Journal of
Physiological Anthropology

Oshita et al.
Journal of Physiological Anthropology(2024) 43:13
https://doi.org/10.1186/s40101-024-00360-9Open Access

ORIGINAL ARTICLE

Associations of body composition
with physical activity, nutritional intake status,and chronotype among female university
students in Japan
Kazushige Oshita1* , Yujiro Ishihara2, Kohei Seike3 and Ryota Myotsuzono3   

Abstract
Background Having higher muscle mass in early adulthood is an important factor in preventing sarcopenia. However, university students undergo lifestyle changes compared to their high school years, which may lead to changes
in body composition, such as an increase in body fat and a decrease in muscle mass. The study aimed to investigate
the association between body composition and lifestyle behaviors, including chronotype, among Japanese female
university students, due to the prevalence of underweight among young females in the country.
Methods The physical activity level (PAL), daily dietary intake status, morningness-eveningness questionnaire(MEQ) score, and body composition of 230 students were assessed in this cross-sectional study. Body composition
was measured using a multifrequency bioelectrical impedance analyzer, and body mass index (BMI), body fat percentage (%BF), and skeletal muscle mass index (SMI) were determined.
Results Individuals who were evening type (ET) had a higher %BF and lower SMI than those who were non-ET,
but no differences in body weight or BMI were found. Although ET individuals had lower total energy intake, protein
intake, and PALs than non-ETs, the differences were small. However, multiple regression analyses showed that SMI
was significantly positively associated with MEQ and PAL, and %BF was significantly negatively associated with MEQ
and PAL.
Conclusion These results suggest that female university students with lateness of chronotype and low physical activity have a body composition imbalance resulting in higher body fat and lower muscle mass. Therefore, young females
may need to take chronotype-specific measures (especially ET individuals) to help them maintain an appropriate
body composition.
Keywords Skeletal muscle mass index, Body fat, Appendicular muscle mass, Morning type, Evening type

*Correspondence:
Kazushige Oshita
oshita@ss.oka-pu.ac.jp
1
Department of Human Information Engineering, Okayama Prefectural
University, Soja, Okayama 719‑1197, Japan
2
Center for Fundamental Education, Okayama University of Science,
Okayama, Japan3
Department of Sport Science, Kyushu Kyoritsu University, Kitakyushu,
Japan

Background
Maintaining muscle mass and strength in middle-aged
individuals, minimizing the decline in older age, andincreasing the peak values achieved in adolescence andyoung adulthood have been noted to delay or prevent
future sarcopenia [1, 2]. Furthermore, although exercise
interventions in frail older adults play an important rolein improving muscle strength and physical function, their© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

effect on increasing muscle mass is likely limited [3].
Therefore, increasing the peak muscle mass, particularly
in early adulthood, may be important for the preventionof sarcopenia. Consequently, identifying the factors associated with muscle mass acquisition in young adults is ofgreat importance.
Among young adults, university students undergo significant lifestyle changes that are thought to affect theirweight [4] and body composition [5]. Further, individuals
have a time orientation, known as the chronotype, andthe sharp maximum of lateness in the chronotype (i.e.,
becoming an evening type (ET)) occurs around the age of
20 years [6]. Recent studies have also reported an association between chronotype and body physique in university students. For instance, a study of 384 students (150
males and 234 females) from the United States found asignificant increase in body mass index (BMI) with age
and lateness of chronotype and later chronotypes gaining
weight faster than early chronotypes [7]. Another study
with a larger sample size also concluded that university
students with ET were more likely to be affected by overweight/obesity [8].
In addition to BMI, several recent studies have reported
associations of chronotype with body composition andlifestyle behaviors. A study of 387 female students in
Saudi Arabia found no significant main effects of chronotype on body composition, nutrient intake status, andenergy expenditure [9]. However, the study participants
had a higher mean body fat percentage (%BF) of 36.6%
and a lower mean muscle mass (skeletal muscle index;
SMI of 5.7 kg/m2) [9], which is the almost same as the
cut-off value for the diagnosis of sarcopenia in older people [2, 10]. Therefore, this study examined a student population with extremely low muscle mass and high bodyfat. In contrast, a study of 327 German students (58%female) found that a later chronotype was associated withhigher visceral fat mass and lower muscle mass [11]. The%BF of the participants in this study was 16.2% in males
and 26.5% in females, calculated from BMI and fat massindex, which falls within the normal range. Although
this study suggested that this association may be related
to physical activity, the nutritional status of the participants was unknown [11]. Therefore, research is needed
to determine the relationship between lifestyle behaviors,
including nutritional intake status, with chronotype andbody composition in the general student population.
As described above, identifying the factors associatedwith high muscle mass in university students is an important consideration for preventing future sarcopenia. Muscle mass in the diagnosis of sarcopenia is assessed by anindex calculated from the sum of the appendicular muscle mass (AMM) [2, 10], commonly referred to as SMI.
It is worth noting that individuals of Asian ethnicity may

