Validation of the PAM-13 instrument in Hungary

Zrubka, Zsombor and Vékás, Péter and Németh, Péter and Dobos, Ágota and Hajdu, Ottó and Kovács, Levente and Gulácsi, László (2021) Validation of the PAM-13 instrument in Hungary. Working Paper. Corvinus University of Budapest. (Unpublished)

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


OBJECTIVES: PAM-13 is a measure of the knowledge, skill, and confidence for managing one’s own health. We developed the Hungarian version of the PAM-13 instrument and tested its psychometric properties. METHODS: In linguistic adaptation and psychometric testing, we followed the WHO and COSMIN guidelines, respectively. From a commercial online panel, we recruited a sample representative of the 40+-year-old general population in terms of gender, age, education, region and type of residence using quotas (n=900). After 10 days, the survey was randomly re-administered in 100 subjects. Responses on the 4-point Likert items were transformed to a 0-100 PAM score. We assessed floor and ceiling effect, test-retest reliability via intraclass-correlation (ICC), factor structure via confirmatory factor-analysis (CFA), internal consistency (Cronbach-alpha), convergent and discriminant validity via analysing correlation with the eHealth Literacy Scale (eHEALS) age, education and income; and known-groups validity assuming that respondents without major lifestyle-related risks or above-median probability of preventive behaviours (participation in cancer screening, cardio-metabolic monitoring and vaccinations according to national guidelines) or health information-seeking have higher PAM scores. RESULTS: Altogether 779 (86.6%) individuals were eligible for the analysis. Mean age was 60.4 (SD=10.6) years, 54.0% were female, 66.5% reported to have chronic disease. Mean PAM score was 59.8 (SD=11.5). No "oor- or ceiling e#ect was detected. Test-retest reliability was assessed in 75 eligible individuals (ICC=0.63). CFA suggested single-factor structure with adequate sample (Kaiser-Meyer-Olkin statistic=0.84) and good fit (RMSEA=0.049). Internal consistency was good (Cronbac alpha=0.77). PAM scores were positively correlated with eHEALS (r=0.41, p<0.001) but not with age (r=0.02, p=0.49), education (polychoric rho=0.00, p=0.99) or income (r=-0.03, p=0.42). PAM scores were higher in individuals without major lifestyle-related risks (p<0.001) and frequent health information seekers (p=0.001) but were not associated with preventive behaviours (p=0.21). CONCLUSIONS: The Hungarian language version of PAM-13 instrument demonstrated good structural and content validity and moderate test-retest reliability.

Item Type:Monograph (Working Paper)
Social welfare, insurance, health care
Funders:The study was funded by the National Research, Development and Innovation Fund of Hungary (Project No. NKFIH-869-10/2019 , Tématerületi Kiválósági Program funding scheme).

