PhD Scientific Days 2026

Health Sciences 3.

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

Előadó neve

Dr. Novák, Attila

Neptune code

SL2PGJ

Előadó munkahelye

Scientific Research Centre of the Hungarian Defence Forces Transformation Command

Előadó telefonszáma

+36204292429

Előadó e-mail címe

novak.attila@phd.semmelweis.hu

Az előadás címe

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

Szerző(k) neve és munkahelye

Attila Novák^1,2

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

Bemutatás módja

Szóbeli

Szekció

Health Sciences 3.

Language of the presentation

Hungarian

Preferred session

Health Sciences

Összefoglaló szövege

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)

University

Semmelweis University

Supervisor

Zoltán Lantos Dr.

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

phd.section.field

after finishing doctoral studies with absolutorium (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem hagyta jóvá

Előadó

9783

Start

11:30

End

11:40