ArticleiScience2022
Quantifying the impact of physical activity on future glucose trends using machine learning.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Trial
- Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial.The Lancet. Digital health · 2023Trial
- Trial
- Identification of Activity, Insulin, and Dietary Thresholds Associated With Exercise-Related Hypoglycaemia and Hyperglycaemia in Children With Type 1 Diabetes: A Free-Living Study Using Explainable Machine Learning.Diabetes, obesity & metabolism · 2026Article
- Understanding Multilevel Correlates of Long-Term Physical Activity Trajectories Among Middle-Aged and Older Adults: A Machine-Learning Analysis.Behavioral sciences (Basel, Switzerland) · 2026Article
- Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.Open life sciences · 2026Article
- Temporal gradient analysis of blood glucose responses to non-standard physical activity: a free-living study in type 1 diabetes.Frontiers in sports and active living · 2026Article
- Development and validation of an interpretable machine learning model for predicting in-hospital hypoglycemia in adults with type 1 diabetes mellitus: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Machine Learning for Sensor Analytics: A Comprehensive Review and Benchmark of Boosting Algorithms in Healthcare, Environmental, and Energy Applications.Sensors (Basel, Switzerland) · 2025Review
- Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a Practical Decision Support Tool.JMIR diabetes · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring.iScience · 2024Article
- Physical Exercise After Solid Organ Transplantation: A Cautionary Tale.Transplant international : official journal of the European Society for Organ Transplantation · 2024Review
- Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.Journal of diabetes science and technology · 2023Article
- On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator.Scientific reports · 2023Article
- The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest.Diabetes technology & therapeutics · 2023Article
- Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes.American journal of physiology. Endocrinology and metabolism · 2023Article
- Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.
Indexed as
Identifiers
What Socratic 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 Socratic graph.