Monitoring Health Status: Development and Preliminary Validation of a Personal Health Index Using the International Classification of Functioning, Disability and Health.

Authors

Ilkka Rautiainen, Lauri Parviainen, Veera Jakoaho, Sami Äyrämö, Jukka-Pekka Kauppi

Published in

JMIR human factors. Volume 13. Pages e84802. Jul 02, 2026. Epub Jul 02, 2026.

Abstract

Effective health monitoring is essential for personalized care and comprehensive health assessment. Personal health indices and profiles offer a concise summary of an individual's overall health, supporting both clinical decision-making and self-management. However, global standardization remains challenging due to diverse practices and data formats across countries.
This study aimed to present a novel model for computing a personal health index and health profile using the International Classification of Functioning, Disability and Health (ICF) framework. The model was designed to handle incomplete and heterogeneous datasets and aimed to provide standardized, interpretable health metrics.
We developed a recursive algorithm that calculates the health index based on the hierarchical structure of the ICF, using all available measurements. The model incorporates time decay and linkage reliability to weight input data. Preliminary validation was conducted on data from 505 individuals, using statistical correlation analyses with self-assessed health measures (EuroQol Visual Analogue Scale and pain ratings), and a sensitivity analysis was performed to assess model robustness.
The computed health index showed moderate positive correlations with EuroQol Visual Analogue Scale scores (all P<.001) and negative correlations with maximum pain trajectories, supporting its validity. Sensitivity analysis confirmed predictable behavior in response to input changes, and the model demonstrated resilience to missing data.
The proposed model offers a flexible and scientifically grounded approach to computing personal health indices and profiles within the ICF framework. It enables the integration of diverse health data sources and supports the visual representation for clinical and personal use. This model has potential applications in health monitoring, rehabilitation planning, and machine learning-based health informatics.

PMID:
42391635
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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