Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure

Lorraine Evangelista, Hassan Ghasemzadeh, Jung Ah Lee, Ramin Fallahzadeh, Majid Sarrafzadeh, Debra K. Moser

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)425-433
Number of pages9
JournalTechnology and Health Care
Volume25
Issue number3
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Fingerprint

Heart Failure
Health
Monitoring
Decision Trees
Defibrillators
Neural Networks (Computer)
Blood pressure
Multilayer neural networks
Decision trees
Software
Learning systems
Demography
Classifiers
Blood Pressure
Technology
Weights and Measures
Research

Keywords

  • E-health
  • telecardiology
  • telehealth

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Biomaterials
  • Information Systems
  • Biomedical Engineering
  • Health Informatics

Cite this

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure. / Evangelista, Lorraine; Ghasemzadeh, Hassan; Lee, Jung Ah; Fallahzadeh, Ramin; Sarrafzadeh, Majid; Moser, Debra K.

In: Technology and Health Care, Vol. 25, No. 3, 01.01.2017, p. 425-433.

Research output: Contribution to journalArticle

Evangelista, Lorraine ; Ghasemzadeh, Hassan ; Lee, Jung Ah ; Fallahzadeh, Ramin ; Sarrafzadeh, Majid ; Moser, Debra K. / Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure. In: Technology and Health Care. 2017 ; Vol. 25, No. 3. pp. 425-433.
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