Health Psychology 2015, Vol. 34, No. 2, 149 –159

© 2014 American Psychological Association 0278-6133/15/$12.00 http://dx.doi.org/10.1037/hea0000110

Self-Affirmation and Responses to Health Messages: A Meta-Analysis on Intentions and Behavior Allison M. Sweeney and Anne Moyer

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Stony Brook University Objective: The present study aimed to quantify the magnitude of the effect of self-affirmation manipulations on health messages’ influence on both intentions and behavior. Methods: A systematic search was conducted for experimental studies comparing participants who self-affirmed prior to reading a threatening health message to those who did not self-affirm. Effect sizes for health intentions and behaviors were aggregated using a random-effects model. Results: Data from 16 studies were included. The aggregate effect sizes for intentions and behavior were significant and small in magnitude (d⫹ ⫽ .26, 95% confidence interval [CI] ⫽ .04 –.48; d⫹ ⫽ .27, 95% CI ⫽ .11–.43, respectively). A meta-regression analysis revealed that among studies assessing both outcomes, the size of an effect on intentions did not predict the size of an effect on behavior, ␤ ⫽ .03, 95% CI ⫽ ⫺.30 –.36. Type of health behavior (damaging vs. promoting), timing of the health behavior (proximal vs. distal), type of self-affirmation manipulation (values vs. kindness), and the specificity of the health message (single vs. multiple health issues) did not moderate the effect of self-affirmation on intentions or behavior. Conclusions: Selfaffirmation influences health messages’ effect on intentions and behavior; however, with the present study finding that intention effect sizes did not predict behavior effect sizes, and with past studies of heath behavior change finding that intentions do not always translate to behavior, little research supports a causal intention-behavior relation among self-affirmation studies. Future research is needed to address which specific health-related responses explain why self-affirmation elicits health behavior change. Keywords: behavior change, health promotion, intentions, self-affirmation

techniques such as message framing (Rothman & Salovey, 1997). An alternative approach to focusing on the characteristics of the message involves changing how people appraise and respond to potentially threatening health information. Prior research suggests that positive beliefs, thoughts, and experiences play an integral role in how people process and respond to negative information (Steele, 1988; Tesser, 1988). Specifically, one method for changing how people evaluate negative information involves leading them to focus on their important values, attributes, or past actions, a process known as self-affirmation (Steele, 1988). By focusing on positive aspects of the self, self-affirmation is theorized to restore or reinforce an individual’s sense of self, thereby making them better equipped to face potential threats to it, including any selfthreat arising from health information. Central to self-affirmation theory is the notion that people are highly motivated to maintain an overall sense of self-integrity (Steele, 1988). To the extent that health information interferes with perceiving oneself as a moral and competent person, health information may threaten one’s self-integrity. Reinforcing sources of self-worth that are important to one’s identity, but that are unrelated to the threat at hand, can help to offset the self-threat of a health message (Sherman, Nelson, & Steele, 2000). For example, an individual with an unhealthy diet who is reminded of his or her strengths, such as being a good parent, is likely to feel less threatened by an appeal to change their diet, as other important aspects of their self-concept have been made salient. According to self-affirmation theory, such an effect occurs because individuals are more motivated to maintain a global sense of self-integrity than they are to address individual self-threats (Steele, 1988).

In attempts to encourage people to change unhealthy behaviors, researchers have continually encountered a key obstacle: People resist persuasive efforts. Those who do not wish to change their behavior use a variety of avoidance strategies, such as ignoring persuasive messages altogether or discrediting them by forming hypercritical evaluations (Blumberg, 2000; Jacks & Cameron, 2003). Relative to positive information, people tend to engage in greater scrutiny when they process negative information (Ditto, Scepansky, Munro, Apanovitch, & Lockhart, 1998). For example, people may generate counterarguments or alternative explanations to discredit unwelcome health messages (Ditto & Boardman, 1995; Ditto & Lopez, 1992; Liberman & Chaiken, 1992). People generally prefer information that reflects well on the self and reminds them of their strengths (see Brown & Dutton, 1995; Sedikides & Strube, 1997; and Taylor & Brown, 1988, for reviews). When health information is personally relevant, prior research suggests that individuals will practice biased defensive processing, presumably to protect a positive self-view (Giner-Sorolila & Chaiken, 1997; Kunda, 1987; Liberman & Chaiken, 1992). One approach to overcoming resistance to health appeals has been to refine the message. Researchers have used a variety of

This article was published Online First August 4, 2014. Allison M. Sweeney and Anne Moyer, Department of Social and Health Psychology, Stony Brook University. Correspondence concerning this article should be addressed to Allison M. Sweeney, Department of Psychology, State University of New York at Stony Brook, Stony Brook, NY 11794. E-mail: allison.sweeney@ stonybrook.edu 149

SWEENEY AND MOYER

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Importantly, self-affirmation reduces motivation to diminish self-threatening information (e.g., generating counterarguments); as a result, self-affirmation leads individuals to practice more objective processing of otherwise threatening information (McQueen & Klein, 2006; Sherman & Cohen, 2006). Self-affirmation inductions have the potential to be a useful tool in health communication, as they can help to resolve the initial self-threat and biased processing evoked by unwanted health information. Supporting the use of self-affirmation as a useful tool for health promotion, an abundance of research suggests that individuals will be more receptive to health information if they are self-affirmed (Reed & Aspinwall, 1998; Sherman et al., 2000; for a review, see Harris & Epton, 2009).

