Association between sensor-based physical activity, sedentary behaviour, sleep, and mental health in university students: a systematic review

Abstract

This systematic review aimed to synthesise evidence on the associations of sensor-measured physical activity, sedentary behaviour, and sleep with depression, anxiety, mood, and broader mental health outcomes among university students. EMBASE, MEDLINE, PsycINFO, and Web of Science were searched, with updates conducted to January 2026. Observational studies were included if they assessed physical activity, sedentary behaviour, or sleep using accelerometers, actigraphy, or wearable sensors in university students and reported depression, anxiety, mood, self-esteem, or related mental health outcomes. Study quality was assessed using the National Institutes of Health observational study quality assessment tool. From 3,078 records, 13 studies published between 2007 and 2025 were included. Sample sizes ranged from 36 to 1,062 participants, and most studies were cross-sectional. For depression-related outcomes, TPA showed inconsistent evidence, with 1 of 4 studies reporting a significant beneficial association. Intensity-specific activity showed clearer patterns: 2 of 4 moderate-to-vigorous physical activity studies and 2 of 3 light physical activity studies reported beneficial associations. Sedentary behaviour findings were mixed, with 1 of 3 studies reporting adverse associations with depressive symptoms. Sleep-related evidence was limited, although 3 of 4 studies suggested that longer sleep or lower daytime sleepiness was associated with fewer depressive symptoms or better mood. Anxiety evidence was weak and inconsistent, while broader mental health, mood, or self-esteem outcomes showed more consistent evidence, with 4 of 5 studies reporting favourable associations. Overall, sensor-based evidence suggests that intensity-specific physical activity may be more informative than total activity for understanding mental health among university students, although the available evidence remains limited and predominantly cross-sectional.

Systematic Review Registration:

https://www.crd.york.ac.uk/prospero/view/CRD42024583750, PROSPERO CRD42024583750.

1 Introduction

Recent data indicate that mental health issues among university students have become increasingly prevalent, reflecting a significant public health concern (Dogra et al., 2018; Litwiller et al., 2022; Moulin et al., 2021; Rehman et al., 2024). This concern is consistent with broader findings in adolescents and young adults, among whom depressive disorders remain a substantial contributor to the global mental health burden, with recent Global Burden of Disease analyses suggesting that the burden is particularly pronounced in the 20–24-year age group (Wu et al., 2025a; Zhao et al., 2025). Recent meta-analytic evidence from China further highlights the scale of the problem, showing that the pooled prevalence of depression among university students was 34.7% across 32 cross-sectional studies, and that prevalence increased from 35.0% before the COVID-19 pandemic to 38.7% during and after the pandemic (Lin et al., 2025). These findings underline the substantial burden of depressive symptoms in university populations and the need for effective, evidence-based strategies to support student mental health.

University students often experience various academic and life pressures, making them prone to mental health issues such as depression (Lin et al., 2025; Zhao et al., 2025). Depression in university students is prevalent and poses concerns due to its potential effects on their academic achievements, social relationships, and overall well-being. In this review, depression and anxiety refer to symptoms of persistent low mood and excessive worry or tension, respectively; mood refers to an individual's current emotional state, while self-esteem reflects an individual's overall evaluation of their self-worth (Radloff, 1977; Spielberger et al., 1971). Clinical guidance for depression commonly emphasises early identification, assessment of symptom severity, psychological therapy, and pharmacological treatment when clinically indicated (American Psychiatric Association, 2022). Alongside these established approaches, lifestyle-related strategies, including physical activity, are increasingly recognised as low-risk and accessible supportive options for depression prevention and management (WHO, 2021).

For university students, physical activity, sedentary behaviour, and sleep are recognised as key factors in maintaining mental health and reducing the risk of depression. Regular physical activity has been linked with improvements in mood, reductions in depressive symptoms, and enhanced psychological health (Holl et al., 2020; Oberste et al., 2020). Likewise, sufficient sleep is critical for emotional regulation, cognitive function, and overall health (Yang et al., 2023). These behaviours are also interrelated in daily life. Whereas prolonged sedentary behaviour has been associated with poorer mental health outcomes (Kandola et al., 2021; Lu et al., 2024).

Although several reviews have provided evidence on the associations of physical activity, sedentary behaviour, or sleep with depression among university students (Fei et al., 2025; Fernandes et al., 2023; Huang et al., 2025; Lin et al., 2025), these behaviours have largely been examined separately rather than within an integrated framework. For example, a recent systematic review and meta-analysis by Huang et al. synthesised 38 studies and reported a significant negative association between physical activity and depression in college students [r = −0.23, (95% CI: −0.307 to −0.173)], suggesting that higher physical activity is generally associated with lower depressive symptoms. The review further indicated that this relationship varied by contextual and behavioural factors, with stronger associations observed after the COVID-19 pandemic, in developing countries, among physical education majors, and at moderate physical activity intensity (Huang et al., 2025). Recent studies also suggest that these behaviours may interact. For instance, evidence from university students indicates that the association between physical activity and depression may differ according to sleep status, supporting the view that sleep may shape or modify the relationship between physical activity and depressive symptoms (Yang et al., 2023). In addition, from a 24-hour movement perspective, physical activity, sedentary behaviour, and sleep are inherently co-dependent because they together make up the daily distribution of time (Dale et al., 2025; Hossian et al., 2025). However, little attention has been given to whether these behaviours should be understood as interrelated behaviours, or as part of a 24 h behavioural composition, in relation to depression among university students.

