Skip to main content
SpringerLink
Log in

Qualitative Attributes and Measurement Properties of Physical Activity Questionnaires

A Checklist

  • Review Article
  • Published:
Sports Medicine Aims and scope Submit manuscript

Abstract

The large number of available physical activity (PA) questionnaires makes it difficult to select the most appropriate questionnaire for a certain purpose. This choice is further hampered by incomplete reporting and unsatisfactory evaluation of the content and measurement properties of the questionnaires. We provide a checklist for appraising the qualitative attributes and measurement properties of PA questionnaires, as a tool for selecting the most appropriate PA questionnaire for a certain target population and purpose. The checklist is called the Quality Assessment of Physical Activity Questionnaire (QAPAQ). This review is one of a group of four reviews in this issue of Sports Medicine on the content and measurement properties of physical activity questionnaires.

Part 1 of the checklist can be used to appraise the qualitative attributes of PA questionnaires, i.e. the construct to be measured by the questionnaire, the purpose and target population for which it was developed, the format, interpretability and ease of use.

Part 2 of the checklist can be used to appraise the measurement properties of a PA questionnaire, i.e. reliability (parameters of measurement error and reliability coefficients), validity (face and content validity, criterion validity and construct validity) and responsiveness.

The QAPAQ can be used to select the most appropriate PA questionnaire for a certain purpose, but it can also be used to design or report a study on measurement properties of PA questionnaires. Using such a checklist will contribute to improving the assessment, reporting and appraisal of the content and measurement properties of PA questionnaires.

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

Table I
Table II
Table II. Contd

Similar content being viewed by others

References

  1. Chinapaw MJM, Mokkink LB, Poppel MNM, et al. Physical activity questionnaires for youth: a systematic review ofmeasurement properties. Sports Med 2010; 40 (7): 539–63

    Article  PubMed  Google Scholar 

  2. Forsen L, Waaler Loland N, Vuillemin A, et al. Self-administered physical activity questionnaires for elderly: asystematic review of measurement properties. Sports Med 2010; 40 (7): 601–23

    Article  PubMed  Google Scholar 

  3. van Poppel MNN, Chinapaw MJM, Mokkink LB, et al. Physical activity questionnaires for adults: a systematicreview of measurement properties. Sports Med 2010; 40 (7): 565–600

    Article  PubMed  Google Scholar 

  4. Lagerros YT, Lagiou P. Assessment of physical activity and energy expenditure in epidemiological research of chronicdiseases. Eur J Epidemiol 2007; 22 (6): 353–62

    Article  PubMed  Google Scholar 

  5. Altschuler A, Picchi T, Nelson M, et al. Physical activity questionnaire comprehension: lessons from cognitive interviews. Med Sci Sports Exerc 2009; 41 (2): 336–43

    Article  PubMed  Google Scholar 

  6. Lagerros YT. Physical activity: the more we measure, the more we know how to measure. Eur J Epidemiol 2009; 24 (3): 119–22

    Article  PubMed  Google Scholar 

  7. Pols MA, Peeters PH, Kemper HC, et al. Methodological aspects of physical activity assessment in epidemiologicalstudies. Eur J Epidemiol 1998; 14 (1): 63–70

    Article  PubMed  CAS  Google Scholar 

  8. Feinstein AR. Clinimetrics. New Haven (CT): Yale University Press, 1987

    Google Scholar 

  9. Terwee CB, Bot SDM, de Boer MR, et al. Quality criteria were proposed for measurement properties of health statusquestionnaires. J Clin Epidemiol 2007; 60: 34–42

    Article  PubMed  Google Scholar 

  10. Mokkink LB, Terwee CB, Knol DL, et al. Protocol of the COSMIN study: COnsensus-based Standards for the selectionof health Measurement INstruments. BMC Med Res Methodol 2006; 6: 2

    Article  PubMed  CAS  Google Scholar 

  11. Rennie KL, Wareham NJ. The validation of physical activity instruments for measuring energy expenditure: problemsand pitfalls. Public Health Nutr 1998; 1 (4): 265–71

    Article  PubMed  CAS  Google Scholar 

  12. Martinez SM, Ainsworth BE, Elder JP. A review of physical activity measures used among US Latinos: guidelines fordeveloping culturally appropriate measures. Ann Behav Med 2008; 36 (2): 195–207

    Article  PubMed  Google Scholar 

  13. Wareham NJ, Rennie KL. The assessment of physical activity in individuals and populations: why try to be moreprecise about how physical activity is assessed? Int J Obes Relat Metab Disord 1998; 22 Suppl. 2: S30–8

