Chronotype, social jetlag, and time perspective

ABSTRACT The phase of entrainment (chronotype) is known to be associated with time perspective (TP), suggesting that the state of circadian system is involved in the long-term planning of human life. However, little is known regarding the influence of circadian misalignment on long-term planning ability. The aim of this study was to investigate the association between social jetlag (SJL) and TP. A total of 1064 schoolchildren and university students (mean age ± standard deviation, 19.2 ± 2.9 years; range, 15–25 years; females, 71.7%) from four cities in the Russian Federation located between 56.9 and 61.7 degrees North completed the Munich ChronoType Questionnaire, the Pittsburgh Sleep Quality Index, the Seasonal Pattern Assessment Questionnaire, and Zimbardo Time Perspective Inventory. Study participants also indicated personal data (age, sex, height weight, place of residence, and achievements). A multiple regression analysis with stepwise inclusion of predictors in the model was performed to evaluate associations between time perspective characteristics (dependent variables) and predictor variables. The change in R2 was used as the measure of effect size. Chronotype was found to be a moderate predictor of future TP (B = 0.034; ΔR2 = 0.037). In addition, sleep quality was found to be a moderate predictor of past negative (B = 0.043; ΔR2 = 0.074), present fatalistic (B = 0.021; ΔR2 = 0.035), and deviation from balanced TP (B = 0.034; ΔR2 = 0.066). Mood seasonality was a moderate predictor of present hedonistic TP (B = 0.016; ΔR2 = 0.038), and social jetlag was a weak predictor of present fatalistic (B = 0.052; ΔR2 = 0.019), future (B = −0.033; ΔR2 = 0.004), and deviation from balanced TP (B = 0.047; ΔR2 = 0.012). In conclusion, this study found a weak but significant association between social jetlag and TP in adolescents and young adults.


