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Abstract

Background: Lifestyle behaviors are a potential adjunctive treatment for depression in emerging adults (18-25 years), but the combination of behaviors associated with depression remains unclear. Methods: 2,019 emerging adults from the National Health and Nutrition Examination Survey between 2011 and 2018 were selected. Latent class analysis (LCA) identified patterns of lifestyle behaviors (diet [Dietary Inflammatory Index], sleep [quantity/quality], physical activity, sedentary behavior, substance use [alcohol/smoking]). Multiple logistic regression examined associations between lifestyle patterns and depression. Results: LCA identified 6 male classes and 5 female classes. Compared to the Healthy reference group, males in the Poor Sleep Sedentary Drinkers (aOR [95% CI]: 4.40 [1.10, 17.55], P = 0.032) and Pro-inflammatory Diet (PID) Smokers Drinkers (aOR [95% CI]: 6.11 [1.94, 19.25], P = 0.002) groups and females in the PID Smokers Drinkers (aOR [95% CI]: 4.96 [2.07, 11.89], P < 0.001), Trouble Sleeping (aOR [95% CI]: 6.65 [2.17, 20.33], P < 0.001), and PID Sedentary (aOR [95% CI]: 4.74 [1.37, 16.35], P = 0.012) groups had increased odds of depression. Conclusions: We found unique sex-specific lifestyle patterns associated with depression (males: diet, sleep [quantity/quality], sedentary behavior, substance use; females: sleep quality). Lifestyle behaviors should be comprehensively targeted to prevent and treat depression in emerging adults.

Keywords

lifestyle diet depression emerging adults

Our findings indicate clustering of lifestyle behaviors among emerging adults in the US and we successfully identified unique lifestyle patterns for males and females.

Introduction

Emerging adulthood is an important, and often positive, developmental period for individuals in their late teens to mid-twenties.¹ However, emerging adults in the United States (US) experience the highest prevalence of depression compared to all other adults, about 1 in 5, which equates to over 6 million individuals.^2,3 High rates of depression increase the risk of suicide, which is the second leading cause of death for this age group.⁴ Emerging adults experience various unique factors that may increase their risk of depression. For instance, emerging adults experience a great deal of stress and pressure related to situations like living alone for the first time, starting work or higher education, and developing relationships.^5-7 These unique pressures, in addition to increased use of social media, can contribute to depression.^5-10 The consequences of depression among this age group are widespread, with negative impacts on work and school performance, relationships, and health.¹¹ Depression negatively impacts overall health and the development of other mental illnesses, as well as comorbid diseases, such as obesity, diabetes, and cardiovascular disease.^12-14 These negative consequences can continue beyond emerging adulthood and are associated with increased mental and physical morbidity and mortality throughout adulthood.^15-17 Therefore, depression in emerging adults is a serious public health burden that warrants additional study.

Despite increased access to psychotherapies and pharmacotherapies in recent years, depression rates among emerging adults have continued to rise.^18-20 As modest increase in treatment cannot keep up with the prevalence of depression among emerging adults, research should explore risk factors that, once targeted, have the potential to reduce symptoms of depression.³ Although numerous factors influence the mental health of emerging adults including financial, academic, and social pressures, focusing on modifiable lifestyle behaviors allows for more targeted and proactive depression management. Modifiable lifestyle behaviors are promising intervention targets for mental illness, especially for emerging adults. Emerging adulthood involves a dynamic shift in physical and social environments, which influences key lifestyle behaviors.⁸ While emerging adulthood presents many challenges, this life stage also presents a unique opportunity for intervention as one’s lifestyle is just beginning to be developed. As a result, lifestyle psychiatry is incorporating a holistic approach to prevent and treat depression by adjunctively targeting modifiable lifestyle behaviors.²¹ Physical activity, diet and nutrition, restorative sleep, social connection, mindfulness and meditation, and mind-body activities have been emphasized as the domains of lifestyle psychiatry due to their role in the pathophysiology of mental illness and effectiveness in improving symptoms of mental illness.^21-23 A lifestyle encompasses various behaviors, including those targeted in lifestyle psychiatry, but other related behaviors, like sedentariness and substance use (e.g., smoking and drinking), should also be considered.^21,24 Among these behaviors, a poor quality diet, poor sleep duration and sleep quality, physical inactivity and sedentariness, tobacco/nicotine use, and alcohol consumption have been independently associated with depression in emerging adults.^25-37

Overall diet quality of US emerging adults is poor and often marked by pro-inflammatory dietary behaviors, including increased intake of fast foods, foods high in salt and sugar, as well as reduced intake of fruits and vegetables and breakfast.^27,38,39 Pro-inflammatory dietary patterns are associated with depression,^40,41 particularly in emerging adults, suggesting that the inflammatory potential of the diet may be targeted to prevent depression.⁴² The relationship between physical activity and depression is well-established in the general adult population,^24,43,44 with both public health organizations and practitioners recommending adequate physical activity to improve mood and prevent depression.^45,46 However, many Americans are not meeting the recommendations for physical activity,⁴⁷ with evidence suggesting physical inactivity is highest among emerging adults.^48-50 Beyond just physical inactivity, sedentary behavior, typically defined as sitting or other activities that expend ≤1.5 metabolic equivalents of energy, has also been associated with depression.^51,52 Unfortunately, research reports an increase in sedentary behavior among emerging adults, likely related to school, work, as well as the high rates of social media usage and other sitting activities, like video games.^53,54 Regarding sleep, emerging adults often neglect sleep for competing priorities related to work, school, and social activities. Both poor sleep duration and quality have been reported in emerging adults and are associated with depression.^28,29,55 In addition, lifestyles of emerging adults are often characterized by high rates of substance use, including tobacco/nicotine products and alcohol.⁵⁶ Substance use is often indicated as a risk factor for depression and is associated with poor mental health outcomes in emerging adults.^32-35 Overall, the unhealthy lifestyle behaviors prevalent among emerging adults may increase their risk of depression. Therefore, research should explore how modifying lifestyle behaviors can reduce risk of depression in emerging adults.

The connections between these unhealthy lifestyle behaviors and depression are well-established.^{27,29,30,37,51} However, independent associations may not show the full picture as lifestyle behaviors co-occur and are often synergistic with one another.⁵⁷ Research exploring the combined effects of lifestyle behaviors (i.e., lifestyle patterns) suggests that the effect of multiple lifestyle behaviors together may be more impactful than targeting an individual behavior alone.^58,59 For instance, the synergistic effect of multiple behaviors was reported to elevate physiological burden in middle-aged adults.⁵⁸ Additionally, specific to depression, a combined healthy lifestyle (3 or more behaviors) was associated with reduced risk of depression in the general adult population.⁵⁹ Observational studies have found similar results in emerging adults outside the US, with unhealthy lifestyle patterns associated with increased risk of depression.^60-63 To date, most of this research has been conducted in populations other than emerging adults in the US.^57-63 Thus, whether this relationship holds true for emerging adults remains unclear. Given the high prevalence of unhealthy lifestyle behaviors among US emerging adults,^{27-29,38,39,48-50,53,54,56} and their relationship with depression,^{32-35,42,44-46,51,52,64} research should explore the combined impact of an unhealthy lifestyle on depression in emerging adults to identify prevention and treatment targets.

Few studies have explored the relationship between combined lifestyle behaviors and depression in emerging adults in the US,^65-67 despite high rates of mental illness and unhealthy lifestyle behaviors in this population.^{5,7,33,36,42-44,51} Moreover, no studies to date have included the inflammatory potential of the diet in their assessment of combined lifestyle behaviors in emerging adults. As diet can directly influence inflammation in the body and this inflammatory response plays a role in the pathophysiology of depression, the inflammatory potential of the diet is an important aspect of lifestyle to investigate.⁶⁸ Furthermore, sleep quality, substance use (e.g., smoking and drinking), and sedentary behavior are important lifestyle behaviors for emerging adults that are less studied in this context and may also influence inflammation in the body.^69-72 An investigation into the lifestyle patterns of emerging adults in the US should consider both well-established and more emerging lifestyle behaviors associated with depression including, a pro-inflammatory diet, poor sleep duration and quality, physical inactivity and sedentary behavior, tobacco/nicotine use, and alcohol consumption.

The lifestyle patterns of US emerging adults and the relationship between lifestyle patterns and depression have yet to be established. In order to develop more effective and targeted interventions for emerging adults in the US, the lifestyle patterns of this population must be understood, including how they are associated with depression. The present study aims to address this gap by utilizing nationally representative data from the US National Health and Nutrition Examination Survey (NHANES) to identify the lifestyle patterns of male and female emerging adults in the US and explore the relationship between lifestyle patterns and depression in this population.

Methods

Study Design & Sampling

This secondary data analysis included participants from NHANES, a nationally representative survey that assesses the health outcomes of adults and children in the US conducted in 2-year cycles since 1999.⁷³ Emerging adults (between the ages of 18-25 years) with complete physical activity, sleep, depression, substance use, and dietary data were included in the analysis. NHANES is administered by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS).⁷³ The sample for each NHANES cycle is obtained through a multi-stage design to ensure the sample is representative of the non-institutionalized civilian population in the US.^74,75 The four-stage sampling design includes (1) primary sampling units (PSUs), which are typically US counties sampled from all 50 states, (2) census blocks within the PSUs, (3) households within census blocks, and (4) individuals in those households.^74,75 Within this design, some subgroups, including individuals of specific races, ethnicities, income levels, and ages, are oversampled to ensure reliability of measures within subgroups.^74,75 Oversampling can differ from cycle to cycle.^74,75

For the present study, we used data from 4 cycles of NHANES covering the years 2011-2018. Table 1 summarizes the response rates and oversampled subgroups for these years. Years 2011-2018 were chosen due to the limited lifestyle data available for our target population prior to 2011, specifically alcohol consumption. In addition, we did not include any cycles after 2018 due to the potential limitations of data collected during the COVID-19 pandemic. The data used in this study were collected through health exams, laboratory tests, and dietary interviews.⁷³ All NHANES survey protocols are reviewed and approved by the NCHS Ethics Review Board (ERB) to ensure the protection of all participants.⁷⁶ Complete information about data collection can be found on the CDC’s website.⁷³

Table 1.

NHANES Oversampling and Response Rates for Years 2011-2018^a.

NHANES Cycle	Unweighted Response Rate	Subgroups oversampled
2011-2012	Interview sample: 72.6% Examined sample: 69.5%	• Hispanic persons • Non-Hispanic Black persons • Non-Hispanic, non-Black Asian persons • Non-Hispanic White persons and persons of other races and ethnicities at or below 130% of the federal poverty level • Non-Hispanic White persons and persons of other races and ethnicities aged 80 years and over
2013-2014	Interview sample: 71.0% Examined sample: 68.5%
2015-2016	Interview sample: 61.3% Examined sample: 58.7%	• Hispanic persons • Non-Hispanic Black persons • Non-Hispanic, non-Black Asian persons • Non-Hispanic White persons and persons of other races and ethnicities at or below 185% of the federal poverty level • Non-Hispanic White persons and persons of other races and ethnicities aged 0-11 years or 80 years and over
2017-2018	Interview sample: 51.9% Examined sample: 48.8%

^aData adapted from CDC NHANES Website.^77-81

Participants

Of the total participants from NHANES cycles 2011-2018, 3489 were between the ages of 18 and 25. Of the 3,489, 421 were excluded due to missing depression data, 8 were excluded due to missing physical activity data, 1 was excluded due to missing sleep data, 577 were excluded due to missing dietary data, and 463 were excluded due to missing alcohol or smoking data. A total of 2,019 participants met inclusion criteria and were included in the analysis. A complete flow diagram of study screening and selection can be seen in Figure 1.

