PhD Scientific Days 2026

Health Sciences 3.

Measuring Less, Deciding Better: A Scalable Decision-Support Framework for Military Readiness

Name of the presenter

Novák, Attila

Institute/workplace of the presenter

Scientific Research Centre of the Hungarian Defence Forces Transformation Command

Authors

Attila Novák^1,2
1: Scientific Research Centre of the Hungarian Defence Forces Transformation Command
2: Doctoral College Health Sciences Division of Semmelweis University

Text of the abstract

Introduction
Lifestyle-related risk factors increasingly constrain military readiness, while existing monitoring systems are often either too resource-intensive or insufficiently actionable at scale. There is a need for integrated approaches that link population-level health surveillance with individual-level decision-making.
Aims
To develop an evidence-informed, scalable decision-support framework that supports military readiness using a minimal, field-feasible measurement approach.
Method
A tiered strategic–operational–tactical framework was developed based on a multi-study research portfolio in military populations. This included training-cycle monitoring (N=265), a body composition program dataset (N=283; 709 measurements), morbidity surveillance data (2011–2020), and exploratory clinical observations. The framework integrates routine health indicators, repeated program-level measurements, and minimal field-based screening tools. Parsimonious prediction models were applied to estimate key body composition parameters, and an integrated Success Index was developed to capture combined physiological (body composition, strength) and behavioural (e.g., stress, eating patterns) adaptation.
Results
Training-cycle data showed that initial favorable body composition changes were not sustained without targeted intervention. Body composition program results demonstrated that minimal measurement sets (body weight, handgrip strength, sex) can achieve high predictive performance (R² ≈ 0.74–0.78). Instead of relying on isolated parameters, the proposed Success Index enabled integrated evaluation of adaptation and clearer responder stratification. Morbidity data revealed substantial readiness-relevant disease burden, highlighting the need for integrated monitoring. Phase angle and handgrip strength were associated with functional status, supporting their use in field-based assessment.
Conclusion
The proposed framework translates empirical findings into a scalable monitoring and decision-support architecture. By combining minimal measurements with composite outcome indicators, it enables low-burden, high-value assessment across levels of command and facilitates more precise intervention pathways. This approach shifts monitoring from descriptive measurement toward actionable decision support in military health systems.
Funding
Institutional support (SE, HDF)