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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: Nurs Forum. 2019 Jul 16;54(4):505–512. doi: 10.1111/nuf.12364

Differences in Hope, Core Self-Evaluations, Emotional Well-Being, and Health Risk Behaviors in Freshman University Students

Stephanie Griggs a,b, Sybil L Crawford c
PMCID: PMC6856360  NIHMSID: NIHMS1036465  PMID: 31309592

Abstract

The purpose of this cross-sectional anonymous survey study was to explore differences in hope, core self-evaluations (CSE), emotional well-being, health risk behaviors, and academic performance by gender, race, and social desirability in a sample of freshman university students. Four hundred and ninety-five freshmen ages 18–21 (M age 18.4), 67% female attending a large public university in the Northeast participated in the study. A Health Risk Behavior Score, with scores ranging from 3–9, was calculated by creating risk categories for drug use, alcohol use, and sexual risk-taking. Hope and health risk behaviors did not differ by gender, however, men reported higher CSE and emotional well-being. There were racial differences in hope, CSE, emotional well-being, and health risk behaviors. Nurses and researchers should consider gender and racial differences when designing or implementing hope interventions. Future researchers should compare their findings with ours for patterns or convergence and divergence and aim for larger representative samples of Non-white groups. These are necessary next steps to advance the understanding of the role hope may play in promoting mental health among diverse college students.

Keywords: hope, core self-evaluations, young adults, emotional well-being, sexual risk-taking, substance use

One in five college students report having an active mental illness (American College Health Association [ACHA], 2018) and many young adults experience psychological distress during their first year of college (Davidson, Feldman, & Margalit, 2012). This distress may negatively affect their academic performance (ACHA, 2018), mental well-being (Abdel-Khalek, 2013; Demirli, Türkmen, & Arık, 2015; Feldman, Davidson, Ben-Naim, Maza, & Margalit, 2016; Stoyles, Chadwick, & Caputi, 2015), increase their health risk behaviors (substance use and sexual risk-taking) (Benotsch, Koester, Luckman, Martin, & Cejka, 2011; Hingson, Zha, & Weitzman, 2009; Monahan, Bracken-Minor, McCausland, McDevitt-Murphy, & Murphy, 2012; Johnson et al., 2018) and increase their risk of suicide (ACHA, 2018).

Suicide is the second leading cause of death in young adults (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2018) and 1.9 % of college students report attempting suicide within the last year (ACHA, 2016). It is important to identify both risk and protective factors, which may be culturally specific to address this public health concern. For instance, historically underrepresented populations (e.g., African Americans and Hispanics) are at a higher risk of suicide (Torres & Rollock, 2007; Hirsch, Visser, Chang, & Jeglic, 2012), but cultural emphasis on suicide as immoral has served as a protective factor in Hispanics (Oquendo et al., 2005). Although, African Americans have a lower lifetime prevalence rate than Non-Hispanic Whites across four categories (mood, anxiety, substance use, any disorder) (Breslau et al., 2006) it is more likely that this lower prevalence is related to the poorer access to diagnosis and treatment. Regardless of race or ethnicity, mood disorders predict suicide along with stressful life events (Hirsch et al., 2012).

Hope is a potential therapeutic target for intervention to improve mental health outcomes in young adults attending college. Hope is a protective factor in suicide risk (Anestis et al., 2014; Hirsch et al., 2012; Hollingsworth, Wingate, Tucker, O’Keefe, & Cole, 2016) and is associated with positive health practices such as less alcohol use, less binge drinking, less smoking, more frequent exercising, and more dietary fat limitation (Berg et al., 2011) among college students. Also, hope is associated with positive mental well-being (Abdel-Khalek, 2013; Connell, O’Cathain, & Brazier, 2014; Demirli et al., 2015; Marques, Pais-Ribeiro, & Lopez, 2011; Marques, Lopez, Fontaine, Coimbra, & Mitchell, 2015), a higher-Grade Point Average (GPA) (Marques et al., 2015; Snyder et al., 2002, [blinded for review purposes], 2017), better school engagement (Marques et al., 2015), a higher likelihood of graduating, and a lower likelihood of dismissal (Snyder et al., 2002).