Figure 1.

Flow diagram of participant screening and selection.

Measures

NHANES utilizes questionnaires and 24-hour dietary recalls to collect lifestyle behavior data. In this study, seven lifestyle variables were assessed: diet, sleep duration, sleep quality, physical activity, sedentary behavior, and substance use including alcohol consumption and smoking history.⁸² Lifestyle behavior data were scored and dichotomized as either meeting recommendations for these behaviors or not. Individuals who met the recommendations for diet, sleep, physical activity, sedentary behavior, and substance use were considered “healthy,” while individuals who did not meet the guidelines were considered “unhealthy.” Scoring for each lifestyle behavior was based on previous literature and is described below.³⁵

Diet

The inflammatory potential of the diet was assessed using the Dietary Inflammatory Index (DII).⁸³ The DII uses up to 45 food parameters, which consist of whole foods, nutrients, and spices, to calculate an overall dietary inflammatory score.⁸³ This study utilized 28 dietary parameters available from NHANES 24-hour recall data to calculate the DII. As two days of dietary recall data were available, day 1 and day 2 totals were averaged. The DII instrument scores can range from −8.87 to 7.98, with higher (positive) scores indicating a pro-inflammatory diet.⁸³ Positive scores (≥0) were defined as “pro-inflammatory diet” and negative scores (<0) were defined as “anti-inflammatory diet.”⁸³

Sleep Duration & Quality

Sleep duration was assessed using responses to the question “How much sleep do you get (hours)?”. As recommended by the National Sleep Foundation, healthy sleep duration for 18-25 year olds is 7-9 hours.⁸⁴ Poor sleep was defined as individuals sleeping <7 hours or >9 hours and adequate sleep was defined as individuals sleeping 7-9 hours.⁸⁴ Sleep quality was measured using responses to the question “Have you ever told a doctor you had trouble sleeping?”. “Trouble sleeping” was defined as individuals who answered “yes,” while “no trouble sleeping” was defined as individuals who answered “no.”

Physical Activity & Sedentary Behavior

Physical activity and sedentary behavior were assessed using the NHANES Physical Activity Questionnaire, which is based on the Global Physical Activity Questionnaire. Physical activity and sedentary behavior are distinct behaviors that influence the amount of energy being expended throughout the day.⁸⁵ Physical activity includes body movements that expend energy above your resting metabolic rate, including activities of daily living (i.e., work-related physical activity) and programed physical activity (i.e., walking, biking, running).⁸⁵ In contrast, sedentary behavior includes waking behaviors with very low energy expenditure (i.e., sitting, reclining, lying down).⁸⁵

Total moderate and vigorous activity from work, leisure, and recreation were scored to determine metabolic equivalents (METs). An individual met the Physical Activity Guidelines for Americans if their total METs was ≥ 600.⁴⁵ METs of 600 includes at least 150 minutes to 300 minutes a week of moderate-intensity or 75 minutes to 150 minutes a week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous-intensity aerobic activity.⁴⁵ Individuals who did not meet these recommendations (METs <600) were scored as “physically inactive,” while individuals who did meet these recommendations (METs ≥600) were scored as “physically active.”⁴⁵

The World Health Organization’s Guidelines on Physical Activity and Sedentary Behavior identify sedentary behavior of greater than 7.5 hours as a risky or unhealthy behavior.⁴⁶ Individuals who reported >7.5 hours were defined as “sedentary” and individuals who reported <7.5 hours were defined as “non-sedentary.”⁴⁶

Substance Use

Individuals reported the number of alcoholic drinks they typically consumed on days they drank alcohol within the past year. Alcohol consumption was scored based on the Dietary Guidelines for Americans.⁸⁶ Individuals were defined as “drinkers” if females reported consuming >1 drink per day and males reported consuming >2 drinks/day.⁸⁶ All others were defined as “nondrinkers.” Smoking history was assessed through one item, which inquires if an individual smoked at least 100 cigarettes in their life, a cut point suggested by the World Health Organization.⁸⁷ Individuals who answered “yes” were defined as “smokers” and individuals who answered “no” were defined as “nonsmokers.”

Depression

Symptoms of depression were assessed using the Patient Health Questionnaire (PHQ-9). The PHQ-9 contains 9 items that assess symptoms of depression with 4 possible responses based on symptom frequency.⁸⁸ This assessment tool identifies an overall depression symptom score by totaling responses, with each score describing a severity category: minimal (score of 0-4), mild (score of 5-9), moderate (score of 10-14), moderately severe (score of 15-19), and severe depression (score of 20-27).⁸⁸ As is consistent with previous literature, moderate, moderately severe, and severe were defined as “with depression symptoms” and minimal and mild as “without depression symptoms.”⁶⁷

Sociodemographic Variables

Sociodemographic data collected included age, race/ethnicity, education level, family income, employment status, sex, marital status, and body mass index (BMI). Age, race/ethnicity, education level, family income, and employment status were included due to potential confounding effects.^89-91 Other sociodemographic variables, such as sex, marital status, and BMI, described the study sample. Race/ethnicity comprised four categories: Non-Hispanic White, Non-Hispanic Black, Hispanic, and Other Race (including multi-racial). Education level consisted of four categories: less than high school, high school graduate/GED or equivalent, some college or AA degree, and college graduate or above. Family income was reported using the ratio of family income to poverty guidelines. Poverty income ratio (PIR) is calculated by dividing total household income by the federal poverty level for their household size.⁹² Individuals with a PIR of ≥1 were defined as higher income and individuals with a PIR of <1 were defined as lower income.⁹² Employment status was summarized using a question that inquired about the “type of work done last week.” Employed was defined as “working at a job or business” or “with a job or business but not at work,” and unemployed was defined as “looking for work” or “not working at a job or business.” Marital status was dichotomized into “married” and “unmarried.” BMI was defined using existing cut points: BMI of 18.5-24.9 kg/m² was considered healthy, BMI of <18.5 kg/m² underweight, BMI of >24.9 kg/m² or <29.9 kg/m² overweight, and BMI ≥30 kg/m² obese.⁹³

Statistical Analysis

Descriptive data were summarized using weighted means and standard errors or counts and weighted percentages. Sample weights in NHANES are needed to account for survey non-response, survey design (including oversampling), and post-stratification adjustments to ensure the sample is representative of the non-institutionalized civilian population in the US.⁷³ For all analyses, we used a combined 8-year sample weight (for years 2011-2018) using the formula wtdr2d/4, indicating that we used dietary 2-day sample weight (wtdr2d) and 4 cycles of NHANES for a total of 8 years.⁹⁴ Complete information about data collection can be found on the CDC’s website.⁹⁴

A latent class analysis (LCA) was conducted to identify lifestyle patterns for emerging adult males and females using seven lifestyle behaviors. To determine the optimal number of latent classes, we fit multiple models from 2 to 8 classes, taking sample weights and cluster features into account, and analyzing the fit statistics, including Akaike’s information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), and entropy. Bootstrap likelihood ratio tests (BLRT) were also used to determine the optimal number of classes. Higher entropy and lower AIC, BIC, and aBIC indicated a better fit, while a significant BLRT P-value indicated that the subsequent class model was a better fit than the previous class model. The best fit was determined using these fit statistics as well as the interpretability and clinical significance of the resulting classes.

Once the optimal model was determined, we analyzed differences between classes using chi-squared tests for categorical variables and analysis of variance (ANOVA) for continuous variables. Multiple logistic regression was used to analyze how lifestyle patterns were associated with odds of depression by sex when controlling for age, race, education level, family income, and employment status. All analyses were conducted using SAS version 9.4 and P-values <0.05 were considered statistically significant.

Results

Sample Characteristics

Demographic Characteristics

The final sample was 50.3% female (n = 1052) with a mean age of 21.8 ± 0.1 years. Regarding race and ethnicity, 55.3% of the sample identified as White, 21.5% of the sample identified as Hispanic, 14.2% of the sample identified as Black, and 9.0% identified as “Other” race/ethnicity. 37.0% of the sample completed some college or an associate’s degree, followed by 29.7% of high school graduates/GED equivalent, 19.9% college graduates or above, and 13.4% less than high school. Almost three quarters of the sample (74.0%) met or exceeded a poverty income ratio of 1.0, indicating higher income. The mean BMI of the sample was 27.1 ± 0.3 kg/m², with 42.7% of the sample having a healthy BMI. Finally, the sample was mostly unmarried (85.8%) and employed (68.5%). Complete demographic characteristics can be seen in Table 2.

Table 2.

Demographic & Lifestyle Characteristics Stratified by Sex.

Variables	Total	Male	Female
Variables	n = 2019	n = 967 (49.7%)	n = 1052 (50.3%)
Age	21.8 (0.1)	21.7 (0.1)	21.9 (0.1)
Race/ethnicity
White	669 (55.3)	314 (54.0)	355 (56.7)
Black	474 (14.2)	220 (13.2)	254 (15.1)
Hispanic (Mexican American + other Hispanic)	532 (21.5)	250 (23.1)	282 (20.0)
Others	344 (9.0)	183 (9.8)	161 (8.2)
Education
Less than high school	351 (13.4)	180 (14.5)	171 (12.3)
High school graduate/GED or equivalent	594 (29.7)	293 (32.4)	301 (27.1)
Some college or AA degree	667 (37.0)	304 (34.1)	363 (39.8)
College graduate or above	407 (19.9)	190 (19.0)	217 (20.8)
PIR
<1.0	599 (26.0)	270 (24.3)	329 (27.7)
≥1.0	1253 (74.0)	612 (75.7)	641 (72.3)
BMI (kg/m²)	27.1 (0.3)	27.0 (0.4)	27.1 (0.4)
Weight status
Underweight	97 (4.2)	40 (3.2)	57 (5.2)
Normal	859 (42.7)	407 (42.6)	452 (42.7)
Overweight	482 (24.0)	253 (24.9)	229 (23.1)
Obese	564 (29.1)	261 (29.3)	303 (29.0)
Marital status
Married	201 (14.2)	81 (11.1)	120 (17.2)
Unmarried	1261 (85.8)	627 (88.9)	634 (82.8)
Employment status
Employed	1251 (68.5)	603 (71.7)	648 (65.4)
Unemployed	768 (31.5)	364 (28.3)	404 (34.6)
Diet
Pro-inflammatory diet	1017 (48.1)	419 (39.6)	598 (56.5)
Anti-inflammatory diet	1002 (51.9)	548 (60.4)	454 (43.5)
Physical activity
Physically inactive	447 (19.7)	146 (15.7)	301 (23.6)
Physically active	1572 (80.3)	821 (84.3)	751 (76.4)
Sedentary activity
Sedentary	792 (36.6)	341 (35.0)	451 (38.3)
Non-sedentary	1227 (63.4)	626 (65.0)	601 (61.7)
Sleep duration
Poor sleep	928 (44.9)	440 (43.8)	488 (46.1)
Adequate sleep	1091 (55.1)	527 (56.2)	564 (53.9)
Sleep quality
Trouble sleeping	300 (17.1)	121 (14.0)	179 (20.2)
No trouble sleeping	1719 (82.9)	846 (86.0)	873 (79.8)
Smoking history
Smokers	491 (27.3)	288 (31.6)	203 (23.1)
Non-smokers	1528 (72.7)	679 (68.4)	849 (76.9)
Alcohol consumption
Drinkers	988 (54.7)	656 (73.7)	332 (36.0)
Non-drinkers	1031 (45.3)	311 (26.3)	720 (64.0)
Depression severity
None/mild (0-9)	1860 (91.2)	911 (95.0)	949 (87.4)
Moderate to severe (10-27)	159 (8.8)	56 (5.0)	103 (12.6)

PIR = ratio of family income to poverty; BMI = body mass index. Continuous variables are represented as weighted mean (standard error) and categorical variables are represented as n (weighted percent).