Page 2 of 9

have lower fat-free mass than those of other ethnicities[12]. In particular, a high proportion of young Japanesewomen are underweight [13, 14] and have body composition concerns, such as normal-weight obesity [15, 16],
which are associated with low fat-free mass. Therefore,studies on SMI in Japanese female university students
have been reported in recent years from the perspectiveof future prevention of low SMI [17–20]. Previous studies showed that nutritional status such as low total energy
intake (TEI) and/or protein intake (PI) has been associated with SMI in female university students [17, 20]. Low
current physical activity level (PAL) was also found to be
associated with low SMI, regardless of previous exercise
participation [19]. Further, a previous study has indicatedthat university students in Japan have a shorter sleep
duration compared to those in other countries [21]. Thismay be due to socially required early wake-up and bedtime schedules, particularly among young people whohave a later chronotype, which can lead to health problems caused by the mismatch between biological andsocial time [22]. Therefore, it is important to investigate
the factors associated with SMI among Japanese female
students, considering chronotype aspects. The present
study examined the relationship between lifestyle factors(PAL and dietary intake) and chronotype in relation tobody composition, including the SMI in Japanese female
university students.

Methods
Participants

Of the 251 total female university students, 230 participants were included in the analysis, excluding 10 with avery high PAL (2.4 or higher, PAL estimation described
below) and 11 with incomplete survey responses (Fig. 1).
The reasoning for exclusion based on PAL was because
an extremely high PAL, such as is observed in athletes,
is likely associated with high muscle mass. In addition,
well-trained athletes are likely to be of the morning type(MT), although this may vary depending on the exercise [23]. The participants were recruited from one junior college and two universities in different prefecturesin Japan, and the academic years were randomly chosen(aged 18–22 years). For ethical reasons, participants were
informed orally and in writing in advance that the survey would be anonymous, that it would be used for the
purpose of this study and not for any other purpose, andthat any data not used for the study would be discarded.
In the event that the results of the survey were published,participants were told that the collected data would bestatistically processed and then published in such a waythat individuals could not be identified, and that thesurvey would only be carried out if participants consented. This study was approved by the Research Ethics

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

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Fig. 1 Flow diagram outlining participant selection for this study

Committee of the Kyushu Kyoritsu University (approval
number: 2022–08).
Survey and procedures

Daily activity, dietary frequency, and chronotype surveys
were conducted using a group interview method with
10–40 participants. The surveys and body composition
measurements were conducted in the morning (9–12a.m.) between late September and early December. The
surveys were conducted with explanations from exercise
and nutrition experts to increase the reliability of the
responses.
Estimation of PAL

The average daily activity and exercise times on weekdays
in the previous month were assessed using a questionnaire-based daily activity diary. The PAL of each activity
was evaluated by calculating the daily average from eachclassified activity using the product of the energy expenditure index, expressed as a multiple of the basal metabolic rate (BMR), and the activity time based on a study
that estimated PAL according to the lifestyle of university students based on the Dietary Reference Intakes forJapanese people (DRIs-J) [19]. BMR was calculated basedon height, weight, age, and sex using the National Institute of Health and Nutrition formula [24, 25]. The totalenergy expenditure (TEE) was calculated by multiplying
the BMR by the PAL.
Nutritional intake status and energy expenditure

