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Translational Behavioral Medicine

, Volume 7, Issue 4, pp 719–730 | Cite as

Adherence with physical activity monitoring wearable devices in a community-based population: observations from the Washington, D.C., Cardiovascular Health and Needs Assessment

  • Leah R Yingling
  • Valerie Mitchell
  • Colby R Ayers
  • Marlene Peters-Lawrence
  • Gwenyth R Wallen
  • Alyssa T Brooks
  • James F. Troendle
  • Joel Adu-Brimpong
  • Samantha Thomas
  • JaWanna Henry
  • Johnetta N Saygbe
  • Dana M Sampson
  • Allan A Johnson
  • Avis P Graham
  • Lennox A Graham
  • Kenneth L WileyJr
  • Tiffany Powell-WileyEmail author
Original Research

Abstract

Wearable mobile health (mHealth) technologies offer approaches for targeting physical activity (PA) in resource-limited, community-based interventions. We sought to explore user characteristics of PA tracking, wearable technology among a community-based population within a health and needs assessment. In 2014–2015, we conducted the Washington, D.C., Cardiovascular Health and Needs Assessment in predominantly African-American churches among communities with higher obesity rates and lower household incomes. Participants received a mHealth PA monitor and wirelessly uploaded PA data weekly to church data collection hubs. Participants (n = 99) were 59 ± 12 years, 79% female, and 99% African-American, with a mean body mass index of 33 ± 7 kg/m2. Eighty-one percent of participants uploaded PA data to the hub and were termed “PA device users.” Though PA device users were more likely to report lower household incomes, no differences existed between device users and non-users for device ownership or technology fluency. Findings suggest that mHealth systems with a wearable device and data collection hub may feasibly target PA in resource-limited communities.

Keywords

mHealth technology Physical activity Community-based participatory research Obesity African-American Activity monitoring 

Notes

Acknowledgements

We would like to acknowledge the participating church communities for warmly welcoming our research team and providing feedback from preliminary stages. Additionally, we acknowledge the D.C. CHOC for their contribution to the study design and their insightful recommendations. We would like to acknowledge Ms. Darlene Allen for her work with blood testing for the study.

We would also like to acknowledge the work on this project by Mr. Praduman Jain and colleagues from Vignet Corporation through use of their Precision Medicine Initiative (PMI) toolkit under contract #HHSN268201400023P. The PMI toolkit enables custom mHealth programs for data collection, population surveillance, interactive informed consent, assessments, remote monitoring, CBPR, consumer engagement, interventions, motivation, and behavior change.

Compliance with ethical standards

Disclosure of potential conflict of interest and funding sources

The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The authors declare that they have no conflict of interest.

This study was funded by grant HL006168. Funding for TP-W, LY, and VM is provided through the Division of Intramural Research of the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH). Funding for CA is provided through a professional services contract (contract #HHSN268201300173P) through the Division of Intramural Research of NHLBI at NIH. Funding for ST and JA-B is provided through the Office of Intramural Training and Education of the NIH. Funding for GW and AB is provided through the Clinical Center, NIH. Funding for MP-L is provided through the Division of Intramural Research of NHLBI at NIH.

