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Abstract

The synergistic role played by multiple lifestyle behaviors on mental health during the transition from adolescence to emerging adulthood has not been extensively studied. This study included 493 participants who self-reported on a range of health behaviors during adolescence and emerging adulthood, and psychological distress and mental wellbeing during emerging adulthood. Latent profile analysis and latent transition analysis were used to analyze the data. Three unique behavioral profiles were observed at baseline: moderately physically active abstainers (i.e., abstaining from alcohol, tobacco, and marijuana), high screen time users, and moderately physically active and moderate risk behaviors. Four unique behavioral profiles were observed at follow-up: highly physically active with moderate risk behaviors, moderately physically active with moderate risk behaviors, physically inactive abstainers, and physically inactive with risky behaviors. Adolescents characterized as moderately active abstainers reported fewer symptoms of psychological distress during emerging adulthood compared to adolescents who displayed moderately active and moderate risk behaviors, and better mental wellbeing than high screen time users.

Keywords

physical activity screen time sleep risky behaviors distress wellbeing

Introduction

The transition from adolescence to emerging adulthood is a critical time in life which can have a profound and long-lasting impact on an individual’s future health and wellbeing (Bonnie et al., 2014). This is a period for which young people establish independence, assume new responsibilities and obligations, and adopt long lasting (un)healthy patterns of behaviours beginning in young adulthood (Kwan et al., 2012; Nelson et al., 2008; Sawyer et al., 2012). Correspondingly, this transition period is characterized by substantial physical, affective, cognitive, and behavioural changes, which may leave young people susceptible to experiencing poorer mental health (Paus et al., 2008; Steinberg, 2005). For example, the burden of common mental health disorders substantially increases during adolescence and the burden of mental health disorders reaches a peak in emerging adulthood (Solmi et al., 2022; Whiteford et al., 2013) and it is estimated that approximately one in every eleven emerging adults will experience a major depressive episode each year (Mojtabai et al., 2016). Therefore, supporting the mental health of young people as they transition out of the adolescent period must be a global public health priority.

There is a growing body of literature demonstrating that healthy lifestyle behaviors including physical activity, screen time, sleep, and alcohol, tobacco and substance use are related to the prevention and treatment of mental health disorders (Firth et al., 2020; Groves et al., 2024). Adolescents who are more physically active (StrÖHle et al., 2007; Suetani et al., 2017) who do not smoke (Chaiton et al., 2009), and who sleep for longer periods of time each night (Orchard et al., 2020) are significantly less likely to have a mental health disorder in emerging adulthood. However, most of the research to date has focused on the association between individual healthy lifestyle behaviors and mental health. Yet, healthy lifestyle behaviors may cluster in unique ways (Leech et al., 2014; Meader et al., 2016), and therefore may have a multiplicative and synergistic effect on mental health outcomes. A recent meta-analysis did demonstrate that people who engage in healthier clusters of lifestyle behaviors report fewer symptoms of depression, anxiety, and general psychological distress, but notes a lack of longitudinal evidence (Bourke et al., 2025). There have been several cross-sectional studies which have demonstrated that clusters of healthy lifestyle behaviors relate to fewer symptoms of mental health conditions in young adults specifically (Brown et al., 2022; Champion et al., 2018; Di Benedetto et al., 2020; El Ansari et al., 2024; Kwan et al., 2016; Ye et al., 2016). Although there have been fewer longitudinal studies on the topic, results from longitudinal studies are consistent with the results reported in cross-sectional studies on the topic (Brown et al., 2021; Collins et al., 2023; Kleppang et al., 2023). However, a recent systematic review and meta-analysis of randomized controlled trials found inconsistent evidence of the effectiveness interventions which target multiple modifiable health behaviors on symptoms of depression and anxiety in adolescents and emerging adults (Bourke et al., 2022). Therefore, examining the nature and magnitude of the prospective association between patterns of healthy lifestyle behaviors during adolescence and emerging adulthood may provide insights into the extent to which, and potentially what type of, multi-faceted lifestyle interventions may be an effective approach to improving mental health outcomes during youth.

While it is important to consider the longitudinal association between the clustering of healthy lifestyle behaviors in adolescence with mental health outcomes in emerging adulthood, an equally important consideration is how lifestyle behaviors change during this dynamic life transition. Emerging adulthood is distinct from adolescence (Arnett, 2000). It is characterized by increased agency and independence, identity exploration, decreased social roles and obligations to others, and decreased social and institutional supports (Arnett, 2000; Arnett et al., 2014; Wood et al., 2018). This period of identity development and shifting interpersonal influences makes emerging adulthood a period of rapid changes in health behavior patterns (Nelson et al., 2008). Inauspiciously, there is consistent evidence that people tend to become unhealthier in emerging adulthood compared to adolescence (Frech, 2012; Harris et al., 2006; Paavola et al., 2004; Wiium et al., 2015). Specifically, emerging adults are more likely to engage in binge drinking, smoke, use illicit drugs, and be physically inactive when compared to their behaviors during adolescence. Therefore, the extent to which adolescents transition to less healthy clusters of lifestyle behaviors may be associated with mental health outcomes in emerging adulthood. Nevertheless, few studies have examined how clusters of health behaviors change longitudinally as individuals transition out of adolescence (Dakin et al., 2021; Lawrence et al., 2020), and to the authors knowledge, no studies have examined how changes in clusters of healthy lifestyle behaviors between adolescence and emerging adulthood are related to mental health outcomes.

The aim of this longitudinal study was to identify distinct latent profiles of healthy lifestyle behaviors during adolescence and emerging adulthood and examine the extent symptoms of psychological distress and mental wellbeing in young adulthood differs between the identified profiles. It is hypothesized that healthy combinations of lifestyle behaviors in adolescence will longitudinally relate to fewer symptoms of psychological distress and better mental wellbeing in emerging adulthood. Additionally, the study aims to determine how participants transition between profiles from adolescence to emerging adulthood and to investigate how different longitudinal transition patterns relate to psychological distress and mental wellbeing outcomes in young adulthood.

Methods

Procedures and Participants

Data for this study were from the Application of intergrateD Approaches to understanding Physical activity during the Transition to emerging adulthood (ADAPT) study (Kwan et al., 2020). This is a prospective cohort study which aimed to examine physical activity predictors and outcomes between the transition from adolescence (when participants were aged 15–18 years) to emerging adulthood (when participants were aged 18–21 years). Participants were recruited from a large school board located in Southern Ontario, Canada, and baseline data collection occurred in Fall, 2019. Adolescents attending a grade 11 class at baseline were eligible to participate in this study. From a total of 2412 adolescents eligible to participate in the study, 1585 responded to the invitation to participate, and 1238 had some valid data at baseline (51.3% valid response rate). Among the 1238 adolescents with valid data at baseline, 498 completed the follow-up assessment at three years post baseline (40.2% retention). Participants with missing data were excluded via listwise deletion, resulting in an analytic sample of 498 participants with valid data at both time points. A detailed description about the study procedures have been described previously (Kwan et al., 2020). The study procedures were approved by the Hamilton Integrated Research Ethics Board and the Hamilton-Wentworth Catholic District School Board.