The nutritional intake status and energy expenditure
were assessed using a food frequency questionnaire
based on food groups (FFQg) (FFQ-NEXT short version,
Kenpakusha, Japan). The FFQg results were analyzed
with Microsoft Excel add-in software Eiyou plus (Kenpakusha, Japan) to calculate the daily nutrient intake ofeach food group and the TEI, PI, fat intake (FI), and carbohydrate intake (CI). The TEI to TEE ratio (TEI/TEE)
was also calculated. Although data from the UK Biobank
project suggest that MTs consume more fruit and vegetables than ETs [26], the present study focused on thesenutrients as factors related to body composition, as inprevious studies [9, 17, 20].
Chronotypes

The Japanese version [27] of the Horne and Ostberg
morningness-eveningness questionnaire (MEQ) [28] wasused to assess chronotypes. It consists of 19 questions

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

and is scored (16–86) based on the answers. Chronotypes
are scored as definitely MT (score: 70–86), moderately
MT (score: 59–69), neither type (score: 42–58), moderately ET (score: 31–41), and definitely ET (score: 16–30)[28].
Body physique and composition

Body weight and composition were measured with abody composition analyzer (MC-780, Tanita, Japan)
using the multifrequency bioelectrical impedance analysis (BIA) method. The participants were asked not to urinate or defecate and to rest in a room at a comfortabletemperature where they did not feel hot or cold before
the measurement. The participants wiped their palms
and soles with alcohol-free wet wipes to moisten andclean the electrode contact areas, stepped onto the electrode portion of the machine, and grasped the hand electrodes with both palms for measurement.
BMI was calculated by dividing body weight (kg) by thesquare of height (­ m2), and SMI was calculated by dividing
AMM (kg) by the square of height ­(m2).
Statistical analyses

The means and standard deviations (SDs) were calculated
for all participants for each measure. As mentioned in the“Background” section, young individuals such as thoseincluded in the present study are expected to be predominantly ETs, and classifying them by chronotype (i.e., MT
or ET) may result in fewer MTs. Consequently, following the classifications in the previous study [28] (see thePage 4 of 9

“Chronotypes” section), participants with an MEQ of 41
or less were classified as the ET group. Participants withan MEQ higher than the mean of all the participants + 0.5
SD (= 47.39 + 4.68 = 52.0) were classified as the non-ET
group (Figs. 1 and 2). The Mann–Whitney U-test wasused to compare variables between the groups. Additionally, the d-value was calculated as the effect size using
Cohen’s method.
The relationships between body composition (%BF,
AMM, and SMI) and body mass, MEQ, PAL, and TEI
were examined using correlation analyses. The relationships between %BF, AMM, and SMI, as dependent
variables and lifestyle factors for which significant or significant trend relationships were found, were examinedusing forced entry multiple regression analysis.
Statistical software StatFlex (ver. 7.0.10, Artec, Osaka,
Japan) was used for these statistical procedures, with astatistical significance level of P ≤ 0.05 and 5% < P ≤ 10%
for a significant trend. Effect sizes were graded as d < 0.2
trivial effect, d = 0.2–0.5 small effect, d = 0.5–0.8 moderate effect, and 0.8 < d large effect [29].

Results
Measurements for all the participants are shownin Table 1 (left), and the chronotype distribution isshown in Fig. 2. Although the distribution of chronotypes for the study participants had a kurtosis of 0.01,
the skewness was 0.20, indicating that the distribution
was slightly skewed toward lower MEQ scores (Fig. 2).
Sixty-one participants were designated as the ET group

Fig. 2 Frequency distributions of MEQ scores. (Sturges’ rule was used to calculate the number of bins in the histogram; 230 participants = 8.8≈9bins)

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

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Table 1 Mean value of each variable for all participants and ET and non-ET groups
All (n = 230)

ET group (n = 61)

Non-ET group (n = 71)

