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| ID | Type | Description | Link |
|---|---|---|---|
| 17-H-0162 |
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| Name | Class |
|---|---|
| George Washington University | OTHER |
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Background:
Heart disease is a leading cause of death. People can reduce their heart disease risk by exercising more. Mobile health technology may make people more successful at increasing their exercise. This includes things like physical activity monitors and smartphone apps.
Objective:
To find out if mobile health technology can increase physical activity.
Eligibility:
African American women ages 21-75 who:
Design:
At visit 1, participants will
For 2 weeks, researchers will collect data about participants physical activity.
Then participants will have a study visit with additional blood tests.
All participants will get messages from the app that encourage exercise.
Some participants will get data from the app about exercise near their home or work.
Some participants may get face-to-face coaching.
Participants may get wireless devices. These measure body weight, blood pressure, and blood glucose. Participants can measure these at home and upload the data to the app for the study.
Participants will have visits after 3 and 6 months. They will repeat the visit 1 tests.
Targeted, effective behavioral interventions are critically needed to ameliorate the disproportionate prevalence of poor cardiometabolic health for African-American women. We propose a sequential, multiple-assignment, randomized trial targeting physical activity (PA) among at-risk African-American women in resource-limited, Washington, D.C. communities using mobile health (mHealth) technology. We hypothesize that by beginning a community-based, adaptive PA intervention with remote coaching tailored to neighborhood environment PA resources, we will see greater increases in PA levels as compared to standard remote coaching. In Aim 1, we will determine if beginning an adaptive intervention with remote coaching tailored to neighborhood environment resources and delivered using mHealth technology (wearables and mobile applications) will lead to a greater PA increase (as measured by steps per day) as compared to standard remote coaching. In Aim 2, we will examine which of four embedded adaptive interventions produce the largest PA increase over the six-month study period. In Aim 3, we will evaluate the feasibility of remote capture of cardiometabolic measures, including blood pressure, weight, and glucose, using mHealth technology. We will also examine intervention effects on cardiometabolic health (adiposity, blood pressure, fasting lipids/glucose, self-reported PA, dietary intake, cigarette smoking). In Aim 4a, we will characterize effects of increasing PA on integrated serologic cytokine/chemokine and lipid inflammatory intermediates to identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the multi-level, community-based PA intervention. In Aim 4b, we examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health. In Aim 5, we will conduct iterative testing of the mobile health technology used in the protocol with a user-centered design approach. In Aim 6a and 6b, we will assess for changes in cardiac structure and function as well as body composition using MRI before and after the intervention. We will also determine the feasibility of measuring behavioral and psychosocial mediating factors of the relationship between PA change and cardiometabolic health in this intervention, including chronic psychological/environmental stress and sedentary behavior/sleep. In addition, because of the COVID-19 pandemic in 2020, we will measure exposure to COVID-19 and psychosocial stress caused by the pandemic as potential confounders of immunologic outcomes and psychosocial stressors in this study. Finally, we will explore the relationships between PA, social determinants of health, and biological markers in this intervention cohort and compare them to other populations using available cohort data. This project provides fundamental knowledge towards the development of tailored, effective behavioral interventions incorporating mHealth technology to promote health among populations most impacted by health disparities.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group 2 Label: PA monitor with standard remote coaching (SRC) | Other | African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area. |
|
| Group 1 Label: PA monitor with remote coaching tailored to place | Other | African American women who are at risk for cardiovascular outcomes in resource-limited communities in the Washington D.C. area. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Step it Up mobile app | Device | Step it Up mobile app |
|
| Measure | Description | Time Frame |
|---|---|---|
| The difference in physical activity (PA) change between an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning w/ standard remote coaching | The difference in physical activity (PA) change (as measured by steps/day) by beginning an adaptive intervention with remote coaching tailored to neighborhood resources (referred to as tailored-to-place coaching) versus beginning with standard remote coaching. | baseline, and up to 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Measure exposure to COVID-19 and psychosocial stress caused by the pandemic | Measure exposure to COVID-19 and psychosocial stress caused by the pandemic as potential confounders of immunologic outcomes and psychosocial stressors | Up to 6 months |
| Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health |
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Individuals eligible for this protocol are overweight or obese (BMI >= 25 kg/m^2) African American women aged 21-75 years who live in Washington, DC Wards 5,7, or 8 and neighboring areas of Prince George s County, MD. Eligible participants should also have access to a smartphone compatible with the mobile app for the protocol that they can use for the study. Eligible participants must be able to provide informed consent independently and also speak and read English at the 8th grade level.