Measures

Independent Variables – Multiple Health Behaviors

Moderate-to-Vigorous Intensity Physical Activity

Moderate-to-vigorous intensity physical activity was assessed at baseline and follow-up using the International Physical Activity Questionnaire – Short Form (IPAQ-SF) (Craig et al., 2003). Participants were asked to report the number of days that they engaged in vigorous intensity and moderate intensity physical activity for at least 10 min at a time in the previous seven days. If they reported at least one day of moderate or vigorous physical activity, they were asked to report how much time they typically engaged in these activities on one of those days. Weekly time spent in moderate and vigorous physical activity was calculated as the number of days a participant was active multiplied by the amount of time they typically spent active on one of those days. Total time spent in moderate-to-vigorous intensity physical activity (MVPA) was calculated by adding the time spent in moderate and the time spent in vigorous physical activity in the previous seven days. Consistent with the IPAQ scoring guide, and to reduce the impact of outliers and remove infeasible values, total time spent in MVPA was truncated at 21 hr in the previous 7 days (i.e., 3 h per day).

Recreational Screen Time

Screen time was assessed at baseline and follow-up using a single item. Participants were asked to report the amount of time they spent watching TV or using a computer, tablet or smartphone during their free time on a typical day in the previous week. Participants were instructed not to include time on a computer at school or at work.

Sleep

Sleep duration was assessed at baseline and follow up. Participants responded to four items that assessed what time they typically went to sleep and woke up during weekdays and on the weekend over the past seven days. Responses were used to calculate the average number of hours participants slept on weekdays and weekends. Average daily sleep was then calculated by multiplying weekday sleep by five and weekend sleep by two, and then summing these products and dividing by seven.

Marijuana Use

Participants were asked to report on the number of occasions that they used marijuana (including a joint, pot, weed, hash, edibles) in the past 12 months at baseline and follow-up. Participants could select from nine response options ranging from never to every day. Reponses were collapsed into four categories: (1) not in the past 12 months, (2) less than once a month, (3) at least once a month, and (4) at least once a week.

Tobacco Smoking

Participants were asked to respond to two questions about tobacco smoking at baseline and follow-up. They were asked at the present time, how often they smoked cigarettes, and at the present time how often they used e-cigarettes of vapes. Response options were daily, occasionally, and not at all. Responses for cigarette smoking and vaping were combined to assess how often participants smoked tobacco.

Binge Drinking

Participants were asked to report on the number of occasions that they consumed five or more drinks on one occasion in the past 30 days at baseline and follow-up. They were instructed that one drink means one 341 ml or 12oz serving of beer whether from a bottle, can or draft, one 142 ml or 5oz glass of wine or bottle of cooler, or one straight or mixed drink with 1.5oz (43 ml) of liquor or spirit. Participants were asked to report on a six-point scale from never to daily or almost daily. Responses were collapsed into three categories: (1) not in the past month, (2) once in the past month, and (3) multiple times in the past month.

Outcomes

Psychological Distress

Symptoms of psychological distress was assessed at follow-up using Kessler’s Psychological Distress scale (K10; Kessler et al., 2002). It has demonstrated excellent convergent validity in large representative samples (Furukawa et al., 2003; Kessler et al., 2010) and among youth (Blake et al., 2023). Each of the ten items is scored on a five-point Likert with a score of one indicating that a symptom has not been present in the past 4 weeks and a score of five indicating a symptom has been present most of the time. Scores from each item are summed to calculate a total score ranging from 10 to 50.

Mental Wellbeing

Mental wellbeing was assessed at follow-up using the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS; Tennant et al., 2007). The WEMWBS has strong psychometric properties in youth (McKay & Andretta, 2017). The WEMWBS assessed both the hedonic (i.e., happiness, satisfaction with life) and eudaimonic (i.e., self-realisation) aspects on mental wellbeing. Participants were asked to report how often they had experienced a range of positive aspects of mental wellbeing in the previous 2 weeks on a five-point scale from 1 (none of the time) to 5 (all of the time). The scale included 14 individual items, and the average item response was calculated as the overall scale score.

Covariates

Participant’s age at baseline, gender, and race were all recorded and included as covariates in the current study.

Statistical Analysis

All statistical analysis was conducted using Mplus (v 8.4). Prior to analysis, the distribution of the independent variables was explored. The data for five participants were removed for having implausible outlier values for sleep (i.e., less than 3 hr or greater than 12 hr of sleep each night) at either baseline or follow-up.

The first step of the analysis involved estimating a cross-sectional latent profile model for the baseline data. The model was estimated with three continuous indicators (MVPA, screen time, sleep) and three categorical indicators (marijuana smoking, tobacco smoking, binge drinking). The model was estimated with a profile invariant, diagonal variance-covariance matrix, where the correlation between indicators within- profiles is fixed at zero and the variance of the indicators is fixed to be invariant across latent profiles. The best fitting model from profile enumeration was determined using the Bayesian Information Criteria (BIC; Gideon, 1978), which has been shown to perform well at identifying the correct number of latent profiles in a range of mixture modelling scenarios (Nylund et al., 2007). The entropy value was reported, which varies between zero and one, with a value closer to one indicating clearer delineation between the latent clusters (Celeux & Soromenho, 1996). Once the optimal number of latent profiles was identified, difference in symptoms of psychological distress and mental wellbeing between profiles was estimated using the manual three step approach (Vermunt, 2017). This approach fixes the conditional probabilities from modal class membership from the best-fitting unconditional model, meaning that additional distal outcomes can be estimated for each profile without influencing the estimation of class membership (Nylund-Gibson et al., 2019). Differences in psychological distress and mental wellbeing at follow-up between each of the profiles were estimated while controlling for participants gender, age, and race as covariates. In the second step, a latent profile analysis was conducted based on participants healthy lifestyle behaviours in emerging adulthood, with differences in psychological distress and mental wellbeing being subsequently examined.

In the third step, a latent transition analysis was estimated. Latent transition analysis is a mixture modelling technique which estimates the relationship between multiple latent profile variables longitudinally (Nylund-Gibson et al., 2023). Results from the latent profile analysis of follow-up data showed that there were substantive differences in the item means and profile-specific item probabilities compared to baseline, so invariance in the latent profiles across time was not imposed in the latent transition model. Instead, the model was estimated with the best fitting model from baseline and follow-up. The manual three-step approach was taken to estimate the latent transition model (Nylund-Gibson et al., 2014). Taking this approach means that the conditional probabilities of modal class membership at baseline and follow-up are fixed based on the results cross-sectional models, ensuring that the interpretation of the latent profiles remains consistent across cross-sectional and longitudinal analyses and meaning that class membership at baseline is not influenced by lifestyle behaviours at follow-up, ensuring temporal precedence. Additionally, using the manual three-step approach facilitates the inclusion of auxiliary variables without influencing the measurement model, and allows for the comparison of symptoms of psychological distress and mental wellbeing based on different latent transition patterns. Differences in psychological distress and mental wellbeing based on different latent transition patterns were assessed controlling for participant’s gender, and race as covariates. Standardized mean differences (i.e., Cohen’s d) are reported in text.

Given that latent profile and latent transition analysis are inherently exploratory (i.e., the number of latent profiles is not known before conducing the analysis), it is not feasible to conduct an a-priori power analysis. Nevertheless, a heuristic of ≥500 participants is generally suggested for latent profile and transition analyses (Nylund-Gibson & Choi, 2018; Nylund-Gibson et al., 2023), however, true power depends on several factors, such as the number of indicators included in the model and the degree of separation between each of the profiles (Tein et al., 2013). With regards to comparisons in distal outcomes between identified profiles and latent transition patterns, differences in outcomes were only reported for comparisons which had 80% power to identify at least a moderate-to-large difference between groups (i.e., d = 0.60). Remaining comparisons were considered to be unreliable, as the chance of detecting a true effect is reduced and the chance of overestimating an effect size is increased (Button et al., 2013).

