Abstract
Objective
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance based on the top 5% of model-predicted mental health outcomes using compositional data analysis.
Methods
A total of 6,084 university students aged 17–24 years in Southwest China self-reported their daily durations of moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SED), and sleep (SLP). They were stratified by gender and then randomly and equally assigned to the “recommendation” group and the “validation” group. Using compositional data analysis, time-use compositions (MVPA, LPA, SED, SLP) were transformed into isometric log-ratios (with quadratic terms as needed) and subsequently used in regression models to predict the three mental health outcomes. All possible combinations of motion components were examined to determine the combination with the highest correlation (top 5%) for each outcome.
Results
Through research and analysis of the recommendation groups, the optimal combination of average (range) time usage is determined as follows: for males, MVPA 92 (60–110) min/day, LPA 361 (310–400) min/day, SED 372 (350–480) min/day, SLP 614 (530–680) min/day; for females, MVPA 58 (40–90) min/day, LPA 290 (180–390) min/day, SED 445 min (400–560), SLP 665 (580–740) min/day. The recommended durations served as benchmarks for the validation group. Participants who met the optimal 24-h movement behavior time showed significantly lower depression (males: β = –1.290, P < 0.05; females: β = –1.040, P < 0.05), anxiety (males: β = –1.350, P < 0.05; females: β = –1.760, P < 0.001), and stress (males: β = –1.280, P < 0.05; females: β = –1.340, P < 0.05) compared to those who did not meet the criteria. Only 14.4% of males and 8.9% of females in the validation sample simultaneously met the recommended times for all four movement behaviors.
Conclusion
The optimal 24-h movement behavior time differs between men and women. Males tend to require a longer optimal MVPA duration than females, while females require a longer optimal SLP duration than males. The findings provide valuable reference for developing 24-h movement guidelines and promoting healthy and balanced lifestyles among university students.
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26534-x.
Keywords: 24-h movement behavior, Mental health, Compositional data analysis, University students, Time-use
Introduction
Mental health problems are now a critical global public health challenge, with disorders like depression and anxiety rising steadily. Over one billion people now live with mental health conditions, with depression and anxiety among the leading contributors to reduced life quality and greater socioeconomic burden [1]. Chronic stress exacerbates various physical and mental illnesses, disrupting individuals’ normal lives [2]. University students face serious mental health challenges under academic and daily pressures, and this also applies to Chinese university students [3]. Han et al. (2025) analyzed 41,620 Chinese university students surveyed in 2023 and reported detection rates of 9.8% for depression, 15.5% for anxiety, and 6.5% for comorbid depression and anxiety [4]. A review study found that 24-h movement behaviors, defined as moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SED), and sleep (SLP) across a 24-h day, are a key factor influencing mental health. When sufficient MVPA, increased LPA, reduced SED, and adequate SLP coexist, they are associated with better mental health [5]. Many studies in Chinese university students indicate that greater time spent in MVPA and LPA within the 24-h movement behavior framework is generally associated with better mental health outcomes [6–8]. In recent years, only a few countries have issued 24-h movement behaviors guidelines aimed at helping individuals structure their daily routines more healthily [9–11].
However, these guidelines are based on combinations of individual behavioral benefits, and by their nature, they largely overlook the covariation among different behaviors [12]. Additionally, university students experience academic pressure, irregular schedules, and specific campus environments, so their behavior patterns may differ from the general adult population [13, 14]. They therefore need tailored 24-h movement guidelines, which are currently unavailable. Dumuid et al. (2020) developed the optimal time balance method, using compositional data analysis within the 24-h movement framework [15], and this method identifies optimal durations and timing windows for 24-h movement behaviors that improve physical and mental health. Subsequently, a series of studies began applying the optimal time balance method to establish optimal movement behavior balance for various populations across different countries [16–22]. Li et al. (2025) analyzed 463 university students, identified gender-specific optimal movement behaviors balance and range for improving physical fitness in males MVPA 142 min (90–150), SED 534 min (450–670), LPA 295 min (110–330), SLP 469 min (350–510)) and in females MVPA 115 min (60–140), SED 536 min (450–680), LPA 306 min (220–320), SLP 482 min (340–540) [21]. However, few studies have explored the optimal balance of movement behaviors for improving university students’ mental health.
Based on this, this study focuses on university students in Southwest China, with the following objectives: (1) to establish optimal 24-h movement behavior balances for male and female students to support mental health; (2) to use the study-defined recommended ranges as benchmarks to assess adherence in Chinese university students; and (3) to validate the mental health effects of these recommended ranges. In all regression analyses, we adjusted for sleep efficiency, sleep quality, parental education, and household income, as these factors are associated with mental health and health-related behaviors in young adults and may confound associations between 24-h movement behaviors and mental health [23–26].