Furthermore, combining evidence from subjective measures, such as self-reported questionnaires, with evidence from objective measures, such as accelerometers and wearable sensors, may obscure the interpretation of findings (Lu et al., 2024). Objective measurement tools are particularly valuable in this context because they enable continuous monitoring of daily activity and sleep patterns while reducing the recall and social-desirability biases associated with self-reported measures (Sutin et al., 2025; Yerramalla et al., 2024). Despite the increasing use of these tools in primary studies, less evidence has specifically synthesised objectively measured physical activity, sedentary behaviour, and sleep in relation to mental health outcomes among university students, including depressive symptoms, anxiety, mood, self-esteem, and broader mental well-being, or examined whether these studies have considered physical activity and sleep as interacting behaviours or as part of the 24 h behavioural composition. Therefore, this systematic review aims to provide a comprehensive synthesis of the associations between sensor-based physical activity, sedentary behaviour, sleep, and mental health outcomes in university students, including depression, anxiety, mood, self-esteem, and broader mental well-being. By integrating this evidence, the review may help identify which movement behaviours and exposure indicators are most consistently associated with student mental health and inform future strategies for mental health promotion in university populations. Understanding these associations among university students is important because mental health difficulties and disrupted movement behaviours may adversely affect academic engagement, performance, retention, and overall quality of life during a critical period of transition to adulthood.

2 Methods

The protocol for this systematic review was registered with PROSPERO on 23 June 2024 (CRD42024583750) (Supplementary Materials S1). The review was conducted and reported in accordance with the PRISMA 2020 statement (Page et al., 2021). The PRISMA framework was used to structure the review process, including record identification, duplicate removal, title and abstract screening, full-text eligibility assessment, documentation of exclusion reasons, and final study inclusion. These stages were summarised in the PRISMA flow diagram, and the reporting of the search strategy, eligibility criteria, data extraction, synthesis approach, and risk-of-bias assessment was aligned with PRISMA 2020 recommendations (Page et al., 2021).

We aimed to identify observational studies that examined the associations between objectively measured physical activity, sedentary behaviour, or sleep and symptoms of depression/emotional, and mental well-being outcomes among university students. A systematic literature search was first conducted in July 2024 and subsequently updated in August 2025 and January 2026. Searches were performed across multiple electronic databases, including EMBASE, MEDLINE, PsycINFO, and Web of Science.

The search was limited to peer-reviewed, full-text articles published in English. Grey literature sources, including preprint servers, dissertation repositories, trial registries, and conference proceedings, were not searched. This decision was made because the review required sufficiently detailed methodological reporting to assess sensor type, device placement, wear protocol, data-processing procedures, exposure definitions, and mental health outcome assessment. Such information may be reported inconsistently or incompletely across heterogeneous grey literature sources. Studies were eligible if they included university or college students and used sensor-based or other objective wearable measures, such as accelerometers, actigraphy, or related wearable devices, to assess physical activity, sedentary behaviour, or sleep. Studies were excluded if they were intervention studies, commentaries, conference abstracts, or review articles. Eligibility was based on participants’ enrolment in a university or college rather than on a fixed numerical age range. Studies involving mixed populations were excluded when data for university or college students could not be extracted separately. Studies relying solely on subjective measures of physical activity, sedentary behaviour, or sleep were excluded. Eligible studies were required to report at least one clearly defined mental health outcome relevant to the review.

2.1 Search strategy

The search strategy combined Medical Subject Headings (MeSH) and free-text terms related to participants, exposures, and outcomes. Key terms included “university students,” “college students,” “physical activity,” “sedentary behaviour,” “sleep,” “depression,” “depressive symptoms,” “depressive mood,” “accelerometer,” “actigraphy,” “wearable device,” “sensor-based,” and, where relevant, terms related to 24-hour movement behaviours. The search strategy was adapted for each database to maximise sensitivity and capture potentially relevant studies (Supplementary Materials S2).

2.2 Study selection

The study selection process was conducted using EndNote software for reference management. Screening and selection of articles were independently undertaken by two reviewers. After duplicates were removed, titles and abstracts were screened for eligibility, and full-text articles were retrieved for studies considered potentially relevant. Discrepancies between reviewers were resolved through discussion or, when necessary, consultation with a third reviewer. The reference lists of included studies were also examined to identify additional relevant citations.

2.3 Data extraction

Data extraction covered multiple domains, including key study characteristics (e.g., author, year of publication, country, study design, sample size, age, sex, and body mass index), details of objective behavioural assessment (including the type of sensor or wearable device, wear location, monitoring protocol, and variables derived for physical activity, sedentary behaviour, and sleep), mental health outcome assessment (including depressive symptoms, anxiety, stress, mood, self-esteem, or broader mental well-being), relevant covariates, statistical methods, and effect estimates describing the associations of interest. A second reviewer independently checked the extracted data for accuracy. When important information was not available in the main text, Supplementary Materials were reviewed. Where relevant, missing information was recorded as “NR” in the extraction tables. In this review, “NR” indicates that the information was not reported in the published article or Supplementary Materials and could not be confirmed from the available sources. Where corresponding authors were contacted for clarification, the analysis was based on the information available in the published report if no response was received after a reminder.

