Gender Differences in the Relationship Between Exercise,Sleep,and Mood in Young Adults

Abstract

Insufficient sleep is a serious public health problem in college students. Exercise is a widely prescribed behavioral treatment for sleep and mood issues; however, more focused and gender-specific prescriptions are needed. The present study examined relationships between exercise, sleep, and mood in undergraduate men and women. Students (N = 866, 19.6 ± 1.4 years, 38.7% women) were recruited from campus recreation facilities and completed demographic, the Pittsburgh Sleep Quality Index, mood (Patient-Reported Outcomes Measurement Information System), and exercise questionnaires. The Department of Health and Human Services Physical Activity Guidelines were used to dichotomize those who did and did not meet weekly aerobic and strength training exercise recommendations. In men, greater exercise frequency associated with less daytime dysfunction (β = 0.147) and less depressive mood (β = −0.64, ps < .05). In women, greater exercise frequency associated with earlier bedtime (β = −12.6), improved sleep quality (β = 0.17), increased positive affect (β = 0.91), less depressive mood (β = −0.71), and less anger (β = −1.24, ps < .05). Compared to men, women reported earlier bedtime, poorer sleep efficiency, and more anxiety and depressive mood (ps < .05, $η_{p}^{2}$ range: 0.01–0.04). Compared to individuals who met physical activity guidelines, those who did not meet the guidelines reported later bedtimes, less positive affect, more anxiety, and more anger (ps < .05 $η_{p}^{2}$ s = 0.01). Among men, those who met physical activity guidelines reported falling asleep more quickly than those who did not meet guidelines ( $η_{p}^{2}$ = 0.01, p = .007); however, no relationship between guideline adherence and sleep latency was observed in women. Adhering to physical activity guidelines may be important for optimal sleep and emotional health. Clinicians should consider gender when creating exercise prescriptions for sleep issues.

Keywords

physical activity guidelines college students sleep quality sleep duration aerobic exercise strength training

Insufficient sleep is a serious and growing public health problem among college students. Approximately 32% of those 18 to 24 years of age report habitually sleeping less than the recommended 7 to 9 hours per night (Liu et al., 2016), and college students rate sleep difficulties second only to stress as a factor that negatively affects academic performance (American College Health Association, 2019). The accumulation of pubertal changes in homeostatic sleep pressure and circadian rhythms (Knutson, 2005), increasing academic and work-life demands (Hershner & Chervin, 2014), and prevalent use of sleep-disrupting substances (e.g., caffeine and alcohol; Patrick et al., 2018) make college students particularly vulnerable to poor sleep. Insufficient sleep and excessive daytime sleepiness lead to adverse physical health outcomes (Hershner & Chervin, 2014; Owens, 2014; e.g., weakened immune system, increased risk for obesity, high blood pressure), poor academic performance (Gaultney, 2010), and increased risk for motor vehicle accidents (Taylor & Bramoweth, 2010).

One of the most rapid and severe consequences of insufficient sleep is the impact on mood. Poor sleep quality associates with next-day changes in mood including blunted positive affect and increased negative affect (Ong et al., 2013; Ong et al., 2017; Simor et al., 2015). In addition, poor sleep efficiency (the amount of actual sleep obtained while in bed for sleep), insufficient sleep duration, and increased daytime sleepiness have been associated with increased emotional reactivity and dysregulation (Ong et al., 2013; Palmer & Alfano, 2017; Simor et al., 2015). Prolonged sleep loss makes it more difficult to adaptively cope with challenging negative emotions, such as feelings of depression, anxiety, or anger, which are implicated in many mental health disorders (Berking & Wupperman, 2012). Adults reporting sleep difficulties are also more likely to call in sick for work, forgo social activities and exercise, and cancel appointments or meetings, which may influence mood and emotional dysfunction and perpetuate a cycle of poor sleep and mood (Berking & Wupperman, 2012; Harvey, 2002; Palmer & Alfano, 2017). In college students, prevalence rates have been estimated as high as 20% for depression, 24% for anxiety, and 16.5% for comorbid depression and anxiety (American College Health Association, 2019). Given the high prevalence of insufficient sleep and poor mental health in this population, as well as the strong association between poor sleep and mental health issues, innovative strategies are critically needed to improve sleep hygiene among college students.

Currently, clinicians recommend physical activity to improve sleep quality (Kredlow et al., 2015). According to the 2018 Physical Activity Guidelines for Americans, college-aged adults should aim for 150 minutes of moderate-intensity aerobic activity (or 75 minutes of vigorous aerobic activity) and two sessions of moderate- to high-intensity muscle strength training activity each week (U.S. Department of Health and Human Services, 2018). A combination of both aerobic activity and strength training provides maximal benefits for cardiovascular and metabolic health (Piercy et al., 2018). Despite the known benefits of regular physical activity for health, less than 50% of college students reported meeting the current guidelines for aerobic activity in 2019 (American College Health Association, 2019). One study found that only 30.9% of college students reported meeting guidelines for both cardio/aerobic and strength training (Farren & Zhang, 2017). Gaining a better understanding of how meeting physical activity guidelines associates with sleep and mood in college students will aid in the dissemination of knowledge about the benefits of meeting physical activity guidelines.

