Introduction Recent legislative amendments in Hungary have resulted in the possibility for early retirement being abolished in the Hungarian Defence Forces. The retirement age for professional soldiers has also increased to 65 years, thereby greatly increasing the average length of military service. This necessitates greater attention to the health and care of service personnel due to the increase of chronic non-communicable diseases with age. The aim of this research was to identify how health behaviours might potentially contribute to diseases in the Hungarian Defence Forces.
Methods All members of the Hungarian Defence Forces undergoing health screenings between 2011 and 2015 were assessed. Health variables analysed were derived from the health screening data sheet which is collected from every member of the Hungarian Defence Forces undergoing a health screening since 2009. Items recorded were connected to health behaviour (physical activity, nutrition, smoking), subjective well-being (psychosomatic backache, fatigue and quality of waking up to describe the quality of sleep), sociodemographic data (age, gender) and the mental toughness quotient (MTQ). A logistic regression model was utilised to predict how health behaviour may affect the development of disease.
Results Factors most associated with the development of disease included psychosomatic backache (P<0.000), age (P<0.001), frequency of undertaking sports (P<0.05), quality of sleeping and waking up (P<0.05) and the assessment of gender differences (P<0.05).
Conclusions Extension of the length of active service will result in an increased burden of disease for members of the Hungarian Defence Forces. This includes both physical and psychological morbidity that could potentially be obstacles for service personnel to perform their military duties. Health behaviours such as psychosomatic backache, the frequency of performing sports and sleep quality may predict the development of disease and should be explored in health screening consultations.
- military health
- predictive factors
- health behavior
- health estimating model
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