Among students from four-year public institutions, African Americans and Hispanics have the lowest six-year completion rate (45.9 percent and 55 percent, respectively) compared to Caucasians and Asians who have a 2/3 completion rate (67.2 percent and 71.7 percent, respectively) (Shapiro et al., 2017). Differences may be related to racial disparities, limited availability of resources, or lower hope (Berg et al., 2011; Hirsch et al., 2012). The Adult Dispositional Hope Scale was initially validated with a predominantly White and middle-class sample (Snyder et al., 1991). Research on hope has been conducted predominantly with American college students of European descent with limited examination of racial or gender variations (Chang & Banks, 2007; Hirsch et al., 2012; Berg et al., 2011). In some of the previous studies there were significant differences noted in hope among races (e.g., lower baseline hope in non-white groups) (Berg et al., 2011; Hirsch et al., 2012), while in other studies hope did not differ across racial groups (Chang & Banks, 2007; Visser et al., 2013).

To design interventions to target mental health in young adult college populations, racial and gender differences should be considered. It is important to determine whether there are patterns of convergence and divergence between Caucasians and other racial groups. Snyder’s Hope Theory (2002) was used to guide this study, where hope is a combination of agency and pathways thinking towards a desired goal. Hope is linked with Core Self-Evaluations (CSE), an emerging construct that is grounded in how one appraises situations and defined as the “overall judgment that individuals have about their value and competency as people” (Smedema, Chan, & Phillips, 2014, p. 400). Based on previous findings, hope theory is useful for predicting better emotional well-being and academic performance and core self-evaluations is a mediator in the relationships [blinded for review purposes]. Therefore, hope and CSE are important protective factors to consider in this population. The purpose of this study1 was to explore differences in hope, CSE, emotional well-being, health risk behaviors, and academic performance by gender, race, and social desirability in freshman university students. The goal was to generate a hypothesis for gender and race, given the limited research on differences.

Methods

Study Design and Procedure

This study was an anonymous self-report online cross-sectional survey design (Research Electronic Data Capture-[REDCap™]) (Harris et al., 2009), hosted at the University of [blinded for review purposes]. After obtaining human subjects’ approval from the University of [blinded for review purposes], we conducted a pilot with 50 random email addresses from the target population to test the survey functionality and recruitment procedures. After minor refinements were made, 4,391 freshman university students between the ages of 18–24 from a large public university in the Northeastern United States were invited to participate via email invitation at the beginning of their second semester (to capture first semester grades). Data were analyzed using the Statistical Package for the Social Sciences (SPSS version 22).

Study Measures

Demographics.

Age, gender, current school status, years since completing high school, race, partner status, military status, living situation, parental involvement, and socioeconomic status was collected.

Adult Dispositional Hope Scale.

A 12-item measure of a respondent’s level of hope ranging from definitely false to definitely true was used. The 12 items include: a 4-question agency subscale (e.g., “I energetically pursue my goals”), a 4-question pathways subscale (e.g., “I can think of many ways to get out of a jam”), and 4-distractor items (not scored) (Snyder et al., 1991). Previous Cronbach’s alphas for the total scale range from 0.74 to 0.84 was (Snyder et al., 1991). Total hope scale scores range from 8–64. Higher scores indicate greater hope.

Core Self-Evaluations Scale.

CSE is a construct comprised of four traits: self-esteem, generalized self-efficacy, emotional stability, and locus of control (Judge, Erez, Bono, & Thorsen, 2003). The 12-item CSE scale rates items (e.g., “I am confident I get the success I deserve in life”, “When I try, I generally succeed”) from strongly disagree to strongly agree with Cronbach’s alphas range from 0.81 to 0.87 for the total scale (Judge et al., 2003). The total scale score was used in the analysis. Scores range from 12–70 with higher scores being indicative of greater levels of CSE (Smedema et al., 2014).

Emotional Well-Being Scale.

A 5-item subscale of the Medical Outcomes Study RAND 36-item health survey (version 1.0) was used (Ware & Sherbourne, 1992). Internal consistency coefficients range from 0.67 to 0.95 (Ware & Sherbourne, 1992). The Emotional Well-Being subscale measures anxiety, depression, loss of behavioral or emotional control, and psychological well-being during the past month (e.g. “how much of the time were you a happy person” for depression and “how much of the time have you felt calm and peaceful” for anxiety) on a 6-point Likert scale (Ware & Sherbourne, 1992). Higher scores indicate better emotional well-being with possible scores ranging from 0 to 100 (Ware & Sherbourne, 1992).

