A meta-analytic review of the relationship between adolescent risky sexual behavior and impulsivity across gender, age, and race.

Clinical Psychology Review 34 (2014) 551–562

Contents lists available at ScienceDirect

Clinical Psychology Review

A meta-analytic review of the relationship between adolescent risky sexual behavior and impulsivity across gender, age, and race Allyson L. Dir ⁎, Ayca Coskunpinar, Melissa A. Cyders Department of Psychology, Indiana University Purdue University-Indianapolis, United States

H I G H L I G H T S • • • • •

Meta-analysis on relationship between impulsivity and risky sex among adolescents. The relationship between impulsivity and adolescent risky sex is significant and small. Effects are similar across unique risky sexual behaviors and impulsivity traits. Gender was a significant moderator in the impulsivity–risky sex relationship. Samples with more females showed stronger effects for impulsivity and risky sex.

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Article history: Received 8 November 2013 Revised 13 August 2014 Accepted 14 August 2014 Available online 16 September 2014 Keywords: Impulsivity Adolescence Risky sexual behavior Gender differences Meta-analysis

a b s t r a c t Background: Impulsivity is frequently included as a risk factor in models of adolescent sexual risk-taking; however, findings on the magnitude of association between impulsivity and risky sexual behavior are variable across studies. The aims of the current meta-analysis were to examine (1) how specific impulsivity traits relate to specific risky sexual behaviors in adolescents, and (2) how the impulsivity–risky sex relationship might differ across gender, age, and race. Method: Eighty-one studies were meta-analyzed using a random effects model to examine the overall impulsivity– risky sex relationship and relationships among specific impulsivity traits and risky sexual behaviors. Results: Overall, results revealed a significant, yet small, association between impulsivity and adolescent risky sexual behavior (r = 0.19, p b 0.001) that did not differ across impulsivity trait. A pattern of stronger effects was associated with risky sexual behaviors as compared to negative outcomes related to these behaviors. Gender moderated the overall relationship (β = 0.22, p = 0.04), such that effect sizes were significantly larger in samples with more females. Age, race, study design, and sample type did not moderate the relationship, although there was a pattern suggesting smaller effects for adolescents in juvenile detention settings. Conclusions: Adolescent samples with more females showed a larger impulsivity–risky sex relationship, suggesting that impulsivity may be a more important risk factor for risky sex among adolescent females. Research and treatment should consider gender differences when investigating the role of impulsivity in adolescent sexual risktaking. © 2014 Elsevier Ltd. All rights reserved.

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Impulsivity as a risk factor for risky sexual behavior 1.1. Sensation seeking . . . . . . . . . . . . 1.2. Negative and positive urgency . . . . . . 1.3. Lack of planning and lack of perseverance . Disaggregation of risky sexual behavior . . . . .

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⁎ Corresponding author at: Department of Psychology, Indiana University Purdue University-Indianapolis, 402 N. Blackford St., LD 124, Indianapolis, IN 46202, United States. Tel./fax: +1 317 274 6752. E-mail address: [email protected] (A.L. Dir).

http://dx.doi.org/10.1016/j.cpr.2014.08.004 0272-7358/© 2014 Elsevier Ltd. All rights reserved.

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Differential relationships across gender, age, and race . . . . . . . . . . . . . . 3.1. Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Race . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The current study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Selection of studies . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Meta-analytic method . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1. Moderator analyses . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Research question 1: general impulsivity and risky sexual behavior . . . . . 6.2.1. Effect size variance across impulsivity and risky sexual behavior type 6.3. Research question 2: gender, age, and race as moderators . . . . . . . . . 6.3.1. Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2. Age and race . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3. Supplemental moderation analyses . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix A. Supplementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Adolescent risky sexual behavior is a major public health concern, due to the disproportionate number of adolescents who experience negative sexual health outcomes (e.g., DiClemente, Salazar, & Crosby, 2007; Kotchick, Shaffer, Miller, & Forehand, 2001). For instance, although adolescents and young adults (age 15–24) account for just 25% of sexually active individuals in the U.S., this group constitutes over half of new sexually transmitted disease (STD) cases each year among all sexually active individuals in the U.S. (Centers for Disease Control and Prevention, 2008). On average, one in four female adolescents have a history of an STD, and African Americans are overrepresented (e.g., Centers for Disease Control and Prevention, 2008; Fergus, Zimmerman, & Caldwell, 2007). Also, females aged 18–24 account for the largest proportion of unintended pregnancies in the U.S. (Finer & Henshaw, 2006). Adolescents are likely at increased risk for such negative sexual health outcomes partly due to their high rates of unprotected sex with multiple partners. Less than 50% of sexually active adolescents report using condoms on a regular basis (e.g., Martinez, Copen, & Abma, 2011), and when asked about their most recent sexual encounter, almost one-third of male and half of female high school students reported not using any form of protection (Centers for Disease Control and Prevention, 2002). Additionally, 14% of sexually active adolescents report four or more lifetime sexual partners (Centers for Disease Control and Prevention, 2004; DiClemente et al., 2010), which is higher than other age groups, especially considering their nascent sexual history. Despite these alarming statistics, some researchers in adolescent development and sexual health emphasize that sexual experimentation is a healthy hallmark of adolescent development (see Smith, Udry, & Morris, 1985; Zimmer-Gembeck & Helfand, 2008); however, the overrepresentation of adolescents in both rates of risky sexual behaviors and negative sexual health outcomes highlights the need to understand risk factors associated with these risky sexual behaviors and possible related medical consequences in order to effectively identify and intervene on those who are at greatest risk for such outcomes.

