title: Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.
abstract: Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.
authors.forename: Nabil
authors.initials: N
authors.lastname: Alshurafa
authors.forename: Costas
authors.initials: C
authors.lastname: Sideris
authors.forename: Mohammad
authors.initials: M
authors.lastname: Pourhomayoun
authors.forename: Haik
authors.initials: H
authors.lastname: Kalantarian
authors.forename: Majid
authors.initials: M
authors.lastname: Sarrafzadeh
authors.forename: Jo-Ann
authors.initials: JA
authors.lastname: Eastwood
mesh_terms: D000328:Adult; D005260:Female; D006293:Health Promotion / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000379:methods*; D012307:Risk Factors; D000068997:Smartphone; D017216:Telemedicine / Q000379:methods*; D016896:Treatment Outcome