Given the consistent gender differences observed in sleep, mood, and physical activity (Campbell et al., 2012; Craft et al., 2014; Grandner, 2017; Kimura, 2005; McLean et al., 2011; Salk et al., 2017; Voderholzer et al., 2003), it is important to consider gender when examining the relationships between these health components. Compared to men, women are more likely to report poor sleep quality and traumatic sleep difficulties (Fatima et al., 2016) and exhibit anxiety and depressive symptoms (McLean et al., 2011; Salk et al., 2017). There are also known gender differences in motivations for exercise, with men reporting enjoyment, challenge, competition, and social recognition, and women reporting weight control, appearance, health, and stress management (Craft et al., 2014; Farren et al., 2017). A recent meta-analysis found that the benefits of acute exercise on objective measures of sleep (i.e., for non–rapid eye movement stage 1 sleep duration and wake time after sleep onset) were stronger for men than women (Kredlow et al., 2015). These findings further support the importance of examining gender differences when assessing the potential benefits of physical activity for sleep and mood.

To date, very few studies have examined the relationships between Physical Activity Guideline adherence, exercise frequency, sleep, and mood in college students. This large cross-sectional study used a novel approach to increase generalizability of the findings to the general college student population by recruiting a large number of college students visiting a campus recreation center during all operational hours and utilizing validated measures of sleep and mood. Additionally, to our knowledge, gender-based stratification of these metrics in a college sample has yet to be explored by Physical Activity Guideline adherence to both aerobic and strength training recommendations for adults. This study was designed to fill this gap and determine if there are gender differences in the relationships between exercise frequency and physical activity guideline adherence, and sleep and mood outcomes. We hypothesized that increased exercise frequency and physical activity guideline adherence would associate with improved sleep and mood. We also hypothesized that these relationships would be stronger in men than in women.

Method

Participants and Protocol

Undergraduates exercisers (N = 1,086) were recruited in person by study staff from campus recreation facilities during facility operating hours (6 a.m.–11 p.m.). Of those screened, n = 926 met study inclusion criteria (18–23 years, undergraduate student, completed a workout immediately prior). Of those eligible, n = 4 chose not to complete the study. Enrolled participants completed a demographic questionnaire as well as exercise, sleep, chronotype, and mood surveys. Data from n = 56 were excluded from analyses due to invalid reporting (sleep duration < 2 hours/night on average in the past 30 days, n = 9; sleep latency > 5 hours/night on average in the past 30 days, n = 4), missing exercise data (n = 28), or a reported gender other than male/female (n = 15). The final analytic sample was N = 866.

The research protocol was approved by the University’s institutional review board. Participants read a consent form prior to filling out the survey and anonymously consented to the research procedures.

Measures

Trained study staff members provided participants with an iPad device, and participants completed the electronic study survey in Qualtrics, a secure online survey platform commonly used by the University to distribute surveys. Participant demographics (age, gender, year of study, race/ethnicity, living arrangement, international student status, and employment status) were assessed via questionnaire. The majority of students who completed the study were men (62.2%), and 97.6% of the students in the sample were traditional college age students (freshman–senior). Students also largely reported living in housing on campus property or within walking distance to the University (90.1%). Student employment was common; 44.2% reported having a job.

The five-item reduced Morningness-Eveningness Ques-tionnaire (rMEQ) was administered to assess chronotype. A total Morningness-Eveningness score was computed according to standard scoring procedures and used to categorized subjects into Chronotype category (score range: 4–25; Morning-type: total score > 17; Neither-type: total score = 12–17; Evening-type: total score < 12; Adan & Almirall, 1991). Compared to the full Morningness Eveningness Questionnaire, the reduced Morningness-Eveningness Questionnaire has demonstrated to be sufficiently sensitive and reliable at categorizing individual level of morningness-eveningness, specifically in college students (Chelminski et al., 2000). The study’s exercise and physical activity survey questions were based off of standardized questions from an internal university lifestyle survey, that is, “During a typical week, how often do you exercise?”; “On how many of the past 7 days did you do moderate-to-vigorous intensity cardiovascular or aerobic exercise for at least 30 minutes?”; “On how many of the past 7 days did you do 8 to 10 strength training exercises for 8 to 12 repetitions each?”; “When you exercise, how many minutes do you typically exercise for?”; “With what intensity do you usually exercise?”; “What is the main reason why you exercise?”; and “What is the main reason why you do not exercise as often (since starting the semester)?”

The Physical Activity Guidelines for Americans (U.S. Department of Health and Human Services, 2018) recommend that adults perform at least 150 minutes of moderate-intensity aerobic activity (or 75 minutes of vigorous aerobic activity) and two sessions of moderate- to high-intensity muscle strength training each week (U.S. Department of Health and Human Services, 2018). Therefore, participants in the current study were dichotomized to meeting the guidelines (i.e., reported at least 3 days of 30+ minutes of moderate- to vigorous-intensity cardiovascular or aerobic exercise and at least 2 days of 8–10 strength training exercisers for 8–12 repetitions each) or not meeting the guidelines (i.e., reported <3 days of 30 minutes moderate- to vigorous-intensity cardiovascular or aerobic exercise and <2 days of 8–10 strength training exercisers for 8–12 repetitions each).

Subjective sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire, a widely utilized, reliable, and valid tool for assessing subjective sleep quality in adults (Buysse et al., 1989, Carpenter & Andrykowski, 1998. Sleep variables (bedtime, sleep latency, sleep duration, time in bed, and sleep efficiency) were calculated from individual PSQI responses. Sleep efficiency was calculated as the ratio of total sleep time to time in bed multiplied by 100, with higher scores indicating better sleep efficiency. Self-reported sleep duration was dichotomized to sufficient sleepers (≥7 hours) and insufficient sleepers (<7 hours) based on current sleep recommendations for young adults (Hirshkowitz et al., 2015). Subscales of the PSQI (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction) yield scores ranging from 0 to 3, with 3 indicating greatest dysfunction. PSQI subscales were summed to create a Global score that dichotomized poor sleepers (score > 5) and good sleepers (score ≤ 5). Participants (n = 136) who reported their average time in bed as less than their time asleep (i.e., calculated sleep efficiency > 100%) were not included for analyses of time in bed, sleep efficiency, habitual sleep efficiency, or Global PSQI score.