Introduction
Modern society clearly distinguishes daily, weekly, and annual rhythms, as reflected by the common phrase, 24/7/12 society.The ability to accurately measure different periods of time is necessary to be able to predict periodically occurring events in the external and internal environmentsa necessary condition for adaptation and the preservation of health.In the process of evolution, a circadian system was formed with a periodicity of about 24 hours (Aschoff 1984;Wever 2013).This circadian system is thought to be responsible for the expression seasonal rhythms (Wehr et al. 2001).However, for the circadian system to work reliably it must be synchronized to a 24-hour light rhythm (Pittendrigh, 1993;Aschoff 1965).
Modern humans spend most of their time indoors; therefore, the effect of the light zeitgeber (solar clock) on humans has decreased.At the same time, the role of the social zeitgeber (social clock) used by people to count daily, weekly, and annual periods has significantly increased.Therefore, the following question may be asked: How large is the role of the circadian system in humans adapted to life in modern society?An extensive body of information on this topic has accumulated in recent years.Taken together, this research indicates that the circadian system still plays an important role in the life of modern humans.Recently described misalignment between the biological and social clocks, called "social jetlag" (SJL; Wittmann et al. 2006), has a number of negative consequencesthe severity of which depends on the severity of the mismatch and its duration.Short-term effects of SJL include decreased academic performance (Haraszti et al. 2014), cognitive function (Panev et al. 2017), and well-being (Levandovski et al. 2011).Long-term negative consequences of SJL include an increased risk of obesity (Roenneberg et al. 2012) and obesity-associated chronic diseases, such as type 2 diabetes (Koopman et al. 2017) and cardiovascular pathologies (Wong et al. 2015).It has been suggested that prolonged exposure to SJL may also increase the risk of cancer (Borisenkov 2011;Gu et al. 2017;VoPham et al. 2018) and reduce life expectancy (Borisenkov 2011).In Japan, which has one of the highest average life expectancies of all nations, the incidence of SJL≥1 h (40.1%;Komada et al. 2019) more than half that observed in Russia (86.4%;Borisenkov et al. 2017).
There exists a mechanism in humans that allows events to be predicted from a more distant perspective.Gerontologists have named this mechanism the "large biological clock" or "ontogenetic clock" (Olovnikov, 2003;Dilman 1971).According to this view, the speed of the large biological clock determines the rate of passage of the main ontogenetic changes in the body (puberty and involution of the reproductive system) and the onset of ageassociated chronic diseases and life expectancy (Anisimov, 1987).It has been further suggested that there is a relationship between circadian system function and the rate of aging (Pittendrigh and Minis 1972;Stevens 2005).The circadian disruption hypothesis (Stevens 2005), which proposes that dysfunction of the circadian system increases the risk of developing age-associated chronic diseases and accelerates the aging process, has been confirmed by experimental, epidemiological, and clinical data (Anisimov, 1987;Anisimov et al. 2004;Filipski et al. 2004;Schernhammer et al. 2003;Vinogradova et al. 2009).The circadian resonance hypothesis (Pittendrigh and Minis 1972), which proposes that inadequate functioning of the circadian system in an individual with a period of τ more than 24 hours can reduce adaptability of the body and cause a reduction in life span, has also been confirmed recently (Wyse et al. 2010).
Evolutionary psychologists refer to this mechanism as Life History Strategy (LHS; Figueredo et al. 2013).There are two main types of LHS: fast and slow.People with a fast LHS enter puberty earlier, have more offspring, and put less effort into raising children (Figueredo et al. 2006).In addition, people with a fast LHS are characterized by risky behavior and reduced attention to their own health, and therefore have a shorter life expectancy (Ellis et al. 2009).Representatives with a slow LHS have opposite features.It has been shown that stress during the critical developmental period can result in a switch to fast LHS (Simpson et al. 2012).In addition, some publications have noted a relationship between the state of the circadian system and LHS (Ponzi et al. 2015).As a rule, persons with a fast LHS belong to the late chronotype, and representatives of the slow LHS to the early one.These data have also been confirmed by studies of the relationships among chronotype, reproductive behavior, and personal traits.People with a late chronotype become sexually active at an earlier age, have more sexual partners, and have shorter relationships with sexual partners (Kasaeian et al. 2019).They are often prone to aggressive behavior (Schlarb et al. 2014) and are more likely to demonstrate suicidal tendencies (Selvi et al. 2011), bad habits (Gau et al. 2007;Wittmann et al. 2006), and problems with reproductive function (Toffol et al. 2013).In other words, they demonstrate short-term mating strategies and neglect their health.Some studies have shown that in people with a late chronotype, this behavior pattern is due to SJL (Komada et al., 2019a;Hasler et al. 2017;Haynie et al. 2017;Wittmann et al. 2006).All of these behaviors are common in individuals with fast LHS.However, it is unknown whether a link exists between SJL and long-term planning ability.
One tool for assessing long-term planning ability is the Zimbardo Time Perspective Inventory (ZTPI; Zimbardo and Boyd 1999).Time perspective (TP) is "the often nonconscious process whereby the continual flows of personal and social experiences are assigned to temporal categories, or time frames, that help to give order, coherence, and meaning to those events" (Zimbardo and Boyd 1999)."In their conceptual model Zimbardo and Boyd (1999) distinguished five TPs: Past-Positive, expressed in a warm, sentimental attitude toward the past; Past-Negative, reflecting a generally aversive view of the past; Present-Fatalistic, revealing a helpless and hopeless attitude toward the future and a consequent focus on the present; Present-Hedonistic, reflecting a hedonistic, risktaking attitude toward time and life; and Futurea general future orientation" (Stolarski et al. 2017).There is a close relationship between TP and LHS (Stolarski et al. 2018).People with present TP display characteristics consistent with a fast LHS, and people with future TP display the characteristics of a slow LHS (Stolarski et al. 2017).An association between TP and the state of the circadian system has also been noted (McGowan et al. 2017;Nowack and van der Meer 2013).People with present TP have a late chronotype, and individuals with future TP have an early chronotype.
Taken together, the results of these previous studies indicate that the circadian system maintains an important role in the life of modern humans.This role is not limited to adaptation to a rapidly changing time environment measured in days and months, but also to long-term planning.Therefore, there is an urgent need to know how chronic dysfunction of the circadian system changes long-term planning, particularly in individuals with SJL.To date, only one study (McGowan et al. 2017) has considered the relationship between SJL and TP.
The aim of this study was to test the hypothesis that SJL impedes long-term planning.If our hypothesis is supported, present TP will prevail in individuals with more pronounced SJL and future TP will prevail in individuals with lower values of SJL.Because maximum SJL values are observed at the age of 16-18 years (Randler et al. 2019), it can be assumed that the most pronounced tendency to focus on the present time will be observed at this age.However, in order to more robustly analyze the relationship between the state of the circadian system and TP, it is necessary to take into account the influence of related factors, such as gender, age, BMI, sleep quality, academic performance, and level of depression, which are known to be associated with the state circadian system and TP.