The Current Review Although it has been well documented that self-affirmation reduces biased processing of potentially threatening information across a variety of domains (Harris & Epton, 2009; McQueen & Klein, 2006; Sherman & Cohen, 2006), it is less clear whether this translates into actual changes in health intentions and behavior. A systematic review and meta-analysis of studies assessing the impact of self-affirmation on health intentions and behaviors is timely for three reasons. First, to date, although there has been one narrative review of the impact of self-affirmation on health-related responses (Harris & Epton, 2009), the magnitude of the effect of self-affirmation on health messages’ influence on intentions and behaviors remains unclear. Furthermore, at the time of Harris and Epton’s (2009) review, there were very few published studies measuring an actual health behavior change following selfaffirmation. In recent years, there has been a substantial increase in the number of studies assessing the effects of self-affirmation on health behavior. Second, past research generally supports the prediction that self-affirmation positively influences receptivity to health-related messages in terms of resulting intentions and behaviors; however, several studies have found nonsignificant differences between affirmed and nonaffirmed participants on measures of behavior (Harris & Napper, 2005; Jessop, Sparks, Buckland, Harris, & Churchill, 2014, Study 2) and intentions (Epton & Harris, 2008; Good & Abraham, 2011; Jessop et al., 2014; Jessop, Simmonds, & Sparks, 2009). Surprisingly, one study found that nonaffirmed participants had higher intentions to change their behavior than affirmed participants (Reed & Aspinwall, 1998). A meta-analysis may help to make better sense of these inconsistencies. Third, it is unclear whether self-affirmation has an effect of similar magnitude on health intentions and behaviors. Some researchers suggest that increasing intentions to change a health behavior helps one to cope with the uncomfortable feelings arising from exposure to threatening health messages (Kok, Ruiter, Van Den Hoek, Schaalma, & De Vries, 2007). One study found that participants reported an increase in health intentions after a self-integrity threat, perhaps as a means to bolster their self-esteem (Arndt, Schimel, & Goldenberg, 2003). Such findings support the possibility that an increase in health intentions after a self-affirmation manipulation could relate more to self-restoration than to the likelihood of actually changing behavior. Relatedly, other reviews have found that in many domains intentions do not always reliably translate into behavior. In a

previous meta-analysis, Sheeran (2002) observed that intentions account for 28% of the variance in behavior (r ⫽ .53). In another meta-analysis, Webb and Sheeran (2006) found that a medium-tolarge effect on intentions (d ⫽ .66) is accompanied by only a small-to-medium effect on behavior (d ⫽ .36) and that intentions and behavior were correlated (r ⫽ .57). It is likely, then, that self-affirmation has a weaker effect on health behavior than on intentions. Yet, several studies of self-affirmation include measures of health intention but not health behavior, suggesting that it is viewed as an informative outcome measure in and of itself (e.g., Good & Abraham, 2011; Klein, Harris, Ferrer, & Zajac, 2011). The present review seeks to establish: (a) whether selfaffirmation has an effect of similar magnitude on health intentions and behavior; (b) whether changes in intentions arising from self-affirmation tend to be accompanied by changes in behavior; and (c) the conditions under which self-affirmation is most likely to impact responses to health information. Regarding this latter aim, four a priori moderators were examined. The moderators are organized into two categories: those related to the type of healthrelated behavior under study and those related to the experimental methods.

Moderators Related to the Type of Health-Related Behavior Under Study Health-Damaging Versus Health-Promoting Behaviors The type of behavior an individual wishes to change may require different solutions based upon the direction of the behavior (stopping vs. starting), the duration of the behavior (one time vs. permanent), and familiarity with the given behavior (Fogg & Hreha, 2010). In this vein, the present review distinguished between studies that focused on decreasing a health-damaging behavior (including smoking or consuming alcohol or caffeine) versus those that focused on increasing a health-promoting behavior (including increasing fruit/vegetable intake, exercise, or sunscreen or condom use, or screening for an illness). Self-affirmation theory posits that individuals act defensively when their self-integrity is threatened, but it is unclear whether information aimed at increasing health-promoting behaviors should elicit a different level of self-threat than information aimed at decreasing health-damaging behaviors. The present review aims to clarify whether the type of behavior (health-damaging vs. -promoting) moderates the influence of self-affirmation on intentions and behavior.

Proximal Versus Distal Measures of Health Behavior The present review compared studies with proximal measures of behavior (e.g., taking a pamphlet at the time of the intervention) to those with distal measures of behavior (e.g., self-report questionnaires administered at various time points). An actual change in a health behavior (e.g., smoking fewer cigarettes) may be substantially more challenging than taking a pamphlet in the presence of an experimenter. Thus, studies with distal measures of behaviors assessed at follow-up are expected to yield smaller effect sizes than studies with proximal measures.

