ArticleIEEE sensors journal2020
Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.
Article in IEEE sensors journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Multivariable Artificial Pancreas to Detect and Mitigate the Effects of Unannounced Physical Activities and Acute Psychological Stress
Leveraging the Exposome and Patient Sensor Data to Enhance Personalized Diabetes Care Across Diverse Communities
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review.Physiological measurement · 2026Article
- A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes.Engineering applications of artificial intelligence · 2025Article
- Interpretable deep learning for personalized energy expenditure prediction using ECG and acceleration signals in incremental exercise.Scientific reports · 2025Article
- Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes.Diabetes technology & therapeutics · 2024Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes.Journal of diabetes science and technology · 2024Article
- The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review.Cureus · 2024Review
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Performance of real-time continuous glucose monitoring during track and field training in adolescents with type 1 diabetes.Pediatric endocrinology, diabetes, and metabolism · 2024Article
- Supervised Learning of Physical Activity Features From Functional Accelerometer Data.IEEE journal of biomedical and health informatics · 2023Article
- Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.Journal of diabetes science and technology · 2023Article
- Strengths and Challenges of Closed-Loop Insulin Delivery During Exercise in People With Type 1 Diabetes: Potential Future Directions.Journal of diabetes science and technology · 2023Article
- Digital Connectivity: The Sixth Vital Sign.Journal of diabetes science and technology · 2022Article
- Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review.Journal of medical Internet research · 2022Article
- Diabetes Technology Meeting 2021.Journal of diabetes science and technology · 2022Article
- Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems.Journal of diabetes science and technology · 2022Article
- Observational Study of Glycemic Impact of Anticipatory and Early-Race Athletic Competition Stress in Type 1 Diabetes.Frontiers in clinical diabetes and healthcare · 2022Article
- Prior Informed Regularization of Recursively Updated Latent-Variables-Based Models with Missing Observations.Control engineering practice · 2021Article
- Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.Computers in biology and medicine · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.
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What OpenQuestion holds
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.