ET vs. non-ET
P

Effect size (d)Age (years)

18.8

±

1.0

18.7

±

0.8

18.8

±

0.8

0.28

0.06

Height (cm)

157.5

±

5.7

156.9

±

5.2

157.8

±

5.4

0.39

0.08
0.09

Weight (kg)

53.1

±

7.7

52.3

±

7.5

53.6

±

6.4

0.20

BMI (kg/m2)

21.4

±

2.6

21.2

±

2.6

21.5

±

2.3

0.25

0.06

%BF (%)

28.8

±

5.6

29.9

±

4.8

27.3

±

5.5

0.01

0.22

AMM (kg)

16.3

±

2.4

15.8

±

2.1

17.0

±

2.5

< 0.01

0.21

SMI (kg/m2)

6.55

±

0.74

6.41

±

0.56

6.81

±

0.86

< 0.01

0.22

MEQ score

47.4

±

9.4

36.0

±

4.0

58.3

±

5.3

PAL

1.72

±

0.26

1.68

±

0.26

1.76

±

0.25

0.04

0.14

TEI (kcal/day)

1781

±

519

1708

±

582

1858

±

490

0.09

0.14

TEI/TEE

0.89

±

0.30

0.88

±

0.32

0.90

±

0.29

0.83

0.03

PI (g/day)

59.2

±

20.1

54.9

±

20.6

62.1

±

20.5

0.04

0.16

FI (g/day)

62.9

±

22.8

61.1

±

24.9

66.6

±

24.5

0.24

0.10

CI (g/day)

237.5

±

67.4

227.6

±

75.7

246.1

±

58.2

0.11

0.14

Values are expressed as means ± SDs(mean MEQ score; 36.0 ± 4.0), and 71 participants were
designated as the non-ET group (58.3 ± 5.3).
The measurements for each group are presented inTable 1 (right). Participant characteristics including
age, height, weight, and BMI did not differ significantly
between the groups. Regarding body composition, the%BF of ETs was significantly lower (27.3 ± 5.5%) thanthat of non-ETs (29.9 ± 4.8%). Furthermore, the AMM
and SMI of non-ETs (17.0 ± 2.5 kg and 6.8 ± 0.9 kg/m2)
were significantly higher than those of ETs (15.8 ± 2.1 kg
and 6.4 ± 0.6 kg/m2). However, the differences were
small (d = 0.21 and 0.22). These results indicate that ET
individuals had higher body fat and lower muscle mass
than those with higher MEQ scores, although no differences in body physique were found, and the measured
differences were negligible.
With respect to physical activity, the PAL of non-ETs(1.76 ± 0.25) was significantly higher than that of ETs(1.68 ± 0.26), but the effect size was trivial (d = 0.14).
This finding indicates that ET individuals are physicallyless active than those with higher MEQ scores, but thedifference is slight.
The daily dietary intake status showed that although
the TEI of non-ETs (1858 ± 490 kcal/day) tended to besignificantly higher than that of ETs (1708 ± 582 kcal/day), the TEI/TEE did not differ significantly between thegroups. Moreover, the FI and CI were not significantly
different between the groups. Furthermore, although the
PI of non-ETs (62.1 ± 20.5 g/day) was significantly higher
than that of ETs (54.9 ± 20.6 g/day), the effect size wastrivial (d = 0.16). Thus, the results indicate that although
ET individuals had lower energy and protein intakes, thedifferences were slight.
Table 2 presents the results of the correlation analyses.
%BF showed a significant positive correlation with bodyweight and BMI and a significant negative correlation
with MEQ and PAL. AMM and SMI showed a significant positive correlation with weight, BMI, MEQ, PAL,
and TEI (significant trend in relationship between SMI
and TEI). The results of the multiple regression analyses
are presented in Table 3. Analyses of %BF with MEQ and

Table 2 The relationships between body composition (%BF, AMM, and SMI) and body mass, MEQ, PAL, and TEI
Weight
%BF
AMM
SMI