Materials and methods
Participants
This cross-sectional study recruited undergraduates aged 18 to 25 years from Chongqing, Sichuan, and Yunnan. Using stratified sampling, we randomly selected one to two districts with undergraduate institutions per province and then randomly selected at least one university per district, yielding seven universities. In July and August 2025, we finalized the study design, questionnaire, and sampling procedures, contacted participating sites, and configured the online survey on the “Question Star” platform. Data were collected online from September 10 to September 24, 2025, using a standardized protocol supported by one to two institutional coordinators at each university who sent daily reminders. The questionnaire assessed sociodemographic characteristics, 24-h movement behaviors, and mental health. Of 8,566 invited students, 7,569 provided electronic informed consent and participated (response rate 88.8%). During data cleaning, we first excluded questionnaires completed in < 125 s (the 5th percentile of the overall completion-time distribution). Second, we removed questionnaires with missing data on key analysis variables, including the 24-h movement behavior components and mental health scores. Finally, we excluded sex-specific outliers in continuous analysis variables, defined as values outside the mean ± 3 standard deviations calculated separately for males and females; this rule was applied to MVPA, LPA, SED, SLP, and depression, anxiety, and stress scores. We further excluded participants who met any of the following criteria: (1) limited physical mobility; (2) diagnosed sleep disorders; (3) diagnosed cognitive impairment; (4) membership in university sports teams. After sex stratification, participants were randomly split within each stratum into recommendation and validation groups using a reproducible simple random half-split in R. Within each sex, baseline comparability between groups was assessed using standardized mean differences. The recommendation groups were used for model development and to derive the recommended 24-h movement behavior balance, and the validation groups were used to confirm these findings. The study was approved by the Ethics Committee of China West Normal University (Approval No. 2025LLSC0047).
Methods
24-h movement behaviors
The 24-h Movement Behaviors Questionnaire (24HMBQ) assessed participants’ daily patterns of sleep, SED, and physical activity (PA). Self-reported durations were collected for weekdays and weekends across sleep, various sedentary behaviors, sedentary interruptions, and PA domains (dormitory life, transport, exercise), including intensity. Reported minutes were summed and averaged to compute daily minutes for each behavior. The 24HMBQ has been validated for good reliability and validity [27].
Mental health outcomes
Mental health status was measured by the 21-item Depression-Anxiety-Stress Scale (DASS-21). This instrument has shown good reliability and validity in Chinese adult samples, with stable internal consistency [28, 29]. The scale includes three subscales: depression, anxiety, and stress. The depression subscale captures low mood and lack of interest; the anxiety subscale reflects physiological arousal; the stress subscale assesses general tension and irritability. Each subscale consists of 7 items [25]. Responses are scored on a 4-point Likert scale from 0 (“does not apply”) to 3 (“applies most of the time”). Participants report how much each statement matched their experience over the past week. Subscale scores are summed and then multiplied by 2 to align with the DASS-42 metric (range 0–42). Higher scores indicate more severe symptoms of depression, anxiety, or stress.
Covariates
Gender and age, parental education, household income, sleep quality, and sleep efficiency were included as covariates. Parental education was categorized by whether parents had received formal higher education. The highest category was graduate school or above. Annual household income was grouped based on per capita disposable income in the surveyed provinces and municipalities. Sleep quality and sleep efficiency were both measured using the Pittsburgh Sleep Quality Index (PSQI). Sleep quality was assessed via item six, and sleep efficiency was calculated as the ratio of actual sleep time (item 4) to time in bed (get-up time from item 3 minus bedtime from item 1), multiplied by 100% [30]. Participants completed a questionnaire covering their sociodemographic details and family background (see Supplementary File S1 for the questionnaire items).
Statistical analysis
Standard descriptive statistics were used to summarize the study population, presenting means with standard deviations or frequencies with percentages. Each participant’s 24-h time was divided into MVPA, LPA, SED, and SLP (i.e., a four-part movement behavior composition). There were no zeros in any of the measured behavioral variables. The 24 h time-use composition was characterized using compositional means. Geometric means were calculated for each component and then normalized to sum to 24 h. Data analysis followed the optimal time zone analysis method of Dumuid et al. (2020) [15]. The compositions and car packages in R version 4.5.1 were used to perform the compositional statistical analyses and to derive the optimal 24-h movement behavior time for university students. The detailed analytical steps were as follows:
Models to explore the relationship between time use and mental health
Participants’ average 24-h time-use composition (MVPA, LPA, SED, and SLP) was expressed as a set of isometric log ratios (ilrs) to allow compositional data analysis (CoDA). Linear regression models with robust estimators were used to regress time-use composition ilrs against mental health (Depression, Anxiety, Stress) Z-scores. If diagnostic plots revealed signs of non-linearity, squared terms of the compositional variables were examined and retained if they significantly improved model fit (partial F-test, P < 0.10). All models were adjusted for gender, age, parental education, family income, sleep quality, and sleep efficiency.
