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Health Coaching Has Differential Effects on Veterans with Limited Health Literacy and Numeracy: a Secondary Analysis of ACTIVATE

  • Sarah S. NouriEmail author
  • Laura J. Damschroder
  • Maren K. Olsen
  • Jennifer M. Gierisch
  • Angela Fagerlin
  • Linda L. Sanders
  • Felicia McCant
  • Eugene Z. Oddone
Original Research

Abstract

Background

Health coaching is an effective behavior change strategy. Understanding if there is a differential impact of health coaching on patients with low health literacy has not been well investigated.

Objective

To determine whether a telephone coaching intervention would result in similar improvements in enrollment in prevention programs and patient activation among Veterans with low versus high health literacy (specifically, reading literacy and numeracy).

Design

Secondary analysis of a randomized controlled trial.

Participants

Four hundred seventeen Veterans with at least one modifiable risk factor: current smoker, BMI ≥ 30, or < 150 min of moderate physical activity weekly.

Methods

A single-item assessment of health literacy and a subjective numeracy scale were assessed at baseline. A logistic regression and general linear longitudinal models were used to examine the differential impact of the intervention compared to control on enrollment in prevention programs and changes in patient activation measures (PAM) scores among patients with low versus high health literacy.

Results

The coaching intervention resulted in higher enrollment in prevention programs and improvements in PAM scores compared to usual care regardless of baseline health literacy. The coaching intervention had a greater effect on the probability of enrollment in prevention programs for patients with low numeracy (intervention vs control difference of 0.31, 95% CI 0.18, 0.45) as compared to those with high numeracy (0.13, 95% CI − 0.01, 0.27); the low compared to high differential effect was clinically, but not statistically significant (0.18, 95% CI − 0.01, 0.38; p = 0.07). Among patients with high numeracy, the intervention group had greater increases in PAM as compared to the control group at 6 months (mean difference in improvement 4.8; 95% CI 1.7, 7.9; p = 0.003). This led to a clinically and statistically significant differential intervention effect for low vs high numeracy (− 4.6; 95% CI − 9.1, − 0.15; p = 0.04).

Conclusions

We suggest that health coaching may be particularly beneficial in behavior change strategies in populations with low numeracy when interpretation of health risk information is part of the intervention.

KEY WORDS

telephone coaching health risk assessment health literacy health numeracy 

Notes

Acknowledgments

We are grateful to the leadership and staff of the VA’s National Center of Health Promotion and Disease Prevention (NCP) for the constant support throughout this project, including Dr. Jane Kim, Chief Consultant (NCP) and Ms. Kathleen Pitman. We also acknowledge the dedication and professionalism of our two health coaches, Ms. Karen Juntilla and Ms. Courtney White-Clark.

Funding Information

This project was funded by the Department of Veterans Affairs, Health Services Research and Development Service (CRE 12-288) and by a fellowship training grant by the National Research Service Award (NRSA) T32HP19025.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they do not have a conflict of interest.