Results

Participant Characteristics and Missing Value Analysis

Among the participants included in the analysis, most were female (68.2%), white (53.5%), and aged 16 years old at baseline (81.8%). Slightly more participants than not engaged in an average of 60 min of MVPA each day (52.4%), however, fewer slept between 8–10 hr each night (22.7%) or limited their recreational screen time to less than 2 hr per day (20.7%). The vast majority of participants had not consumed more than 5 alcoholic beverages on a single occasion in the past month (73.6%), had never used marijuana (75.1%), and had never smoked tobacco (81.9%) at baseline.

Participants with missing data at the follow-up assessment were significantly more physically active at baseline compared to participants with valid follow-up data (p < .001), and significantly younger (p = .017). Additionally, participants with missing data at follow-up were significantly more likely to binge drink at baseline (p = .002), and were significantly more likely to be male (p < .001). Missing data was not related to participants levels of recreational screen time or sleep at baseline, whether participants smoked marijuana or tobacco at baseline, or participant’s race.

Baseline Latent Profile Analysis

The model fit statistics from the estimation of the mixture model at baseline with increasing numbers of estimated latent profiles in displayed in Appendix A. Results showed that the 3-profile solution was the best fitting model. Lifestyle behaviors for each of the latent profiles are displayed in Table 1. The majority of participants were assigned to profile 1 (74.3%) which was characterized as moderately active abstainers. Fewer participants were assigned to profile 2 (16.9%), which were high screen time users, or profile 3 (8.7%) which were characterized as moderately active with moderate risk behaviors.

Table 1.

Lifestyle Behaviors for each Latent Profile at Baseline

	Profile 1	Profile 2	Profile 3
	“Moderately active abstainers” (n = 366)	“High screen time users” (n = 83)	“Moderately active with moderate risk behaviors” (n = 43)
MVPA hrs/week	8.39 (7.88, 8.90)	7.15 (5.56, 8.74)	9.45 (7.98, 10.92)
Screen time hrs/day	4.02 (3.75, 4.29)	14.54 (13.58, 15.50)	4.13 (2.46, 3.80)
Sleep	7.34 (7.25, 7.43)	7.18 (6.74, 7.62)	7.09 (6.90, 7.28)
Marijuana
Not in the past 12 months	98.4%	81.8%	14.5%
Less than once a month	1.6%	7.4%	31.7%
At least once a month	0.0%	6.4%	30.6%
At least once a week	0.0%	4.4%	23.2%
Tobacco
Never	96.7%	75.6%	28.8%
Occasionally	3.3%	12.7%	47.5%
Daily	0.0%	11.6%	23.7%
Binge drinking
Not in the past month	90.3%	79.3%	32.3%
Once in the past month	8.2%	2.2%	35.4%
Multiple times in the past month	1.6%	18.5%	32.3%

Note. MVPA = moderate-to-vigorous physical activity.

The longitudinal association between profile membership in adolescence and psychological distress and mental wellbeing during emerging adulthood are displayed in Table 2. Results demonstrated that the moderately active abstainers reported significantly fewer symptoms of psychological distress compared to participants who were in the moderately active with moderate risk behaviors profile as adolescents (d = −0.42, p = .001). Additionally, the moderately active abstainers reported significantly more positive mental wellbeing compared to adolescents in the high screen time use profile as adolescents (d = 0.90, p < .001).

Table 2.

Differences in Psychological Distress (K10) and Mental Wellbeing (WEMWBS) at Follow-up Between Profiles at Baseline

K10
	Mean	Profile 1	Profile 2	Profile 3
Profile 1	24.32 (23.60, 25.04)	^-	−1.63 (−3.89, 0.63)	−2.70 (−4.15, −1.24)**
Profile 2	25.95 (24.10, 27.79)		-	−1.07 (−4.12, 1.98)
Profile 3	27.01 (24.96, 29.06)			-

WEMWBS
	Mean	Profile 1	Profile 2	Profile 3
Profile 1	48.27 (46.20, 50.34)	-	3.41 (2.24, 4.58)**	2.43 (−0.65, 5.50)
Profile 2	44.86 (42.19, 47.53)		-	−0.98 (−3.35, 1.38)
Profile 3	45.84 (42.40, 49.28)			-

Note. Results are presented as mean (95% confidence interval); *p < .01 **p < .001, WEMWBS = warwick edinburgh mental wellbeing scale.

Follow-Up Latent Profile Analysis

Model fit statistics for the latent profile analysis at follow-up are displayed in Appendix A. Although results demonstrated improving model fit with increasing number of profiles, there were small profiles which consisted of less than 5% of the total study population for models which estimated more than four latent profiles, indicating potentially spurious profiles. Therefore, the four-profile solution was chosen as the optimal solution at follow-up. Lifestyle behaviors for each of the profiles at follow-up are displayed in Table 3. Participants were relatively evenly split between the four profiles which included Profile 1 (23.4%) which was characterized by highly active with moderate risk behaviors, Profile 2 (23.6%) which was characterized by moderately active with moderate risk, Profile 3 (31.3%) which was characterized by inactive abstainers, and Profile 4 (21.7%) which was characterized by inactive with risky behaviors. Sleep duration was relatively consistent across the profiles.

Table 3.

Lifestyle Behaviors for each Latent Profile at Follow-up

	Profile 1	Profile 2	Profile 3	Profile 4
	“Highly active with moderate risk behaviors” (n = 115)	“Moderately active with moderate risk behaviors” (n = 116)	“Inactive abstainers” (n = 154)	“Inactive with risky behaviors” (n = 107)
MVPA hrs/week	19.99 (19.60, 20.38)	10.74 (9.66, 11.82)	2.13 (0.66, 3.60)	2.89 (2.15, 3.63)
Screen time hrs/day	5.32 (4.67, 5.97)	4.84 (4.11, 5.57)	5.30 (4.63, 5.97)	5.00 (4.24, 5.76)
Sleep	6.89 (6.52, 7.26)	7.18 (6.83, 7.53)	7.11 (6.91, 7.31)	7.99 (7.79, 8.19)
Marijuana
Not in the past 12 months	51.8%	53.7%	86.5%	1.7%
Less than once a month	14.9%	11.6%	12.6%	23.9%
At least once a month	15.3%	15.1%	0.9%	29.3%
At least once a week	17.9%	19.6%	0.0%	45.1%
Tobacco
Never	71.6%	78.9%	97.8%	48.0%
Occasionally	12.0%	12.6%	1.1%	29.1%
Daily	16.4%	8.5%	1.1%	22.9%
Binge drinking
Not in the past month	32.2%	38.0%	70.3%	13.0%
Once in the past month	22.9%	17.0%	17.8%	12.9%
Multiple times in the past month	44.9%	45.0%	11.8%	74.1%

Note. MVPA = moderate-to-vigorous physical activity.

Differences in symptoms of psychological distress and mental wellbeing between each of the identified profiles at follow-up are displayed in Table 4. Results indicate that there were no significant differences in symptoms of distress or levels of mental wellbeing between any of the profiles at follow-up.

Table 4.

Differences in Psychological Distress (K10) and Mental Wellbeing (WEMWBS) Between Profiles at Follow-up

K10
	Mean	Profile 1	Profile 2	Profile 3	Profile 4
Profile 1	24.93 (24.09, 25.77)	^-	0.88 (−0.99, 2.75)	0.14 (−1.03, 1.30)	−1.37 (−4.18, 1.44)
Profile 2	24.05 (21.99, 26.10)		-	−0.74 (−2.84, 1.35)	−2.25 (−5.97, 1.47)
Profile 3	24.79 (23.72, 25.86)			-	−1.50 (−3.70, 0.70)
Profile 4	26.29 (24.02, 28.56)				-

WEMWBS
	Mean	Profile 1	Profile 2	Profile 3	Profile 4
Profile 1	48.94 (46.09, 51.80)	-	1.58 (−1.03, 4.18)	2.57 (−0.27, 5.42)	2.77 (−1.31, 6.85)
Profile 2	47.37 (44.89, 49.84)		-	1.00 (−1.27, 3.26)	1.19 (−2.27, 4.65)
Profile 3	46.37 (43.93, 48.81)			-	0.20 (−1.85, 2.2)
Profile 4	46.17 (42.12, 50.22)				-

Note. Results are presented as mean (95% confidence interval), WEMWBS = warwick edinburgh mental wellbeing scale.