A comprehensive set of time-use compositions was generated to capture all possible combinations of daily activity behaviors (MVPA, LPA, SED, and SLP) in 10-min increments within the empirically observed ranges. Following the Goldilocks day optimal time use approach, the grid resolution was set at 10 min, and the outer bounds were restricted to values within the mean plus or minus three standard deviations of each behavior to limit extrapolation beyond the observed sample footprint [15, 17–19]. This created a 3D grid of equally spaced data points representing hypothetical participants, all with different time-use compositions. The outer boundaries of the 3D grid were truncated at ± 3 SD of the behaviors’ univariate distributions, yielding 7,349 unique time-use compositions for males and 6,715 for females. The limits (in minutes/day) for the hypothetical composition grid were set for each sex. For males, the ranges were MVPA 10–110, LPA 140–390, SED 340–730, and SLP 460–730. For females, the ranges were MVPA 10–100, LPA 140–390, SED 340–740, and SLP 470–740. (Note, although MVPA durations of 0 min/day were within –3 SD of the sample mean, zero values were excluded because all behaviors were required to be expressed as log ratios before use in compositional model prediction.) The compositional regression models described above were used to estimate mental health outcomes for the grid of hypothetical time-use compositions (expressed as ilrs). Model predicted mental health scores were plotted over the full range of each daily activity behavior in the hypothetical grid, and a LOESS smooth curve was fitted to depict the relationship.
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(2)
Finding the best daily activity composition for mental health
Predicted mental health scores were generated for all daily composition combinations (with 10-min increments within the observed bounds). These predictions were then ordered from lowest to highest, and the central range of the best 5% was defined as the “best mental health zone.” The 5% cutoff was selected to reflect contemporary thresholds for statistical and clinical significance (eg, alpha of 0.05, 95th percentile) and to allow overlap among the best zones for each individual outcome. The overlapping region across outcomes was labelled the “best overall mental health zone,” representing the set of time-use compositions predicted to yield optimal scores across all measures. The best mental health zones were plotted in 3D quaternary plots, and the region where all the best mental health zones overlapped (“best overall mental health zone”) was described by its compositional center (range). The best overall mental health zone describes the mix of daily behaviors associated with optimal mental health. To further examine potential selection bias in the split-sample procedure, the within-sex random split was repeated 100 times using different random seeds, and the stability of the resulting recommended ranges was summarized.
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(3)
Evaluating compliance with the study defined recommended ranges
Compliance with the study defined recommended ranges was evaluated in the validation group. Participants were classified as compliant if MVPA, LPA, SED, and SLP each fell within the corresponding recommended ranges proposed in this study simultaneously; otherwise, they were classified as non-compliant. Associations between compliance status and mental health were examined using sex stratified multivariable linear regression models for depression, anxiety, and stress, with compliance status as the primary independent variable (non-compliant as the reference). In these compliance analyses, depression, anxiety, and stress were analyzed using the original DASS 21 subscale scores to facilitate clinical interpretability. We conducted sex stratified multivariable linear regression analyses, adjusting for age, parental education, family income, sleep quality, and sleep efficiency. Adjusted regression coefficients (β) with 95% confidence intervals and p-values were reported.
Results
Descriptive statistics
After data cleaning and applying the exclusion criteria (Fig. 1), 6,084 participants were included in the analyses (1,888 males, 31.00%; 4,196 females, 69.00%). Within each sex, participants were randomly split into a recommendation set and a validation set, with 944 males and 2,098 females in each set. Baseline characteristics were well balanced between the recommendation and validation sets within each sex, with most standardized mean differences below 0.10 and all below 0.20 (Supplementary File S2). The overall and sex-specific characteristics of the participants are presented in Table 1. The mean age was 18.75 ± 1.29 years. For the 24-h movement behaviors, the geometric mean composition (closed to 1,440 min/day) was 34.64 min/day (2.41%) for MVPA, 261.57 min/day (18.16%) for LPA, 538.81 min/day (37.42%) for sedentary behavior, and 604.98 min/day (42.01%) for sleep; the corresponding arithmetic means were 40.62 min/day (2.82%), 262.18 min/day (18.21%), 537.17 min/day (37.30%), and 600.03 min/day (41.67%). Mean (± SD) mental health scores were 4.96 ± 4.63 for depression, 5.35 ± 4.92 for anxiety, and 6.75 ± 5.81 for stress.
Fig. 1.
The procedure used for cleaning invalid and missing data in this study
Table 1.