Four adult Patient-Reported Outcomes Measurement Information System (Version 1.0) questionnaires were used to assess mood over the past 7 days: Positive Affect (short form–15a [SF15a]), Anxiety (SF8a), Depression (SF8a), and Anger (SF5a). The Patient-Reported Outcomes Measurement Information System questionnaires have been determined to be valid and reliable for use to measure mood in the U.S. general population; short forms demonstrated good reliability across score distributions and moderate to strong correlations to other widely validated and accepted measures for mood (Cella et al., 2010; Schalet et al., 2016). Standard scoring procedures were used (scores were normed to the U.S. population as t scores with M = 50 and SD = 10; Bevans et al., 2014). Higher scores on depression, anxiety, and anger indicate worse health (e.g., greater depression), while higher scores on the positive affect scale indicate better health (e.g., greater positive affect).

Statistical Analyses

Descriptive statistics (means and frequencies) were calculated for demographic variables. The distribution and spread of the data were checked, and for continuous variables, we performed normality testing. Assumptions for statistical analyses (i.e., normality, equality of variance, independence, linearity) were checked and met prior to conducting subsequent statistical tests. Between-participant analysis of variance (ANOVA) and Pearson/spearman chi-squared tests were performed to compare continuous and categorical exercise variables by gender. To assess multiple continuous dependent variables (sleep, mood) by multiple independent variables (gender, physical activity guideline adherence), a two-way multivariate ANOVA (MANOVA) was performed. Main effects for gender and guideline adherence and their interaction are reported. Gender-stratified between-participant ANOVAs were performed for sleep and mood variables in an exploratory analysis to examine gender-specific relationships between exercise, sleep, and mood variables. For ANOVA analyses, (η²) was presented as a measure of effect size, calculated as the ratio of the effect variance to the total variance (effect size classification: 0.01 = small, 0.06 = medium, >0.14 = large) and partial η² were reported for MANOVAs (Cohen, 1973). The phi correlation coefficient (ϕ) and Cramer’s V were calculated as a measure of the strength of the association between variables for chi-square testing (effect size classification: 0.1 = small, 0.3 = medium, 0.5 = large; Cohen, 2013).

Linear regressions analyzed the relationship between exercise session frequency and continuous variables for sleep and mood. Ordinal logistic regression analysis was used to examine the relationship between exercise session frequency and PSQI subcomponents. Statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 26.0 (Armonk, NY).

Results

Descriptive statistics calculated for men and women are shown in Table 1. Compared to men, women were more likely to be employed (χ²(1, 866) = 12.18, ϕ = 0.12, p < .001) and less likely to have an evening preference (χ²(1, 866) = 14.12, Cramer’s V = 0.13, p < .001).

Table 1.

Descriptive Statistics for Study Sample (N = 866).

Variables/category	Men (n = 531)	Women (n = 335)
Age, years, M (SD)	19.6 (1.4)	19.5 (1.3)
Year of study, n (%)
Freshman/first year	174 (32.8)	101 (30.1)
Sophomore/second year	119 (22.4)	85 (25.4)
Junior/third year	122 (23.0)	73 (21.8)
Senior/fourth year	102 (19.2)	69 (20.6)
Fifth year	12 (2.3)	7 (2.1)
Other	2 (0.4)	0 (0)
Race, n (%)
American Indian/Alaska Native	0 (0)	0 (0)
Asian	204 (38.4)	89 (26.6)
Black or African American	33 (6.2)	17 (5.1)
Native Hawaiian/Pacific Islander	1 (0.2)	1 (0.3)
White/Caucasian	188 (35.4)	143 (42.7)
Hispanic/Latinx	45 (8.5)	34 (10.1)
Middle Eastern	10 (1.9)	9 (2.7)
More than one race	42 (7.9)	39 (11.6)
Other	8 (1.5)	3 (0.9)
International student, n (%)
Yes	29 (5.5)	17 (5.1)
No	502 (94.5)	318 (94.9)
Residential status, n (%)
On-campus	353 (66.5)	219 (65.4)
Off-campus	124 (23.4)	84 (25.1)
Commute	54 (10.2)	32 (9.6)
Employment status, n (%)
Yes, non–university affiliated job	116 (21.8)	88 (26.3)
Yes, university-affiliated job	94 (17.7)	85 (25.4)
No	321 (60.5)	162 (48.4)
Chronotype, n (%)
Morning-type	65 (12.2)	39 (11.6)
Neither-type	292 (55.0)	224 (66.9)
Evening-type	174 (32.8)	72 (21.5)

A comparison of exercise characteristics between men and women is presented in Table 2. Compared to women, men reported more exercise sessions per week (F(1, 864) = 44.70, p < .001, η² = 0.05), fewer moderate- to vigorous-intensity aerobic activity days (F(1, 864) = 17.14, p < .001, η² = 0.02), and more strength training days (F(1, 864) = 101.03, p < .001, η² = 0.11). Compared to men, women were more likely to meet the aerobic activity guideline (χ²(1, 866) = 19.16, ϕ = 0.15, p < .001) and less likely to meet the strength training guideline (χ²(1, 866) = 52.82, ϕ = 0.25, p < .001). The proportion of participants meeting both aerobic and strength training guidelines did not differ by gender (χ²(1, 866) = 2.69, ϕ = 0.06, p = .10).

Table 2.

Exercise Variables by Gender.