Latent Transition Analysis

Results from the latent transition analysis are displayed in Figure 1. Participants in Profile 1 (moderately active abstainers) at baseline most commonly transitioned to Profile 3 (inactive abstainers) at follow-up (36.3%), followed by Profile 2 (moderately active with moderate risk behaviors; 23.5%), Profile 1 (highly active with moderate risk behaviors; 23.0%) and Profile 4 (inactive with risky behaviors; 17.2%). Participants in Profile 2 (high screen time users) at baseline were most likely to transition to Profile 4 at follow-up (inactive with risky behaviors; 38.6%) followed by Profile 1 or 2 (moderate or highly active with moderate risk behaviors; both 26.5%), and Profile 3 (inactive abstainers; 8.4%). Participants in Profile 3 (moderately active with moderate risk behaviors) at baseline were most likely to transition into Profile 3 (inactive abstainers; 34.9%) followed by Profile 4 (inactive with risky behaviors; 25.6%), Profile 1 (highly active with moderate risk behaviors; 20.9%) and Profile 2 (moderately active with moderate risk behaviors; 18.6%).

Figure 1.

Transition Between Classes From Adolescents to Emerging Adulthood

Differences in symptoms of psychological distress and mental wellbeing between participants with different latent transition patterns are displayed in Table 5. There were small cell sizes of participants who transitioned from Profiles 2 (high screen time users) and 3 (moderately active with moderate risk behaviors) at baseline to various profiles at follow-up, thus these results may not be reliable. Therefore, only results are for participants transitioning from Profile 1 (moderately active abstainers) at baseline to various profiles at follow-up are presented. Results demonstrated that there was no significant difference in symptoms of psychological distress of mental wellbeing between participants who transitioned from Profile 1 (moderately active abstainers) at baseline to various profiles at follow-up.

Table 5.

Differences in Psychological Distress (K10) and Mental Wellbeing (WEMWBS) Among Participants who Transitioned From Profile 1 at Baseline to Various Profiles at Follow-up

K10
Transitioned to	Mean	Profile 1	Profile 2	Profile 3	Profile 4
Profile 1	23.47 (20.99, 25.94)	^-	−0.16 (−2.47, 2.15)	−1.47 (−3.77, 0.83)	−2.30 (−7.85, 3.25)
Profile 2	23.63 (21.70, 25.56)		-	−1.31 (−3.59, 0.97)	−2.14 (−6.79, 2.52)
Profile 3	24.94 (23.91, 25.97)			-	−0.82 (−4.29, 2.64)
Profile 4	25.76 (22.57, 28.96)				-

WEMWBS
Transitioned to	Mean	Profile 1	Profile 2	Profile 3	Profile 4
Profile 1	49.25 (45.28, 53.22)	-	1.51 (−2.23, 5.26)	2.28 (−2.94, 7.51)	1.14 (−6.46, 8.74)
Profile 2	47.74 (44.94, 50.52)		-	0.77 (−2.28, 3.82)	−0.37 (−5.42, 4.68)
Profile 3	46.97 (44.47, 49.47)			-	−1.14 (−3.73, 1.45)
Profile 4	48.11 (43.30, 52.92)				-

Note. Results are presented as mean (95% confidence interval), WEMWBS = warwick edinburgh mental wellbeing scale.

Discussion

The aim of this study was to examine how multiple lifestyle behaviors during youth were related to symptoms of psychological distress and mental wellbeing in emerging adulthood. Results demonstrated that adolescents with the healthiest combinations of lifestyle behaviors reported significantly better mental health in emerging adulthood. Specifically, participants who were moderately active abstainers in adolescence reported fewer symptoms of psychological distress when compared to participants who were moderately active but engaged in moderately high levels of risky behaviors during adolescence. Additionally, moderately active abstainers reported significantly higher levels of mental wellbeing in emerging adulthood when compared to adolescents who were high screen time users. Interestingly, lifestyle behaviors during emerging adulthood were not associated with mental health and wellbeing in emerging adulthood, nor were there difference in mental health outcomes among participants who were moderately active abstainers as an adolescent who transitioned into various profiles of healthy lifestyle behaviors during emerging adulthood.

The results from the current study support the hypothesis that engaging in healthy lifestyle behaviors as an adolescent can protect against developing symptoms of psychological distress and promote positive mental wellbeing in emerging adulthood. With regards to psychological distress, the finding that participants who were moderately active but also engaged in moderate levels of risky behaviors as adolescents reported greater symptoms of psychological distress in emerging adulthood compared to participants who were moderately active abstainers as adolescents is consistent with previous literature which has demonstrated a prospective relationship between alcohol use, smoking, and marijuana use in adolescents with symptoms of distress in emerging adulthood (Brook et al., 2002; Gobbi et al., 2019; Wells et al., 2004). Adolescence is a period of heightened neuroplasticity, and is a sensitive period of brain development (Fuhrmann et al., 2015). Binge drinking, tobacco smoking, and marijuana use may impact the architecture and functioning of adolescent’s brains, including regions implicit in reward processing and executive functioning (Blest-Hopley et al., 2018; Lydon et al., 2014; Pérez-García et al., 2022); regions of the brain which may also be altered in individual’s with depression (Drevets et al., 2008; Zhang et al., 2018). Therefore, increasing the age of onset of binge drinking and marijuana use, and reducing the number of adolescents who smoke tobacco may reduce the incidences of mental health disorders as youth transition beyond secondary school.

With regards to mental wellbeing, children who were high screen users in adolescence reported significantly worse mental wellbeing in emerging adulthood. The existing research on the longitudinal association between screen time and mental health and wellbeing has been somewhat mixed (Tang et al., 2021). This could potentially be a result of the fact that different types of screen time may be differently associated with mental health outcomes in young people (Sanders et al., 2024). For example, cognitively active screen activities may actually be beneficial for mental health (Hallgren et al., 2020). However, evidence demonstrates that excessive screen use is unfavorably related to several aspects of wellbeing in young people including satisfaction with life and loneliness (Khan et al., 2021; Lawrence et al., 2022). Not all screen time is intrinsically harmful. For example, mentally active screen time, such as playing video games, may not be as detrimental to mental health as mentally passive sedentary behaviors in emerging adults (Peng et al., 2024). However, the high levels of screen time observed among the high screen time users in the current study, the impact on mental wellbeing is detrimental (Przybylski & Weinstein, 2017). As smartphones have become ubiquitous in developed societies, even among youths, it is not surprising that the prevalence of smartphone addiction has increased (Olson et al., 2022), which might contribute to very high levels of screen time observed in the current study, especially considering that the majority of the sample were girls (Leonhardt & Overå, 2021). Intervening in young people with problematic smartphone use patterns may be essential to promote positive aspects of mental wellbeing in adolescents and emerging adults (White et al., 2024).

