Trial reportJournal of diabetes science and technology2018
Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.
Trial report in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Telemedicine-assisted structured self-monitoring of blood glucose in management of T2DM results of a randomized clinical trial.BMC medical informatics and decision making · 2023Trial
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review.Journal of medical Internet research · 2023Article
- Current Technologies for Managing Type 1 Diabetes Mellitus and Their Impact on Quality of Life-A Narrative Review.Life (Basel, Switzerland) · 2023Review
- Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges.International journal of telemedicine and applications · 2023Review
- Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit.Frontiers in neuroscience · 2022Article
- Diabetes Technology Meeting 2020.Journal of diabetes science and technology · 2021Article
- Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.Sensors (Basel, Switzerland) · 2021Review
- Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.Sensors (Basel, Switzerland) · 2020Review
- Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.Sensors (Basel, Switzerland) · 2020Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- A review of big data and medical research.SAGE open medicine · 2020Review
- Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.Diabetes technology & therapeutics · 2019Article
- Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study.JMIR mHealth and uHealth · 2019Article
- HYPO-CHEAT's aggregated weekly visualisations of risk reduce real world hypoglycaemia.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn type 1 diabetes mellitus (T1DM), patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions. Artificial intelligence applications can help people with type 1 diabetes in decision making and allow them to react at time scales shorter than the scheduled face-to-face visits. This work presents a decision support system (DSS), based on glucose prediction, to assist patients in a mobile environment.
methodsThe system's impact on therapeutic corrective actions has been evaluated in a randomized crossover pilot study focused on interprandial periods. Twelve people with type 1 diabetes treated with insulin pump participated in two phases: In the experimental phase (EP) patients used the DSS to modify initial corrective decisions in presence of hypoglycemia or hyperglycemia events. In the control phase (CP) patients were asked to follow decisions without knowing the glucose prediction. A telemedicine platform allowed participants to register monitoring data and decisions and allowed endocrinologists to supervise data at the hospital. The study period was defined as a postprediction (PP) time window.
resultsAfter knowing the glucose prediction, participants modified the initial decision in 20% of the situations. No statistically significant differences were found in the PP Kovatchev's risk index change (-1.23 ± 11.85 in EP vs -0.56 ± 6.06 in CP). Participants had a positive opinion about the DSS with an average score higher than 7 in a usability questionnaire.
conclusionThe DSS had a relevant impact in the participants' decision making while dealing with T1DM and showed a high confidence of patients in the use of glucose prediction.
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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.