Citation: Carmen Giurgescu, Lara Fahmy, Jaime Slaughter-Acey, Alexandra Nowak, Cleopatra Caldwell, Dawn P Misra. Can support from the father of the baby buffer the adverse effects of depressive symptoms on risk of preterm birth in Black families?[J]. AIMS Public Health, 2018, 5(1): 89-98. doi: 10.3934/publichealth.2018.1.89
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The electronic cigarette (e-cigarette) was first introduced a decade ago.[1] Across time, the popularity and design of these devices have changed remarkably. The first device contained a small cartridge of nicotine solution, resembled a cigarette, and was promoted as a smoking cessation tool. Today e-cigarettes are modifiable, with users able to personalize the amount of nicotine (if any) and the flavor of the solution.[2] Paralleling these changes in technology has been an increase in the number of people using e-cigarettes.
Between 2011 and 2017, current e-cigarette use rose from 1.5% to 11.7% among U.S. high school students.[3] In 2014, 3.7% of U.S. adults reported current e-cigarette use.[4] Use among young adults 18 to 24 years of age is higher than in the adult general population; 13.6% reported using the devices every day, some days, or occasionally.[5]
A similar trend has been noted among college students. A 2009 random sample of North Carolina college students showed that 4.9% of students were “ever” users and 1.5% had used e-cigarettes in the past month.[6] Of more than 95,000 randomly selected college students on 137 campuses in 2016, 14.8% were “ever” users and 3.7% had used in the last 30 days.[7] It is important to note that nearly 20% of the respondents in that sample were 25 years or older. Among college students, increased use may be related to them being more likely to accept the use of e-cigarettes in public compared to using traditional cigarettes in public.[8] Other studies suggest that the greater information exposure to e-cigarettes and alternative tobacco products puts college students at greater risk for e-cigarette use.[9]
A number of studies have found lower levels of well-being among college students who use tobacco when compared to non-tobacco users.[10,11] Well-being is a multidimensional construct that is conceptualized in a number of different ways. It may include psychological components, positive and negative affect, satisfaction with life, and overall happiness.[12,13,14] Others assert that quality of life (QOL) is a subjective sense of well-being that includes multiple dimensions.[15] No matter the conceptual approach used to determine QOL or well-being, an evaluation of one's life is required.
QOL related to smoking and disease states among smokers has been examined in a number of cross-sectional and cohort studies. See Goldenberg, Danovitch, and IsHak (2014) for a review.[16] In general, smoking status and the failure to quit smoking are associated with lower levels of QOL. One study in that review found lower levels of QOL among hookah users.[17] That study was conducted with middle-aged adults (mean 42.1 years) in a Middle Eastern country.
Although there have been a number of studies examining the relationship between QOL and smoking, there's a paucity of research examining QOL and smoking among college students.[18,19,20] Extant studies focus on QOL as it relates to specific diseases (e.g. asthma), disabilities, or stressful life events. None have examined QOL among college students who use e-cigarettes. Therefore, the purpose of this study was to explore differences in QOL among college students who use and do not use e-cigarettes. The specific aim of this study was to examine associations between QOL and e-cigarette use among college students.
A random sample of 5,000 undergraduate students, attending a university in the southeastern United States who were 18 to 26 years of age, received an electronic invitation to participate in an anonymous online survey focused on well-being from campus health promotion services. Students who completed the survey had the option to enter a lottery for prizes. The prizes included massages, a Fitbit, Google Chrome Cast, Roku, an iPad, and a bicycle. The student body was comprised of nearly 15,000 undergraduates, 50% were female, and 73% were white. The final sample consisted of 1,132 students (23% response rate). All data were de-identified. This study is a secondary analysis of cross-sectional data collected for program evaluation in February 2016; the University of Louisville Institutional Review Board reviewed and approved this study.
The students completed the World Health Organization Quality of Life-BREF survey.[21,22] The survey is an abbreviated quality of life (QOL) measure that contains 26 items from the original 100-item survey.[22] The original version had 24 facets of QOL and the BREF contains an item assessing each facet. A 5-point Likert-type scale is used and depending on the item, responses range from “very poor” to “very good”, “very dissatisfied” to “very satisfied”, “not at all” to “an extreme amount”, “not at all” to “completely”, and “never” to “always”. The QOL measure includes physical health (7 items), psychological (6 items), social relationships (3 items), and environmental (8 items) domains. Three items on the scale are reverse scored. The raw data are transformed to a 0 to 100 scale using a standard procedure outlined by WHO. Higher scores on the scale reflect higher levels of QOL. Mean scores for the domains among international college students range from 63.43 to 70.63 for physical health, 62.58 to 64.24 for psychological, 63.41 to 67.27 for social relationships, and 52.3 to 57.6 for environmental.[23,24] Cronbach's alphas for the domains among U.S. college students ranged from .70 to .75 in one study[25] and .73 to .86 in another.[26] There is one additional item that assesses overall QOL, “how would you rate your quality of life?” and one item that assesses health, “how satisfied are you with your health?” Both items are scored using a 5-point Likert-type scale that can be transformed to a 0 to 100 scale.
