Document Type : Research Paper
Authors
1
Assistant Professor in Sports Psychology, Department of Psychology and Counselling, Fatemeh Al-Zahra Campus, Farhangian University, Tehran, Iran
2
Assistant Professor of Sports Psychology Department, Sport Sciences Research Institute, Tehran, Iran
Abstract
Extended Abstract
Background and Purpose
Obesity poses significant physical and psychological health risks, including diabetes, cardiovascular disease, depression, and anxiety. It is influenced by genetic, behavioral, and cognitive factors, particularly initial dysfunctional schema. These schemas—often related to beauty in women and power in men—can contribute to disordered eating and excessive exercise behaviors. Body Mass Index (BMI), widely used for weight classification, defines normal weight as 18.5–24.9, overweight as 25–29.9, and obesity as ≥30, according to WHO guidelines. Research highlights associations between BMI and variables such as gender, age, parental education, socioeconomic status, and place of residence. Sociocultural norms and gender stereotypes further shape body image and eating patterns through schema activation. Schema therapy, which integrates cognitive-behavioral techniques, attachment theory, and emotion-focused strategies, has demonstrated effectiveness in reducing BMI. Moreover, links have been established among childhood trauma, maladaptive schemas, and obesity. This study examines the relationship between BMI and maladaptive schemas, mediated by demographic (age, gender, education) and socioeconomic (occupation, income) factors. By integrating psychological, social, and demographic characteristics, this research facilitates the design of holistic obesity interventions that enhance body image and emotional regulation.
Materials and Methods
This descriptive survey employed convenience sampling to examine athletes active in Iranian sports clubs in 2023. Based on Kaiser’s rule and Morgan’s table, 395 participants completed an online demographic survey and the Young Schema Questionnaire–Short Form (YSQ-SF). The sample included athletes (81%), coaches (8%), and sports managers (6%), with an almost equal gender distribution (49% male, 49% female, 1% undisclosed). Participants had a mean age of 21 years (SD = 16; range: 17–64), an average weight of 79 kg, and a mean height of 166 cm (SD = 25). Athletic involvement ranged from recreational (54%) to amateur (23%), semi-professional (7%), and professional (4%). Competitive achievements included none (67%), regional (12%), provincial (14%), national (4%), and international (1%). Eligibility required participants to be at least 17 years old, with a minimum of one year of athletic experience and at least a high school diploma. Data were collected through (1) a demographic questionnaire covering personal and athletic characteristics; (2) the YSQ-SF (Young, 2005), a 75-item, 6-point Likert scale instrument measuring 15 maladaptive schemas across five domains (α = 0.97; subscales = 0.72–0.94); and (3) Body Mass Index (BMI), calculated as weight (kg)/height (m²). Following approval from the Ministry of Sports, participants provided informed consent and completed the questionnaires online.
Results
Mean scores for maladaptive schemas (4.00) and BMI (40.00) were observed. Pearson correlations indicated significant associations between schemas and BMI with gender, education, marital status, and employment (p < .01). Linear and curvilinear regression analyses identified education (β = 1.00) and BMI (β = 0.068) as the strongest predictors of maladaptive schemas. The results of the path analysis (SEM) indicated the following: a) Model fit indices: χ²/df = 105.528 (p < .0001), SRMR = 0.10 (acceptable), and NFI = 1.035 (slightly below the ideal threshold but still supported by the other indices). B) Goodness-of-fit (GOF): BMI = 0.15, socioeconomic factors = 0.034, and demographic factors = 0.12, reflecting a weak to moderate overall model fit. Key Findings: 1. Direct effects: Schemas had a significant impact on BMI (t = 2.045, p = .021). Socioeconomic factors significantly influenced BMI (t = 3.597, p < .001). Demographic factors showed the strongest direct effect on BMI (t = 6.129, p < .001). Socioeconomic factors also strongly predicted demographic factors, representing the strongest pathway (β = .168, p < .001). 2. Indirect effects: Schemas indirectly affected BMI through socioeconomic factors (p < .05). A longer pathway was also observed, where schemas influenced socioeconomic factors, which in turn affected demographic factors, ultimately impacting BMI (p < .01)..
Conclusion
The study highlights several important findings. First, there is a positive relationship between maladaptive schemas and BMI, consistent with previous evidence, with schemas such as rumination and emotional inhibition being linked to unhealthy eating behaviors. Second, socioeconomic factors, including education and income, were inversely associated with BMI, likely reflecting the benefits of improved health literacy and greater access to resources. Third, gender differences were observed, with men showing a higher risk of obesity, suggesting the influence of gender-specific health behaviors. Finally, mediation analyses revealed that schemas affect BMI indirectly through socioeconomic and demographic pathways. The implications of these findings suggest the need for targeted strategies across multiple domains. Psychological interventions, such as schema-focused therapy implemented within sports clubs, may help address maladaptive patterns linked to unhealthy behaviors. Socioeconomic support, including nutrition programs and educational scholarships, can further promote healthier lifestyles by improving access to resources and knowledge. Moreover, gender-tailored approaches are essential, with self-worth enhancement programs being particularly beneficial for women, while performance-related weight management strategies may be more effective for men. Limitations include non-random sampling, reliance on self-report measures, and unexamined mediators such as social support. Recommendations for future research include longitudinal designs, mixed-method approaches, and multidimensional interventions. Overall, this study emphasizes the importance of integrated strategies that address psychological, social, and demographic factors in promoting athlete health.
Article Message
This study highlights the complex interplay of psychological, social, and cultural factors influencing Body Mass Index (BMI) and maladaptive behaviors such as overeating. The results emphasize that cognitive schemas—core mental structures shaped by person–environment interactions—act as filters through which individuals perceive health, process information, and form behaviors. These schemas are further shaped by contextual variables such as education, occupation, marital status, and gender norms. The findings advocate for moving beyond reductionist models (e.g., focusing solely on nutrition) toward comprehensive, multi-level interventions that address individual, organizational, and policy dimensions to promote sustainable health outcomes in athletes.
Ethical Considerations
Approved by IR.SSRI.REC.1401.1868. Informed consent, confidentiality, and ethical guidelines were followed.
Authors’ Contributions
Equal contributions to design, execution, and writing.
Conflicts of Interest
None declared.
Acknowledgement
We thank participating athletes, clubs, and officials.
Keywords
Main Subjects