Variables	Men (n = 531)	Women (n = 335)	p
Exercise sessions (sessions/week), M (SD)	4.6 (1.3)	3.9 (1.5)	<.001
Both aerobic and strength training guidelines, n (%)^a			.10
Met both guidelines	236 (44.4)	168 (50.1)
Did not meet both guidelines	295 (55.6)	167 (49.9)
Aerobic activity (days/week), M (SD)	2.6 (1.9)	3.1 (1.6)	<.001
Aerobic activity guidelines, n (%)^b			<.001
Met cardio/aerobic guidelines	265 (49.9)	218 (65.1)
Did not meet cardio/aerobic guidelines	266 (50.1)	117 (34.9)
Strength training (days/week), M (SD)	3.7 (1.9)	2.4 (1.7)	<.001
Strength training guidelines, n (%)^c			<.001
Met strength training guidelines	465 (87.6)	225 (67.2)
Did not meet strength training guidelines	66 (12.4)	110 (32.8)
Exercise duration (minutes), n (%)			<.001
0−15	3 (0.6)	1 (0.3)
16−30	14 (2.6)	15 (4.5)
31−45	80 (15.1)	84 (25.1)
46−60	194 (36.5)	153 (45.7)
60+	240 (45.2)	82 (24.5)
Exercise intensity, n (%)^d			<.001
High intensity	282 (53.1)	80 (23.9)
Moderately high	202 (38.0)	174 (51.9)
Moderate	41 (7.7)	77 (23.0)
Low to moderate	5 (0.9)	3 (0.9)
Light	1 (0.2)	1 (0.3)
Exercise reason, n (%)			<.001
Maintain or lose weight	82 (15.4)	153 (45.7)
Gain weight or muscle mass	238 (44.8)	26 (7.8)
General health	103 (19.4)	84 (25.1)
Enjoyment	49 (9.2)	23 (6.9)
Stress relief	43 (8.1)	41 (12.5)
Social interaction	4 (0.8)	1 (0.3)
Other	12 (2.3)	7 (2.1)

Note. p < .05 indicated in boldface.

3 days of 30 minutes moderate- to vigorous-intensity cardiovascular or aerobic exercise and 2 days of 8–10 strength training exercisers for 8–12 repetitions each. ^b3 days of 30 minutes moderate- to vigorous-intensity cardiovascular or aerobic exercise. ^c2 days of 8–10 strength training exercisers for 8–12 repetitions each. ^dHigh intensity = high-intensity activities that result in sustained heavy breathing and perspiration (heavy weight lifting, plyometrics, sprinting, kickboxing); Moderately high = moderately high–intensity aerobic and sport activities that result in heavy breathing and perspiration (distance cycling, running stairs, jumping rope, light weight lifting, soccer, lacrosse); Moderate = moderate-intensity aerobic activities (normal bike riding, jogging, low-impact aerobics); Low to moderate = low- to moderate-intensity aerobic and sports activities (recreational volleyball, moderate speed walks); Light = light aerobic exercise (normal walking, golfing, bowling).

A 2 (gender) × 2 (guideline adherence) MANOVA revealed significant main effects of gender (bedtime: F(1, 859) = 14.94, p < .001, $η_{p}^{2}$ = 0.02; calculated sleep efficiency: F(1, 726) = 8.36, p = .004, $η_{p}^{2}$ = 0.01; sleep efficiency subscale: F(1, 726) = 6.98, p = .008, $η_{p}^{2}$ = 0.01; anxiety: F(1, 859) = 31.65, p < .001, $η_{p}^{2}$ = 0.04; depression: F(1, 859) = 7.12, p = .008, $η_{p}^{2}$ = 0.01) and of guideline adherence (bedtime: F(1, 859) = 6.19, p = .013, $η_{p}^{2}$ = 0.01; positive affect: F(1, 859) = 6.29, p = .012, $η_{p}^{2}$ = 0.01; anxiety: F(1, 859) = 4.34, p = .04, $η_{p}^{2}$ = 0.01; anger: F(1, 859) = 7.83, p = .005, $η_{p}^{2}$ = 0.01) for several sleep and mood variables. In addition, there was a significant gender × guideline adherence interaction effect for sleep latency (F(1, 859) = 3.97, p = .047, $η_{p}^{2}$ = 0.01). Compared to men, women reported significantly earlier bedtime, poorer sleep efficiency, more anxiety, and more depression (Table 3). Compared to individuals who met physical activity guidelines, those who did not meet the guidelines reported significantly later bedtimes, less positive affect, more anxiety, and more anger (Table 4).

Table 3.

Sleep and Mood Variables by Gender.

Variables/category	Men (n = 531)	Women (n = 335)	p	Difference
Sleep variables, M (SD)
Bedtime (hh:mm)	0:53 (1:18)	0:32 (1:14)	<.001	0:21
Sleep duration (hours)	8.0 (1.2)	8.1 (1.3)	.317	−0.1
Sleep time (hours)	7.0 (1.0)	6.9 (1.1)	.392	0.1
Insufficient sleep (<7 hours/night), n (%)	198 (37.3)	122 (36.4)	.796
Sufficient sleep (≥7 hours/night), n (%)	333 (62.7)	213 (63.6)
Calculated sleep efficiency (%)	87.4 (7.9)	85.6 (9.1)	.005	1.8
Sleep latency (minutes)	22.2 (20.9)	21.6 (17.7)	.684	0.6
PSQI variables, M (SD)
Subjective sleep quality	1.14 (0.65)	1.10 (0.65)	.389	0.04
Sleep latency	1.23 (0.88)	1.16 (0.92)	.266	0.07
Sleep duration	0.75 (0.66)	0.79 (0.71)	.369	−0.04
Habitual sleep efficiency	0.32 (0.62)	0.45 (0.77)	.011	−0.13
Sleep disturbances	1.13 (0.51)	1.20 (0.52)	.064	−0.07
Use of sleep medications	0.26 (0.67)	0.27 (0.72)	.855	−0.01
Daytime dysfunction	1.15 (0.87)	1.20 (0.90)	.487	−0.05
Global PSQI Score	6.11 (2.78)	6.33 (2.90)	.313	−0.22
Poor sleeper, n (%)	255 (55.8)	153 (56.0)	.949
Good sleeper, n (%)	202 (44.2)	120 (44.0)
Mood variables, M (SD)
Positive affect	46.3 (8.5)	47.0 (9.4)	.301	−0.7
Anxiety	55.5 (8.1)	58.7 (8.0)	<.001	−3.2
Depression	50.3 (9.1)	51.9 (8.8	.011	−1.6
Anger	50.1 (10.5)	51.0 (9.3)	.199	−0.9