Research involving human participants

Statement of human rights: All procedures involving human participants performed in the Washington, D.C. CV Health and Needs Assessment study were in accordance with the ethical standards of the National Heart, Lung, and Blood Institute (NHLBI) Institutional Review Board study and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Kochanek, K.D., et al., Mortality in the United States, 2013. NCHS Data Brief, 2014(178): p. 1–8.Google Scholar
  2. 2.
    Smedley BD, S.A., Nelson AR, editors, Unequal treatment: confronting racial and ethnic disparities in health care. Washington DC: 2002 by the National Academy of Sciences; 2003.Google Scholar
  3. 3.
    Cooper, R., Cutler, J., Desvigne-Nickens, P., Fortmann, S. P., Friedman, L., Havlik, R., et al. (2000). Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the national conference on cardiovascular disease prevention. Circulation, 102(25), 3137–3147.CrossRefPubMedGoogle Scholar
  4. 4.
    Mensah GA M.A., Ford ES, Greenlund KJ, Croft JB, State of disparities in cardiovascular health in the United States. Circulation 2005;111(10):1233–1241. doi:  10.1161/01.cir.0000158136.76824.04.
  5. 5.
    Diez-Roux, A. V., et al. (1997). Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol, 146(1), 48–63.CrossRefPubMedGoogle Scholar
  6. 6.
    Diez Roux, A. V., et al. (2001). Neighborhood of residence and incidence of coronary heart disease. N Engl J Med, 345(2), 99–106.CrossRefPubMedGoogle Scholar
  7. 7.
    Elosua, R., et al. (2013). Dose-response association of physical activity with acute myocardial infarction: do amount and intensity matter? Prev Med, 57(5), 567–572.CrossRefPubMedGoogle Scholar
  8. 8.
    Haskell, W. L., et al. (2007). Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation, 116(9), 1081–1093.CrossRefPubMedGoogle Scholar
  9. 9.
    Walton-Moss, B., et al. (2014). Community based cardiovascular health interventions in vulnerable populations: a systematic review. The Journal of cardiovascular nursing, 29(4), 293–307.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Case, M. A., et al. (2015). Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA, 313(6), 625–626.CrossRefPubMedGoogle Scholar
  11. 11.
    Thorndike, A. N., et al. (2014). Activity monitor intervention to promote physical activity of physicians-in-training: randomized controlled trial. PLoS One, 9(6), e100251.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Asch, D. A., Muller, R. W., & Volpp, K. G. (2012). Automated hovering in health care—watching over the 5000 hours. N Engl J Med, 367(1), 1–3.CrossRefPubMedGoogle Scholar
  13. 13.
    Crossing the Quality Chasm: The IOM Health Care Quality Initiative. 2001, Institute of Medicine: Washington, DC.Google Scholar
  14. 14.
    Smith, J.C. and B.R. Schatz. Feasibility of mobile phone-based management of chronic illness. in AMIA annual symposium proceedings. 2010. American Medical Informatics Association.Google Scholar
  15. 15.
    Pew Research Internet Project: cell phone and smartphone ownership demographics, http://www.pewinternet.org/data-trend/mobile/cellphone-and-smartphone-ownership-demographics/, Accessed October 23, 2014.
  16. 16.
    Shuger, S. L., et al. (2011). Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act, 8, 41.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313(5), 459–460.CrossRefPubMedGoogle Scholar
  18. 18.
    Fitzsimons, C. F., et al. (2013). Using an individualised consultation and activPAL feedback to reduce sedentary time in older Scottish adults: results of a feasibility and pilot study. Prev Med, 57(5).Google Scholar
  19. 19.
    Hurling, R., et al. (2007). Using internet and mobile phone technology to deliver an automated physical activity program: randomized controlled trial. J Med Internet Res, 9(2), e7.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Pellegrini, C. A., et al. (2012). The comparison of a technology-based system and an in-person behavioral weight loss intervention. Obesity (Silver Spring), 20(2), 356–363.CrossRefGoogle Scholar
  21. 21.
    Reijonsaari, K., et al. (2012). The effectiveness of physical activity monitoring and distance counseling in an occupational setting—results from a randomized controlled trial (CoAct). BMC Public Health, 12, 344.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Slootmaker, S. M., et al. (2009). Feasibility and effectiveness of online physical activity advice based on a personal activity monitor: randomized controlled trial. J Med Internet Res, 11(3), e27.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Tabak, M., op den Akker, H., & Hermens, H. (2014). Motivational cues as real-time feedback for changing daily activity behavior of patients with COPD. Patient Educ Couns, 94(3), 372–378.CrossRefPubMedGoogle Scholar
  24. 24.
    Polzien, K. M., et al. (2007). The efficacy of a technology-based system in a short-term behavioral weight loss intervention. Obesity (Silver Spring), 15(4), 825–830.CrossRefGoogle Scholar
  25. 25.
    Atienza, A. A., & Patrick, K. (2011). Mobile health: the killer app for cyberinfrastructure and consumer health. Am J Prev Med, 40(5 Suppl 2), S151–S153.CrossRefPubMedGoogle Scholar
  26. 26.
    Guyll, M., Spoth, R., & Redmond, C. (2003). The effects of incentives and research requirements on participation rates for a community-based preventive intervention research study. J Prim Prev, 24(1), 25–41.CrossRefGoogle Scholar
  27. 27.
    Yingling, L. R., et al. (2016). Community engagement to optimize the use of web-based and wearable technology in a Cardiovascular Health and Needs Assessment Study: a mixed methods approach. JMIR mHealth uHealth, 4(2), e38.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    United States Census Bureau: www.census.gov. Accessed 10 April 2015.
  29. 29.
    Chobanian AV, B.G., Black HR, Cushman WC, Green LA, Izzo JL, Jones DW, Materson BJ, Oparil S, Wright JT, Roccella EJ, Committee tNHBPEPC., Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension. 2003.Google Scholar
  30. 30.
    National Heart, L., and Blood Institute Expert Panel. [Accessed April 3, 2011];Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Available at: http://www.nhlbi.nih.gov/guidelines/obesity.
  31. 31.
    Mercer, K., et al. (2016). Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR Mhealth Uhealth, 4(2), e40.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Arsand, E., et al. (2015). Performance of the first combined smartwatch and smartphone diabetes diary application study. J Diabetes Sci Technol, 9(3), 556–563.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Bunz, U. (2004). The computer-email-web (CEW) fluency scale-development and validation. International journal of human-computer interaction, 17(4), 479–506.CrossRefGoogle Scholar
  34. 34.
    Tudor-Locke, C., & Bassett Jr., D. R. (2004). How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med, 34(1), 1–8.CrossRefPubMedGoogle Scholar
  35. 35.
    Burke, L. E., et al. (2015). Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation, 132(12), 1157–1213.CrossRefPubMedGoogle Scholar
  36. 36.
    Gebreab, S. Y., et al. (2015). The impact of lifecourse socioeconomic position on cardiovascular disease events in African Americans: the Jackson heart study. J Am Heart Assoc, 4(6), e001553.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Chang, R. C.-S., et al. (2016). Reciprocal reinforcement between wearable activity trackers and social network services in influencing physical activity behaviors. JMIR mHealth and uHealth, 4, e84.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Ramo, D. E., et al. (2014). Facebook recruitment of young adult smokers for a cessation trial: methods, metrics, and lessons learned. Internet Interventions, 1(2), 58–64.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Carter-Harris, L., et al. (2016). Beyond traditional newspaper advertisement: leveraging Facebook-targeted advertisement to recruit long-term smokers for research. Journal of Medical Internet Research, 18(6), e117.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Bock, B., et al. (2013). A text message delivered smoking cessation intervention: the initial trial of TXT-2-Quit: randomized controlled trial. JMIR mHealth and uHealth, 1(2), e17.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Burke, L. E., et al. (2012). Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med, 43(1), 20–26.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Kontos, E. Z., Bennett, G. G., & Viswanath, K. (2007). Barriers and facilitators to home computer and internet use among urban novice computer users of low socioeconomic position. J Med Internet Res, 9(4), e31.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Kontos, E. Z., et al. (2010). Communication inequalities and public health implications of adult social networking site use in the United States. J Health Commun, 15(Suppl 3), 216–235.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Chou, W. Y., et al. (2009). Social media use in the United States: implications for health communication. J Med Internet Res, 11(4), e48.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Jackson, L. A., et al. (2008). Race, gender, and information technology use: the new digital divide. Cyberpsychol Behav, 11(4), 437–442.CrossRefPubMedGoogle Scholar
  46. 46.
    Wang, J. Y., Bennett, K., & Probst, J. (2011). Subdividing the digital divide: differences in internet access and use among rural residents with medical limitations. J Med Internet Res, 13(1), e25.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Smith, A., Mobile Access 2010. Pew Internet and American Life Project, July 7 (2010) Available from: http://pewinternet.org.ezproxy.nihlibrary.nih.gov/Reports/2010/Mobile-Access-2010.aspx.
  48. 48.
    Leena, K., Tomi, L., & Arja, R. R. (2005). Intensity of mobile phone use and health compromising behaviours—how is information and communication technology connected to health-related lifestyle in adolescence? J Adolesc, 28(1), 35–47.CrossRefPubMedGoogle Scholar
  49. 49.
    Coughlin, S. S., & Smith, S. A. (2016). A review of community-based participatory research studies to promote physical activity among African Americans. Journal of the Georgia Public Health Association, 5(3), 220–227.PubMedPubMedCentralGoogle Scholar
  50. 50.
    Kumanyika, S. K., Whitt-Glover, M. C., & Haire-Joshu, D. (2014). What works for obesity prevention and treatment in black Americans? Research directions. Obes Rev, 15(Suppl 4), 204–212.CrossRefPubMedGoogle Scholar

