ReviewJournal of diabetes science and technology2015
A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps.
Review in Journal of diabetes science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis 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
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review.Journal of medical Internet research · 2017Pooled it
- One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- Accuracy and User Performance Evaluation of a New, Wireless-enabled Blood Glucose Monitoring System That Links to a Smart Mobile Device.Journal of diabetes science and technology · 2017Trial
- A New, Wireless-enabled Blood Glucose Monitoring System That Links to a Smart Mobile Device: Accuracy and User Performance Evaluation.Journal of diabetes science and technology · 2017Trial
- Artificial Intelligence-Driven Digital Tools for Diabetes Self-management in Children: A Scoping Review.International journal of endocrinology and metabolism · 2027Review
- Caregiver experiences of an integrative patient-centered digital health application for pediatric type 1 diabetes care: Findings from a pilot clinical trial.PLOS digital health · 2025Article
- The Clinical Impact of Flash Glucose Monitoring-a Digital Health App and Smartwatch Technology in Patients With Type 2 Diabetes: Scoping Review.JMIR diabetes · 2023Article
- Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children.JAMA network open · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Matching science to reality: how to deploy a participant-driven digital brain health platform.Frontiers in dementia · 2023Article
- Six Digital Health Technologies That Will Transform Diabetes.Journal of diabetes science and technology · 2023Article
- Digital Diabetes Management: A Literature Review of Smart Insulin Pens.Journal of diabetes science and technology · 2022Review
- Assessing the Feasibility of a Novel mHealth App in Hematopoietic Stem Cell Transplant Patients.Transplantation and cellular therapy · 2021Article
- Diabetes clinic reinvented: will technology change the future of diabetes care?Therapeutic advances in endocrinology and metabolism · 2021Review
- Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements.Sensors (Basel, Switzerland) · 2020Article
- Potential Benefits and Risks Resulting From the Introduction of Health Apps and Wearables Into the German Statutory Health Care System: Scoping Review.JMIR mHealth and uHealth · 2020Article
- Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.Sensors (Basel, Switzerland) · 2020Review
- Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal.JMIR research protocols · 2019Article
- Bridging the integration gap between patient-generated blood glucose data and electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Review of Emerging Technologies in Diabetes Management for Multiple-Dose Insulin-Injecting Patients With Type 2 Diabetes Who Self-monitor Blood Glucose.The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.
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.