Characteristics of participants
| Overall (n = 6,084) |
Male | Female | |||
|---|---|---|---|---|---|
| Recommendation (n = 944) |
Validation (n = 944) |
Recommendation (n = 2,098) |
Validation (n = 2,098) |
||
| Age (years), mean ± SD | 18.75 ± 1.29 | 18.65 ± 1.25 | 18.65 ± 1.20 | 18.81 ± 1.34 | 18.80 ± 1.31 |
| Sex, n (%) | |||||
| Male | 1888 (31.00%) | - | - | - | - |
| Female | 4196 (69.00%) | - | - | - | - |
| Activity variables (min/day), compositional mean (%) | |||||
| MVPA | 34.64 (2.41%) | 39.76 (2.76%) | 36.73 (2.55%) | 33.33 (2.31%) | 32.94 (2.29%) |
| LPA | 261.57 (18.16%) | 261.67 (18.18%) | 263.21 (18.28%) | 262.49 (18.23%) | 259.83 (18.04%) |
| SED | 538.81 (37.42%) | 536.47 (37.25%) | 534.88 (37.14%) | 537.14 (37.30%) | 543.22 (37.72%) |
| SLP | 604.98 (42.01%) | 602.10 (41.81%) | 605.18 (42.03%) | 607.03 (42.15%) | 604.01 (41.95%) |
| Activity variables (min/day), arithmetic mean (%) | |||||
| MVPA | 40.62 (2.82%) | 44.92 (3.12%) | 42.61 (2.96%) | 39.46 (2.74%) | 38.95 (2.70%) |
| LPA | 262.18 (18.21%) | 262.37 (18.22%) | 263.75 (18.32%) | 263.10 (18.27%) | 260.45 (18.09%) |
| SED | 537.17 (37.30%) | 535.14 (37.16%) | 533.30 (37.03%) | 535.41 (37.18%) | 541.58 (37.61%) |
| SLP | 600.03 (41.67%) | 597.57 (41.50%) | 600.32 (41.69%) | 602.03 (41.81%) | 599.02 (41.60%) |
| Mental health, mean ± SD | |||||
| Depression | 4.96 ± 4.63 | 5.43 ± 5.16 | 5.35 ± 5.07 | 4.69 ± 4.29 | 4.84 ± 4.30 |
| Anxiety | 5.35 ± 4.92 | 5.40 ± 5.12 | 5.55 ± 5.29 | 5.22 ± 4.60 | 5.37 ± 4.66 |
| Stress | 6.75 ± 5.81 | 6.69 ± 6.19 | 6.55 ± 6.14 | 6.75 ± 5.65 | 6.88 ± 5.62 |
| Father's education, n (%) | |||||
| Middle school or below | 3656 (60.1%) | 589 (62.3%) | 546 (57.8%) | 1257 (59.9%) | 1264 (60.2%) |
| High school | 1565 (25.7%) | 213 (22.6%) | 256 (27.1%) | 551 (26.3%) | 545 (26.0%) |
| Junior college or bachelor’s degree | 828 (13.6%) | 130 (13.8%) | 137 (14.5%) | 284 (13.5%) | 277 (13.2%) |
| Postgraduate or above | 35 (0.6%) | 12 (1.3%) | 5 (0.6%) | 6 (0.3%) | 12 (0.6%) |
| Mather's education, n (%) | |||||
| Middle school or below | 4166 (68.4%) | 658 (69.7%) | 634 (67.2%) | 1439 (68.6%) | 1435 (68.4%) |
| High school | 1221 (20.1%) | 169 (17.9%) | 200 (21.2%) | 419 (20%) | 433 (20.6%) |
| Junior college or bachelor’s degree | 669 (11.0%) | 111 (11.8%) | 106 (11.2%) | 231 (11%) | 221 (10.5%) |
| Postgraduate or above | 28 (0.5%) | 6 (0.6%) | 4 (0.4%) | 9 (0.4%) | 9 (0.5%) |
| Monthly household income, n (%) | |||||
| < 5000RMB | 2802 (46.1%) | 410 (43.4%) | 407 (43.1%) | 988 (47.1%) | 997 (47.5%) |
| 5001 ~ 10000RMB | 2345 (38.5%) | 375 (39.7%) | 363 (38.5%) | 798 (38%) | 809 (38.6%) |
| 10,001 ~ 20000RMB | 726 (11.9%) | 125 (13.2%) | 128 (13.6%) | 240 (11.4%) | 233 (11.1%) |
| > 20001RMB | 211 (3.5%) | 34 (3.7%) | 46 (4.9%) | 72 (3.5%) | 59 (2.8%) |
| Sleep efficiency, n (%) | |||||
| > 85% | 2600 (42.7%) | 443 (46.8%) | 437 (46.3%) | 891 (42.5%) | 829 (39.5%) |
| 75% ~ 85% | 2600 (42.7%) | 382 (40.5%) | 398 (42.2%) | 912 (43.5%) | 908 (43.3%) |
| 65% ~ 75% (Contains 75%) | 703 (11.6%) | 93 (9.9%) | 87 (9.2%) | 238 (11.3%) | 285 (13.6%) |
| < 65% | 181 (3.0%) | 26 (2.8%) | 22 (2.3%) | 57 (2.7%) | 76 (3.6%) |
| Sleep quality, n (%) | |||||
| Very good | 1769 (29.1%) | 294 (31.1%) | 310 (32.8%) | 587 (28.0%) | 578 (27.5%) |
| Fairy good | 3567 (58.6%) | 536 (56.8%) | 505(53.5%) | 1257 (59.9%) | 1269 (60.5%) |
| Fairy bad | 683 (11.2%) | 101 (10.7%) | 113 (12%) | 237 (11.3%) | 232 (11.1%) |
