Metacognitive function in young adults is impacted by physical activity, diet, and sleep patterns

The study investigates whether lifestyle behaviours—physical activity, diet, and sleep—are associated with metacognitive functioning in healthy young adults (ages 17–35). While object-level cognition (e.g., attention, working memory) is known to vary with these behaviours, it remains unclear if meta-level processes that monitor and regulate cognition are similarly affected. Metacognition comprises monitoring and control processes and reflects strategic deployment and optimization of cognitive resources. Prior work shows metacognition influences engagement in lifestyle behaviours, but evidence that lifestyle behaviours affect metacognition is mixed and limited, especially in young adults. The authors aim to: (1) establish associations between physical activity (PA), diet, and sleep with metacognitive function; (2) replicate findings in a second sample; and (3) disentangle individual effects given the interdependence of PA, diet, and sleep.

Object-level cognitive function has documented associations with physical activity, diet, and sleep across the lifespan, with emerging evidence in young adults. Metacognition plays a role in lifestyle behaviours (e.g., athletes’ metacognitive strategies, metacognitive beliefs related to diet and sleep). Evidence for lifestyle behaviours affecting metacognition is limited and heterogeneous: PA shows small positive effects on higher-level functions/metacognition in children and improved metacognitive performance with fitness in obese preadolescents, but adult interventions show mixed effects on metacognitive monitoring; diet-related factors can impair metacognition (e.g., caffeine cravings); and sleep deprivation often spares metacognitive monitoring but may impair control. Young adults’ metacognitive systems are sensitive to performance changes and differ from older adults’ in strategy use under cognitive load. Given the interdependence of PA, diet, and sleep, parsing their unique contributions to metacognition is warranted.

Design: Two studies using online self-report measures with multiple regression analyses; Study Two adds a within-subjects design including all metacognitive measures in the same participants and exploratory factor analysis (EFA). Recruitment: University of British Columbia Department of Psychology Human Subject Pool (Spring 2021 for Study One, Spring 2022 for Study Two). Ethics: UBC Behavioural Research Ethics Board, certificate H19-02890; informed consent obtained; participants received course credit. Data collection: Surveys hosted on Qualtrics; stages included consent, demographics (age, sex), and randomized blocks of predictor and response measures. Measures: Predictors—International Physical Activity Questionnaire (IPAQ; MET-min/week over 7 days), Perceived Stress Scale-10 (PSS-10), Pittsburgh Sleep Quality Index (PSQI; past month composite, higher is worse sleep), Rapid Eating and Activity Assessment for Participants—Short Version (REAP-S; higher scores indicate healthier diet). Responses—three metacognition instruments administered: Inventory of Metacognitive Self-Regulation (IMSR; subscales: knowledge of cognition, objectivity, problem representation, subtask monitoring, evaluation), Meta-Cognitions Questionnaire (MCQ; subscales: positive beliefs about worry, uncontrollability and danger, cognitive confidence, negative beliefs, cognitive self-consciousness), Metacognitive Awareness Inventory (MAI; knowledge of cognition: declarative/procedural/conditional; regulation of cognition: planning, information management, monitoring, debugging, evaluation). Data processing: Scoring followed instrument manuals; outliers in predictors identified via Tukey’s fences (±1.5×IQR from Q1/Q3) and removed; longstring analysis excluded respondents with repetitive responses exceeding 90% of item count; cases with missing variable scores removed. Analysis: Multiple regression models predicted metacognition subscale scores from age, sex, perceived stress (PSS), diet (REAP-S), sleep (PSQI), and PA (IPAQ); variance inflation factors checked (<1.5). Study One samples: IMSR n=493 (of 536; 43 removed), MCQ n=523 (of 577; 54 removed), MAI n=529 (of 589; 60 removed). Study Two sample: n=564 (of 626; 62 removed) completed all predictors and all three metacognition measures. EFA (Study Two): Correlation matrix of all metacognition subscales; KMO=0.91; Bartlett’s χ²(153)=4640.35, p<.001; scree plot, Velicer’s MAP, and parallel analysis supported a four-factor solution. Method: minimum residual factoring with oblimin rotation; fit indices indicated modest fit (TLI≈0.91, RMSEA≈0.09).

