Dataset Information

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

ABSTRACT: A mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patient's devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article.We developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge design, both groups initially received an application without Diastat. Group 1 activated Diastat after 4 weeks, whereas Group 2 activated Diastat 12 weeks after startup (T1). End points were glycated hemoglobin (HbA1c) level and number of out-of-range (OOR) measurements (i.e., outside the range 72-270 mg/dL).Thirty patients were recruited to the study, and 15 were assigned to each group after the initial meeting. There were no significant differences between groups at T1 in HbA1c or OOR events. Overall, all patients had a decrease of 0.6 percentage points in mean HbA1c (P < 0.001) and 14.5 in median OOR events over 2 weeks (P < 0.001).The study does not provide evidence that data-driven feedback improves glycemic control. The decrease in HbA1c was sizeable and significant, even though the study was not powered to detect this. The overall improvement in glycemic control suggests that, in general, mobile phone-based interventions can be useful in diabetes self-management.

SUBMITTER: Skrovseth SO

PROVIDER: S-EPMC4504254 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

Skrøvseth Stein Olav SO Årsand Eirik E Godtliebsen Fred F Joakimsen Ragnar M RM

Diabetes technology & therapeutics 20150309 7

<h4>Background</h4>A mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patient's devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article.<h4>Materials and methods</h4>We developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge de ...[more]

PMID: 25751133

Similar Datasets

Project description:ObjectivesPositive affect (PA) plays a crucial role in the development, course, and recovery of depression. Recently, we showed that a therapeutic application of the experience sampling method (ESM), consisting of feedback focusing on PA in daily life, was associated with a decrease in depressive symptoms. The present study investigated whether the experience of PA increased during the course of this intervention.DesignMulticentre parallel randomized controlled trial. An electronic random sequence generator was used to allocate treatments.SettingsUniversity, two local mental health care institutions, one local hospital.Participants102 pharmacologically treated outpatients with a DSM-IV diagnosis of major depressive disorder, randomized over three treatment arms.InterventionSix weeks of ESM self-monitoring combined with weekly PA-focused feedback sessions (experimental group); six weeks of ESM self-monitoring combined with six weekly sessions without feedback (pseudo-experimental group); or treatment as usual (control group).Main outcomeThe interaction between treatment allocation and time in predicting positive and negative affect (NA) was investigated in multilevel regression models.Results102 patients were randomized (mean age 48.0, SD 10.2) of which 81 finished the entire study protocol. All 102 patients were included in the analyses. The experimental group did not show a significant larger increase in momentary PA during or shortly after the intervention compared to the pseudo-experimental or control groups (χ2(2) = 0.33, p = .846). The pseudo-experimental group showed a larger decrease in NA compared to the control group (χ2(1) = 6.29, p =.012).ConclusionPA-focused feedback did not significantly impact daily life PA during or shortly after the intervention. As the previously reported reduction in depressive symptoms associated with the feedback unveiled itself only after weeks, it is conceivable that the effects on daily life PA also evolve slowly and therefore were not captured by the experience sampling procedure immediately after treatment.Trial registrationTrialregister.nl/trialreg/index.asp. NTR1974.

Project description:Personalized feedback is a promising self-help for problem gamblers. Such interventions have shown consistently positive results with other addictive behaviours, and our own pilot test of personalized normative feedback materials for gamblers yielded positive findings. The current randomized controlled trial evaluated the effectiveness, and the sustained efficacy, of the personalized feedback intervention materials for problem gamblers.Respondents recruited by a general population telephone screener of Ontario adults included gamblers with moderate and severe gambling problems. Those who agreed to participate were randomly assigned to receive: 1) the full personalized normative feedback intervention; 2) a partial feedback that contained all the feedback information provided to those in condition 1 but without the normative feedback content (i.e., no comparisons provided to general population gambling norms); or 3) a waiting list control condition. The primary hypothesis was that problem gamblers who received the personalized normative feedback intervention would reduce their gambling more than problem gamblers who did not receive any intervention (waiting list control condition) by the six-month follow-up.The study found no evidence for the impact of normative personalized feedback. However, participants who received, the partial feedback (without norms) reduced the number of days they gambled compared to participants who did not receive the intervention. We concluded that personalized feedback interventions were well received and the materials may be helpful at reducing gambling. Realistically, it can be expected that the personalized feedback intervention may have a limited, short term impact on the severity of participants' problem gambling because the intervention is just a brief screener. An Internet-based version of the personalized feedback intervention tool, however, may offer an easy to access and non-threatening portal that can be used to motivate participants to seek further help online or in person.ClinicalTrials.govNCT00578357.