Unlike adolescence, healthy lifestyle behaviors during emerging adulthood were not significantly related to symptoms of psychological distress or mental wellbeing. Additionally, the changes in profiles among those who were in the moderately healthy abstainers were not related to symptoms of psychological distress or mental wellbeing. One potential explanation for this result is that, although there was a clear profile which had the unhealthiest lifestyle behaviors, there was not a consistently salubrious profile for emerging adults. Those who abstained from alcohol, tobacco, and marijuana were relatively inactive, while emerging adults who were highly active were likely to binge drink, and to a lesser extent smoke tobacco and use marijuana. This is consistent with cross-sectional research which has demonstrated a positive correlation between physical activity and alcohol consumption in this population (Dodge et al., 2016). However, the association between physical activity and binge drinking may be less pronounced in young women (Barry et al., 2013; Buscemi et al., 2011), meaning that the number of emerging adults assigned to the moderately active with moderate risk behaviors profile may be larger in a more gender balanced sample. The results from the current study are similar to results that have been reported in previous studies examining the clustering of healthy lifestyle behaviors in emerging adults, which have found that the healthiest cluster only exhibit moderately healthy lifestyle behaviors (Collins et al., 2023; Kwan et al., 2016) or that few emerging adults engage in consistently healthy behaviors (Lawrence et al., 2017). This highlights how, contrary to popular belief, emerging adults are surprisingly unhealthy (Stroud et al., 2015). Additionally, there was no clear pattern between adolescents who engage in healthy lifestyle behaviors transitioning to any particular latent profile at follow-up. This highlights how emerging adulthood is a distinct life period from adolescence, where an individual has increased independence, decreased social and institutional obligations and supports, and participates in identity exploration, which may leave them susceptible to engaging in risky and unhealthy behaviors (Arnett, 2000; Arnett et al., 2014; Nelson et al., 2008; Wood et al., 2018). Therefore, supporting youth to continue to engage in healthy lifestyle behaviors prior to and after the transition beyond high school may be necessary to ensure favorable outcomes in this crucial developmental period of youth and is a growing public health priority. Given that multiple behavior change interventions may be more effective (Zhang et al., 2024), taking a holistic human-centered approach to improving lifestyle behaviors where the largest benefits can be gained for each individual may be effective in this cohort (Bourke et al., 2025). Nevertheless, more research is needed to determine how best to support an overall healthy combination of lifestyle behaviors among emerging adults.

Limitations

This study had several strengths, including the use of a person-centered analysis to examine the conjoint longitudinal association between lifestyle behaviors and indicators of mental health in emerging adults. Nevertheless, there are several limitations which must be acknowledged. Firstly, all data in the current study was self-reported. Despite the use of valid and reliable instruments, there are several biases which may be introduced when examining self-reported data. These include recall bias, social desirability bias, and common method bias (although this is less of a concern when examining longitudinal data). Future studies could benefit from the use of device-assessed physical activity, sedentary behavior, and sleep (e.g., accelerometry) and higher resolution documenting of health-risk behaviors such as binge drinking, tobacco smoking, and marijuana use. Additionally, using more objective assessments for mental health, such as diagnostic interviews, may strengthen the results of future research. Second, there was some inconsistencies in the temporal frame that lifestyle behaviors were being assessed. For example, physical activity and screen time were assessed in the previous seven days, current smoking and vaping behaviors were assessed, marijuana use was assessed over the participants lifetime, and binge drinking was assessed over the past month. Another limitation of the current study was that psychological distress and mental wellbeing were not assessed at baseline, so changes in our outcomes of interest could not be assessed, meaning that reverse causation cannot be ruled out. Additionally, important socio-demographic data which may have confounded the results were not available (e.g., socioeconomic status indicators) and therefore could not be controlled for in the present analysis. Finally, attrition analysis demonstrated that data at follow-up was not missing completely at random. Specifically, participants with missing data were more active and more likely to binge drink at baseline, indicating that they may have been more likely to fall into the moderately active with moderate risk behaviors profile at baseline. Therefore, the number of participants in the moderately active with moderate risk behaviors profile may have been underestimated in the present analysis. Although statistical methods such as Full Information Maximum Likelihood (FIML) and multiple imputation were considered, they could not be implemented. For example, FIML cannot handle cases with missing data on all indicator variables (such is the case when a participant is lost to follow-up), while multiple imputation may lead to different optimal class solutions for each imputed dataset. This limitation should be taken into account when interpreting the profile distribution.

Conclusion

Results from this study demonstrate that engaging in more healthy lifestyle behaviors in adolescence may protect against psychological distress and promote mental wellbeing. Neither clusters of lifestyle behaviors in emerging adulthood, nor transitions in lifestyle behaviors from adolescence to emerging adulthood, were related to mental health outcomes. Nevertheless, results demonstrated that most participants transitioned to less healthy lifestyle behaviors in emerging adulthood, so it may be crucial to support the maintenance of a healthy lifestyle in this important transition period.

Footnotes

ORCID iD

Matthew Bourke

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The ADAPT study was supported by Social Sciences and Humanities Research Council of Canada: [Grant Number ]. MB is supported by The University of Queensland Strategic Funding for the 360-Kids Community Network Health Research Accelerator (HERA) program. MYK is supported by a SSHRC Tier 2 Canada Research Chair.

Declaration of Conflicting Interests

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

Transparency Statements

(1) This is a secondary analysis of a preregistered study (); (2) the analysis conducted in this manuscript was not preregistered; (3) data is available from the corresponding author upon request; (4) analytic code is available from the corresponding author upon request; (5) the materials used in this study are publicly available.

Author Biographies

Matthew Bourke: Dr. Matthew Bourke is a Research Fellow working on the Health Research Accelerator 360-Kids Community Network program at the University of Queensland. Matthew’s research focuses on how engaging in combinations of healthy behaviours can support optima development in young people, with a specific interest in mental health and wellbeing.

Denver Brown: Dr. Denver Brown is an Assistant Professor in the Department of Kinesiology at Kansas State University, where he directs the Infant, Child, and youth Health (INCH) Lab. His research aims to improve the health and well-being of young people by promoting the adoption and maintenance of physically active lifestyles. His current work centers on two main lines of inquiry: 1) application and refinement of theories of physical activity behavior change; and 2) examining how 24-h movement behaviors interact to influence mental health symptoms as part of a complex, dynamic system. Dr. Brown earned his PhD in Kinesiology from McMaster University in Canada and completed postdoctoral fellowships in the Mental Health and Physical Activity Research Centre at the University of Toronto and the Department of Family Medicine at McMaster University.

Matthew Y.W. Kwan: Dr. Matthew Kwan is an Associate Professor and a Tier 2 Canada Research Chair in the Department of Child and Youth Studies and Director of the Infant, Child, and Youth Health Lab at Brock University. Dr. Kwan has formal training in Kinesiology with a specific focus in health and exercise psychology, but his current research interests reflects a broader interdisciplinary science comprised of psychology, behavioural medicine, and population health.

Appendix

References

Arnett

J. J.

(2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469

Arnett

J. J.

Žukauskienė

Sugimura

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

Barry

A. E.

Whiteman

Piazza-Gardner

A. K.

Jensen

A. C.

(2013). Gender differences in the associations among body mass index, weight loss, exercise, and drinking among college students. Journal of American College Health: J of ACH, 61(7), 407–413. https://doi.org/10.1080/07448481.2013.823973

Blake

J. A.

Farugia

T. L.

Andrew

Malacova

Lawrence

Thomas

H. J.

Scott

J. G.

(2023). The kessler psychological distress scale in australian adolescents: Analysis of the second australian child and adolescent survey of mental health and wellbeing. Australian and New Zealand Journal of Psychiatry, 58(4), 345–354. https://doi.org/10.1177/00048674231216601

Blest-Hopley

Giampietro

Bhattacharyya

(2018). Residual effects of cannabis use in adolescent and adult brains — a meta-analysis of fmri studies. Neuroscience & Biobehavioral Reviews, 88(2018), 26–41. https://doi.org/10.1016/j.neubiorev.2018.03.008

Bonnie

R. J.

Stroud

Breiner

(Eds.), (2014). Investing in the health and well-being of young adults. The National Academies Press.