1. WHO. Chronic diseases and health promotion. 2019 2019.09.01.]; Available from:
2. OECD, Fiscal Sustainability of Health Systems: Bridging Health and Finance
Perspectives. 2015, Paris: OECD Publishing.
3. Institute of Medicine, Crossing the quality chasm: A new Health System for the 21
century. 2005, Washington, DC.
4. World Health Organization, WHO global strategy on people-centred and integrated
health services - Interim report. 2015, WHO: Geneva.
5. WHO, Action plan for the prevention and control of noncommunicable diseases
in the WHO European Region. 2016, Copenhagen: WHO Regional Office for Europe.
6. WHO, Healthy, prosperous lives for all: the European Health Equity Status Report.
2019, Copenhagen: World Health Organization Regional Office for Europe.
7. Bircher, J. and S. Kuruvilla, Defining health by addressing individual, social, and
environmental determinants: new opportunities for health care and public health. J
Public Health Policy, 2014. 35(3): p. 363-86.
8. Wallston, K.A., B.S. Wallston, and R. DeVellis, Development of the Multidimensional
Health Locus of Control (MHLC) Scales. Health Educ Monogr, 1978. 6(2): p. 160-70.
9. Bandura, A., Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 1977. 84(2): p. 191-215.
10. Lorig, K., Self-management of chronic illness: a model for the future. Generations,
1993. 17(3): p. 11-14.
11. Prochaska, J., C. Redding, and K. Evers, The transtheorethical model model and
stages of change, in Health Behaviour and Health Education Theory, Research and
Practice 4th Edition, K. Glanz, B. Rimer, and K. Viswanath, Editors. 2008, Jossey-Bass:
San Francisco, CA.
12. Coleman, K., et al., Evidence on the Chronic Care Model in the new millennium.
Health Aff (Millwood), 2009. 28(1): p. 75-85.
13. Morton, K., et al., Using digital interventions for self-management of chronic physical
health conditions: A meta-ethnography review of published studies. Patient Educ
Couns, 2017. 100(4): p. 616-635.
14. NICE, Evidence standards framework for digital health technologies. 2019: London.
15. Hibbard, J.H., et al., Development of the Patient Activation Measure (PAM):
conceptualizing and measuring activation in patients and consumers. Health Serv
Res, 2004. 39(4 Pt 1): p. 1005-26.
16. Insignia Health. The Science of the PAM survey. 2019; Available from:
17. Mosen, D., et al., Is Patient Activation Associated With Outcomes of Care for Adults
With Chronic Conditions? Journal of Ambulatory Care Management, 2007. 30(1): p.
18. Harvey, L., et al., 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): p. 338-43.
19. Graffigna, G., S. Barello, and A. Bonanomi, The role of Patient Health Engagement
Model (PHE-model) in affecting patient activation and medication adherence: A
structural equation model. PLoS One, 2017. 12(6): p. e0179865.
20. Remmers, C., et al., Is patient activation associated with future health outcomes and
healthcare utilization among patients with diabetes? J Ambul Care Manage, 2009.
32(4): p. 320-7.
21. Aziz, A., R. Reynolds, and A. Ansari, PROCESS AND SYSTEMS: A population-based
model of care for people with inflammatory bowel disease - patient-reported
outcomes. Future Healthc J, 2019. 6(1): p. 30-35.
22. Schnock, K.O., et al., Acute Care Patient Portal Intervention: Portal Use and Patient
Activation. J Med Internet Res, 2019. 21(7): p. e13336.
23. Carroll, J.K., et al., "Get Ready and Empowered About Treatment" (GREAT) Study: a
Pragmatic Randomized Controlled Trial of Activation in Persons Living with HIV. J Gen
Intern Med, 2019. 34(9): p. 1782-1789.
24. Korm. határozat az „Egészséges Magyarország 2014–2020” Egészségügyi Ágazati
Stratégia 2017–2018 évekre vonatkozó cselekvési tervéről, in 1886/2016. (XII. 28.)
2017, Egészségügyi Közlöny LXVI. évfolyam 1. : Hungary.
25. KSH, Population census 2011. 2011, Hungarian Central Statistical Office: Online.
26. Skolasky, R.L., et al., Psychometric properties of the Patient Activation Measure
among individuals presenting for elective lumbar spine surgery. Qual Life Res, 2009.
18(10): p. 1357-66.
27. Kosar, C. and D.B. Besen, Adaptation of a patient activation measure (PAM) into
Turkish: reliability and validity test. Afr Health Sci, 2019. 19(1): p. 1811-1820.
28. Terwee, C.B., et al., Quality criteria were proposed for measurement properties of
health status questionnaires. J Clin Epidemiol, 2007. 60(1): p. 34-42.
29. WHO. Process of translation and adaptation of instruments. na 4th Nov, 2019];
Available from:
30. Hibbard, J.H., et al., Development and testing of a short form of the patient
activation measure. Health Serv Res, 2005. 40(6 Pt 1): p. 1918-30.
31. Lindsay, A., et al., Patient Activation Changes as a Potential Signal for Changes in
Health Care Costs: Cohort Study of US High-Cost Patients. J Gen Intern Med, 2018.
33(12): p. 2106-2112.
32. Zrubka, Z., et al., Psychometric properties of the Hungarian version of the eHealth
Literacy Scale. Eur J Health Econ, 2019. 20(Suppl 1): p. 57-69.
33. Norman, C.D. and H.A. Skinner, eHEALS: The eHealth Literacy Scale. J Med Internet
Res, 2006. 8(4): p. e27.
34. van der Vaart, R., et al., Does the eHealth Literacy Scale (eHEALS) measure what it
intends to measure? Validation of a Dutch version of the eHEALS in two adult
populations. J Med Internet Res, 2011. 13(4): p. e86.
35. Neter, E. and E. Brainin, Perceived and Performed eHealth Literacy: Survey and
Simulated Performance Test. JMIR Hum Factors, 2017. 4(1): p. e2.
36. Weiss, B.D., et al., Quick assessment of literacy in primary care: the newest vital sign.
Ann Fam Med, 2005. 3(6): p. 514-22.
37. Koltai, J. and E. Kun, [The practical measurement of health literacy in Hungary and in
