Abstract
Background
Social jetlag is very common among modern people and is an important factor influencing mental health. However, evidence regarding the relationship between social jetlag and depressive symptoms, particularly among young people (ages 10–24), is lacking. Therefore, this review aims to synthesize these studies and assess the relationship between social jetlag and depressive symptoms in young people.
Methods
We searched PubMed, the Cochrane Library, Embase, and Web of Science for relevant publications from their respective inception dates to May 4, 2024. The quality of selected studies was evaluated using the Newcastle-Ottawa Scale. Meta-analysis was performed using Review Manager 5.3 to calculate the combined odds ratio and correlation coefficients(r). Data were analyzed for sensitivity assessment using Stata 18.0.
Results
A total of eight studies were ultimately included in the meta-analysis. The analysis results showed that high social jetlag (≥ 2 hours) was associated with increased odds of depressive symptoms among young people(OR = 1.44, 95% CI:1.18–1.77, I2 = 85%), whereas low social jetlag (1–2 hours) was not significantly associated (OR = 1.05, 95% CI:1.00-1.09,I2 = 47%). Meta-analysis of correlations showed that social jetlag was significantly but weakly associated with depressive symptoms (r = 0.16, 95% CI: 0.03–0.28,I2 = 73%). The results of the sensitivity analysis indicate that the relationship between higher social jetlag (> 2 hours) and adolescent depression is robust.
Conclusions
Social jetlag in young people is correlated with depressive symptoms, especially among those with high social jetlag (≥ 2 h). Future longitudinal studies are needed to assess the causal relationship between the two.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12888-025-07066-x.
Keywords: Social jetlag, Depressive symptoms, Meta-analysis, Systematic review, Adolescents, Young adults
Introduction
Depression is an important mental health issue worldwide, particularly among adolescents (ages 10–19) and young adults (ages 20–24), collectively referred to as young people [1, 2]. They are in a rapid growth phase and dramatic physical, psychosocial, and emotional changes increase their vulnerability to psychological disorders [3]. Studies have shown that the global prevalence of depressive symptoms among adolescents has increased from 24% between 2001 and 2010 to 37% between 2011 and 2020 [4]. The same trend has been observed in young adults [5]. Depressive symptoms in young people have become important influencing factors that lead to cognitive and social impairment, self-injury, suicide, and decreased quality of daily life, placing a significant burden of illness and disability on young people [1, 6, 7]. Therefore, it is crucial to identify the risk factors for depressive symptoms in young people. Previous studies have demonstrated that depressive symptoms are influenced by sleep-related variables. For example, sleep disorders, sleep quality, and circadian rhythm disruption are key mechanisms that contribute to depressive symptoms [8–10].
Among the many sleep-related factors associated with depressive symptoms, a circadian rhythm disruption known as“social jetlag”(SJL) has received increasing attention [11, 12]. Our daily lives are influenced by three types of clocks: biological, solar, and social [13]. The biological clock is regulated by endogenous physiological mechanisms and closely associated with the solar clock. Social clocks are determined by social factors that require people to work and rest, such as waking up early to go to work/school, and staying up late to study/work or play [14]. Social jetlag occurs when the intrinsic sleep schedule driven by the biological clock conflicts with the sleep schedule determined by the social clock [11].
Social jetlag was quantified as the absolute value difference in the sleep midpoint between work/school days (MSW) and free days (MSF): SJL=|MSF - MSW|) [8]. Some studies also consider the existence of negative SJL, where the midpoint of sleep on workdays is later than on free days, and thus use the actual difference (SJL = MSF - MSW) [15]. The midpoint of sleep on “free days” is intended to capture innate tendencies of the biological clock, while the midpoint of sleep on work/school days measures the impact of social activities on the sleep schedule [15]. Jankowski argues that the currently used social jetlag formula (SJL = MSF - MSW) not only captures the misalignment between social and biological time but also captures the sleep debt resulting from sleep deprivation on workdays. To eliminate the interference of sleep debt, he suggests adopting a sleep-corrected social jetlag formula, which takes the form of: SJLsc = |MSFsc - MSWsc|. MSFsc is the sleep-start time on free days plus half of the average weekly sleep duration, and MSWsc is the sleep-start time on workdays plus half of the average weekly sleep duration [16].