2004) and adolescents (e.g., Broaddus & Bryan, 2008; DiClemente et al., 2008, 2010; Kahn, Kaplowitz, Goodman, & Emans, 2002; Khurana et al., 2012; Zimmerman, 2010). Impulsivity likely influences risky sexual behaviors among adolescents because (1) adolescence often marks the initiation of sexual exploration (e.g., Smith et al., 1985), (2) learning about sexual encounters is likely related to impulsivity's effects on expectancy formation (Smith & Anderson, 2001); (3) adolescence corresponds with neurobiological changes related to increases in impulsive behavior and reward seeking (e.g., Romer, 2010; Steinberg, 2008), and (4) adolescence is characterized by decreased parental monitoring and increased peer influences on behavior (e.g., Dahl, 2004; Smetana, Campione-Barr, & Metzger, 2006). However, evidence for the relationship of impulsivity and risky sexual behavior in adolescents is mixed (e.g., Breakwell, 1996; Brown, DiClemente, & Park, 1992; White & Johnson, 1988). One potential reason for such inconsistencies is that impulsivity is a heterogeneous trait. The current study uses the UPPS-P framework (Lynam, Smith, Cyders, Fischer, & Whiteside, 2007) to separate trait impulsivity into separate unique aspects of impulsive behavior: (1) lack of perseverance, defined as the tendency to not finish tasks; (2) lack of planning, defined as the tendency to act without thinking; (3) sensation seeking, defined as the tendency to engage in exciting adventures and seek arousal; (4) negative urgency, defined as the tendency to act rashly in response to extreme negative emotions; and (5) positive urgency, defined as the tendency to act rashly in response to extreme positive emotions (Lynam et al., 2007). Identification and measurement of these separate traits have revealed discrete relationships with a number of risky behaviors (e.g., Coskunpinar, Dir, & Cyders, 2013; Cyders, Flory, Rainer, & Smith, 2009; Fischer & Smith, 2008; Gunnarsson, Gustavsson, Tengström, Franck, & Fahlke, 2008; Romer, 2010; Zapolski, Stairs, Settles, Combs, & Smith, 2010), including risky sexual behavior (e.g., Deckman & DeWall, 2011; Sher & Trull, 1994; Sultan & Pipon, 1996; Zapolski, Cyders, & Smith, 2009). However, much of this evidence is based on findings from adult samples, and thus this should be examined in adolescent groups.

1. Impulsivity as a risk factor for risky sexual behavior 1.1. Sensation seeking Impulsivity is a prominent personality-based risk factor that is consistently included in models of risky sexual behavior for both adults (Deckman & DeWall, 2011; Hoyle, Fejfar, & Miller, 2000; Miller et al.,

Sensation seeking is a robust predictor of risky sexual behavior among young adults (e.g., Hoyle et al., 2000; Justus, Finn, & Steinmetz,


2000) and adolescents (e.g., Desrichard & Denarié, 2005; DiClemente et al., 2010; Zimmerman et al., 2007). Adolescent sensation seekers report more frequent sex (Zapolski et al., 2009), less condom use (e.g., Donohew et al., 2000; Robbins & Bryan, 2004), more lifetime sexual partners (Kalichman, Tannenbaum, & Nachimson, 1998; Kalichman et al., 1994; Spitalnick et al., 2007; Tercek, 2008), more frequent alcoholinduced sexual encounters (e.g., Cooper, Peirce, & Huselid, 1994), earlier sex initiation (Kahn et al., 2002; Tercek, 2008), and more diverse risky sexual experiences (e.g., Donohew et al., 2000; Vélez-Blasini, 2008; Zuckerman & Kuhlman, 2000). Research on risky sexual behavior in adolescents has disproportionately focused on sensation seeking; thus, the influence of other impulsivity traits is not yet well understood. 1.2. Negative and positive urgency Negative and positive urgency (i.e., risk taking in response to negative or positive emotional states, respectively; Cyders & Smith, 2008) are thought to increase in adolescence due to neurobiological changes in systems underlying emotional experiences and impulse control (Cyders & Smith, 2008), and thus might be a particularly important factor for sexual risk-taking among adolescents. Urgency has been linked to increased neural responding to emotional stimuli in the amygdala and orbitofrontal cortex, suggesting a mechanism by which brain responsivity contributes to urgency and emotion-based risktaking (Cyders et al., 2014). Adolescence is characterized by increased emotional volatility and risk-taking, particularly when experiencing affect (e.g., Steinberg, 2008). These behaviors are thought to be affected by incomplete prefrontal cortex formation during adolescence, which leads to inability to regulate extreme emotional experiences effectively (e.g., Cyders & Smith, 2008). In fact, negative urgency is associated with STD acquisition (Tercek, 2008) in adolescents, and both negative and positive urgency are associated with general risky sexual behavior in young adults (Deckman & DeWall, 2011; Zapolski et al., 2009). Thus, adolescents might be drawn to risky sexual behaviors in response to intense negative or positive emotions.

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2000). This broadly heterogeneous conceptualization has led to inconsistency in the measurement of risky sexual behavior. For example, while some studies measure risky sex as one specific sexual behavior, others measure risky sex as a composite score of multiple sexual behaviors, and these measurement differences are likely contributing to incongruent findings on specific factors that are involved in the risk process. The current study uses the framework proposed by Hoyle et al. (2000), which disaggregates risky sexual behavior into the following separate domains: (1) unprotected sex, (2) multiple lifetime sexual partners, and (3) hazardous sexual activity, defined as sexual encounters with added risky situational factors (i.e., sex with a stranger, sex while intoxicated, sex with multiple partners simultaneously). Using these separate domains has revealed more robust and unique relationships with personality traits across literature based on adult samples (Hoyle et al., 2000). For example, sensation seeking demonstrated the most robust effects with overall risky sexual behavior, and had a significantly stronger effect with number of sexual partners than with condom use (Hoyle et al., 2000; Miller et al., 2004). One unique issue in the adolescent literature is that some conceptualize any adolescent sexual activity as risky, which creates further inconsistency in measurement (e.g., Beadnell et al., 2007; Metzler, Noell, & Biglan, 1992). This is problematic because some risky sexual behaviors are more highly related to negative health outcomes, such as unprotected sex with multiple partners (Beadnell et al., 2007). Therefore, we further disaggregated risky sexual behavior in order to consider the unique aspects of adolescent sex, including age at sexual initiation, frequency of sexual activity, STD history, and history of unplanned/unintended pregnancy. Taken together, disaggregating adolescent risky sexual behavior may explain divergent findings and offer evidence for discrete relationships between risk factors and specific behaviors. Therefore, the second goal of the current review is to examine how impulsivity is associated with specific domains of risky sexual behaviors among adolescents. 3. Differential relationships across gender, age, and race

1.3. Lack of planning and lack of perseverance

3.1. Gender

Although less well established, lack of planning is linked to early sexual debut (Raffaelli & Crockett, 2003), unprotected sex, STD acquisition, more lifetime sexual partners (Kahn et al., 2002; Tercek, 2008), and unplanned pregnancy (Rawlings, Boldero, & Wiseman, 1995) among adolescents. Likewise, self-regulation, a trait that entails elements of lack of planning and perseverance (Lengua, 2002; Peterson & Zill, 1986), is also a correlate of adolescent sexual behavior (Raffaelli & Crockett, 2003). The direction of this relationship might vary; for instance, some might plan to engage in safe sexual practices (high planning), but fail to follow through with such a plan (low perseverance), while others might plan to engage in a risky sexual encounter and follow through with such a plan (high planning and high perseverance). Taken together, specific impulsivity traits might have discrete relationships with risky sexual behaviors, based on evidence in adult samples (e.g., Deckman & DeWall, 2011; Hoyle et al., 2000; Miller et al., 2004); however, this has not been examined among one of the most high risk populations: adolescents. Therefore, one goal of the current review is to examine the pattern of relationships between specific impulsivity traits and risky sexual behavior among adolescents.