2.4 Data synthesis

Data synthesis was primarily conducted using a narrative approach, given the heterogeneity across the included studies in terms of study design, behavioural indicators, measurement protocols, outcome measures, and statistical reporting. A quantitative meta-analysis was not performed because the included studies differed substantially in exposure definitions, outcome measures, measurement units, and statistical reporting. For example, sensor-derived exposures included total physical activity (TPA), moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour, daily steps, sleep duration, sleep efficiency, daytime sleepiness, and 24 h movement-related indicators ¹. Mental health outcomes were assessed using different instruments, including CES-D, PHQ-9, SAS, STAI, SF-36 mental health, POMS, PROMIS, and daily mood ratings. In addition, the reported effect estimates were not directly comparable, as studies reported correlations, regression coefficients, odds ratios, stratified estimates, and isotemporal substitution estimates. Because these estimates were derived from different analytical models and could not be converted consistently into a common effect size, results were extracted and presented descriptively rather than quantitatively pooled.

Findings were synthesised according to the type of objectively measured behaviour, including physical activity, sedentary behaviour, sleep, and, where relevant, 24 h movement-related indicators. Cross-sectional and prospective studies were considered separately where appropriate. The synthesis focused on the direction and significance of associations between objectively measured behavioural variables and mental health outcomes, including depression, anxiety, mood, self-esteem, and broader mental well-being, as well as on methodological features that may have contributed to consistency or inconsistency across studies. Where effect estimates were reported, these were extracted and presented descriptively rather than quantitatively pooled.

2.5 Risk of bias assessment

The risk of bias in the included studies was independently assessed by two reviewers using the National Institutes of Health study quality assessment tool for observational cohort and cross-sectional studies (National Heart, Lung and Blood Institute, 2021). This tool evaluates several aspects of study quality, including design, data collection, and the appropriateness of analytical methods. Any disagreements between the reviewers were resolved through consensus (Supplementary Materials S3).

3 Results

3.1 Study selection

Following the updated searches in January 2026, the final study selection process was completed. The screening process began with 3,078 records identified from database searches and reference lists. After removing 1,199 duplicates, 1,879 records were screened by title and abstract, leading to the exclusion of 1,807 records. A total of 72 full-text articles were assessed for eligibility, with most excluded due to irrelevant outcomes, wrong populations, study design issues, or lack of accelerometer data. This process resulted in 13 studies being included in the review (as shown in Figure 1).

Figure 1

3.2 Study characteristics and risk of bias

Table 1 presents the characteristics and main findings of the 13 studies included in this review. Overall, the included studies were published between 2007 and 2025 and were conducted across six countries, including Japan, the United States, South Korea, China, Spain, and the Netherlands. Most studies used a cross-sectional design, while two studies adopted prospective designs. Sample sizes varied substantially, ranging from 36 participants in Suh, 2018 to 1,062 participants in Zhang et al., 2025. The participants were mainly university or college students, with mean ages generally falling within late adolescence and young adulthood, ranging from approximately 18–27 years.

The included studies examined a range of sensor-derived movement behaviours, including TPA, moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour, daily steps, sleep duration, sleep quality, sleep efficiency, wake after sleep onset, and total sleep time. Mental health outcomes also varied across studies and included depressive symptoms, anxiety symptoms, daily mood, general mental health, self-esteem, and total mood disturbance. Depression-related outcomes were most frequently assessed, while fewer studies examined anxiety or broader mental health outcomes.

The methodological quality assessment indicated that four studies were rated as Good and the remaining nine as Fair; no study was rated as Poor (Supplementary Materials S3). Common methodological limitations included the predominance of cross-sectional designs, unclear participation rates, lack of sample size justification, limited temporal ordering between exposures and outcomes, infrequent repeated exposure assessment, and inconsistent adjustment for potential confounders. Nevertheless, most studies clearly defined their aims and study populations and used well-established measures of sensor-based movement behaviours and mental health outcomes.

3.3 Association between physical activity, sleep, sedentary and depression

For depressive symptoms or depressive mood, the evidence was mixed and appeared to vary by the type of movement behaviour examined. Studies examining total or overall physical activity did not show consistent findings. Among the studies that directly examined TPA or overall activity in relation to depression-related outcomes, one study reported a significant beneficial association, showing that higher total physical activity (TPA) was associated with lower CES-D scores (Kirschner et al., 2022). In contrast, three studies reported no significant association between overall physical activity and depressive symptoms or depressive mood (Kawada et al., 2007; Tao et al., 2019; Wu et al., 2025b). Therefore, the evidence for TPA as a correlate of depression was limited and inconsistent.