BMI

r

0.57

0.75

P

< 0.01< 0.01

MEQ− 0.17< 0.01PAL− 0.30< 0.01

TEI− 0.11

0.11

r

0.68

0.40

0.21

0.43

0.13

P

< 0.01< 0.01< 0.01< 0.01

0.05

r

0.55

0.50

0.25

0.36

0.11

P

< 0.01< 0.01< 0.01< 0.010.10

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

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Table 3 Results of the multiple regression analysis
Dependent variable: %BF
Modeled variableβStd. β (95% CI)

Modeled variableβStd. β (95% CI)

MEQ*− 0.04− 0.13 (− 0.25 ~  − 0.01)

Weight**0.48

0.66 (0.57 ~ 0.75)

MEQ**− 0.10− 0.17 (− 0.26 ~  − 0.08)

PAL**− 8.81− 0.41 (− 0.50 ~  − 0.31)

PAL**− 1.38− 0.29 (− 0.41 ~  − 0.16)

R2 = 0.11, F = 14.17, P< 0.01

R2 = 0.53, F = 84.60, P < 0.01

Dependent variable: AMM
Modeled variableβStd. β (95% CI)

Modeled variableβStd. β (95% CI)

MEQ*

0.04

0.14 (0.02 ~ 0.25)

Weight**0.19

0.61 (0.52 ~ 0.70)

PAL**

3.78

0.40 (0.28 ~ 0.52)

MEQ*

0.02

0.10 (0.01 ~ 0.18)

TEI< 0.010.07 (− 0.05 ~ 0.19)

PAL**

2.77

0.29 (0.20 ~ 0.38)

TEI< 0.010.06 (− 0.03 ~ 0.15)

R2 = 0.21, F = 19.74, P < 0.01

R2 = 0.56, F = 73.01, P < 0.01

Dependent variable: SMI
Modeled variableβStd. β (95% CI)

Modeled variableβStd. β (95% CI)

MEQ**

0.01

0.19 (0.07 ~ 0.31)

Weight**0.05

0.49 (0.39 ~ 0.60)

PAL**

0.94

0.33 (0.20 ~ 0.45)

MEQ**

0.01

0.15 (0.05 ~ 0.26)

TEI< 0.010.05 (− 0.07 ~ 0.17)

PAL**

0.69

0.24 (0.14 ~ 0.35)

TEI< 0.010.04 (− 0.06 ~ 0.15)

R2 = 0.17, F = 15.33, P < 0.01*

P ≤ 0.05**

R2 = 0.40, F = 37.34, P < 0.01

P < 0.01

PAL (R2 = 0.11, P < 0.01) showed that %BF had a significant negative relationship with MEQ and PAL. Analysis
of AMM in relation to MEQ, PAL, and TEI (R2 = 0.21,
P < 0.01) showed that AMM had a significant positive
association with MEQ and PAL. Analysis of SMI by
MEQ, PAL, and TEI (R2 = 0.17, P < 0.01) showed that SMI
was significantly positively associated with MEQ and
PAL. These relationships were similar when body weight
was added to the modeled variable, and %BF, AMM, and
SMI were significantly positively associated with bodyweight, respectively.