Circadian preference shifts later with age during development, reaching its latest value at the age of 20 years, and then gradually shifts forward [17]. In other words, the biological clocks of young people promote sleep later than those of younger children and older adults. However, they are not free to organize their weekday wake-up times because of the demands of social activities, such as waking up early for school. This makes them more susceptible to the effects of social jetlag [18].
Increasingly, studies have focused on the link between social jetlag and depression risk in young people, but the conclusions remain controversial [19–24]. Some studies have found a positive association between social jetlag and depression risk [19–21]; however, others have concluded that no such association exists [22–24]. Furthermore, according to our search, only a few systematic reviews have provided comprehensive evidence on the relationship between social jetlag and depression among young people [25, 26]. However, no meta-analysis has been published on this topic. Many original studies have emerged that have not yet been integrated. Therefore, this systematic review and meta-analysis aimed to better understand the relationship between social jetlag and depressive symptoms in young people by analyzing available data on their association.
Materials and methods
Protocol and registration
The study protocol was registered with the International Prospective Register of Systematic Reviews (Registration No. CRD42024543708). This study adheres to the PRISMA and the Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. PRISMA provides a comprehensive framework for systematic reviews, while MOOSE is tailored to observational studies, which dominate our study [27, 28]. Using both methods could meet review criteria while taking into account the specifics for observational studies, thereby providing good quality and credibility [29].
Eligibility criteria
The inclusion criteria were as follows: (1) Studies in which the participants were adolescents (ages 10–19) or young adults (ages 20–24), (2) Measure of social jetlag, (3) Measure of depressive symptoms, and (4) Assessment of the association between social jetlag and depressive symptoms.
The exclusion criteria were as follows: (1) Non-observational studies; (2) studies that did not accurately describe social jet lag and depressive symptom measures; (3) conference abstracts; (4) studies that could not extract the information required for this review or had incomplete information; and (5) studies with samples aged < 10 or ≥ 25 years.
Measurement methods for social jetlag and depressive symptoms: (1) Social jetlag: The usual bedtime and wake-up times on school days and free days were obtained from the participants through self-reports or questionnaire surveys. Social jetlag was calculated using the formula SJL = |MSF - MSW| or SJLsc = |MSFsc - MSWsc|(MSF = midsleep of free days; MSW = midsleep of school day; MSFsc = sleep onset on free days + half of the average weekly sleep duration; MSWsc = sleep onset on workdays + half of the average weekly sleep duration) [15, 16]. (2)Depressive symptoms: The retrieved literature mainly used various depression screening scales, such as the Patient Health Questionnaire, Birleson Depression Self-Rating Scale for Children, and the Self-Rating Depression Scale, to determine whether participants exhibited depressive symptoms [20, 30, 31].
Search and selection
We searched PubMed, the Cochrane Library, Embase, and Web of Science databases for relevant publications from their inception dates to May 4, 2024. The citation lists of eligible studies were also checked. The search strings are listed in Table S1. During database searches, no filters were applied based on factors such as publication date, language, or research design. Two independent reviewers (Sun and Yu) screened titles, abstracts, and full-text articles for eligibility. Disagreements were resolved by a third reviewer (Tung).
Data extraction and quality assessment
Data were extracted and verified by two researchers (Sun and Yang) using a predefined Microsoft Excel spreadsheet. The following data were extracted from each article: author, year of publication, country, study type, age range, sample size, number and percentage of female participants, measurement method of social jetlag, level of social jetlag, measure of depressive symptoms, and effect value reflecting the association between social jetlag and depressive symptoms.