| Very bad | 65 (1.1%) | 13 (1.4%) | 16 (1.7%) | 17 (0.8%) | 19 (0.9%) |
MVPA moderate-to-vigorous-intensity physical activity, LPA light physical activity, SED sedentary behavior, SLP sleep
Association between 24-h movement behaviors and mental health
The activity composition ilrs were significantly associated with all mental health indicators (all p ≤ 0.01) (Table 2). Quadratic associations between activity composition and stress were observed in male, and quadratic associations between activity composition and depression, anxiety and stress were observed in female.
Table 2.
Association between activity composition ilrs and mental health variables and best mental health zones (Best 5% of Time-Use Compositions Within the Observed Range of Activity Behaviors)
| Sex | Variable | Model summary (for ilrs) | Activity center (range) associated with top 5% of mental health outcomes (min/d) | ||||
|---|---|---|---|---|---|---|---|
| Fa | P | MVPA (sample center = 57)d | LPA (sample center = 262)d | Sedentary (sample center = 523)d | Sleep (sample center = 598)d | ||
| Male | Depression | 2.7 | 0.040 | 80 (60:100) | 370 (340:390) | 500 (450:540) | 500 (480:530) |
| Anxiety | 4.5 | 0.004 | 90 (80:100) | 330 (290:370) | 370 (360:390) | 650 (610:690) | |
| Stressb | 5.1 | < 0.001 | 90 (70:100) | 340 (310:370) | 370 (350:390) | 640 (610:680) | |
| Overall best mental health zonec (overlapping zone)(min/day) | 92 (60:110) | 361 (310:400) | 372 (350:480) | 614 (310:400) | |||
| Female | Depressionb | 4.5 | < 0.001 | 80 (50:90) | 350 (310:380) | 500 (450:540) | 520 (490:580) |
| Anxietyb | 5.5 | < 0.001 | 40 (20:80) | 180 (150:220) | 495 (440:540) | 720 (690:740) | |
| Stressb | 3.7 | < 0.001 | 50 (30:80) | 190 (160:230) | 470 (410:520) | 720 (710:730) | |
| Overall best mental health zonec (overlapping zone)(min/day) | 58 (40:90) | 290 (180:390) | 445 (400:560) | 665 (570:740) | |||
Ilr isometric log ratio, MVPA moderate-to-vigorous-intensity physical activity, LPA light physical activity, SED sedentary behavior, SLP sleep
aMultiple correlation coefficient for overall activity composition (set of ilrs), adjusted for gender, age, parental education, family income, sleep quality, and sleep efficiency
bModel includes additional squared term for ilrs
cDefined as the compositional center (range) of the overlapping area of the best 5% of depression, anxiety and stress
dCompositional center is calculated by finding the geometric mean of each activity variable and then linearly adjusting these means so that they collectively sum to 1440 min
Figures 2 and 3 illustrate the associations between Z-scores for depression, anxiety, and stress and individual activity components (MVPA, LPA, SED, and SLP) as estimated by the compositional regression models. When interpreting these figures, it should be noted that the total daily time is fixed at 24 h; thus, an increase in one behavior must be offset by a decrease in another. The vertical spread of predicted mental-health Z-scores at a given duration of activity (e.g., 60 min/d MVPA) indicates that mental health outcomes at this level of MVPA vary according to the distribution of the remaining behaviors. The shapes of the fitted loess curves depict the average associations between each behavior and mental health indicators, considering all possible allocations of the remaining behaviors.
Fig. 2.
Associations between 24-h movement behavior combinations of male university students and depression, anxiety, and stress
Fig. 3.
