Trends of self-reported non-adherence among type 2 diabetes medication users in the United States across three years using the self-reported Medication Adherence Reasons Scale

Unni, Elizabeth J.; Gupta, Shaloo; Sternbach, N.

doi:10.1016/j.numecd.2021.09.018

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Recent evidence suggests that some individuals with prediabetes benefit from taking medications, such as Metformin, in addition to participating in the DPP [ 38 ]. Medication adherence is often suboptimal, and strategies to promote compliance are necessary [ 39 ]. Medication adherence is one of several recent features added to the digital DPP investigated in this study.…”

Section: Discussionmentioning

confidence: 99%

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Hori¹,

Sia²,

Lockwood³

et al. 2022

Behavioral Sciences

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Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.

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Section: Discussionmentioning

confidence: 99%

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Hori¹,

Sia²,

Lockwood³

et al. 2022

Behavioral Sciences

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“…In this regard, it has been shown that emerging technologies, like continuous glucose monitoring, connected insulin pens and artificial pancreas, may help to maximize therapy adherence and consequently improve BG control. [7][8][9] However, the impaired adherence to insulin therapy in T2D patients is still poorly investigated [10][11][12] and differences between T1D and T2D may exist. This calls for developing tailored solutions to properly predict and treat diabetes complications in target populations.…”

Section: Introductionmentioning

confidence: 99%

Short- and Long-Term Effects on Glucose Control of Nonadherence to Insulin Therapy in People With Type 2 Diabetes An In Silico Study

Visentin,

Cobelli,

Sieber

et al. 2024

J Diabetes Sci Technol

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Background: Strict adherence to multiple daily insulin (MDI) therapy is a cornerstone for the achievement of good glucose control in people with advanced type 2 diabetes (T2D). Here, we aim to in silico assess glucose control in T2D subjects with poor adherence to MDI therapy. Methods: We tuned the Padova T2D Simulator, originally describing early-stage T2D physiology, around advanced T2D people. One hundred in silico advanced T2D subjects were generated and equipped with optimal MDI therapy: specifically, basal and bolus insulin amounts and injection times were individualized for each subject by applying titration algorithms that iteratively update insulin dose based on glucose deviation from its target. Then, the effect of nonadhering to MDI therapy was assessed using standard glucose control metrics calculated in two 6-month 3-meal/day in silico scenarios: in Scenario 1, subjects received the optimal basal and prandial insulin bolus at each meal; in Scenario 2, subjects received optimal basal insulin and randomly delayed or skipped the prandial insulin bolus in 3 lunches during working days and 1 dinner during weekends. Results: A statistically significant degradation was found in all glucose control outcome metrics in Scenario 2 versus Scenario 1: e.g., percent time above 180 mg/dL increased by 22.2% and glucose management index by 0.2%. Conclusions: Impaired adherence to MDI therapy in T2D leads to glucose control deteriorations in both short and long terms. Interestingly, short-term hyperglycemia seems being contrasted by residual endogenous insulin secretion, which statistically increased by 3-fold after delayed/skipped insulin boluses compared with optimal ones.

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“…According to the World Health Organization (WHO), the definition of treatment adherence is "the extent to which a patient follows medical instructions" (Organization, 2003). Adherence to T2D treatment requires taking the prescribed medications, regular selfmonitoring, following a healthy diet, and adopting a physically active lifestyle (Unni et al, 2022). Studies have shown that adherence to the treatment of T2D patients can lead to a reduction in emergency visits, the risk of hospitalization, and healthcare costs (Evans et al, 2022;Shani et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Determinants of Adherence to Treatment in Type 2 Diabetic Patients: A Directed Qualitative Content Analysis Based on Pender’s Health Promotion Model

Shahabi,

Hosseini,

Aghamolaei

et al. 2023

Qual Health Res

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Adherence to the treatment of type 2 diabetes (T2D), which includes the use of prescribed drugs, following a healthy diet, and adopting a physically active lifestyle, is important to control the disease and improve the patients’ quality of life. The present study aimed to explain the determinants of adherence to treatment in patients with T2D based on Pender’s Health Promotion Model (HPM). The present research used a qualitative content analysis and was based on Pender’s HPM. The data were collected through in-depth semi-structured interviews with 20 T2D patients using a purposive sampling in Bandar Abbas, southern Iran. Maximum variation was considered in terms of gender, age, history of diabetes, type of drug used, education, and occupation. The data collection continued until data saturation. At the same time, the collected data were analyzed using the directed content analysis. MAXQDA 10 was used to manage codes and facilitate data analysis. The data analysis led to the extraction of 10 categories and 19 subcategories as the determinants of adherence to the treatment of T2D patients. The participants shared their experiences about personal characteristics and their ecological environment, specific cognition and emotions, and the behavioral results and experience as the determinants of adherence to T2D treatment. The categories extracted from this study in the form of HPM constructs provided a framework to explain treatment adherence. This information can help policy makers and planners in designing future programs.

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Trends of self-reported non-adherence among type 2 diabetes medication users in the United States across three years using the self-reported Medication Adherence Reasons Scale

Cited by 8 publications

References 20 publications

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Short- and Long-Term Effects on Glucose Control of Nonadherence to Insulin Therapy in People With Type 2 Diabetes An In Silico Study

Determinants of Adherence to Treatment in Type 2 Diabetic Patients: A Directed Qualitative Content Analysis Based on Pender’s Health Promotion Model

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