international comparison]. Orv Hetil, 2016. 157(50): p. 2002-2006.
38. Mansfield, E.D., et al., Canadian adaptation of the Newest Vital Sign(c), a health
literacy assessment tool. Public Health Nutr, 2018. 21(11): p. 2038-2045.
39. Herdman, M., et al., Development and preliminary testing of the new five-level
version of EQ-5D (EQ-5D-5L). Qual Life Res, 2011. 20(10): p. 1727-36.
40. Rencz, F., et al., Pns401 the First Parallel Eq-5d-3l and Eq-5d-5l Composite Time
Trade-Off Valuation Study in Europe. Value in Health, 2019. 22.
41. Cox, B., et al., The reliability of the Minimum European Health Module. Int J Public
Health, 2009. 54(2): p. 55-60.
42. Parker, R.N. and R. Fenwick, The Pareto Curve and Its Utility for Open-Ended Income
Distributions in Survey Research. Social Forces, 1983. 61(3).
43. European Central Bank Eurosystem. Euro foreign exchange reference rates. 2020
2020.04.01.]; Available from:
44. Veenhoven, R., Happiness in Nations: Overview of happiness surveys using Measure
type: 112G / 11-step numeral Happiness, in World Database of Happiness. 1993,
Erasmus University Rotterdam, Happiness Economics Research Organisation:
45. Al-Janabi, H., T.N. Flynn, and J. Coast, Development of a self-report measure of
capability wellbeing for adults: the ICECAP-A. Qual Life Res, 2012. 21(1): p. 167-76.
46. Coast, J., et al., Valuing the ICECAP capability index for older people. Soc Sci Med,
2008. 67(5): p. 874-82.
47. Coast, J., et al., An assessment of the construct validity of the descriptive system for
the ICECAP capability measure for older people. Qual Life Res, 2008. 17(7): p. 967-76.
48. Flynn, T.N., et al., Scoring the Icecap-a capability instrument. Estimation of a UK
general population tariff. Health Econ, 2015. 24(3): p. 258-69.
49. Stein, C.J. and G.A. Colditz, Modifiable risk factors for cancer. Br J Cancer, 2004.
90(2): p. 299-303.
50. Lim, S.S., et al., Validation of a new predictive risk model: measuring the impact of
the major modifiable risks of death for patients and populations. Popul Health Metr,
2015. 13: p. 27.
51. Ng, R., et al., Smoking, drinking, diet and physical activity-modifiable lifestyle risk
factors and their associations with age to first chronic disease. Int J Epidemiol, 2020.
49(1): p. 113-130.
52. WHO, Global Status Report About Noncommunicable Diseases. 2014, Geneva: WHO.
53. McGorrian, C., et al., Estimating modifiable coronary heart disease risk in multiple
regions of the world: the INTERHEART Modifiable Risk Score. Eur Heart J, 2011. 32(5):
p. 581-9.
54. Di Angelantonio, E., et al., Body-mass index and all-cause mortality: individualparticipant-data meta-analysis of 239 prospective studies in four continents. The
Lancet, 2016. 388(10046): p. 776-786.
55. Taghizadeh, N., J.M. Vonk, and H.M. Boezen, Lifetime Smoking History and CauseSpecific Mortality in a Cohort Study with 43 Years of Follow-Up. PLoS One, 2016.
11(4): p. e0153310.
56. Pirie, K., et al., The 21st century hazards of smoking and benefits of stopping: a
prospective study of one million women in the UK. The Lancet, 2013. 381(9861): p.
57. Stamatakis, E., et al., Sitting Time, Physical Activity, and Risk of Mortality in Adults. J
Am Coll Cardiol, 2019. 73(16): p. 2062-2072.
58. Wang, X., et al., Fruit and vegetable consumption and mortality from all causes,
cardiovascular disease, and cancer: systematic review and dose-response metaanalysis of prospective cohort studies. BMJ, 2014. 349: p. g4490.
59. Xi, B., et al., Relationship of Alcohol Consumption to All-Cause, Cardiovascular, and
Cancer-Related Mortality in U.S. Adults. J Am Coll Cardiol, 2017. 70(8): p. 913-922.
60. NIAAA, NIAAA Newsletter in NIAAA Newsletter 2004, Office of Research Translation
and Communications, NIAAA, NIH: Online. p. 3.
61. NM rendelet a kötelező egészségbiztosítás keretében igénybe vehető betegségek
megelőzését és korai felismerését szolgáló egészségügyi szolgáltatásokról és a
szűrővizsgálatok igazolásáról, in 51/1997. (XII. 18.) 1997, Magyar Közlöny 1997/114;
Publication date: 18/12/1997: Hungary.
62. A Nemzeti Népegészségügyi Központ módszertani levele a 2020. évi védőoltásokról.
2020, ÁNTSZ [National Public Health and Medical Officer Service]: Online.
63. Insignia Health, Best Practices for Analysing PAM data, Insignia Health, Editor.,
Insignia Health, : online.
64. Mokkink, L.B., et al., The COSMIN checklist for assessing the methodological quality
of studies on measurement properties of health status measurement instruments: an
international Delphi study. Qual Life Res, 2010. 19(4): p. 539-49.
65. Prinsen, C.A.C., et al., COSMIN guideline for systematic reviews of patient-reported
outcome measures. Qual Life Res, 2018. 27(5): p. 1147-1157.
66. Terwee, C.B., et al., Rating the methodological quality in systematic reviews of
studies on measurement properties: a scoring system for the COSMIN checklist. Qual
Life Res, 2012. 21(4): p. 651-7.
67. Lim, C.R., et al., Floor and ceiling effects in the OHS: an analysis of the NHS PROMs
data set. BMJ Open, 2015. 5(7): p. e007765.
68. McGraw, K.O. and S.P. Wong, Forming inferences about some intraclass correlation
coefficients. Psychological Methods, 1996. 1(1): p. 30-46.
69. Rosseel, Y., lavaan: AnRPackage for Structural Equation Modeling. Journal of
Statistical Software, 2012. 48(2).
70. Kaiser, H. and J. Rice, Little jiffy, mark IV. Educational and psychological
measurement, 1974 34(1): p. 111-117.

ID Code:6242
Deposited By: Péter Vékás
Deposited On:10 Feb 2021 13:43
Last Modified:10 Feb 2021 13:43

Repository Staff Only: item control page


Downloads per month over past year

View more statistics