Daily Physical Activity in Patients With COPD After Hospital Discharge in a Minority Population

Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device

Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients’ hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions.

Patient-centered mobile health technology intervention to improve self-care in patients with chronic heart failure: Protocol for a feasibility randomized controlled trial

This randomized controlled trial aims to determine the feasibility and preliminary efficacy of a patient-centered, mobile health technology intervention (iCardia4HF) in patients with chronic Heart Failure (HF). Participants (n = 92) are recruited and randomized 1:1 to the intervention or control group. The intervention group receives a commercial HF self-care app (Heart Failure Storylines), three connected health devices that interface with the app (Withings weight scale and blood pressure monitor, and Fitbit activity tracker), and a program of individually tailored text-messages targeting health beliefs, self-care self-efficacy, HF-knowledge, and physical activity. The control group receives the same connected health devices, but without the HF self-care app and text messages. Follow-up assessments occur at 30 days and 12 weeks. The main outcome of interest is adherence to HF self-care assessed objectively through time-stamped data from the electronic devices and also via patient self-reports. Primary measures of HF self-care include medication adherence and adherence to daily weight monitoring. Secondary measures of HF self-care include adherence to daily self-monitoring of HF symptoms and blood pressure, adherence to low-sodium diet, and engagement in physical activity. Self-reported HF self-care and health-related quality of life are assessed with the Self-care Heart Failure Index and the Kansas City Cardiomyopathy Questionnaire, respectively. Hospitalizations and emergency room visits are tracked in both groups over 12 weeks as part of our safety protocol. This study represents an important step in testing a scalable mHealth solution that has the potential to bring about a new paradigm in self-management of HF.