Copyright information

© Society of Behavioral Medicine (outside the US) 2017

Authors and Affiliations

  • Leah R Yingling
    • 1
  • Valerie Mitchell
    • 1
  • Colby R Ayers
    • 2
  • Marlene Peters-Lawrence
    • 3
  • Gwenyth R Wallen
    • 4
  • Alyssa T Brooks
    • 4
  • James F. Troendle
    • 5
  • Joel Adu-Brimpong
    • 1
  • Samantha Thomas
    • 1
  • JaWanna Henry
    • 6
  • Johnetta N Saygbe
    • 1
  • Dana M Sampson
    • 7
  • Allan A Johnson
    • 8
  • Avis P Graham
    • 8
  • Lennox A Graham
    • 8
  • Kenneth L WileyJr
    • 9
  • Tiffany Powell-Wiley
    • 1
    Email author
  1. 1.Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaUSA
  2. 2.Donald W. Reynolds Cardiovascular Clinical Research CenterUniversity of Texas Southwestern Medical CenterDallasUSA
  3. 3.Office of the Clinical Director, Division of Intramural Research, National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaUSA
  4. 4.Clinical CenterNational Institutes of HealthBethesdaUSA
  5. 5.Office of Biostatistics Research, National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaUSA
  6. 6.Office of the National Coordinator for Health Information TechnologyWashingtonUSA
  7. 7.Office of Minority Health, U.S. Department of Health and Human ServicesRockvilleUSA
  8. 8.College of Nursing and Allied Health SciencesHoward UniversityWashingtonUSA
  9. 9.Division of Genomic Medicine, National Human Genome Research InstituteNational Institutes of HealthBethesdaUSA

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