rather than causal First, the cross-sectional design precludes causal inference; central or bridge nodes should be interpreted as correlational signals leverage points. These findings are hypothesis-ge

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Network analysis of lifestyle behaviors and anxiety in children and adolescents: gender and school-stage heterogeneity (2026) · doi

  rather than causal First, the cross-sectional design precludes causal inference; central or bridge nodes should be interpreted as correlational signals leverage points. These findings are hypothesis-generating, and claims about mechanisms or intervention relevance require longitudinal or experimental evidence. Second, the reliance on self-reported measures may introduce biases. The IPAQ-SF may not be appropriate for the youngest participants, and measurement invariance of the GAD-7 across school stages was not formally tested. Sleep and dietary behaviors were assessed using brief self-developed items. Supplementary item-level analyses suggested that the central role of MPA was preserved, whereas the constituent sleep- and diet- related items showed heterogeneous contributions, particularly for sleep. Future research should employ validated instruments such as the Pittsburgh Sleep Quality Index. Third, estimated

  Keywords: sleep causal central self items rather first cross sectional design precludes inference bridge nodes interpreted
• The interplay of sleep architecture and exercise in executive function of middle-aged and older adults (2026) · doi

  Future studies should prioritize several key research priorities. First, well-designed randomized controlled trials combining exercise inter- vention with sleep manipulation (e.g., sleep extension, selective sleep deprivation and pharmacological sleep regulation) were required to verify whether exercise-induced cognitive improvements were mediated by sleep architecture changes, while accounting for potential bidirec- tional and reciprocal interactions (10). Second, longitudinal repeated- measures designs with multi-timepoint assessments of sleep architecture, physical activity and executive function helped clarify temporal causality and distinguish unidirectional, bidirectional and reciprocal relationships (66). Third, closed-loop intervention strategies that adjusted real-time exercise prescriptions based on wearable device-monitored sleep param- eters (e.g., switching to low-intensity mind–body exercise after poor sleep) optimized the synergistic effects of exercise and sleep regulation (67). Moreover, multimodal approaches integrating functional magnetic resonance imaging, electroencephalography and actigraphy, combined with computational modeling, facilitated the construction of individual- ized response prediction models for combined intervention (68). Finally, the development of feasible, population-tailored intervention protocols (e.g., morning light-exercise combined therapy, midday short napping and evening relaxation training) and their translational application in community settings remained critical for clinical implementation. Several limitations constrain the interpretability of this review. First, most cited studies are cross-sectional or observational, precluding causal inference and leaving unmeasured confounding unresolved. Second, the hypothesized pathway in which sleep architecture is involved in exercise effects on executive function lacks direct testing; no randomized controlled trial has manipulated sleep architecture within an exercise intervention to examine whether altering sleep archi- tecture changes the cognitive outcomes, which would be necessary to support a causal role. Third, mechanistic evidence, particularly for the glymphatic system and lactate signaling, derives primarily from animal models, limiting direct translation to aging humans due to species dif- ferences. Fourth, individual differences (e.g., genetic background, cog- nitive reserve, comorbidities) in middle-aged and older adults and considerable heterogeneity in the operationalization of sleep architec- ture and executive function across studies further complicate synthesis.

  Keywords: sleep exercise architecture intervention executive function combined several first randomized controlled regulation whether cognitive changes
• Associations between physical activity and sleep quality among college students: the chain-mediating roles of rumination and emotion regulation strategies (2026) · doi

  Although this study has certain strengths in terms of sample size and variable integration, several limitations should be noted and addressed in future research. activity, rumination, First, this study employed a cross-sectional design, with all variables measured at a single time point. Therefore, it does not inferences regarding the relationships allow for strict causal among physical emotion regulation strategies, and sleep quality. Although the findings support the statistical plausibility of the proposed chain mediation model, the directionality of the associations among variables requires further verification through longitudinal or experimental research. Future studies may adopt multi-wave longitudinal designs to examine whether changes in physical activity are accompanied by dynamic changes in rumination, emotion regulation strategies, and sleep quality, thereby providing clearer evidence regarding temporal relationships among these variables. Second, this study relied primarily on self-report questionnaires to assess physical activity, rumination, emotion regulation strategies, and sleep quality. As a result, the findings may be influenced by common method bias and social desirability effects. Although statistical tests were conducted to assess common method bias, potential biases associated with self-report measures cannot be fully ruled out. Future research may benefit from incorporating objective measures, such as wearable device–based assessments of physical activity and sleep, or experimental tasks and behavioral indicators to assess cognitive and emotion regulation processes, in order to enhance the objectivity and interpretability of the findings. Third, the sample consisted of college students, with a relatively homogeneous source, which may limit the generalizability of the findings. College students represent a specific population with unique characteristics in terms of daily routines, developmental stage, and sources of stress. Whether the observed association patterns apply to other age groups or occupational populations remains to be determined. Future studies may replicate the proposed model in diverse populations to compare similarities and differences in the relationships between physical activity and sleep quality across developmental stages and social roles. In addition, although sex and age were controlled, several potentially important confounding variables were not included in the present study, such as depressive symptoms, anxiety, caffeine intake, chronotype, and bedtime regularity. These factors are known to be associated with both physical activity and sleep quality and may therefore have influenced the magnitude of the observed associations. For example, individuals with higher levels of depressive or anxiety symptoms may be less physically active, more prone to rumination, and more likely to report poorer sleep quality, which may have contributed to the magnitude of the observed indirect associations. Similarly, irregular sleep schedules, evening chronotype, or higher caffeine intake may independently contribute to poorer sleep quality, potentially overlapping with the pathways identified in the present model. Therefore, the findings of this study should be interpreted with caution. Future research is needed to incorporate a broader range of psychological and behavioral variables to further examine the robustness and generalizability of the proposed model. Despite these limitations, the relatively large sample size and the consistency of the observed associations across analyses provide support for the robustness of the findings. In addition, the measure of physical activity used in this study focused on overall activity level and did not capture the timing or type of exercise. As prior research suggests that the sleep-related associations of physical activity may vary according to exercise modality and time of day, especially for vigorous activity performed late in the evening, future studies should incorporate more detailed assessments of physical activity patterns. Moreover, the present study did not assess or exclude pre- existing clinically diagnosed sleep disorders. It is therefore possible that some participants had significant sleep pathology, which may have influenced the observed associations. Future studies should include screening for clinical sleep disorders and consider this factor in sampling or statistical adjustment. Overall, this study integrates physical activity, rumination, and emotion regulation strategies to provide an informative framework for understanding sleep quality among college students. With further methodological refinement and the incorporation of more diverse measurement approaches, future research may build on this framework to advance theoretical understanding and provide more rigorous empirical evidence for sleep health promotion.

  Keywords: sleep activity physical future quality ndings associations rumination variables emotion regulation observed among strategies model