SynthesisJMIR mHealth and uHealth2021
Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review.
Synthesis in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 8 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
50 citing papers in PubMed, 8 syntheses or guidelines pooled it, 125 citations in OpenAlex.
- Diagnostic accuracy of ECG smart chest patches versus PPG smartwatches for atrial fibrillation detection: a systematic review and meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis.Frontiers in digital health · 2025Pooled it
- Cultural Tailoring and Implementation Science for Cardiometabolic Interventions in Asian Americans: A Systematic Review.Nursing open · 2024Pooled it
- Effectiveness of wearable technology-based physical activity interventions for adults with type 2 diabetes mellitus: A systematic review and meta-regression.Journal of diabetes · 2024Pooled it
- Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature.Journal of medical Internet research · 2023Pooled it
- The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Efficacy of Mobile Health Applications to Improve Physical Activity and Sedentary Behavior: A Systematic Review and Meta-Analysis for Physically Inactive Individuals.International journal of environmental research and public health · 2022Pooled it
- Machine Learning and Smart Devices for Diabetes Management: Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Effects of Incontro, Alleanza, Responsabilita, Autonomia Intervention Model Combined with Orem Self-Care Model and the Use of Smart Wearable Devices on Perceived Stress and Self-Efficacy in Patients after Total Hip Arthroplasty.Computational intelligence and neuroscience · 2022Trial
- Digital Health Interventions for Type 2 Diabetes: A Narrative Review of Mobile Technologies and Their Impact on Patients' Outcomes.Health science reports · 2026Article
- Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us.The Journal of clinical endocrinology and metabolism · 2026Article
- Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.Statistics in medicine · 2026Article
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Interpretable non-invasive glucose monitoring: an attention-based deep learning framework for visualizing hemodynamic correlates in PPG signals.Frontiers in bioengineering and biotechnology · 2026Article
- Sugar slay: a gamified decision support ecosystem for type 1 diabetes.Frontiers in digital health · 2026Article
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Big Data and AI-Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach.International journal of obesity (2005) · 2025Article
- Rethinking the Diabetes-Cardiovascular Disease Continuum: Toward Integrated Care.Journal of clinical medicine · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences.
objectiveThis study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus.
methodsA systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale.
resultsThe search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level.
conclusionsThe use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.
Indexed as
Identifiers
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.