Back to Search Start Over

Psychophysiological Models to Identify and Monitor Elderly with a Cardiovascular Condition

Authors :
Victor L. Kallen
Amine N. Issa
Jacqueline Stam
Jan-Willem Marck
Bruce D. Johnson
Nico L. U. van Meeteren
Epidemiologie
RS: CAPHRI - R3 - Functioning, Participating and Rehabilitation
Source :
Sensors, Vol 20, Iss 3240, p 3240 (2020), Sensors (Basel, Switzerland), Sensors, 11, 20, Sensors, 20(11):3240. MDPI AG, Sensors; Volume 20; Issue 11; Pages: 3240
Publication Year :
2020

Abstract

The steadily growing elderly population calls for efficient, reliable and preferably ambulant health supervision. Since cardiovascular risk factors interact with psychosocial strain (e.g., depression), we investigated the potential contribution of psychosocial factors in discriminating generally healthy elderly from those with a cardiovascular condition, on and above routinely applied physiological assessments. Fifteen elderly (aged 60 to 88) with a cardiovascular diagnosis were compared to fifteen age and gender matched healthy peers. Six sequential standardized lab assessments were conducted (one every two weeks), including an autonomic test battery, a 6-min step test and questionnaires covering perceived psychological state and experiences over the previous two weeks. Specific combinations of physiological and psychological factors (most prominently symptoms of depression) effectively predicted (clinical) cardiovascular markers. Additionally, a highly significant prognostic model was found, including depressive symptoms, recently experienced negative events and social isolation. It appeared slightly superior in identifying elderly with or without a cardiovascular condition compared to a model that only included physiological parameters. Adding psychosocial parameters to cardiovascular assessments in elderly may consequently provide protocols that are significantly more efficient, relatively comfortable and technologically feasible in ambulant settings, without necessarily compromising prognostic accuracy.

Details

Language :
English
ISSN :
14248220
Volume :
20
Issue :
11
Database :
OpenAIRE
Journal :
Sensors
Accession number :
edsair.doi.dedup.....ea50729b6cac3e2c238e6a3feae3fc77