SELF-AFFIRMATION AND HEALTH RESPONSES

Moderators Related to the Experimental Method

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Type of Self-Affirmation Manipulation One commonly used self-affirmation manipulation, the values affirmation manipulation (Steele & Liu, 1983), involves ranking a list of values (e.g., social, political) in order of personal value. Participants then elaborate on their values by rating their level of agreement with several scaled items relating to their top-ranked value. In place of using these scaled items, other researchers have asked participants to write about why their top value is important to them (Good & Abraham, 2011; Harris & Napper, 2005). A second commonly used manipulation, the kindness affirmation manipulation (Reed & Aspinwall, 1998), involves elaborating on past acts of kindness. The manipulation involves answering items, such as “Have you ever forgiven another person when they have hurt you?” Few studies have established whether there are differences in the effectiveness of these two manipulations. One, however, considered this in the context of sunscreen use and found no significant main effect of condition on intentions among a kindness affirmation group, a values affirmation group, a positive traits affirmation group, and a control group (Jessop et al., 2009). The present review aims to clarify whether the kindness or values affirmation manipulations differ in the extent to which they impact health-related intentions and behavior across studies.

Specificity of the Health Message The health messages included in self-affirmation studies sometimes emphasize one specific health concern, such as breast or skin cancer. Others emphasize a variety of related health issues (e.g., a message about smoking might emphasize lung cancer, strokes, and gangrene; see Armitage, Harris, Hepton, & Napper, 2008). The present review assessed whether health message specificity (one vs. multiple health issues) moderated the influence of selfaffirmation on intentions and behavior. Note that the specificity of the emphasis of the health message does not correspond to the specificity of the behavior intended to be changed.

Objectives and Hypotheses The general aim of the present review is to quantify the magnitude of the effect of self-affirmation manipulations on health messages’ influence on both intentions and behavior. The first specific objective is to establish whether self-affirmation inductions have an effect of similar magnitude on health intentions and behavior. Thus, separate standardized mean difference effect sizes (d⫹) were calculated for intentions and behavior. Consistent with past reviews of intention-behavior change (Webb & Sheeran, 2006), we hypothesized that self-affirmation would yield a larger effect size for intentions than behavior. Second, the review aims to establish whether studies finding a difference in intentions between self-affirmed and nonself-affirmed groups tend to find a difference in behavior. Thus, among studies that measured both outcomes, a meta-regression was conducted to assess the extent to which intention effect sizes predict behavior effect sizes. Third, through the use of moderator analyses, the review aims to identify whether aspects of the health-related behavior under study or the

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experimental method account for variability in the effectiveness of self-affirmation. Studies with proximal measures of health behavior are predicted to yield larger effect sizes. Type of health behavior, type of self-affirmation manipulation, and health message specificity are included as exploratory moderators.

Method Identification and Selection of Studies for Inclusion Eligible studies contained a manipulation of self-affirmation and a measure of intentions and/or health behavior in response to a health-related message. There were no date or language restrictions, and both peer-reviewed journal articles and theses/dissertations were included. Studies were identified by searching the following databases: PsycINFO, Medline, ERIC, PsycArticles, Psychology and Behavioral Science Collection, Web of Science, and ProQuest. The search strategy involved combining the search terms self-affirmⴱ and health and (health behavior or intention or attitude to health or health information or health promotion or threat or defensive processing or risk behavior or risk perception). The search string was not developed with the assistance of a reference librarian. Some of the keywords were generated using the medical search heading builder; others were generated from keywords and topics from Harris and Epton’s (2009) narrative review on self-affirmation and health outcomes. In addition, the reference lists of Harris and Epton’s (2009) narrative review and eligible empirical articles were examined to identify other articles not captured in the database search. Finally, members of the American Psychological Association Division 38 Health Psychology listserv were contacted with a request to submit published or unpublished manuscripts on self-affirmation and health-related intentions and/or behavior; however, no members responded. Articles with adult human participants were included. Two independent coders (Allison M. Sweeney and Anne Moyer) reviewed the titles and abstracts to identify potentially eligible reports. The full text of these reports was then examined to determine whether or not they met the inclusion criteria. The search was conducted in June 2013, and again in January 2014 to check for recent publications. Interrater reliability for the effect sizes were both excellent (Intentions: intraclass correlation coefficient [ICC] ⫽ .96; Behavior: ICC ⫽ .97), and for the coding of the moderator analyses (␬ ⫽ .80). Disagreements were resolved through discussion. To be eligible for inclusion, studies must have (a) been designed so that the self-affirmation manipulation occurred before participants read a health message, (b) compared at least one group receiving a self-affirmation manipulation with a control condition, (c) contained a health message with at least some words (written or spoken), (d) contained a health message without any specific persuasive elements, including fear appeals,1 and (e) included self-reported intentions to change a health behavior and/or a measure of health behavior. Eligible studies included measures of health intentions that were specific to one or two health behaviors. 1 Studies that included additional persuasive strategies were excluded because this review was concerned primarily with quantifying the effect of self-affirmation on intentions and behavior, independent of other behavior change strategies. This criterion led to the exclusion of three studies (Klein et al., 2010; Schneider, Gadinger, & Fischer, 2012; Zhao & Nan, 2010).

