Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
Translational Behavioral Medicine

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig 1
Fig 2

Similar content being viewed by others

References

  1. Kochanek, K.D., et al., Mortality in the United States, 2013. NCHS Data Brief, 2014(178): p. 1–8.

  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.

  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.

    Article  CAS  PubMed  Google Scholar 

  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. Diez-Roux, A. V., et al. (1997). Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol, 146(1), 48–63.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Case, M. A., et al. (2015). Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA, 313(6), 625–626.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  13. Crossing the Quality Chasm: The IOM Health Care Quality Initiative. 2001, Institute of Medicine: Washington, DC.

  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.

  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. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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).

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  28. United States Census Bureau: www.census.gov. Accessed 10 April 2015.

  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.

  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. Mercer, K., et al. (2016). Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR Mhealth Uhealth, 4(2), e40.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bunz, U. (2004). The computer-email-web (CEW) fluency scale-development and validation. International journal of human-computer interaction, 17(4), 479–506.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Jackson, L. A., et al. (2008). Race, gender, and information technology use: the new digital divide. Cyberpsychol Behav, 11(4), 437–442.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Suite 5-3330, Bethesda, MD, 20892, USA

    Leah R Yingling BS, Valerie Mitchell BA, Joel Adu-Brimpong BS, Samantha Thomas BS, Johnetta N Saygbe BS & Tiffany Powell-Wiley MD, MPH

  2. Donald W. Reynolds Cardiovascular Clinical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Colby R Ayers MS

  3. Office of the Clinical Director, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Marlene Peters-Lawrence RN

  4. Clinical Center, National Institutes of Health, Bethesda, MD, USA

    Gwenyth R Wallen RN, PhD & Alyssa T Brooks PhD

  5. Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    James F. Troendle PhD

  6. Office of the National Coordinator for Health Information Technology, Washington, DC, USA

    JaWanna Henry MPH

  7. Office of Minority Health, U.S. Department of Health and Human Services, Rockville, MD, USA

    Dana M Sampson MS, MBA

  8. College of Nursing and Allied Health Sciences, Howard University, Washington, DC, USA

    Allan A Johnson PhD, Avis P Graham PhD, RD, LDN & Lennox A Graham DM

  9. Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Kenneth L Wiley Jr PhD

Authors
  1. Leah R Yingling BS
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valerie Mitchell BA
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Colby R Ayers MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marlene Peters-Lawrence RN
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gwenyth R Wallen RN, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alyssa T Brooks PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. James F. Troendle PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joel Adu-Brimpong BS
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Samantha Thomas BS
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. JaWanna Henry MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Johnetta N Saygbe BS
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dana M Sampson MS, MBA
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Allan A Johnson PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Avis P Graham PhD, RD, LDN
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Lennox A Graham DM
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kenneth L Wiley Jr PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Tiffany Powell-Wiley MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tiffany Powell-Wiley MD, MPH.

Ethics declarations

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.

Additional information

Trial registration: NCT01927783

Implications

Practice: Community-based behavioral interventions targeting cardiometabolic health in resource-limited communities should consider incorporation of wearable mHealth technology.

Policy: Efforts to reduce barriers to using mHealth technology in resource-limited settings may aid in decreasing cardiometabolic health disparities in at-risk populations.

Research: Future research is needed to determine how wearable mHealth technology can be leveraged to promote increased PA and improve cardiometabolic health in resource-limited communities.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yingling, L.R., Mitchell, V., Ayers, C.R. et al. Adherence with physical activity monitoring wearable devices in a community-based population: observations from the Washington, D.C., Cardiovascular Health and Needs Assessment. Behav. Med. Pract. Policy Res. 7, 719–730 (2017). https://doi.org/10.1007/s13142-016-0454-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13142-016-0454-0

Keywords

Search

Navigation