EXCLUSION CRITERIA:
Pilot Study INCLUSION CRITERIA:
Optional MRI Tests
Subjects will be screened for implanted metal objects or devices that may be incompatible with MRI (i.e. cerebral aneurysm clip, cochlear implant, pacemaker, etc.) These subjects will be eligible to proceed with study enrollment, but will not undergo the optional MRI study.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Marie Marah, R.N. | Contact | (301) 640-1701 | marie.marah@nih.gov | |
| Tiffany M Powell-Wiley, M.D. | Contact | (301) 594-3735 | powelltm2@mail.nih.gov |
| Name | Affiliation | Role |
|---|---|---|
| Tiffany M Powell-Wiley, M.D. | National Heart, Lung, and Blood Institute (NHLBI) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Institutes of Health Clinical Center | Recruiting | Bethesda | Maryland | 20892 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26811276 | Background | Writing Group Members; Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 2016 Jan 26;133(4):447-54. doi: 10.1161/CIR.0000000000000366. No abstract available. | |
| 21899451 |
| Label | URL |
|---|---|
| NIH Clinical Center Detailed Web Page | View source |
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| Global Positioning System (GPS) Device | Device | Global Positioning System (GPS) Device |
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| Bluetooth-enabled scale | Device | Bluetooth-enabled scale |
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| Bluetooth-enabled glucometer | Device | Bluetooth-enabled glucometer |
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| MRI: Image Reconstruction and Analysis Software (Device Manufacturer: NIH) | Device | Image Reconstruction and Analysis Software |
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| MRI: Research pulse sequences (Device Manufacturer: NIH) | Device | pulse sequences |
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| MRI: radiofrequency coils (Device Manufacturer: Siemens Medical Solutions USA, Inc.) | Device | radiofrequency coils |
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| AMRA Researcher Image reconstruction software | Device | Image reconstruction software |
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Examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health, such as chronic stress and sedentary behavior/sleep |
| Up to 6 months |
| Identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes | Characterize effects of increasing PA on integrated serologic cytokine/chemokine and lipid inflammatory intermediates to identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the multi-level, community-based PA intervention | Up to 6 months |
| Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures | Examine the effect of an adaptive community-based intervention targeting Physical Activity on Cardiovascular health measures (BMI, blood pressure, fasting lipids, fasting plasma glucose, dietary intake, [self-reported minutes of moderate/vigorous PA, cigarette smoking)](streamdown:incomplete-link) | Up to 6 months |
| Examine the feasibility of incorporating methods for remote capture of CV health measures | Examine the feasibility of incorporating methods for remote capture of CV health measures (weight, blood pressure, blood glucose) in a target community-based population | Up to 6 months |
| Determine which embedded adaptive interventions produce the largest PA increase | Determine which of four embedded adaptive interventions produce the largest PA increase over six months | Up to 6 months |
| Exploratory Aim: Examine the relationships between PA, social determinants of health, and biological markers in this intervention population and through comparison to other populations using available cohort data | To measure biological markers that may include vascular markers (i.e. extracellular vesicles, markers of vascular and endothelial function), transcriptomic (i.e, RNA sequencing), epigenomic, proteomic, and metabolomic markers, immune cell measures, and markers of inflammation and chronic stress. | up to 6 months |
| Background |
| Boggs DA, Rosenberg L, Cozier YC, Wise LA, Coogan PF, Ruiz-Narvaez EA, Palmer JR. General and abdominal obesity and risk of death among black women. N Engl J Med. 2011 Sep 8;365(10):901-8. doi: 10.1056/NEJMoa1104119. |
| 19833999 | Background | Lightwood J, Bibbins-Domingo K, Coxson P, Wang YC, Williams L, Goldman L. Forecasting the future economic burden of current adolescent overweight: an estimate of the coronary heart disease policy model. Am J Public Health. 2009 Dec;99(12):2230-7. doi: 10.2105/AJPH.2008.152595. Epub 2009 Oct 15. |
| 41057147 | Derived | Troendle JF, Sur A, Leifer ES, Powell-Wiley T. Sensitivity Analyses for Missing in Repeatedly Measured Outcome Data. Stat Med. 2025 Oct;44(23-24):e70282. doi: 10.1002/sim.70282. |
| 33371027 | Derived | Tamura K, Vijayakumar NP, Troendle JF, Curlin K, Neally SJ, Mitchell VM, Collins BS, Baumer Y, Gutierrez-Huerta CA, Islam R, Turner BS, Andrews MR, Ceasar JN, Claudel SE, Tippey KG, Giuliano S, McCoy R, Zahurak J, Lambert S, Moore PJ, Douglas-Brown M, Wallen GR, Dodge T, Powell-Wiley TM. Multilevel mobile health approach to improve cardiovascular health in resource-limited communities with Step It Up: a randomised controlled trial protocol targeting physical activity. BMJ Open. 2020 Dec 21;10(12):e040702. doi: 10.1136/bmjopen-2020-040702. |
| ID | Term |
|---|---|
| D009765 | Obesity |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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