Evidence for different effects of impulsivity on risky sexual behavior across adolescent males and females is inconsistent (e.g., Crockett, Raffaelli, & Shen, 2006; Cyders, 2013; Kraft & Rise, 1994); however, there is substantial evidence that adolescent males and females have varying (1) impulsivity trait levels (e.g., d'Acremont & Van der Linden, 2005) and (2) patterns of risky sexual behavior (Kotchick et al., 2001; Leigh, Morrison, Trocki, & Temple, 1994; Seidman & Reider, 1994). Adolescent males often show more significant changes in dopaminergic activity during adolescence (Spear, 2011) and report higher levels of sensation seeking and risk-taking (Bongers, Koot, Van der Ende, & Verhulst, 2003; Eysenck, Easting, & Pearson, 1984), while females report higher levels of negative urgency (d'Acremont & Van der Linden, 2005). Moreover, males start having sex earlier than females, report more lifetime sexual partners, and engage in multiple risky sexual behaviors (Grunbaum et al., 2002, 2004; Kotchick et al., 2001; Newman & Zimmerman, 2000; Romer & Hennessy, 2007), while females report higher rates of STDs (Murphy, Rotheram-Borus, & Reid, 1998).

2. Disaggregation of risky sexual behavior

There are significant biological, individual, cognitive, contextual, and social changes that occur during adolescence (e.g., Bauermeister, Zimmerman, Caldwell, Xue, & Gee, 2010; Moilanen, Crockett, Raffaelli, & Jones, 2010; Sales et al., 2012; Smetana et al., 2006) that likely influence the relationship between impulsivity and risky sexual behavior (e.g., Blakemore & Robbins, 2012; Brenhouse & Andersen, 2011; Harden & Tucker-Drob, 2011; Romer, 2010; Somerville & Casey, 2010; Somerville, Jones, & Casey, 2010). For example, changes in neurobiological

Conceptualizations of risky sexual behavior subsume a number of different sexual activities, including unprotected sex, sex with an uncommitted partner, multiple sexual partners, sex while intoxicated or high, and other sexual activities with increased risk of STD or unplanned pregnancy (e.g., Beadnell, Wilsdon, Wells, Morison, & Gillmore, 2007; Fortunato, Young, Boyd, & Fons, 2010; Hoyle et al.,

3.2. Age

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systems involved in impulse control and reward sensitivity occur throughout adolescence, and parallel increases in risky sexual behavior across this time period (e.g., Blakemore & Robbins, 2012; Romer, 2010; Steinberg, 2008). Relatedly, sexual activity becomes more normative across adolescence, and may be more closely tied to impulsivity in early adolescence (e.g., Grunbaum et al., 2002, 2004).

impulsivity (e.g., protected vs. unprotected sex), or compared groups of higher versus lower levels of impulsivity on a sex outcome. Authors of studies that did not include sufficient data were emailed and given one month to provide the necessary information. A flowchart, including numbers of studies excluded based on each criterion, is included in Fig. 1.

3.3. Race

5.2. Meta-analytic method

There is evidence that the relationship between impulsivity and risky sex is more robust among Caucasians than African Americans (Hipwell, Keenan, Loeber, & Battista, 2010), suggesting that impulsivity may be a more salient risk factor for risky sexual behavior among Caucasians compared to African Americans. African American adolescents report higher rates of risky sexual behavior and negative outcomes, such as higher rates of HIV acquisition, more frequent sexual activity, more lifetime sexual partners, earlier age at sexual initiation, and lower rates of condom use (Black, Ricardo, & Stanton, 1997; Brown et al., 1992; Grunbaum et al., 2002, 2004; Kann et al., 1998; Kotchick et al., 2001; Salazar et al., 2011; Stanton et al., 1993), suggesting that sexual risk-taking is more normative in African American youth, and less tied to impulsive tendencies. Alternatively, socioeconomic and environmental risk factors, including parent–child relationships and communication are likely to have a stronger influence on sexual risktaking in African American youth (e.g., Li, Fiegelman, & Stanton, 2000; Santelli, Lowry, Brener, & Robin, 2000), which would suggest different prevention and treatment approaches.

Pearson's r was used as the effect size statistic for the relationship between impulsivity and risky sexual behavior. Studies that did not report a correlation were converted to r based on conversion formulas (as suggested by Borenstein, Hedges, Higgins, & Rothstein, 2009; Lipsey & Wilson, 2001) and an effect size calculator (provided by Wilson, 2010). All effect sizes were coded such that higher positive values indicated higher levels of impulsivity and higher levels of risky sexual behavior. Mean effect sizes were calculated using SPSS 21.0 and macros provided by Wilson (2010). The overall relationship between impulsivity and risky sexual behavior, as well as relationships between specific impulsivity facets and risky sexual behaviors, were examined. In cases where one sample provided multiple effect sizes for an association, effect sizes were averaged to produce one mean effect size per distinct sample, thus ensuring independence of effect sizes in the meta-analytic analyses. Effect sizes were converted using a Fisher's Z transformation and weighted based on their inverse variance weight to account for differences in sample size (using SPSS macros by Wilson, 2010). Effect sizes of less than 0.10 were considered small, effects of 0.25 were considered medium, and effect sizes greater than or equal to 0.40 were considered large (Lipsey & Wilson, 2001). A random effects model was used in order to generate the most conservative estimates of effect sizes (Lipsey & Wilson, 2001). Traditionally, the Q-test is a commonly used measure of heterogeneity, and a significant Q-test suggests that the variation across the weighted mean effect sizes is greater than is expected by chance. However, the Q-test does not provide a quantitative value representing how much heterogeneity there is across effect sizes. The I2 index is a measure of heterogeneity calculated from Q that calculates the proportion of heterogeneity in effect sizes; values range from 0 to 1.0 (0–100%) with higher values indicating more true heterogeneity (Higgins & Thompson, 2002; Huedo-Medina, Sanchez-Meca, Marin-Martinez, & Botella, 2006). Therefore, the I2 index was used to determine the proportion of heterogeneity across effect sizes. Fail-safe N analyses were also conducted to estimate how many studies with null findings would be necessary to drop the effect sizes to non-significance (Lipsey & Wilson, 2001; Rosenthal, 1979). In addition to the fail-safe N, the Egger's regression test of asymmetry was used to examine publication bias and effect size asymmetry; the more values deviate from zero, the higher the level of publication bias (Egger, Smith, Schneider, & Minder, 1997).