Students self-reported their age, sex, sexual identity (lesbian, gay bisexual [LGB]), year in school, grade point average, and race. The seven categories for race (American Indian/Alaskan Native, Asian, Black/African American, Hispanic/Latino, Pacific Islander, Two or More Races, and White) were collapsed into two categories (White or Non-White). Tobacco use was assessed by asking students to self-report how many days in the past 30 they used: 1) traditional cigarettes and 2) e-cigarettes. Responses included “never used”, “0 days”, “1-2 days”, “3-5 days”, “6-9 days”, “10-19 days”, “20-29 days”, and “all 30 days”. The data were dichotomized to reflect any use in the past 30 days. The main outcome variable (tobacco use) was categorized into non-users, dual users (i.e., used both traditional cigarettes and e-cigarettes), sole e-cigarette users, and sole traditional cigarette users.
Study participant characteristics were expressed as frequency (%) for categorical variables. Associations between demographic characteristics and tobacco use were examined using Chi-square tests, Fisher's exact test, or ANOVA as appropriate for the level of data. Multivariate logistic regression models were conducted to examine the relationship between tobacco use and QOL. For each model, tobacco non-users were the reference group. Demographic characteristics that were significantly associated with tobacco use were included in the models. Models were adjusted for sex, race, and sexual identity. Data were analyzed using SAS, version 9.4 (SAS Institute, Inc., Cary, North Carolina) and P-values <.05 were regarded as statistically significant.
Summary statistics of the study sample appear in Table 1. The majority of participants were female (62%), white (78%), and heterosexual (87%). Participants were relatively mixed by academic level. The average age of participants was 21.2 years (SD = 1.8) and the average GPA was 3.05 (SD = 0.86). When compared to the university population, females and whites were overrepresented and freshmen were underrepresented in the sample. The QOL scores were as follows: overall QOL (M = 71.2, SD = 17.7), physical health (M = 71.1, SD = 14.5), psychological well-being (M = 63.0, SD = 17.4), social relations (M = 64.9, SD = 20.9), and environmental health (M = 67.1, SD = 14.8).
Current Tobacco Use | ||||||
Entire Sample N=1132 (%) |
Non-Use n=947 (%) |
Dual Use n=42 (%) |
Sole E-Cigarette n=37 (%) |
Sole Cigarette n=106 (%) |
||
Age - mean (sd) | 21.2 (1.8) | 21.2 | 21.6 | 21.4 | 21.3 | |
GPA - mean (sd) | 3.05 (.86) | 3.1 | 3.2 | 2.8 | 3.0 | |
Sex* | Female | 706 (63) | 608 (64) | 16 (38) | 15 (41) | 67 (63) |
Male | 426 (37) | 339 (36) | 26 (62) | 22 (59) | 39 (37) | |
Race | White | 922 (78) | 773 (82) | 31 (74) | 31 (84) | 87 (82) |
Non-White | 250 (22) | 214 (18) | 11 (26) | 6 (16) | 19 (18) | |
Sexual Identity* | Heterosexual | 984 (87) | 845 (89) | 31 (74) | 32 (86) | 76 (72) |
LGB | 147 (13) | 101 (11) | 11 (26) | 5 (14) | 30 (28) | |
Academic Level | Freshman | 205 (18) | 169 (18) | 4 (9) | 8 (22) | 24 (23) |
Sophomore | 292 (26) | 249 (26) | 12 (29) | 6 (16) | 25 (24) | |
Junior | 267 (24) | 233 (25) | 10 (24) | 8 (22) | 16 (15) | |
Senior | 368 (32) | 296 (31) | 16 (38) | 15(40) | 41 (38) | |
WHOQOL-BREF Scores | ||||||
Quality of Life-mean (sd)* | 71.2 (17.7) | 72.2 | 67.6 | 68.9 | 64.6 a | |
Physical Health-mean (sd) | 71.1 (14.5) | 71.7 | 67.7 | 71.7 | 67.3 | |
Psychological-mean (sd) * | 63.0 (17.4) | 64.2 | 57.1 | 60.8 | 55.5 a | |
Social Relations-mean (sd)* | 64.9 (20.9) | 66.7 | 63.5 | 65.8 | 59.0 a | |
Environmental-mean (sd)* | 67.1 (14.8) | 67.7 | 63.0 | 67.5 | 63.2 a | |
Note. a Post hoc difference from nonusers at P < .05. *P < .01 |
Study results based on tobacco use appear in Table 1. Of the 1,132 participants, 947 were non-tobacco users, 42 used both traditional cigarettes and e-cigarettes (dual use), 37 used only e-cigarettes (sole e-cigarette users), and 106 used only traditional cigarettes (sole traditional cigarette users). E-cigarettes were used by 6.97% of the participants, either solo or along with traditional cigarettes. There was no difference in tobacco use by age, academic level, or GPA between the groups. Male college students were more likely than females to use e-cigarettes, either solo or in combination with traditional cigarettes (χ2 = 19.4, P < .01). Lesbian, gay, and bisexual students are more likely than heterosexual students to use traditional cigarettes, either solo or in combination with e-cigarettes (χ2 = 32.9, P < .01). There were significant associations between tobacco use and overall QOL, physical health, psychological well-being, social relations, and environmental health (all, P < .01). Post-hoc analyses showed significant differences in these scores were found between non-users and sole-cigarette users (all, P < .05).