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In addition, the selected studies (a) measured a self-reported health behavior through a follow-up survey or (b) measured whether participants took a pamphlet or a sample of a health-related product, or through the amount they were willing to pay for a healthrelated product. On the basis of these criteria, we included 16 studies.

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Data Abstraction If studies presented more than one self-affirmation or control group, such as by separating participants with a low versus a high health risk or relevancy (as was the case for seven studies), the conditions with participants most likely to be threatened by the health information (e.g., higher health risk participants) were selected for inclusion in the meta-analysis. For studies that included more than one type of self-affirmation manipulation (which was the case for one study), conditions were selected to facilitate balanced groups in the moderator analysis of self-affirmation manipulation type (values vs. kindness). For studies with an experimental design comparing multiple types of health issues (this occurred in one study), conditions were selected to be as similar as possible to the other health issues studied in reports included in the review. We included studies with continuous (e.g., alcohol consumption; n ⫽ 8 studies) and dichotomous (e.g., number of participants taking a pamphlet; n ⫽ 4 studies) measures of behavior. If health behavior data were presented in both a dichotomous and a continuous format (e.g., Armitage, Harris, & Arden, 2011), data presented in a continuous format (e.g., means and standard deviations) were selected. If studies assessed behavior at multiple time points (as was done in two studies), data at the latest possible time point was selected. If studies did not explicitly report on the number of participants per condition (which occurred in 4 studies), authors were contacted with a request for this information. If authors were unresponsive (which occurred in two cases), ns were estimated based on the total reported N assuming an equal number of participants per condition. When possible, we selected data presented as raw means and standard deviations; and, in the interest of including as many studies in the review as possible, effect sizes were also calculated from data presented as adjusted means (which was the case for one study), and as frequencies. If a study presented insufficient data to calculate effect sizes (which occurred in four cases), researchers were contacted with a request to provide the necessary information. Two of the four researchers responded with the requested information. In addition to the potential moderator variables, data was collected on the region in which the data was collected (United States or Europe/United Kingdom), type of participant (student, nonstudent or a mix), racial/ethnic makeup of participants, proportion of females in the sample, the mean age of participants, type of control task, presentation of the health message (text, video, or text with images) and time point at which follow-up behavioral data was collected (same day, one week, one month).

Meta-Analytic Procedure Standardized mean difference effect sizes were calculated and aggregated using the statistical package, Comprehensive MetaAnalysis (v.2; CMA; Borenstein, Hedges, Higgins, & Rothstein, 2005). The aggregate effect sizes (d⫹) are corrected for sample size

bias and are based on random-effects assumptions (Schmidt, Oh, & Hayes, 2009). For each of the aggregate effect sizes, heterogeneity tests using the Q statistic were conducted to determine whether there was significant variability among each set of effect sizes (i.e., larger variability than would be expected from sampling error alone; Lipsey & Wilson, 2001). I2 values are reported to index the total percentage of variability in a set of effect sizes arising from between-study differences (e.g., I2 ⫽ 50 indicates that 50% of variability is due to sampling error and 50% to between-study differences; Higgins & Thompson, 2002). To test for systematic differences in effect sizes between studies, moderator analyses were conducted using the analogue-to-analysis of variance procedure in CMA using a mixedeffects model (Viechtbauer, 2005).2

Results The bibliographic search yielded 112 titles and abstracts. Reviews by two independent coders revealed that 23 studies were eligible for further review. After review of the full text of these reports, 16 studies were included in the final review, with 14 measuring intentions and 12 measuring behavior. A summary of the study selection process is provided in Figure 1. The total sample sizes ranged from 31 to 185. The mean age of participants ranged from 17.76 to 38.19, M ⫽ 25.19, SD ⫽ 7.00.3 Females made up 50 –100% of the participants in all studies, with five of the studies consisting only of females. The studies were conducted primarily in Europe or the United Kingdom (68.75%) and the participants were primarily undergraduate students (68.75%). See Table 1 for further description of the studies included in the review. As shown in Figure 2, the effect sizes (d⫹) for health intentions of self-affirmed versus control participants ranged from ⫺0.86 to 1.67. Positive effect sizes indicate increased intentions to decrease a health-damaging behavior or to increase a health-promoting behavior. The aggregate effect size for health intentions was d⫹ ⫽ .26, 95% CI ⫽ .04 –.48. For health behaviors, the effect sizes (d⫹) of self-affirmed versus control participants ranged from ⫺0.15 to 0.60. Positive effect sizes indicate that participants reduced a health-damaging behavior or increased a health-promoting behavior. For health behavior, the aggregate effect size was d⫹ ⫽ .27, 95% CI ⫽ .11–.43. The effect sizes for both health intentions and behavior are considered small in magnitude (Cohen, 1988). Using the meta-regression procedure in CMA, intention effect sizes were entered as a continuous predictor of behavior effect sizes in a mixed model meta-regression analysis. The results indicated that among the 10 studies assessing both outcomes, intentions effect sizes were not a significant predictor of behavior effect sizes, ␤ ⫽ .03, SE ⫽ .17, 95% CI ⫽ ⫺.30 –.36. This analysis suggests that the presence of an effect of intentions does not predict the presence of an effect of behavior. 2 Studies assessing behavior included both continuous and dichotomous measures. To pool data together from these two types of studies, dichotomous measures were re-expressed as a standardized mean difference (Deeks, Higgins, & Altman, 2011). 3 Descriptive statistics, including mean age and percent female, represent estimates. Most studies reported the mean age and gender for all of their participants, but in some instances only some of the conditions from a study were germane to our analyses.