4. The current study This is the first empirical review to examine the relationship between specific facets of impulsivity and risky sexual behaviors among adolescents, and how this relationship might vary across gender, age, and race. Understanding how risk patterns might differ across sample characteristics will foster the development of targeted and more effective intervention and prevention strategies in this high-risk group. We hypothesized that the relationship would differ across impulsivity facet and sexual behavior, and across gender, age, and race of the sample. 5. Methods 5.1. Selection of studies Relevant studies were identified via literature searches, using Medline, PsychInfo, PsychArticles, Web of Science, EMBase, and PubMed (published before November 2013), as well as reference section reviews, forward searches, and email alerts. Searches were performed based on all keyword combinations of terms for impulsivity and sexual behavior (see Appendix A), as used in previous reviews (e.g., Coskunpinar et al., 2013; Hoyle et al., 2000). Studies were included if they used both self-report measures of impulsivity and sexual behavior, and were based on adolescent samples with a mean age or age range between 10 and 19 years (with an upper limit of age range at 25), based on previous conceptualizations of adolescence (Sales et al., 2012; Smetana et al., 2006). Impulsivity measures were assigned to separate impulsivity traits based on a factor analysis by Whiteside and Lynam (2001) and by previous metaanalyses (e.g., Coskunpinar et al., 2013; Cyders & Coskunpinar, 2012; see Appendix B), and sexual behaviors that we assigned were based on the Hoyle et al. (2000) framework (i.e., unprotected sex, sex with multiple partners, hazardous sex), with additional categories to account for unique aspects of adolescent sex (i.e., age at sexual initiation, frequency of sexual activity, STD history, and history of unplanned pregnancy; see Appendix B). Studies were included if they provided an effect size representing the relationship between sexual behavior and impulsivity, compared groups defined on a sexual behavior on

5.2.1. Moderator analyses Gender (percentage of the sample that is female), age (mean age of the sample), and race (percentage of the sample that is Caucasian) were coded as continuous moderator variables and tested using metaregression (MetaReg macro by Wilson, 2010). Supplemental moderation analyses were conducted with clinical sample status (clinical, nonclinical, juvenile detention), gender (all male or all female samples), and study design (correlation, longitudinal, group comparison) coded as categorical moderators using meta-analysis of variance (MetaF macro by Wilson, 2010). 6. Results 6.1. Sample The final study sample consisted of 81 studies (75 peer-reviewed journal articles and six dissertation manuscripts) and 98 independent


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Initial retrieval of studies meeting the following criteria (n = 389):

Studies excluded for the following reasons (n = 220): -

Literature reviews without original data (n = 15) No self-report impulsivity measure (n = 75) No self-report measure of sex (n = 16) Risk behavior composite – sex not measured individually (n = 27) - Full-text of articles unavailable (n = 16) - Mean age and range not within proposed limits (n = 71)

Studies coded for inclusion in meta-analysis (n = 169): Studies excluded for the following reasons (n = 88): - Impulsivity self-report measure cannot map onto UPPS framework (n = 29) - Self-report measure of sex cannot map onto risky sexual behaviors (n = 2)a - Studies with sample sizes duplicated from another coded study (n = 8) - No mean or age range reported (n = 29) - No direct impulsivity-sex association (n = 15)b - Insufficient information to calculate effect size or insufficient effect size to convert to r (n = 5)b

Studies included in final calculation of effect sizes and in meta-analysis sizes (n = 81) Fig. 1. Flow chart for selection of studies included in the meta-analysis. aStudies excluded used self-report measures of attitudes towards risky sex or condom use — not actual incidence of behavior. b25 authors were emailed to obtain appropriate data for calculating effect sizes and 7 authors responded and provided data to calculate effect sizes.

samples (some studies reported separate effects for multiple independent samples). The mean size of the samples was 548.72 (SD = 852.18; range 19–6,663; n = 98 distinct samples), and mean sample age was 16.25 (SD = 1.78; range 11–19.9; 53.5% of samples represented middle adolescents aged 14–16). On average, samples were 53.06% female (SD = 35.87; range 0–100) and 44.36% Caucasian (SD = 36.92; range 0–100% Caucasian; n = 5 Caucasian-only samples). African Americans were the dominant minority group and on average made up 39.31% of the sample (SD = 36.92, range 0–100% African American; n = 16 African American-only samples). The majority of samples were nonclinical (n = 78; n = 8 juvenile detainee samples; n = 12 clinical samples). Sensation seeking was the most common impulsivity construct measured (n = 115 associations), and hazardous sexual activity was the most common risky sexual behavior measured (n = 64 associations) (see Appendix C for studies and original studyreported effect sizes used in the current study analyses); where more than one effect size is reported, this indicates multiple independent samples included in the single study (indicated by separate rows in Appendix C), or multiple effect sizes reported for one sample, which were then averaged for the current analyses to ensure independent effects (indicated by effect sizes reported in the same row, separated by a comma, in Appendix C). Appendix D reports citations for studies whose data were included in the analyses.

Table 1 presents the mean effect sizes and related statistics for both the overall mean effect size and specific association effect sizes (only results for effect sizes that are based on at least two studies are presented). There were a total of 98 overall mean effect sizes (mean effect sizes for 98 independent samples from 81 studies) and 214 specific association mean effect sizes. 6.2. Research question 1: general impulsivity and risky sexual behavior The overall weighted mean effect size between impulsivity and risky sexual behavior was small at r = 0.19 (SE = 0.02; 95% C.I. [0.16–0.22]; based on 98 independent effect sizes). The overall effect was significantly different from zero (z = 11.38 at p b 0.001; Q = 1275.38, df = 97, p b 0.001), and a fail-safe N analysis determined that it would take 123 missing studies with null effects to reduce the overall mean effect to non-significance. Egger's regression test of asymmetry resulted in a precision value of −0.001 (p = 0.44), indicating no significant amount of asymmetry, which posits no significant amount of publication bias (see Egger et al., 1997). The funnel plot (see Fig. 2) indicates a generally symmetric distribution of effect sizes, with most effects around the mean intercept, thus noting that publication bias is unlikely. Effect sizes were generally normally distributed, with few outliers (Fig. 3). A test for the homogeneity of effect sizes indicated that 92% of the