Table 2 provides the results of the multivariate logistic models examining the relationship between tobacco use patterns and quality of life. The Hosmer and Lemeshow tests for the models were non-significant (P = .10 - P = .56) indicating the models fit the data. After adjusting for race, sex, and sexual identity, overall QOL, physical health, social well-being, and environmental health scores were not associated with being a dual or sole e-cigarette user. However, as psychological well-being increased by 10-units, the odds of being a dual user compared to a non-user decreased (Odds Ratio (OR) = 0.83; 95% Confidence Interval (CI) [0.70, 0.98]). For every 10-unit increase in overall QOL, physical health, psychological well-being, or social relations the adjusted odds of being a sole cigarette user compared to a non-user were significantly lower (OR = .83, 95% CI [.74, .92], P < .001); (OR = .86, 95% CI [.75, .99], P = .029); (OR = .80, 95% CI [.72, .90], P < .001); (OR = .87, 95% CI [.79, .96], P = .003), and (OR = .84, 95% CI [.74, .96], P = .01), respectively. After adjusting for race, sex, and sexual identity there was no association between dual vs. sole cigarette user, dual vs. sole e-cigarette user, and sole e-cigarette vs. sole cigarette user and any of the QOL measures.
Dual Use | Sole E-cig Use | Sole Cigarette Use | |||||||
OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P | |
QOL | 0.88 | [0.74, 1.04] | .135 | 0.90 | [0.75, 1.08] | .244 | 0.83 | [0.74, 0.92] | <.001 |
Physical health | 0.84 | [0.68, 1.03] | .097 | 1.00 | [0.79, 1.26] | .971 | 0.86 | [0.75, 0.99] | .029 |
Psychological well-being | 0.83 | [0.70, 0.98] | .026 | 0.90 | [0.74, 1.08] | .239 | 0.80 | [0.72, 0.90] | <.001 |
Social relations | 0.97 | [0.84, 1.12] | .688 | 1.00 | [0.86, 1.17] | .997 | 0.87 | [0.79, 0.96] | .003 |
Environmentalhealth | 0.83 | [0.69, 1.02] | .072 | 1.00 | [0.80, 1.24] | .988 | 0.84 | [0.74, 0.96] | .010 |
Note. Logistic regression adjusted for race, sex, sexual identity. Represents odds ratio of being in each category compared to non-users per 10-unit increase on WHOQOL-BREF scale. |
The purpose of this study was to examine associations between QOL and tobacco use patterns among college students. Interestingly, our findings suggest that as college students' perceived quality of life increases the likelihood that they will consume traditional cigarettes decreases. For dual use, psychological well-being appears to play a key role; that is, as psychological well-being increases likelihood of engaging in dual use decreases. As with traditional cigarette use, e-cigarette use appears to be connected to psychological distress.[27] Additional research is needed to further examine potential relationships between well-being and traditional cigarette and e-cigarette use and, when such relationships exist, to explicate their causes.
Although the percentage of e-cigarette users in our sample (6.97%) was comparable to the number of e-cigarette users nationally (4.1%) during Spring 2016,[7] the high overall rate of tobacco use is a cause for concern. When use rates for all tobacco products examined (i.e., sole cigarette use, sole e-cigarette use, and dual use) are combined, our results show that over 16% of study participants consumed one or more forms of tobacco. Additional work to prevent youth and young adult tobacco use as well as to encourage quitting is needed. Tobacco takes a heavy toll on young users—in terms of the likelihood of continued use as well as the cumulative health effects of such use. Given the interest of college students in newer and novel tobacco products, such as e-cigarettes, these populations seem especially important ones to target with prevention and cessation messaging.