Bourke

Patten

R. K.

Dash

Pascoe

Craike

Firth

Bailey

Jacka

Parker

A. G.

(2022). The effect of interventions that target multiple modifiable health behaviors on symptoms of anxiety and depression in young people: A meta-analysis of randomized controlled trials. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 70(2), 208–219. https://doi.org/10.1016/j.jadohealth.2021.08.005

Bourke

Wang

H. F. W.

McNaughton

S. A.

Thomas

Firth

Trott

Cairney

(2025). Clusters of healthy lifestyle behaviours are associated with symptoms of depression, anxiety, and psychological distress: A systematic review and meta-analysis of observational studies. Clinical Psychology Review, 118(2025), Article 102585. https://doi.org/10.1016/j.cpr.2025.102585

Brook

D. W.

Brook

J. S.

Zhang

Cohen

Whiteman

(2002). Drug use and the risk of major depressive disorder, alcohol dependence, and substance use disorders. Archives of General Psychiatry, 59(11), 1039–1044. https://doi.org/10.1001/archpsyc.59.11.1039

10.

Brown

D. M. Y.

Kwan

M. Y.

Arbour-Nicitopoulos

K. P.

Cairney

(2021). Identifying patterns of movement behaviours in relation to depressive symptoms during adolescence: A latent profile analysis approach. Preventive Medicine, 143(2021), Article 106352. https://doi.org/10.1016/j.ypmed.2020.106352

11.

Brown

D. M. Y.

Faulkner

G. E. J.

Kwan

M. Y. W.

(2022). Healthier movement behavior profiles are associated with higher psychological wellbeing among emerging adults attending post-secondary education. Journal of Affective Disorders, 319(2022), 511–517. https://doi.org/10.1016/j.jad.2022.09.111

12.

Buscemi

Martens

M. P.

Murphy

J. G.

Yurasek

A. M.

Smith

A. E.

(2011). Moderators of the relationship between physical activity and alcohol consumption in college students. Journal of American College Health: J of ACH, 59(6), 503–509. https://doi.org/10.1080/07448481.2010.518326

13.

Button

K. S.

Ioannidis

J. P. A.

Mokrysz

Nosek

B. A.

Flint

Robinson

E. S. J.

Munafò

M. R.

(2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. https://doi.org/10.1038/nrn3475

14.

Celeux

Soromenho

(1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195–212. https://doi.org/10.1007/BF01246098

15.

Chaiton

M. O.

Cohen

J. E.

O'Loughlin

Rehm

(2009). A systematic review of longitudinal studies on the association between depression and smoking in adolescents. BMC Public Health, 9(1), 356. https://doi.org/10.1186/1471-2458-9-356

16.

Champion

K. E.

Mather

Spring

Kay-Lambkin

Teesson

Newton

N. C.

(2018). Clustering of multiple risk behaviors among a sample of 18-year-old australians and associations with mental health outcomes: A latent class analysis. Frontiers in Public Health, 6(2018), 135. https://doi.org/10.3389/fpubh.2018.00135

17.

Collins

Hoare

Allender

Olive

Leech

R. M.

Winpenny

E. M.

Jacka

Lotfalian

(2023). A longitudinal study of lifestyle behaviours in emerging adulthood and risk for symptoms of depression, anxiety, and stress. Journal of Affective Disorders, 327(2023), 244–253. https://doi.org/10.1016/j.jad.2023.02.010

18.

Craig

C. L.

Marshall

A. L.

SjÖStrÖM

Bauman

A. E.

Booth

M. L.

Ainsworth

B. E.

Pratt

Ekelund

U. L. F.

Yngve

Sallis

J. F.

Oja

(2003). International physical activity questionnaire: 12-country reliability and validity. Medicine & Science in Sports & Exercise, 35(8), 138–195. https://journals.lww.com/acsm-msse/fulltext/2003/08000/international_physical_activity_questionnaire_.20.aspx

19.

Dakin

Manneville

Langlois

Legrand

Lecomte

Briançon

Omorou

A. Y.

(2021). Longitudinal patterns of lifestyle behaviours in adolescence: A latent transition analysis. British Journal of Nutrition, 126(4), 621–631. https://doi.org/10.1017/S0007114520004316

20.

Di Benedetto

Towt

C. J.

Jackson

M. L.

(2020). A cluster analysis of sleep quality, self-care behaviors, and mental health risk in australian university students. Behavioral Sleep Medicine, 18(3), 309–320. https://doi.org/10.1080/15402002.2019.1580194

21.

Dodge

Clarke

Dwan

(2016). The relationship between physical activity and alcohol use among adults in the United States: A systematic review of the literature. American Journal of Health Promotion: AJHP, 31(2), 97–108. https://doi.org/10.1177/0890117116664710

22.

Drevets

W. C.

Price

J. L.

Furey

M. L.

(2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure and Function, 213(1), 93–118. https://doi.org/10.1007/s00429-008-0189-x

23.

El Ansari

Sebena

El-Ansari

Suominen

(2024). Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: Evidence from students at a university in Finland. BMC Public Health, 24(1), 1103. https://doi.org/10.1186/s12889-024-18421-0

24.

Firth

Solmi

Wootton

R. E.

Vancampfort

Schuch

F. B.

Hoare

Gilbody

Torous

Teasdale

S. B.

Jackson

S. E.

Smith

Eaton

Jacka

F. N.

Veronese

Marx

Ashdown-Franks

Siskind

Sarris

Rosenbaum

Carvalho

A. F.

Stubbs

(2020). A meta-review of “lifestyle psychiatry”: The role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 19(3), 360–380. https://doi.org/10.1002/wps.20773

25.

Frech

(2012). Healthy behavior trajectories between adolescence and young adulthood. Advances in Life Course Research, 17(2), 59–68. https://doi.org/10.1016/j.alcr.2012.01.003

26.

Fuhrmann

Knoll

L. J.

Blakemore

S.-J.

(2015). Adolescence as a sensitive period of brain development. Trends in Cognitive Sciences, 19(10), 558–566. https://doi.org/10.1016/j.tics.2015.07.008

27.

Furukawa

T. A.

Kessler

R. C.

Slade

Andrews

(2003). The performance of the k6 and k10 screening scales for psychological distress in the australian national survey of mental health and well-being. Psychological Medicine, 33(2), 357–362. https://doi.org/10.1017/S0033291702006700

28.

Gideon

(1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136

29.

Gobbi

Atkin

Zytynski

Wang

Askari

Boruff

Ware

Marmorstein

Cipriani

Dendukuri

Mayo

(2019). Association of cannabis use in adolescence and risk of depression, anxiety, and suicidality in young adulthood: A systematic review and meta-analysis. JAMA Psychiatry, 76(4), 426–434. https://doi.org/10.1001/jamapsychiatry.2018.4500

30.

Groves

C. I.

Huong

Porter

C. D.

Summerville

Swafford

Witham

Hayward

Kwan

M. Y. W.

Brown

D. M. Y.

(2024). Associations between 24-h movement behaviors and indicators of mental health and well-being across the lifespan: A systematic review. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), 9. https://doi.org/10.1186/s44167-024-00048-6

31.