Materials and methods
The study was conducted from 2015 to 2019 among residents of four settlements.The geographical coordinates of the settlements are shown in Table 1.

Subjects and data collection
To collect data, questionnaires were administered to schoolchildren and university students.The average age of the survey participants was 19.2 ± 2.9 years (range, 15-25 years; females, 71.7%).The schoolchildren were administered paper questionnaires (questionnaires were filled out by 157 adolescents aged 15-18 years, living in Syktyvkar) and an online survey was conducted among university students.Of the 1316 study participants, 189 people refused to fill out the questionnaire and 63 failed to fill out all forms.These data were excluded from the analysis.The final analysis included the responses from 1064 questionnaires.Table 1 presents the sociodemographic and anthropometric characteristics of the individuals who participated in the study.Table 2 shows the average values and SDs of the studied indicators.Additional information on the frequency distribution of the indicator is presented in Figure 1S (see Supplemental Materials).

Ethical measures
This study was approved by the ethics committee of the Institute of Physiology of Komi Science Center, Ural Branch of RAS, and was conducted in accordance with the ethical and methodological standards established by the Journal for human biological rhythm research (Portaluppi et al. 2008).

Instruments
Each participant in the study provided personal data (place of residence, sex, age, height, weight, and academic performance) and filled out four questionnaires (the Munich ChronoType Questionnaire [MCTQ], the Pittsburgh Sleep Quality Index [PSQI], the Seasonal Pattern Assessment Questionnaire [SPAQ], and the ZTPI).All participants were also required to answer the question "What mean score have you received for the last quarter (examinations)?"These data were used to calculate the grade point average (GPA).Weight and height were used to calculate body mass index (BMI) as weight in kilograms divided by height in meters squared.Sexand-age-specific BMI percentiles were calculated using BMI growth charts (World Health Organization (WHO) 2007).Four groups of participants were identified according to their BMI values (BMIc): (1) underweight (BMI percentile below 5%), (2) normal weight (BMI percentiles ranging from 5% to 84.9%), ( 3) overweight (BMI percentile in a range from 85% to 94.9%), and (4) obese (BMI percentile over 95%).

MCTQ
The test consists of questions about sleep and wakefulness on work/school days and weekends and permits an evaluation of chronotype (MSF SC ), SJL, average weekly sleep duration (SlD), sleep efficiency (SlE), sleep onset (SlO W(F) ), sleep onset latency (SOL W(F) ), awakening (Aw W(F) ) , and sleep inertia (SlI W(F) ) on week (free) days.In addition, the MCTQ contains a question regarding the amount of time spent outside on week (free) days (TSO W(F) ).The formulas and calculation methods of the characteristics listed above, as well as the average values of indicators for young residents of Russia, are described in a previous paper (Borisenkov et al. 2015).

PSQI
To assess the quality of sleep, we used the Russian version of the PSQI (Semenova and Danilenko 2009).This measure consists of 19 questions related to sleep quality, including sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime sleepiness for a period of one month.Global PSQI scores range from 0 to 21 points; in our sample the scores ranged from 0 to 17, with an overall group mean ± SD of 5.9 ± 2.6.In accordance with the recommendation of the test authors (Buysse et al. 1989) a PSQI score ≤5 was considered to be indicative of a good-quality sleeper and a PSQI score >5 was considered to be indicative of a poor-quality sleeper.

