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. 2021 Aug 14;42(11):9053–9062. doi: 10.1007/s12144-021-02160-1

Relationship between self-efficacy, social rhythm, and mental health among college students: a 3-year longitudinal study

Qiongcui Huang 1,#, Xueqian Wang 1,2,#, Yongjing Ge 1,3, Dan Cai 1,✉
PMCID: PMC8364412  PMID: 34413621

Abstract

Although many empirical studies have aimed to find variances in positive mental health among different groups and the relationship between mental health and other variables, few studies examined the developmental trend of mental health levels and the cross-lagged relationship between self-efficacy, social rhythm, and mental health. Furthermore, few of them explored the interrelationship between self-efficacy and social rhythm among college students over time. This study thus aimed to clarify the longitudinal cross-lagged relationship between self-efficacy, social rhythm, and positive mental health among college students. A total of 764 students (627 females, mean age 21.03 ± 0.84 years at T1) participated in a 3-year-long study (T1, T2, T3) and were asked to complete the General Self-Efficacy, the Brief Social Rhythm, and the Positive Mental Health Scales. The results indicate that the development of positive mental health among college students showed an upward trend during those 3 years. There was a significant relationship between positive mental health, social rhythm, and self-efficacy. Positive mental health and social rhythm significantly predicted self-efficacy in the following year. Positive mental health in T2 could predict both T3 social rhythm marginally and T3 self-efficacy significantly. However, T1 social rhythm was not indirectly associated with T3 mental health via T2 self-efficacy, and T1 self-efficacy was not indirectly associated with T3 mental health via T2 social rhythm. This finding clarified the relationship between positive mental health, self-efficacy, and social rhythm, and provided evidence that positive mental health is the basis for self-efficacy and social rhythm among young adults. Therefore, school psychologists in universities should pay close attention to the positive mental health of young adults to form high levels of self-efficacy and social rhythm.

Keywords: Self-efficacy, Social rhythm, Mental health, College students, Longitudinal study

Introduction

According to the Washington Department of Health and Human Services (2020), mental health involves individuals’ emotional, psychological, and social well-being. It affects how one thinks, feels, and acts. It also indicates how one will handle stress, relate to others, and make choices. Mental health is important at every stage of life, from childhood, over adolescence and adulthood, to old age. In recent decades, positive aspects of mental health have played a significant role in public mental health care. Some researchers have proposed that the definition of positive mental health is not only a property of an individual, but is heavily influenced by social phenomena as well (Karlsson, 2012). According to the dual-factor model of mental health, there are three core components of mental health: emotional, positive, and social well-being (Keyes, 2007). Some studies have shown that positive mental health is a predictor of recovery from mental illness, whereas Iasiello et al. (2019) found that it could protect people from affective disorders. Moreover, positive mental health is an important factor in predicting remission from anxiety disorders and depression (Teismann et al., 2018). Mental health is influenced by many factors, including everyday behaviors that can be altered by individuals (Velten et al., 2014). A study suggested that personal well-being, physical health, psychological well-being, and environmental health predict adolescents’ positive mental health (Singh & Junnarkar, 2015). In addition, some protective factors such as sufficient sleep and physical activity and a healthy diet were associated with better mental health outcomes (Kaneita et al., 2007; Cairns et al., 2014). Furthermore, a study aimed at investigating the relationship between daily stressors, self-efficacy, and mental health in samples of college students from Germany, Russia, and China, and found that perceived self-efficacy was a mediator between the effects of daily stress and positive mental health (Schönfeld et al., 2016). Based on these studies, we tested the relationship between self-efficacy, social rhythm, and mental health among Chinese college students.