Associations between 24-h movement behavior combinations of female university students and depression, anxiety, and stress. Note: Estimated association between individual activity behaviors on the x axis and mental health (Z-scores) on the y axis. Models adjusted for age, parental education, household income, sleep quality, and sleep efficiency. Gray broken line shows compositional mean value of behavior in the sample, and red broken line shows value of behavior at estimated optimal composition
The gender-stratified analyses revealed that for males, MVPA and LPA were negatively associated with all mental health indicators, while SED was positively associated. SLP exhibited a negative association with anxiety, a U-shaped association with stress, and a positive association with depression. For females, MVPA and LPA were negatively associated with all mental health indicators, SED was positively associated, LPA showed a negative association with depression and a U-shaped association with anxiety and stress, and SLP was negatively associated with all mental-health indicators.
Optimal time use of 24-h movement behaviors for mental health
The center (compositional mean) and range (min; max) of the set of predictive time-use compositions associated with the best 5% (95th percentile) of mental health indicators are presented in Table 2. Gender-stratified analyses suggested that the best composition for overall mental health for males was different to that of females. Males’ optimal mental health zones included greater MVPA and less SLP compared with females, while reductions in SED benefitted both sexes. To describe the overall best mental health zone, the overlapping area of the best individual mental health indicators zones was described (Figs. 4 and 5). The center of the overall best mental health zone for males was an ‘optimal’ 24-h day that comprised center(range): MVPA 92 min (60–110), LPA 361 min (310–400), SED 372 min (350–480) and SLP 614 min (530–680). For females, the corresponding durations were MVPA 58 min (40–90), LPA 290 min (180–390), SED 445 min (400–560) and SLP 665 min (580–740). Robustness checks based on 100 within-sex random half-splits indicated that the primary recommended durations were stable across resamples (Supplementary File S3).
Fig. 4.
Overall optimal time zone of 24-h movement in male
Fig. 5.
Overall optimal time zone of 24-h movement in female. Note: Best mental health zones. Models adjusted for gender and age, parental education, household income, sleep quality, and sleep efficiency were included as covariates. MVPA = moderate-to-vigorous-intensity physical activity; LPA = light physical activity; SED = sedentary behavior; SLP = sleep. Each panel shows a different face of the same quaternary tetrahedron. Activities are at 100% (24 h) at the corresponding apices of the tetrahedron and 0% at the opposite base. A data point in the exact center of the tetrahedron would have equal shares of each activity (25%, or 6 h). Each of the polygons represents the compositions associated with the top 5% of mental health indicators. Insets show magnified best mental health zones. The compositional mean of the overlap zone between the polygons (shown in red) is indicated by the black dot (min/d): for males MVPA = 92, LPA = 361, SED = 372, SLP = 614; for females MVPA = 58, LPA = 290, SED = 445, SLP = 665
Recommendation volume verification
Meeting the recommended levels is defined as having MVPA, LPA, SED, and SLP each fall within their respective recommended ranges simultaneously; otherwise, the levels are deemed unmet. Among 944 male students in the validation group, only 136 (14.4%) fulfilled all four behavior criteria. By individual behavior, the counts and rates were MVPA 190 (20.1%), LPA 321 (34.0%), SED 286 (30.3%), and SLP 264 (28.0%). Among 2,098 female students, just 188 (8.9%) met all four standards simultaneously. The figures for each behavior were MVPA 291 (13.9%), LPA 634 (30.2%), SED 574 (27.4%), and SLP 836 (39.8%).
The model used standard linear regression to assess how meeting the recommended 24-h movement behavior durations relates to depression, anxiety, and stress. Compared to the non-compliant group, adherence was linked to significantly lower depression (β = –1.290, P < 0.05; β = –1.040, P < 0.05), anxiety (β = –1.350, P < 0.05; β = –1.760, P < 0.001), and stress (β = –1.280, P < 0.05; β = –1.340, P < 0.05). These findings (Table 3) indicate that meeting the 24-h movement behavior standard proposed in this study is associated with better mental health among university students.
Table 3.