Hallgren

Dunstan

D. W.

Owen

(2020). Passive versus mentally active sedentary behaviors and depression. Exercise and Sport Sciences Reviews, 48(1). 20, 27. https://doi.org/10.1249/JES.0000000000000211. https://journals.lww.com/acsm-essr/fulltext/2020/01000/passive_versus_mentally_active_sedentary_behaviors.5.aspx

32.

Harris

K. M.

Gordon-Larsen

Chantala

Udry

J. R.

(2006). Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Archives of Pediatrics and Adolescent Medicine, 160(1), 74–81. https://doi.org/10.1001/archpedi.160.1.74

33.

Kessler

R. C.

Andrews

Colpe

L. J.

Hiripi

Mroczek

D. K.

Normand

S. L. T.

Walters

E. E.

Zaslavsky

A. M.

(2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), 959–976. https://doi.org/10.1017/S0033291702006074

34.

Kessler

R. C.

Green

J. G.

Gruber

M. J.

Sampson

N. A.

Bromet

Cuitan

Furukawa

T. A.

Gureje

Hinkov

C.-Y.

Lara

Lee

Mneimneh

Myer

Oakley-Browne

Posada-Villa

Sagar

Viana

M. C.

Zaslavsky

A. M.

(2010). Screening for serious mental illness in the general population with the k6 screening scale: Results from the who world mental health (wmh) survey initiative. International Journal of Methods in Psychiatric Research, 19(S1), 4–22. https://doi.org/10.1002/mpr.310

35.

Khan

Lee

E.-Y.

Rosenbaum

Khan

S. R.

Tremblay

M. S.

(2021). Dose-dependent and joint associations between screen time, physical activity, and mental wellbeing in adolescents: An international observational study. The Lancet. Child & adolescent health, 5(10), 729–738. https://doi.org/10.1016/S2352-4642(21)00200-5

36.

Kleppang

A. L.

Vettore

M. V.

Hartz

Haugland

S. H.

Stea

T. H.

(2023). Patterns of unhealthy behaviours during adolescence and subsequent anxiety and depression in adulthood: A prospective register linkage study of the hunt survey and health registries. International Journal of Behavioral Nutrition and Physical Activity, 20(1), 6. https://doi.org/10.1186/s12966-023-01408-2

37.

Kwan

M. Y.

Arbour-Nicitopoulos

K. P.

Duku

Faulkner

(2016). Patterns of multiple health risk-behaviours in university students and their association with mental health: Application of latent class analysis. Health promotion and chronic disease prevention in Canada: Research, Policy and Practice, 36(8), 163–170. Profils de multiples comportements à risque pour la santé des étudiants universitaires et leurs liens avec la santé mentale : utilisation de l'analyse des classes latentes. https://doi.org/10.24095/hpcdp.36.8.03

38.

Kwan

M. Y.

Cairney

Faulkner

G. E.

Pullenayegum

E. E.

(2012). Physical activity and other health-risk behaviors during the transition into early adulthood: A longitudinal cohort study. American Journal of Preventive Medicine, 42(1), 14–20. https://doi.org/10.1016/j.amepre.2011.08.026

39.

Kwan

M. Y. W.

Dutta

Bray

S. R.

Brown

D. M. Y.

Cairney

Dunton

G. F.

Graham

J. D.

Rebar

A. L.

Rhodes

R. E.

(2020). Methods and design for the adapt study: Application of integrated approaches to understanding physical activity during the transition to emerging adulthood. BMC Public Health, 20(1), 426. https://doi.org/10.1186/s12889-020-08484-0

40.

Lawrence

Hunter

S. C.

Cunneen

Houghton

S. J.

Zadow

Rosenberg

Wood

Shilton

(2022). Reciprocal relationships between trajectories of loneliness and screen media use during adolescence. Journal of Child and Family Studies, 31(5), 1306–1317. https://doi.org/10.1007/s10826-021-02066-3

41.

Lawrence

E. M.

Mollborn

Hummer

R. A.

(2017). Health lifestyles across the transition to adulthood: Implications for health. Social Science & Medicine, 193(2017), 23–32. https://doi.org/10.1016/j.socscimed.2017.09.041

42.

Lawrence

Mollborn

Goode

Pampel

(2020). Health lifestyles and the transition to adulthood. Socius: Sociological Research for a Dynamic World, 6(2020), Article 2378023120942070. https://doi.org/10.1177/2378023120942070

43.

Leech

R. M.

McNaughton

S. A.

Timperio

(2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: A review. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 4. https://doi.org/10.1186/1479-5868-11-4

44.

Leonhardt

Overå

(2021). Are there differences in video gaming and use of social media among boys and girls? a mixed methods approach. International Journal of Environmental Research and Public Health, 18(11), 6085. https://doi.org/10.3390/ijerph18116085

45.

Lydon

D. M.

Wilson

S. J.

Child

Geier

C. F.

(2014). Adolescent brain maturation and smoking: What we know and where we’re headed. Neuroscience & Biobehavioral Reviews, 45(2014), 323–342. https://doi.org/10.1016/j.neubiorev.2014.07.003

46.

McKay

M. T.

Andretta

J. R.

(2017). Evidence for the psychometric validity, internal consistency and measurement invariance of warwick edinburgh mental well-being scale scores in scottish and Irish adolescents. Psychiatry Research, 255(2017), 382–386. https://doi.org/10.1016/j.psychres.2017.06.071

47.

Meader

King

Moe-Byrne

Wright

Graham

Petticrew

Power

White

Sowden

A. J.

(2016). A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health, 16(1), 657. https://doi.org/10.1186/s12889-016-3373-6

48.

Mojtabai

Olfson

Han

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

49.

Nelson

M. C.

Story

Larson

N. I.

Neumark-Sztainer

Lytle

L. A.

(2008). Emerging adulthood and college-aged youth: An overlooked age for weight-related behavior change. Obesity, 16(10), 2205–2211. https://doi.org/10.1038/oby.2008.365

50.

Nylund

K. L.

Asparouhov

Muthén

B. O.

(2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396

51.

Nylund-Gibson

Choi

A. Y.

(2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440–461. https://doi.org/10.1037/tps0000176

52.

Nylund-Gibson

Garber

A. C.

Carter

D. B.

Chan

Arch

D. A. N.

Simon

Whaling

Tartt

Lawrie

S. I.

(2023). Ten frequently asked questions about latent transition analysis. Psychological Methods, 28(2), 284–300. https://doi.org/10.1037/met0000486

53.

Nylund-Gibson

Grimm

Quirk

Furlong

(2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 439–454. https://doi.org/10.1080/10705511.2014.915375

54.

Nylund-Gibson

Grimm

R. P.

Masyn

K. E.

(2019). Prediction from latent classes: A demonstration of different approaches to include distal outcomes in mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 26(6), 967–985. https://doi.org/10.1080/10705511.2019.1590146

55.

Olson

J. A.

Sandra

D. A.

Colucci

É. S.

Al Bikaii

Chmoulevitch

Nahas

Raz

Veissière

S. P. L.

(2022). Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Computers in Human Behavior, 129(2022), Article 107138. https://doi.org/10.1016/j.chb.2021.107138

56.

Orchard

Gregory

A. M.

Gradisar

Reynolds

(2020). Self-reported sleep patterns and quality amongst adolescents: Cross-sectional and prospective associations with anxiety and depression. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 61(10), 1126–1137. https://doi.org/10.1111/jcpp.13288

57.

Paavola

Vartiainen

Haukkala

(2004). Smoking, alcohol use, and physical activity: A 13-year longitudinal study ranging from adolescence into adulthood. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 35(3), 238–244. https://doi.org/10.1016/j.jadohealth.2003.12.004

58.