As an important part of social cognitive theory, self-efficacy refers to the degree of confidence individuals show in their ability to act in a particular situation (Skaalvik & Skaalvik, 2017). Based on social cognitive theory, individuals’ healthy behavior is not driven by internal forces, nor is it automatically shaped and controlled by external stimuli. Instead, it is a result of the interaction between environmental, behavioral, and personal factors (Bandura, 1977). In other words, when facing the pressures of the surrounding environment, individuals who have internal confidence in controlling, eliminating, or reducing the severity of a threatening event are not defeated by it. Thus, stress response is the result of low self-efficacy in controlling threats or overloaded environmental pressure. Whether an individual adopts healthy behaviors is closely related to the level of self-efficacy.

Previous studies have demonstrated that self-efficacy and mental health are significantly correlated. For instance, individuals with higher self-efficacy are more likely to adopt healthy behaviors, so the positive factors of mental health show a higher level, while the negative factors of mental health show a lower level (Holden, 1991; Sylvia, 2015). Furthermore, a study examined the relationship between self-efficacy and other psychological structures across five countries, suggesting that self-efficacy is positively associated with optimism, self-esteem, and self-regulation (Luszczynska et al., 2005a); that is, people with a high sense of self-efficacy believe they have the ability to solve problems and accomplish their goals, so they are more satisfied with life than those with lower self-efficacy are (Azizli et al., 2015). Moreover, a study that examined young adults suggested that self-efficacy and hope were significantly positively correlated with mental health, and these two predictor variables were able to predict it (Bai et al., 2017). In addition, some studies have suggested that self-efficacy is associated with mental disorders, such as anxiety and depression. People with low self-efficacy are more likely to experience negative emotions and high levels of anxiety, depression, and distress (Karademas & Kalantzi-Azizi, 2004; Lee & Hayes-Skelton, 2018; Schönfeld et al., 2016). Therefore, increasing self-efficacy can reduce some negative attitudes and the effects of stress on health; help to avoid social risks, low self-esteem, and depression; and in turn, promote mental health (Parto, 2011). Although there are some studies on this topic, we found that the mutual relationship between self-efficacy and mental health is unknown.

In the social zeitgeber theory, “social zeitgeber” refers to personal relationships, social demands, or tasks that serve to entrain biological rhythms (Ehlers et al., 1988). There are some external clues that can control biological rhythms, including eating, sleeping, getting up, exercising, going to school or working, entertaining, participating in social activities, and so on, whereas disruption or irregularity in time cues that trigger one’s biological and social behavior can lead to increased symptoms of mental illnesses (Gorwood, 2012; Lieverse et al., 2013; Wever, 1979). Therefore, in our study, social rhythm reflects one’s life regularity, which includes sleep times, diet, and social contacts. As described above, the level of self-efficacy is correlated with an individual’s social rhythm. People with higher self-efficacy are more likely to adopt healthy behaviors and may have a regular social rhythm. Shojaei et al. (2018) suggested that circadian rhythm is one of the factors affecting an increase or decrease in the quality of working life and self-efficacy. Few studies have examined social rhythms and self-efficacy. Bihlmaier and Schlarb (2016) investigated the correlation between self-efficacy and sleep behavior in school-aged children. They found that healthy children with regular sleep behavior had higher self-efficacy than children with sleep problems did. Higher self-efficacy was related to lower sleep problems, such as delayed sleep onset. The same results are available in another study aimed at college students where researchers tested self-efficacy, sleep problems, sleep characteristics, and insomnia. Results showed that self-efficacy and insomnia had a significant association (Schlarb et al., 2012). In other words, students without sleep problems have a higher self-efficacy than those with insomnia.