Project description:Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Project description:BackgroundA system that combines technology and web-based coaching can help treat chronic conditions such as diabetes. However, the effectiveness of apps in mobile health (mHealth) interventions is inconclusive and unclear due to heterogeneous interventions and varying follow-up durations. In addition, randomized controlled trial data are limited, and long-term follow-up is lacking, especially for apps integrated into electronic medical records.ObjectiveWe aimed to assess the effect of an electronic medical record-integrated mobile app for personalized diabetes self-care, focusing on the self-monitoring of blood glucose and lifestyle modifications, on glycemic control in patients with type 2 diabetes mellitus.MethodsIn a 26-week, 3-arm, randomized, controlled, open-label, parallel group trial, patients with type 2 diabetes mellitus and a hemoglobin A1c (HbA1c) level of ≥7.5% were recruited. The mHealth intervention consisted of self-monitoring of blood glucose with the automatic transfer of glucose, diet, and physical activity counseling data (iCareD system). Participants were randomly assigned to the following three groups: usual care (UC), mobile diabetes self-care (MC), and MC with personalized, bidirectional feedback from physicians (MPC). The primary outcome was the change in HbA1c levels at 26 weeks. In addition, diabetes-related self-efficacy, self-care activities, and satisfaction with the iCareD system were assessed after the intervention.ResultsA total of 269 participants were enrolled, and 234 patients (86.9%) remained in the study at 26 weeks. At 12 weeks after the intervention, the mean decline in HbA1c levels was significantly different among the 3 groups (UC vs MC vs MPC: -0.49% vs -0.86% vs -1.04%; P=.02). The HbA1c level decreased in all groups; however, it did not differ among groups after 26 weeks. In a subgroup analysis, HbA1c levels showed a statistically significant decrease after the intervention in the MPC group compared with the change in the UC or MC group, especially in patients aged <65 years (P=.02), patients with a diabetes duration of ≥10 years (P=.02), patients with a BMI of ≥25.0 kg/m2 (P=.004), patients with a C-peptide level of ≥0.6 ng/mL (P=.008), and patients who did not undergo treatment with insulin (P=.004) at 12 weeks. A total of 87.2% (137/157) of the participants were satisfied with the iCareD system.ConclusionsThe mHealth intervention for diabetes self-care showed short-term efficacy in glycemic control, and the effect decreased over time. The participants were comfortable with using the iCareD system and exhibited high adherence.Trial registrationClinical Research Information Service, Republic of Korea KCT0004128; https://tinyurl.com/bdd6pa9m.

Project description:BackgroundShort-duration studies show that salsalate improves glycemia in type 2 diabetes mellitus (T2DM).ObjectiveTo assess 1-year efficacy and safety of salsalate in T2DM.DesignPlacebo-controlled, parallel trial; computerized randomization and centralized allocation, with patients, providers, and researchers blinded to assignment. (ClinicalTrials.gov: NCT00799643).Setting3 private practices and 18 academic centers in the United States.PatientsPersons aged 18 to 75 years with fasting glucose levels of 12.5 mmol/L or less (≤225 mg/dL) and hemoglobin A1c (HbA1c) levels of 7.0% to 9.5% who were treated for diabetes.Intervention286 participants were randomly assigned (between January 2009 and July 2011) to 48 weeks of placebo (n = 140) or salsalate, 3.5 g/d (n = 146), in addition to current therapies, and 283 participants were analyzed (placebo, n = 137; salsalate, n = 146).MeasurementsChange in hemoglobin A1c level (primary outcome) and safety and efficacy measures.ResultsThe mean HbA1c level over 48 weeks was 0.37% lower in the salsalate group than in the placebo group (95% CI, -0.53% to -0.21%; P < 0.001). Glycemia improved despite more reductions in concomitant diabetes medications in salsalate recipients than in placebo recipients. Lower circulating leukocyte, neutrophil, and lymphocyte counts show the anti-inflammatory effects of salsalate. Adiponectin and hematocrit levels increased more and fasting glucose, uric acid, and triglyceride levels decreased with salsalate, but weight and low-density lipoprotein cholesterol levels also increased. Urinary albumin levels increased but reversed on discontinuation; estimated glomerular filtration rates were unchanged.LimitationTrial duration and number of patients studied were insufficient to determine long-term risk-benefit of salsalate in T2DM.ConclusionSalsalate improves glycemia in patients with T2DM and decreases inflammatory mediators. Continued evaluation of mixed cardiorenal signals is warranted.

Dataset Information

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

Publications

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets