Validity of a New Patient Engagement Measure: The Altarum Consumer Engagement (ACE) Measure

Christopher C Duke, Wendy D Lynch, Brad Smith, Julie Winstanley, Christopher C Duke, Wendy D Lynch, Brad Smith, Julie Winstanley

Abstract

Objective: The objective of this study was to report on the validation of new scales [called the Altarum Consumer Engagement (ACE) Measure] that are indicative of an individual's engagement in health and healthcare decisions. The instrument was created to broaden the scope of how engagement is measured and understood, and to update the concept of engagement to include modern information sources, such as online health resources and ratings of providers and patient health.

Methods: Data were collected through an online survey with a US population of 2079 participants. A combination of Principal Component Analysis (PCA) and detailed Rasch analyses were conducted to identify specific subscales of engagement. Results were compared to another commonly used survey instrument, and outcomes were compared for construct validity.

Results: The PCA identified a four-factor structure composed of 21 items. The factors were named Commitment, Informed Choice, Navigation, and Ownership. Rasch analyses confirmed scale stability. Relevant outcomes were correlated in the expected direction, such as health status, lifestyle behaviors, medication adherence, and observed expected group differences.

Conclusions: This study confirmed the validity of the new ACE Measure and its utility in screening for and finding group differences in activities related to patient engagement and health consumerism, such as using provider comparison tools and asking about medical costs.