Two authors (Sun and He) independently assessed the quality of the included studies using an adapted version of the Newcastle-Ottawa-Scale (NOS) [32]. Any disagreements were resolved through consultation with the third author (Tung). The studies included in this review were cross-sectional studies, and this version of the NOS scale is specifically designed for cross-sectional studies. A more detailed description of the NOS scale can be found in Annex 1. Based on the NOS content, we scored each study on three dimensions: selection, comparability, and outcome. Each study had a NOS score of 0–10 and was categorized according to the score as high-quality (7–10 points), medium-quality (4–6 points), or poor-quality (0–3 points) [33].
Data analysis
The relationship between social jetlag and depressive symptoms in young people was quantitatively analyzed using Review Manager software, version 5.3. Odds ratios (OR), Pearson’s correlation coefficients (r), and corresponding 95% confidence intervals (CI) were used to express the combined results. The correlation coefficient was first subjected to Fisher’s z-transformation to reduce bias [34]. The homogeneity between the enrolled studies was investigated using I2 statistics. If I² ≤ 50%, there is no significant heterogeneity among the studies, and the meta-analysis will use a fixed-effects model. If I²> 50%, there was significant heterogeneity among the studies, and the meta-analysis used a random-effects model [35]. In addition, we used stata18.0 to perform sensitivity analyses thereby exploring sources of heterogeneity.
Results
Search of selected studies and characteristics
Figure 1 shows a PRISMA flowchart of the study inclusion process. Four databases–PubMed, the Cochrane Library, Embase, and Web of Science–were searched to obtain 7867 studies, and 1346 duplicates were excluded. Next, through title and abstract screening to exclude studies, 40 articles proceeded to the full-text review stage. After excluding studies that did not meet the criteria, a total of 8 studies were included in the meta-analysis [10, 20, 22, 24, 30, 31, 36, 37].
Fig. 1.
Flow chart of the systematic review process
The characteristics of the included studies are summarized in Table 1. The studies were conducted in five countries: China (n = 4), Japan (n = 1), South Korea (n = 1), Brazil (n = 1), and Scotland (n = 1), and included a total of 148,410 participants. The results of four studies are presented in the form of odds ratio (OR): three of them simultaneously compared the prevalence of depressive symptoms in young people with a social jetlag of > 2 h and 1–2 h to those with only 0–1 h of social jetlag [20, 30, 36]. One study reported a difference between groups with a social jetlag of > 2 h and < 2 h [10]. In addition, four research results were presented in the form of Pearson correlation coefficients [22, 24, 31, 37]. The quality assessment of the literature (Table 2) showed that four studies were of moderate quality and four studies were of high quality.
Table 1.
Characteristics of selected studies
| Author (country) |
Publication year |
Study Type | Age (range/mean ± SD) | Sample size (n) | Sex (%female) |
Measure of social jetlag | Measure of depressive symptoms | Result |
|---|---|---|---|---|---|---|---|---|
| Li et al. (China) | 2024 | cross-sectional | 10–18/13.0 ± 1.8 | 106,979 | 45.51 |
SJL=|MSF- MSW|a SJLSC=|MSFsc-MSWsc|b |
The 2-item Patient Health Questionnaire |
OR SJL < 1 h 1 SJL1–2 h 1.00 (0.94–1.05) SJL ≥ 2 h 1.15 (1.06–1.24) SJLsc < 1 h 1 1–2 h 1.16 (1.10–1.22) ≥ 2 h 1.35 (1.25–1.46) adjusted for age, sex, one child, parental marital status, boarding at school, chronic somatic diseases, BMI, physically inactive, smoking or drinking, chronotype, sleep duration, insomnia, frequent nightmares |
| Zhang et al. (China) | 2023 | cross-sectional | seventh grade-eighth grade/13.50 ± 0.76 | 37,871 | 46 | SJL=|MSF- MSW| | The 2-item Patient Health Questionnaire |