Although female sole cigarette users outnumbered male sole cigarette users and the number of female tobacco users outnumbered male tobacco users, males were more frequently sole e-cigarette users and dual users. This finding, especially when combined with similar findings in previous studies,[28] suggests that because male college students are more likely to use e-cigarettes they are more at risk of potential health effects associated with such consumption. Although exact health effects are not yet clear, emerging findings point to several potential dangers.[28,29,30] Further, some studies have indicated that dual users may consume more nicotine or report greater withdrawal than sole e-cigarette or sole cigarette users;[31,32] thus, future studies should examine overall levels of nicotine consumption by college males.
Previous work with sexual minorities has reported high levels of smoking,[33,34,35,36] and more recent studies indicate that use of both traditional cigarettes and e-cigarettes tends to be high in LGB populations.[37,38] Our results also indicate a propensity among LGB students to smoke traditional cigarettes, either through sole use or in combination with e-cigarettes. Given the increased likelihood that LGB individuals will consume traditional and e-cigarettes, future research should seek to better understand the drivers for such use,[39,40,41] especially for adopting e-cigarette or other novel tobacco product use. In addition, health communication campaigns, such as the FDA's “This Free Life”, which targets young LGB adults, should attempt to reduce the tobacco burden in these communities.
Despite interesting findings that contribute to the literature on college students' QOL perceptions and e-cigarette use, this study has several limitations. First, all data were self-reported and thus subject to the potential of associated biases (e.g., memory, mood). Second, although our study had similar percentages of e-cigarette users to other studies with college students, our findings were shaped by the questions asked and the study design did not allow for more in-depth responses to glean a richer understanding from participants who used e-cigarettes, either in a sole or dual capacity. Third, despite a response rate similar to other surveys of this type,[7] differences may exist between participants who chose to respond to the survey invitation and individuals who declined. Fourth, our survey was conducted on one campus and may not represent the views of all college students.
In spite of these limitations, our results support previous findings, extend past work in new ways, and point to future research directions. First, male college students are more likely to use e-cigarettes, either solo or in combination with traditional cigarettes. These use patterns, in our study as well as previous studies, suggest that targeted health messaging is needed for this population to raise awareness of (1) scientific uncertainty surrounding e-cigarette safety as well as (2) the likelihood of nicotine increases with dual use and associated dangers of such consumption. Second, LGB students are more likely to use traditional cigarettes, either solo or in combination with e-cigarettes. This finding, especially when combined with similar findings in past studies, indicates that health communication campaigns on campuses are needed for LGB college students. The dangers of traditional cigarettes are well documented and combining their use with newer tobacco products, such as e-cigarettes, has the potential to exacerbate negative health effects. Third, this study shows interesting links between perceived quality of life and use of tobacco products. In particular, as with consumption of traditional cigarettes,[27] psychological distress appears to be connected to use of e-cigarettes. Further, our findings suggest increases in psychological well-being result in decreased likelihood of dual use. The results of this study, one of the first to examine QOL and e-cigarette use, call for additional inquiry to more fully understand linkages in overall well-being and tobacco product consumption, especially with newer tobacco products, such as e-cigarettes. Fourth, both the e-cigarette use rate and overall patterns of tobacco consumption in our study point out that much work remains to be done on college campuses to lessen the likelihood that college students will begin using tobacco products and to promote cessation among students who are tobacco users. The results of this study lay important groundwork for future health prevention measures directed to groups most likely to use e-cigarettes and for future inquiries into the role that perceived QOL may play in tobacco initiation and continued use.
The authors acknowledge support from the University of Louisville School of Nursing and Campus Health Services Office of Health Promotion. This work also was supported, in part, by the National Heart, Lung and Blood Institute (NHLBI) and the FDA Center for Tobacco Products (CTP) (grant number P50HL120163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, FDA, East Tennessee State University, University of Louisville, or Wright State University.
All authors declare that they have no conflicts of interest.