Paus

Keshavan

Giedd

J. N.

(2008). Why do many psychiatric disorders emerge during adolescence? Nature Reviews Neuroscience, 9(12), 947–957. https://doi.org/10.1038/nrn2513

59.

Peng

Zheng

Zhang

(2024). Latent profile analysis of mental health in postgraduates: The impact of mentally-passive or active screen time and sedentary behaviors. Mental Health and Physical Activity, 27(2024), Article 100628. https://doi.org/10.1016/j.mhpa.2024.100628

60.

Pérez-García

J. M.

Suárez-Suárez

Doallo

Cadaveira

(2022). Effects of binge drinking during adolescence and emerging adulthood on the brain: A systematic review of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 137(2022), Article 104637. https://doi.org/10.1016/j.neubiorev.2022.104637

61.

Przybylski

A. K.

Weinstein

(2017). A large-scale test of the goldilocks hypothesis: Quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychological Science, 28(2), 204–215. https://doi.org/10.1177/0956797616678438

62.

Sanders

Noetel

Parker

Del Pozo Cruz

Biddle

Ronto

Hulteen

Parker

Thomas

De Cocker

Salmon

Hesketh

Weeks

Arnott

Devine

Vasconcellos

Pagano

Sherson

Conigrave

Lonsdale

(2024). An umbrella review of the benefits and risks associated with youths’ interactions with electronic screens. Nature Human Behaviour, 8(1), 82–99. https://doi.org/10.1038/s41562-023-01712-8

63.

Sawyer

S. M.

Afifi

R. A.

Bearinger

L. H.

Blakemore

S.-J.

Dick

Ezeh

A. C.

Patton

G. C.

(2012). Adolescence: A foundation for future health. Lancet (London, England), 379(9826), 1630–1640. https://doi.org/10.1016/S0140-6736(12)60072-5

64.

Solmi

Radua

Olivola

Croce

Soardo

Salazar de Pablo

Il Shin

Kirkbride

J. B.

Jones

Kim

J. H.

Kim

J. Y.

Carvalho

A. F.

Seeman

M. V.

Correll

C. U.

Fusar-Poli

(2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295. https://doi.org/10.1038/s41380-021-01161-7

65.

Steinberg

(2005). Cognitive and affective development in adolescence. Trends in Cognitive Sciences, 9(2), 69–74. https://doi.org/10.1016/j.tics.2004.12.005

66.

StrÖHle

Höfler

Pfister

Müller

A.-G.

Hoyer

Wittchen

H.-U.

Lieb

(2007). Physical activity and prevalence and incidence of mental disorders in adolescents and young adults. Psychological Medicine, 37(11), 1657–1666. https://doi.org/10.1017/S003329170700089X

67.

Stroud

Walker

L. R.

Davis

Irwin

C. E.

(2015). Investing in the health and well-being of young adults. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 56(2), 127–129. https://doi.org/10.1016/j.jadohealth.2014.11.012

68.

Suetani

Mamun

Williams

G. M.

Najman

J. M.

McGrath

J. J.

Scott

J. G.

(2017). Longitudinal association between physical activity engagement during adolescence and mental health outcomes in young adults: A 21-year birth cohort study. Journal of Psychiatric Research, 94(2017), 116–123. https://doi.org/10.1016/j.jpsychires.2017.06.013

69.

Tang

Werner-Seidler

Torok

Mackinnon

A. J.

Christensen

(2021). The relationship between screen time and mental health in young people: A systematic review of longitudinal studies. Clinical Psychology Review, 86(2021), Article 102021. https://doi.org/10.1016/j.cpr.2021.102021

70.

Tein

J.-Y.

Coxe

Cham

(2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. https://doi.org/10.1080/10705511.2013.824781

71.

Tennant

Hiller

Fishwick

Platt

Joseph

Weich

Parkinson

Secker

Stewart-Brown

(2007). The warwick-edinburgh mental well-being scale (wemwbs): Development and UK validation. Health and Quality of Life Outcomes, 5(1), 63. https://doi.org/10.1186/1477-7525-5-63

72.

Vermunt

J. K.

(2017). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. https://doi.org/10.1093/pan/mpq025

73.

Wells

J. E.

Horwood

L. J.

Fergusson

D. M.

(2004). Drinking patterns in mid-adolescence and psychosocial outcomes in late adolescence and early adulthood. Addiction, 99(12), 1529–1541. https://doi.org/10.1111/j.1360-0443.2004.00918.x

74.

White

R. L.

Vella

Biddle

Sutcliffe

Guagliano

J. M.

Uddin

Burgin

Apostolopoulos

Nguyen

Young

Taylor

Lilley

Teychenne

(2024). Physical activity and mental health: A systematic review and best-evidence synthesis of mediation and moderation studies. International Journal of Behavioral Nutrition and Physical Activity, 21(1), 134. https://doi.org/10.1186/s12966-024-01676-6

75.

Whiteford

H. A.

Degenhardt

Rehm

Baxter

A. J.

Ferrari

A. J.

Erskine

H. E.

Charlson

F. J.

Norman

R. E.

Flaxman

A. D.

Johns

Burstein

Murray

C. J. L.

Vos

(2013). Global burden of disease attributable to mental and substance use disorders: Findings from the global burden of disease study 2010. Lancet (London, England), 382(9904), 1575–1586. https://doi.org/10.1016/S0140-6736(13)61611-6

76.

Wiium

Breivik

Wold

(2015). Growth trajectories of health behaviors from adolescence through young adulthood. International Journal of Environmental Research and Public Health, 12(11), 13711–13729. https://doi.org/10.3390/ijerph121113711

77.

Wood

Crapnell

Lau

Bennett

Lotstein

Ferris

Kuo

(2018). Emerging adulthood as a critical stage in the life course. Handbook of life course health development, 123–143.

78.

Y.-l.

Wang

P.-g.

G.-c.

Yuan

Phongsavan

Q.-q.

(2016). Associations between multiple health risk behaviors and mental health among Chinese college students. Psychology Health & Medicine, 21(3), 377–385. https://doi.org/10.1080/13548506.2015.1070955

79.

Zhang

A. L.

Liu

White

B. X.

Liu

X. C.

Durantini

Chan

M.-p. S.

Dai

Zhou

Leung

O'Keefe

Palmese

Albarracín

(2024). Health-promotion interventions targeting multiple behaviors: A meta-analytic review of general and behavior-specific processes of change. Psychological Bulletin, 150(7), 798–838. https://doi.org/10.1037/bul0000427

80.

Zhang

F.-F.

Peng

Sweeney

J. A.

Jia

Z.-Y.

Gong

Q.-Y.

(2018). Brain structure alterations in depression: Psychoradiological evidence. CNS Neuroscience & Therapeutics, 24(11), 994–1003. https://doi.org/10.1111/cns.12835

Lifestyle Behavior Patterns During the Transition From Adolescence to Emerging Adulthood: Associations With Mental Health and Wellbeing

Abstract

Keywords

Introduction

Methods

Procedures and Participants

Measures

Independent Variables – Multiple Health Behaviors

Moderate-to-Vigorous Intensity Physical Activity

Recreational Screen Time

Sleep

Marijuana Use

Tobacco Smoking

Binge Drinking

Outcomes

Psychological Distress

Mental Wellbeing

Covariates

Statistical Analysis

Results

Participant Characteristics and Missing Value Analysis

Baseline Latent Profile Analysis

Follow-Up Latent Profile Analysis

Latent Transition Analysis

Discussion

Limitations

Conclusion

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

Transparency Statements

Author Biographies

Appendix

References