Profiling of patients with type 2 diabetes based on medication adherence data

Markovič, Rene; Grubelnik, Vladimir; Završnik, Tadej; Vošner, Helena Blažun; Kokol, Peter; Perc, Matjaž; Marhl, Marko; Završnik, Matej; Završnik, Jernej

doi:10.3389/fpubh.2023.1209809

Cited by 4 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research showed that the onset of long COVID could be associated with metabolic dysfunction [23][24][25]. A study performed in the same region reports that metformin is often the first line of treatment for diabetes patients [26]. Metformin has been shown to have a potential protective role in COVID-19 [27] and long COVID [28,29] so it might be the contributing factor for the shorter duration of PCS among diabetic patients.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Diabetes and Being Overweight on Patients with Post-COVID-19 Syndrome

Kirbiš,

Sobotkiewicz,

Schaubach

et al. 2023

Infectious Disease Reports

Self Cite

View full text Add to dashboard Cite

In the aftermath of the COVID-19 pandemic, post-COVID-19 syndrome (PCS) remains a challenge and may continue to pose a major health problem in the future. Moreover, the influences of type 2 diabetes and being overweight on PCS remain unclear. This study aimed to assess these influences. We performed an observational study from October 2020 to July 2022, which included 466 patients (269 males and 197 females) with a median age of 65. They were hospitalized due to COVID-19 pneumonia and had persistent symptoms after 1 month of COVID-19 infection. The patients were divided into four groups according to the study objectives: patients with type 2 diabetes, overweight patients, overweight patients with type 2 diabetes, and average-weight patients without type 2 diabetes. The clinical and demographic data collected during hospitalization and regular visits to the Community Healthcare Center dr. Adolf Drolc Maribor were analyzed. Our results showed that type 2 diabetes patients had more difficult courses of treatment and longer hospitalizations. Moreover, more type 2 diabetes patients underwent rehabilitation than the other study groups. The prevailing symptoms of our patients with PCS were dyspnea and fatigue, mostly among female patients with type 2 diabetes. Our study also showed that more women with type 2 diabetes and overweight women with type 2 diabetes suffered from secondary infections. Furthermore, more overweight patients were treated in the intensive care unit than patients from the other groups. However, our study showed an interesting result: patients with type 2 diabetes had the shortest PCS durations. Type 2 diabetes and being overweight are risk factors for PCS onset and prolonged duration. Therefore, our data that revealed a shorter duration of PCS in type 2 diabetes patients than the other investigated groups was unexpected. We believe that answering the questions arising from our unexpected results will improve PCS treatment in general.

show abstract

Section: Discussionmentioning

confidence: 99%

The Effects of Diabetes and Being Overweight on Patients with Post-COVID-19 Syndrome

Kirbiš,

Sobotkiewicz,

Schaubach

et al. 2023

Infectious Disease Reports

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this section, we look at the various AI algorithms that are used when dealing with this kind of data. Markovic et al [32] applied a Novel NLP to medical records to create a patient-specific drug profile for diabetic patients.…”

Section: A Ai Techniques and Healthcarementioning

confidence: 99%

DFU-SIAM a Novel Diabetic Foot Ulcer Classification With Deep Learning

Toofanee,

Dowlut,

Hamroun

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Diabetes affects roughly 537 million people in the world, and it is predicted to reach 783 million by 2045. Diabetic Foot Ulcer (DFU) is a major issue with diabetes that may lead to lower limb amputation. The rapid evolution of DFU demands immediate intervention to prevent the terrible consequences of amputation and related complications.This research introduces a novel approach utilizing deep neural networks and machine learning for the accurate classification of diabetic foot ulcer (DFU) images. The proposed method harnesses the cutting-edge capabilities of Convolutional Neural Networks (CNN) and Vision Image Transformers (ViT) within a Siamese Neural Network (SNN) Architecture. By employing similarity learning, the model efficiently categorizes DFU images into four distinct classes: None, Infection, Ischemia, or Both. The training process involves the use of the DFU2021 dataset, with all ethical clearances duly obtained. Notably, the model exhibits remarkable performance on both the validation and test data, indicating a significant breakthrough in the field of DFU disease image classification. The potential of this innovative model extends beyond classification; it holds promise as an integral component of a comprehensive detection tool and longitudinal treatment protocol validation for DFU disease.

show abstract

“…It could also enable patient empowerment by allowing them to play an active role in the management of their disease ( 11 ). eHealth also has a positive impact on medication adherence ( 12 ) and in the management of polypharmacy ( 13 ), which is a major challenge among patients with chronic diseases like diabetes ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

eHealth profile of patients with diabetes

Jendly,

Santschi,

Tancredi

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

BackgroundDigital health technology can be useful to improve the health of patients with diabetes and to support patient-centered care and self-management. In this cross-sectional study, we described the eHealth profile of patients with diabetes, based on their use of digital health technology, and its association with sociodemographic characteristics.MethodsWe used data from the “Qualité Diabète Valais” cohort study, conducted in one region of Switzerland (Canton Valais) since 2019. Participants with type 1 or type 2 diabetes completed questionnaires on sociodemographic characteristics and on the use of digital health technology. We defined eHealth profiles based on three features, i.e., ownership or use of (1) internet-connected devices (smartphone, tablet, or computer), (2) mHealth applications, and (3) connected health tools (activity sensor, smart weight scale, or connected blood glucose meter). We assessed the association between sociodemographic characteristics and participants’ eHealth profiles using stratified analyses and logistic regression models.ResultsSome 398 participants (38% women) with a mean age of 65 years (min: 25, max: 92) were included. The vast majority (94%) were Swiss citizens or bi-national and 68% were economically inactive; 14% had a primary level education, 51% a secondary level, and 32% a tertiary level. Some 75% of participants had type 2 diabetes. Some 90% of the participants owned internet-connected devices, 43% used mHealth applications, and 44% owned a connected health tool. Older age and a lower educational level were associated with lower odds of all features of the eHealth profile. To a lesser extent, having type 2 diabetes or not being a Swiss citizen were also associated with a lower use of digital health technology. There was no association with sex.ConclusionWhile most participants owned internet-connected devices, only about half of them used mHealth applications or owned connected health tools. Older participants and those with a lower educational level were less likely to use digital health technology. eHealth implementation strategies need to consider these sociodemographic patterns among patients with diabetes.

show abstract

Profiling of patients with type 2 diabetes based on medication adherence data

Cited by 4 publications

References 57 publications

The Effects of Diabetes and Being Overweight on Patients with Post-COVID-19 Syndrome

The Effects of Diabetes and Being Overweight on Patients with Post-COVID-19 Syndrome

DFU-SIAM a Novel Diabetic Foot Ulcer Classification With Deep Learning

eHealth profile of patients with diabetes

Contact Info

Product

Resources

About