[1] | Kochanek KD, Murphy SL, Xu J, et al. (2016) Deaths: Final data for 2014. National Center for Health Statistics: Hyattsville. |
[2] | Martin JA, Hamilton BE, Osterman MJ, et al. (2017) Births: Final data for 2015. National Vital Statistics Report 2017, National Center for Health Statistics: Hyattsville, MD. |
[3] |
Hedegaard M, Henriksen TB, Secher NJ, et al. (1993) Psychological distress in pregnancy and preterm delivery. BMJ 307: 234–239. doi: 10.1136/bmj.307.6898.234
![]() |
[4] |
Rini CK, Dunkelschetter C, Wadhwa PD, et al. (1999) Psychological adaptation and birth outcomes: The role of personal resources, stress, and sociocultural context in pregnancy. Health Psychol 18: 333–345. doi: 10.1037/0278-6133.18.4.333
![]() |
[5] |
Dole N, Savitz DA, Siega-Riz AM, et al. (2003) Maternal stress and preterm birth. Am J Epidemiol 157: 14–24. doi: 10.1093/aje/kwf176
![]() |
[6] |
Dole N, Savitz DA, Siegariz AM, et al. (2004) Psychosocial factors and preterm birth among African American and White women in central North Carolina. Am J Public Health 94: 1358–1365. doi: 10.2105/AJPH.94.8.1358
![]() |
[7] |
Wadhwa PA, Sandman CA, Porto M, et al. (1993) The association between prenatal stress and infant birth weight and gestational age at birth: A prospective investigation. Am J Obstet Gynecol 169: 858–865. doi: 10.1016/0002-9378(93)90016-C
![]() |
[8] |
Ding XX, Wu YL, Xu SJ, et al. (2014) Maternal anxiety during pregnancy and adverse birth outcomes: A systematic review and meta-analysis of prospective cohort studies. J Affective Disord 159: 103–110. doi: 10.1016/j.jad.2014.02.027
![]() |
[9] |
McDonald SW, Kingston D, Bayrampour H, et al. (2014) Cumulative psychosocial stress, coping resources, and preterm birth. Arch Womens Mental Health 17: 559–568. doi: 10.1007/s00737-014-0436-5
![]() |
[10] |
Dayan J, Creveuil C, Marks MN, et al. (2006) Prenatal depression, prenatal anxiety, and spontaneous preterm birth: A prospective cohort study among women with early and regular care. Psychosom Med 68: 938–946. doi: 10.1097/01.psy.0000244025.20549.bd
![]() |
[11] |
Orr ST, James SA, Prince CB (2002) Maternal prenatal depressive symptoms and spontaneous preterm births among African-American women in Baltimore, Maryland. Ame J Epidemiol 156: 797–802. doi: 10.1093/aje/kwf131
![]() |
[12] |
Jesse DE, Seaver W, Wallace DC (2003) Maternal psychosocial risks predict preterm birth in a group of women from Appalachia. Midwifery 19: 191–202. doi: 10.1016/S0266-6138(03)00031-7
![]() |
[13] |
Misra D, Strobino D, Trabert B (2010) Effects of social and psychosocial factors on risk of preterm birth in Black women. Paediatr Perinat Epidemiol 24: 546–554. doi: 10.1111/j.1365-3016.2010.01148.x
![]() |
[14] |
Grote NK, Bridge JA, Gavin AR, et al. (2010) A meta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction. Arch Gen Psychiatry 67: 1012–1024. doi: 10.1001/archgenpsychiatry.2010.111
![]() |
[15] |
Szegda K, Markenson G, Bertone-Johnson ER, et al. (2014) Depression during pregnancy: A risk factor for adverse neonatal outcomes? A critical review of the literature. J Matern-Fetal Neonat Med 27: 960–967. doi: 10.3109/14767058.2013.845157
![]() |
[16] |
Misra D, Strobino D, Trabert B (2010) Effects of social and psychosocial factors on risk of preterm birth in Black women. Paediatr Perinat Epidemiol 24: 546–554. doi: 10.1111/j.1365-3016.2010.01148.x
![]() |
[17] |
Giurgescu C, Engeland CG, Templin TN (2015) Symptoms of depressive predict negative birth outcomes in African American women: A pilot study. J Midwifery Women's Health 60: 570–577. doi: 10.1111/jmwh.12337
![]() |
[18] |