The findings were somewhat more supportive for activity intensity-specific indicators. For MVPA, two of four studies reported significant beneficial associations, indicating that higher MVPA was associated with lower depressive symptoms or lower odds of depressive symptoms (Zhang et al., 2025; Zhou et al., 2024). The remaining two studies found no statistically significant association between MVPA and depressive symptoms (Kirschner et al., 2022; Zhang et al., 2025). For LPA, two of three studies reported beneficial associations with depression-related outcomes, including lower depressive symptom scores or improvements when sedentary behaviour was replaced with LPA (Kirschner et al., 2022; Zhou et al., 2024), while one study reported no significant association (Zhang et al., 2024). These findings suggest that intensity-specific indicators, particularly MVPA and LPA, may provide more consistent evidence than TPA alone.

For sedentary behaviour, the evidence was also mixed. Zhou et al., 2024 found that greater sedentary behaviour was associated with higher depressive symptoms, and that replacing 30 min per day of sedentary behaviour with LPA or MVPA was associated with lower CES-D scores. However, two studies reported no significant association between sedentary behaviour and depressive symptoms (Kirschner et al., 2022; Zhang and Gu, 2022). Thus, although some evidence suggests that reducing sedentary behaviour may be beneficial for depressive symptoms, this finding was not consistent across studies.

For sleep-related exposures, three studies examined sleep duration, sleep guideline adherence, or daytime sleepiness in relation to depression-related outcomes. Kawada et al. (2007) found that longer total sleep time was associated with lower CES-D scores, but only among female students. Zhang and Gu, 2022 found no significant association between meeting the sleep duration guideline and depressive symptoms. Shimamoto, Suwa and Mizuno, 2021 reported that greater daytime sleepiness was positively associated with PHQ-9 scores, indicating that poorer sleep-related functioning was associated with greater depressive symptoms. Taken together, the limited sleep-related evidence suggests that insufficient sleep or greater daytime sleepiness may be associated with worse depression-related outcomes.

Two studies considered movement behaviours from a 24 h perspective. Zhang and Gu, 2022 examined adherence to 24 h movement behaviour guidelines and found no significant associations between LPA, MVPA, sedentary behaviour, or sleep guideline adherence and depressive symptoms. However, the direction of the odds ratios suggested potentially lower odds of depressive symptoms among students meeting MVPA, sedentary behaviour, and sleep recommendations: MVPA ≥ 60 min/day, OR = 0.56, 95% CI 0.20–1.59; sedentary behaviour <3 h/day, OR = 0.86, 95% CI 0.43–1.69; and sleep ≥7 h/night, OR = 0.75, 95% CI 0.37–1.52. In contrast, Zhou et al. (2024) used an isotemporal substitution model and found that replacing 30 min per day of sedentary behaviour with LPA was associated with lower CES-D scores (β = −0.202, 95% CI −1.371 to −0.146), while replacing sedentary behaviour with MVPA showed a stronger association (β = −0.308, 95% CI −0.970 to −0.073). These findings suggest that considering the reallocation of time between sedentary behaviour and physical activity may provide more informative evidence than examining each behaviour separately. Overall, for depression-related outcomes, the most consistent beneficial evidence was observed for MVPA and LPA, while findings for TPA, sedentary behaviour, and sleep were less consistent.

3.4 Association between physical activity, sleep, sedentary and anxiety

For anxiety outcomes, the evidence was limited and less consistent. Three studies examined anxiety-related outcomes using sensor-derived physical activity, sedentary behaviour, or sleep indicators (Felez-Nobrega et al., 2020; Suh, 2018; Zhou et al., 2024). For physical activity and sedentary behaviour, two studies provided relevant evidence. Felez-Nobrega et al. (2020)reported no significant associations between LPA, MVPA, or sedentary behaviour and state anxiety, trait anxiety, or perceived stress. In contrast, Zhou et al., 2024 found that higher sedentary behaviour was associated with higher anxiety scores, whereas higher MVPA was associated with lower anxiety scores. In the same study, LPA was positively associated with anxiety scores in the single-activity model, but replacing sedentary behaviour with LPA was not significantly associated with anxiety symptoms in the isotemporal substitution model. Replacing 30 min per day of sedentary behaviour with MVPA was associated with lower SAS scores (β = −0.147, 95% CI −1.863 to −0.034). Therefore, current evidence for anxiety suggests a possible beneficial association for MVPA and a possible adverse association for sedentary behaviour. The clearest evidence came from Zhou et al., 2024, which suggested that higher MVPA and replacing sedentary behaviour with MVPA may be associated with lower anxiety symptoms. However, because only a small number of studies examined anxiety and because outcome measures and analytical approaches differed, firm conclusions cannot be drawn.

3.5 Association between physical activity, sleep, sedentary and mental well-being

For broader mental health, daily mood, and self-esteem, the findings were more consistently favourable (Fennell et al., 2022; Kalmbach et al., 2018; Kirschner et al., 2022; Tao et al., 2019; Wilson et al., 2016), four studies reported beneficial associations between sensor-derived movement behaviours and mental health outcomes. Wilson et al., 2016 found that higher objectively measured physical activity was associated with better SF-36 mental health scores, although this relationship was moderated by personality traits. Kalmbach et al., 2018 found that both longer sleep and higher daily steps were associated with better daily mood. Fennell et al., 2022 reported that higher step counts were associated with lower POMS depression, anger, and total mood disturbance. Kirschner et al., 2022 found that higher TPA and LPA were associated with higher self-esteem, whereas higher sedentary behaviour was associated with lower self-esteem.