Note. p < .05 indicated in boldface. PSQI = Pittsburgh Sleep Quality Index.

Table 4.

Sleep and Mood Variables by Physical Activity Guideline Adherence Category.

Variables/category	Met guidelines^a (n = 404)	Did not meet guidelines (n = 462)	p	Difference
Sleep variables, M (SD)
Bedtime (hh:mm)	0:38 (0:16)	0:51 (0:17)	.012	−0:13
Sleep duration (hours)	8.0 (1.2)	8.1 (1.3)	.288	−0.1
Sleep time (hours)	6.93 (1.12)	6.97 (1.05)	.537	−0.04
Insufficient Sleep (<7 hours), n (%)	153 (37.9)	167 (36.1)	.600
Sufficient Sleep (≥7 hours), n (%)	251 (62.1)	295 (63.9)
Calculated sleep efficiency (%)	87.1 (8.1)	86.4 (8.7)	.269	0.7
Sleep latency (minutes)	20.5 (17.5)	23.3 (21.4)	.038	−2.8
PSQI variables, M (SD)
Subjective sleep quality	1.09 (0.67)	1.15 (0.63)	.157	−0.06
Sleep latency	1.15 (0.92)	1.25 (0.87)	.108	−0.10
Sleep duration	0.75 (0.70)	0.78 (0.65)	.661	−0.03
Habitual sleep efficiency	0.34 (0.63)	0.39 (0.73)	.316	−0.05
Sleep disturbances	1.14 (0.49)	1.18 (0.53)	.328	−0.04
Use of sleep medications	0.27 (0.69)	0.25 (0.69)	.647	0.02
Daytime dysfunction	1.13 (0.89)	1.21 (0.87)	.162	−0.08
Global PSQI Score	6.08 (2.87)	6.28 (2.78)	.340	−0.20
Poor sleeper (>5), n (%)	184 (55.3)	224 (56.4)	.752
Good sleeper (≤5), n (%)	149 (44.7)	173 (43.6)
Mood variables, M (SD)
Positive affect	47.6 (9.6)	46.0 (8.5)	.010	1.6
Anxiety	56.2 (8.4)	57.2 (8.1)	.085	−1.0
Depression	50.3 (9.1)	51.4 (9.0)	.084	−1.1
Anger	49.5 (10.1)	51.2 (10.0)	.013	−1.7

Note. p < .05 indicated in boldface. PSQI = Pittsburgh Sleep Quality Index.

3 days of 30 minutes moderate- to vigorous-intensity cardiovascular or aerobic exercise and 2 days of 8–10 strength training exercisers for 8–12 repetitions each.

Among men, those who met physical activity guidelines reported significantly shorter sleep latency (F(1, 529) = 7.31, p = .007, η² = 0.01) than those who did not, whereas there was no difference in sleep latency between guideline adherence groups among women (p = .76; Table 5). Men who met physical activity guidelines also reported more positive affect (F(1, 527) = 4.67, p = .03, η² = 0.09; Table 4) than men who did not. Women who met physical activity guidelines reported significantly earlier bedtimes (F(1, 333) = 4.58, p = .03, η² = 0.014) and less anger (F(1, 332) = 8.13, p = .005, η² = .024) than women who did not (Table 5).

Table 5.

Sleep and Mood Variables by Gender and Physical Activity Guideline Adherence Category.

