Assistive Digital Technologies for Adherence Monitoring and Evaluation Using Multimodal Patient Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F25%3A00388007" target="_blank" >RIV/68407700:21460/25:00388007 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.grantjournal.com/issue/1402/PDF/1402buchtelova.pdf" target="_blank" >https://www.grantjournal.com/issue/1402/PDF/1402buchtelova.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Assistive Digital Technologies for Adherence Monitoring and Evaluation Using Multimodal Patient Data

Popis výsledku v původním jazyce

Adherence to therapeutic recommendations is a key determinant of treatment effectiveness across medical disciplines, yet its reliable assessment remains challenging in real-world settings. This paper presents an assistive digital framework for adherence monitoring and evaluation based on multimodal patient- generated data. The proposed system integrates wearable-derived physiological data, mobile self-reported behavioral inputs, and contextual clinical information into a unified assessment pipeline. An interpretable algorithmic approach combining categorical evaluation, normalization, and weighted aggregation is introduced to quantify daily adherence. The framework emphasizes transparency, flexibility, and practical usability rather than black-box prediction models. Pilot study confirmed technical feasibility, reliable data integration, and high user acceptance, supporting the applicability of the approach in real-world conditions.

Název v anglickém jazyce

Assistive Digital Technologies for Adherence Monitoring and Evaluation Using Multimodal Patient Data

Popis výsledku anglicky

Adherence to therapeutic recommendations is a key determinant of treatment effectiveness across medical disciplines, yet its reliable assessment remains challenging in real-world settings. This paper presents an assistive digital framework for adherence monitoring and evaluation based on multimodal patient- generated data. The proposed system integrates wearable-derived physiological data, mobile self-reported behavioral inputs, and contextual clinical information into a unified assessment pipeline. An interpretable algorithmic approach combining categorical evaluation, normalization, and weighted aggregation is introduced to quantify daily adherence. The framework emphasizes transparency, flexibility, and practical usability rather than black-box prediction models. Pilot study confirmed technical feasibility, reliable data integration, and high user acceptance, supporting the applicability of the approach in real-world conditions.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Grant Journal

ISSN

1805-0638

e-ISSN

1805-0638

Svazek periodika

14

Číslo periodika v rámci svazku

02

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

33-37

Kód UT WoS článku

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EID výsledku v databázi Scopus

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