MC group were quite significantly less likely than those in the control group to avoid their melanoma risk; while 23.40% of control
avoiding their risk information to accepting it, but did not push them that extra length to receive even more information about melanoma or ways to reduce their risk. A possible explanation is that the survey had already provided the participants with a good deal of information about melanoma and the risky behaviors that lead to it, and participants may have felt as though they had learned enough already.
The present study provides the first indication of mental contrasting’s ability to reduce health information avoidance, which has important implications both for MC’s wide-ranging effectiveness, as well as for improving our nation’s health. Mental contrasting has been shown to improve goal pursuit in a number of domains, including diet and exercise (Adriaanse et al., 2010; Johannessen et al., 2011; Sheeran et al., 2013), smoking cessation (Oettingen, Mayer, & Thorpe, 2010), self-discipline and academic performance (Duckworth et al., 2011, 2013; Gollwitzer et al., 2011), and job performance (Oettingen, Stephens, et al., 2010), and now its utility can be extended to reduce health information avoidance. Further, only one other study (Oettingen, Mayer, & Thorpe, 2010) has utilized the form of mental contrasting in which negative undesired futures are contrasted with aspects of reality endangered by that future—rather than positive
desired futures contrasted with obstacles standing in the way of that future—thus, this study provides additional support that mental contrasting can be carried out in this manner.
This study offers a new method of reducing health information avoidance that, if implemented into health-care settings, has the potential to improve health and save lives. Early detection is central to the ACS’s mission to fight for a cancer-free world, and it heavily
implementing known interventions. However, such interventions have not been fully adopted by individuals, physicians, health care systems, and society at large (Institute of Medicine et al., 2003). An extensive review of the literature concluded that people are less likely to adopt interventions that are complex or time-consuming (van Dulmen et al., 2007). Mental contrasting is different than past interventions developed to reduce health information avoidance (namely, self-affirmation strategies) in that it is brief, simple, and engaging (Achtziger et al., 2009; Oettingen, 2012). Thus, participating in a mental contrasting exercise at the beginning of a visit to a doctor’s office or clinic could be both feasible and effective, and could therefore help Americans make use of the screening resources available for their health and longevity. Future research should accumulate additional evidence on mental contrasting’s success at prompting individuals to learn personal health risks to substantiate this study’s findings. Future research may want to replicate this study using cancers other than melanoma, particularly ones with low screening rates, such as breast cancer, colorectal cancer, and lung cancer (ACS, 2019). Future studies should also extend this research to non-cancerous diseases that also have low screening rates, such as HIV (Centers for Disease Control and Prevention, 2017).
Future research on MC’s effect on health information avoidance may benefit from an expectancy measure. Some past research has found mental contrasting to be
expectations of their ability to fend off melanoma and thus could not determine whether their choice to avoid or accept their risk was expectancy-dependent.