Social rhythm and behavioral patterns in daily routines are related to mental health; some changes in regular biological daily patterns such as bedtime, social contacts, mealtime, and working schedules lead to mental illnesses (Ehlers et al., 1988; Grandin et al., 2006; Lieverse et al., 2013). Furthermore, another study found that poor sleep quality was associated with difficulties in mental and physical health among college students (Pilcher & Ott, 1998). As the social zeitgeber theory describes, stressful life events can disturb social zeitgeber, and accordingly, a change occurs in the stability of social rhythms. Eventually, this leads to depression, which in turn affects the biological rhythm of the individual (Ehlers et al., 1988). Many studies have proved this theory (Boland et al., 2016; Lieverse et al., 2013). Previous studies have found that social rhythm disruptions may contribute to or increase the symptoms of psychological disorders, such as bipolar disorder and depression. Furthermore, Shen et al. (2008) suggested that individuals with bipolar disorder had lesser social rhythm regularity compared to normal controls, and a low social rhythm regularity predicted affective episodes in participants with bipolar disorder. Similarly, university students with sleep disorders showed a lower frequency of social interactions than the controls did (Carney et al., 2006).

These studies indicated that social rhythms related to negative mental health and irregular social rhythms may cause mental illness. According to the dual-factor model of mental health, mental illness is associated with mental health, but they are two distinct dimensions (Westerhof & Keyes, 2010). Therefore, both negative and positive aspects of mental health should be emphasized. In fact, limited studies on social rhythm and positive mental health have been conducted. One study showed that social rhythm was significantly associated with life satisfaction, anxiety, and stress, and that regular social rhythms can reflect good mental health. Meanwhile, less social rhythm regularity predicts anxiety and stress (Velten et al., 2018). In addition, some studies have found that sleep quality may predict the positive and negative dimensions of mental health among college students. (Peach et al., 2016).

In terms of the developmental trend of college students’ mental health, previous studies drew inconsistent conclusions. A 3-year-long longitudinal study suggested that during college life, students’ scores of positive mental health first showed a downward and then an upward trend (Cai et al., 2017). However, another longitudinal study found a different result: students’ scores of positive mental health showed a downward trend (Bieda et al., 2019). These contradictory results provided us with some information, but more research should be conducted to explore the current situation and trends regarding college students’ mental health. Therefore, a purpose of this study was to investigate trends in the mental health of college students.

Existing research indicates that self-efficacy is closely related to mental health, and individuals with high self-efficacy have better mental health (Azizli et al., 2015; Abdel-Khalek & Lester, 2017); thus, it can be speculated that self-efficacy can affect the development of mental health. In addition, it is essential to evaluate the relationship between social rhythms and the mental health of college students. Previous studies have focused on social rhythm disruptions and mental illness (Boland et al., 2012; Frank et al., 2005; Shen et al., 2008; Sylvia et al., 2009), but only a few have examined the mechanism of the action of social rhythm on positive mental health (Cai et al., 2017; Margraf et al., 2016). Second, when first-year Chinese students enter the university, their curriculum arrangements and lifestyles are quite different from those of middle school students. In particular, they have more freedom to arrange their own lifestyles. Such a great change in life rhythm probably made it difficult for them to perceive control over internal states in their freshmen year, which was confirmed to have a negative effect on individuals’ behavior and physical and mental health (Cai et al., 2017; Zhang et al., 2020; Pallant, 2000; Wu et al., 2020). However, based on current studies, it is difficult to determine the developmental trend of college students’ social rhythm.

Extensive research has been conducted on the relationship between mental health and other variables (Cai et al., 2017; Duan, 2016; Hu et al., 2020; Lamers et al., 2015). However, it remains unknown whether they reciprocally influence one another or if one causes the other. For this reason, the developmental cascades of mental health, self-efficacy, and social rhythm in college students are yet to be examined. This study thus considered the theoretical perspective of the development cascade model to examine interrelations among mental health, self-efficacy, and social rhythm (Masten & Cicchetti, 2010), which is whether social rhythm could influence mental health by promoting self-efficacy, or whether self-efficacy helps to promote mental health through a high level of social rhythm over time. Based on the above discussion, we hypothesized that (1) the level of mental health of college students would change with grade increase; (2) self-efficacy would positively correlate with mental health and social rhythm at each time point; (3) self-efficacy and social rhythm would predict each other, which in turn would subsequently promote the level of mental health; and (4) mental health would predict both self-efficacy and social rhythm over time.

