IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i2p1000-d1026414.html
   My bibliography  Save this article

Smart Walk : A Culturally Tailored Smartphone-Delivered Physical Activity Intervention for Cardiometabolic Risk Reduction among African American Women

Author

Listed:
  • Rodney P. Joseph

    (Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd St., Phoenix, AZ 85004, USA)

  • Michael Todd

    (College of Nursing and Health Innovation, Arizona State University, 500 N 3rd St., Phoenix, AZ 85004, USA)

  • Barbara E. Ainsworth

    (School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China)

  • Sonia Vega-López

    (College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
    Southwest Interdisciplinary Research Center, Arizona State University, Phoenix, AZ 85004, USA)

  • Marc A. Adams

    (College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA)

  • Kevin Hollingshead

    (College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA)

  • Steven P. Hooker

    (College of Health and Human Services, San Diego State University, San Diego, CA 92182, USA)

  • Glenn A. Gaesser

    (College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA)

  • Colleen Keller

    (Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd St., Phoenix, AZ 85004, USA)

Abstract

This article reports the results of Smart Walk : a randomized pilot trial of an 8-month culturally tailored, smartphone-delivered physical activity (PA) intervention for African American women with obesity. Sixty participants (age range = 24–49 years; BMI range = 30–58 kg/m 2 ) were randomized to the Smart Walk intervention (n = 30) or a wellness comparison intervention (n = 30). Results supported the acceptability and feasibility of the intervention, as demonstrated by participant retention (85% at 4 months and 78% at 8 months), Smart Walk app use, and intervention satisfaction (i.e., 100% of PA participants completing the intervention [n = 24] reported they would recommend it to friend). Smart Walk participants also reported greater increases in moderate-to-vigorous PA (4-month between-arm difference in change [b] = 43.3 min/week; p = 0.018; Cohen’s d = 0.69; 8-month b = 56.6 min/week; p = 0.046; d = 0.63) and demonstrated clinically relevant, although not statistically significant ( p -values > 0.05), baseline to 4 months improvements in cardiorespiratory fitness (b = 1.67 mL/kg/min; d = 0.40), systolic blood pressure (b = −3.33 mmHg; d = 0.22), diastolic blood pressure (b = −4.28 mmHg; d = 0.37), and pulse wave velocity (b = −0.46 m/s; d = 0.33). Eight-month cardiometabolic outcomes followed similar trends, but had high rates of missing data (45–53%) due to COVID-19 restrictions. Collectively, findings demonstrated favorable outcomes for acceptability and feasibility, while also highlighting key areas for refinement in future research.

Suggested Citation

  • Rodney P. Joseph & Michael Todd & Barbara E. Ainsworth & Sonia Vega-López & Marc A. Adams & Kevin Hollingshead & Steven P. Hooker & Glenn A. Gaesser & Colleen Keller, 2023. "Smart Walk : A Culturally Tailored Smartphone-Delivered Physical Activity Intervention for Cardiometabolic Risk Reduction among African American Women," IJERPH, MDPI, vol. 20(2), pages 1-25, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1000-:d:1026414
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/2/1000/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/2/1000/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barbara Resnick & Sheryl Itkin Zimmerman & Denise Orwig & Anne-Linda Furstenberg & Jay Magaziner, 2000. "Outcome Expectations for Exercise Scale," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 55(6), pages 352-356.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Natan Feter & Tiago Silva Santos & Eduardo Lucia Caputo & Marcelo Cozzensa Silva, 2019. "What is the role of smartphones on physical activity promotion? A systematic review and meta-analysis," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(5), pages 679-690, June.
    4. Ammar W Ashor & Jose Lara & Mario Siervo & Carlos Celis-Morales & John C Mathers, 2014. "Effects of Exercise Modalities on Arterial Stiffness and Wave Reflection: A Systematic Review and Meta-Analysis of Randomized Controlled Trials," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-15, October.
    5. Pamela McCoy & Sophia Leggett & Azad Bhuiyan & David Brown & Patricia Frye & Bryman Williams, 2017. "Text Messaging: An Intervention to Increase Physical Activity among African American Participants in a Faith-Based, Competitive Weight Loss Program," IJERPH, MDPI, vol. 14(4), pages 1-12, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    6. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    7. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    8. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    9. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    10. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    11. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    12. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    13. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    14. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    15. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    16. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    17. Michelle Symons & Carmem Meira Cunha & Karolien Poels & Heidi Vandebosch & Nathalie Dens & Clara Alida Cutello, 2021. "Physical Activity during the First Lockdown of the COVID-19 Pandemic: Investigating the Reliance on Digital Technologies, Perceived Benefits, Barriers and the Impact of Affect," IJERPH, MDPI, vol. 18(11), pages 1-23, May.
    18. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    19. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    20. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1000-:d:1026414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.