OR SJL < 1 h: 1 SJL1-2 h:1.10 (1.02–1.19) SJL ≥ 2 h: 1.51 (1.39–1.65) adjusted for age, gender, grade, ethnicity, parental marital status, single child status, boarding status, smoking & drinking status, and chronic somatic disease conditions |
| Tamura et al. (Japan) | 2023 | cross-sectional | 12–15/13.6 ± 0.9 | 1493 | 52.4 | SJL = MSF- MSW | The 18-item Birleson Depression Self-Rating Scale for Children (DSRS-C) |
OR Males SJL<0 h:1.85 (1.39–2.46) SJL 0–1 h: 1 SJL1-2 h:1.03 (0.81–1.29) SJL ≥ 2 h:1.56 (1.00-2.44) Females SJL<0 h: 1.21 (0.59–2.45) SJL 0–1 h: 1 SJL1-2 h:1.25 (0.97–1.60) SJL ≥ 2 h:1.84 (1.36–2.50) adjusted for age, menarche/voice changes and age at menarche/voice changes, BMI, household size, breakfast consumption, participation in extracurricular activities, participation in a tutoring school, routine daytime napping for 30 min or more (weekdays), consuming caffeinated drinks after dinner, and time using smartphones and video games. |
| Qu et al. (China) | 2023 | cross-sectional | 18–22 | 1042 | 61.9 | SJL=|MSF- MSW| | The Patient Health Questionnaire 9 (PHQ-9) |
OR SJL < 2 h: 1 SJL ≥ 2 h:1.43 (0.67–3.12) adjusted model controlled age, gender, smoking, alcohol consumption, life events, problematic mobile phone use, physical activity |
| Jia et al. (China) | 2023 | cross-sectional | 17.08–22.74/19.04 ± 0.89 | 415 | 73.01 | SJL=|MSF- MSW| | The Self-Rating Depression Scale (SDS) |
correlation coefficient r = 0.1 |
| Jang et al. (South Korea) | 2023 | cross-sectional | 21–23 | 198 | 87.9 | SJLsc=|MSFsc-MSWsc| |
Center for Epidemiological Studies Depression Scale (CES-D) |
correlation coefficient r = 0.34 |
| Lyall (Scotland) | 2020 | cross-sectional | 10–14 | 61 | 63.93 | SJL = MSF- MSW | Mood and Feelings Questionnaire (MFQ): Short version |
correlation coefficient r = 0.15 |
| de Souza et al. (Brazil) | 2013 | cross-sectional | 12–21 | 351 | 70.37 | SJL=|MSF- MSW| |
Beck Depression Inventory (BDI) |
correlation coefficient r = 0.07 |
a: SJL: social Jetlag; MSF = midsleep of freedays; MSW = midsleep of workdays
b: social jetlag; MSFsc = sleep onset on free days + half of the average weekly sleep duration; MSWsc = sleep onset on workdays + half of the average weekly sleep duration
Table 2.
The results of the quality evaluation of the included studies
| Study (first author) | Selection (Maximum 5 stars) | Comparability (Maximum 2 stars) |
Outcome (Maximum 3 stars) |
Total score | ||||
|---|---|---|---|---|---|---|---|---|
| Representativeness of the sample | Sample size | Non-respondents | Ascertainment of the exposure | Based on design and analysis | Assessment of outcome | Statisticaltes | ||
| Li et al. (2024) | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 8 |
| Zhang et al. (2023) | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 8 |
| Tamura et al. (2023) | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 8 |
| Qu et al. (2023) | 1 | 1 | 0 | 1 | 2 | 1 | 1 | 7 |
| Jia et al. (2023) | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
| Jang et al. (2023) | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 6 |
| Lyall (2020) | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 5 |
| de Souza et al. (2013) | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 5 |
Relationship between social jetlag and depressive symptoms in young people
For dichotomous outcomes, we used the OR as the effect size to be combined. According to the classification method used in most studies, this review categorizes social jetlag into 0–1 h (no social jetlag), 1–2 h (low social jetlag), and ≥ 2 h (high social jetlag). For social jetlag and depressive symptoms represented by continuous scores, meta-analyses were performed using Fisher’s Z transformation. Fisher’s Z was back-transformed to Pearson’s r for interpretation [38].
