Measuring arm function early after stroke: Is the DASH good enough?

Journal of Neurology, Neurosurgery and Psychiatry

Volumn 87, Issue 6, 2016, Pages 604-610

  Baker, Karen ^a   Barrett, Louise ^b   Playford, E Diane ^a   Aspden, Trefor ^c   Riazi, Afsane ^c   Hobart, Jeremy ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

^c UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARM DISEASE; ARM MOVEMENT; ARTICLE; CEREBROVASCULAR ACCIDENT; CLASSICAL TEST THEORY; CLINICAL ASSESSMENT TOOL; CONTROLLED STUDY; CORRELATION ANALYSIS; DISABILITIES OF THE ARM SHOULDER AND HAND; EFFECT SIZE; FEMALE; FUNCTIONAL ASSESSMENT; HUMAN; INTERMETHOD COMPARISON; INTERNAL CONSISTENCY; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; PSYCHOMETRY; RASCH MEASUREMENT THEORY; RATING SCALE; REFERENCE VALUE; RELIABILITY; ADOLESCENT; ARM; DISABILITY; FOLLOW UP; HEMIPLEGIA; INNERVATION; MIDDLE AGED; OUTCOME ASSESSMENT; REPRODUCIBILITY; STATISTICS AND NUMERICAL DATA; STROKE; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; ARM; DISABILITY EVALUATION; FEMALE; FOLLOW-UP STUDIES; HEMIPLEGIA; HUMANS; MALE; MIDDLE AGED; OUTCOME ASSESSMENT (HEALTH CARE); PSYCHOMETRICS; REPRODUCIBILITY OF RESULTS; STROKE; YOUNG ADULT;

EID: 84937596767     PISSN: 00223050     EISSN: 1468330X     Source Type: Journal    
DOI: 10.1136/jnnp-2015-310557     Document Type: Article

References (44)