Study One: IMSR—Physical activity predicted Objectivity (β=0.11, 95% CI [0.007, 0.22], t=2.09, p=.04) and Knowledge of Cognition (β=0.10, 95% CI [0.00, 0.20], t=1.97, p=.05); marginal for Problem Representation (β=0.10, 95% CI [-0.003, 0.20], t=1.92, p=.06). Perceived stress negatively predicted Subtask Monitoring and Knowledge of Cognition. Diet and sleep were not significant predictors for IMSR subscales. Models explained significant variance for Problem Representation and Knowledge of Cognition. MCQ—PA not associated with any subscales. Diet predicted Cognitive Confidence (β=-0.10, 95% CI [-0.20, -0.01], t=-2.17, p=.03). Sleep predicted Uncontrollability and Danger (β=0.10, 95% CI [0.007, 0.19], t=2.12, p=.04) and Cognitive Self-Consciousness (β=0.12, 95% CI [0.008, 0.24], t=2.11, p=.04). Perceived stress predicted all MCQ subscales; worse diet, sleep, and higher stress were associated with negative metacognitive outcomes. Models explained significant variance for all MCQ subscales. MAI—Physical activity and lower perceived stress were significantly predictive of all components; diet and sleep showed no associations. Models explained significant variance for each MAI subscale. Study Two: IMSR—PA predicted Objectivity (β=0.10, 95% CI [0.003, 0.19], t=2.02, p=.04) and Knowledge of Cognition (β=0.10, 95% CI [0.006, 0.19], t=2.09, p=.04). Perceived stress negatively associated with these subscales. Better diet associated with higher Evaluation and Problem Representation; sleep not related to IMSR. Models explained significant variance for Evaluation, Objectivity, and Knowledge of Cognition. MCQ—PA not significant. Perceived stress predicted all subscales except Positive Beliefs; sleep associated with Uncontrollability of Danger and Cognitive Control; diet not associated. Models explained significant variance for all MCQ subscales. MAI—PA significantly predicted all subscales except Evaluation; perceived stress and worse diet consistently related to poorer outcomes; sleep not predictive. Models explained significant variance for all MAI subscales except Evaluation. Exploratory Factor Analysis (Study Two): Four factors emerged—(1) Knowledge of Cognition (e.g., MAI declarative/procedural/conditional; IMSR knowledge; MAI information management), (2) Offline Regulation of Cognition (e.g., IMSR objectivity; MAI planning/monitoring/evaluation), (3) Online Regulation of Cognition (e.g., IMSR evaluation/subtask monitoring/problem representation; MAI debugging), and (4) Metacognitive Worry (MCQ subscales). Associations by factor: PA positively related to Knowledge of Cognition and Offline Regulation; sleep related to Metacognitive Worry; diet related to Online Regulation. Fit indices indicated modest model fit (TLI≈0.91; RMSEA≈0.09).

Across two studies, lifestyle behaviours were differentially associated with metacognitive functioning in young adults. Physical activity consistently related to greater metacognitive knowledge of cognition and to offline regulation (planning, monitoring before/after tasks), but not to online task-concurrent regulation or metacognitive worry. Diet related to online regulation of cognitive processes during tasks, whereas sleep quality related to metacognitive worry (e.g., uncontrollability/danger, cognitive confidence) but not to knowledge or regulation domains. Perceived stress showed pervasive negative associations with metacognitive measures. These findings address the research question by demonstrating that lifestyle behaviours predict distinct facets of metacognition beyond object-level cognitive tests and suggest metacognition may be more labile to lifestyle variability in young adults. The significance lies in identifying specific lifestyle targets for enhancing separate metacognitive capacities, implying potential for tailored health behaviour interventions. The results also support the idea that metacognition may mediate relationships between lifestyle factors and objective cognitive performance, enriching models of how health behaviours interact with cognitive systems.

The paper shows that physical activity, diet, and sleep patterns uniquely and differentially associate with metacognitive functions in healthy young adults. Physical activity links to metacognitive knowledge and offline regulation; diet relates to online regulation; and sleep correlates with metacognitive worry. An exploratory factor model consolidates these patterns into four metacognitive clusters. These contributions highlight that lifestyle behaviours influence meta-level cognitive processes and could be targeted to optimize self-regulation of cognition. Future research should employ experimental and longitudinal designs to determine causality, integrate objective assessments (e.g., device-based PA/sleep, behavioural metacognitive tasks) with validated self-reports, and test targeted interventions to enhance specific metacognitive domains via lifestyle modifications. Public health messaging for young adults should emphasize the bidirectional relationship between metacognition and lifestyle behaviours to support short- and long-term cognitive health.

Causality cannot be inferred due to observational design; metacognitive status and lifestyle behaviours may influence each other bidirectionally. Reliance on self-reported measures for both lifestyle behaviours and metacognition introduces potential biases and deviations from objective indices. Sampling from a university subject pool and pandemic-related context yielded large but convenience samples, with baseline differences between Spring 2021 and Spring 2022 in physical activity, diet, and sleep; although controlled statistically, these differences may affect generalizability. Models are based on self-report subscales and show modest factor-analytic fit; replication with objective and behavioural measures is needed.