Academic Performance.

Participants were asked to provide first semester Grade Point Average (GPA) and satisfaction with academic performance on a 5-point Likert scale (ranging from not at all satisfied to extremely satisfied) one week after grades were available to the students (to reduce recall bias).

Assessment of Health Risk Behaviors.

Three health risk behaviors, alcohol use, drug use, and sexual risk-taking, were assessed as components of health risk. All three were measured by self-report. As explained in detail below, each behavior was assigned a value of 1, 2, or 3, based on specific criteria for that behavior. These individual scores then were summed to create a composite health risk score. A similar approach to generating a composite score has been documented in previous research on health behaviors (Sternfield et al., 2017; Wang et al., 2018).

Alcohol Use Disorders Identification Test-C (AUDIT-C).

The AUDIT-C measures hazardous drinking or active alcohol use disorders (including alcohol abuse and dependence) (Reinert & Allen, 2007). The AUDIT-C has a good internal consistency (Cronbach’s alpha = 0.94) and temporal stability (correlation coefficient = 0.97) (Reinert & Allen, 2007). Respondents were asked to rate how often they used alcohol in the past year (e.g., “How often did you have a drink containing alcohol?” and “How many drinks did you have on a typical day when you were drinking in the past year?” on a 5-point Likert scale (never to 4 or more times per week; 1 or 2 to 10 or more) (Reinert & Allen, 2007). Two items from the AUDIT-C were asked in this survey (one item – was inadvertently left out of the final survey). Scores range from 0–8 with higher scores indicating more alcohol use. Based on the observed distribution, a score of 3 was given for scores of 5–8, a score of 2 for scores 3–4, and a score of 1 for scores 0–2.

Drug Abuse Screening Test (DAST-10).

The DAST-10 is a 10-item measure of a respondent’s drug use, not including alcohol or (abbreviated version of an original 28-item scale) (Cocco & Carey, 1998). The DAST-10 has been shown to have good internal consistency (Kuder–Richardson Formula 20 [rKR-20] = 0.86), and temporal stability (test–retest intraclass correlation coefficient = 0.71) (Cocco & Carey, 1998). Questions in the DAST (e.g., “Have you used drugs other than those required for medical reasons?”, “Do you abuse more than one drug at a time?”, and “Have you had blackouts or flashbacks as a result of drug use?”) refer to the past 12 months (Cocco & Carey, 1998). Scores range from 0–10 with higher scores indicating more drug use (Cocco & Carey, 1998). Based on the observed distribution, a score of 3 was given for scores >2, a score of 2 for scores of 1, and a score of 1 for scores of 0 (indicating no drug use).

Sexual Risk-Taking with Uncommitted Partners (SRT).

The SRT is an 8-item scale that was adapted for this study from an open response format to a 5-point Likert scale (0, 1, 2, 3, 4 or more) for questions such as number of sex partners, sex with uncommitted partners, sex with someone not known well, sex before discussing risk factors etc. (Turchik & Garske, 2009). Internal consistency alphas in a college sample ranged from 0.88 to 0.90 (Turchik & Garske, 2009). Participants were asked to consider only the last 6 months when indicating the frequency of sexual behavior (e.g., “How many partners have you had sex with?”, “How many times have you had sex with a new partner before discussing sexual history, IV drug use, disease status and other current sexual partners?”, and “How many partners have you had sex with that you didn’t trust?”). Scores range from 0–32 with higher scores indicating greater sexual risk-taking (Turchik & Garske, 2009). Based on the observed distribution, a score of 3 was given for scores > 5, a score of 2 for scores 1–4, and a score of 1 for scores of 0.

Marlowe-Crowne Social Desirability Scale (MCS).

Positive mental health factors and health-risk behaviors may be subject to social desirability bias (e.g., underreporting of health risk and overreporting of hope). The MCS is a 13-item short form version of the original 33-item scale measuring a respondent’s social desirability bias using a true/false format (Reynolds, 1982). The MCS has demonstrated acceptable reliability (rKR-20 = 0.76) in undergraduate students (Reynolds, 1982). Respondents identify if statements concerning personal attitudes or characteristics (e.g., “It is sometimes hard for me to go on with my work if I am not encouraged” or “I sometimes feel resentful when I don’t get my way”) are true or false. Scores range from 0–13 with higher scores reflecting greater social desirability bias. The rKR-20 reliability in our sample was 0.67.