The prevalence of depressive symptoms between high SJL (≥ 2 hours) and no SJL (0–1 h) were compared, and the results are shown in Fig. 2a. The results of the heterogeneity test showed I2 = 85%, indicating a high degree of heterogeneity among the included studies; therefore, a random-effects model was used. The results of the random effects model showed high social jetlag is positively correlated with a higher prevalence of depressive symptoms (OR = 1.44, 95% CI:1.18–1.77, I2 = 85%). This means that young people with a social jetlag of more than 2 hours are 1.44 times more likely to suffer from depressive symptoms than young people with a social jetlag of less than 1 h.
Fig. 2.
(a) Forest plot of high social jetlag (≥ 2 h) and depressive symptoms (b) Forest plot of low social jetlag (1–2 h) and depressive symptoms (c) Forest plot of correlation meta-analyses
However, as shown in Fig. 2b, for young people with low social jetlag (1–2 hours), the odds of depressive symptoms were not significantly higher than for young people with no social jetlag (0–1 h) (OR = 1.05, 95% CI:1.00-1.09, I2 = 47%).
A meta-analysis of the correlations between continuous outcomes is presented in Fig. 2c. The results indicated a significantly positive association between young people’s social jetlag and depressive symptoms, although the association was weak (r = 0.16, 95% CI: 0.03–0.28, I2 = 73%).
Sensitivity analysis
The sensitivity analysis results for the association between high social jetlag (≥ 2 hours) and depressive symptoms indicate that after excluding any single study, the combined results of the remaining studies still maintain significance and are consistent with the meta-analysis results (Table 3). This suggests that the result that high social jetlag (≥ 2 hours) is significantly associated with depressive symptoms is reliable. After excluding the study by Li [20], I2 was 0%, indicating that this study may have introduced a great deal of heterogeneity.
Table 3.
Results of the sensitivity analysis
| Study omitted | OR/correlation coefficient(r) | 95%CI | I2(%) |
|---|---|---|---|
| SJL ≥ 2 h | OR | ||
| Li 2024 | 1.53 | 1.41–1.66 | 0.0 |
| Qu 2022 | 1.44 | 1.16–1.79 | 89.0 |
| Tamura 2023(females) | 1.36 | 1.09–1.70 | 86.0 |
| Tamura 2023(males) | 1.43 | 1.14–1.79 | 86.0 |
| Zhang 2023 | 1.44 | 1.07–1.94 | 71.0 |
| SJL1-2 h | OR | ||
| Li 2024 | 1.10 | 1.03–1.19 | 0.0 |
| Tamura 2023(females) | 1.04 | 0.97–1.12 | 45.0 |
| Tamura 2023(males) | 1.07 | 0.97–1.17 | 65.0 |
| Zhang 2023 | 1.04 | 0.93–1.16 | 30.0 |
| Correlation | correlation coefficient(r) | ||
| Jia 2023 | 0.19 | 0.01–0.38 | 81.4 |
| Jang 2023 | 0.09 | 0.02–0.16 | 0.0 |
| Lyall 2020 | 0.17 | 0.01–0.31 | 83.9 |
| Souza 2013 | 0.20 | 0.02–0.37 | 78.2 |
The results of the sensitivity analysis of the relationship between low social jetlag (1–2 h) and depressive symptoms showed that the combined results of the remaining studies were statistically significant after excluding the study by Li [20], which was inconsistent with the results of the meta-analysis (Table 3). This suggests that the lack of a significant association between low social jetlag (1–2 h) and depressive symptoms is unreliable.
Sensitivity analysis conducted on studies using correlation coefficients as effect sizes found that the combined results indicated a statistically significant association between social jetlag and depressive symptoms even after excluding any single study (Table 3). After excluding the study by Jang [37], the I2 was 0%, suggesting that this study may have introduced significant heterogeneity.