Discussion
The distribution of chronotypes was slightly skewed
toward lower MEQ scores (Fig. 2). This finding suggeststhat many of the students in this study had lower MEQ
scores (i.e., ETs), as previous research has shown thatpeak chronotype lateness occurs around the age of 20[6]. The mean MEQ score for non-ETs was 58.3, whichis “neither type” close to MT [28]. The mean SMI for
all participants in this study was 6.55 kg/m2, which wasslightly higher than the mean of 6.4 ± 0.5 kg/m2 found ina study of 426 female Japanese university students using
the same body composition device [17]. Furthermore,
although the correlation was weak, SMI was significantly
positively correlated with the MEQ, and the SMI of ETs
was significantly lower than that of non-ETs. On theother hand, the %BF results were the opposite, i.e., %BF
was significantly negatively correlated with MEQ and washigher in ETs than in non-ETs. Therefore, ET individuals
have an imbalance in body composition resulting in lessmuscle mass and more body fat but show no differences
in body mass.
Differences in body composition are likely related tolifestyle behaviors. In previous studies, SMI was significantly associated with TEI in female university students [17] or students with high PAL [20]. However, inthe present study, %BF and SMI did not significantly
correlate with TEI, whereas AMM was significantly
associated with TEI, although the correlation was weak(r = 0.13). The mean TEI in the previous study was low(1562 ± 489 kcal), suggesting that many participants
could not consume sufficient nutrients for protein synthesis in accordance with their physical activity andthat TEI was related to SMI [17]. The TEI in the present
study was 1781 kcal for all participants, and the TEI wasslightly lower than the TEE (TEI/TEE was approximately
0.9). Such a difference in energy intake may explain thediscrepancy of results from this and previous studies on
the association between TEI and SMI.
Regarding dietary intake and chronotype, recent systematic reviews have found that both MTs and ETs had
similar energy intakes [30, 31]. In the present study, theTEI of ETs tended to be significantly lower than that ofOshita et al. Journal of Physiological Anthropology(2024) 43:13

non-ETs, but the effect size was small, and no significant difference in TEI/TEE was found. These findings
suggest that the difference in TEI between chronotypes
is not large. However, the distribution of nutrients inthe three meals may differ between chronotypes, even if
the difference in the TEI is small. An ET was more likely
than an MT to skip breakfast or consume fewer calories
during breakfast while consuming more calories during
dinner and snacks in the afternoon or at night [30–32].
Additionally, irregular meal patterns were also foundto be associated with chronotype among Japanese university students [33]. Therefore, they may have different daily intake patterns despite having the same dailyenergy intake. Furthermore, the frequency of breakfast
intake among Japanese female university students had
shown a significant positive correlation with SMI [17]. In
another study, ensuring adequate protein intake for eachof the three meals as well as total daily protein intake wasimportant for high muscle mass among Japanese university students [34]. These findings suggest that a moredetailed analysis of the dietary intake is required in futurestudies.
According to a previous review, the relationshipbetween physical activity and chronotype among university students is inconsistent [35]. In the present study,
multiple regression analyses showed that both AMM and
SMI were significantly positively associated with MEQ
and PAL, and %BF was significantly negatively associated with MEQ and PAL. This suggests that lateness ofchronotype and lower physical activity are associatedwith lower muscle mass and higher body fat. However,the PALs of both groups in the present study were within
the normal range of the DRIs-J classification, and theeffect size was trivial. A previous study suggested that
although weekday physical activity is unlikely to show
differences by chronotype, weekend physical activity (i.e.,free from academic/social obligations) of ETs is lower
than that of MTs [36]. Thus, although only weekday PAL
was assessed in this study, the difference in PAL could
have been be greater if the total weekly PAL was assessed.
A similar pattern was observed for nutrient intake. The
PI was significantly lower in non-ETs in this study, which
was not surprising, as the TEI was also significantly lower
tend (i.e., lower total food intake). However, no significant differences were observed between the groups in
FI and CI, suggesting that these intakes may have been
relatively higher in ETs. In previous studies, the intake oflipids, carbohydrates, sodium, and polyunsaturated fatty
acids [32] or sucrose and saturated fatty acids [37] washigher in ETs than in MTs, and a later chronotype wasassociated with a lower overall dietary quality index [38].
Furthermore, this difference was more pronounced on
weekends [37]. Therefore, examining not only daily PAL