In contrast, Tao et al., 2019 did not report a significant direct predictive association between objectively measured physical activity and PROMIS mental health or quality-of-life outcomes. These findings suggest that movement behaviours may be more consistently associated with broad affective or well-being indicators than with diagnostic symptom scales alone.

3.6 Moderators in the association between physical activity and mental health

Few studies formally examined moderation effects. Wilson et al., 2016 examined whether personality traits moderated the association between objectively measured physical activity and mental health among female college students. The study found that the association between objective physical activity and SF-36 mental health differed according to levels of neuroticism and extraversion. Specifically, a significant three-way interaction was observed between objective physical activity, neuroticism, and extraversion (p = .01). Higher physical activity was associated with better mental health among neurotic-introverted students, but this association was not observed among extraverted students. This suggests that personality traits may influence whether physical activity is associated with better mental health, and that students with higher psychological vulnerability may benefit more from physical activity. Kawada et al., 2007 reported that total sleep time was significantly associated with CES-D scores only among female students, suggesting a possible sex-specific association; however, a formal sex interaction was not reported.

Other studies adjusted for selected participant characteristics or study-specific factors, such as sex, age, body mass index, baseline mental health, sleep parameters, or contextual variables. However, some reported findings were based mainly on correlations, stratified analyses, or partially adjusted models, meaning that confounding control was not applied consistently across all effect estimates.

Table 2 summarises the accelerometer data collection and processing methods used across the included studies. Overall, substantial heterogeneity was observed in device type, wear location, monitoring duration, valid-wear criteria, cut-points, and non-wear definitions. Across the 13 included studies, 11 studies reported the device used and wear location. Commonly used devices included ActiGraph models, activPAL, Fitbit. Wear location varied considerably, including the wrist, hip, thigh, waist, right leg, and non-dominant wrist. This variation is important because accelerometer-derived estimates of physical activity, sedentary behaviour, and sleep may differ depending on device placement.

The measurement period also varied between studies. Most studies collected accelerometer data for 7 days, while others used shorter monitoring periods, such as 2 days in Suh, 2018 and 4 days in Kawada et al., 2007, or longer protocols, such as 14 days in Shimamoto, Suwa and Mizuno, 2021. Most studies used 24-hour or waking-hour protocols, but reporting of valid wear criteria was inconsistent. Valid-day requirements were reported in only part of the studies, ranging from 3 to 4 valid days, and valid wear duration ranged from 8 to 20 h per day where reported.

Epoch length and cut-points were also inconsistently reported. Among studies that reported epoch size, 60 s epochs were most common, while Kirschner et al., 2022 used 10 s epochs. Cut-points varied across studies and included counts per minute, MET-based thresholds, and step-rate-based thresholds. For example, some studies defined sedentary behaviour as <100 counts per minute, while others used step-based thresholds or MET-based classifications. Weekend inclusion was reported in several studies, but not consistently across all studies. Reasons for non-wear were often related to bathing, swimming, sleeping, or water-based activities, although many studies did not clearly report non-wear reasons or validation procedures.

Table 3 summarises the measurement methods used to assess depression, anxiety, mood, and broader mental health outcomes across the included studies. Overall, substantial heterogeneity was observed in the mental health assessment tools. The most commonly used instrument was the Center for Epidemiologic Studies Depression Scale (CES-D), which was applied in five studies to assess depressive symptoms. Other depression-related tools included the Patient Health Questionnaire-9 (PHQ-9) and a short depressive mood scale based on the CES-D. Anxiety was assessed using the Self-rating Anxiety Scale (SAS), the Korean version of the Spielberger State-Trait Anxiety Inventory, and the Spanish version of the State-Trait Anxiety Inventory. Several studies assessed broader mood or mental health constructs rather than depression or anxiety alone, including the SF-36 Mental Health subscale, Mood 24/7 daily mood rating, Profile of Mood States (POMS), and Patient-Reported Outcomes Measurement Information System (PROMIS).

Most instruments were self-reported questionnaires, with item numbers ranging from a single-item daily mood rating to 58-item multidimensional mood scales. Scoring direction also differed between instruments. For example, higher scores on CES-D, PHQ-9, SAS, STAI, and POMS total mood disturbance indicate worse symptoms, whereas higher scores on the SF-36 Mental Health subscale and Mood 24/7 daily mood rating indicate better mental health or better daily mood. These differences should be considered when interpreting the direction of associations between sensor-measured movement behaviours and mental health outcomes.

4 Discussion

This review provides an objective-measurement-focused synthesis of the associations between movement behaviours and depression, anxiety, mood, and broader mental health outcomes in university students. Overall, in summary, the narrative synthesis suggests that the most consistent evidence was observed for intensity-specific physical activity, particularly MVPA and LPA, in relation to lower depressive symptoms, and for daily steps, TPA, LPA, and sleep in relation to better broader mood, self-esteem, or mental health outcomes. Evidence for anxiety was more limited and inconsistent, with only one study clearly supporting beneficial associations of MVPA and adverse associations of sedentary behaviour. Findings from 24 h movement behaviour studies suggest that behavioural reallocation may be important: replacing sedentary behaviour with LPA or MVPA appeared beneficial for depressive symptoms, while replacing sedentary behaviour with MVPA appeared beneficial for anxiety. However, guideline-adherence analyses did not show statistically significant associations. Sleep-related findings generally suggested that longer sleep or lower daytime sleepiness may be associated with better mood or fewer depressive symptoms, although studies used different sleep indicators and were too few for firm conclusions. Because of heterogeneity in exposure definitions, device protocols, mental health measures, and analytical approaches, these findings should be interpreted as a narrative synthesis rather than as evidence of a pooled quantitative effect.