    Google Scholar 

  14. Vanhees L, Lefevre J, Philippaerts R, et al. How to assess physical activity? How to assess physical fitness? Eur J Cardiovasc Prev Rehabil 2005; 12 (2): 102–14

    Article  PubMed  Google Scholar 

  15. Ainsworth BE. How do I measure physical activity in my patients? Questionnaires and objective methods. Br JSports Med 2009; 43 (1): 6–9

    Article  CAS  Google Scholar 

  16. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability andvalidity. Med Sci Sports Exerc 2003; 35 (8): 1381–95

    Article  PubMed  Google Scholar 

  17. Staten LK, Taren DL, Howell WH, et al. Validation of the Arizona Activity Frequency Questionnaire using doublylabeled water. Med Sci Sports Exerc 2001; 33 (11): 1959–67

    Article  PubMed  CAS  Google Scholar 

  18. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity inepidemiological studies. Am J Clin Nutr 1982; 36 (5): 936–42

    PubMed  CAS  Google Scholar 

  19. Dolan SH, Williams DP, Ainsworth BE, et al. Development and reproducibility of the bone loading history questionnaire. Med Sci Sports Exerc 2006; 38 (6): 1121–31

    Article  PubMed  Google Scholar 

  20. Tsubono Y, Tsuji I, Fujita K, et al. Validation of walking questionnaire for population-based prospective studiesin Japan: comparison with pedometer. J Epidemiol 2002; 12 (4): 305–9

    Article  PubMed  Google Scholar 

  21. Ainsworth BE, Sternfeld B, Richardson MT, et al. Evaluation of the kaiser physical activity survey in women. Med Sci Sports Exerc 2000; 32 (7): 1327–38

    Article  PubMed  CAS  Google Scholar 

  22. Gionet NJ, Godin G. Self-reported exercise behavior of employees: a validity study. J Occup Med 1989; 31 (12): 969–73

    Article  PubMed  CAS  Google Scholar 

  23. Ainsworth BE, Jacobs Jr DR, Leon AS, et al. Assessment of the accuracy of physical activity questionnaire occupationaldata. J Occup Med 1993; 35 (10): 1017–27

    PubMed  CAS  Google Scholar 

  24. Chasan-Taber L, Erickson JB, McBride JW, et al. Reproducibility of a self-administered lifetime physical activityquestionnaire among female college alumnae. Am JEpidemiol 2002; 155 (3): 282–9

    Article  Google Scholar 

  25. Kirschner B, Guyatt G. A methodological framework for assessing health indices. J Chron Dis 1985; 38: 27–36

    Article  Google Scholar 

  26. Lohr KN, Aaronson NK, Alonso J, et al. Evaluating quality of life and health status instruments: development ofscientific review criteria. Clin Ther 1996; 18 (5): 979–92

    Article  PubMed  CAS  Google Scholar 

  27. Altman DG. Practical statistics for medical research. London: Chapman and Hall, 1991

    Google Scholar 

  28. Giraudeau B, Mary JY. Planning a reproducibility study: how many subjects and how many replicates per subject foran expected width of the 95 per cent confidence interval ofthe intraclass correlation coefficient. Stat Med 2001; 20: 3205–14

    Article  PubMed  CAS  Google Scholar 

  29. Mokkink LB, Terwee CB, Patrick DL, et al. International consensus on taxonomy, terminology, and definitionsof measurement properties for health-related patientreportedoutcomes: results of the COSMIN study. J Clin Epidemiol. In press

  30. de Vet HCW, Terwee CB, Knol DL, et al. When to use agreement versus reliability measures. J Clin Epidemiol 2006; 59: 1033–9

    Article  PubMed  Google Scholar 

  31. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1 (8476): 307–10

    Article  PubMed  CAS  Google Scholar 

  32. Wilbur J, Holm K, Dan A. A quantitative survey to measure energy expenditure in midlife women. J Nurs Meas 1993; 1 (1): 29–40

    PubMed  CAS  Google Scholar 

  33. Streiner DL, Norman GR. Health measurement scales: a practical guide to their development and use. New York: Oxford University Press, 2003

    Google Scholar 

  34. McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Method 1996; 1: 30–46

    Article  Google Scholar 

  35. Matton L, Wijndaele K, Duvigneaud N, et al. Reliability and validity of the Flemish Physical Activity Computerized Questionnaire in adults. Res Q Exerc Sport 2007; 78 (4): 293–306

    Article  PubMed  Google Scholar 

  36. Rigby AS. Statistical methods in epidemiology: v. Towards an understanding of the kappa coefficient. Disabil Rehabil 2000; 22 (8): 339–44