Achtziger, A., Fehr, T., Oettingen, G., Gollwitzer, P. M., & Rockstroh, B. (2009). Strategies of intention formation are reflected in continuous MEG activity. Social Neuroscience, 4, 11-27. https://doi.org/10.1080/17470910801925350
Adriaanse, M. A., Oettingen, G., Gollwitzer, P. M., Hennes, E. P., de Ridder, D., & de Wit, J. (2010). When planning is not enough: Fighting unhealthy snacking habits by mental contrasting with implementation intentions (MCII). European Journal of Social Psychology, 40(7), 1277-1293. https://doi.org/10.1002/ejsp.730
Ajekigbe, A. T. (1991). Fear of mastectomy: The most common factor responsible for late presentation of carcinoma of the breast in Nigeria. Clinical Oncology, 3(2), 78-80. https:// doi.org/10.1016/s0936-6555(05)81167-7
American Cancer Society. (2019). Cancer prevention & early detection facts & figures 2019-2020. http://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/ cancer-prevention-and-early-detection-facts-and-figures/cancer-prevention-and-early-detection-facts-and-figures-2019-2020.pdf
Armitage, C. J., Harris, P. R., & Arden, M. A. (2011). Evidence that self-affirmation reduces alcohol consumption: Randomized exploratory trial with a new, brief means of self-affirming. Health Psychology, 30(5), 633-641. https://doi.org/10.1037/a0023738 Carroll, P., Sweeny, K., & Shepperd, J. (2006). Forsaking optimism. Review of General
Centers for Disease Control and Prevention. (2017). HIV testing. Retrieved March 15, 2020, from https://www.cdc.gov/vitalsigns/hiv-testing/index.html
Cutler, S. J., & Hodgson, L. G. (2003). To test or not to test: Interest in genetic testing for Alzheimer’s disease among middle-aged adults. American Journal of Alzheimer’s
Disease and Other Dementias, 18(1), 9-20. https://doi.org/10.1177/153331750301800106 de la Ronde, C., & Swann, W. B. (1993). Caught in the crossfire: Positivity and self-verification
strivings among people with low self-esteem. In Baumeister, R. F., Self-esteem: The puzzle of low self-regard, 147-165. New York: Plenum Press.
Duckworth, A. L., Grant, H., Loew, B., Oettingen, G., & Gollwitzer, P. M. (2011).
Self-regulation strategies improve self-discipline in adolescents: benefits of mental contrasting and implementation intentions. Educational Psychology, 31(1), 17-26.
Duckworth, A. L., Kirby, T. A., Gollwitzer, A., & Oettingen, G. (2013). From fantasy to action: Mental contrasting with implementation intentions (MCII) improves academic
performance in children. Social Psychological and Personality Science, 4(6), 745-753. https://doi.org/10.1177/1948550613476307
Emanuel, A. S., Kiviniemi, M. T., Howell, J. L., Hay, J. L., Waters, E. A., Orom, H., & Shepperd, J. A. (2015). Avoiding cancer risk information: Prevalence and correlates.
Social Science & Medicine, 147, 113-120.
Ferrer, R. A., Klein, W. M. P., Persoskie, A., Avishai-Yitshak, A., Jones, K., Villegas, M. E., & Sheeran, P. (2018). When does risk perception predict protection motivation for health threats? A person-by-situation analysis. PLoS ONE, 13(3).
Gollwitzer, A., Oettingen, G., Kirby, T. A., Duckworth, A. L., & Mayer, D. (2011). Mental contrasting facilitates academic performance in school children. Motivation and Emotion,
35(4), 403-412. https://doi.org/10.1007/s11031-011-9222-0
Hart, W., Albarracin, D., Eagly, A. H., Brechan, I., Lindberg, M., & Merrill, M. (2009). Feeling validated versus being correct? A meta-analysis of selective exposure to information.
Psychological Bulletin, 135(4), 555-588. https://doi.org/10.1037/a0015701 Howell, J. L., Crosier, B. S., & Shepperd, J. A. (2014). Does lacking threat-management
resources increase information avoidance? A multi-sample, multi-method investigation.
Journal of Research in Personality, 50, 102-109. https://doi.org/10.1016/j.jrp.2014.03.003
Howell, J. L., Ratliff, K. A., & Shepperd, J. A. (2016). Automatic attitudes and health information avoidance. Health Psychology, 35(8), 816-823.
Howell, J. L., & Shepperd, J. A. (2012). Reducing information avoidance through affirmation.
Association for Psychological Science, 23(2), 141-145. https://doi.org/10.1177/0956797611424164
Institute of Medicine, National Research Council, & National Cancer Policy Board. (2003).
Johannessen, K. B., Oettingen, G., & Meyer, D. (2011). Mental contrasting of a dieting wish improves self-reported health behaviour. Psychology & Health, 27, 43-58. https://doi.org/ 10.1080/08870446.2011.626038
Kappes, A., & Oettingen, G. (2011). From wishes to goals: Mental contrasting connects future and reality. Manuscript under review.