References

  1. 1.
    Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res 2005;40:1918–1930.CrossRefGoogle Scholar
  2. 2.
    Greene J and Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520–26.CrossRefGoogle Scholar
  3. 3.
    Sontag U, Wiesner J, Fahrenkrog S, Renneberg B, Braun V, Heintze C. Motivational interviewing and shared decision making in primary care. Pat Educ Couns. 2012;87(1):62–6.CrossRefGoogle Scholar
  4. 4.
    Harvey L, Fowles JB, Xi M, Terry P. When activation changes, what else changes? The relationship between change in patient activation measure (PAM) and employees' health status and health behaviors. Patient Educ Couns. 2012;88(2):338–43.CrossRefGoogle Scholar
  5. 5.
    Hendren S, Griggs JJ, Epstein RM, et al. Study protocol: a randomized controlled trial of patient nativation-activation to reduce cancer health disparities. BMC Cancer. 2010;10:551.CrossRefGoogle Scholar
  6. 6.
    Hill B, Richardson B, Skouteris H. Do we know how to design effective health coaching interventions: a systematic review of the state of the literature. Am J Health Promot. 2015;29(5):158–68.CrossRefGoogle Scholar
  7. 7.
    Bosworth HB, Olsen MK, Grubber JM, et al. Two self-management interventions to improve hypertension control: a randomized trial. Ann Intern Med. 2009;151(10):687–95.CrossRefGoogle Scholar
  8. 8.
    Bosworth HB, Olsen MK, Dudley T, et al. Patient education and provider decision support to control blood pressure in primary care: a cluster randomized trial. Amer Heart J. 2009;157(3):450–6.CrossRefGoogle Scholar
  9. 9.
    Damschroder LJ, Lutes LD, Goodrich DE, Gillon L, Lowery JC. A small-change approach delivered via telephone promotes weight loss in Veterans: Results from the ASPIRE-VA pilot study. Patient Educ Couns. 2010;79(2): 262–6.CrossRefGoogle Scholar
  10. 10.
    Eakin EG, Lawler SP, Vandelanotte C, Owen N. Telephone interventions for physical activity and dietary behavior change: a systematic review. Am J Prev Med. 2007;32(5):419–434.CrossRefGoogle Scholar
  11. 11.
    Alexander JA, Hearld LR, Mittler JN, Harvey J. Patient-physician role relationships and patient activation among individuals with chronic illness. Health Serv Res. 2012;47(3):1201–23.CrossRefGoogle Scholar
  12. 12.
    Cunningham PJ, Hibbard J, Gibbons CB. Raising low ‘patient activation’ rates among Hispanic immigrants may equal expanded coverage in reducing access disparities. Health Aff. 2011;30(10):1888–94.CrossRefGoogle Scholar
  13. 13.
    Hibbard JH and Cunningham PJ. How engaged are consumers in their health and health care, and why does it matter? Res Brief. 2009;8:1–9.Google Scholar
  14. 14.
    Martin LT, Schonlau M, Haas A, et al. Patient Activation and Advocacy: Which Literacy Skills Matter Most? J Health Commun. 2011;16(3):177–190.CrossRefGoogle Scholar
  15. 15.
    Smith SG, Curtis LM, Wardle J, von Wagner C, Wolf MS. Skill set or mind set? Associations between health literacy, patient activation and health. PLoS One. 2013;8(9): e74373.CrossRefGoogle Scholar
  16. 16.
    Eneanya ND, Winter M, Cabral H, et al. Health literacy and education as mediators of racial disparities in patient activation within an elderly patient cohort. J Health Care Poor Underserved. 2016;27(3):1427–40.CrossRefGoogle Scholar
  17. 17.
    Gwynn KB, Winter MR, Cabral HJ, et al. Racial disparities in patient activation: evaluating the mediating role of health literacy with path analyses. Patient Educ Couns. 2016;99(6):1033–7.CrossRefGoogle Scholar
  18. 18.
    Institute of Medicine (US) Committee on Health Literacy. Health literacy: a prescription to end confusion. Washington, D.C: National Academies Press; 2004.Google Scholar
  19. 19.
    Kirsch IS, Jungeblut A, Jenkins L, Kolstad A. Adult literacy in America: a first look at the findings of the National Adult Literacy Survey (NALS). Washington, DC: U.S. Department of Education, National Center for Education Statistics; 1993.Google Scholar
  20. 20.
    Kutner M, Greenberg E, Baer J. A first look at the literacy of America’s adults in the 21st century. Washington, DC: U.S. Department of Education, National Center for Education Statistics; 2005.Google Scholar
  21. 21.
    Goodman M, Finnegan R, Mohadjer L, Krenzke T, Hogan J. Literacy, numeracy, and problem solving in technology-rich environments among U.S. adults: Results from the program for the international assessment of adult compe- tencies 2012: First look (NCES 2014-008). Washington, DC: U.S. Department of Education, National Center for Education Statistics; 2013.Google Scholar
  22. 22.
    OECD. OECD skills outlook 2013: first results from the survey of adult skills. OECD Publishing; 2013, http://www.oecd-ilibrary.org/education/oecd-skills-outlook-2013_9789264204256-en. Accessed May 2017.
  23. 23.
    Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107.CrossRefGoogle Scholar
  24. 24.
    Baker DW, Wolf MS, Feinglass J, Thompson JA, Gazmararian JA, Huang J. Health literacy and mortality among elderly persons. Arch Intern Med. 2007; 167:1503–9.CrossRefGoogle Scholar
  25. 25.
    Nelson W, Reyna VF, Fagerlin A, Lipkus I, Peters E. Clinical Implications of Numeracy: Theory and Practice. Ann Behav Med. 2008;35(3):261–274.CrossRefGoogle Scholar
  26. 26.