Lifestyle Behaviors & Depression

For the total sample, the dietary inflammatory potential was evenly distributed, with 51.9% of the sample consuming an anti-inflammatory diet and 48.1% consuming a pro-inflammatory diet. A majority of the sample was physically active (80.3%) and non-sedentary (63.4%). A little more than half of the sample (55.1%) reported adequate sleep with only 17.1% reporting trouble sleeping. Over a quarter of the sample (27.3%) were smokers and more than half of the sample were drinkers (54.7%). Regarding depression, the average depression score was 3.3 ± 0.1 indicating minimal depression symptoms. When depression symptom severity was categorized, almost 9% of the sample had moderate to severe depression symptoms (8.8%). Complete lifestyle and depression information can be seen in Table 2.

Latent Class Analysis

Indicators of fit for LCA models of different classes stratified by sex can be seen in Table 3. The 7-class models for males and females had the smallest AIC, BIC, and aBIC values and highest entropy, indicating good separation between classes. Although the 7-class model showed optimal statistical fit, this model also contained classes with a very small proportion of participants (<4%), raising concerns over model stability and interpretability (class 7 for males [2.9%]; classes 2 and 7 for females [3.9% and 1.5%, respectively]).^95,96 These classes were difficult to separate from existing classes, did not offer novel, meaningful subgroups, and may have limited the validity of our regression analysis. Therefore, to ensure interpretability and clinical relevance, a 6-class model was selected for males and 5-class model was selected for females. These models were statistically sound, showed good separation between classes, and contained meaningful and interpretable subgroups of lifestyle behaviors. Conditional probability of lifestyle behaviors across latent class groups can be seen in Figure 2 for males and Figure 3 for females.

Table 3.

Indicators of Fit for Models With Different Numbers of Latent Classes.

	Male					Female
Class	AIC	BIC	aBIC	Entropy	BLRT P-value	AIC	BIC	aBIC	Entropy	BLRT P-value
2	3479702.70	3479775.81	3479728.17	0.381074477	<0.001*	3547248.23	3547322.61	3547274.96	0.9998236	<0.001*
3	2723827.61	2723939.72	2723866.67	0.758506517	<0.001*	2709748.02	2709862.07	2709789.02	0.7070079	<0.001*
4	2198300.35	2198451.45	2198352.99	0.723529808	<0.001*	2426038.23	2426191.94	2426093.48	0.669251	<0.001*
5	1790734.27	1790924.36	1790800.50	0.504887125	<0.001*	2127910.44	2128103.82	2127979.95	0.6937624	<0.001*
6	1370210.75	1370439.84	1370290.57	0.562562478	<0.001*	1889090.48	1889323.52	1889174.24	0.6530696	<0.001*
7	1187836.516	1188104.60	1187929.92	0.647506554	<0.001*	1601938.83	1602211.55	1602036.86	0.752929	<0.001*
8	The model did not converge					1438443.71	1438756.09	1438555.99	0.69801	<0.001*

AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = adjusted BIC, BLRT = Bootstrap Likelihood Ratio Test. Seven variables were put into the LCA models: Diet-DII, Physical Activity, Sedentary Activity, Sleep Duration, Sleep Quality, Smoking History, Alcohol Consumption.

Figure 2.

Conditional probability of lifestyle behaviors across latent class groups among male emerging adults.

Figure 3.

Conditional probability of lifestyle behaviors across latent class groups among female emerging adults.

Lifestyle Behaviors & Demographic Characteristics Across Latent Classes Among Males

For males, Class 1 was labeled as Pro-inflammatory Diet (PID) but Non-Substance User. This group had high probabilities of following a pro-inflammatory diet (100%). PID but Non-Substance User also had moderate probabilities of physical inactivity (33.8%), sedentariness (51.6%), and poor sleep (58.8%), but had lower probabilities of using substances like alcohol (24.1% drinkers) and tobacco/nicotine (6.6% smokers). PID but Non-Substance User were slightly younger than the other classes (mean age 20.8 ± 0.2 years), had a higher proportion of Black (23.1%) and Hispanic (26.1%) individuals, and had the highest obesity among the male classes (39.9%).

Class 2 among males was labeled Poor Sleep Sedentary Drinkers. These individuals had high probabilities of engaging in unhealthy behaviors including sedentary behavior (99.7%), poor sleep (85.9%), trouble sleeping (48.5%), smoking (40.7%) and drinking (100%). However, these individuals had high probabilities of engaging in physical activity (98.1%) and following an anti-inflammatory diet (89.0%). Poor Sleep Sedentary Drinkers were mostly White (63.1%) and had the highest percentage of individuals who graduated college (36.9%), had a PIR greater than or equal to 1.0 (85.8%), and were employed (85.1%).

Males grouped into Class 3 were labeled Healthy but Drinkers. These males had high probabilities of engaging in healthy behaviors, including an anti-inflammatory diet (99.5%), physical activity (90.7%), non-sedentary behavior (100%), adequate sleep (63.0%), no trouble sleeping (100%), and not smoking (62.1%) but were also drinkers (98.1%). Healthy but Drinkers were mostly White (54.7%) and Hispanic (29.0%) and completed high school/GED (36.9%) and some college (40.0%).

Class 4 was considered the Healthy group among males. Males grouped into the Healthy group had high probabilities of engaging in healthy behaviors, including an anti-inflammatory diet (100%), physical activity (94.6%), non-sedentary (72.4%), adequate sleep (56.1%), no trouble sleeping (87.4%), non-smokers (100%), and non-drinkers (100%). Healthy males were mostly White (47.2%), less educated (41.7% high school/GED), employed (70.9%) and had a high percentage (80.4%) of those with a PIR ≥1.0. The Healthy group also had the highest percentage of males who were married (16.3%).

The fifth class for males was labeled PID Smokers Drinkers. Individuals in this group had high probabilities of engaging in physical activity (100%) and non-sedentariness (100%) but also had high probabilities of engaging in unhealthy behaviors, such as pro-inflammatory diet (88.8%), poor sleep (48.0%), trouble sleeping (26.8%), smoking (51.9%), and drinking (91.8%). These males were mostly White (53.2%), had a higher percentage of individuals with a PIR <1.0 (28.1%), and had a high proportion of obesity (32.5%).

Males grouped into Class 6 were labeled Sedentary Drinkers. These individuals had high probabilities of engaging in healthy behaviors, including anti-inflammatory diets (61.4%), adequate sleep (87.5%), no trouble sleeping (100%), and were physically active (65.7%) non-smokers (65.9%). However these males also had high probabilities of engaging in unhealthy behaviors, like sedentariness (100%) and drinking (99.1%). Regarding demographic information, this group was mostly White (64.1%), highly educated (32.9% completed college or more), and had a PIR ≥1.0 (79.8%). Complete lifestyle and demographic information by class for males can be seen in Table 4.

Table 4.

Demographic & Lifestyle Characteristics Stratified by Class Among Male Emerging Adults.