Kappes, A., Oettingen, G., & Pak, H. (2012). Mental contrasting and the self-regulation of responding to negative feedback. Personality and Social Psychology Bulletin, 38(7), 845-857. https://doi.org/10.1177/0146167212446833
Kappes, A., Singmann, H., & Oettingen, G. (in press). Mental contrasting instigates goal-pursuit by linking obstacles of reality with instrumental behavior. Journal of Experimental Social Psychology. Advance online publication. https://doi.org/10.1016/j.jesp.2012.02.002 Lyter, D. W., Valdiserri, R. O., Kingsley, L. A., Amoroso, W. P., & Rinaldo, C. R. (1987). The
HIV antibody test: Why gay and bisexual men want or do not want to know their results.
Public Health Reports, 102(5), 468-474.
National Cancer Institute (n.d.). Melanoma risk assessment tool. Retrieved November 4, 2019, from https://mrisktool.cancer.gov/calculator.html
Melanoma Research Alliance (n.d.). Melanoma Statistics. https://www.curemelanoma.org/about-melanoma/melanoma-statistics-2/
Oettingen, G. (2012). Future thought and behaviour change. European Review of Social Psychology, 23(1), 1-63. https://doi.org/10.1080/10463283.2011.643698
and Social Psychology Bulletin, 35(5), 608-622. https://doi.org/10.1177/0146167208330856
Oettingen, G., Mayor, D., & Thorpe, J. S. (2010). Self-regulation of commitment to reduce cigarette consumption: Mental contrasting of future with reality. Psychology and Health,
25, 961-977. https://doi.org/10.1080/08870440903079448
Oettingen, G., & Reininger, K. M. (2016). The power of prospection: mental contrasting and behavior change. Social and Personality Psychology Compass, 10(11), 591-604. https://doi.org/10.1111/spc3.12271
Oettingen, G., Stephens, E. J., Mayer, D., & Brinkmann, B. (2010). Mental contrasting and the self-regulation of helping relations. Social Cognition, 28, 490-508.
Sheeran, P., Harris, P., Vaughan, J., Oettingen, G., & Gollwitzer, P. M. (2013). Gone exercising: Mental contrasting promotes physical activity among overweight, middle-aged, low-SES fishermen. Health Psychology, 32(7), 802-809. https://doi.org/10.1037/a0029293
Smith, S. M., Fabrigar, L. R., & Norris, M. E. (2008). Reflecting on six decades of selective exposure research: Progress, challenges, and opportunities. Social and Personality Psychology Compass, 2(1), 464-493. https://doi.org/10.1111/j.1751-9004.2007.00060.x Sweeny, K., Melnyk, D., Miller, W., & Shepperd, J. A. (2010). Information avoidance: Who,
what, when, and why [Abstract]. Review of General Psychology, 14(4), 340-353. https://doi.org/10.1037/a0021288
Thompson, H. S., Valdimarsdottir, H. B., Duteau-Buck, C., Guevarra, J., Bovbjerg, D. H., Richmond-Avellaneda, C., & Amarel, D. (2002). Psychosocial predictors of BRCA counseling and testing decisions among urban African-American women. Cancer Epidemiology Biomarkers and Prevention, 11, 1579-1585.
Van der Steenstraten, I. M., Tibben, A., Roos, R. A. C., van de Kamp, J. J. P., & Niermeijer, M. F. (1994). Predictive testing for Huntington disease: Nonparticipants compared with participants in the Dutch program. American Journal of Human Genetics, 55(4), 618-625. van Dulmen, S., Sluijs, E., van Dijk, L., de Ridder, D., Heerdink, R., & Bensing, J. (2007).
Patient adherence to medical treatment: A review of reviews. BMC Health Services Research, 7. https://doi.org/10.1186/1472-6363-7-55