Variables/category	Men			Women
Variables/category	Met guidelines (n = 236)	Did not meet guidelines (n = 295)	p	Met guidelines (n = 168)	Did not meet guidelines (n = 167)	p
Sleep variables, M (SD)
Bedtime (hh:mm)	0:48 (0:15)	0:57 (0:21)	.201	0:23 (0:16)	0:40 (0:11)	.033
Sleep duration (hours)	7.9 (1.2)	8.1 (1.2)	.135	8.1 (1.3)	8.1 (1.4)	.983
Sleep time (hours)	6.9 (1.1)	7.0 (1.0)	.267	6.9 (1.2)	6.9 (1.1)	.670
Insufficient Sleep (<7 hours), n (%)	76 (38.4)	94 (36.3)	.359	49 (36.3)	50 (36.2)	.546
Sufficient Sleep (≥7 hours), n (%)	122 (61.6)	165 (63.7)		86 (63.7)	88 (63.8)
Calculated sleep efficiency (%)	87.9 (7.4)	86.9 (8.3)	.176	85.8 (8.9)	85.3 (9.3)	.686
Sleep latency (minutes)	19.5 (15.0)	24.4 (24.3)	.007	21.9 (20.4)	21.3 (14.6)	.761
PSQI variables, M (SD)
Subjective sleep quality	1.12 (0.65)	1.14 (0.65)	.550	1.05 (0.70)	1.15 (0.59)	.149
Sleep latency	1.14 (0.86)	1.30 (0.89)	.030	1.17 (1.00)	1.15 (0.84)	.867
Sleep duration	0.76 (0.68)	0.74 (0.64)	.685	0.74 (0.73)	0.85 (0.68)	.212
Habitual sleep efficiency	0.27 (0.56)	0.35 (0.67)	.182	0.44 (0.72)	0.46 (0.82)	.775
Sleep disturbances	1.10 (0.47)	1.16 (0.54)	.143	1.24 (0.51)	1.19 (0.51)	.933
Use of sleep medications	0.24 (0.61)	0.28 (0.71)	.486	0.33 (0.79)	0.21 (0.64)	.135
Daytime dysfunction	1.11 (0.88)	1.19 (0.86)	.341	1.14 (0.91)	1.25 (0.88)	.269
Global PSQI score	5.88 (2.60)	6.29 (2.90)	.125	6.38 (3.22)	6.28 (2.57)	.787
Poor sleeper (>5), n (%)	107 (54.0)	148 (57.1)	.285	77 (57.0)	76 (55.1)	.419
Good sleeper (≤5), n (%)	91 (46.0)	111 (42.9)		58 (43.0)	62 (44.0)
Mood variables, M (SD)
Positive affect	48.0 (9.8)	46.2 (9.1)	.031	47.0 (9.4)	45.6 (7.4)	.131
Anxiety	55.0 (8.1)	56.0 (8.2)	.157	58.0 (8.5)	59.3 (7.5)	.124
Depression	49.6 (9.1)	50.8 (9.1)	.136	51.3 (9.0)	52.4 (8.7)	.251
Anger	49.5 (10.8)	50.6 (10.3)	.251	49.6 (9.2)	52.4 (9.2)	.005

Note. p < .05 indicated in boldface. PSQI = Pittsburgh Sleep Quality Index.

Each additional exercise session was associated with a 0.147 ± 0.06 decrease in the log odds of worse daytime dysfunction (p = .015, Wald = 5.97, Exp(β) = 0.864, 95% confidence interval (CI) [0.767, 0.972]), lower scores for depression (p = .030, β = −0.64, R² = .009, 95% CI [−1.22, −0.062]), and earlier bedtime (p < .001, β = −12.60, R² = 0.062, 95% CI [−17.87, −7.29]) among men. Each additional exercise was associated with a 0.166 ± 0.075 decrease in the log odds of worse sleep quality (p = .036, Wald = 4.86, Exp(β) = 0.847, 95% CI [0.732, 0.982]), greater positive affect (p = .004, β = 0.91, R² = 0.024, 95% CI [0.29, 1.53]), lower scores for depression (p = .033, β = −0.71, R² = 0.014, 95% CI [−1.36, −0.06]), and lower scores for anger (p < .001, β = −1.24, R² = 0.037, 95% CI [−1.92, −0.56]) among women. In men and women, each additional exercise session was associated with a significant 0.110 ± 0.051 decrease in the log odds for shorter PSQI subcomponent sleep duration (p = .032, Wald = 4.57, Exp(β) = 0.896, 95% CI [0.810, 0.991]). In men and women, exercise session frequency was not associated with sleep duration, time, efficiency, latency, PSQI subcomponents (sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, daytime dysfunction), global PSQI score, or anxiety (ps > .05).

Discussion

Consistent with previous studies (Becker et al., 2018; Beiter et al., 2015; Buckworth & Nigg, 2004; Farren & Zhang, 2017), we demonstrated that even in a large, diverse sample of physically active college students, only 46.7% reported meeting the current physical activity guidelines. Poor sleep and mood were highly prevalent, with 55.9% reporting poor sleep quality and 37.0% reporting insufficient sleep (<7 hours/night). Additionally, mood disturbances were common; 40.5% of students reported moderate to severe levels of anger, anxiety, or depression. Meeting the physical activity guidelines and exercising more days per week associated with significant improvements in sleep and mood. This evidence provides further support for programs aimed at increasing the number students who meet the current guidelines, and demonstrates additional benefits of exercise for sleep and mood. Furthermore, findings from this study build on previous research describing the benefits of physical activity for mental and physical health (Chennaoui et al., 2015; Choi et al., 2018; Kredlow et al., 2015; Penedo & Dahn, 2005).

To increase physical activity among students, universities should consider implementing models like the Temple University Kinesiology Physical Activity Program, which successfully enrolls approximately 1,200 students per semester in over 80 structured physical activity workshops for academic credit (Temple University College of Public Health, 2020). On a global level, the physical activity initiative, Exercise Is Medicine on Campus, has demonstrated success since 2007 (Lobelo et al., 2014) with over 280 university campuses enrolled (American College of Sports Medicine, 2020). In this program, trained health/fitness professionals provide individualized exercise prescriptions to students who visit campus health facilities to prevent and treat chronic diseases. Successful integration of Exercise Is Medicine on Campus has led to first-year seminars about exercise resources on campus and the importance of exercise, campus-wide integration of active health technology to monitor patient progress, and alumni outreach events to promote exercise in workplaces (American College of Sports Medicine, 2020; Lobelo et al., 2014). Similar programs should consider incorporating sleep measures when evaluating the program’s effectiveness in health promotion.