Data Analysis

Prior to analysis, data were screened for missing data, out-of-range, and distributions of continuous variables. Cronbach’s alphas were computed for measures for the full sample and separately by gender and race. To explore differences in gender and race on the variables of interest, a series of t-tests, one-way analyses of variances (ANOVA), and correlational analyses were conducted for normally distributed variables. For race, multiple pair-wise comparisons and Bonferroni correction for multiple testing were conducted (Plichta, Kelvin, & Munro, 2012).

Results

Participants

This study involved 495 freshmen (331 females, 161 males, and 3 other gender) ages 18–21. The response rate was 11.2% and sample was generally similar to the target population regarding race and GPA (accessible recruitment site population: 48.3% female, 65.1% White, 9.1% Asian, 4.8% Hispanic, 3.5% African American, 7% Other, 10.6% unknown race, and 3.26 mean GPA). There was a slight overrepresentation by women and Asian students (48.3% and 9.1% in target population respectively). The sample included Caucasians, Hispanics, African Americans, Asians, and Other with a slight overrepresentation of Hispanics, African Americans, and Asians (4.7%, 3.5%, 9.1% respective comparisons). Most participants lived on campus (97.8%), never served in the military (99%), and reported being able to meet their monthly living expenses (84.6%). (see table 1)

Table 1.

Means, Standard Deviations, and Frequencies of Demographic information (N= 495)

Characteristic N Mean ± SD
Age 494 18.37 ± 0.535
G.P.A. 433 3.44 ± 0.51
  Female 280 3.50 ± 0.46
  Male 150 3.35 ± 0.57
Count (%)
Gender 494
  Female 331 (66.9)
  Male 161 (32.5)
  Other 3 (0.6)
Race 495
  White 344 (69.5)
  Asian 80 (16.2)
  Hispanic or Latino 26 (5.3)
  Other 24 (4.8)
  Black or African American 21 (4.2)
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Gender differences

Hope scores did not differ between men (M = 49.55, SD = 8.61) and women (M = 49.39, SD = 7.44) (t = .212, df = 490, p = .83). Health risk behaviors did not differ by gender (p = 0.06). However, men reported higher CSE and emotional well-being and a lower mean G.P.A. (t = −2.89, df = 428, p < 0.01). (See table 4 below)

Table 4.

Racial ANOVA Means ± Standard Deviations

Variable (F statistics) Caucasian Hispanic African American Asian Other
Hope (F(4, 494) = 5.7, p < 0.001) 50.4 ± 6.7 49.3 ± 8.6 47.5 ± 10.6 47 ± 9.5 44.8 ± 10.3
Core Self-Evaluations (F(4, 491) = 4.8, p < 0.01) 42.3 ± 7.7 41.7 ± 6.6 39.6 ± 8.9 39.1 ± 6.9 37.6 ± 8.4
Emotional Well-Being (F(4, 491) = 3.9, p < 0.01) 63.2 ± 18.6 65.9 ± 20.9 68.2 ± 20.9 60.8 ± 18.3 49.2 ± 22.1
Health Risk Behaviors (F(4, 488) = 14.35 p < 0.001) 6.5 ± 1.7 6.0 ± 1.6 5.4 ± 1.3 5.0 ± 1.3 5.6 ± 1.7
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Legend:

1.

Significant differences are bolded

Racial Differences

The multi-item scales demonstrated adequate reliability (see table 2). When analyzed by race, the scales were found to be reliable (α = .61 to .93) for all scales. There were statistically significant differences by race for hope, emotional well-being, and health risk behaviors. Caucasians had the highest hope and CSE compared to other racial groups with significant hope differences from Asians and ‘Other race’ and CSE from ‘Other race’. There were statistically significant differences for Emotional Well Being for all races with African Americans reporting the highest Emotional Well-Being followed by Hispanics, Caucasians, Asians, and ‘Other race’.

Table 2.

Cronbach’s alpha reliability coefficients, means, and standard deviations for total scale score and by gender.