SPAQ
Seasonal changes in mood and behavior were evaluated using the SPAQ (Rosenthal et al. 1984).Global seasonality scores (GSS), and a winter pattern of mood seasonality (SAD W ) were evaluated from the test, as described in Borisenkov et al. (2015).

Statistical analysis
The SPSS software package was used for the statistical analysis of data.A Pearson's correlation analysis was carried out, as well as multiple regression analysis, in which PA-, PA+, PR H , PR F , FUT, and DBTP were used as dependent variables, and the characteristics presented in Tables 1 and 2 were used as independent variables (predictors).Categorical variables were included in the model using the codes shown in Table 1.A stepwiseinclusion procedure was used to determine the final set of predictors in the model.To assess multicollinearity, the variance inflation factor (VIF) was assessed.A predictor was excluded from the analysis if VIF was ≥5.

Results
The average chronotype (MSF SC ) of the study subjects was 4 h 50 min, the average sleep duration according to the MCTQ was 7 h 20 min, and mean sleep efficiency was 88.7% (Table 2).Of the study subjects, 50.5% had poor sleep quality (PSQI >5).A total of 81.2% of young people had SJL ≥1 h, and 46.4% had SJL ≥2 h.S-SADw and SADw was noted in 15.9% and 8.6% of the subjects.Young participants had a TSO W of 2.5 h, and a TSO F 3.5 h.The results of a correlation analysis between some of the sleep characteristics obtained from the PSQI and MCTQ are presented in Table 3.Both tools provided indicators that were closely correlated with each other; however, a closer relationship between PSQI and MCTQ data on school days was noted.The data for each indicator were normally distributed (Figure 1S).Therefore, parametric multiple regression analysis was used.

Present hedonistic TP
Time perspective PR H was more common among the residents of Syktyvkar (B = −0.015;ΔR 2 = 0.005), in younger study subjects (B = −0.021;ΔR 2 = 0.026), in people with insufficient weight (B = −0.083;ΔR 2 = 0.007), and those who spent more time outdoors on weekends (B = 0.019; ΔR 2 = 0.008).This type of TP was more often detected in individuals with SJL (B = 0.024; ΔR 2 = 0.004), and those with a seasonality of mood and behavior (B = 0.016; ΔR 2 = 0.038).Young people with PR H reported spending more time in bed after waking up on weekends (B = 0.101; ΔR 2 = 0.008), fell asleep faster on school days (B = −0.103;ΔR 2 = 0.006), and were more often bad sleepers (B = 0.012; ΔR 2 = 0.004).Together, these factors explained 10.6% of the variability in PR H (Table 4; Model 3).B: non-standardized regression coefficient; β: standardized regression coefficient; P: significance of regression coefficient; R 2 : total variance accounted by predictors at their stepwise inclusion in the model; ΔR 2 : portion of the variance accounted for by separate predictors in the model; VIF: variation inflation factor; 1 in this model the DBTP was evaluated.Therefore, interpretation of the signs of the regression coefficients was opposite to that used in models 1 to 5; other abbreviations as in Table 2.

