Affiliations: [a] Program in Nursing Science, University of California Irvine, Irvine, CA, USA | [b] School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA | [c] School of Computer Science & Director, Wireless Health Institute, University of California, Los Angeles, CA, USA | [d] College of Nursing, University of Kentucky, Lexington, KY, USA
Correspondence:
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Corresponding author: Lorraine S. Evangelista, UCI Program in Nursing Science, Irvine, CA 92697, USA. Tel.: +1 949 824 1771; E-mail:[email protected]
Abstract: BACKGROUND: It is unclear whether subgroups of patients may benefit
from remote monitoring systems (RMS) and what user characteristics and
contextual factors determine effective use of RMS in patients with heart
failure (HF). OBJECTIVE: The study was conducted to determine whether certain
user characteristics (i.e. personal and clinical variables) predict use of
RMS using advanced machine learning software algorithms in patients with HF. METHODS: This pilot study was a single-arm experimental study with
a pre- (baseline) and post- (3 months) design; data from the baseline
measures were used for the current data analyses. Sixteen patients provided
consent; only 7 patients (mean age 65.8 ± 6.1, range 58-83) accessed the
RMS and transmitted daily data (e.g. weight, blood pressure) as instructed
during the 12 week study duration. RESULTS: Baseline demographic and clinical characteristics of users
and non-users were comparable for a majority of factors. However, users were
more likely to have no HF specialty based care or an automatic internal
cardioverter defibrillator. The precision accuracy of decision tree,
multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for
predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively. CONCLUSION: Our preliminary data show that a small set of baseline
attributes is sufficient to predict subgroups of patients who had a higher
likelihood of using RMS. While our findings shed light on potential
end-users more likely to benefit from RMS-based interventions, additional
research in a larger sample is warranted to explicate the impact of user
characteristics on actual use of these technologies.
