ArticleIEEE journal of biomedical and health informatics2020
A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.
Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Wearable Technology and Machine Learning for Prediction of Performance-Based and Patient-Reported Outcome Measures: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.PloS one · 2020Trial
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.Frontiers in digital health · 2026Article
- Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).Scientific reports · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal.NPJ digital medicine · 2024Review
- AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.Scientific reports · 2024Article
- Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones.Scientific reports · 2024Article
- A deep learning framework for noninvasive fetal ECG signal extraction.Frontiers in physiology · 2024Article
- Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application.Behavioral sciences (Basel, Switzerland) · 2023Article
- Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study.JMIR formative research · 2023Article
- Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset.Bioengineering (Basel, Switzerland) · 2022Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique.Frontiers in big data · 2022Article
- A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment.Computational intelligence and neuroscience · 2022Article
- Digital health device measured sleep duration and ideal cardiovascular health: an observational study.BMC cardiovascular disorders · 2021Observational
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.