Prediction Using Connected Technologies for Diabetes
| Status: | Active, not recruiting |
|---|---|
| Conditions: | Endocrine, Diabetes, Diabetes |
| Therapuetic Areas: | Endocrinology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 10/6/2018 |
| Start Date: | July 9, 2018 |
| End Date: | May 2019 |
Prediction Using a Randomized Evaluation of Data Collection Integrated Through Connected Technologies for Diabetes Management
In this study, adults with pre-diabetes will be prospectively enrolled for data collection to
design prediction models that integrate electronic health record data and patient-generated
activity data. Patients will be randomized to receive either a waist-worn or wrist-worn
wearable device for 6 months to capture patient-generated activity data.
design prediction models that integrate electronic health record data and patient-generated
activity data. Patients will be randomized to receive either a waist-worn or wrist-worn
wearable device for 6 months to capture patient-generated activity data.
Patients with suboptimal glycemic control could be better managed if these higher risk
patients could be identified and effective interventions were then targeted towards them.
However, most practice settings perform infrequent laboratory testing every 3 to 6 months, if
not at longer intervals. Current models to predict change in glycemic control perform poorly
and do not take into account the behaviors that occur between these intervals. In this study,
we will compare different methods to use data on daily health behaviors collected by wearable
devices to enhance risk prediction models. Adults with pre-diabetes will complete a series of
surveys and baseline assessments and then will be randomly assigned to use a waist-worn or
wrist-worn wearable device for 6 months. Measures of HbA1c and LDL will be obtained at
baseline and at 6 months.
patients could be identified and effective interventions were then targeted towards them.
However, most practice settings perform infrequent laboratory testing every 3 to 6 months, if
not at longer intervals. Current models to predict change in glycemic control perform poorly
and do not take into account the behaviors that occur between these intervals. In this study,
we will compare different methods to use data on daily health behaviors collected by wearable
devices to enhance risk prediction models. Adults with pre-diabetes will complete a series of
surveys and baseline assessments and then will be randomly assigned to use a waist-worn or
wrist-worn wearable device for 6 months. Measures of HbA1c and LDL will be obtained at
baseline and at 6 months.
Inclusion Criteria:
1. Be 18 years or older
2. Be able to provide informed consent
3. Have a smartphone or tablet compatible with the wearable device smartphone application
and be willing to use the wearable device for 6 months
4. Baseline hemoglobin A1c of 5.7 to 6.4
Exclusion Criteria:
Participants will no be eligible if they have any medical condition or other reason that
will likely prohibit them from completing the 6-month study
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