Variables	Class 1: PID but Non-substance User(n = 171)	Class 2: Poor Sleep Sedentary Drinkers(n = 65)	Class 3: Healthy but Drinkers(n = 249)	Class 4: Healthy(n = 124)	Class 5: PID Smokers Drinkers(n = 210)	Class 6: Sedentary Drinkers(n = 127)	Overall P-value
Age	20.8 (0.2)	22.5 (0.4)	21.9 (0.2)	21.0 (0.2)	21.8 (0.2)	22.1 (0.3)	<0.001*
Race/ethnicity							0.004*
White	38 (35.8)	27 (63.1)	78 (54.7)	33 (47.2)	77 (53.2)	52 (64.1)
Black	54 (23.1)	12 (9.3)	48 (8.7)	26 (15.3)	47 (15.9)	32 (11.9)
Hispanic (Mexican American + Other Hispanic)	40 (26.1)	13 (20.1)	85 (29.0)	29 (22.5)	57 (23.0)	19 (13.9)
Others	39 (15.1)	13 (7.5)	38 (7.7)	36 (15.0)	29 (7.9)	24 (10.1)
Education							<0.001*
Less than high school	40 (20.3)	13 (11.5)	34 (11.3)	19 (9.2)	49 (18.4)	22 (17.3)
High school graduate/GED or equivalent	57 (34.7)	17 (17.8)	87 (36.9)	37 (41.7)	67 (34.5)	23 (22.0)
Some college or AA degree	51 (28.8)	19 (33.8)	85 (40.0)	33 (27.3)	71 (35.1)	40 (27.8)
College graduate or above	23 (16.1)	16 (36.9)	43 (11.9)	35 (21.8)	23 (12.0)	42 (32.9)
PIR							0.314
<1.0	46 (29.4)	11 (14.2)	78 (26.7)	31 (19.6)	66 (28.1)	32 (20.2)
≥1.0	109 (70.6)	46 (85.8)	154 (73.3)	79 (80.4)	125 (71.9)	85 (79.8)
BMI (kg/m²)	28.4 (0.8)	26.2 (0.9)	25.9 (0.6)	26.9 (1.1)	27.9 (0.7)	26.8 (0.7)	0.103
Weight status							0.72
Underweight	10 (4.4)	2 (0.8)	7 (4.2)	5 (3.1)	8 (1.7)	7 (3.1)
Normal	66 (35.4)	30 (44.4)	110 (44.4)	63 (45.4)	80 (39.4)	55 (51.0)
Overweight	34 (20.2)	19 (32.7)	72 (25.6)	30 (24.8)	58 (26.4)	31 (20.4)
Obese	61 (39.9)	13 (22.1)	59 (25.8)	26 (26.7)	63 (32.5)	33 (25.5)
Marital status							0.883
Married	11 (11.5)	4 (10.1)	28 (10.8)	11 (16.3)	16 (8.2)	8 (9.9)
Unmarried	86 (88.5)	45 (89.9)	171 (89.2)	61 (83.7)	159 (91.8)	99 (90.1)
Employment status							0.121
Employed	90 (60.6)	48 (85.1)	169 (73.9)	68 (70.9)	140 (73.4)	79 (73.3)
Unemployed	81 (39.4)	17 (14.9)	80 (26.1)	56 (29.1)	70 (26.6)	48 (26.7)
Diet							0.018*
Pro-inflammatory	171 (100.0)	9 (11.0)	4 (0.5)	0 (0.0)	182 (88.8)	51 (38.6)
Anti-inflammatory	0 (0.0)	56 (89.0)	245 (99.5)	124 (100.0)	28 (11.2)	76 (61.4)
Physical activity							<0.001*
Physically inactive	53 (33.8)	2 (1.9)	24 (9.3)	9 (5.4)	0 (0.0)	37 (34.3)
Physically active	118 (66.2)	63 (98.1)	225 (90.7)	115 (94.6)	210 (100.0)	90 (65.7)
Sedentary activity							<0.001*
Sedentary	81 (51.6)	64 (99.7)	0 (0.0)	48 (27.6)	0 (0.0)	127 (100.0)
Non-sedentary	90 (48.4)	1 (0.3)	249 (100.0)	76 (72.4)	210 (100.0)	0 (0.0)
Sleep duration							<0.001*
Poor sleep	99 (58.8)	53 (85.9)	92 (37.0)	59 (43.9)	102 (48.0)	22 (12.5)
Adequate sleep	72 (41.2)	12 (14.1)	157 (63.0)	65 (56.1)	108 (52.0)	105 (87.5)
Sleep quality							<0.001*
Trouble sleeping	23 (8.5)	29 (48.5	0 (0.0)	10 (12.6)	50 (26.8)	0 (0.0)
No trouble sleeping	148 (91.5)	36 (51.5)	249 (100.0)	114 (87.4)	160 (73.2)	127 (100.0)
Smoking history							<0.001*
Smokers	13 (6.6)	26 (40.7)	93 (37.9)	0 (0.0)	109 (51.9)	46 (34.1)
Non-smokers	158 (93.4)	39 (59.3)	156 (62.1)	124 (100.0)	101 (48.1)	81 (65.9)
Alcohol consumption							<0.001*
Drinkers	32 (24.1)	65 (100.0)	241 (98.1)	0 (0.0)	192 (91.8)	124 (99.1)
Non-drinkers	139 (75.9)	0 (0.0)	8 (1.9)	124 (100.0)	18 (8.2)	3 (0.9)
Depression severity							0.020*
None/mild (0-9)	162 (94.9)	59 (94.1)	239 (97.5)	119 (96.3)	187 (88.4)	124 (98.8)
Moderate (10-14)	6 (4.3)	6 (5.9)	7 (1.7)	5 (3.7)	16 (7.6)	3 (1.2)
Moderately severe (15-19)	1 (0.3)	0 (0.0)	3 (0.8)	0 (0.0)	5 (2.0)	0 (0.0)
Severe (20-27)	2 (0.5)	0 (0.0)	0 (0.0)	0 (0.0)	2 (2.0)	0 (0.0)

PID = pro-inflammatory diet; PIR = ratio of family income to poverty; BMI = body mass index. Continuous variables are represented as weighted mean (standard error) and categorical variables are represented as frequency (weighted percent). *P < 0.05, P-value for continuous variables was obtained by performing T-test, and P-value for categorical variables was obtained by performing chi square-test.

Lifestyle Behaviors & Demographic Characteristics Across Latent Classes Among Females

For females, Class 1 was labeled PID Smokers Drinkers. Individuals in this group had high probabilities of engaging in unhealthy behaviors, such as pro-inflammatory diet (73.1%), poor sleep (53.9%), trouble sleeping (21.3%), smoking (100%), and drinking (73.1%) but also had high probabilities of engaging in healthy behaviors, such as physical activity (81.5%) and non-sedentariness (80.7%). These females were mostly White (64.3%) and had the lowest percentage of Black individuals (8.9%). The PID Smokers Drinkers female group were also less educated (19.9% less than HS), and had a low PIR (33.6% PIR <1.0), high proportion of overweight (29.1%) and obesity (33.4%), and the highest proportion of unemployment (36.5%).

Females in Class 2 were labeled Trouble Sleeping. These females had the highest probability of trouble sleeping (100%) but also had high probabilities of engaging in healthy behaviors, including high physical activity (100%), non-sedentariness (100%), adequate sleep (50.7%), non-smoking (75.0%), non-drinking (84.0%). Females in this group also had a higher probability of following a pro-inflammatory diet (53.5%). The Trouble Sleeping group was mostly White (72.2%) females, who were educated (51.8% some college), with a high proportion of individuals with a PIR ≥1.0 (77.5%), and the lowest proportion of individuals with obesity among the female classes (23.2%).

Females grouped into Class 3 were considered the Healthy group. These individuals had the highest probability of engaging in healthy behaviors among females with the exception of drinking. The Healthy group had the highest probability of following an anti-inflammatory diet (64.7%), engaging in physical activity (100%), non-sedentariness (100%), adequate sleep (76.0%), no trouble sleeping (100%), non-smoking (100%), but some drinking (39.7%). This group was mostly White (54.2%) but had a high proportion of Hispanic females (23.2%) compared to the other classes. The Healthy female group were also highly educated (42.9% some college; 23.5% college graduate or above) and had a low proportion of individuals with obesity (27.9%).

Class 4 consisted of females who were labeled as PID Poor Sleep. These females had high probabilities of engaging in unhealthy behaviors including a pro-inflammatory diet (66.8%), inactivity (33.4%), sedentariness (71%), drinking (30.2%), and had the highest probability of poor sleep (63.5%) among the female classes. Interestingly, these individuals had low probabilities of reporting trouble sleeping (5.9%) and smoking (0.3%). The PID Poor Sleep group had the highest proportion of Black (19.8%) and Hispanic (23.2%) females among the female classes, were highly educated (26% completed college or above), had low obesity (27.6%), a high proportion of unmarried females (85.9%), and moderate proportion of employment (66.2%).

The fifth and final class for females was labeled PID Sedentary. This group had the highest probability of females following pro-inflammatory dietary patterns (83.9%). This group also had high probabilities of engaging in unhealthy behaviors, like trouble sleeping (60.2%), physical inactivity (31.2%), sedentariness (100%), and smoking (53.7%). However, the PID Sedentary group had high probabilities of engaging in healthy behavior, such as adequate sleep (100%) and not drinking (100%). This group was mostly White (71.2%) but had the highest proportion of Other race/ethnicity (11.2%) compared to the other classes. PID Sedentary also had the highest proportion of individuals who were married (35.9%), with a PIR below 1.0 (83.5%), higher proportion of individuals with obesity (39.9%) and employment (75.6%). Complete lifestyle and demographic information by class for females can be seen in Table 5.

Table 5.

Demographic & Lifestyle Characteristics Stratified by Class Among Female Emerging Adults.

Variables	Class 1: PID Smokers Drinkersn = 135	Class 2: Trouble Sleepingn = 72	Class 3: Healthyn = 242	Class 4: PID Poor Sleepn = 506	Class 5: PID Sedentaryn = 37	Overall P-value
Age	22.3 (0.3)	22.5 (0.3)	21.4 (0.2)	21.8 (0.2)	22.2 (0.7)	<0.001*
Race/ethnicity						0.010*
White	61 (64.3)	35 (72.2)	82 (54.2)	134 (49.5)	18 (71.2)
Black	24 (8.9)	15 (9.0)	48 (13.7)	144 (19.8)	9 (12.2)
Hispanic (Mexican American + Other Hispanic)	30 (16.2)	13 (13.7)	79 (23.6)	144 (23.2)	3 (5.4)
Others	20 (10.5)	9 (5.1)	33 (8.4)	84 (7.4)	7 (11.2)
Education						0.018*
Less than high school	32 (19.9)	12 (10.9)	22 (6.9)	81 (11.2)	8 (23.3)
High school graduate/GED or equivalent	37 (34.1)	18 (16.1)	69 (26.8)	146 (25.1)	15 (32.1)
Some college or AA degree	58 (39.4)	27 (51.8)	93 (42.9)	156 (37.8)	6 (18.1)
College graduate or above	8 (6.6)	15 (21.3)	58 (23.5)	123 (26.0)	8 (26.5)
PIR						0.331
<1.0	54 (33.6)	23 (22.5)	80 (28.8)	144 (25.8)	8 (16.5)
≥1.0	75 (66.4)	48 (77.5)	144 (71.2)	314 (74.2)	25 (83.5)
BMI (kg/m²)	28.3 (1.1)	26.4 (1.2)	26.6 (0.5)	26.8 (0.6)	30.0 (1.5)	0.115
Weight status						0.331
Underweight	6 (4.3)	3 (8.2)	12 (3.9)	34 (5.7)	0 (0.0)
Normal	47 (33.2)	29 (41.6)	114 (45.0)	222 (45.2)	15 (41.5)
Overweight	31 (29.1)	18 (26.9)	51 (23.3)	111 (21.5)	7 (18.6)
Obese	50 (33.4)	22 (23.2)	64 (27.9)	132 (27.6)	15 (39.9)
Marital status						0.319
Married	15 (17.9)	10 (17.7)	29 (17.5)	51 (14.1)	6 (35.9)
Unmarried	103 (82.1)	43 (82.3)	151 (82.5)	277 (85.9)	23 (64.1)
Employment status						0.906
Employed	83 (63.5)	45 (66.4)	162 (65.7)	306 (66.2)	21 (75.6)
Unemployed	52 (36.5)	27 (33.6)	80 (34.3)	200 (33.8)	16 (24.4)
Diet						<0.001*
Pro-inflammatory	95 (73.1)	45 (53.3)	93 (35.3)	326 (66.8)	29 (83.9)
Anti-inflammatory	40 (26.9)	27 (46.7)	149 (64.7)	180 (33.2)	8 (16.1)
Physical activity						<0.001*
Physically inactive	27 (18.5)	0 (0.0)	0 (0.0)	201 (33.4)	13 (31.2)
Physically active	108 (81.5)	72 (100.0)	242 (100.0)	305 (66.6)	24 (68.8)
Sedentary activity						<0.001*
Sedentary	28 (19.3)	0 (0.0)	0 (0.0)	364 (71.0)	37 (100.0)
Non-sedentary	107 (80.7)	72 (100.0)	242 (100.0)	142 (29.0)	0 (0.0)
Sleep duration						<0.001*
Poor sleep	68 (53.9)	41 (49.3)	55 (24.0)	310 (63.5)	0 (0.0)
Adequate sleep	67 (46.1)	31 (50.7)	187 (76.0)	196 (36.5)	37 (100.0)
Sleep quality						<0.001*
Trouble sleeping	26 (21.3)	72 (100.0)	0 (0.0)	39 (5.9)	23 (60.2)
No trouble sleeping	109 (78.7)	0 (0.0)	242 (100.0)	467 (94.1)	14 (39.8)
Smoking history						<0.001*
Smokers	135 (100.0)	18 (23.0)	0 (0.0)	1 (0.3)	19 (53.7)
Non-smokers	0 (0.0)	54 (77.0)	242 (100.0)	505 (99.7)	18 (46.3)
Alcohol consumption						<0.001*
Drinkers	90 (73.1)	14 (16.0)	85 (39.7)	125 (30.2)	0 (0.0)
Non-drinkers	45 (26.9)	58 (84.0)	157 (60.3)	381 (69.8)	37 (100.0)
Depression severity						<0.001*
None/Mild (0-9)	107 (79.4)	59 (74.8)	232 (95.6)	471 (90.6)	29 (84.3)
Moderate (10-14)	18 (14.3)	7 (20.2)	8 (3.9)	24 (6.4)	3 (3.7)
Moderately severe (15-19)	9 (6.0)	5 (4.1)	1 (0.3)	7 (1.3)	4 (11.7)
Severe (20-27)	1 (0.3)	1 (1.0)	1 (0.2)	4 (1.7)	1 (0.3)

