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For the sub-study, this digital navigation tool will both inform/educate, engage, support, and navigate participants and providers through the process of clinical trial participation via personalization (data profiling, adaptive and customized messaging, and tailored digital navigation) in a sample of 100 participants with diabetes and hypertension.
To identify provider- and system-level facilitators (solutions) of clinical trial participation that can be in corporated in the digital navigation tool. We will pay special emphasis on the behavior research volunteerism and altruism and individuals who are at risk for cardio metabolic conditions. To develop solutions that will address patient-, provider and system-level barriers preventing clinical trial participation Objective 2: To test the adherence, feasibility, and efficacy of a research volunteerism and altruism component of a health and wellness web app. that provides personalized newsfeeds and curriculum about research and health-related volunteer and altruistic activities versus traditional patient navigation procedures and resources in clinical trial awareness.'awareness/knowledge, attitudes, willingness to participate in clinical trial, self-efficacy, and share knowledge about clinical trial opportunities to social network. To determine which elements of the digital tool and mobil app are associated with increased awareness/knowledge, intent to participate, and contagion
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control group | Active Comparator | Participants will receive non-personalized information (one-size-fits-all) diet, sleep and physical activity recommendations via messaging delivered by app. |
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| Intervention Group | Experimental | Participants will receive personalized messages about achieving healthy diet, physical activity and sleep as well as summary of their performance for the week and month. They will also receive personalized content about research volunteerism and altruistic activities. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| control group | Behavioral | will receive standard medical care where research assistant will give pamphlets, with one size fits all recommendations for diet/nutrition, physical activity and sleep, to 10 subjects with comorbid HTN and T2D. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in clinical trials participation Change in User Adherence | Will show that one-size-fits-all versus personalization leads to greater adherence. Adherence will be based on whether the subject met the daily and weekly behavior recommendations. | Baseline Visit, 4 week follow up visit, 6 month follow up visit |
| Change in glucose level Change in blood pressure | The amount of sugar levels will be determined the Harris-Benedict Calculator, which consists of height, weight, weight age and activity levels | Baseline Visit, 4 week follow up visit, 6 month follow up visit |
| Change in body mass index Change in blood pressure | Change in body mass index will be determined by the self reported information the vb=vodt | Baseline Visit, 4 week follow up visit, 6 month follow up visit |
| Change in physical activity | FitBit tracking will measure increase in physical activity adherence. | Baseline Visit, 4 week follow up visit, 6 month follow up visit |
| Change in physical activity by self-report | Increase in physical activity will be measured by self-report diaries collected by study staff through health and wellness app platforms. | Baseline Visit, 4 week follow up visit, 6 month follow up visit |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Azizi Seixas | NYU Langone | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| NYU Langone | New York | New York | 10016 | United States |
Individual participant data that underlie the results reported in this article, after deidentification (text, tables, figures, and appendices).
Proposals may be submitted up to 36 months following article publication. After 36 months the data will be available in our University's data warehouse but without investigator support other than deposited metadata. Information regarding submitting requests and accessing data may be found at (Link to be provided).
Investigators whose proposed use of the data has been approved by an independent review committee ("learned intermediary") identified for this purpose.
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| ID | Term |
|---|---|
| D002318 | Cardiovascular Diseases |
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| ID | Term |
|---|---|
| D035061 | Control Groups |
| ID | Term |
|---|---|
| D015340 | Epidemiologic Research Design |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D012107 | Research Design |
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| Lifestyle management | Behavioral | Will use passive ubiquitous sensing through FitBit to 1)learn behavioral profiles of subjects who have pre-HTN/HTN and/or pre-diabetes /Diabetes 2) provide personalized recommendations through mobile based messaging app that will increase the likelihood of engaging in health diet/nutrition, physical activity and sleep practices |
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| D008722 | Methods |