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Table 1 Mean effect sizes and I2 test of homogeneity for each association. Impulsivity construct

Risky sexual behavior

K

N

ES

SE

95% CI

Z

I2

Fail-safe N

All SS LPL LPS NUR All All All All All All All SS SS SS SS SS SS SS LPL LPL LPL LPL LPL LPL LPL LPS LPS LPS LPS LPS LPS NUR NUR NUR NUR NUR NUR

All All All All All Unprotected Partners Hazardous Age Pregnancy STD history Frequency Unprotected Partners Hazardous Age Pregnancy STD history Frequency Unprotected Partners Hazardous Age Pregnancy STD history Frequency Unprotected Partners Hazardous Age STD history Frequency Unprotected Partners Hazardous Age STD history Frequency

98 71 28 18 12 28 28 45 10 5 7 29 22 19 36 6 3 5 21 11 11 10 5 4 5 4 3 3 8 4 1 9 4 4 5 2 2 1

54,191 38,749 14,748 7318 5471 14,779 13,667 25,897 4727 2295 4038 14,869 7700 10,538 23,229 2089 1561 3665 11,653 9523 3181 3754 1229 2158 2060 1341 372 1934 2863 2651 93 3704 784 2643 3713 217 2071 272

.19 .19 .16 .17 .12 .12 .19 .21 .21 .09 .08 .21 .13 .20 .21 .19 .07 .02 .20 .14 .15 .16 .12 .14 .17 .17 .18 .12 .15 .19 .19 .18 .16 .11 .11 .11 .09 .09

.02 .02 .02 .01 .01 .01 .02 .03 .04 .04 .02 .03 .02 .03 .02 .05 .07 .05 .03 .04 .04 .04 .05 .06 .06 .06 .08 .07 .04 .06 .15 .04 .06 .07 .05 .08 .09 .12

.16–.22 .14–.23 .13–.19 .14–.20 .09–.15 .10–.14 .15–.23 .16–.27 .12–.29 .01–.16 .03–.13 .15–.27 .08–.18 .15–.26 .17–.25 .09–.29 −.07–.22 −.09–.12 .15–.25 .06–.22 .07–.23 .07–.24 .01–.24 .02–.25 .06–.28 .05–.28 .02–.33 −.02–.25 .06–.23 .08–.31 −.10–.48 .10–.25 .04–.29 −.02–.24 .01–.21 −.04–.25 −.11–.28 −.14–.32

11.38⁎⁎ 8.36⁎⁎ 9.62⁎⁎ 11.77⁎⁎ 8.85⁎⁎ 11.04⁎⁎ 9.93⁎⁎ 8.11⁎⁎ 4.82⁎⁎ 2.25⁎ 3.31⁎ 6.70⁎⁎ 4.91⁎⁎ 7.36⁎⁎ 10.47⁎⁎ 3.69⁎⁎

.92 .94 .57 .21 0 .16 .75 .94 .86 .57 .50 .92

123 89 29 19 9 25 34 62 13 2 3 40 18 24 49 7 0 0 27 9 10 10 3 3 5 4 3 1 2 4 0 10 5 2 3 1 0 0

1.03 .32 7.97⁎⁎ 3.51⁎⁎ 3.61⁎⁎ 3.75⁎⁎ 2.21⁎ 2.35⁎ 2.95⁎ 2.83⁎ 2.23⁎ 1.73 3.29⁎⁎ 3.22⁎ 1.31 4.35⁎⁎ 2.53⁎ 1.68 2.06⁎ 0.85 1.26 0.76

Note. Impulsivity construct: All = overall impulsivity or risky sexual behavior composite. SS = sensation seeking. LPL = lack of planning. LPS = lack of perseverance. NUR = negative urgency. Risky Sexual Behavior: Unprotected = unprotected sex. Partners = number of sexual partners. Hazardous = hazardous sexual activity. Age = age at sexual initiation. STD history = history of STD diagnosis. Pregnancy = history of unplanned pregnancy. Frequency = frequency of sexual intercourse. K = number of associations. N = number of participants included in association across studies. ES = weighted effect size. SE = standard error. 95% CI = 95% confidence interval for effect sizes. Z = z-test for significance of effect. I2 = proportion measure of heterogeneity. ⁎ p b .05. ⁎⁎ p b .01.

variability in effect sizes was due to true heterogeneity between studies and not sampling error (I2 = 0.92; Huedo-Medina et al., 2006); thus, moderation analyses were conducted on the overall effect size for impulsivity and risky sexual behavior. 6.2.1. Effect size variance across impulsivity and risky sexual behavior type Independent effect sizes were examined for each impulsivity trait type for their relationship with risky sexual behaviors (collapsed across specific behavior type). Effect sizes across the UPPS traits were r = 0.12 for negative urgency (p b 0.001, based on 12 independent effects), r = 0.16 for lack of planning (p b 0.001, based on 28 independent effects), r = 0.17 for lack of perseverance (p b .001, based on 18 independent effects), and r = 0.19 for sensation seeking (p b 0.001, based on 71 independent effects). There were no studies that provided effect sizes for positive urgency. All UPPS traits had significant effects with overall risky sexual behavior, and the pattern of effects suggests no marked differences between these effects. Independent effect sizes were also examined for specific risky sexual behavior types with impulsivity (collapsed across all traits). Effect sizes were somewhat smaller for negative sexual outcomes, such as history of STD (p b 0.05, based on 7 independent effects) and unplanned pregnancy (r = 0.11, p b 0.05), and tended to be larger for risky sexual behaviors, such as r = .12 for unprotected sex (p b .001, based on 28 independent effects), r = .19 for multiple sexual partners (p b .001, based on 28 independent

effects), and r = .21 for age at sexual debut and hazardous sexual activity (p b .001, based on 10 independent effects for age at sexual debut and 45 independent effects for hazardous sexual activity). All risky

Fig. 2. Funnel plot of average study effect size (ES) against precision estimate.


