Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Ochnik Dominika, Cholewa-Wiktor Marta, Jakubiak Monika, Pataj Magdalena
Dyscypliny:
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Rok wydania:	2024
Wersja dokumentu:	Drukowana \| Elektroniczna
Język:	angielski
Wolumen/Tom:	14
Numer artykułu:	5173
Strony:	1 - 18
Impact Factor:	3,8
Web of Science® Times Cited:	2
Scopus® Cytowania:	2
Bazy:	Web of Science \| Scopus
Efekt badań statutowych	NIE
Finansowanie:	The costs of conducting this research were fully covered by the Lublin University of Technology. The APC was equally covered by the Academy of Silesia from the subsidy of the Ministry of Education and Science for the Silesian Academy in Katowice, the Lublin University of Technology, and Maria Curie-Skłodowska University (Institute of Management and Quality Sciences and Institute of Social Communication and Media) in Lublin, Poland.
Materiał konferencyjny:	NIE
Publikacja OA:	TAK
Licencja:
Sposób udostępnienia:	Witryna wydawcy
Wersja tekstu:	Ostateczna wersja opublikowana
Czas opublikowania:	W momencie opublikowania
Data opublikowania w OA:	2 marca 2024
Abstrakty:	angielski
	eHealth tools usage is vital for health care systems and increased significantly after the COVID‑19 pandemic, which aggravated mental health issues. This cross‑sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18–87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS‑4, GAD‑2, and PHQ‑2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.

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eHealth tools use and mental health: a cross-sectional network analysis in a representative sample

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