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SELF-AFFIRMATION AND HEALTH RESPONSES

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Figure 1. CONSORT diagram.

Moderators of the Relationship Between Self-Affirmation and Health Intentions and Behavior

self-affirmation manipulation, and the specificity of health message. Table 2 shows the results of these analyses.

A significant amount of variability was found among the effect sizes reflecting the relation between self-affirmation and health intentions, Q(13) ⫽ 38.92, p ⬍ .01, I2 ⫽ 66.60, 95% I2CI ⫽ 41.31– 80.99; however, a nonsignificant amount was found among the effect sizes reflecting the relation between self-affirmation and behaviors, Q(11) ⫽ 15.02, p ⫽ .18, I2 ⫽ 26.75, 95% I2CI ⫽ 0 – 62.95. The test for heterogeneity of variance is a conservative test, particularly for small samples (Lipsey & Wilson, 2001). Thus, moderator analyses were conducted to assess whether any of the a priori moderators significantly accounted for the variability in effect sizes both among studies assessing intentions and behavior. Moderator analyses were conducted for the four categorical moderators: type of health behavior, proximal versus distal measures of health behavior, type of

Moderators related to the type of health-related behavior under study. The effect of type of health behavior was nonsignificant for intentions, QBetween (B) (1) ⫽ .62, p ⫽ .43 and behavior, QB (1) ⫽ .40, p ⫽ .53. For both outcomes, studies focusing on health-damaging behaviors yielded larger effect sizes than those focusing on health-promoting behaviors. Among studies of behavior, the effect of proximal versus distal measures of health behavior was nonsignificant, QB (1) ⫽ .60, p ⫽ .44. Studies with distal measures of behavior yielded an effect size that was medium and significantly different from zero, whereas studies with proximal measures yielded an effect size that was not significantly different from zero. This difference was not in the hypothesized direction.

Exercise behavior Exercise behavior Caffeine intake Alcohol intake

Fruit/vegetable intake Caffeine intake Condom use

Jessop (2014); Study 1 Jessop (2014); Study 2 Klein (2011); Study 2 Meier (2010)

Pietersma (2011)

Note.

Yes

Yes

No

Yes

Yes

No

Yes

Yes

Health promoting

Health promoting Health damaging

Health promoting Health damaging

Health damaging

Health promoting Health promoting Health promoting Health damaging

Health promoting Health promoting Health damaging

Health damaging

Health damaging

Health damaging

Type of health behavior

84

47

—

33

147

90

50

61

67

82

36

170

93

34

—

57

n for intention

84

—

61

33

131

90

—

53

54

82

31

—

87

—

185

57

n for behavior

Proximal

—

Distal; 2 week FU Distal; 1 month FU Distal; 1 week FU Proximal

Distal; 1 week FU Distal; 1 week FU —

Distal; 1 month FU Proximal

Distal; 1 week FU —

Distal; 1 month FU —

Proximal

Measurement of health behavior

Values affirmation

Kindness affirmation Kindness affirmation Values affirmation Kindness affirmation Values affirmation Values affirmation Kindness affirmation Kindness affirmation Kindness affirmation Values affirmation Values affirmation Values affirmation Kindness affirmation Values affirmation Values affirmation

Type of selfaffirmation manipulation

One health issue

Multiple health issues Multiple health issues Multiple health issues Multiple health issues One health issue One health issue One health issue Multiple health issues Multiple health issues One health issue Multiple health issues Multiple health issues One health issue One health issue Multiple health issues

Specificity of health message

U.K./Europe

U.K./Europe

U.S.

U.S.

U.K./Europe

U.S.

U.S.

U.K./Europe

U.K./Europe

U.K./Europe

U.K./Europe

U.K./Europe

U.K./Europe

U.S.

U.K./Europe

U.K./Europe

Location

Nonstudent sample

Nonstudent sample Nonstudent sample Student sample Student sample Student sample Student sample Nonstudent sample Students and nonstudents Student sample Student sample Student sample Student sample Student sample Student sample Student sample

Participants

For ⴱ college age and ⴱ adult age, mean age was not available in the text. The dash (⫺) indicates that data were not obtained or applicable. FU ⫽ follow-up.