Associations of Lifestyle Groups With Depression

The association between lifestyle group and depression is shown in Table 6. For males, when compared to the Healthy reference group, those in the Poor Sleep Sedentary Drinkers and PID Smokers Drinkers groups had significantly increased odds of having more severe depression symptoms. The Poor Sleep Sedentary Drinkers group had more than 4 times the odds of having more severe depression symptoms (aOR [95% CI]: 4.40 [1.10, 17.55], P = 0.032) and the PID Smokers Drinkers group had more than 6 times the odds of having more severe depression symptoms (aOR [95% CI]: 6.11 [1.94, 19.25], P = 0.002) compared to the Healthy reference group.

Table 6.

The Association Between Class and Depression Among Emerging Adults.

Class	Adj. OR (95% CI)	P-value
Male
Class 4 (Healthy)	1.00 (REF)	-
Class 1 (PID but Non-Substance User)	1.72 (0.43, 6.87)	0.435
Class 2 (Poor Sleep Sedentary Drinkers)	4.40 (1.10, 17.55)	0.032*
Class 3 (Healthy but Drinkers)	1.92 (0.46, 8.02)	0.363
Class 5 (PID Smokers Drinkers)	6.11 (1.94, 19.25)	0.002*
Class 6 (Sedentary Drinkers)	0.71 (0.17, 3.05)	0.642
Female
Class 3 (Healthy)	1.00 (REF)	-
Class 1 (PID Smokers Drinkers)	4.96 (2.07, 11.89)	<0.001*
Class 2 (Trouble Sleeping)	6.65 (2.17, 20.33)	<0.001*
Class 4 (PID Poor Sleep)	1.87 (0.76, 4.61)	0.163
Class 5 (PID Sedentary)	4.74 (1.37, 16.35)	0.012*

^aPHQ-9 score ≥10, indicating moderate to severe depression symptoms. PID = pro-inflammatory diet. P < 0.05, Adjusted odds ratio (95% CI) and P-values were obtained by performing multiple logistic regression, adjusted for age, race, education level, family income, and employment status.

For females, when compared to the Healthy reference group, those in the PID Smokers Drinkers, Trouble Sleeping, and PID Sedentary groups had significantly increased odds of having more severe depression symptoms. Females in the PID Smokers Drinkers group had almost 5 times the odds of having more severe depression symptoms (aOR [95% CI]: 4.96 [2.07, 11.89], P < 0.001), while females in the Trouble Sleeping group had more than 6 times the odds of having more severe depression symptoms (aOR [95% CI]: 6.65 [2.17, 20.33], P < 0.001) compared to the Healthy reference group. The PID Sedentary group had over 4 times the odds of having more severe depression symptoms (aOR [95% CI]: 4.74 [1.37, 16.35], P = 0.012), compared to the Healthy reference group.

Discussion

To our knowledge, this is the first study to explore the relationship between lifestyle patterns of emerging adults in the US, including the inflammatory potential of the diet, sleep quality as well as physical activity and sedentary time, sleep duration, smoking history, and alcohol consumption. We identified multiple high-risk behaviors in our sample of emerging adults, including 48.1% following a pro-inflammatory diet, 36.6% with >7.5 hours of sedentary time, 44.9% with <7 or >9 hours of sleep per day, and 54.7% drinking past the recommended limit for alcohol intake. Our findings indicate clustering of lifestyle behaviors among emerging adults in the US and we successfully identified unique lifestyle patterns for males and females. These patterns included, PID but Non-Substance User, Poor Sleep Sedentary Drinkers, Healthy but Drinkers, Healthy, PID Smokers Drinkers, and Sedentary Drinkers for males, and PID Smokers Drinkers, Trouble Sleeping, Healthy, PID Poor Sleep, and PID Sedentary for females. Compared to their corresponding Healthy reference group, males in the Poor Sleep Sedentary Drinkers and PID Smokers Drinkers groups and females in the PID Smokers Drinkers, Trouble Sleeping, and PID Sedentary groups had increased odds of more severe depression symptoms. These results emphasize the need to promote a healthy lifestyle and comprehensively target these behaviors for the prevention and treatment of depression in emerging adults.

Given the high prevalence of depression in US emerging adults, effective, low-risk strategies should be identified. Our study not only supported the idea that lifestyle behaviors cluster together in US emerging adults, but also highlighted possible intervention targets in the form of comprehensive lifestyle behaviors. We observed clustering of lifestyle behaviors and identified six lifestyle patterns for males and five lifestyle patterns for females. Clustering of lifestyle behaviors has been seen in adolescents, older adults, and now emerging adult populations.^62,63,97-99 While research typically studies lifestyle behaviors in isolation, an individual’s lifestyle is made up of multiple behaviors that co-occur and often have a synergistic relationship with one another.⁵⁷ Unhealthy lifestyle behaviors tend to cluster together and are associated with poor mental health outcomes, including increased risk of depression.^{35,60,67,99,100} Studies that utilize clustering techniques, like LCA, help us better understand how specific lifestyle behaviors that cluster or co-occur can create a combined effect on depression risk that is greater than the effect of the individual behaviors. Typically the group with the highest rates of unhealthy behaviors^67,99,100 or highest number of unhealthy behaviors^35,60 is associated with increased odds of mental health problems. Our study adds to this body of research that emphasizes the need for future studies to explore how these behaviors co-occur and how they relate to risk of mental illness.

Clustering of behaviors may vary for populations of different ages and geographic origins and depending on which lifestyle variables are being studied. Previous studies in emerging adults have typically identified three to five lifestyle patterns, while our study identified five lifestyle patterns for males and six lifestyle patterns for females.^{25,62,63,65-67,99} Our study was the first study to explore this clustering phenomenon in US emerging adults, stratified by sex using seven lifestyle variables, and as a result, we were able to present unique sex differences in lifestyle patterns and how they relate to risk of depression. Our study identified only one overlapping lifestyle pattern for males and females, PID Smokers Drinkers. Previous studies that have stratified lifestyle patterns by sex found three to five overlapping patterns for males and females.^63,65 Grant et al. conducted a LCA in US emerging adults using three lifestyle variables: fruit and vegetable intake, physical activity, and alcohol consumption and identified five common classes for both sexes.⁶⁵ The inclusion of an overall measure of diet quality beyond fruit and vegetable intake plus the addition of sedentary time, sleep quality, and smoking history, may have resulted in more distinct patterns between sexes in our study. Sex and gender related differences in lifestyle behaviors have previously been reported in emerging adults.^101-103 Distinct approaches to diet, sleep, exercise, drinking, and smoking have been observed, with research suggesting that sex and gender related biological and psychosocial factors may influence lifestyle behaviors.^101-103 Future research should further explore these differences to develop more informed and targeted lifestyle interventions.

Our findings presented multiple lifestyle patterns associated with an increased risk of depression. Looking at these patterns overall, high-risk lifestyles were typically characterized by high rates of substance use, following a pro-inflammatory diet, and/or poor sleep duration or quality. These behaviors may contribute to depression through various mechanisms, such as inflammation.^71,104 Both excessive alcohol intake and smoking have been shown to increase inflammatory biomarkers, including C-reactive protein and interleukin-6.^71,104 Use of these substances can also lead to highs and subsequent lows or “crashes” in mood, which may contribute to worsening depression symptoms overall. Excessive alcohol intake may also lead to social consequences, like poor job or academic performance, strained relationships, and isolation that can lead to depressed mood.¹⁰⁵ Regarding diet, dietary intake can contribute to inflammation through the gut-microbiome and the production of inflammatory cytokines.¹⁰⁶ A pro-inflammatory diet is also often devoid of nutrients and high in “empty calories,” which may not provide adequate nutrition required for proper mood stabilization.¹⁰⁷ Similarly, when individuals experience poor sleep quality, they are more likely to experience issues with neurological functioning and emotional regulation, which can lead to depressed mood.^108,109

Consistent with previous studies in emerging adults, this study found that lifestyle patterns with high rates of substance use were associated with increased risk of mental illness.^61-63,65,66 However, most of these studies identified patterns with high rates of substance use and inadequate PA or high sedentary behavior.^61-63 Interestingly, our study found that among the high-risk patterns, physically activity, specifically meeting the Physical Activity Guidelines for Americans, was not enough to protect individuals from risk of depression when other unhealthy lifestyle behaviors were present. For instance, Poor Sleep Sedentary Drinkers, had high rates of alcohol use and sedentary behavior and also high rates of PA. Similarly, Peterson et al and Dong et al discussed how emerging adults can simultaneously be physically active and sedentary.^54,63 The impact of sedentary behavior separate from physical activity has been previously emphasized, with the Physical Activity Guidelines for Americans including recommendations specific to sedentary behavior starting with the 2nd edition in 2018.⁴⁵ Overall, a strong link is present between substance use and depression symptoms in the literature but more research is needed to explore how substance use co-occurs with other unhealthy lifestyle behaviors in emerging adulthood.

We identified a unique class among females, Trouble Sleeping, characterized by high probability of reporting poor sleep quality to a doctor. While behaviors like diet and physical activity are commonly studied included in lifestyle pattern research, sleep quality has more recent evidence highlighting its role as a risky health behavior within a lifestyle.^109,110 In relation to mental illness, few studies have included sleep in their analysis of lifestyle patterns and depression in emerging adults^{60,62,67,99,111} with only two studies including sleep quality^60,111 and one including both sleep quantity and sleep quality.¹¹¹ Using hierarchical regression analysis, Wickham et al. identified both sleep quality and sleep quantity as the strongest predictors of depression symptoms in their sample of emerging adults, which aligns with our findings.¹¹¹ However, Wickham et al. did not stratify by sex and we only found this relationship to be true among females.¹¹¹ Interestingly, both sleep disturbances and depression have been reported as more common among emerging adult females, suggesting a need to screen and target sleep disturbances among females.⁵⁵ Given the relationship between sleep and depression as well as its potential as a high-risk modifiable lifestyle behavior, future studies should consider both sleep quality and sleep quantity when assessing overall lifestyle.