Method

Participants

The participants were college students from a university in Shanghai, China. All of them were randomly selected from the majors of humanities, education, mathematics and science, and construction engineering. The study lasted 3 years (2014–2016) with one distribution of questionnaires each year (referred to as T1, T2, and T3, respectively). A total of 1622 students took the test in 2014. In 2015, 1277 junior students were selected. In T3, 1055 senior students participated in the study because some students moved to another campus. Furthermore, participants who completed <80% of the three target scales, who were suspected not to respond sincerely, or who missed one or more surveys were excluded. Of these, 764 students participated for 3 years, of which 627 were female and 137 were male. The average age (at T1) of the longitudinal sample was 21.03 ± 0.84 years, ranging from 18 to 24.

Measures

Self-Efficacy

The General Self-Efficacy Scale was used to measure self-efficacy. It was created by Ralf Schwarzer, a German clinical and health psychologist (Schwarzer et al., 1999). The original scale of 20 items was reduced to five, which were scored on a 4-point Likert scale (1 = disagree to 4 = strongly agree). A high score indicated that a person had higher self-efficacy. Sample items were “It is easy for me to stick to my aims and accomplish my goals,” and “I am confident that I could deal efficiently with unexpected events.” The Chinese version of the General Self-Efficacy Scale was compiled by Zhang in 1995 and has good reliability and validity (Zhang & Schwarzer, 1995). In this study, the Cronbach’s alphas for the 3 years were T1 = 0.89, T2 = 0.91, and T3 = 0.88, respectively.

Social Rhythm

The Brief Social Rhythm Scale was used to measure the rhythm of life, and participants’ daily activities such as mealtimes, bedtimes, and wake-up times during the school week and on the weekend were investigated (Margraf et al., 2016). Cai et al. (2017) used the Brief Social Rhythm Scale to examine social rhythms in a sample of Chinese college students. This scale consists of 10 items rated on a 6-point Likert scale (1 = very regular to 6 = very irregular). Sample items were “Going to bed Mondays through Fridays,” “Going to bed on the weekend,” and “Meeting other people at school or work Mondays through Fridays.” Final scores were recoded reversely; therefore, participants with higher scores had a regular life rhythm. The internal consistency for the 3 years was T1 = 0.87, T2 = 0.89, and T3 = 0.88, respectively.

Positive Mental Health

Positive Mental Health Scale was used to measure the participants’ levels of positive mental health (Margraf et al., 2016). It contained nine items, and participants answered on a 4-point Likert scale (1 = disagree to 4 = strongly agree). Higher scores indicated higher levels of mental health. There are some items, “All in all, I am satisfied with my life,” “I feel that I am actually well equipped to deal with life and its difficulties,” and “Much of what I do brings me joy.” Wu et al. (2020) applied the translation–backtranslation–revision method to create the Chinese version. The Positive Mental Health Scale exhibited strong cross-cultural measurement invariance across student samples from Germany, Russia, and China (Velten et al., 2018). The internal consistency of this study for the 3 years was T1 = 0.90, T2 = 0.94, and T3 = 0.93.

Data Collection

All participants were asked to complete the questionnaires in the classroom, with a pencil and paper provided by survey conductors, who were two trained graduate students majoring in psychology. The students who agreed to participate in the survey entered the pre-arranged classroom in groups and conducted the questionnaire survey. The General Self-Efficacy Scale, the Brief Social Rhythm Scale, and Positive Mental Health Scale were applied in May each year. Participants took approximately 20 min to complete the questionnaires. After completing the survey, they received a compensation of approximately 5 yuan.

Results

Descriptive Statistics for all Measures

The means and standard deviations are listed in Table 1. The t-test was used to compare the differences between men and women in terms of self-efficacy, social rhythm, and positive mental health. The results show that there were no gender differences in self-efficacy and social rhythm scores at each time point. However, a significant difference was found between males and females in terms of positive mental health at all three time points. Specifically, the level of positive mental health was significantly lower in men than in women, and the values were as follows: T1 (t = 2.18, p < 0.05, Cohen’s d = 0.22), T2 (t = 2.04, p < 0.05, Cohen’s d = 0.20), and T3 (t = 3.9, p < 0.001, Cohen’s d = 0.36).