4.1 Comparison with previous reviews

Findings of the present study are broadly consistent with previous reviews showing that physical activity, sleep, and sedentary behaviour are related to mental health outcomes in the university students. Huang et al., 2025 conducted a systematic review and meta-analysis of 38 studies on physical activity and depression among college students. The main meta-analytic result showed a significant negative association between physical activity and depression, with a pooled correlation of r = −0.238 (95% CI: −0.307 to −0.173, p < 0.001), indicating that higher physical activity was associated with lower depressive symptoms. Their subgroup analyses further suggested that this association may be stronger for moderate-intensity physical activity. Rehman et al., 2024 reviewed evidence on regular physical activity, sleep, and mental health among university students, including eight observational studies with a reported mean age of 19.9 ± 0.88 years. The outcomes included sleep quality, sleep duration, depression, anxiety, and stress. Quantitatively, physical activity was associated with a lower risk of poor sleep quality, with an estimate of 0.75 (95% CI: 0.59–0.91), and a slightly lower risk of anxiety, with an estimate of 0.91 (95% CI: 0.83–0.99). Dogra et al. (2018) conducted an earlier systematic review (n = 22) examining physical activity, sedentary time, and fitness in relation to depression, stress, and anxiety among post-secondary students. They identified only five studies with low or moderate risk of bias; all five were cross-sectional. Because of heterogeneity in exposures, outcomes, and study quality, the review did not provide a pooled effect estimate, but concluded that physical activity, sedentary time, and fitness may be associated with mental health outcomes in post-secondary students.

However, the present review found weaker evidence for TPA than these broader reviews. Among studies directly examining TPA or overall activity in relation to depression-related outcomes, only one of four reported a significant beneficial association. This difference may partly reflect measurement method (Janz et al., 1995; Sallis et al., 1996). Previous reviews included studies using self-reported physical activity as well as objective measures, whereas the present review was restricted to sensor-based exposures. Self-reported physical activity may overestimate total activity and is influenced by recall bias, social desirability, and participants’ interpretation of activity intensity (Haskell, 2012; Sallis, 1991; Sallis et al., 1993). In contrast, sensor-based TPA captures all movement across the day, including commuting, study-related movement, part-time work, and compulsory or stressful activity. This may reduce measurement bias in total volume, but it may also make total activity less specific as a mental health exposure (Harris et al., 2009; Lee and Shiroma, 2014). As a result, between-person differences in sensor-derived total activity may be smaller or less psychologically meaningful, making significant associations harder to detect.

4.2 Intensity-specific physical activity and depressive symptoms

The present review also extends previous evidence by suggesting that intensity-specific indicators may be more informative than TPA. Two of four studies examining MVPA reported beneficial associations with depression-related outcomes, and two of three studies examining LPA reported beneficial associations. This partly aligns with Huang et al., 2025, who found that the association between physical activity and depression varied by activity intensity, with moderate intensity showing stronger associations. However, our findings add that LPA may also be relevant. Kirschner et al., 2022 found that higher LPA was associated with lower depressive symptoms and higher self-esteem, while Zhou et al., 2024 showed that replacing sedentary behaviour with LPA was associated with lower CES-D scores. This suggests that the mental health benefits of movement in university students may not be limited to MVPA.

For anxiety outcomes, Felez-Nobrega et al., 2020 found no significant associations between activPAL-derived LPA, MVPA, and state anxiety, trait anxiety, or perceived stress. In contrast, Zhou et al., 2024 found that higher sedentary behaviour and LPA were associated with higher anxiety scores in single-activity models, while higher MVPA was associated with lower anxiety scores. Their substitution analysis showed that replacing sedentary behaviour with MVPA, but not LPA, was associated with lower anxiety symptoms.

The more consistent findings for MVPA and LPA compared with TPA may reflect differences in biological, psychological, and contextual mechanisms (Buchan et al., 2021; Pascoe et al., 2020). Given that university students spend substantial time studying, sitting, and commuting, increasing light-intensity movement may be a realistic and scalable strategy (Pengpid and Peltzer, 2025). MVPA may improve depressive symptoms through established pathways such as increased neurotrophic factors, endorphin release, improved sleep regulation, reduced inflammation, and enhanced self-efficacy (Lubans et al., 2016; White et al., 2024).