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and dietary intake but also their variation over the week
would provide more evidence of the relationship between
lifestyle and body composition, considering chronotype.
Previous studies have suggested that the association
between BMI and chronotype may vary based on factors
such as location, age, and gender [9], as well as among
different racial/ethnic groups [7]. However, our study
with Japanese female students found that body composition was related to chronotype and physical activity, similar to a previous study with German students (which did
not investigate nutrient intakes) [11]. It is therefore possible that this relationship may be observed across racial/ethnic groups. However, it is important to note that studies have reported variations in chronotype among different racial/ethnic groups, with a higher prevalence of MT
in blacks compared to whites [39]. As the chronotypeof the present study population skewed toward the laterside, different findings might have been observed in a
population with a greater prevalence of MT. In addition,although this study was conducted during the almost
autumn season in Japan (late September to early December), chronotype variation has been reported in various
seasons [40]. Seasonal differences should therefore beconsidered in future research.
This study had several limitations. First, because PAL,
nutrient intake, and chronotype were assessed using aquestionnaire, future research will need to assess thesefactors objectively. In particular, chronotype must be
assessed using clock gene analysis, as a previous study
suggested that the intra-week variation in physicalactivity differed between clock genes and questionnaire-based assessments of chronotype [36]. Secondly,social jetlag (SJL), which refers to the difference in the
midpoint of sleep between work/school days and freedays, is a topic of interest in chronobiology and healthresearch. In the Japanese population, a late chronotype has been found to be associated with an increase
in SJL, and females may be at higher risk of developing
adverse health symptoms because they have a higher
SJL than males [22]. A previous study found that having
electronic devices in the bedroom and using the Internet before going to bed are associated with SJL [41].
Although the participants in the present study attended
university during the day, their nocturnal activity
remains unknown. As a recent report has shown that anincrease in SJL is linked to less physical activity in Japanese female students [42], future research should investigate sleep status and night-time activities as well as
chronotype. Finally, as this was a cross-sectional study
conducted at three universities with a limited numberof participants, future research should examine changes
in body composition in relation to changes in chronotypes and lifestyle behaviors. Although the sample size

Oshita et al. Journal of Physiological Anthropology(2024) 43:13

in this study was similar to that in recent studies on theassociation between chronotype and body composition in university students [7, 9, 11], further research is
required to investigate this association at more universities with a larger number of participants.

Conclusions
This study examined the relationships between physicalactivity, dietary intake, chronotype, and body composition in Japanese female university students. ET individuals had higher body fat and lower AMM and SMI thanthose with high MEQ scores, but no differences in bodymass were found. Although ET individuals had lower
PAL, TEI, and PI than those with high MEQ scores, thedifferences were small. However, multiple regression
analyses showed that %BF was negatively associated with
MEQ and PAL, whereas AMM and SMI were positively
associated with MEQ and PAL. These results suggestthat lateness of chronotype and low physical activity areassociated with higher body fat and lower muscle massin female university students. Previous reports haveshown that people around the age of 20, such as university students, have peak chronotype lateness [6]. Further,
university students undergo lifestyle changes comparedto their high school years, which may lead to changes inbody weight [4] or composition [5]. Therefore, chronotype-specific interventions (especially for ET individuals)
are necessary among young females to help maintain an
appropriate body composition.
Abbreviations
SMI	Skeletal muscle mass index
AMM	Appendicular muscle mass
PAL	Physical activity level
TEI	Total energy intake
PI	Protein intake
ET	Evening type
BMR	Basal metabolic rate
DRIs-J	Dietary Reference Intakes for Japanese people
TEE	Total energy expenditure
FFQg	Food frequency questionnaire based on food groupsFI	Fat intake
CI	Carbohydrate intake
MEQ	Morningness-eveningness questionnaireMT	Morning type
BIA	Bioelectrical impedance analysis
BMI	Body mass index
SD	Standard deviation
std β	Standard β
SJL	Social jetlag
Authors’ contributions
OK designed this study and interpreted the data. OK, IY, SK, and MR collected
and analyzed the data. OK was a major contributor to writing the manuscript,
and IY, SK, and MR substantively revised it. The authors read and approved thefinal manuscript.
Funding
This work was supported by the Wesco Science Promotion Foundation.

Page 8 of 9

Availability of data and materials
The datasets generated and analyzed during the current study are available
from the corresponding author upon reasonable request.

Declarations
Ethics approval and consent to participate
All participants provided written informed consent before enrolment in thepresent study, which was approved by the Research Ethics Committee of
Kyushu Kyoritsu University (2022–08).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 23 February 2024 Accepted: 26 April 2024

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