Associations between 24-h movement behaviors and mental health among university students
| Sex | Variable | Compliance status | Depression | Anxiety | Stress | |||
|---|---|---|---|---|---|---|---|---|
| Β (95%CI) | p | Β (95%CI) | p | Β (95%CI) | p | |||
| Male | 24-h movement behaviors | Non-compliant (n = 808) | Reference | Reference | Reference | |||
| Compliant (n = 136) | −1.290 (−2.240, −0.344) | 0.007 | −1.350 (−2.350, −0.346) | 0.008 | −1.280 (−2.380, −0.171) | 0.023 | ||
| Female | 24-h movement behaviors | Non-compliant (n = 1,910) | Reference | Reference | Reference | |||
| Compliant (n = 188) | −1.040 (−1.950, −0.125) | 0.025 | −1.760 (−2.740, −0.792) | 0.001 | −1.340 (−2.520, −0.155) | 0.026 | ||
β represents the regression coefficient, indicating the average change in the mental health outcome score, expressed as the original DASS 21 subscale score, when moving from the non-compliant to the compliant group, holding all other variables constant; 95%CI: 95% confidence intervals
Discussion
This study employed the Optimal time zone analysis method to identify 24-h movement durations most favorable for mental health. For male students, those durations are MVPA 92 min, LPA 361 min, SED 372 min, and SLP 614 min. For female students, optimal durations are MVPA 58 min, LPA 290 min, SED 445 min, and SLP 665 min. These durations add up to 24 h, though real life varies. In response, practical intervals were proposed. For males, MVPA should range are 60–110 min, LPA 310–400 min, SED 350–480 min, and SLP 530–680 min. For females, MVPA should range from 40–90 min, LPA 180–390 min, SED 400–560 min, and SLP 580–740 min. Meeting all four of these intervals was significantly associated with better mental health, supporting the case for flexible, evidence-based 24-h movement behavior guidelines for students.
Currently, only one study has explored optimal 24-h movement behavior time for university students’ physical health. Li et al. (2025) surveyed 463 students aged 15–24 in Jinhua, Zhejiang Province, China, and applied the Goldilocks Day method to estimate durations that improve physical fitness. Their findings for male students were MVPA 142 min, LPA 295 min, SED 534 min, and SLP 469 min daily. For female students, the recommended durations were MVPA 115 min, LPA 306 min, SED 536 min, and SLP 482 min [21]. Compared with Li et al. (2025), the present study reported different recommended durations. The main reason lies in the type of health outcomes analyzed. Li et al. (2025) focused on physical fitness, while this study emphasizes mental health. Mental health tends to benefit more from longer sleep and moderate activity than from very high MVPA. Therefore, the recommended MVPA duration is lower, and the recommended sleep duration is higher. Conversely, sample size, regional lifestyles, time constraints, measurement methods, model specifications, and the practical feasibility in generating time combinations may all contribute to these discrepancies.
Male students had higher recommended MVPA and LPA durations than female students, whereas their recommended SLP and SED durations were lower. This pattern may reflect sex differences in time use and behavioral constraints in university settings. Previous studies consistently show that male university students tend to be more physically active than female students [31–33]. In contrast, female university students often report longer sleep latency, more frequent nighttime awakenings, and poorer sleep quality than their male counterparts [34], which may increase the need for longer or more stable sleep to support emotional regulation, hormonal balance, and psychological recovery. Collectively, these findings highlight the importance of sex specific health recommendations in the context of student mental health. A single uniform target may be less feasible for one sex or may fail to address the dominant behavioral constraints linked to mental health. Sex differentiated guidance may therefore improve acceptability and implementation by aligning recommendations with sex specific activity and sleep patterns and by prioritizing the behaviors most relevant for mental health within each sex. To strengthen the evidence base for sex specific recommendations, future studies should replicate these findings across university student samples from different universities and regions.
MVPA and LPA remain central topics in physical activity research. Most guidelines follow the World Health Organization (WHO) recommendation that adults accumulate 150 to 300 min of MVPA per week, but few provide daily targets. Using rigorous analyses, we derived daily 24 h movement behavior recommendations for university students, including an optimal MVPA of 92 min per day for males (range 60 to 110 min). This range reflects a mental health-oriented optimum rather than current typical behavior. In our sample, students spent more time in sedentary behavior and sleep and less time in MVPA and LPA, consistent with patterns commonly reported among Chinese university students [35], indicating that reaching the optimal MVPA range would require gradual increases. In practice, WHO guidance can serve as a baseline, with students progressing toward the study-defined optimal range where feasible. Supporting the plausibility of higher MVPA targets, Li et al. (2025) applied the optimal time zone approach in Chinese university students and reported higher MVPA values (142 min per day) for physical fitness outcomes [21]. Moreover, prior regression studies suggest that at least 60 min of daily MVPA is associated with lower depressive symptoms in university students [36], supporting 60 min per day as a practical starting point and a step toward the upper end of the study-defined range.