Consistent with previous studies observing gender differences in sleep and exercise (Kredlow et al., 2015; Silva et al., 2019; Sullivan Bisson et al., 2019), we observed that exercise associated with different sleep outcomes in men and women with small to medium effect sizes observed. In men, those who met current physical activity guidelines reported taking less time to fall asleep than those who did not. Difficulty falling asleep at night is a characteristic of insomnia, a sleep disorder that affects 9% to 12% of adults (Bhaskar et al., 2016) and approximately 6.1% of college students (American College Health Association, 2019). In the present study, 32.4% of students reported taking 30 minutes or more to fall asleep in the past month, which is indicative of a clinical sleep issue. Individuals who suffer from insomnia symptoms often report difficulty falling asleep due to extended time for worry, rumination, or frustration (Baron & Culnan, 2019). Despite the small effect size observed in the relationship between exercise and sleep latency among men, this finding is important as it supports future studies that aim to determine if meeting recommendations for physical activity guidelines reduce insomnia symptom severity in college-aged men.

We also found that additional exercise sessions associated with lower odds for daytime dysfunction in men. Daytime dysfunction describes having difficulty carrying out standard waking activities due to sleepiness and fatigue and can have physical (e.g., bodily pain, mobility) or mental (e.g., emotional problems, productivity) causes (Fortier-Brochu et al., 2010). In our study, 18% of students reported having trouble staying awake while driving, eating meals, or engaging in a social activity at least once per week, and 42% reported significant problems gathering enough enthusiasm to get things done. Interestingly, previous exercise interventions have not been successful at reducing daytime fatigue (Hartescu et al., 2015; Murawski et al., 2019). One potential reason may have been the socially isolated nature of these programs: Loneliness is a significant predictor of daytime dysfunction, independent of sleep duration (Hawkley et al., 2010). In our study, social interaction was not observed as common reason for exercise; however, men who exercise more frequently increase their opportunities for socialization and, subsequently, spend less time alone than those who exercise less frequently. Additional studies are needed to further examine the potential relationships between sleep, exercise, loneliness, and daytime functioning in men. Findings from this study demonstrate the potential benefits of increasing exercise session frequency to mitigate the negative effects of daytime dysfunction, specifically among men.

In women, those who met the physical activity guidelines went to bed 17 minutes earlier than those who did not meet the guidelines and each additional exercise session associated with going to bed 13 minutes earlier. Time constraints related to work, commute, and social obligations in a 24-hour day often result in reallocation of time dedicated to exercise and sleep (Grgic et al., 2018). Despite exercising more, women who met the guidelines were able to shift bedtime earlier. Maintaining an earlier bedtime may allow for more time to exercise by improving productivity (Malone, 2011; Massar et al., 2019). Time use is a particularly important area to examine for women. Women were more likely to report time burden as a main reason for not exercising as often as they used to (women: 90.7%, men: 77.0%) and were also more likely to be employed (women: 51.7%, men: 39.5%). Future studies are needed to systematically measure time use in college-aged students, particularly women, to determine what behaviors could be modified to increase time for sleep and exercise and improve productivity.

Exercise frequency was also associated with lower odds for poor subjective sleep quality in women. This is consistent with previous studies demonstrating the benefits of regular exercise for subjective sleep in young adults (Buman & King, 2010; Chang et al., 2016; Choi et al., 2018). There are known gender differences in sleep quality. Women exhibit worse subjective measures of sleep quality (i.e., self-report questionnaire; Fatima et al., 2016; Mallampalli & Carter, 2014; Silva et al., 2019; Voderholzer et al., 2003) but better objective measures of sleep quality (i.e., sleep efficiency assessed using overnight polysomnography; Goel et al., 2005) compared with men. Consistent with these findings, we observed worse self-report measures of sleep quality and efficiency in women compared to men. Although the effect sizes observed were small, improving poor subjective sleep quality in women is critical given its association with a number of mental health problems, including worse mood, poorer emotional regulation, and increased likelihood of mood disorders (Millman, 2005; Palmer & Alfano, 2017; Pires et al., 2016; Zhai et al., 2015). Women are at increased risk for sleep disturbances and certain sleep disorders across the life span (e.g., pregnancy, postpartum period, menstruation and menopause; Grandner, 2019; Nowakowski et al., 2013). The discordance between subjective and objective sleep measures among women (Mallampalli & Carter, 2014) is an area of active research within the sleep field. Future studies should examine how aerobic and strength training exercise affect subjective and objective sleep measures in women as well as how exercise affects sleep during female-specific biological events.

Consistent with previous studies observing gender differences in mood and exercise (Busch et al., 2016; McDowell et al., 2016; Plante et al., 2014), we observed that exercise associated with different mood outcomes in men and women. In men, those who met the physical activity guidelines exhibited more positive affect than those who did not, with a moderately large effect size observed. This finding suggests that men who incorporate both cardiovascular and strength training in their fitness routine may also receive the added benefits for mood. Given the cross-sectional design, it is also plausible that the association may be bidirectional in that men who struggle with positive affect may benefit from meeting physical activity guidelines and be a potential target for university exercise programming. In women, a small effect was observed for guideline adherence and anger; those who met the physical activity guidelines exhibited less anger than those who did not, and each additional exercise session associated with greater positive affect and less anger. Previous studies demonstrate the benefits of combining various forms of exercise for improved mood among women. For example, the U.S. army basic combat training, a 9- to 10-week course including highly structured aerobic and muscular strength and endurance training, was shown to have positive effects for overall mood, including anger and depression (Lieberman et al., 2014). Another study found that acute sessions of moderate-intensity resistance exercise and Hatha Yoga, which uses meditation and regulated breathing during exercise-based physical postures, had positive effects on affect and reduced anxiety among women (Fishman et al., 2019). Additionally, a 24-week prescription of resistance training was associated with a significant decrease in negative affect among healthy, physically active older women (age 65–70 years; Ericson et al., 2018). Our findings support the importance of women performing both aerobic and strength training exercise to obtain optimal mental health benefits. Given the higher prevalence of anxiety among women compared to men in the general population (McLean et al., 2011) and the lower frequency of strength training reported by women in the present study, university fitness programming should consider barriers to resistance training activities specifically among women.