Giurgescu C, Sanguanklin N, Engeland CG, et al. (2015) Relationships among psychosocial factors, biomarkers, preeclampsia, and preterm birth in African American women: A piol. Appl Nurs Res 28: 1–6. doi: 10.1016/j.apnr.2014.12.004
![]() |
[19] | Paul K, Boutain D, Agnew K, et al. (208) The relationship between racial identity, income, stress and C-reactive protein among parous women: Implications for preterm birth disparity research. J Natl Med Assoc 100: 540–546. |
[20] |
Giurgescu C, Misra DP, Caldwell CH, et al. (2015) The impact of neighborhood quality, perceived stress, and social support on depressive symptoms during pregnancy in African American women. Soc Sci Med 130: 172–180. doi: 10.1016/j.socscimed.2015.02.006
![]() |
[21] |
Giurgescu C, Zenk SN, Templin TN, et al. (2015) The impact of neighborhood environment, social support, and avoidance coping on depressive symptoms of pregnant African American women. Womens Health Issues 25: 294–302. doi: 10.1016/j.whi.2015.02.001
![]() |
[22] |
Hetherington E, Doktorchik C, Premji SS, et al. (2015) Preterm birth and social support during pregnancy: A systematic review and meta-analysis. Paediatr Perinat Epidemiol 29: 523–535. doi: 10.1111/ppe.12225
![]() |
[23] |
Giurgescu C, Templin TN (2015) Father involvement and psychological well-being of pregnant women. MCN Am J Matern Child Nurs 40: 381. doi: 10.1097/NMC.0000000000000183
![]() |
[24] |
Nylen KJ, O'Hara MW, Engeldinger J (2013) Perceived social support interacts with prenatal depression to predict birth outcomes. J Behav Med 36: 427–440. doi: 10.1007/s10865-012-9436-y
![]() |
[25] |
Jonsdottir SS, Thome M, Steingrimsdottir T, et al. (2017) Partner relationship, social support and perinatal distress among pregnant Icelandic women. Women Birth 30: e46–e55. doi: 10.1016/j.wombi.2016.08.005
![]() |
[26] |
Bloch JR, Webb DA, Matthew L, et al. (2010) Beyond marital status: The quality of the mother-father relationship and its influence on reproductive health behaviors and outcomes among unmarried low income pregnant women. Matern Child Health J 14: 726–734. doi: 10.1007/s10995-009-0509-7
![]() |
[27] |
Sealyjefferson S, Giurgescu C, Helmkamp L, et al. (2015) Perceived physical and social residential environment and preterm delivery in African-American women. Am J Epidemiol 182: 485–493. doi: 10.1093/aje/kwv106
![]() |
[28] |
Slaughter-Acey JC, Sealy-Jefferson S, Helmkamp L, et al. (2016) Racism in the form of micro aggressions and the risk of preterm birth among black women. Ann Epidemiol 26: 7–13. doi: 10.1016/j.annepidem.2015.10.005
![]() |
[29] | Center for Disease Control and Prevention, Defining adult overweight and obesity. Available from: https://www.cdc.gov/obesity/adult/defining.html. |
[30] |
Radloff LS (1977) The CES-D scale: A self-report depression scale for research in the general population. App Psychol Meas 1: 385–401. doi: 10.1177/014662167700100306
![]() |
[31] | Radloff LS, Locke BZ (1986) The community mental health assessment survey and CES-D scale, In: Weissman MM and Myers JK, Editors, Community Surveys of Psychiatric Disorders, New Brunswick, NJ: Rutgers University Press, 177–189. |
[32] |
Orr ST, Blazer DG, James SA, et al. (2007) Depressive symptoms and indicators of maternal health status during pregnancy. J Womens Health 16: 535–542. doi: 10.1089/jwh.2006.0116
![]() |
[33] | Antonucci TC (1986) Social support networks: A hierarchical mapping technique. Generations 10: 10–12. |
[34] | Kahn RL, Antonucci TC (1980) Convoys over the life course: Attachment, roles, and social support, In: Baltes PB, Editors, Life-span development and behavior, New York: Academic Press, 254–283. |
[35] | Taipale P, Hiilesmaa V (2001) Predicting delivery date by ultrasound and last menstrual period in early gestation. Obstet Gynecol 97: 189–194. |