These mechanisms may operate through biological, psychological, behavioural, and social pathways. Biologically, physical activity may influence neuroplasticity, neurotrophic factors, inflammatory regulation, endorphin release, and stress-related neuroendocrine pathways (White et al., 2024). Psychologically, regular movement may enhance self-esteem, self-efficacy, perceived competence, and emotion regulation, while also providing distraction from negative rumination (Biddle et al., 2019). Behaviourally, physical activity may improve sleep quality, reduce prolonged sedentary time, and support more structured daily routines. Socially, activity undertaken in campus, group, or recreational settings may increase social connectedness and peer support (Xiao et al., 2024; Yang et al., 2024). These pathways may act together rather than independently, which may partly explain why intensity-specific movement indicators showed more consistent associations with depressive symptoms than total activity alone.

4.3 Sedentary behaviour

In the present review, only one of three studies reported that higher sedentary behaviour was associated with worse depression-related outcomes, while two studies found no significant association. For anxiety, the evidence was also limited, with one study suggesting that higher sedentary behaviour was associated with higher anxiety symptoms and that replacing sedentary time with MVPA was associated with lower anxiety. This pattern is broadly consistent with previous reviews showing that sedentary behaviour may be related to poorer mental health, but that associations are often weaker and more heterogeneous than those observed for physical activity (Dogra et al., 2018).

Similarly, broader evidence suggests that sedentary behaviour is not a single uniform exposure; its association with mental health may depend on whether sitting occurs during passive screen use, study, transport, social interaction, or rest (Faulkner and Biddle, 2013; Gilchrist et al., 2021; Hamer, Coombs and Stamatakis, 2014). These behaviours may have different emotional meanings and may therefore show different associations with mental health. Therefore, total sedentary time may dilute associations if mentally harmful sitting, such as prolonged passive screen use or socially isolated sitting, is combined with neutral or even beneficial sitting, such as studying productively or spending time with peers.

4.4 Sleep, sleepiness, and mood-related outcomes

These findings are broadly consistent with previous reviews suggesting that sleep is closely related to mental health in university students and young adults (Gilchrist et al., 2021; Hofman et al., 2022; Rehman et al., 2024). Three of four sleep-related studies supported this pattern, Kawada et al., 2007 found that longer total sleep time was associated with lower CES-D scores among female students, and Shimamoto et al., 2021 found that greater daytime sleepiness was associated with higher PHQ-9 scores. Kalmbach et al., 2018 also found that shorter sleep predicted worse next-day mood among medical interns, while worse mood also predicted shorter sleep the following night. In contrast, Zhang and Gu, 2022 found no significant association between meeting the sleep duration guideline and depressive symptoms.

University students often experience irregular schedules, academic pressure, screen exposure, and social demands, which may affect both sleep and mental health. Poor sleep may increase vulnerability to negative mood the next day, while depressive or anxious symptoms may also make it harder to maintain regular sleep. This bidirectional relationship may help explain why sleep-related indicators, such as total sleep time and daytime sleepiness, were associated with mental health outcomes in some studies (Rehman et al., 2024).

However, the evidence remains difficult to compare because the included studies used different sleep indicators, including total sleep time, sleep efficiency, wake after sleep onset, daytime sleepiness, and sleep guideline adherence. Total sleep duration alone may not capture important aspects of sleep health, such as timing, regularity, fragmentation, or weekday–weekend variability (Cabanas-Sanchez et al., 2021; Henderson et al., 2023). Future studies should therefore examine multiple dimensions of sleep and consider sleep together with physical activity and sedentary behaviour within a 24 h movement framework.

4.5 24 h movement composition

Only two studies in this review examined movement behaviours from a 24 h perspective, and their findings were not fully consistent. Zhang and Gu, 2022 found no significant associations between 24 h movement guideline adherence and depressive symptoms, although the odds ratios suggested potentially lower odds of depressive symptoms among students meeting MVPA, sedentary behaviour, and sleep recommendations. In contrast, Zhou et al. (2024) used an isotemporal substitution model and found that replacing sedentary behaviour with LPA or MVPA was associated with lower depressive symptoms, while replacing sedentary behaviour with MVPA was associated with lower anxiety symptoms.

This pattern suggests that the mental health relevance of sedentary behaviour may depend on what behaviour it replaces. Physical activity, sedentary behaviour, and sleep occur within a fixed 24 h day, so increasing one behaviour necessarily reduces time in another (Larisch et al., 2020). Guideline-adherence models may miss smaller changes in daily time use, whereas isotemporal substitution models estimate realistic behavioural reallocations, such as replacing 30 min of sedentary time with LPA or MVPA (Dale et al., 2025). Future studies should therefore use compositional or isotemporal approaches to better understand how the daily balance of movement behaviours relates to depression, anxiety, and mental well-being in university students (Pengpid and Peltzer, 2025).

4.6 Moderators and heterogeneity of effects

Evidence on moderators was limited, suggesting that few studies have examined whether the association between movement behaviours and mental health differs across student subgroups (Kawada et al., 2007; Tao et al., 2019; Wilson et al., 2016). Although studies addressed confounding to some extent in the quality assessment, adjustment strategies were heterogeneous (Felez-Nobrega et al., 2020; Kalmbach et al., 2018; Zhang et al., 2025; Zhang and Gu, 2022; Zhou et al., 2024). However, confounding control was not applied consistently across all effect estimates. Several factors may be associated with depression progression in university students, including baseline depressive symptom severity, sex, age, socioeconomic status, academic workload, sleep quality, sedentary behaviour, screen time, diet, body mass index or physical health status, chronotype, medication use, psychological treatment, social support, stressful life events, and activity context. These factors may influence both movement behaviours and mental health outcomes (Biddle et al., 2019; White et al., 2024). Therefore, variation in covariate adjustment across the included studies may have contributed to heterogeneity in the observed associations between sensor-derived movement behaviours and depression-related outcomes.

