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April 18, 2026Journal of Marine Medical Society0 citationsOpen Access

Associative Modeling for Determination of Biological Age Based on Lifestyle Behaviors among Indian Civil Pilots

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SMSudhanshu Shekhar MohapatraDKDeepak Kumar KhukharRKRaman Kumar

Key Points

  • The aim is to develop a predictive model to determine biological age based on lifestyle behaviors among Indian civil pilots.
  • Conducted a cross-sectional observational study with 278 healthy pilots.
  • Participants completed a comprehensive lifestyle questionnaire.
  • Clinical evaluations included measurements of blood pressure, BMI, and lipid profiles.
  • Utilized Multidimensional Scaling (MDS) to analyze data complexity and identify key lifestyle factors.
  • Applied multiple linear regression to predict biological age using selected lifestyle behaviors.
  • MDS highlighted two dimensions of health: Health Risk Behaviors and Lifestyle Balance.
  • The regression model explained 95% of the variation in biological age with an R2 of 0.95.
  • The derived Health-15 Score correlated with a younger biological age, indicating effective lifestyle modifications can decelerate aging.

Abstract

Abstract Background: Biological age (BA) provides a more precise reflection of physiological status and resilience than chronological age alone, particularly in high-stakes professions. For aviators, whose operational environment involves unique stressors such as irregular schedules, circadian disruption, and sedentary periods, understanding the impact of lifestyle on BA is crucial for optimizing health, safety, and operational efficiency. Aims & Objectives: This study aimed to develop a robust predictive model for determining BA among healthy Indian civil pilots. The primary objectives were to assess current lifestyle behaviors and clinical health indicators, explore their complex interrelationships with aging markers, and construct a predictive equation using composite lifestyle scores to identify accelerated or decelerated aging. Materials & Methods: A cross-sectional observational study was conducted involving 278 healthy commercial pilots (mean age 47.4 years). Participants completed a comprehensive, validated questionnaire covering lifestyle factors—including diet, physical activity, sleep quality, and substance use—and underwent standard clinical evaluations for blood pressure, BMI, and lipid profiles. Multidimensional Scaling (MDS) was employed to reduce data complexity and identify key variables, followed by a multiple linear regression model to predict BA. Results: MDS analysis revealed two distinct dimensions influencing pilot health: “Health Risk Behaviors” (characterized by high blood pressure, cholesterol, smoking, and alcohol intake) and “Lifestyle Balance” (characterized by physical activity, hydration, and balanced diet). The regression model demonstrated significant predictive capability (R2 = 0.95), explaining 95% of the variation in BA using 15 selected lifestyle factors. The derived “Health-15 Score” strongly correlated with a younger biological age, validating the “Smart Age” concept where favourable behaviors effectively decelerated aging. Conclusion: This study establishes a strong link between modifiable lifestyle factors and biological aging in pilots. The findings highlight the critical importance of lifestyle interventions in reducing pilot BA. Implementing targeted programs based on the Health-15 Score can significantly enhance the functional longevity and operational readiness of aviation professionals.

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Cite This Study

Mohapatra et al. (2026) studied this question.

synapsesocial.com/papers/69e320e740886becb653ffc6https://doi.org/10.4103/jmms.jmms_138_25
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