    Wu JR, Moser DK, DeWalt DA, Rayens MK, Dracup K. Health literacy mediates the relationship between age and health outcomes in patients with heart failure. Circ Heart Fail. 2016;9(1):e002250.CrossRefGoogle Scholar
  27. 27.
    Gazmararian JA, Baker DW, Williams MV, Parker RM, Scott TL, Green DC, et al. Health literacy among Medicare enrollees in a managed care organization. JAMA. 1999;10(28):545–51.CrossRefGoogle Scholar
  28. 28.
    Bennett IM, Chen J, Soroui JS, White S. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Ann Fam Med. 2009;7:204–11.CrossRefGoogle Scholar
  29. 29.
    Howard DH, Sentell T, Gazmararian JA. Impact of health literacy on socioeconomic and racial differences in health in an elderly population. J Gen Intern Med. 2006;21:857–61.CrossRefGoogle Scholar
  30. 30.
    Sentell TL, Halpin HA. Importance of adult literacy in understanding health disparities. J Gen Intern Med. 2006;21:862–6.CrossRefGoogle Scholar
  31. 31.
    Oddone EZ, Damschroder LJ, Gierisch JM, et.al. A Coaching by Telephone Intervention on Engaging Patients to Address Modifiable Cardiovascular Risk Factors: A Randomized Controlled Trial. J Gen Intern Med, 2018 (in press).Google Scholar
  32. 32.
    Oddone EZ, Damschroder LJ, Gierisch J, et al.. A Coaching by Telephone Intervention for Veterans and Care Team Engagement (ACTIVATE): A study protocol for a Hybrid Type I effectiveness-implementation randomized controlled trial. Contemp Clin Trials. 2017;55:1–9.CrossRefGoogle Scholar
  33. 33.
    Veterans Health Administration. MyHealtheVet: HealtheLiving Assessment <https://www.myhealth.va.gov/mhv-portal-web/web/myhealthevet/ss20170509-birds-eye-view-of-your-wellness-and-your-health-risks>. Accessed May 2017.
  34. 34.
    Hibbard JH, Mahoney E R, Stock R, Tusler M (2007). Do increases in patient activation result in improved self-management behaviors? Health Serv Res 2007; 42(4): 1443–1463.CrossRefGoogle Scholar
  35. 35.
    Remmers C, Hibbard J, Mosen DM, Wagenfield M, Hoye RE, Jones C. Is patient activation associated with future health outcomes and healthcare utilization among patients with diabetes? J Ambulatory Care Manage 2009;32:320–327.CrossRefGoogle Scholar
  36. 36.
    Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561–6.CrossRefGoogle Scholar
  37. 37.
    Powers, BJ, Trinh JV, Bosworth HB. Can this patient read and understand written health information? JAMA. 2010;34(1):76–84.CrossRefGoogle Scholar
  38. 38.
    Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med 2004;36(8):588–94.Google Scholar
  39. 39.
    Zikmund-Fisher BJ, Smith DM, Ubel PA, Fagerlin A. Validation of the Subjective Numeracy Scale: effects of low numeracy on comprehension of risk communications and utility elicitations. Med Decis Making. 2007;27(5):663–71.CrossRefGoogle Scholar
  40. 40.
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making. 2007;27(5):672–80.CrossRefGoogle Scholar
  41. 41.
    McNaughton CD, Cavanaugh KL, Kripalani S, Rothman RL, Wallston KA. Validation of a Short, 3-Item Version of the Subjective Numeracy Scale. Med Decis Making. 2015;35(8):932–6.CrossRefGoogle Scholar
  42. 42.
    Fitzmaurice, GM, Laird NM, and Ware JH. Applied longitudinal analysis. Vol. 998. Hoboken NJ: John Wiley & Sons, 2012.Google Scholar
  43. 43.
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Helping patients decide: Ten steps to better risk communication. JNCI. 2011;103(19): 1436–1443.CrossRefGoogle Scholar
  44. 44.
    Malloy-Weir LJ, Schwartz L, Yost J, McKibbon KA. Empirical relationships between numeracy and treatment decision making: A scoping review of the literature. Pat Educ Couns. 2016;99(3):310–325.CrossRefGoogle Scholar
  45. 45.
    Lawson KL, Jonk Y, O’Connor H, Riise KS, Eisenberg DM, Kreitzer MJ. The impact of telephonic health coaching on health outcomes in a high-risk population. Glob Adv Health Med. 2013;2(3):40–47.CrossRefGoogle Scholar
  46. 46.
    Galesic M and Garcia-Retamero R. Do low-numeracy people avoid shared decision making? Health Psychol. 2011;30(3):336–341.CrossRefGoogle Scholar
  47. 47.
    McCormack L, Thomas V, Lewis MA, Rudd R. Improving low health literacy and patient engagement: A social ecological approach. Pat Educ Couns. 2017;100(1):8–13.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  • Sarah S. Nouri
    • 1
    Email author
  • Laura J. Damschroder
    • 2
  • Maren K. Olsen
    • 3
    • 4
    • 5
  • Jennifer M. Gierisch
    • 2
    • 4
  • Angela Fagerlin
    • 6
    • 7
  • Linda L. Sanders
    • 3
    • 4
  • Felicia McCant
    • 4
  • Eugene Z. Oddone
    • 3
    • 4
  1. 1.Division of General Internal Medicine, Department of Medicine University of CaliforniaSan FranciscoUSA
  2. 2.VA Center for Clinical Management ResearchVA Ann Arbor Healthcare SystemAnn ArborUSA
  3. 3.Division of General Internal Medicine, Department of MedicineDuke University Medical CenterDurhamUSA
  4. 4.Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT)Durham VA Health Care SystemDurhamUSA
  5. 5.Department of Biostatistics and BioinformaticsDuke UniversityDurhamUSA
  6. 6.Salt Lake City VA Informatics Decision-Enhancement and Analytic Sciences (IDEAS 2.0) Center for InnovationSalt Lake CityUSA
  7. 7.Department of Population Health SciencesUniversity of UtahSalt Lake CityUSA

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