Physical Activity Type and Psychosocial Factors as Predictors of Mental Distress in U.S. College Students

Technical Summary: Physical Activity Type and Psychosocial Factors as Predictors of Mental Distress in U.S. College Students

Problem Statement
Mental health disorders, particularly depression and anxiety, represent a critical public health challenge for U.S. college students, driven by academic, social, and lifestyle stressors. While physical activity (PA) is widely recognized as a preventive strategy, existing literature lacks clarity on whether the mental health benefits of exercise vary by specific activity type, frequency, or duration. Furthermore, it remains unclear whether these associations persist after adjusting for significant psychosocial burdens such as stress, loneliness, and depression history. This study addresses the gap in understanding the independent predictive power of PA type versus psychosocial factors in a nationally representative sample of college students.

Methodology

• Data Source: The study utilized the 2023 Behavioral Risk Factor Surveillance System (BRFSS), the first and only cycle to capture detailed physical activity type (EXRACT12), session duration, and total weekly minutes for a large sample.
• Sample: The analytic sample consisted of 1,862 U.S. college students aged 18–24. Three PA categories with insufficient sample sizes (household activities, aerobics, elliptical/EFX) were excluded.
• Variables:
  • Outcome: Self-reported poor mental health days in the past 30 days (continuous, 0–30) and a binary indicator of frequent mental distress (≥14 days).
  • Predictors: PA type (walking as reference), frequency, duration, total minutes, strength-training frequency, depression diagnosis, life satisfaction, emotional support, loneliness, stress frequency, and socioeconomic indicators (income, hardship).
• Statistical Analysis:
  • Group Differences: Welch's one-way ANOVA and Kruskal–Wallis tests examined differences in mental health days across PA types, followed by TukeyHSD post-hoc tests.
  • Regression Modeling: Six nested multiple linear regression models (Models 0–5) were constructed to assess incremental variance explained. A parallel binary logistic regression was performed for frequent mental distress.
  • Inference: Heteroscedasticity-consistent (HC3) standard errors were used due to confirmed heteroscedasticity. Survey-weighted sensitivity analyses were conducted using LLCPWT sampling weights.

Key Results

1. Physical Activity Type Differences: Welch's ANOVA confirmed significant differences in poor mental health days across PA types ($F(7, 148.09) = 8.34, p < 0.0001$). Post-hoc analysis revealed that running/jogging, weight lifting, and other activities were associated with significantly fewer poor mental health days compared to walking (the reference group), with small effect sizes ($d = 0.28–0.46$).
2. Predictive Power of PA Modality: Adding PA modality variables (frequency, duration, total minutes, strength frequency) to the model did not significantly improve model fit beyond PA type and demographics ($LRT\ p = 0.341$).
3. Dominance of Psychosocial Factors: The inclusion of psychosocial factors (stress, loneliness, depression history, life satisfaction) produced a substantial increase in explained variance ($R^2$ increased from 0.079 to 0.443; $LRT\ p < 0.0001$). In the full model (adjusted $R^2 = 0.433$), stress was the strongest predictor (standardized $\beta = -0.608$), followed by loneliness, depression diagnosis, and life satisfaction.
4. Independent Effects: After full adjustment for psychosocial and socioeconomic factors, running/jogging was the only PA type that remained independently associated with fewer poor mental health days ($\beta = -1.14, p < 0.01$). Weight lifting and other activities lost significance.
5. Logistic Regression: For frequent mental distress (≥14 days), students who rarely or never felt stressed had 94–95% lower odds of distress compared to those always stressed. Running/jogging was the only PA type associated with significantly lower odds of frequent distress ($OR = 0.61, 95\% CI [0.38, 0.95]$).

Significance and Claims
The paper claims to provide the first nationally representative test of these relationships using the unique 2023 BRFSS dataset. The primary significance of the findings lies in reframing the role of physical activity in campus wellness strategies:

• Psychosocial Context is Primary: The study concludes that psychosocial burden (specifically stress, loneliness, and depression history) vastly outweighs exercise behavior in explaining mental distress. The variance explained by psychosocial factors is significantly greater than that explained by PA type, frequency, or duration.
• Specificity of Activity: While various activities showed unadjusted benefits, only running/jogging maintained an independent protective association after rigorous adjustment. This suggests that the benefits of other activities may be largely mediated by the psychosocial characteristics of the individuals who engage in them.
• Strategic Implications: The authors argue that university health promotion should not rely on increasing physical activity participation as a standalone solution. Instead, institutions should prioritize stress management and social connectedness interventions as primary strategies, positioning structured aerobic exercise (like running) as a valuable but secondary complement.

Limitations Acknowledged
The authors explicitly note that the cross-sectional design precludes causal inference, leaving open the possibility of reverse causation (e.g., poor mental health reducing exercise engagement). Additionally, reliance on self-reported data introduces potential response bias, and the specific PA type module used in 2023 was discontinued in subsequent BRFSS cycles, limiting future replication with the same variables. The study also acknowledges that the moderate adjusted $R^2$ (~43%) indicates significant unexplained variance in mental health outcomes.