Table 1.

Mean and standard deviation (SD) descriptions of social rhythm, self-efficacy, and mental health of different genders at each time point

T1 T2 T3
Male Female Overall Male Female Overall Male Female Overall
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
SE

13.86

(3.00)

 14.22 (2.99) 14.16 (2.99) 14.05 (3.27) 14.12 (3.13) 14.11 (3.15) 14.93 (2.56) 14.84 (2.43) 14.85 (2.45)
SR 45.82 (9.81) 46.67 (7.47) 46.50 (7.95) 44.89 (9.86) 45.03 (7.84) 44.99 (8.26) 44.42 (9.29) 45.17 (8.24) 45.06 (8.45)
MH 19.66 (5.17) 20.70 (4.37) 20.52 (4.55) 19.91 (5.75) 20.97 (4.63) 20.76 (4.87) 19.60 (4.97) 21.31 (4.60) 21.01 (4.71)
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Note. SE, self-efficacy; SR, social rhythm; MH, mental health; T1, time 1; T2, Time 2; T3, Time 3

Repeated measure ANOVAs were conducted to test for any differences in participants’ self-efficacy, social rhythm, and mental health over time. Significant differences were found in the 3 years of self-efficacy (F = 25.06, p < 0.001, η2 = 0.032). Pairwise comparisons showed that self-efficacy at T1 was significantly lower than that at T3 (t = −6.31, p < 0.001), and that self-efficacy at T2 was significantly lower than that at T3 (t = −6.34, p < 0.001).

There were significant differences in the 3 years of social rhythm (F = 13.39, p < 0.001, η2 = 0.017). Pairwise comparisons showed that the social rhythm at T1 was significantly higher than at T2 (t = 4.87, p < 0.001) and T3 (t = 4.27, p < 0.001). There was no significant difference between the social rhythms at T2 and T3.

Furthermore, there were significant differences among the evaluations of 3 years of mental health (F = 4.19, p < 0.05, η2 = 0.005). Pairwise comparisons showed that mental health at T1 was significantly lower than that at T3 (t = −2.95, p < 0.01). There was no significant difference in mental health between the T2 and T3 groups.

Correlation between Self-Efficacy, Social Rhythm, and Mental Health

Statistical analyses were performed using the SPSS version 22. Table 2 shows the correlations of self-efficacy, social rhythm, and mental health at three time points. Correlation analyses indicated that self-efficacy correlated positively with mental health each year (Pearson’s r, .50–.62) and across years (Pearson’s r,.30–.37). In addition, there was a significant positive correlation between social rhythm and mental health at each year (Pearson’s r, .31–.38) and across years (Pearson’s r, .19–.27). Self-efficacy was positively correlated with social rhythm at each year (Pearson’s r, .27–.30) and across years (Pearson’s r, .11–.26).

Table 2.

Correlations of three variables for participants at each time point

1 2 3 4 5 6 7 8 9
MH (T1) –
MH (T2) .49*** –
MH (T3) .50*** .50*** –
SE (T1) .62*** .33*** .34*** –
SE (T2) .31*** .50*** .30*** .37*** –
SE (T3) .35*** .37*** .57*** .39*** .35*** –
SR (T1) .38*** .23*** .27*** .29*** .19*** .26*** –
SR (T2) .24*** .37*** .22*** .21*** .30*** .22*** .45*** –
SR (T3) .24*** .19*** .31*** .22*** .11*** .27*** .36*** .37*** –
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Note. SE, self-efficacy; SR, social rhythm; MH, mental health; T1, Time 1; T2, Time 2; T3, Time 3; *** P < 0.001

Cross-Lagged Panel Modeling

The correlations between self-efficacy, social rhythm, and mental health (Table 2) showed that their scores were significantly correlated at each time point (e.g., SR and MH at T1, r = 0.38, p < 0.001) and across time points (e.g., for MH at T2 and SE at T3, r = 0.37, p < 0.001).