4.7 Methodological considerations and future directions

Several methodological issues may explain the inconsistent findings across studies. First, most included studies used relatively conventional exposure indicators, such as TPA, MVPA, LPA, daily steps, total sedentary time, or total sleep time. These indicators are useful but may not fully capture the behavioural features that are most relevant to mental health (Danilevicz et al., 2024; Lear et al., 2024; Shivgulam and O’Brien, 2023). As discussed above, TPA or step counts may combine leisure movement, commuting, part-time work, and compulsory activity, while total sedentary time may combine studying, passive screen use, transport, and social sitting. Similarly, sleep duration alone may not reflect sleep timing, regularity, or fragmentation. Future studies should therefore move beyond total duration and examine more refined exposure variables, including intensity, timing, accumulation patterns, bout duration, domain or context, sleep regularity, and 24 h time-use reallocation (Lahens et al., 2022; Verswijveren et al., 2022; Walsh et al., 2022).

Methodological heterogeneity in sensor protocols and study design remains a major limitation. Across the included studies, devices differed by brand, wear location, monitoring duration, epoch length, cut-points, valid-wear criteria, and non-wear definitions. These differences may affect estimates of physical activity, sedentary behaviour, steps, and sleep, making findings difficult to compare across studies (Harris et al., 2009; Lee and Shiroma, 2014; Ott et al., 2000). In addition, most studies were cross-sectional, which limits causal interpretation. Future research should use standardised accelerometer processing methods, clearly report wear-time and non-wear criteria, and prioritise longitudinal or repeated-measures designs. Combining sensor-based data with contextual information, such as activity diaries, screen-use data, ecological momentary assessment, or academic schedule information, may help clarify not only how much students move, sit, and sleep, but also when, why, and under what conditions these behaviours relate to mental health.

4.8 Strengths and limitations

A key strength of this review is its specific focus on sensor-based measures of physical activity, sedentary behaviour, and sleep, rather than combining objective and self-reported exposures. This provides a clearer synthesis of evidence based on objectively measured movement behaviours and reduces the influence of recall bias and social desirability bias. Another strength is that the review considered multiple movement behaviours, including TPA, MVPA, LPA, sedentary behaviour, daily steps, and sleep-related indicators, allowing the findings to be interpreted within a broader movement behaviour framework. The review also summarised accelerometer protocols, mental health measurement tools, and study quality, which helps explain why findings differed across studies and provides useful guidance for future research.

However, several limitations should be acknowledged. The review was restricted to peer-reviewed English-language articles indexed in four electronic databases and did not include grey literature sources. Therefore, publication bias cannot be excluded. The number of included studies was relatively small, especially for anxiety, sleep, and 24 h movement behaviour analyses, and most studies were cross-sectional, limiting causal interpretation. Considerable heterogeneity was also observed in device type, wear location, monitoring period, cut-points, valid-wear criteria, and mental health questionnaires, which prevented meta-analysis. Future studies should use longitudinal designs, standardised accelerometer protocols, repeated mental health assessments, and 24 h movement behaviour models to better clarify how physical activity, sedentary behaviour, and sleep relate to mental health in university students.

5 Conclusion

This systematic review suggests that sensor-derived movement behaviours are associated with mental health outcomes in university students, but the evidence varies by behaviour type and outcome. The most consistent findings were observed for intensity-specific physical activity, particularly MVPA and LPA, which were more often associated with lower depressive symptoms than TPA. Broader mental health outcomes, including mood and self-esteem, also showed relatively consistent favourable associations with higher physical activity, higher step counts, longer sleep, or lower sedentary behaviour.

Evidence for anxiety was limited and less consistent, with only a small number of studies examining anxiety-related outcomes. Sleep-related findings suggested that longer or better sleep and lower daytime sleepiness may be related to better mental health, but the small number of studies and differences in sleep indicators limit firm conclusions. Emerging 24 h movement behaviour evidence indicates that reallocating sedentary time to LPA or MVPA may be relevant for depressive symptoms, and reallocating sedentary time to MVPA may be relevant for anxiety.

Overall, this review highlights the value of sensor-based measurement in studying movement behaviours and mental health in university students. However, the current evidence is limited by heterogeneous accelerometer protocols, varied mental health measures, and the predominance of cross-sectional designs. Future research should use longitudinal designs, standardised sensor-processing methods, repeated mental health assessments, and more refined and innovative physical activity exposure variables, such as activity intensity, timing, accumulation patterns, bout duration, domain/context, and 24 h time-use reallocation models, to better understand how physical activity, sedentary behaviour, and sleep are related to mental health in university students. From a practical perspective, these findings may inform university health-promotion initiatives that encourage feasible increases in physical activity, particularly LPA and MVPA, reductions in prolonged sedentary time, and healthier sleep behaviours, although stronger longitudinal evidence is needed before specific recommendations can be made.

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