We found that exercise session frequency was positively associated with less depressive mood in men and women. Previous studies demonstrate that aerobic exercise is as effective as first-line treatments, such as selective serotonin reuptake inhibitors, for reducing depressive symptoms (Rimer et al., 2012; Wipfli et al., 2008). For example, an 8-week moderate-intensity aerobic training intervention, consistent with the Physical Activity Guidelines for Americans for aerobic activity (Rethorst & Trivedi, 2013; U.S. Department of Health and Human Services, 2018), was associated with a clinically meaningful decrease in depressive symptoms among young adults compared to a stretching control group (Olson et al., 2017). Aerobic exercise at a consistent dose (i.e., energy expenditure and frequency) was also an effective treatment for mild to moderate major depressive disorder (Dunn et al., 2005). In addition to aerobic exercise, yoga exercise interventions have demonstrated success at reducing depressive symptoms among undergraduates (Falsafi, 2016). While numerous pharmacological treatments are available for depression, many patients are unable to continue prescriptions due to intolerable side effects; for these individuals, exercise may be particularly beneficial (Thase, 2016). Findings from the present study are consistent with previous studies demonstrating the benefits of meeting cardiovascular/aerobic physical activity guidelines and regular exercise for improving depressive symptoms (Dunn et al., 2005; Kvam et al., 2016; Olson et al., 2017). However, considering the variation observed in exercise frequency and type by gender in this sample, future studies should consider the role of gender preferences for dose and mode of exercise to optimize adherence and advance treatment of depression. Findings from this study support that meeting the exercise guidelines is beneficial for both sleep and mood among men as well as women. While the directionality of the associations presented highlight the benefits of exercise for sleep, it is important to consider that exercise and sleep share a reciprocally positive, bidirectional relationship (Youngstedt & Kline, 2006). Adults who exercise are more likely to exhibit better sleep quality and less like to suffer from sleep-disordered breathing (Atkinson & Davenne, 2007; Youngstedt & Kline, 2006). From a physiologic standpoint, maintenance of physical activity is associated with improved cardiovascular health, a health outcome also implicated in reduced risk for poor sleep quality (Alves et al., 2016, Hershner & Chervin, 2014; Owens, 2014). In this sample, among both men and women, exercise session frequency was associated with a lower odds of short sleep duration, which may suggest that increasing number of sessions may contribute to more sleep. From a psychological perspective, scheduled exercise can serve as a time management strategy necessary for good sleep hygiene (e.g., consistent bedtimes, wake times), and subsequently result in more time set aside for sleep.

In the alternative causal pathway model, sleep may drive exercise patterns and performance. For example, those with better sleep (increased sleep efficiency, shortened sleep onset latency, fewer awakenings after sleep onset, and more deep sleep) exhibit greater exercise exertion and higher physical activity levels (Brand et al., 2014; Foti et al., 2011). Sleep debt is thought to catalyze muscle degradation pathways and can impair recovery, which make it more difficult to sustain routine physical activity (Dattilo et al., 2011). It is plausible that those with better sleep have more energy to exercise, and thus, these individuals choose to exercise more often. Sleep loss has also been shown to reduce mood and motivation, and thus, individuals who are sleep deprived may exhibit a lower desire to exercise or exercise less often (Palagini et al., 2019).

Overall, the present study supports the current health recommendations to meet physical activity guidelines. Positive relationships were observed among both men and women for all significant associations between predictors and sleep and mood outcomes. However, the current “one-size-fits-all” approach for recommendations for physical activity implies maximal benefits for health for both men and women, yet there are gender differences in health benefits derived from exercise. For example, low- to moderate-intensity physical activity was associated with better subjective sleep quality and protection from cardiovascular disease and diabetes in women but to a lesser extent in men (Hu et al., 1999; Sattelmair et al., 2011; Sullivan Bisson et al., 2019). The use of gender-disaggregated analyses for understanding the benefits of meeting physical activity guidelines on sleep and mood may clarify how gender plays a role in healthy decision making and has implications for gender-based exercise prescription for improving sleep and mood.

Limitations of this study include the cross-sectional study design, which prevents causal inference and reliance on self-report measures exercise and sleep. Given the epidemiologic reports of discordance between subjective and objective sleep (Mallampalli & Carter, 2014), experimental studies should incorporate objective measures of both exercise (e.g., intensity, duration, time of day) and sleep, and consider gender influences on the perceptions of physical activity interventions. Additionally, the mood questionnaires, which were intended to capture mood over the last week, were administered immediately following a workout. Consequently, the responses may have inadvertently captured effects of acute exercise on mood rather than long-term physical activity.

In summary, meeting the guidelines for both cardiovascular/aerobic activity and strength training may be associated with benefits for sleep and mood, namely, earlier bedtime, more positive affect, and less anxiety and anger. Additionally, we found a significant interaction between gender and physical activity guideline adherence for sleep latency such that guideline adherence shortened time to fall asleep in men and not women. Findings from this study highlight the importance of taking an individual’s gender and specific sleep issue into account when implementing a nonpharmacologic exercise prescription to improve sleep and mood in practice.

Footnotes

Acknowledgements

The authors thank Rutgers, The State University of New Jersey, Rutgers Recreation and staff, especially Bethann Wittig and Samantha Plum, and the student participants for making this work possible.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Emily E. Glavin

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