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impulsivity and hazardous sex (β = 0.31, p = 0.03) and multiple partners (β = 0.37, p = 0.05). Samples with males only (k = 19 independent samples, n = 6366) reported an average effect of r = 0.16 (CI 95% [0.12–0.20], z = 7.42, p b 0.001) between impulsivity and risky sexual behavior, whereas samples with females only (k = 28 independent samples, n = 12,900) reported an average effect of r = 0.24 (CI 95% [0.16–0.32], z = 5.82, p b 0.001). 6.3.2. Age and race Sample mean age (β = − 0.10, p = 0.38) and race (β = 0.10, p = 0.40) did not moderate the impulsivity–risky sex relationship.

Fig. 3. Frequency plot of average study effect size (ES). Two studies were outliers in the effect size distribution (Young, King, Abbey, & Boyd, (2009) had an overall effect of r = .74; and Collins et al., (2004) had an overall effect of r = .60; see Appendix C for complete study details). As a supplemental analysis, these two studies were removed and the overall relationship between impulsivity and risk sexual behavior was reassessed; the effect size was still significant after the removal of the two outlying studies (r = 0.17, p b .0001).

sexual behaviors had significant effects with overall impulsivity, and the magnitudes of effects were similar across all risky sexual behavior types. Table 1 presents the overall relationship between impulsivity and risky sexual behavior, as well as the more specific effects examined above. Additionally, this table presents the individual associations between specific unidimensional impulsivity traits and specific risky sexual behaviors based on independent samples; although samples only contributed one effect size to each of these values, samples were allowed to contribute an effect size in multiple different associations (i.e., sample A contributed one effect size to the SS–unprotected sex association and another effect size to the SS–hazardous sex association). In general, the relationships between impulsivity traits and risky sexual behaviors were significantly different from zero, and were of a similar magnitude to each other and to the overall relationship of r = 0.19. There were a few exceptions of interest, however. Sensation seeking's relationships with unplanned pregnancy (r = 0.07, ns) and with STD history (r = 0.02, ns) were smaller than the overall effect and other risky sexual behavior effects, and were non-significant. Additionally, negative urgency generally had smaller relationships with all risky sexual behavior outcomes (r = 0.09, ns, for STD history and sex frequency; r = 0.11, ns, for number of sexual partners and age of sexual onset; r = 0.11, p b 0.05, for hazardous sexual activity), except unprotected sex (r = 0.16, p b 0.05). Lack of perseverance also had two non-significant effect sizes with number of sexual partners (r = 0.12, ns) and STD history (r = 0.19, ns). 6.3. Research question 2: gender, age, and race as moderators 6.3.1. Gender Gender significantly moderated the overall impulsivity–risky sex relationship (β = 0.22, p = 0.04, based on 97 independent effects), such that effect sizes were more robust in samples with more females. Across the four UPPS traits, gender had a significant moderating effect across effect sizes for sensation seeking (β = 0.27, p = 0.02), such that effect sizes for sensation seeking with general risky sexual behavior were more robust for samples with more females. Likewise, samples with more females showed more robust relationships between overall

6.3.3. Supplemental moderation analyses We also examined other sample characteristics as potential moderators of the overall impulsivity–risky sex relationship. Effect sizes across sample type were all significant and ranged from r = .12 for juvenile detention samples (p = .02, k = 8) to r = .16 for clinical samples (p b .001, k = 12) and r = .20 for nonclinical samples (p b .001, k = 78). There were no significant differences across effect sizes (Qbetween = 3.31, p = .19). Study design did not significantly moderate the overall effect size (Qbetween = 1.31, p = .52), however, all effect sizes were significantly different from zero (correlation: r = .17, p b .001, k = 46; longitudinal: r = .19, p b .001, k = 28; group comparison: r = .22, p b .001, k = 24). 7. Discussion This review sought to examine the relationship between specific impulsivity traits and risky sexual behaviors among adolescents and how it might vary based on gender, age, and race. Overall, results from the meta-analysis revealed a significant, yet small, association between impulsivity and risky sexual behavior among adolescents that was similar across impulsivity trait, but did show some variability across risky behavior conceptualization, with stronger effects for risky sexual behaviors, such that general impulsivity and sensation seeking had larger relationships with hazardous sexual activity and the number of sexual partners, and weaker relationships with actual negative outcomes, such as unplanned pregnancy and STD history. Race, age, sample status, and study design did not moderate the relationship. However, results suggest somewhat smaller effects in juvenile detention samples than non-clinical samples, suggesting the effects of external restrictions on behaviors. Most importantly, gender did significantly moderate this relationship, such that the overall relationship between impulsivity and risky sexual behavior was stronger in samples with more females. These results suggest that impulsivity may be a stronger risk factor for risky sexual behavior for adolescent females compared to males. Recent evidence based on college samples is consistent with the current study findings (Dir, Coskunpinar, Steiner, & Cyders, 2013; Dir, Cyders, & Coskunpinar, 2013; Turchik, Garske, Probst, & Irvin, 2010). Other risk factors for adolescent sexual risk-taking also tend to have stronger effects for females, such as depressive symptoms, low self-esteem, and substance use (e.g., Costa, Jessor, Donovan, & Fortenberry, 1995; Kowaleski-Jones & Mott, 1998; Zimmer-Gembeck & Helfand, 2008). Previous studies with adults have not found differences in the relationship between impulsivity and risky sexual behaviors (e.g., Crockett et al., 2006; Hoyle et al., 2000; Kraft & Rise, 1994). Impulsivity might have a greater effect on sexual risk-taking for female adolescents because (1) such behaviors are seen as less socially acceptable for female rather than male adolescents (e.g., Aubrey, 2004; Brady & Halpern-Felsher, 2007), and (2) sexual risk-taking is more likely to result in negative outcomes for females (e.g., Bryan, Schmiege, & Magnan, 2012). Female adolescents are at an increased risk of acquiring STDs due to biological changes in the female reproductive system that occur during puberty and menarche that leave females more susceptible to infection (Biro & Rosenthal, 1995; Gevelber & Biro, 1999). Further, risk of unplanned pregnancy is conspicuously more