Sherman (2000); Study 2 van Koningsbruggen (2009) van Koningsbruggen (2009)

Reed (1998)

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Sunscreen use

Jessop (2009)

Yes

Type 2 diabetes

Alcohol intake

Harris (2005)

No

Yes

Yes

Yes

No

Yes

Yes

Behavior

Yes

Yes

Caffeine intake Fruit/vegetable intake Sunscreen use

Caine (2004); Study 2 Epton (2008)

No

Yes

Intention

Caffeine intake

Alcohol intake

Armitage (2011)

Good (2011)

Smoking

Health behavior

Armitage (2008)

First author and year

Table 1 Descriptives for Studies Included in the Review

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38.19

College ageⴱ 23.62

College ageⴱ College ageⴱ College ageⴱ 20.60

20.65

25.02

33.33

18.80

17.76

69.04

76.19

51.67

100.00

75.47

51.11

100.00

51.72

73.33

100.00

100.00

100.00

100.00

55.00

66.20

Adult ageⴱ College ageⴱ 21.50

82.46

Gender (% female) 32.40

M Age

154 SWEENEY AND MOYER

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SELF-AFFIRMATION AND HEALTH RESPONSES

Figure 2.

155

Forest plots for intentions (top) and behavior (bottom).

Moderators related to the experimental method. The effect of type of self-affirmation manipulation was nonsignificant for both intentions, QB (1) ⫽ .31, p ⫽ .58, and behavior, QB (1) ⫽ 1.31, p ⫽ .25. For intentions, studies with the values affirmation manipulation yielded a medium effect size that was significantly different from zero. Conversely, for behavior, studies with the kindness affirmation manipulation yielded a medium effect size that was significantly different from zero. The effect of specificity of the health message was nonsignificant for intentions, QB (1) ⫽ .00, p ⫽ .96, and behavior, QB (1) ⫽ 2.77, p ⫽ .10. For both

outcomes, studies with messages addressing multiple health issues yielded small to medium effect sizes that were significantly different from zero.

Publication Bias To assess whether publication bias impacted the aggregate effect sizes for intentions and behavior, a trim-and-fill analysis was conducted. Duval and Tweedie’s (2000) trim-and-fill analysis estimates whether there are missing effect size values, imputes the

Table 2 Moderator Analyses Intention Moderator Type of health behavior Health damaging Health promoting Proximal vs. distal measure of behavior Distal measures Proximal measures Type of self-affirmation manipulation Kindness affirmation Values affirmation Specificity of health messages One health issue Multiple health issues Note. 95% CI ⫽ 95% confidence interval.

k

d

95% CI

7 7

0.39 0.17

⫺0.13–0.90 0.02–0.32

Behavior p

k

d

95% CI

p

.14 .03

5 7

0.33 0.22

0.05–0.61 0.02–0.42

.02 .03

8 4

0.33 0.17

0.17–0.50 ⫺0.20–0.55

⬍.01 .37

6 8

0.19 0.32

⫺0.16–0.53 0.01–0.63

.29 .05

7 5

0.35 0.17

0.14–0.56 ⫺0.06–0.40

⬍.01 .16

6 8

0.27 0.26

⫺0.22–0.76 0.06–0.45

.28 ⬍.01

5 7

0.09 0.37

⫺0.19–0.37 0.20–0.53

.53 ⬍.01

SWEENEY AND MOYER

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missing values, and then recalculates the aggregate effect size. Using a random-effects model, among studies of intentions, zero studies were estimated as missing, such that the prediction for d⫹ remained unadjusted. Among studies of behavior, three studies with medium to large positive effect sizes were estimated as missing, with the adjusted d⫹ predicted as .37, 95% CI ⫽ .19 –.54. Together, these results suggest minimal publication bias among the studies in the review.

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Discussion The present meta-analytic review examined whether or not those who self-affirmed display increased health intentions and behavior change relative to those who did not self-affirm in response to a health message. The meta-analysis yielded small effect sizes for both health-related intentions and behaviors, with the difference between those who self-affirmed and those who did not being significantly different from zero. In addition, among studies assessing both outcomes, the size of effects on intentions was not a significant predictor of the size of effects on behavior. A large amount of heterogeneity was detected in the overall analysis for health intentions. However, type of health behavior (damaging vs. promoting), timing of the measure of health behavior (proximal vs. distal), type of self-affirmation manipulation (values vs. kindness), and the specificity of the health message (single vs. multiple health issues) did not moderate the effect of self-affirmation on health intentions or behavior.

Strengths and Limitations of the Review Strengths of the review. The present review adds to previous research on self-affirmation and health outcomes in several ways. Whereas past reviews (Harris & Epton, 2009) were limited by the scarcity of studies on self-affirmation and health behavior, this is the first systematic review to quantify the effects of selfaffirmation on intentions and behavior. By providing evidence that self-affirmation has an effect of similar magnitude on health intentions and behaviors, this quantitative summary generates empirical support for the prediction that self-affirmation has the potential to impact health behavior, as well as health intentions. One strength of meta-analytic reviews is that they can draw attention to gaps in an existing literature. One limitation brought to light by this review pertains to the issue of generalizability. Although a few nonstudent populations were included (e.g., Armitage et al., 2008; Armitage et al., 2011), most of the studies in this review included groups of participants who were young women from Western countries; this pattern was observed in Harris and Epton’s (2009) review as well. As suggested by an anonymous reviewer, it is feasible that young adults may be more concerned with self-threats, whereas older adults care more about their physical health. To better address concerns of generalizability, future researchers should consider testing the effects of self-affirmation among different populations, including older adults. A second limitation brought to light by the review pertains to establishing whether a change in intentions following a selfaffirmation induction causes a change in behavior. In the present review, the meta-regression results indicate that the size of an effect on intentions does not predict the size of an effect on behavior; however, such an analysis does not indicate whether an

individual with high intentions is likely to show a large change in behavior. To assess the latter, one would need to know the correlation between intentions and behavior in each study, which was reported in very few studies. Past reviews of general behavior change, such as Webb and Sheeran (2006), have included studies that report the correlation between intentions and behaviors, thereby making it possible to conduct mediational analyses to test for a causal relationship. Surprisingly, among the studies included in the present review, researchers did not tend to report the correlation between intentions and behavior when they were both assessed. Two exceptions are Armitage and colleagues (2008) who reported a medium correlation (r ⫽ .41) between intentions and behavior, and Reed and Aspinwall (1998) who reported a small correlation (r ⫽ .01). In recent studies of self-affirmation, emphasis has been placed on measuring health behavior in addition to other health-related responses (e.g., Epton & Harris, 2008), but it remains unclear how self-affirmation inductions facilitate health behavior change in response to health messages. Specifically, limited research has addressed whether changes in behavior arising from self-affirmation are a result of changes in intentions. To date, only a few studies have addressed whether intentions mediate the effect of self-affirmation on behavior. One study found that among participants who were at a higher risk for developing Type 2 diabetes, intentions to take an online diabetes risk test mediated the effect of self-affirmation on participation in an online risk test (van Koningsbruggen & Das, 2009). Another study reported that moderately involved individuals who were self-affirmed ate more cooked vegetables than those who were not self-affirmed; the effect of the interaction between level of involvement and selfaffirmation condition on health behavior was mediated by intentions (Pietersma & Dijkstra, 2011). However, the same pattern was not reported for fruit intake. Taken together, these findings offer initial evidence for a causal relation between intentions and behavior at the individual level in studies of self-affirmation; however, it is evident that further research is needed to confirm and extend these results. With the present results indicating that self-affirmation has similar effects on intentions and behavior, it is tempting to assume that changes in intentions arising from self-affirmation should give rise to changes in behavior. However, with past reviews finding that across several domains intentions do not always translate to behavior (Webb & Sheeran, 2006), it is important to base such assumptions on experimental evidence. It remains plausible that an increase in health intentions after a self-affirmation induction causes a change in behavior; but, to date, there is limited empirical support for this claim. Alternatively, other responses may prove to be better predictors of health behavior change arising from selfaffirmation inductions than intentions. For example, Epton and Harris (2008) found that the effect of self-affirmation on behavior was mediated by response-efficacy (i.e., believing that changing one’s behavior will benefit one’s health), such that participants in the self-affirmation condition showed increased response-efficacy relative to the control condition. Furthermore, other research indicates that intentions help to explain the effect of self-affirmation on behavior when additional variables are considered. For example, Armitage and colleagues (2008) found that health message acceptance mediated the relation between self-affirmation condition and intentions, and intentions mediated the relation between

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SELF-AFFIRMATION AND HEALTH RESPONSES

message acceptance and behavior. In showing that very few selfaffirmation studies report on the correlation between health-related intentions and behavior, we hope the present review will encourage researchers to address this notable gap in future research. Another important contribution of the present review is that it provides researchers with empirical evidence on which to base decisions relating to the design of future studies on health outcomes and self-affirmation. For example, future research may benefit from having initial evidence that the type of selfaffirmation manipulation does not significantly impact the effectiveness of self-affirmation. Similarly, researchers interested in a variety of health behaviors may find it useful to have empirical evidence that the type of health behavior (e.g., promoting vs. damaging) and the time of measurement (proximal vs. distal health behaviors) do not substantially moderate the effectiveness of selfaffirmation on intentions and behavior. Limitations. Although the present review sought to include as many studies as possible, the aggregate effect sizes were based on a relatively small number of studies. The number of studies may have limited our power to detect significant moderating variables. The review did not find any significant moderators; however, one should not assume based on these moderator analyses that message characteristics, aspects of the self-affirmation manipulation, outcome measurement, and method are unimportant. The overall analyses for health intentions yielded a significant amount of heterogeneity, which suggests that there is variance to be explained, but the moderators that we examined do not capture the full story. Future research is needed to further understand the influence of self-affirmation on health intentions and behavior. Furthermore, the trim and fill publication bias analysis indicated that, among studies of behavior, the review was missing a small number studies with medium to large positive effect sizes. This result suggests that the true effect sizes of self-affirmation on health behavior may have been underestimated. In the data abstraction for the present review, conditions were selected in which individuals would be most likely to be affected by self-affirmation (e.g., high-risk and high-relevancy participants). As a result, it is important to acknowledge that the review may have maximized the likelihood of finding a significant effect of self-affirmation. The decision to use only high-risk/relevancy groups was guided by the idea that self-affirmation is not thought to influence health responses among individuals for whom a health behavior is not relevant. Instead, past research has found that affirming participants prior to reading information that is not threatening to them reduces information processing, leading to less positive attitudes toward the information (Briñol, Petty, Gallardo, & DeMarree, 2007).4 Given that self-affirmation acts as a buffer against threats to one’s self-integrity (Steele, 1988), health information should not be perceived as a self-threat among individuals for whom a behavior is not relevant. Thus, we reasoned that a review that focused on those groups most likely to be threatened by a health message would be a more useful summary of this body of research. Finally, in the interest of generating a strong test of the potentially moderating roles of aspects related to the behavior under study and the experimental methods, the present review does not reflect the full breadth of self-affirmation research. For example, in addition to the values and kindness affirmation manipulations, there are other means for affirming the self, including a manipu-

