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Enregistrement W4407266308 · doi:10.1097/ms9.0000000000002990

SmartAdjust technology and beyond: a new era in diabetes management – a correspondence

2025· editorial· en· W4407266308 sur OpenAlexaff
Ahmad Furqan Anjum, Maher Ali Rusho, Aymar Akilimali

Notice bibliographique

RevueAnnals of Medicine and Surgery · 2025
Typeeditorial
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Research
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineDiabetes mellitusIntensive care medicineDiabetes managementType 2 diabetesEndocrinology

Résumé

récupéré en direct d'OpenAlex

To the Editor, Diabetes care innovations have dramatically transformed management approaches, with technology being the main focus. These advancements optimize patient results and promote the overall well-being of individuals with diabetes. CGM systems and HCL systems have significantly transformed how diabetes is managed, resulting in better HbA1c levels and lower rates of hypoglycemia[1]. Advanced insulin pens and automated insulin delivery systems are being developed, offering patients more convenient and easy-to-use choices[2]. Integrating patient-generated health data with electronic health records improves individualized care by enabling real-time monitoring[3]. The FDA approval process plays a vital role in guaranteeing novel medical technology’s safety and effectiveness while striking a balance between innovation and safeguarding patients. The FDA enforces stringent testing protocols for high-risk medical gadgets, guaranteeing their advantages above any potential hazards[4]. FDA has recently cleared the use of automated insulin dosing (AID) devices, utilizing SmartAdjust technology, for adults with Type 2 DM[5]. This represents a significant advancement in managing Type 2 DM. SmartAdjust technology combines wearable devices and AI to control diabetes. It helps anticipate blood glucose levels, detect risk events, and modify insulin doses. This technology improves the lifestyle and quality of life for patients[6]. The system utilizes an individual’s CGM measurement and then adjusts insulin administration every 5 minutes, either increasing, decreasing, or pausing its delivery. A smartphone application consistently displays glucose levels[7]. This system was utilized only for Type 1 DM patients[8] previously, but now it has been cleared to use for Type 2 DM patients[5].Highlights What’s Known: Continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) systems have significantly improved diabetes management by optimizing HbA1c levels and reducing hypoglycemia. What’s New: The FDA recently cleared SmartAdjust technology for Type 2 diabetes mellitus (DM), marking a major advancement in automated insulin delivery using artificial intelligence (AI) and wearable devices. Clinical Implications: SmartAdjust technology offers more personalized, real-time glucose management, potentially enhancing patient outcomes and quality of life in diabetes care. The automatic insulin dosage system actively assists in correcting high blood sugar levels and safeguarding against low blood sugar levels. There are no requirements for administering many injections daily, using tubes, or pricking fingers for blood tests. A study shows that an automated insulin dosing system, such as an automated self-adjusting subcutaneous insulin algorithm (SQIA), showed a greater percentage of glucose levels falling within the desired range of 70–180 mg/dL compared to traditional methods, with 71.0% versus 69.0% respectively, and significantly decreased the occurrences of severe hyperglycemia and hypoglycemia. Also, the implementation of the SQIA resulted in a more than 12-fold decrease in insulin order revisions, demonstrating a significant enhancement in physician productivity[9]. Studies have demonstrated that automated insulin delivery systems can increase the Time in Range (TIR) by around 10.87% and decrease HbA1c levels by 0.37%, all without raising the likelihood of severe hypoglycemia or diabetic ketoacidosis[10]. The use of d-Nav® in individuals over an extended period resulted in a TIR of 69.8%, indicating successful management of glucose levels[8]. Utilizing HCL systems leads to a notable decrease in diabetes distress, resulting in a 16% enhancement in distress levels during a 6-month period[11]. Patients experience improved treatment satisfaction and decreased anxiety associated with the automated management of diabetes[12]. Automated algorithms optimize insulin control in clinical settings, successfully regulating glucose levels[13]. The FDA analyzed data from a 13-week clinical trial, including 289 persons diagnosed with Type 2 DM who utilized Insulet SmartAdjust technology. The study, encompassing heterogeneous demography in terms of race, age, education, and income, demonstrated enhanced regulation of blood sugar levels among all cohorts. The participants, who had diverse levels of familiarity with diabetes and insulin and frequently used other diabetes drugs, did not encounter any significant negative occurrences associated with the technology. The documented adverse effects were predominantly mild to moderate severity, encompassing hyperglycemia, hypoglycemia, and skin irritation[5]. Another study of 30 adults with Type 2 DM showed that integrating automated insulin delivery and home health care services effectively improved glycemic control. This intervention was also observed to be safe and efficacious for patients with Type 2 DM who were on multiple daily insulin injections and home health care. Patients with poor glycemic control should consider AID a reliable and safe option[14]. Apart from all the potential benefits and efficacy of automated insulin dosage, some issues may influence the efficacy and safety of the automated insulin dosage with SmartAdjust technology. Integration and interconnection of several devices increases the risk of technical complications affecting insulin management and monitoring glucose[15]. The control algorithms must address various states because physical activity and meal composition are among the parameters that may greatly affect insulin requirements[15,16]. To avoid using ineffective automated systems, organized education and user support should be provided suitably[17]. Current systems have difficulties in effectively addressing changes in glucose levels; this prompts inadequate regulation of blood sugar levels[18]. The limitations in the pharmacokinetics of rapid-acting insulin entail that the user is required to give information about the amount of carbohydrates consumed, thus challenging the full automation process[17]. However, there is a steady improvement in the technology and algorithms that could be adopted to improve the artificial insulin dosage systems. It is important to address these issues to increase the level of protection for users and the effectiveness of the treatment. Implementing SmartAdjust technology is also a remarkable accomplishment for treating diabetes patients and delivering better blood glucose control and quality of lifestyles. Despite important benefits such as a better TIR and a reduction in HbA1c, this innovation encounters limitations like technical difficulties and the need for specific training. The issues mentioned above should be discussed to capture the full potential of automated insulin administration systems and effectively control diabetes. As technology continues to develop, these systems hold the potential to revolutionize the field of diabetic care and treatment.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,042

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,016
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0040,006
Science ouverte0,0020,001
Intégrité de la recherche0,0080,017
Charge utile insuffisante (le modèle a refusé de juger)0,0130,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,340
Écart entre enseignants0,304 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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Même revueAnnals of Medicine and SurgeryMême sujetDiabetes Management and ResearchTravaux en français237 207