Cross-lagged analysis was used to investigate the interaction between self-efficacy, social rhythm, and positive mental health at the three time points (Fig. 1). Because there were no gender differences in the self-efficacy and social rhythm scores at each time point, we used one cross-lagged model in our analysis. The cross-lagged model had a good fitting index, χ2(9) = 159.32, p < 0.001, CFI = 0.928, NFI = 0.926, RMSEA = 0.148 (90% CI [0.128, 0.169]). After controlling for the correlation and stability of self-efficacy, social rhythm, and positive mental health, T1 self-efficacy did not significantly predict positive mental health in T2 (β = 0.04, p > 0.05). Positive mental health in T1 significantly predicted T2 self-efficacy (β = 0.10, p < 0.05). Similarly, after controlling for other T2 variables, T2 self-efficacy did not significantly predict positive mental health in T3 (β = 0.06, p > 0.05). T2 positive mental health significantly predicted T3 self-efficacy (β = 0.23, p < 0.001).

Fig. 1.

Fig. 1

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Cross-lagged structural model for self-efficacy, social rhythm, and mental health. Note that the standardized coefficients are displayed in the model. e, measurement error; SE, self-efficacy; SR, social rhythm; MH, mental health; T1, Time 1; T2, Time 2; T3, Time 3; *p < 0.05, **p < 0.01, ***p < 0.001, ▲p < 0.1

In addition, T1 social rhythm did not significantly predict positive mental health in T2 (β = 0.05, p > 0.05), and positive mental health in T1 did not significantly predict T2 social rhythm (β = 0.05, p > 0.05). Furthermore, T2 social rhythm did not significantly predict positive mental health in T3 (β = 0.03, p > 0.05). Positive mental health in T2 predicted T3 social rhythm; thus, the predictive power reached marginal significance (β = 0.07, p = 0.06).

The results showed that T1 self-efficacy did not significantly predict T2 social rhythms (β = 0.06, p > 0.05). T1 social rhythm predicted T2 self-efficacy, and predictive power approached significance (β = 0.07, p = 0.06). T2 self-efficacy also did not significantly predict T3 social rhythm (β = −0.03, p > 0.05), and T2 social rhythm significantly predicted T3 self-efficacy (β = 0.07, p < 0.05).

However, the results show that there were no development cascade effects in this model. In other words, T1 self-efficacy was not found to have a significant indirect effect on T3 mental health via T2 social rhythm. The indirect effect of T1 social rhythm on T3 mental health via T2 self-efficacy was also not significant.

Discussion

The present study found a significant relationship between self-efficacy, social rhythm, and mental health among college students over the period of 3 years, and the results showed that college students’ mental health increased every year, while self-efficacy and social rhythm seemed to have decreased first at T2 and then increased at T3. In addition, our analyses explored the bidirectional relationship between self-efficacy, social rhythm, and mental health. These findings enrich previous longitudinal study results and enhance the understanding of the relationship between self-efficacy, social rhythm, and mental health among college students.

The results showed that mental health among females was higher than among males at each time point, which was consistent with previous studies (Jeyagurunathan et al., 2017; Yue et al., 2017). Females were more likely to express negative emotions with their friends and often reported better social relationships (Ryff & Keyes, 1995). A better social relationship is linked to higher levels of social support, which plays a positive role in improving individuals’ mental health (Keyes, 2006; Wang et al., 2018). The results further show that the level of mental health among college students increased every year, which confirms our first hypothesis. One possible explanation is that the college students in our study had just entered a new environment in their freshman year, which was different from that of middle school students. Therefore, they needed time to adapt to their new lifestyles and changes in their surrounding environment. Based on the above reasons, the scores for positive mental health during the first year were relatively low. However, after adjusting to college life and possibly finding their life purpose (Zhang et al., 2019), students’ level of positive mental health improved significantly.

As hypothesized, the correlation analysis showed a positive relationship between self-efficacy and mental health, which is consistent with previous studies. The level of mental health can be reflected in college students’ sense of self-efficacy. A strong sense of self-efficacy predicted better health outcomes (Bandura, 1977) and enhanced personal well-being (Karademas & Kalantzi-Azizi, 2004). In contrast, low self-efficacy was related to psychological symptoms of anxiety and distress (Bandura, 1977; Schwarzer, 1993; Sandin et al., 2015; Schönfeld et al., 2016). Promoting self-efficacy may improve the mental health of individuals with social anxiety disorder (Lee & Hayes-Skelton, 2018; Goldin et al., 2012). Therefore, self-efficacy was an important factor that affected one’s life satisfaction, personal well-being, and positive attitudes (Azizli et al., 2015; Judge et al., 2002; Luszczynska et al., 2005b). Furthermore, we found a positive relationship between social rhythms and mental health. The results also indicated that a person’s mental health could be prompted by enhancing social rhythm regularity (Margraf et al., 2016). Social rhythm regularity is a strong predictor of positive mental health, suggesting that improving social rhythms helps to increase psychological well-being among college students (Cai et al., 2017; Julia et al., 2018). In particular, people with a regular rhythm have good sleep quality, which in turn may help to increase the positive aspects of mental health, such as a sense of well-being (Hamilton et al., 2007; Morgan, 2003; Peach et al., 2016). Many studies demonstrated that a stable social rhythm was beneficial in the reduction of mental illness symptoms, for instance, and modifying the biological rhythm would reduce the psychological and physiological symptoms of depression (Salgado-Delgado et al., 2011). There is no doubt that a regular life rhythm provides a more positive mindset and helps to protect people from mental illness. Finally, we found that self-efficacy was positively related to the social rhythm of college students at each time point, indicating that a high level of self-efficacy was more likely related to a regular social rhythm. This result was consistent with a previous study, which suggested that there was a negative correlation between self-efficacy and sleep problems (Przepiórka et al., 2019). Furthermore, Angelika et al. (2012) regarded self-efficacy as a protective resource for maintaining sleep hygiene, which involves regular sleep schedules. In other words, individuals with high self-efficacy have fewer sleep problems, and their sleep time is more regular than in those with low self-efficacy.

We did not find any developmental cascades of mental health, self-efficacy, and social rhythm from the cross-lagged analyses in this study. Specifically, self-efficacy and social rhythm failed to predict one other, and these two variables failed to promote mental health. This conclusion is contrary to that of H3. One explanation may be that the cross-lagged pathways between self-efficacy, social rhythm, and mental health are completely sophisticated, and no relevant research has drawn a clear conclusion. Another explanation for such results may be that social rhythm mainly focused on the regularity of life during the school week and on the weekend, such as going to bed and meeting friends. Nevertheless, it does not reflect an individual’s sleep quality. Furthermore, Velten et al. (2014) concluded that a higher frequency of physical exercise could predict positive mental health. However, the predictive relationship between social rhythms and physical exercise remains unknown. To the best of our knowledge, this study is the first to test the developmental cascade effects of self-efficacy, social rhythm, and mental health. Future research could consider additional variables, such as physical activity and sleep quality. However, there were some longitudinal relationships between these three variables. First, students’ mental health could significantly predict self-efficacy in the following year. This means that individuals with a higher level of positive mental health may be more confident in overcoming difficulties. In other words, they have high self-efficacy. Similar results could be found in other studies, which suggested that positive mental health could help people regulate their emotions better and be more capable of facing and accepting problems (Bieda et al., 2019; Yiengprugsawan et al., 2012). In addition, the cross-lagged panel model showed that social rhythm could not predict mental health in the following year, while mental health could predict social rhythm in the following year. A study of the relationship between lifestyle and mental health in adults investigated whether a regular social rhythm could reflect positive mental health (Velten et al., 2018). Studies examining positive mental health have found a greater impact on social rhythms and self-efficacy. Lastly, the results indicate that social rhythm can predict self-efficacy significantly across years. Individuals with regular social rhythms, such as regular sleep, frequent exercise, eating healthy food, and getting sufficient rest may have high self-efficacy, because a regular life allows them to believe that they are capable of dealing with the issues they encounter. This result was consistent with a study aimed at improving children’s self-efficacy, which found that lifestyle intervention resulted in a significant improvement in children’s sense of self-efficacy (De Villiers et al., 2016).