    Article  PubMed  CAS  Google Scholar 

  37. Nunnally JC, Bernstein IH. Psychometric theory. 3rd ed. New York: McGraw-Hill, 1994

    Google Scholar 

  38. McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveysadequate? Qual Life Res 1995; 4: 293–307

    Article  PubMed  CAS  Google Scholar 

  39. Patterson P. Reliability, validity, and methodological response to the assessment of physical activity via self-report. Res Q Exerc Sport 2000; 71 (2 Suppl.): S15–20

    Google Scholar 

  40. Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeledwater. Obesity (Silver Spring) 2007; 15 (10): 2371–9

    Article  Google Scholar 

  41. Brown WJ, Burton NW, Marshall AL, et al. Reliability and validity of a modified self-administered version of theActive Australia physical activity survey in a sample ofmid-age women. Aust N Z J Public Health 2008; 32 (6): 535–41

    Article  PubMed  Google Scholar 

  42. Terwee CB, Dekker FW, Wiersinga WM, et al. On assessing responsiveness of health-related quality of life instruments:guidelines for instrument evaluation. Qual Life Res 2003; 12 (4): 349–62

    Article  PubMed  CAS  Google Scholar 

  43. Deyo RA, Centor RM. Assessing the responsiveness of functional scales to clinical change: an analogy to diagnostictest performance. J Chron Dis 1986; 39: 897–906

    Article  PubMed  CAS  Google Scholar 

  44. Troiano RP. Can there be a single best measure of reported physical activity? Am J Clin Nutr 2009; 89 (3): 736–7

    Article  PubMed  CAS  Google Scholar 

  45. Cortina JM. What is coefficient alpha? An examination of theory and applications. J Appl Psychol 1993; 78: 98–104

    Article  Google Scholar 

  46. Fayers PM, Hand DJ. Causal variables, indicator variables and measurement scales: an example from quality of life. J R Statist Soc A 2002; 165: 233–61

    Article  Google Scholar 

  47. Streiner DL. Being inconsistent about consistency: when coefficient alpha does and doesn’t matter. J Pers Assess 2003; 80 (3): 217–22

    Article  PubMed  Google Scholar 

  48. Mokkink LB, Terwee CB, Patrick DL, et al. The COSMIN checklist for assessing the methodological quality of studieson measurement properties of health status measurementinstruments: an international Delphi study. Qual Life Res 2010; 19: 539–49

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors received no funding for the conduct of this study or the writing of this review. The authors have no conflicts of interest that are directly relevant to the content of this review.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands

    Caroline B. Terwee, Lidwine B. Mokkink & Henrica C. W. de Vet

  2. Department of Public and Occupational Health and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands

    Mireille N. M. van Poppel, Mai J. M. Chinapaw & Willem van Mechelen

Authors
  1. Caroline B. Terwee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lidwine B. Mokkink
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mireille N. M. van Poppel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mai J. M. Chinapaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Willem van Mechelen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Henrica C. W. de Vet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caroline B. Terwee.

Appendix

Appendix

Parameters of Measurement Error and Reliability

Reliability

Intraclass Correlation Coefficient (ICC)

figure Equ1

Equation 1 is a general formula for the intraclass correlation coefficient (ICC). Many different ICCs can be calculated. For test-retest reliability, a two-way random effects model is preferred. For more information about different ICCs see McGraw and Wong.[34]

Measurement Error

Standard Error of Measurement (SEM)

figure Equ2

In equation 2, SEM is an indication of the error of one single score, and can be used to calculate a confidence interval around a single score. Some people prefer to include the variance between time points (vart) in the SEM because they consider this variance part of the measurement error,[30] while others do not.

Using equation 3, the SEM can be converted into the smallest detectable change (SDC):

figure Equ3

SDC reflects the smallest change in score in one person that can be interpreted as a ‘true’ change, i.e. beyond measurement error.[30] The SDC reflects the confidence interval around a single change score, thus a change score of one individual.[33]

In research, where the interest is in mean changes in groups of people, the measurement is reduced by a factor √n (where n is the sample size). SDCgroup reflects the smallest mean change score in a group that can be interpreted as a ‘true’ change, beyond measurement error.[30] In equation 4, the SDCgroup reflects the confidence interval around a mean change score in a group.

figure Equ4

Equation 5 presents limits of agreement (LOA).

figure Equ5

The LOA and SDC are the same because:

figure Equ6

Rights and permissions

Reprints and permissions

About this article

Cite this article

Terwee, C.B., Mokkink, L.B., van Poppel, M.N.M. et al. Qualitative Attributes and Measurement Properties of Physical Activity Questionnaires. Sports Med 40, 525–537 (2010). https://doi.org/10.2165/11531370-000000000-00000

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/11531370-000000000-00000

Keywords

Search

Navigation