References

    1. Ancker JS, Barrón Y, Rockoff ML, Hauser D, Pichardo M, Szerencsy A, et al. Use of an electronic patient portal among disadvantaged populations. J Gen Intern Med. 2011;26(10):1117–1123. doi: 10.1007/s11606-011-1749-y.
    1. Jordan JE, Briggs AM, Brand CA, Osborne RG. Enhancing patient engagement in chronic disease self-management support initiatives in Australia: the need for an integrated approach. Med J Aust. 2008;189(10):S9–S13.
    1. Gruman J, Holmes-Rovner M, French ME, Jeffress D, Sofaer S, Shaller D, et al. From patient education to patient engagement: implications for the field of patient education. Patient Educ Couns. 2010;78(3):350–356. doi: 10.1016/j.pec.2010.02.002.
    1. Weingart SN, Zhu J, Chiappetta L, Stuver SO, Schneider EC, Epstein AM, et al. Hospitalized patients’ participation and its impact on quality of care and patient safety. Int J Qual Health Care. 2011;23(3):269–277. doi: 10.1093/intqhc/mzr002.
    1. 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 Ambul Care Manage. 2009;32(4):320–327. doi: 10.1097/JAC.0b013e3181ba6e77.
    1. Kennedy AD, Sculpher MJ, Coulter A, Dwyer N, Rees M, Abrams KR, et al. Effects of decision aids for menorrhagia on treatment choices, health outcomes, and costs: a randomized controlled trial. JAMA. 2002;288(21):2701–2708. doi: 10.1001/jama.288.21.2701.
    1. Clark NM, Janz NK, Dodge JA, Mosca L, Lin X, Long Q, et al. The effect of patient choice of intervention on health outcomes. Contemp Clin Trials. 2008;29(5):679–686. doi: 10.1016/j.cct.2008.04.002.
    1. Naik AD, Kallen MA, Walder A, Street RLJ. Improving hypertension control in diabetes mellitus: the effects of collaborative and proactive health communication. Circulation. 2008;117(11):1361–1368. doi: 10.1161/CIRCULATIONAHA.107.724005.
    1. Lorig K, Ritter PL, Laurent DD, Plant K, Green M, Jernigan VB, et al. Online diabetes self-management program: a randomized study. Diabetes Care. 2010;33(6):1275–1281. doi: 10.2337/dc09-2153.
    1. Bloomfield HE, Krause A, Greer N, Taylor BC, MacDonald R, Rutks I, et al. Meta-analysis: effect of patient self-testing and self-management of long-term anticoagulation on major clinical outcomes. Ann Intern Med. 2011;154(7):472–482. doi: 10.7326/0003-4819-154-7-201104050-00005.
    1. Basu A, Meltzer D. Value of information on preference heterogeneity and individualized care. Med Decis Making. 2007;27(2):112–127. doi: 10.1177/0272989X06297393.
    1. Stiggelbout AM, Molewijk AC, Otten W, Timmermans DR, van Bockel JH, Kievit J. Ideals of patient autonomy in clinical decision making: a study on the development of a scale to assess patients’ and physicians’ views. J Med Ethics. 2004;30(3):268–274. doi: 10.1136/jme.2003.003095.
    1. Smith MS, Wallston KA, Smith CA. The development and validation of the Perceived Health Competence Scale. Health Educ Res. 1995;10(1):51–64. doi: 10.1093/her/10.1.51.
    1. Simon D, Kriston L, Loh A, Spies C, Scheibler F, Wills C, et al. Confirmatory factor analysis and recommendations for improvement of the Autonomy-Preference-Index (API) Health Expect. 2010;13(3):234–243.
    1. Simon D, Schorr G, Wirtz M, Vodermaier A, Caspari C, Neuner B, et al. Development and first validation of the shared decision-making questionnaire (SDM-Q) Patient Educ Couns. 2006;63(3):319–327. doi: 10.1016/j.pec.2006.04.012.
    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(6 Pt 1):1918–1930. doi: 10.1111/j.1475-6773.2005.00438.x.
    1. Benbassat J, Pilpel D, Tidhar M. Patients’ preferences for participation in clinical decision making: a review of published surveys. Behav Med. 1998;24(2):81–88. doi: 10.1080/08964289809596384.
    1. Hudon C, Fortin M, Haggerty JL, Lambert M, Poitras ME. Measuring patients’ perceptions of patient-centered care: a systematic review of tools for family medicine. Ann Fam Med. 2011;9(2):155–164. doi: 10.1370/afm.1226.
    1. Small N, Bower P, Chew-Graham CA, Whalley D, Protheroe J. Patient empowerment in long-term conditions: development and preliminary testing of a new measure. BMC Health Serv Res. 2013;13:263. doi: 10.1186/1472-6963-13-263.
    1. Maibach EW, Weber D, Massett H, Hancock GR, Price S. Understanding consumers’ health information preferences: development and validation of a brief screening instrument. J Health Commun. 2006;11(8):717–736. doi: 10.1080/10810730600934633.
    1. Guyatt GH, Mitchell A, Molloy DW, Capretta R, Horsman J, Griffith L. Measuring patient and relative satisfaction with level or aggressiveness of care and involvement in care decisions in the context of life threatening illness. J Clin Epidemiol. 1995;48(10):1215–1224. doi: 10.1016/0895-4356(95)00024-X.
    1. Santesso N, Rader T, Wells GA, O’Connor AM, Brooks PM, Driedger M, et al. Responsiveness of the Effective Consumer Scale (EC-17) J Rheumatol. 2009;36(9):2087–2091. doi: 10.3899/jrheum.090363.
    1. Bennett DA. How can I deal with missing data in my study? Aust N Z J Public Health. 2001;25(5):464–469. doi: 10.1111/j.1467-842X.2001.tb00294.x.
    1. Jolliffe I. Principal components analysis. Wiley StatsRef: Statistics Reference Online; 2014. . Accessed 28 May 2015.
    1. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334. doi: 10.1007/BF02310555.
    1. RUMM Laboratory Pty Ltd . RUMM2030. Professional. Duncraig: RUMM Laboratory Pty Ltd; 2011.
    1. Rasch G. Probabilistic models for some intelligence and attainment tests. In: B. D. Wright. Expanded ed. with foreword and afterword, 1992. Chicago: University of Chicago Press; 1960.
    1. Linacre J. Applying the Rasch Model. Fundamental measurement in the human sciences. 2nd ed. Mahwah: Lawrence Erlbaum Associates Publishers; 2007.
    1. Bond TG, Fox CM. Applying the Rasch Model: fundamental measurement in the human sciences. 2. Mahwah: Lawrence Erlbaum Associates Publishers; 2007.
    1. Wright BD, Stone MH. Best test design. Chicago: Mesa Press; 1979.
    1. Tennant A, Conaghan PG. The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthritis Rheum. 2007;57(8):1358–1362. doi: 10.1002/art.23108.
    1. Pallant JF, Tennant A. An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS) Br J Clin Psychol. 2007;46(Pt 1):1–18. doi: 10.1348/014466506X96931.
    1. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310(6973):170. doi: 10.1136/bmj.310.6973.170.
    1. Smith EV., Jr Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. J Appl Meas. 2002;3(2):205–231.

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