Publication bias test
Create funnel plots to infer the presence of publication bias. The funnel plots results indicate that the distribution of studies is not symmetrical, suggesting the possibility of some publication bias(Fig. 3).
Fig. 3.
(a) Funnel plot of high social jetlag (≥ 2 h) and depressive symptoms (b) Funnel plot of low social jetlag (1–2 h) and depressive symptoms (c) Funnel plot of correlation meta-analyses
Discussion
Eight studies reporting an association between social jetlag and depressive symptoms in young people, with a total sample size of 148,410, were included in this study. The studies measured the impact using two types of effect size: odds ratio (OR) and correlation coefficients. The meta-analysis results using OR as effect sizes indicated that high social jetlag (≥ 2 h) was linked to higher odds of depressive symptoms in young people. In contrast, a low social jetlag (1–2 h) did not show a significant association. However, the results of the sensitivity analysis showed that a low social jetlag (1–2 h) was also significantly associated with depressive symptoms after excluding one study. A meta-analysis using correlation coefficients as the effect size revealed a weak but significant link between social jetlag and depressive symptoms.
The underlying mechanisms of the association between social jetlag and depressive symptoms in young people
High social jetlag is significantly positively correlated with depressive symptoms in young people; this finding is similar to that of a previous review [25]. Social jetlag is very common among adolescents and young adults, and its distribution among them is significantly higher than that of the general and working populations [39]. Social jetlag represents the disruption of circadian rhythms over the course of a week and is often accompanied by sleep deprivation [15]. Stable circadian rhythms and adequate sleep are essential for maintaining mental health [40, 41].
Previous studies have explored the relationship between social jet lag and depressive symptoms from the perspective of neural mechanisms [31, 42–44]. Social jet lag affects the ventral striatum, a brain region closely associated with the human reward system. This, in turn, can lead to depression as a result of reduced reward responsiveness, reward expectations, and willingness to make an effort to obtain rewards [31, 42]. Circadian rhythm disorders caused by social jetlag may also lead to the overactivation of the HPA axis, phase shifts in the cortisol rhythm, and increased cortisol exposure, thereby affecting mood regulation, which could also be a neurobiological basis for depressive symptoms [43, 44]. In addition, the use of electronic devices by young people before bedtime is another factor that must be considered. Previous studies have found that the use of various electronic devices before sleep (such as smartphones, computers, and tablets) is associated with both social jetlag and emotional issues [45]. Exposure to blue light emitted by electronic devices at night may lead to circadian rhythm disorders, such as social jetlag, which in turn can trigger emotional problems, such as depression [45, 46]. Therefore, bedtime screen use should be considered in the management of depressive symptoms among young adults.
However, the mild circadian rhythm disruption caused by a 1–2 h low social jetlag may have less impact on the physical and mental health of young people compared to at least 2 h of high social jetlag. Therefore, the results of this meta-analysis showed no statistically significant association with depressive symptoms in young people. Sensitivity analyses, in turn, showed that 1–2 h of social time difference was also associated with depressive symptoms.
Heterogeneity of meta-analysis
When conducting a meta-analysis, excessive heterogeneity among studies may render the combined results unreliable. The I2 statistic is commonly used to determine the percentage of variation in selected studies due to heterogeneity rather than chance. In this study, the heterogeneity for the meta-analysis combining the association between high social jetlag (≥ 2 h) and depressive symptoms using the OR as the effect size, and the meta-analysis using the correlation coefficients as the effect size, were 85% and 73%, respectively, both greater than 50%.
After excluding individual studies, we found that heterogeneity mainly originated from two studies: one by Li [20] and the other by Jang [37]. The excessive heterogeneity between Li [20] and other studies may be due to the very broad age range of participants in this study, which included all groups from 10 to 18 years of age, from primary to junior high school. This resulted in significant differences in sample characteristics compared to other studies. Jang [37] used a corrected formula to calculate social jetlag, whereas other studies used an uncorrected formula. This may explain the substantial heterogeneity observed in this study.
