A novel pen-based Bluetooth-enabled insulin delivery system with insulin dose tracking and advice
Abstract
INTRODUCTION: Diabetes is growing in prevalence internationally. As more individuals require insulin as part of their treatment, technology evolves to optimize delivery, improve adherence, and reduce dosing errors. Insulin pens outperform vial and syringe in simplicity, dosing accuracy, and user preference. Bolus advisors improve dosing confidence and treatment adherence. The InPen System offers a novel approach to treatment via a wireless pen that syncs to a mobile application featuring a bolus advisor, enabling convenient insulin dose tracking and more accurate bolus advice among other features. Areas covered: Existing technology for insulin delivery and bolus advice are reviewed. The mechanics and functionality of the InPen device are delineated. Findings from formative testing and usability studies of the InPen system are reported. Future directions for the InPen system in the treatment of diabetes are discussed. Expert opinion: Diabetes management is complex and largely data-driven. The InPen System offers a promising new opportunity to avail insulin pen-users of features known to improve treatment efficacy, which have otherwise primarily been available to those using pumps. Given that the majority of insulin users do not use insulin pumps, the InPen System is poised to improve glucose control in a significant portion of the diabetes population.
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Many people treated with insulin fail to reach their glycemic targets, which is known to increase the risk of microvascular and macrovascular complications, resulting in substantial morbidity and mortality.
“Many people treated with insulin fail to reach their glycemic targets, which is known to increase the risk of microvascular and macrovascular complications, resulting in substantial morbidity and mortality.13,14,24”
From Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen (2020) · cites this paper as [24]
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- Authors
- Bailey, T. S., & Stone, J. Y.
- Affiliations
- AMCR Institute (US)
- Corresponding author
- Stone, Jenine Y.
- Venue
- Expert Opinion on Drug Delivery
- Publisher
- Informa UK Limited
- Published
- 2017-04-10
- Volume
- 14
- Issue
- 5
- Pages
- 697-703
- ISSN
- 1742-5247 (ISSN-L)
- 1744-7593
- OpenAlex
- W2602552145
- Cited by
- 60
- References
- 40
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 5 · 2023: 5 · 2022: 11 · 2021: 5 · 2020: 12 · 2019: 2 · 2018: 6 · 2017: 1
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Opinion on Drug Delivery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- insulin pens, Bluetooth, diabetes management, mobile application, insulin delivery devices, medication adherence, dosing errors, glycemic control, usability
- MeSH terms
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Injections, Insulin, Insulin Infusion Systems, Syringes, Drug Delivery Systems, Treatment Outcome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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