External factors would also have an impact on both lifestyle behaviors and depression in emerging adults. This population faces various social, financial, and academic pressures in the form of increased use of social media, rising student loan burdens, and high achievement culture.^9,10,112,113 These external factors may contribute to depression and may also lead to high-risk behaviors. We identified high-risk lifestyle patterns that include high rates of substance use, sedentary behavior, inflammatory dietary intake, and troubled/poor sleep. Pressure to succeed at both school and work as well as financial burden of student loans may lead to increased stress that causes emerging adults to turn to substances or junk foods as a coping mechanism. These pressures may also result in sleep disturbances. Furthermore, the proliferation of social media in addition to working or studying for long hours can lead to increased sedentary behaviors even if these individuals are meeting physical activity requirements. Use of social media, student loan burden, and high achievement culture have continued to increase in recent years, as have global factors, including the COVID-19 pandemic, cultural and political polarization, and increased awareness of global threats.^113-115 In addition to modifiable lifestyle behaviors, future studies should aim to consider these external factors as possible confounding variables when exploring the relationship between lifestyle behaviors and mental illness in emerging adults.

This study was the first to explore the relationship between lifestyle patterns and depression in emerging adults assessing diet using the DII. Previous studies have used other dietary patterns, like the Healthy Eating Index (HEI)⁶⁷ or Dietary Approaches to Stop Hypertension (DASH)⁶¹ dietary pattern, or specific components of diet, like fruit and vegetable intake or breakfast intake.^62,63,99 These high-risk eating patterns or behaviors were not typically associated with depression in previous research.^61-63,67,99 In contrast, we identified two high-risk classes among females (PID Smokers Drinkers, PID Sedentary) and one high-risk class among males (PID Smokers Drinkers) characterized in part by high probabilities of pro-inflammatory dietary patterns. The inflammatory potential of the diet was high in our sample of emerging adults, with almost half (48.1%) following a pro-inflammatory dietary pattern. High rates of dietary inflammation are likely related to increased intake of fast foods and processed foods in this population, which are higher in added sugars, saturated fats, and sodium.¹¹⁶ Assessing dietary inflammation in emerging adults provides unique insights into the role that diet plays as part of an overall lifestyle. Future research should continue to assess dietary inflammation given its strong association with depression.^40,41,68,117

Limitations

While this study provides unique insights into the relationship between lifestyle patterns and depression in emerging adults in the US, we must consider the study limitations. First, while NHANES is a rich, nationally representative dataset, it is also cross-sectional in nature and therefore we cannot determine the causality of this relationship. Nor can we ignore the bidirectional relationship between lifestyle and depression as we cannot rule out the possibility that depression leads to unhealthy lifestyle behaviors. Specifically, the clusters of lifestyle behaviors highlighted in this study (combinations of poor sleep quality, pro-inflammatory dietary pattern, and substance use) may all be coping mechanisms for depressed moods. Poor sleep quality may be a side effect of depression, as it is listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM) as one criteria for major depressive disorder.¹¹⁸ Individuals who are depressed may crave junk foods or comfort foods that are high in saturated fats, added sugars, or other inflammatory components as a way to self-soothe.¹¹⁹ In addition, substances like alcohol and tobacco or nicotine products are often used as coping mechanisms by individuals experiencing depression.^34,105,120 Second, the lifestyle behavior data collected in NHANES are self-reported and thus we cannot rule out the possibility of self-report bias. Third, while LCA is considered a robust and statistically sound technique for analyzing patterns among a population, it is not without limitations. One such limitation is the potential loss of information in recoding continuous variables into binomial or categorical variables.⁹⁵ Finally, while this study included the largest number of lifestyle variables among studies exploring lifestyle patterns among emerging adults (seven variables), other important lifestyle behaviors and external factors were not included as they were not measured in NHANES. Lifestyle psychiatry highlights social connection, mindfulness and meditation, and mind-body activities, which were not included in the present study. In addition, external factors influence the mental wellbeing of emerging adults, including not only financial, social, and academic pressures but also a changing geopolitical landscape. Future studies should aim to consider these additional lifestyle behaviors and external factors in their analysis.

Implications and Future Directions

The present study highlighted multiple lifestyle patterns among emerging adults in the US associated with increased risk for depression. These lifestyle patterns, Poor Sleep Sedentary Drinkers and PID Smokers Drinkers for males and PID Smokers Drinkers, Trouble Sleeping, and PID Sedentary for females, not only provide insight into how unhealthy lifestyle behaviors cluster together but also provide direction for future lifestyle interventions. Our findings indicate that lifestyle interventions in emerging adults in the US should target sleep quality and quantity, inflammatory potential of the diet, substance use (e.g., smoking and drinking), and sedentary behavior for the prevention or adjunctive treatment of depression. Furthermore, our findings suggest that interventions may need to be tailored for males and females, as lifestyle behaviors cluster differently between the sexes.

Conclusions

Emerging adulthood presents a unique opportunity for intervention as lifestyle is often just beginning to be developed during this period and these lifestyle behaviors may persist throughout adulthood. Using a nationally representative sample of US emerging adults, we successfully identified clusters of lifestyle behaviors and distinct lifestyle patterns associated with increased risk of depression. These findings support the hypothesis that clusters of unhealthy lifestyle behaviors (overall unhealthy lifestyle) increase risk of depression, thus, adopting a healthy lifestyle may be an effective prevention or treatment strategy to reduce risk of depression.

Footnotes

ORCID iDs

Noelle Armstrong

Furong Xu

Ethical Considerations

This secondary data analysis included publicly available de-identified data from the National Health and Nutrition Examination Survey (NHANES). All NHANES survey protocols are reviewed and approved by the National Center for Health Statistics Ethics Review Board (ERB) to ensure the protection of all participants.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

This secondary data analysis included participants from the National Health and Nutrition Examination Survey (NHANES). All data and research materials are available through the Center for Disease Control and Prevention’s National Center for Health Statistics website for NHANES at .

References

Arnett

Žukauskienė

Sugimura

. The new life stage of emerging adulthood at ages 18–29 years: implications for mental health. Lancet Psychiatry. 2014;1(7):569-576. doi:10.1016/S2215-0366(14)00080-7

Gustavson

Knudsen

Nesvåg

Knudsen

Vollset

Reichborn-Kjennerud

. Prevalence and stability of mental disorders among young adults: findings from a longitudinal study. BMC Psychiatry. 2018;18(1):65. doi:10.1186/s12888-018-1647-5

Goodwin

Dierker

Galea

Hoven

Weinberger

. Trends in U.S. depression prevalence from 2015 to 2020: the widening treatment gap. Am J Prev Med. 2022;63(5):726-733. doi:10.1016/j.amepre.2022.05.014

Curtin

Tejada-Vera

Bastian

. Deaths: Leading Causes for 2021. National Center for Health Statistics (U.S.); 2024. doi:10.15620/cdc/147882

Finan

Ohannessian

Gordon

. Trajectories of depressive symptoms from adolescence to emerging adulthood: the influence of parents, peers, and siblings. Dev Psychol. 2018;54:1555-1567. doi:10.1037/dev0000543

Długosz

. Factors influencing mental health among American youth in the time of the Covid-19 pandemic. Pers Indiv Differ. 2021;175:110711. doi:10.1016/j.paid.2021.110711

Zhao

Chen

Peng

. Prevalence and associated factors of depression and anxiety symptoms among college students: a systematic review and meta-analysis. JCPP (J Child Psychol Psychiatry). 2022;63(11):1222-1230. doi:10.1111/jcpp.13606

Reifman

Niehuis

. Extending the five psychological features of emerging adulthood into established adulthood. J Adult Dev. 2023;30(1):6-20. doi:10.1007/s10804-022-09412-9

Abi-Jaoude

Naylor

Pignatiello

. Smartphones, social media use and youth mental health. CMAJ (Can Med Assoc J). 2020;192(6):E136-E141. doi:10.1503/cmaj.190434

10.

Khalaf

Alubied

Khalaf

Rifaey

. The impact of social media on the mental health of adolescents and young adults: a systematic review. Cureus. 2023;15(8):e42990. doi:10.7759/cureus.42990

11.

Patalay

Gage

. Changes in millennial adolescent mental health and health-related behaviours over 10 years: a population cohort comparison study. Int J Epidemiol. 2019;48(5):1650-1664. doi:10.1093/ije/dyz006

12.

O’Neil

Nicholls

Redfern

Brown

Hare

. Mental health and psychosocial challenges in the COVID-19 pandemic: food for thought for cardiovascular health care professionals. Heart Lung Circ. 2020;29(7):960-963. doi:10.1016/j.hlc.2020.05.002

13.

Wang

Zhao

, et al. Shared biological mechanisms of depression and obesity: focus on adipokines and lipokines. Aging. 2023;15(12):5917-5950. doi:10.18632/aging.204847

14.

Fanelli

Raschi

Hafez

, et al. The interface of depression and diabetes: treatment considerations. Transl Psychiatry. 2025;15(1):1-15. doi:10.1038/s41398-025-03234-5

15.

Keck

Vivier

Cassisi

, et al. Examining the role of anxiety and depression in dietary choices among college students. Nutrients. 2020;12(7):2061. doi:10.3390/nu12072061

16.

Walker

McGee

Druss

. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry. 2015;72(4):334-341. doi:10.1001/jamapsychiatry.2014.2502

17.

Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019 - PubMed. https://pubmed.ncbi.nlm.nih.gov/35026139/. Accessed 28 June 2024.

18.

Jorm

Patten

Brugha

Mojtabai

. Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry. 2017;16(1):90-99. doi:10.1002/wps.20388

19.

Mojtabai

Olfson

Han

. National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics. 2016;138(6):e20161878. doi:10.1542/peds.2016-1878

20.

Cantor

McBain

Bravata

Whaley

. Telehealth and In-Person Mental Health Service utilization and spending, 2019 to 2022. JAMA Health Forum. 2023;4(8):e232645. doi:10.1001/jamahealthforum.2023.2645

21.

Noordsy

Abbott-Frey

Chawla

. Special report: lifestyle psychiatry emphasizes behaviors supporting mental health. Psychiatr News. 2024;59(03). doi:10.1176/appi.pn.2024.03.3.19

22.

Malhi

Bassett

Boyce

, et al. Royal Australian and New Zealand college of psychiatrists clinical practice guidelines for mood disorders. Aust N Z J Psychiatr. 2015;49(12):1087-1206. doi:10.1177/0004867415617657

23.

Marx

Manger

Blencowe

, et al. Clinical guidelines for the use of lifestyle-based mental health care in major depressive disorder: World Federation of societies for biological psychiatry (WFSBP) and Australasian society of lifestyle medicine (ASLM) taskforce. World J Biol Psychiatr. 2022;24:1-54. doi:10.1080/15622975.2022.2112074

24.