This review also has some limitations. First, the circadian rhythm system does not remain in the same state throughout the week; rather, it changes with variations in sleep time [47]. Therefore, social jetlag, as a mismatch between the biological and social clocks, may be an overly simple model. Second, since we included studies based primarily on cross-sectional data, they may have been subject to reverse causality. In other words, depressive symptoms may be responsible for circadian rhythm disruption and increased social jet lag. Although recent meta-analytic evidence suggests that sleep disturbance in adolescents typically precedes the onset of depression [48], this study should still be cautiously interpreted. Third, heterogeneity among the original studies was high, which may be due to differences in the methods used to measure social jetlag across various studies, as well as differences in the age of the study participants. Fourth, the population included in the study came from East Asia, South America, and Europe and may not represent other populations. Fifth, when using OR as the effect size, one included study had a different reference group (“<2 hours” as the reference) compared to other studies (“<1 hours” as the reference), which may lead bias estimations. Sixth, in quantitative research, effect size plays a crucial role in reflecting the practical significance of the differences or relationships between variables [49]. However, in our meta-analysis, the effect size correlation coefficients (r = 0.16) was relatively low, indicating that there might have been interference from other factors or limitations of the measurement tools. These limitations may have rendered the statistical results less meaningful. Therefore, it is important to consider a combination of other factors when exploring depressive symptoms in young adults. Seventh, due to the limited number of studies included, as well as the differences in effect sizes used among selected studies, it is very difficult to conduct subgroup analyses based on factors such as age or cultural background. Finally, although the studies we included using odds ratios as effect sizes had already been adjusted for confounding factors, there was insufficient information to assess the impact of interfering factors on depression. Moreover, studies using correlation coefficients as effect sizes did not take into account the control for confounding factors, which reduced the reliability of the results.
Conclusion
This systematic review and meta-analysis found that social jetlag in young people is significantly associated with depressive symptoms, especially in groups with high social jetlag (≥ 2 h). However, not only due to limitations in the number of current studies, but also some included studies only had correlation coefficients without adjustment for confounding factors, the ture impacts of low social jetlag (1–2 h) on depressive symptoms in young people are still uncertain. The results of this systematic review suggest that the presence of social jetlag should be considered when preventing depressive symptoms among young people. Future research should include more high-quality studies of the relationship between social jetlag and depressive symptoms in young adults. For example, studies could be conducted from various perspectives, such as different levels of social jetlag severity, different ages, and different sociocultural contexts. In particular, in-depth cohort studies and randomized controlled trials are needed to clarify the relationship between the two and explore the underlying mechanisms.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
This study was presented orally at the 2024 Zhejiang Provincial Medical Association Academic Conference on Clinical Epidemiology and Evidence-Based Medicine. We appreciate the suggestions from the judges at this conference.
Abbreviations
- SJL
Social jetlag
- NOS
The Newcastle-Ottawa scale
- OR
Odds ratio
- MSF
Midsleep of freedays
- MSW
Midsleep of workdays
Author contributions
Tao-Hsin Tung and Siwen Sun conceived this review. Siwen Sun, Yupei Yang, Fuyang Yu, and Yang He contributed to studies search and screening, data extraction and analysis, and draft writing. Chengwen Luo and Meixian Zhang contributed to the methodology. Haixiao Chen and Tao-Hsin Tung supervised and reviewed.
Funding
This work was supported by the National Natural Science Foundation of China [funding ID 72374157].
Data availability
All data generated or analyzed during this study are included in this published article and the supplementary material.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Siwen Sun and Yupei Yang are equally contributed to this study.
Contributor Information
Haixiao Chen, Email: chenhx@enzemed.com.
Tao-Hsin Tung, Email: ch2876@yeah.net.
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Data Availability Statement
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