[36] | Obstetriciansgynecologists ACO (2014) Committee opinion, number 611. Method for estimating due date. Am Coll Obstet Gynecol 863–866. |
[37] | Misra DP, Caldwell C, Young AA, et al. (2010) Do fathers matter? Paternal contributions to birth outcomes and racial disparities. Am J Obstet Gynecol 202: 99–100. |
[38] |
Srinivasjois RM, Shah S, Shah PS (2012) Biracial couples and adverse birth outcomes: A systematic review and meta-analyses. Acta Obstet Gynecol Scand 91: 1134–1146. doi: 10.1111/j.1600-0412.2012.01501.x
![]() |
[39] |
Lewin A, Mitchell SJ, Waters D, et al. (2015) The protective effects of father involvement for infants of teen mothers with depressive symptoms. Matern Child Health J 19: 1016–1023. doi: 10.1007/s10995-014-1600-2
![]() |
[40] |
Shah MK, Gee RE, Theall KP (2014) Partner support and impact on birth outcomes among teen pregnancies in the United States. J Pediatr Adolesc Gynecol 27: 14–19. doi: 10.1016/j.jpag.2013.08.002
![]() |
[41] |
Brewin D, Nannini A (2014) Using a life course model to examine racial disparities in low birth weight during adolescence and young adulthood. J Midwifery Womens Health 59: 417–427. doi: 10.1111/jmwh.12110
![]() |
[42] |
Wilusz MJ, Peters RM, Cassidy-Bushrow AE (2014) Course of Depressive Symptoms across Pregnancy in African American Women. J Midwifery Womens Health 59: 411–416. doi: 10.1111/jmwh.12057
![]() |
[43] |
Misra DP, Slaughter-Acey J, Giurgescu C, et al. (2017) Why do Black women experience higher rates of preterm birth? Curr Epidemiol Rep 4: 1–15. doi: 10.1007/s40471-017-0094-z
![]() |
1. | Lingling Gao, Yiqun Gan, Amanda Whittal, Sonia Lippke, Problematic Internet Use and Perceived Quality of Life: Findings from a Cross-Sectional Study Investigating Work-Time and Leisure-Time Internet Use, 2020, 17, 1660-4601, 4056, 10.3390/ijerph17114056 | |
2. | Penny Heidke, Corneel Vandelanotte, Christopher Irwin, Susan Williams, Sonia Saluja, Saman Khalesi, Associations between health‐related quality of life and health behaviors in Australian nursing students , 2021, 1441-0745, 10.1111/nhs.12832 | |
3. | Enrique Ramón-Arbués, Emmanuel Echániz-Serrano, Blanca Martínez-Abadía, Isabel Antón-Solanas, Ana Cobos-Rincón, Iván Santolalla-Arnedo, Raúl Juárez-Vela, Benjamin Adam Jerue, Predictors of the Quality of Life of University Students: A Cross-Sectional Study, 2022, 19, 1660-4601, 12043, 10.3390/ijerph191912043 | |
4. | Nicholas A Carlisle, Sarah MacCarthy, Karlie Burrell, Jeffrey Wickliffe, Environmental Exposure Inequities Among Sexual and Gender Minority Populations in the United States: A Scoping Review, 2024, 18, 1178-6302, 10.1177/11786302241296225 | |
5. | Yue Cao, Xuxi Zhang, Ian M. Fearon, Jiaxuan Li, Xi Chen, Yuming Xiong, Fangzhen Zheng, Jianqiang Zhang, Xinying Sun, Xiaona Liu, The effects of electronic cigarette use patterns on health-related symptom burden and quality of life: analysis of US prospective longitudinal cohort study data, 2024, 12, 2296-2565, 10.3389/fpubh.2024.1433678 |
Current Tobacco Use | ||||||
Entire Sample N=1132 (%) |
Non-Use n=947 (%) |
Dual Use n=42 (%) |
Sole E-Cigarette n=37 (%) |
Sole Cigarette n=106 (%) |
||
Age - mean (sd) | 21.2 (1.8) | 21.2 | 21.6 | 21.4 | 21.3 | |
GPA - mean (sd) | 3.05 (.86) | 3.1 | 3.2 | 2.8 | 3.0 | |
Sex* | Female | 706 (63) | 608 (64) | 16 (38) | 15 (41) | 67 (63) |
Male | 426 (37) | 339 (36) | 26 (62) | 22 (59) | 39 (37) | |
Race | White | 922 (78) | 773 (82) | 31 (74) | 31 (84) | 87 (82) |
Non-White | 250 (22) | 214 (18) | 11 (26) | 6 (16) | 19 (18) | |
Sexual Identity* | Heterosexual | 984 (87) | 845 (89) | 31 (74) | 32 (86) | 76 (72) |
LGB | 147 (13) | 101 (11) | 11 (26) | 5 (14) | 30 (28) | |
Academic Level | Freshman | 205 (18) | 169 (18) | 4 (9) | 8 (22) | 24 (23) |