Future TP
Time perspective FUT was more often detected in people with high academic performance (B = 0.154; ΔR 2 = 0.048).This type of TP was characteristic of people with an early chronotype (B = 0.034; ΔR 2 = 0.037), mild SJL (B = −0.033;ΔR 2 = 0.004), and who had a higher quality of sleep (B = −0.014;ΔR 2 = 0.005).They spent less time in bed after waking up on week days (B = −0.244;ΔR 2 = 0.013) and weekends (B = −0.085;ΔR 2 = 0.005), but more often had problems falling asleep on weekends (B = 0.109; ΔR 2 = 0.005).Together, these factors explained 10.0% of the variability in FUT (  (Rönnlund and Carelli 2018).The authors showed that poor sleep quality was associated with past negative TP.However, unlike in this study, the authors did not find an association between sleep quality and other types of TP, possibly because of methodological differences; a different tool was used to evaluate sleep quality (Karolinska Sleep Questionnaire).Our findings are consistent with those of other studies that have also shown depression to be positively associated with past negative TP (Carelli and Wiberg 2012;Zimbardo and Boyd 1999) and present fatalistic TP (Anagnostopoulos and Griva 2012;Zimbardo and Boyd 1999), and negatively associated with balanced TP (Wiberg et al. 2017).However, we did not find a negative association between depression and future TP, as reported earlier (Kooij et al. 2018;Zimbardo and Boyd 1999).Again, this difference may be due to methodological differences; we used various tools for assessing depression, including the SPAQ, whereas the other authors used the Beck Depression Inventory.It is also possible that impaired sleep function and the circadian system have an indirect effect on the relationship between the psycho-emotional state and TP.People with SJL have a higher risk of developing depression (Levandovski et al. 2011) and depression is common in individuals with poor sleep quality (Hayashino et al. 2010).
Chronotype was found to be a moderate predictor of future TP, with people with an early chronotype focused on future TP.Our data were consistent with the results of previous studies (McGowan et al. 2017;Nowack and van der Meer 2013;Ponzi et al. 2015;Stolarski et al. 2013).Previous studies have shown a positive association between present TP and MSF SC and a negative relationship between future TP and MSF SC (McGowan et al. 2017;Nowack and van der Meer 2013).In this paper, we have shown for the first time that SJL, a characteristic of individuals with a late chronotype, is associated with a present hedonistic and fatalistic TP, not the late chronotype itself.We have also shown that people with future and balanced TP are less likely to have SJL.However, a small effect size indicated that this association was weak.McGowan et al. (2017) also noted a positive relationship between DBTP and SJL.The authors did not find associations between SJL and the other TPs, possibly because they used a reduced ZTPI scale.
This study had several advantages.The sample size in the study was large and data were simultaneously collected in four settlements using a single set of tools.The comprehensive nature of the study, including the collection of personal data and indicators characterizing the sleep-wake rhythm, sleep quality, mood seasonality, and time perspective, made it possible to reliably assess the hypothesis that there an association between state of circadian system and TP.The work also had a number of limitations.First, the sex ratio of the subjects was significant biased toward females.Second, we studied schoolchildren only in Syktyvkar.Therefore, results regarding gender and geographical differences of the studied indicators should be treated with caution.Third, the cross-sectional design of the study did not allow us to determine cause-and-effect relationships between any of the studied indicators.Fourth, the range of change in indicators characterizing the time perspective in this study was narrow, especially for PA+, PR H , and FUT (Figure 1S).This could be because the subjects in the study were restricted in age range (i.e. 15 to 25 years), which could, in turn, explain why only a weak association was found between the state of the circadian system and time perspective.Future work is needed to investigate the relationship between the state of the circadian system and the time perspective across a wider age range.

Conclusion
This study confirmed previous data on the association between the state of the circadian system and TP subscales.Chronotype was found to be a moderate predictor of future TP.In addition, sleep quality was found to be a moderate predictor of PA-, PR F , and DBTP.Mood seasonality was a moderate predictor of PR H .We also found that SJL was a weak predictor of PR F , FUT, and DBTP.Taken together, the data indicate that SJL has a limited but significant association with TP in adolescents and young adults.

Table 1 .
Socio-demographic and anthropometric characteristics of the surveyed persons.
W : winter pattern of mood seasonality; S-SAD W : sub-syndrome of SAD W .

Table 2 .
Mean values and standard deviations of parameters measured.

Table 3 .
Comparison of some sleep characteristics obtained by PSQI and MCTQ (n = 1064).
1Pearson's r; in all cases P < 0.0001 to P < 0.00001; abbreviations as in Table2.

Table 4 .
Factors affecting time perspective.

Table 4 ;
Model 5).DiscussionSleep quality was a sleep characteristic moderately associated with all TP subscales except PA+.At the same time, sleep efficiency and sleep duration were not included in any of the models as predictors.Moderate associations between GSS and PA-, and PR H were noted, and as well as between SAD W and PA+, PR F , and DBTP.These data indicate that individuals with FUT, PA+, and DBTP had good sleep quality and low mood seasonality.At the same time, poor sleep quality and a predisposition to seasonal depression were more likely to occur in individuals with PA-, PR H , and PR F .Previous work has examined the relationship between sleep quality and TP