Title
Psychophysiological Models to Identify and Monitor Elderly with a Cardiovascular Condition: The Added Value of Psychosocial Parameters to Routinely Applied Physiological Assessments
Author
Kallen, V.
Marck, J.W.
Stam, J.
Issa, A.
Johnson, B.
van Meeteren, N.
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.
Subject
Elderly
Cardiovascular
Heart Rate Variability
Heart Rate Recovery
Respiratory
Psychosocial functioning
Prognostic model
Depression
Loneliness
To reference this document use:
http://resolver.tudelft.nl/uuid:fbc3951f-d127-4a8c-8321-b15779f46b75
DOI
https://doi.org/10.3390/s20113240
TNO identifier
877999
Source
Sensors, 20 (20)
Document type
article