Sophomore | 292 (26) | 249 (26) | 12 (29) | 6 (16) | 25 (24) | |
Junior | 267 (24) | 233 (25) | 10 (24) | 8 (22) | 16 (15) | |
Senior | 368 (32) | 296 (31) | 16 (38) | 15(40) | 41 (38) | |
WHOQOL-BREF Scores | ||||||
Quality of Life-mean (sd)* | 71.2 (17.7) | 72.2 | 67.6 | 68.9 | 64.6 a | |
Physical Health-mean (sd) | 71.1 (14.5) | 71.7 | 67.7 | 71.7 | 67.3 | |
Psychological-mean (sd) * | 63.0 (17.4) | 64.2 | 57.1 | 60.8 | 55.5 a | |
Social Relations-mean (sd)* | 64.9 (20.9) | 66.7 | 63.5 | 65.8 | 59.0 a | |
Environmental-mean (sd)* | 67.1 (14.8) | 67.7 | 63.0 | 67.5 | 63.2 a | |
Note. a Post hoc difference from nonusers at P < .05. *P < .01 |
Dual Use | Sole E-cig Use | Sole Cigarette Use | |||||||
OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P | |
QOL | 0.88 | [0.74, 1.04] | .135 | 0.90 | [0.75, 1.08] | .244 | 0.83 | [0.74, 0.92] | <.001 |
Physical health | 0.84 | [0.68, 1.03] | .097 | 1.00 | [0.79, 1.26] | .971 | 0.86 | [0.75, 0.99] | .029 |
Psychological well-being | 0.83 | [0.70, 0.98] | .026 | 0.90 | [0.74, 1.08] | .239 | 0.80 | [0.72, 0.90] | <.001 |
Social relations | 0.97 | [0.84, 1.12] | .688 | 1.00 | [0.86, 1.17] | .997 | 0.87 | [0.79, 0.96] | .003 |
Environmentalhealth | 0.83 | [0.69, 1.02] | .072 | 1.00 | [0.80, 1.24] | .988 | 0.84 | [0.74, 0.96] | .010 |
Note. Logistic regression adjusted for race, sex, sexual identity. Represents odds ratio of being in each category compared to non-users per 10-unit increase on WHOQOL-BREF scale. |
Current Tobacco Use | ||||||
Entire Sample N=1132 (%) |
Non-Use n=947 (%) |
Dual Use n=42 (%) |
Sole E-Cigarette n=37 (%) |
Sole Cigarette n=106 (%) |
||
Age - mean (sd) | 21.2 (1.8) | 21.2 | 21.6 | 21.4 | 21.3 | |
GPA - mean (sd) | 3.05 (.86) | 3.1 | 3.2 | 2.8 | 3.0 | |
Sex* | Female | 706 (63) | 608 (64) | 16 (38) | 15 (41) | 67 (63) |
Male | 426 (37) | 339 (36) | 26 (62) | 22 (59) | 39 (37) | |
Race | White | 922 (78) | 773 (82) | 31 (74) | 31 (84) | 87 (82) |
Non-White | 250 (22) | 214 (18) | 11 (26) | 6 (16) | 19 (18) | |
Sexual Identity* | Heterosexual | 984 (87) | 845 (89) | 31 (74) | 32 (86) | 76 (72) |
LGB | 147 (13) | 101 (11) | 11 (26) | 5 (14) | 30 (28) | |
Academic Level | Freshman | 205 (18) | 169 (18) | 4 (9) | 8 (22) | 24 (23) |
Sophomore | 292 (26) | 249 (26) | 12 (29) | 6 (16) | 25 (24) | |
Junior | 267 (24) | 233 (25) | 10 (24) | 8 (22) | 16 (15) | |
Senior | 368 (32) | 296 (31) | 16 (38) | 15(40) | 41 (38) | |
WHOQOL-BREF Scores | ||||||
Quality of Life-mean (sd)* | 71.2 (17.7) | 72.2 | 67.6 | 68.9 | 64.6 a | |
Physical Health-mean (sd) | 71.1 (14.5) | 71.7 | 67.7 | 71.7 | 67.3 | |
Psychological-mean (sd) * | 63.0 (17.4) | 64.2 | 57.1 | 60.8 | 55.5 a | |
Social Relations-mean (sd)* | 64.9 (20.9) | 66.7 | 63.5 | 65.8 | 59.0 a | |
Environmental-mean (sd)* | 67.1 (14.8) | 67.7 | 63.0 | 67.5 | 63.2 a | |
Note. a Post hoc difference from nonusers at P < .05. *P < .01 |
Dual Use | Sole E-cig Use | Sole Cigarette Use | |||||||
OR | 95% CI | P | OR | 95% CI | P | OR | 95% CI | P | |
QOL | 0.88 | [0.74, 1.04] | .135 | 0.90 | [0.75, 1.08] | .244 | 0.83 | [0.74, 0.92] | <.001 |
Physical health | 0.84 | [0.68, 1.03] | .097 | 1.00 | [0.79, 1.26] | .971 | 0.86 | [0.75, 0.99] | .029 |
Psychological well-being | 0.83 | [0.70, 0.98] | .026 | 0.90 | [0.74, 1.08] | .239 | 0.80 | [0.72, 0.90] | <.001 |
Social relations | 0.97 | [0.84, 1.12] | .688 | 1.00 | [0.86, 1.17] | .997 | 0.87 | [0.79, 0.96] | .003 |
Environmentalhealth | 0.83 | [0.69, 1.02] | .072 | 1.00 | [0.80, 1.24] | .988 | 0.84 | [0.74, 0.96] | .010 |
Note. Logistic regression adjusted for race, sex, sexual identity. Represents odds ratio of being in each category compared to non-users per 10-unit increase on WHOQOL-BREF scale. |