1. Nakayama H, Stig Jørgensen H, Otto Raaschou H, et al. Recovery of upper extremity function in stroke patients: the Copenhagen stroke study. Arch Phys Med Rehabil 1994; 75: 394-8.
2. Lawrence ES, Coshall C, Dundas R, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke 2001; 32: 1279-84.
3. Velstra IM, Ballert CS, Cieza A A systematic literature review of outcome measures for upper extremity function using the international classification of functioning, disability, and health as reference. PM R 2011; 3: 846-60.
4. Connell LA, Tyson SF. Clinical reality of measuring upper-limb ability in neurologic conditions: a systematic review. Arch Phys Med Rehabil 2012; 93: 221-8.
5. Lang CE, Bland MD, Bailey RR, et al. Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. J Hand Ther 2013; 26: 104-15.
6. Mathiowetz V, Volland G, Kashman N, et al. Adult norms for the box and block test of manual dexterity. Am J Occup Ther 1985; 39: 386-91.
7. Lyle RC A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res 1981; 4: 483-92.
8. Baker K, Cano SJ, Playford ED. Outcome measurement in stroke: a scale selection strategy. Stroke 2011; 42: 1787-94.
9. Department of Health. Guidance on the routine collection of patient reported outcome measures (PROMs). London, UK: Department of Health, 2008.
10. Food and Drug Administration. Patient reported outcome measures: use in medical product development to support labelling claims. U.S. Department of Health and Human Services, 2009.
11. Duncan PW, Jorgensen HS, Wade DT. Outcome measures in acute stroke trials: a systematic review and some recommendations to improve practice. Stroke 2000; 31: 1429-38.
12. Hudak PL, Amadio PC, Bombardier C. Development of an upper extremity outcome measure: the DASH (disabilities of the arm, shoulder, and hand) [corrected]. The Upper Extremity Collaborative Group (UECG). Am J Ind Med 1996; 29: 602-8.
13. Ring D, Kadzielski J, Fabian L, et al. Self-reported upper extremity health status correlates with depression. J Bone Joint Surg Am 2006; 88: 1983-8.
14. Hoang-Kim A, Pegreffi F, Moroni A, et al. Measuring wrist and hand function: common scales and checklists. Injury 2011; 42: 253-8.
15. Ashford S, Slade M, Malaprade F, et al. Evaluation of functional outcome measures for the hemiparetic upper limb: a systematic review. J Rehabil Med 2008; 40: 787-95.
16. Lord FM, Novick MR. Statistical theories of mental test scores. Reading, MA: Addison-Wesley, 1968.
17. Rasch G. Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Education Research, 1960.
18. Cano SJ, Barrett LE, Zajicek JP, et al. Beyond the reach of traditional analyses: using Rasch to evaluate the DASH in people with multiple sclerosis. Mult Scler 2011; 17: 214-22.
19. Hobart JC, Freeman JA, Lamping DL, et al. The SF-36 in multiple sclerosis (MS): why basic assumptions must be tested. J Neurol Neurosurg Psychiatry 2001; 71: 363-70.
20. Ware JE Jr, Harris WJ, Gandek B, et al. MAP-R for windows: multitrait/multi-item analysis program-revised users guide. Boston, MA: Health Assessment Lab, 1997.
21. McHorney CA, Ware JE Jr, Lu JF, et al. The MOS 36-Item Short-Form Health Survey ( SF-36): III. Tests of data quality, scaling assumptions and reliability across diverse patient groups. Med Care 1994; 32: 40-66.
22. Likert RA A technique for the measurement of attitudes. Arch Psychol 1932; 140: 5-55.
23. McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Qual Life Res 1995; 4: 293-307.
24. Holmes WC, Shea JA. Performance of a new, HIV/AIDS-Targeted quality of life instrument in asymptomatic seropositive individuals. Qual Life Res 1997; 6: 561-71.
25. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika 1951; 16: 297-334.
26. Eisen M, Ware JE Jr, Donald CA, et al. Measuring components of childrens health status. Med Care 1979; 17: 902-21.
27. Wright BD, Masters GN. Rating scale analysis: Rasch measurement. Chicago: MESA, 1982.
28. Andrich D. Rasch models for measurement. Beverly Hills, CA: Sage Publications, 1988.
29. Hobart JC, Cano SJ. Improving the evaluation of therapeutic interventions in multiple sclerosis: the role of new psychometric methods. Health Technol Assess 2009; 13.
30. Andrich D A rating formulation for ordered response categories. Psychometrika 1978; 43: 561-73.
31. Marais I, Andrich D. Formalizing dimension and response violations of local independence in the unidimensional Rasch model. J Appl Meas 2008; 9: 200-15.
32. Andrich D. An index of person separation in latent trait theory, the traditional KR20 index, and the Guttman scale response pattern. Educ Res Perspect 1982; 9: 95-104.
33. Andrich D, Sheridan B, Luo G. RUMM2030: a windows program for the analysis of data according to Rasch Unidimensional models for measurement. Perth, WA: RUMM Laboratory Pty Ltd, 2010.
34. Cohen J. Statistical power analysis for the behavioural sciences. 2nd edn. Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1988.
35. Luo G, Andrich D. Estimating parameters in the Rasch model in the presence of null categories. J Appl Meas 2005; 6: 128-46.
36. 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: 1-18.
37. Barrett LE, Cano SJ, Zajicek JP, et al. Can the ABILHAND handle manual ability in MS? Mult Scler 2013; 19: 806-15.
38. Hobart J, Cano S, Baron R, et al. Achieving valid patient-reported outcomes measurement: a lesson from fatigue in multiple sclerosis. Mult Scler 2013; 19: 1773-83.
39. Stenner AJ, Smith M, Burdick D. Towards a theory of construct definition. J Educ Meas 1983; 20: 305-16.
40. Novick MR. The axioms and principal results of classical test theory. J Math Psychol 1966; 3: 1-18.
41. Hagquist C, Andrich D. Measuring subjective health among adolescents in Sweden. Soc Indic Res 2004; 68: 201-20.
42. Andrich D, Hagquist C. Real and artificial differential item functioning. J Educ Behav Stat 2012; 37: 387-416.
43. Andrich D. Rating scales and Rasch measurement. Expert Rev Pharmacoecon Outcomes Res 2011; 11: 571-85.
44. Andrich D. The legacies of R. A. Fisher and K. Pearson in the application of the polytomous Rasch model for assessing the empirical ordering of categories. Educ Psychol Meas 2013; 73: 553-80.