Demographical, Clinical, and Psychological Characteristics of Users and Nonusers of an Online Platform for T2DM Patients (e-VitaDM-3/ZODIAC-44)

Roelofsen, Yvonne; Vugt, M. van; Hendriks, Steven H.; Hateren, Kornelis J. J. van; Groenier, Klaas H.; Snoek, Frank J.; Kleefstra, Nanne; Huijsman, Robbert; Bilo, Henk J. G.

doi:10.1155/2016/6343927

Cited by 6 publications

(13 citation statements)

References 31 publications

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Section: Methodsmentioning

confidence: 99%

“…In this retrospective cohort study, patients were selected using the electronic health records (EHRs) of two large Dutch diabetes care networks (DCNs) that routinely collect individual patient data and have been frequently used for research. [15][16][17][18][19] General practitioners and practice nurses from the participating practices recorded these data in the EHRs from the start of diabetes diagnosis. They use the information in the EHRs for the treatment and follow-up of their patients and as proof that they provided the care as agreed upon with health insurers for declaration purposes; therefore, it can be considered accurate.…”

Section: Study Design and Patientsmentioning

confidence: 99%

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A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Hertroijs

Elissen

Brouwers

et al. 2017

Diabetes Obesity Metabolism

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AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient‐centred care.MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n = 10 528; validation cohort, n = 3777). Latent growth mixture modelling identified distinct glycaemic 5‐year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice.ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver‐operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy.ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.

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Section: Methodsmentioning

confidence: 99%

Section: Study Design and Patientsmentioning

confidence: 99%

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Hertroijs

Elissen

Brouwers

et al. 2017

Diabetes Obesity Metabolism

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“…In a previous study, the online care platform used for this study (e-Vita) showed that users of the platform had lower glycated hemoglobin (HbA1c) levels and reported higher quality of life, better well-being, lower diabetes-related distress, and better medication adherence than non-users [5]. However, the usage of this platform has been shown to be minimal in the past [5,31]. Only 11% of the patients who were interested in the use of a care platform logged on at least once [31].…”

Section: Description Of the Platformmentioning

confidence: 92%

“…However, the usage of this platform has been shown to be minimal in the past [5,31]. Only 11% of the patients who were interested in the use of a care platform logged on at least once [31]. An improved version of the online platform e-Vita was used in our study.…”

Section: Description Of the Platformmentioning

confidence: 99%

Effects of the Proactive Interdisciplinary Self-Management (PRISMA) Program on Online Care Platform Usage in Patients with Type 2 Diabetes in Primary Care: A Randomized Controlled Trial

Pon

Kleefstra

Cleveringa

et al. 2020

Journal of Diabetes Research

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Online care platforms can support patients with type 2 diabetes (T2DM) in managing their health. However, in the use of eHealth, a low participation rate is common. The Proactive Interdisciplinary Self-Management (PRISMA) program, aimed at improving patients’ self-management skills, was expected to encourage patients to manage their disease through the use of an online platform. Therefore, the objective of the current study was to investigate whether a group education program can improve the use of an online care platform in patients with T2DM treated by primary care providers in the Netherlands. In a randomized controlled trial, patients with T2DM received either PRISMA with usual care or usual care only. During a six-month follow-up period in 2014-2015, usage (number of log-ons and time spent per session) of an online care platform (e-Vita) aimed at improving T2DM self-management was assessed. A training about the functionalities of e-Vita was offered. The sample consisted of 203 patients. No differences were found between the intervention and control groups in the number of patients who attended the platform training (interested patients) (X21=0.58; p=0.45), and the number of patients who logged on at least once (platform users) (X21=0.46; p=0.50). In addition, no differences were found between the groups in the type of users—patients who logged on twice or more (active users) or patients who logged on once (nonactive users) (X21=0.56; p=0.45). The PRISMA program did not change platform usage in patients with T2DM. In addition, only a small proportion of the patients logged on twice or more. Patients probably need other encouragements to manage their condition using an online platform.

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“…A fifth research project was conducted to evaluate the impact of the PHR on health care utilization (cost-effectiveness). The evaluations of all three PHRs so far showed that the actual use of the PHR is lagging [28–30] and only marginal (health) improvements were found [29–32]. Concrete reasons for the underperformance of the PHR remained unclear in these studies.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the implementation of a personal health record for chronic primary and secondary care: a mixed methods approach

Sieverink

Kelders

Braakman-Jansen

et al. 2019

BMC Med Inform Decis Mak

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BackgroundPersonal health records (PHRs) provide the opportunity for self-management support, enhancing communication between patients and caregivers, and maintaining and/or improving the quality of chronic disease management. Their implementation is a multi-level and complex process, requiring a holistic approach that takes into account the technology, its users and the context of implementation. The aim of this research is to evaluate the fidelity of a PHR in chronic care (the degree to which it was implemented as intended) in order to explain the found effects.MethodsA convergent parallel mixed methods design was used, where qualitative and quantitative data were collected in parallel, analyzed separately, and finally merged. Log data of 536 users were used to gain insight into the actual long-term use of the PHR (the dose). Focus group meetings among caregivers (n = 13) were conducted to assess program differentiation (or intended use). Interviews with caregivers (n = 28) and usability tests with potential end-users (n = 13) of the PHR were used to understand the responsiveness and the differences and similarities between the intended and actual use of the PHR.ResultsThe results of the focus groups showed that services for coaching are strongly associated with monitoring health values and education. However, the PHR was not used that way during the study period. In the interviews, caregivers indicated that they were ignorant on how to deploy the PHR in current working routines. Therefore, they find it difficult to motivate their patients in using the PHR. Participants in the usability study indicate that they would value a PHR in the future, given that the usability will be improved and that the caregivers will use it in daily practice as well.ConclusionsIn this study, actual use of the PHRs by patients was influenced by the responsiveness of caregivers. This responsiveness is likely to be strongly influenced by the perceived support when defining the differentiation and delivery of the PHR. A mixed-methods approach to understand intervention fidelity was of added value in providing explanations for the found effects that could not be revealed by solely focusing on the effectiveness of the technology in an experimental trial.

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Demographical, Clinical, and Psychological Characteristics of Users and Nonusers of an Online Platform for T2DM Patients (e-VitaDM-3/ZODIAC-44)

Cited by 6 publications

References 31 publications

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Effects of the Proactive Interdisciplinary Self-Management (PRISMA) Program on Online Care Platform Usage in Patients with Type 2 Diabetes in Primary Care: A Randomized Controlled Trial

Evaluating the implementation of a personal health record for chronic primary and secondary care: a mixed methods approach

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