Firth

Solmi

Wootton

, et al. A meta-review of “lifestyle psychiatry”: the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders. World Psychiatry. 2020;19(3):360-380. doi:10.1002/wps.20773

25.

Collins

Dash

Allender

Jacka

Hoare

. Diet and mental health during emerging adulthood: a systematic review. Emerg Adulthood. 2022;10(3):645-659. doi:10.1177/2167696820943028

26.

Molendijk

Molero

Ortuño Sánchez-Pedreño

Van Der Does

Angel Martínez-González

. Diet quality and depression risk: a systematic review and dose-response meta-analysis of prospective studies. J Affect Disord. 2018;226:346-354. doi:10.1016/j.jad.2017.09.022

27.

Ogden

Ansai

Fryar

Wambogo

Brody

. Depression and diet quality, US adolescents and young adults: national health and nutrition examination survey, 2015-March 2020. J Acad Nutr Diet. 2025;125(2):247-255. doi:10.1016/j.jand.2024.08.007

28.

Aschbrenner

Naslund

Salwen-Deremer

, et al. Sleep quality and its relationship to mental health, physical health and health behaviours among young adults with serious mental illness enrolled in a lifestyle intervention trial. Early Interv Psychiatry. 2022;16(1):106-110. doi:10.1111/eip.13129

29.

Marino

Andrade

Campisi

, et al. Association between disturbed sleep and depression in children and youths: a systematic review and meta-analysis of cohort studies. JAMA Netw Open. 2021;4(3):e212373. doi:10.1001/jamanetworkopen.2021.2373

30.

Petruzzello

Box

. The kids are alright—right? Physical activity and mental health in college students. Kinesiol Rev. 2020;9(4):279-286. doi:10.1123/kr.2020-0039

31.

Nguyen

HPT

PBT

Tong

, et al. Mechanisms linking physical activity with psychiatric symptoms across the lifespan: a systematic review. Sports Med. 2023;53(11):2171-2190. doi:10.1007/s40279-023-01895-0

32.

Becker

Arnold

Martin

Rice

. Systematic review of electronic cigarette use (vaping) and mental health comorbidity among adolescents and young adults. Nicotine Tob Res. 2021;23(3):415-425. doi:10.1093/ntr/ntaa171

33.

Bandiera

Loukas

Wilkinson

Perry

. Depressive symptoms predict current E-Cigarette use among college students in Texas. Nicotine Tob Res. 2017;19(9):1102-1106. doi:10.1093/ntr/ntx014

34.

McConaha

McCabe

Falcon

. Anxiety, depression, coping, alcohol use and consequences in young adult college students. Subst Use Misuse. 2024;59(2):306-311. doi:10.1080/10826084.2023.2270550

35.

Armstrong

Woolf

. The relationship between lifestyle behaviors and mental illness in women in college. Nutrients. 2024;16(14):2211. doi:10.3390/nu16142211

36.

Marsden

Loukas

Chen

Perry

Wilkinson

. Associations between frequency of cigarette and alternative tobacco product use and depressive symptoms: a longitudinal study of young adults. Addict Behav. 2019;99:106078. doi:10.1016/j.addbeh.2019.106078

37.

Bataineh

Wilkinson

Sumbe

, et al. The association between tobacco and cannabis use and the age of onset of depression and anxiety symptoms: among adolescents and young adults. Nicotine Tob Res. 2023;25(8):1455-1464. doi:10.1093/ntr/ntad058

38.

Cowan-Pyle

Bailey

Gao

, et al. Dietary quality and diet-related factors among emerging adults (18–23 y) in the United States are a cause for concern: National health and nutrition examination survey 2015–2018. J Nutr. 2024;154(8):2524-2533. doi:10.1016/j.tjnut.2024.06.015

39.

Lipsky

Nansel

Haynie

, et al. Diet quality of US adolescents during the transition to adulthood: changes and predictors12. Am J Clin Nutr. 2017;105(6):1424-1432. doi:10.3945/ajcn.116.150029

40.

Zhan

Huang

, et al. Association of dietary inflammatory index (DII) and depressive disorders. J Inflamm Res. 2021;14:6959-6973. doi:10.2147/JIR.S344002

41.

Wang

Zhou

Chen

, et al. Dietary inflammatory index and depression: a meta-analysis. Public Health Nutr. 2019;22(4):654-660. doi:10.1017/S1368980018002628

42.

Wang

Sun

Wang

, et al. The role of dietary inflammatory index and physical activity in depressive symptoms: results from NHANES 2007–2016. J Affect Disord. 2023;335:332-339. doi:10.1016/j.jad.2023.05.012

43.

Schuch

Vancampfort

Richards

Rosenbaum

Ward

Stubbs

. Exercise as a treatment for depression: a meta-analysis adjusting for publication bias. J Psychiatr Res. 2016;77:42-51. doi:10.1016/j.jpsychires.2016.02.023

44.

Lopresti

Hood

Drummond

. A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. J Affect Disord. 2013;148(1):12-27. doi:10.1016/j.jad.2013.01.014

45.

Piercy

Troiano

Ballard

, et al. The physical activity guidelines for Americans. JAMA. 2018;320(19):2020-2028. doi:10.1001/jama.2018.14854

46.

WHO Guidelines on Physical Activity and Sedentary Behaviour. WHO. Accessed February 16, 2025.https://www.who.int/publications/i/item/9789240015128

47.

Arena

Pronk

Woodard

. The influence of social vulnerability and culture on physical inactivity in the United States – identifying hot spots in need of attention. Am J Med. 2024;137(2):113-121. doi:10.1016/j.amjmed.2023.10.025

48.

Kwan

King-Dowling

Veldhuizen

Ceccacci

Cairney

. Examining device-assessed physical activity during the transition into emerging adulthood: results from the MovingU study. J Adolesc Health. 2021;69(3):477-481. doi:10.1016/j.jadohealth.2021.01.005

49.

Poobalan

Aucott

Clarke

Smith

WCS

. Physical activity attitudes, intentions and behaviour among 18–25 year olds: a mixed method study. BMC Public Health. 2012;12(1):640. doi:10.1186/1471-2458-12-640

50.

Bélanger

Giroux

Registe

PPW

, et al. Adolescent physical activity profiles as determinants of emerging adults’ physical activity. Int J Behav Nutr Phys Activ. 2025;22(1):35. doi:10.1186/s12966-025-01732-9

51.

Huang

Gan

, et al. Sedentary behaviors and risk of depression: a meta-analysis of prospective studies. Transl Psychiatry. 2020;10:26. doi:10.1038/s41398-020-0715-z

52.

Bames

Behrens

Benden

. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab-Physiol Appl Nutr Metab. 2012;37:540-542.

53.

Primack

Shensa

Sidani

Escobar-Viera

Fine

. Temporal associations between social media use and depression. Am J Prev Med. 2021;60(2):179-188. doi:10.1016/j.amepre.2020.09.014

54.

Peterson

Sirard

Kulbok

DeBoer

Erickson

. Sedentary behavior and physical activity of young adult university students. Res Nurs Health. 2018;41(1):30-38. doi:10.1002/nur.21845

55.

Barsha

RAA

Hossain

. Trouble sleeping and depression among US women aged 20 to 30 years. Prev Chronic Dis. 2020;17:E29. doi:10.5888/pcd17.190262

56.

Moran

Villanti

Johnson

Rath

. Patterns of alcohol, tobacco, and substance use among young adult peer crowds. Am J Prev Med. 2019;56(6):e185-e193. doi:10.1016/j.amepre.2019.02.010

57.

Ding

Rogers

Ploeg

Stamatakis

Bauman

. Traditional and emerging lifestyle risk behaviors and all-cause mortality in middle-aged and older adults: evidence from a large population-based Australian cohort. PLoS Med. 2015;12(12):e1001917. doi:10.1371/journal.pmed.1001917

58.

Siew

RVK

Nabe-Nielsen

Turner

Bujtor

Torres

. The role of combined modifiable lifestyle behaviors in the association between exposure to stressors and allostatic load: a systematic review of observational studies. Psychoneuroendocrinology. 2022;138:105668. doi:10.1016/j.psyneuen.2022.105668

59.

Wang

Arafa

Liu

Eshak

Dong

. Combined healthy lifestyle and depressive symptoms: a meta-analysis of observational studies. J Affect Disord. 2021;289:144-150. doi:10.1016/j.jad.2021.04.030

60.

Wang

cong

Yuan

Phongsavan

. Associations between multiple health risk behaviors and mental health among Chinese college students. Psychol Health Med. 2016;21(3):377-385. doi:10.1080/13548506.2015.1070955

61.

Collins

Hoare

Allender

, et al. A longitudinal study of lifestyle behaviours in emerging adulthood and risk for symptoms of depression, anxiety, and stress. J Affect Disord. 2023;327:244-253. doi:10.1016/j.jad.2023.02.010

62.

Champion

Mather

Spring

Kay-Lambkin

Teesson

Newton

. Clustering of multiple risk behaviors among a sample of 18-year-old Australians and associations with mental health outcomes: a latent class analysis. Front Public Health. 2018;6:135. doi:10.3389/fpubh.2018.00135

63.

Dong

Chen

, et al. Patterns of health-risk behaviours and their associations with anxiety and depression among Chinese young adults by gender: a latent class analysis. Psychol Rep. 2024;1-27. doi:10.1177/00332941241258922

64.

Ballesio

Aquino

MRJV

Feige

, et al. The effectiveness of behavioural and cognitive behavioural therapies for insomnia on depressive and fatigue symptoms: a systematic review and network meta-analysis. Sleep Med Rev. 2018;37:114-129. doi:10.1016/j.smrv.2017.01.006

65.

Grant

Becnel

Giano

Williams

Martinez

. A latent profile analysis of young adult lifestyle behaviors. Am J Health Behav. 2019;43(6):1148-1161. doi:10.5993/AJHB.43.6.12

66.

Jao

Robinson

Kelly

Ciecierski

Hitsman

. Unhealthy behavior clustering and mental health status in United States college students. J Am Coll Health. 2019;67(8):790-800. doi:10.1080/07448481.2018.1515744

67.

Xie

Fan

Luo

Chen

. Associations of lifestyle patterns with overweight and depressive symptoms among United States emerging adults with different employment statuses. Int J Public Health. 2023;68:1606451. doi:10.3389/ijph.2023.1606451

68.

Tolkien

Bradburn

Murgatroyd

. An anti-inflammatory diet as a potential intervention for depressive disorders: a systematic review and meta-analysis. Clin Nutr. 2019;38(5):2045-2052. doi:10.1016/j.clnu.2018.11.007

69.

Yin

Gong

Zhang

, et al. Associations between sleep disturbance, inflammatory markers and depressive symptoms: mediation analyses in a large NHANES community sample. Prog Neuropsychopharmacol Biol Psychiatry. 2023;126:110786. doi:10.1016/j.pnpbp.2023.110786

70.