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relevant for females and also carries different implications for female versus male adolescents (e.g., Kowaleski-Jones & Mott, 1998; Michels, Kropp, Eyre, & Halpern-Felsher, 2005). Females are also at a higher risk for sexual assault in situations involving alcohol use due to the disinhibiting effects of alcohol (e.g., Abbey, 2011; Buzy et al., 2004; Champion et al., 2004). While heterosexual males are not immune to risk, males are less susceptible to (e.g., STD acquisition and sexual assault; Abbey, 2011; Sales et al., 2012) or are less affected by (e.g., impregnating a partner; Kowaleski-Jones & Mott, 1998) such risks. Thus, risky sexual behaviors are inherently less risky for males, resulting in a less robust relationship between impulsivity and participation in such behaviors for this group. Such differences in risks influence individuals' beliefs and perceptions of how risky a sexual encounter may be, and this evaluation or perception of risk influences whether one chooses to engage in sexual activity (Horvath & Zuckerman, 1993). While males and females generally hold similar attitudes regarding the risk for STDs (e.g., DiClemente, Forrest, & Mickler, 1990), females are more concerned about the risk of unplanned pregnancy compared to males (e.g., Kowaleski-Jones & Mott, 1998). Females also report more beliefs about the potential social risks (Crawford & Popp, 2003) and emotional distress (while males report more positive affect) after engaging in sexual activity, subsequently influencing the decision to engage in sexual activity in the future (Brady & Halpern–Felsher, 2007). This pattern has also been demonstrated in models of sexting behaviors: Females perceive sexting as riskier and have more negative expectancies about sexting outcomes, thus leading to lower likelihood of sexting (Dir, Coskunpinar et al., 2013). On the other hand, males perceived sexting as a more rewarding experience, and thus were less inhibited by potential risks (Dir, Coskunpinar et al., 2013). Impulsivity influences engagement in risky sexual behaviors through, in part, making it more likely for the individual to form positive beliefs about the outcomes of such behaviors (Smith & Anderson, 2001). Impulsive individuals tend to focus more on the rewarding aspects of a behavior, and act without considering potential risks and consequences (e.g., Horvath & Zuckerman, 1993; Smith & Anderson, 2001). Since males tend to have higher rates of impulsivity (Cyders, 2013) they are more likely to learn and remember positive outcomes associated with risky sexual behaviors, and as such are more likely to engage in risky sexual behaviors and to be less inhibited by beliefs about behavioral risks. These findings have important research and clinical implications. For one, researchers should consider including gender as a moderator in models incorporating impulsivity as a risk factor for risky sexual behavior, since models on predominantly female samples (as often found in college student samples) might not generalize to male samples. Clinically, the accumulating evidence suggests that females and males might benefit from unique intervention and prevention strategies. Traditionally, school-based education programs, such as abstinence education (Bearman, Jones, & Udry, 1997; Trenholm et al., 2007) or combined HIV and drug abuse prevention programs (e.g., D.A.R.E.; Hansen & McNeal, 1997), have served as the primary prevention approaches for adolescent risky sex; however, lackluster support for efficacy has spurred suggestions for alternatives to current prevention and intervention techniques (e.g., Fisher & Misovich, 1990; Steinberg, 2008). Study findings suggest that impulsivity may be an important risk factor to target in intervention and prevention behavioral programs for females; however, this may not be as effective for males. Instead, interventions that focus on skill-building and exploring motivations and attitudes around risky sexual behaviors have been effective in increasing condom use among both adolescent (Jemmott, Jemmott, & Fong, 1998) and adult (Solomon & DeJong, 1989) males. Additionally, interventions that incorporate family components, such as parental monitoring and communication skills have also proven effective for adolescent males (e.g., Kincaid, Jones, Sterrett, & McKee, 2012). Some view adolescent risk-taking as an uncontrollable and inevitable

developmental process, and, as such, suggest that prevention efforts should focus on intervening at the level of the environment instead of trying to change the individual (e.g., Reyna & Farley, 2006), such as implementing stricter adolescent curfew laws, which may cut down on sexual risk-taking by limiting opportunities for social interaction (e.g., see Steinberg, 2008). Another important finding from the current study is that the overall effect size between impulsivity and risky sexual behavior was significant, yet small, which corroborates findings from young adult samples (Hoyle et al., 2000). Other factors, such as parental monitoring, permissive attitudes towards sex, and religious affiliation, showed medium to large effect sizes with adolescent sexual risk-taking (ZimmerGembeck & Helfand, 2008). Although impulsivity has a small direct effect on risky sexual behavior, which questions the utility of the trait, these results should be interpreted cautiously. It is more likely that impulsivity is a distal predictor of risky sexual behavior. As discussed above, impulsivity influences the development of positive beliefs and expectancies about the effects of alcohol on sex (Dermen & Cooper, 1994), increasing engagement in alcohol-related sexual activity (Hendershot, Stoner, George, & Norris, 2007; Kalichman et al., 1998; Smith, Toadvine, & Kennedy, 2009). Likewise, impulsivity also influences motives for having sex, increasing subsequent sexual activity (Cooper, Agocha, & Sheldon, 2000). Additionally, more proximal risk factors may dampen or mask the effects of impulsivity on risky sexual behavior (e.g., Buhi & Goodson, 2007; Cooper, 2002; Crockett et al., 2006; Hoyle et al., 2000; Price & Hyde, 2009). For example, adolescents have more environmental constraints and limited freedom compared to young adults (White et al., 2006), and it could be that an impulsive adolescent who is prone to sexual risk-taking cannot act on these impulses because of environmental constraints, such as parental supervision (e.g., Bachman, Wadsworth, O'Malley, Johnston, & Schulenberg, 1997). This would suggest that the effects of parental monitoring on sexual risk-taking can override the effects of impulsivity on sexual risk-taking; however, further examination of the influence of these factors on actual behavior is warranted. Furthermore, the small relationship between impulsivity and risky sexual behaviors among adolescents might suggest that many sexual acts in this group are not really impulsive, but might rather be planned or occur in the context of a committed romantic partner. This interpretation is viable; sexuality development is normative during adolescence (e.g., Zimmer-Gembeck & Helfand, 2008), and some sexual acts can be considered less risky (e.g., protected sex with a committed partner). The relationship between impulsivity and risky sexual behavior did not vary based on type of impulsivity trait assessed, suggesting that all UPPS traits are similarly and significantly related to risky sexual behavior, although effects are small. However, due to the overrepresentation of sensation seeking and limited data on other impulsivity traits across the literature, it might be difficult to detect true differences between trait effects, and thus, further research examining the role of other facets of impulsivity in risky sexual behavior is warranted. Research on adolescent risky sexual behavior and impulsivity has largely focused on the role of sensation seeking (e.g., Spitalnick et al., 2007; Vélez-Blasini, 2008; Zuckerman & Kuhlman, 2000) and has discounted potential effects of other dispositions to rash action on adolescent risky sexual behaviors. Additionally, the current findings did not assess the unique contributions of the traits, after controlling for the other traits, which might shed light on the active mechanisms of risk. Regardless, these findings suggest that research should examine evidence for the remaining impulsivity traits, and develop treatments or intervention strategies that target all impulsivity traits, or that individualize treatment based on an individual's impulsive tendencies. Treatment and intervention strategies, especially for females, may benefit from targeting all impulsivity traits, or individualizing treatment based on an individual's impulsive tendencies. For example, many prevention approaches focus on targeting sensation seekers (e.g., Donohew et al., 2000; Palmgreen,


