Evaluation of a Web-Based Simulation Tool for Self-Management Support in Type 1 Diabetes: A Pilot Study

Colmegna, Patricio; Bisio, Alessandro; McFadden, Ryan; Wakeman, Christian; Oliveri, Mary C.; Nass, Ralf; Breton, Marc D.

doi:10.1109/jbhi.2022.3209090

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…Currently, there is increasing interest in decision aids for screening and treating chronic diseases, such as breast cancer (Elkin et al, 2017;Lin et al, 2022;Osaka & Nakayama, 2017;Sivell et al, 2012), stroke (Saposnik et al, 2016;Schott et al, 2019)and diabetes (Buse et al, 2021;Colmegna et al, 2023;Yu et al, 2020). However, there is lack of a systematic review of the characteristics, content and effectiveness of ACP decision aids applied in the field of palliative care.…”

Section: What Does This Paper Contribute To the Wider Global Clinical...mentioning

confidence: 99%

Exploration of decision aids to support advance care planning: A scoping review

Xu,

Han,

et al. 2024

Journal of Clinical Nursing

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BackgroundAdvance care planning is a process through which people communicate their goals and preferences for future medical care. Due to the complexity of the decision‐making process, decision aids can assist individuals in balancing potential benefits and risks of treatment options.ObjectiveWhile decision aids have the potential to better promote advance care planning, their characteristics, content and application effectiveness are unclear and lack systematic review. Therefore, we aimed to explore these three aspects and establish a foundation for future research.DesignScoping review.MethodsThis scoping review adheres to the framework proposed by Arksey and O'Malley and the PRISMA‐ScR list. Six English‐language databases were systematically searched from the time of construction until 1 December 2023. Two researchers conducted the article screening and data extraction, and the extracted data was presented in written tables and narrative summaries.ResultsOf the 1479 titles and abstracts, 20 studies fulfilled the inclusion criteria. Types of decision aids were employed, mainly websites and videos. Decision aid's primary components center around 11 areas, such as furnishing information, exploring treatment and care preferences. The main manifestations were a significant increase in knowledge and improved recognition of patients' target value preferences. Among the aids, websites and videos for advance care planning have relatively high content acceptability and decision‐making process satisfaction, but their feasibility has yet to be tested.ConclusionsDecision aids were varied, with content focused on describing key information and exploring treatment and care preferences. Regarding application effects, the aids successfully facilitated the advance care planning process and improved the quality of participants' decisions. Overall, decision aids are efficient in improving the decision‐making process for implementing advance care planning in cancer and geriatric populations. In the future, personalised decision aids should be developed based on continuous optimization of tools' quality and promoted for clinical application.Reporting MethodThe paper has adhered to the EQUATOR guidelines and referenced the PRISMAg‐ScR checklist.No Patient or Public ContributionThis is a review without patient and public contribution.Registration: https://doi.org/10.17605/OSF.IO/YPHKF, Open Science DOI: 10.17605/OSF.IO/YPHKF.

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Section: What Does This Paper Contribute To the Wider Global Clinical...mentioning

confidence: 99%

Exploration of decision aids to support advance care planning: A scoping review

Xu,

Han,

et al. 2024

Journal of Clinical Nursing

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“…Diabetes is generally categorized into two types: Type1 [5], [6] and Type 2 [7], [8] diabetes. Type1 diabetes happens when the body stops producing insulin [9]. Generally, this type of diabetes happens in childhood or in young adults although the disease can happen any time.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Timely Diabetes Detection Through Advanced Predictive Modeling and Patient Medical Statistics

Mehan

2023

INDJCSE

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Diabetes has become prevalent across a significant portion of the global population. While medication offers one avenue for disease management, its dosage often escalates with age, eventually necessitating insulin injections. Alternatively, lifestyle adjustments provide another means of control. Detecting diabetes early holds the potential to enhance patient well-being and avert the onset of the disease. This study undertakes a thorough parametric analysis of diabetes, comparing Linear, Multi-Linear, Polynomial, and Logistic Regression Models. The goal is to identify the optimal approach for predicting diabetes. Leveraging a dataset encompassing 768 patients from the National Institute of Diabetes and Digestive and Kidney Diseases, USA, the research considers both Patient Medical Statistics (PMS) and a Combination of Patients Medical Statistics (CPMS), featuring variables such as Pregnancy, Blood Sugar (BS), Blood Pressure (BP), Body Fat (BF), Insulin Level (IL), Body Mass Index (BMI), Diabetes Pedigree Function (DPF), and Age. Evaluation metrics, including Root Mean Square Error (RMSE) and Coefficient of Determination (R2), guide model selection. By employing R2, the ranking of PMS and CPMS is determined to yield a highly efficient model. Empirical findings indicate the Multiple Linear Regression model as presenting the lowest RMSE among all regression models. However, the comparative assessment underscores the suitability of Logistic Regression due to its discrete nature, demonstrating superior accuracy in prediction compared to other models.

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