Linsley

Bishop

Roberts

Hamrouni

Demashkieh

Paine

. Interactions between sedentary behaviour, moderate‐vigorous intensity physical activity and acute psychological stress‐induced inflammatory responses. Stress Health. 2025;41(3):e70038. doi:10.1002/smi.70038

71.

Elisia

Lam

Cho

, et al. The effect of smoking on chronic inflammation, immune function and blood cell composition. Sci Rep. 2020;10(1):19480. doi:10.1038/s41598-020-76556-7

72.

Feldman

McPherson

Biesecker

, et al. Neuroimaging of inflammation in alcohol use disorder: a review. Sci China Inf Sci. 2020;63(7):170102. doi:10.1007/s11432-019-2857-5

73.

CDC . About NHANES. National health and nutrition examination survey. 2024. https://www.cdc.gov/nchs/nhanes/about/index.html. Accessed 16 February 2025.

74.

Johnson

Dohrmann

Burt

Mohadjer

. National health and nutrition examination survey: sample design, 2011-2014. Vital Health Stat 2. 2014;2(162):1-33.

75.

Chen

Clark

Riddles

Mohadjer

Fakhouri

THI

. National health and nutrition examination survey, 2015-2018: sample design and estimation procedures. Vital Health Stat. 2020;2(184):1-35.

76.

CDC . Ethics review board approval. National health and nutrition examination survey. 2024. https://www.cdc.gov/nchs/nhanes/about/erb.html. Accessed 16 February 2025.

77.

NHANES response rates and population totals. Accessed May 4, 2025.https://wwwn.cdc.gov/nchs/nhanes/ResponseRates.aspx

78.

NHANES 2011-2012 overview. Accessed May 4, 2025.https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overview.aspx?BeginYear=2011

79.

NHANES 2013-2014 overview. Accessed May 4, 2025.https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overview.aspx?BeginYear=2013

80.

NHANES 2015-2016 overview. Accessed May 4, 2025.https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overview.aspx?BeginYear=2015

81.

NHANES 2017-2018 overview. Accessed May 4, 2025.https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overview.aspx?BeginYear=2017

82.

NHANES questionnaires, datasets, and related documentation. https://wwwn.cdc.gov/nchs/nhanes/. Accessed 12 May 2025.

83.

Shivappa

Steck

Hurley

Hussey

Hébert

. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689-1696. doi:10.1017/S1368980013002115

84.

Hirshkowitz

Whiton

Albert

, et al. National sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233-243. doi:10.1016/j.sleh.2015.10.004

85.

Thivel

Tremblay

Genin

Panahi

Rivière

Duclos

. Physical activity, inactivity, and sedentary behaviors: definitions and implications in occupational health. Front Public Health. 2018;6:288. doi:10.3389/fpubh.2018.00288

86.

Snetselaar

de Jesus

DeSilva

Stoody

. Dietary guidelines for Americans, 2020–2025. Nutr Today. 2021;56(6):287-295. doi:10.1097/NT.0000000000000512

87.

World Health Organization . Guidelines for Controlling and Monitoring the Tobacco Epidemic. World Health Organization; 1998.

88.

Kroenke

Spitzer

Williams

JBW

. The PHQ-9. J Gen Intern Med. 2001;16(9):606-613. doi:10.1046/j.1525-1497.2001.016009606.x

89.

Lantz

Golberstein

House

Morenoff

. Socioeconomic and behavioral risk factors for mortality in a national 19-year prospective study of U.S. adults. Soc Sci Med. 2010;70(10):1558-1566. doi:10.1016/j.socscimed.2010.02.003

90.

Faught

McLaren

Kirkpatrick

Hammond

Minaker

Olstad

. Socioeconomic disadvantage across the life course is associated with diet quality in young adulthood. Nutrients. 2019;11(2):242. doi:10.3390/nu11020242

91.

Lantz

House

Lepkowski

Williams

Mero

Chen

. Socioeconomic factors, health behaviors, and MortalityResults from a nationally representative prospective study of US adults. JAMA. 1998;279(21):1703-1708. doi:10.1001/jama.279.21.1703

92.

Bureau UC . How the Census Bureau Measures Poverty. Census.gov. https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html. Accessed 1 July 2025.

93.

Weir

Jan

. BMI classification percentile and cut off points. In: Statpearls. StatPearls Publishing; 2025. https://www.ncbi.nlm.nih.gov/books/NBK541070/. Accessed 1 July 2025.

94.

NHANES tutorials - weighting module. Accessed April 22, 2025.https://wwwn.cdc.gov/nchs/nhanes/tutorials/weighting.aspx

95.

Lanza

Rhoades

. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157-168. doi:10.1007/s11121-011-0201-1

96.

Nylund-Gibson

Choi

. Ten frequently asked questions about latent class analysis. Transl Issues Psychol Sci. 2018;4(4):440-461. doi:10.1037/tps0000176

97.

Oftedal

Kolt

Holliday

, et al. Associations of health-behavior patterns, mental health and self-rated health. Prev Med. 2019;118:295-303. doi:10.1016/j.ypmed.2018.11.017

98.

Yang

, et al. A latent class analysis of psychological symptoms and health risk behaviors among Chinese adolescents. Asian J Psychiatr. 2021;55:102518. doi:10.1016/j.ajp.2020.102518

99.

Qin

Duan

, et al. Associations between healthy lifestyle behavioral patterns and mental health problems: a latent class analysis of 161,744 Chinese young adults. J Affect Disord. 2024;347:414-421. doi:10.1016/j.jad.2023.11.087

100.

Deasy

Coughlan

Pironom

Jourdan

Mcnamara

. Psychological distress and lifestyle of students: implications for health promotion. Health Promot Int. 2015;30(1):77-87. doi:10.1093/heapro/dau086

101.

Kritsotakis

Psarrou

Vassilaki

Androulaki

Philalithis

. Gender differences in the prevalence and clustering of multiple health risk behaviours in young adults. J Adv Nurs. 2016;72(9):2098-2113. doi:10.1111/jan.12981

102.

Varì

Scazzocchio

D’Amore

Giovannini

Gessani

Masella

. Gender-related differences in lifestyle may affect health status. Ann DellIstituto Super Sanità. 2016;52(2):158-166.

103.

Olfert

Barr

Charlier

Greene

Zhou

Colby

. Sex differences in lifestyle behaviors among U.S. college freshmen. Int J Environ Res Publ Health. 2019;16(3):482. doi:10.3390/ijerph16030482

104.

Karoly

Bidwell

Mueller

Hutchison

. Investigating the relationships between alcohol consumption, cannabis use and circulating cytokines: a preliminary analysis. Alcohol Clin Exp Res. 2018;42(3):531-539. doi:10.1111/acer.13592

105.

Magee

Connell

. The role of substance use coping in linking depression and alcohol use from late adolescence through early adulthood. Exp Clin Psychopharmacol. 2021;29(6):659-669. doi:10.1037/pha0000436

106.

Mac

. Inflammation and depression: research designs to better understand the mechanistic relationships between depression, inflammation, cognitive dysfunction, and their shared risk factors. Brain Behav Immun Health. 2021;15:100278. doi:10.1016/j.bbih.2021.100278

107.

Zielińska

Łuszczki

Dereń

. Dietary nutrient deficiencies and risk of depression (review article 2018–2023). Nutrients. 2023;15(11):2433. doi:10.3390/nu15112433

108.

O’Leary

Bylsma

Rottenberg

. Why might poor sleep quality lead to depression? A role for emotion regulation. Cognit Emot. 2017;31(8):1698-1706. doi:10.1080/02699931.2016.1247035

109.

Jiang

Ding

. Relationship of depression and sleep quality, diseases and general characteristics. World J Psychiatr. 2022;12(5):722-738. doi:10.5498/wjp.v12.i5.722

110.

Moskal

Teeters

. Associations between depression, sleep quality, alcohol craving, and alcohol-related consequences among emerging adults. Subst Use Misuse. 2025;60(6):828-835. doi:10.1080/10826084.2025.2454656

111.

Wickham

Amarasekara

Bartonicek

Conner

. The big three health behaviors and mental health and well-being among young adults: a cross-sectional investigation of sleep, exercise, and diet. Front Psychol. 2020;11:579205. doi:10.3389/fpsyg.2020.579205

112.

Lindgren

Tristao

Neighbors

. The association between student loan debt and perceived socioeconomic status and problematic drinking and mental health symptoms: a preliminary investigation. Addict Behav. 2023;139:107576. doi:10.1016/j.addbeh.2022.107576

113.

Anderson

Valiauga

Tallo

, et al. Contributing factors to the rise in adolescent anxiety and associated mental health disorders: a narrative review of current literature. J Child Adolesc Psychiatr Nurs. 2025;38(1):e70009. doi:10.1111/jcap.70009

114.

Waller

Anderson

. Quantifying social organization and political polarization in online platforms. Nature. 2021;600(7888):264-268. doi:10.1038/s41586-021-04167-x

115.

Lass-Hennemann

Sopp

Ruf

, et al. Generation climate crisis, COVID-19, and Russia–Ukraine-war: global crises and mental health in adolescents. Eur Child Adolesc Psychiatr. 2024;33(7):2203-2216. doi:10.1007/s00787-023-02300-x

116.

Poobalan

Aucott

Clarke

Smith

WCS

. Diet behaviour among young people in transition to adulthood (18-25 year olds): a mixed method study. Health Psychol Behav Med. 2014;2(1):909-928. doi:10.1080/21642850.2014.931232

117.

Kheirouri

Alizadeh

. Dietary inflammatory potential and the risk of incident depression in adults: a systematic review. Adv Nutr. 2019;10(1):9-18. doi:10.1093/advances/nmy100

118.

American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed. American Psychiatric Association; 2013. doi:10.1176/appi.books.9780890425787. Accessed 4 March 2024.

119.

Paans

NPG

Gibson-Smith

Bot

, et al. Depression and eating styles are independently associated with dietary intake. Appetite. 2019;134:103-110. doi:10.1016/j.appet.2018.12.030

120.

Friedman

. Smoking to cope: addictive behavior as a response to mental distress. J Health Econ. 2020;72:102323. doi:10.1016/j.jhealeco.2020.102323

The Association Between Lifestyle Patterns and Depression in United States Emerging Adults: A Latent Class Analysis

Abstract

Keywords

Introduction

Methods

Study Design & Sampling

Participants

Measures

Diet

Sleep Duration & Quality

Physical Activity & Sedentary Behavior

Substance Use

Depression

Sociodemographic Variables

Statistical Analysis

Results

Sample Characteristics

Demographic Characteristics

Lifestyle Behaviors & Depression

Latent Class Analysis

Lifestyle Behaviors & Demographic Characteristics Across Latent Classes Among Males

Lifestyle Behaviors & Demographic Characteristics Across Latent Classes Among Females

Associations of Lifestyle Groups With Depression

Discussion

Limitations

Implications and Future Directions

Conclusions

Footnotes

ORCID iDs

Ethical Considerations

Funding

Declaration of Conflicting Interests

Data Availability Statement

References