SED and SLP are core elements of 24-h movement behaviors. Numerous studies associate lower SED and greater SLP with better mental health in university students [35, 37, 38]. Many guidelines thus recommend limiting SED to 8 h daily and SLP 7–9 h nightly. However, evidence suggests that the SED subtype is an important modifier of associations with mental health. Mentally active SED (e.g., studying) is generally associated with lower depressive symptoms, whereas mentally passive SED (e.g., recreational screen time) is associated with higher depressive symptoms [39]. In this study, optimal SED was 372 min (350–480) for males and 445 min (400–560) for females. The difference reflects that this study’s recommendations arise from a university student sample, while the general guidelines target all adults. University students often show distinct behavior patterns compared to the broader adult group [13]. Compliance with the SED standard was low in the validation sample, with just 286 male students (30.3%) and 574 female students (27.4%) meeting it. Previous studies have likewise noted that low SED adherence is a major driver of poor overall compliance with 24-h movement behavior guidelines among Chinese university students [40]. Taken together, our SED recommendation should prioritize reducing mentally passive SED and interrupting prolonged sitting where feasible, with the goal of keeping total SED within the recommended levels defined by this study. SLP plays a vital role in physical and mental health [41]. In this study, the optimal sleep duration was 614 min (530–680) for males and 665 min (580–740) for females. Compliance with the sleep duration recommendation was similarly low, with 264 males (28.0%) and 574 females (27.4%) meeting the target. This rate is consistent with earlier sleep studies in university populations. A meta-analysis reported a mean sleep duration of 7.08 h among Chinese university students, with 43.9% sleeping less than 7 h [42]. A prospective study documented that 39.5% of Chinese university students slept under 7 h per night [43]. These figures underscore that sleep patterns in this population remain in need of improvement.
Strengths and limitations
This study has several strengths. Firstly, the study incorporates the Optimal time zone analysis method into research on Chinese university students’ mental health. Secondly, it provides distinct optimal 24-h movement behavior times for male and female university students. Thirdly, it uses compositional data analysis (CoDA) to handle substitution and covariation among MVPA, LPA, SED, and SLP, thereby assessing their joint association with mental health rather than treating behaviors in isolation. Finally, the modeling process includes testing quadratic (nonlinear) terms and validation sample analysis, which enhances the robustness and practical value of the optimal recommendations.
Several limitations should be noted. Firstly, the recommended durations derive primarily from cross-sectional associations between 24-h movement behaviors and mental health. While validity was tested, causal inference remains limited. Secondly, the use of self-reported questionnaires to measure 24-h movement behaviors may introduce subjective bias and recall error. Future studies should adopt wearable devices (e.g., accelerometers) to gather objective behavior data and improve accuracy. Thirdly, this work focused solely on association with mental health. Future research should expand to other physical and mental health indicators to investigate optimal 24-h behavior durations for a wider range of health outcomes. Finally, this study did not differentiate between SED types, but their impacts on mental health may vary. Future research could explore the time allocation of different SED subtypes to promote optimal physical and mental health.
Conclusion
This study used the Optimal time zone analysis method to derive recommended daily durations of 24-h movement behaviors (MVPA, LPA, SED, SLP) for Chinese university students based on mental health indicators (depression, anxiety, and stress). For male students, the recommended durations are MVPA 92 min (60–110), LPA 361 min (310–400), SED 372 min (350–480), and SLP 614 min (530–680). For female students, the recommended durations are MVPA 58 min (40–90), LPA 290 min (180–390), SED 445 min (400–560), and SLP 665 min (580–740). These recommendations expand existing movement behavior guidelines by providing targets specific to the university student population. Validation analyses show that meeting these recommended levels is significantly associated with better mental health. Moreover, the observed sex differences in the recommended composition underscore the need for sex specific approaches when translating 24-h movement guidance into mental health promotion strategies for students. The findings offer valuable input for developing public health policy, crafting 24-h movement guidelines specific to university students, organizing broad epidemiological investigations of student behavior, and creating intervention programs.
Supplementary Information
Acknowledgements
We are very grateful to all the participants, researchers involved in this study.
Abbreviations
- MVPA
Moderate-to-vigorous-intensity physical activity
- LPA
Light physical activity
- SED
Sedentary behavior
- SLP
Sleep
- CoDA
Compositional data analysis
- CI
Confidence interval
- MD
Mean difference
- SD
Standard deviation
Authors’ contributions
CLL: Writing-original draft, Methodology, Investigation, Formal analysis, Data curation. YPQ: Investigation, Conceptualization. NZ: Investigation. YWL: Investigation. XLY: Investigation, Data curation. LJW: Writing-review & editing, Validation, Supervision, Project administration, Conceptualization.
Funding
This study was funded by the 2024 National College Students' Innovation and Entrepreneurship Training Program (ID: 202310638018X). The National Social Science Fund of China (22BTY048).
Data availability
The data that support the findings of this study are available from first author, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of first author.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Tenets of the Declaration of Helsinki. Complete research objectives and survey contents were explained to the participants. All participants provided electronic informed consent and agreed to the required measurement and survey completion procedures. Ethical approval for this study was obtained from the Ethics Committee of China West Normal University (Approval No. 2025LLSC0047).
Consent for publication
This manuscript does not contain an individual person’s data; therefore, consent for publication is not required.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from first author, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of first author.