It is worth pointing out that in comparison to previous studies that used positive mental health solely as an outcome measure index (Bai et al., 2017; Velten et al., 2018), the present study considered positive mental health as a predictive factor and found that positive mental health had a greater impact on social rhythm and self-efficacy; that is, mental health was the basis for self-efficacy and social rhythm in younger adult populations. These findings provide new insights for future research. Moreover, the longitudinal study displayed trends in the mental health of college students, which provided us with useful information regarding changes in college students’ mental health.

This study has some limitations. First, all participants came from the same university, although they attended six different colleges. The results would be more reliable if we could have obtained a wider range of representative samples. In addition, the proportion of students (about 65%) who participated in the survey was relatively small. Second, self-reported measures were used to assess self-efficacy, social rhythm, and mental health. Multiple methods should be considered in future research to reduce the measurement errors. Finally, future longitudinal research could focus on finding other variables that predict the negative aspects of mental health, such as stress, depression, and anxiety.

Despite these limitations, this study preliminarily investigated the development trend and relationship between self-efficacy, social rhythm, and mental health through a 3-year longitudinal design of a large sample of college students. College students are at an important stage in their lives, which may result in changes to their mental health status. Such longitudinal research can make up for the inconsistent conclusions in previous studies regarding the general development trend of college students’ mental health. Furthermore, the cross-lagged structural model for self-efficacy, social rhythm, and mental health showed that positive mental health had a greater impact on social rhythm and self-efficacy. In other words, college students with a high level of positive mental health are more likely to have a regular life and high self-efficacy. This emphasizes the importance of focusing on and helping college students improve their positive mental health in their daily lives. Accordingly, school psychologists in universities could set up relevant courses and form a variety of interest groups to enhance college students’ sense of well-being and improve their levels of positive mental health. Furthermore, considering the impact of COVID-19, relevant departments must appeal to college students to properly arrange their diet sleeping and social life, and form a regular social rhythm to enhance their self-efficacy. Thus, encouraging college students to maintain a regular social rhythm and strengthen their positive mental health to have a high level of self-efficacy when facing difficulties are of great significance.

Conclusion

The present study provides evidence of the longitudinal relationship between self-efficacy, social rhythm, and positive mental health among college students. The mutually reinforcing relationship between self-efficacy, social rhythm, and positive mental health differed during the college years. The developmental cascades of mental health, self-efficacy, and social rhythm does not hold, that is self-efficacy and social rhythm failed promote mental health via another one. However, positive mental health and social rhythm significantly predicted self-efficacy in the following year. Improving the positive mental health and keeping a regular social rhythm can further promote college students’ self-efficacy and let them have a better quality of life.

Acknowledgments

The authors acknowledge Prof. Jürgen Margraf and Dr. Xiao Chi Zhang of Ruhr-University Bochum for their great efforts in supervising this project. This work was supported by Shuguang Program supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (20SG45).

Declarations

Conflict of Interest

All authors declare that they have no conflict of interest.

Ethical Approval

Ethical approval was obtained from the Research Ethics Committee of Corresponding Author’s University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Qiongcui Huang and Xueqian Wang contributed equally to this work.

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Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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