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lation in which participants focus on positive aspects of the self (Jessop et al., 2009) and a manipulation that combines the value affirmation procedure with the “if-then” format of implementation intentions (Armitage et al., 2011). In the present review we did not systematically exclude studies based on the type of selfaffirmation manipulation; however when presented with a study with multiple types of self-affirmation manipulations (Jessop et al., 2009), we prioritized selecting conditions that would facilitate roughly equal conditions for the moderator analysis. The authors acknowledge that this decision may have contributed to an underestimation of the variability of the effect of self-affirmation inductions on intentions and behavior. Relatedly, whereas the present review focused on studies with written health messages, selfaffirmation inductions have been used successfully in combination with persuasive health-related images (e.g., Harris, Mayle, Mabbott, & Napper, 2007; Schüz, Schüz, & Eid, 2013). Implications for practice and research. An advantage of self-affirmation inductions is that they are relatively brief, inexpensive, and easy to implement. For example, one study found that leading smokers to self-affirm by simply asking them to write down their desirable traits increased intentions to quit smoking relative to a control group (Harris et al., 2007). Importantly, past research has found some success in delivering self-affirmation inductions in contexts outside the lab; for example, Jessop and colleagues (2009) administered self-affirmation inductions to beach-goers before providing them with information about sunscreen use. Such studies suggest that self-affirmation has the potential to be a useful intervention tool in applied contexts. In future work, practitioners may consider structuring public health campaigns in such a way that that individuals are first reminded of a value or personal strength before they receive a health appeal (see Charlson et al., 2007 for a related approach). As indicated in the present review, there is evidence that selfaffirmation impacts immediate responses to health messages, including self-reported health intentions and behaviors measured over short periods of time. A next step for self-affirmation research in the health domain may be to identify under what conditions self-affirmation gives rise to long-term behavioral change. In other domains, researchers have found that self-affirmation can have long-lasting positive effects. For example, in a field study focused on academic achievement, minority students in middle school were randomly assigned to receive a series of brief self-affirmation manipulations or a control task (Cohen, Garcia, Purdie-Vaughns, Apfel, & Brzustoski, 2009). Over 2 years, students in the selfaffirmation group showed a positive increase in grade point average and self-perception relative to the control group. Such findings suggest that delivering a self-affirmation induction at a time when the self is especially vulnerable (e.g., when students first encounter academic challenges), plays an important role in changing the trajectory of one’s actions and thoughts about the self. It remains 4 Consistent with past research, of the studies in this review, four provided means and standard deviations comparing the effect of selfaffirmation on health intentions between high and low relevancy/risk participants. Among low relevancy/risk participants, the effect of selfaffirmation on intentions (relative to a control group) was negative, d⫹ ⫽ ⫺.26, CI ⫽ ⫺.60 –.08. Conversely, among high relevancy/risk participants, the effect of self-affirmation on intentions (relative to a control group) was positive, d⫹ ⫽ .37, CI ⫽ ⫺.59 –1.34.

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to be tested whether a similar pattern holds true in the health domain. To address this possibility, future self-affirmation research may consider targeting individuals who have made an initial attempt to change a health behavior but were unsuccessful in doing so.

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Conclusions Overall, the meta-analysis presents a promising view that selfaffirmation impacts behavior, in addition to intentions. Finding ways to encourage individuals to change unhealthy behaviors has been a long-standing challenge for psychologists. Not only does self-affirmation appear to be a useful tool for reducing biased defensive processing of health information, it also has an impact on people’s actions. We hope evidence of small to moderate effectiveness of such manipulations will act as an impetus for more studies on the effectiveness of self-affirmation on health intentions and behaviors.

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Received October 15, 2013 Revision received April 7, 2014 Accepted April 17, 2014 䡲