Scale # of items N Mean ± SD t-test α
Hope Scale 8 484 49.3 ± 7.9 t = .212, df = 490, p = 0.83 0.87
   Female 331 49.5 ± 8.6
   Male 161 49.4 ± 7.4
Core Self-Evaluation Scale 12 484 41.4 ± 7.7 t = 2.65, df 487, p = 0.008 0.87
   Female 331 40.8 ± 7.7
   Male 158 42.8 ± 7.6
Emotional Well-Being Scale 5 488 62.5 ± 19.2 t = 2.26, df = 247.3, p = 0.025 0.84
   Female 329 60.9 ± 19.4
   Male 160 65.6 ± 18.6
Health Risk Behaviors Scale 18 493 6.1 ± 1.7 t = 1.90, df = 488, p = 0.06 0.66
   Female 331 6.0 ± 1.7
   Male 159 6.3 ± 1.7
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Legend:

1.

Significant differences p < 0.05 are bolded.

2.

Higher scores indicate higher hope, higher core self-evaluations, higher emotional well-being, more health risk taking, and more social desirability bias.

Caucasians reported the highest health risk behaviors (M = 6.5, SD = 1.7), and that difference was significant from African Americans (M = 5.4, SD = 1.3) and Asians (M = 5.0, SD = 1.3). There were no significant differences among races for G.P.A. ( p = 0.28) or satisfaction with academic performance (p = 0.53).

Social desirability differences

Social desirability was associated with higher hope (r = .34, p = .000) and higher emotional well-being (r = .365, p = .000). Therefore, social desirability explains some of the variance in how participants score on these variables.

Discussion

Mental health is a major concern on colleges campuses. It is important to identify potential therapeutic targets to improve mental health and reduce health risk behaviors in those attending college. Overall, racial disparities and differences are not well understood nor accounted for when designing these interventions. Racial and gender differences in hope, CSE, emotional well-being, and health risk behaviors were noted in the current study. This suggests that race and gender may explain some of the variance in both protective factors (hope, CSE, and emotional well-being) as well as health risk behaviors.

There were statistically significant differences by race for hope, emotional well-being, drug use, alcohol use, and sexual risk-taking. Caucasians had the highest hope and CSE. There were no significant racial differences found in academic performance. These findings have varied in other studies. For example, in Berg and colleagues (2011)’s study higher hope was associated with being older, female, and non-Hispanic white, however their study dichotomized race (white and non-white) as their population was mostly Caucasian (84.4%). In two studies hope was not different between racial groups (Chang & Banks, 2007; Visser et al., 2013). It may be, when considering culture that Caucasians are more attuned to the potential for goal attainment due to their historically dominant social position in the United States as suggested by Chang & Banks, 2007. For emotional well-being, African Americans reported the highest emotional well-being. This notion was supported in previous research where African Americans had a lower lifetime prevalence rate than Non-Hispanic Whites across four categories (mood, anxiety, substance use, any disorder) (Breslau et al., 2006). However, it is unknown if this lower prevalence is related to the poorer access to diagnosis and treatment in this racial group (Breslau et al., 2006).

Caucasians reported the highest health risk behaviors compared to other racial groups. This has been supported in previous research where Caucasians reported the highest drug use and alcohol use (Berg, Ritschel, Swan, An, & Ahluwalia, 2011; Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality., 2014). This suggests that race may explain some of the variance in health risk behaviors.

There were no gender differences in hope, which is consistent with some research (Chang, 1998) and has varied in other research (Berg et al., 2011). Additionally, men in the sample reported higher CSE, emotional well-being, drug use and alcohol use which is consistent in other research and women in the sample reported greater academic achievement. This confirmed findings in other studies that have reported higher mental health in men, (Marques et al., 2011; Ostroff, Woolverton, Berry, & Lesko, 1996) and higher academic achievement in women (Johnson, McGue, & Iacono, 2006; Marques et al., 2011). However, gender differences were not noted for CSE in a previous study (Miller and Nichols, 2011).

Nursing Implications

Nurses working across settings from acute care to college health should consider the incidence of mental illness and regularly screen for suicide when working with young adults, particularly those in their first year of college. Hope is a potentially modifiable target for intervention for young adults experiencing significant stressors. Though there were no gender differences in hope noted, men reported higher CSE and emotional well-being than the women in the study. Nurses might consider tailoring group hope interventions for women and men attending college to improve their emotional well-being and build their social support. For individual treatment, nurses could promote hope in young adults by having them participate in cognitive goal mapping of social, educational, or health goals. Young adults should also be encouraged to identify potential challenges and strategies to overcome those challenges identified - to encourage pathways thinking.