Donohew, Lorch, Hoyle, & Stephenson, 2001); however, results demonstrate that other traits have similar effects on risky sex, and thus, behavioral techniques that target other impulsive tendencies, such as negative urgency or lack of planning, should be considered (see Zapolski, Settles, Cyders, & Smith, 2010 for review), especially given the possibility of downstream, cumulative effects of the traits on more proximal risk factors. Effect sizes across unique risky sexual behaviors did vary; however, many effect sizes with many risky sexual behaviors were small, which could be related to the disaggregation of risky sex into these separate categories. Still, differences in effect sizes did reveal a meaningful pattern, with somewhat stronger relationships with risky sexual behaviors and weaker relationships for actual negative outcomes, such as unplanned pregnancy and STD history. This pattern held true for the overall relationship, as well as for sensation seeking. This suggests that impulsivity is more highly related to risky sexual practices, but that the experience of a negative outcome related to these practices is likely also related to other unmeasured factors. Of importance is that there was significant heterogeneity in how risky sexual behavior was defined and measured across studies. Many studies used nonspecific measures of risky sexual behavior developed for the particular study and had incongruent units of analysis (e.g., measuring number of lifetime partners vs. partners in the last year; dichotomous vs. continuous measures of condom use), which makes it difficult to compare results across studies. This is likely due to the lack of “gold standard” measure for risky sexual behavior (George, Zawacki, Simoni, Stephens, & Lindgren, 2005; Marcus, Fulton, & Turchik, 2011; Turchik & Garske, 2009), and thus, questions the reliability and validity of these measures without a well-validated and baseline measure to compare to. Therefore, current findings should be used to inform future research efforts to establish a consistent conceptualization of risky sexual behavior and identify and operationalize unidimensional risky sexual behaviors in order to develop reliable and valid unidimensional measures that can be used for various populations. Additionally, better discrimination between normative adolescent sexual experimentation and high-risk sexual activity (e.g., Beadnell et al., 2007; Lanza & Collins, 2008; Newman & Zimmerman, 2000), as well as measurement of risky sexual behaviors versus outcomes, would likely clarify incongruent findings. Age and race did not moderate the overall relationship between risky sex and impulsivity. This suggests that the relationship between impulsivity and risky sexual behavior is the same across adolescent development and across racial groups, and that similar identification and prevention strategies might be effective across such sample characteristics. However, limitations regarding how age and race were coded for meta-analytic examination did not allow for a thorough examination of these variables, and thus, may have contributed to null results. For one, null results for age could be due to a restriction in age range in samples, since the majority of the samples represented middle adolescents (ages 14–16; 53.5% samples). This is consistent with other findings examining impulsivity and early sexual initiation (Khurana et al., 2012) and adolescent alcohol use (Stautz & Cooper, 2013) that failed to show unique trajectories and differences across a restricted age range. Also, coding for mean age or race percentage of each sample without considering within sample ranges masks potential withinstudy effects and also exacerbates heterogeneity across studies with similar means. Additionally, a few studies included in the metaanalysis reported significant moderating effects for age (e.g., Sales et al., 2012), which were masked by coding only the overall sample effect size and mean age. Therefore, these results should be interpreted with caution and examined more directly in future work.

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the addition of null results. The findings are also limited by the nature of the studies assessed: Variance across studies related to methodology, sample characteristics, and measurement may have obscured results, and although many of these factors were examined as potential moderators, there may be other sources of unexplained variance (e.g., Borenstein et al., 2009; Lipsey & Wilson, 2001). The random effects model was used to account for some of this variability, but likely more remains. Also, most studies were cross-sectional, some based on college student samples, and ethnic minorities were underrepresented as a whole across the work. Additionally, many associations could not be tested due to lack of data available, which limits generalizability. Importantly, there were a disproportionate number of effect sizes for sensation seeking compared to other UPPS traits, and thus, conclusions regarding relationships between these impulsivity facets and risky sexual behavior should be examined further. Finally, coding of age, gender, and race for the meta-analytic analysis might have masked important differences. Also, conclusions regarding impulsivity are limited to conceptualizations based on self-report measures. Lab task measures of impulsivity measure other aspects of impulsive behavior (e.g., Cyders & Coskunpinar, 2012; Dick et al., 2010), and thus may show different relationships with risky sexual behavior. Additionally, the framework used to disaggregate risky sexual behavior (see Hoyle et al., 2000) has not been validated with adolescent samples. More importantly, as discussed above, there was also inconsistency in how risky sexual behavior was measured and defined across studies, and there is a lack of a “gold standard” measure for risky sexual behavior (George et al., 2005; Marcus et al., 2011; Turchik & Garske, 2009). 9. Conclusions This review was the first to aggregate existing data on impulsivity and risky sexual behavior across the adolescent literature. Moreover, this was the first study to examine discrete relationships between unidimensional impulsivity traits and specific risky sexual behaviors among adolescents, and how these relationships may vary depending on age, gender, and race. Results from a review of 81 studies suggest that the relationship between impulsivity and adolescent risky sexual behavior is small, and that this relationship is likely larger in females. Therefore, research should model gender differences when investigating the role of impulsivity in risky sexual behavior, and should seek to develop gender-specific prevention approaches. Additionally, research should be more inclusive of other facets of impulsivity in models of sexual risk-taking, since findings suggest that unidimensional impulsivity traits have similar relationships with risky sexual behavior. Lastly, efforts should be made to develop a consensual conceptualization of risky sexual behavior in order to accurately identify risk factors so that effective intervention and prevention techniques can be designed. Role of funding sources There was no funding source for this study. No funding source was connected to the study design, collection, analysis or interpretation of the data, writing of the manuscript, or the decision to submit the paper for publication. Contributors Dir and Cyders designed the study. Dir and Coskunpinar conducted literature searches and coded the studies for the meta-analysis. Dir conducted statistical analysis and wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest The authors declare that there are no conflicts of interest.

8. Limitations Appendix A. Supplementary data First, the file drawer problem applies to the current study, such that there may be inflated effect sizes due to publication bias (Rosenthal, 1979); however, fail-safe N analyses suggest robust effects even with

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.cpr.2014.08.004.

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