Research Implications

When designing interventions to target hope and CSE in young adult college populations, racial and gender differences should be considered (Chang & Banks, 2007; Hirsch, Visser, Chang, & Jeglic, 2012). Caucasians had a significantly higher hope and CSE in the current study. Historically, minority groups have experienced individual and institutional racism particularly affecting the African American community. This institutional racism along with failed social efforts to eliminate discrimination may contribute to the perception that race-related obstacles will prevent goal attainment (Milam, Richardson, Marks, Kemper, & Mccutchan, 2004) (e.g., poorer access to care and treatment, poorer education, etc.). It is therefore warranted to consider this when designing interventions and may require additional resources or sessions given this lower baseline of hope. On the other hand, given previous research of a lower lifetime prevalence rate of mental disorders among African Americans compared to Non-Hispanic Whites (Breslau et al., 2006) and our findings supporting African Americans having a higher emotional well-being it may be that either (a) there is another protective factor other than hope not accounted for in our design or (b) those with higher emotional well-being were more responsive to this survey (response bias). More research is needed before we can draw definitive implications for cultivating hope within different racial groups. Also, we were not powered to detect an interaction between gender, race, and age. Future researchers should compare their findings with ours for patterns or convergence and divergence and aim for larger representative samples of Non-white groups. Also, future researchers could target students most in need to determine the appropriate dose, duration, and need for booster sessions to increase hope and sustain this increase over time.

Limitations and Strengths

This study was cross-sectional using self-report surveys and future researchers should consider examining these differences over time. Participants were mostly Caucasian (69.5%) from one public university in the Northeast. Also, there is a potential for under-representativeness as participants were all successfully enrolled in college, with higher SES and may possess certain adaptive characteristics, so racial differences should be interpreted with caution. For gender, only males and females could be analyzed due to the small cell count in reported ‘other gender’ (n = 3). Additionally, drug use should be interpreted with caution for African Americans with the low cell count and the possibility that the DAST-10 was less useful for identifying subtleties of drug use in this population. Despite these limitations, there was an adequate sample to examine differences across five races in hope, core self-evaluations, emotional well-being, and health risk behaviors. Therefore, socio-demographic characteristics should be considered when designing future research and interventions targeting protective factors or health risk behaviors.

Conclusion

Nurses and researchers should consider culturally specific and culturally sensitive hope interventions. Understanding differences among and within minority groups and different genders in health risk and protective factors (hope and CSE) is important to address. Further research is needed to determine the racial and gender variability in protective factors as well as health risk behaviors due to limited racial variation in sampling in previous research. These are necessary next steps to advance the understanding of the role hope may play in promoting mental health among diverse college students.

Table 3.

Cronbach’s alpha reliability coefficients for the Adult Dispositional Hope Scale and Core Self Evaluation Scale by Race

Adult Dispositional Hope Scale Core Self Evaluation Scale
Race N Mean ± SD α N Mean ± SD α
White 339 50.4 ± 6.7 0.83 338 42.3 ± 7.7 0.87
Hispanic/ Latino 24 49.4 ± 8.6 0.90 26 41.7 ± 6.6 0.76
Black/ African American 20 47.5 ± 10.6 0.90 20 36.9 ± 8.9 0.89
Asian 77 47 ± 9.5 0.93 77 39.1 ± 6.9 0.79
Other 24 44.8 ± 10.3 0.88 23 37.5 ± 8.4 0.85
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Acknowledgement:

Carol Bova, PhD, ANP, RN, and Donna Perry, PhD, RN for their mentorship and support of this dissertation conducted at the Graduate School of Nursing, University of Massachusetts Medical School.

Funding: This study was funded by the Beta Zeta-at-Large Chapter of Sigma Theta Tau and the Massachusetts Society of Professors. The author is currently funded by the National Institute for Nursing Research (NINR), T32 NR 0008346.

Footnotes

Conflict of Interests

The authors declare no conflict of interests.

1

A full report of this study was published in the Journal of XXX. This is the first time the results of this aim were published. [insert reference after blind review]

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