Advertisement

Revisiting the disabilities of the arm, shoulder and hand (DASH) and QuickDASH in rheumatoid arthritis

  • B. ProdingerEmail author
  • A. Hammond
  • A. Tennant
  • Y. Prior
  • S. Tyson
Open Access
Research article
Part of the following topical collections:
  1. Epidemiology of musculoskeletal disorders

Abstract

Background

Limitations in upper limb functioning are common in Musculoskeletal disorders and the Disabilities of the Arm, Shoulder and Hand scale (DASH) has gained widespread use in this context. However, various concerns have been raised about its construct validity and so this study seeks to examine this and other psychometric aspects of both the DASH and QuickDASH from a modern test theory perspective.

Methods

Participants in the study were eligible if they had a confirmed diagnosis of Rheumatoid Arthritis (RA). They were mailed a questionnaire booklet which included the DASH. Construct validity was examined by fit to the Rasch measurement model. The degree of precision of both the DASH and QuickDASH were considered through their Standard Error of Measurement (SEM).

Results

Three hundred and thirty-seven subjects with confirmed RA took part, with a mean age of 62.0 years (SD12.1); 73.6% (n = 252) were female. The median standardized score on the DASH was 33 (IQR 17.5–55.0). Significant misfit of the DASH and QuickDASH was observed but, after accommodating local dependency among items in a two-testlet solution, satisfactory fit was obtained, supporting the unidimensionality of the total sets and the sufficiency of the raw (ordinal or standardized) scores.

Conclusion

Having accommodated local response dependency in the DASH and QuickDASH item sets, their total scores are shown to be valid, given they satisfy the Rasch model assumptions. The Rasch transformation should be used whenever all items are used to calculate a change score, or to apply parametric statistics within an RA population.

Significance and innovations

  • Most previous modern psychometric analyses of both the DASH and QuickDASH have failed to fully address the effect of a breach of the local independence assumption upon construct validity.

  • Accommodating this problem by creating ‘super items’ or testlets, removes this effect and shows that both versions of the scale are valid and unidimensional, as applied with a bi-factor equivalent solution to an RA population.

  • The Standard Error of Measurement of a scale can be biased by failing to take into account the local dependency in the data which inflates reliability and thus making the SEM appear better (i.e. smaller) than the true value without bias.

Background

Limitations in upper limb functioning, and associated impact upon daily activities are common in musculoskeletal disorders, as well as many other long-term conditions. One recent large-scale survey in China reported prevalence of rheumatic pain in the neck, shoulder and elbow as 5.6, 3.1 and 1.4% respectively [1]. Another study in the USA, with a sample whose mean age was 68 years, found symptoms associated with the neck and shoulder to have a prevalence of 8 and 13% respectively [2]. An earlier large-scale study in those aged 16 years and above in the UK found the prevalence of pain, swelling or stiffness of shoulder, elbow and hand at 6.9, 3.1 and 6.6% respectively [3]. Thus, upper limb problems are common in the population, and particularly so among those with musculoskeletal disorders and in older people.

It is not surprising therefore that several Patient Reported Outcome Measures (PROMs) have been developed with the intention of assessing the extent of impairment of function, or of limitations in activities associated with the upper limb function in both children and adults [4, 5] . One such PROM, the Disabilities of the Arm, Shoulder and Hand scale (DASH) has gained widespread use across many chronic conditions [6, 7]. However, at the same time, concerns have been raised from a classical test theory perspective about the viability of the summated score of its 30 items, suggesting that the scale comprises more than one domain [8, 9, 10]. Similar concerns have been expressed from a modern test theory perspective when data from the scale have been fit to the Rasch model, indicating lack of dimensionality, a breach of the local independence assumption among its items, and lack of consistent scaling properties of its items [11, 12, 13]. Meanwhile, a shortened 11-item version, the QuickDASH, has emerged which has also been criticized from both test perspectives [14, 15, 16].

This study seeks to examine the internal construct validity and other psychometric aspects of both the DASH and QuickDASH, from a modern test theory perspective, in a population of those with Rheumatoid Arthritis.

Methods

The National Research Ethics Service Committee North West - Greater Manchester North (12/NW/0841) and the University of Salford School of Health Sciences Ethics Panel provided ethical approval for this study. All participants provided written, informed consent.

Participants

Two recruitment strategies were applied in parallel. Firstly, research nurses recruited in 17 Rheumatology out-patient clinics or identified participants from department databases. Secondly, participants willing to be contacted for future studies of a previously conducted outcome measure study from the same Rheumatology out-patient clinics originally were re-checked for eligibility prior to consent. The following eligibility criteria were applied: 1) confirmed diagnosis of rheumatoid arthritis (RA); 2) being able to read, write and understand English; and 3) had not (or were not about to) altered their disease-modifying medication regimen in the last three months.

Procedures

A questionnaire booklet which included the information about the to be recruited study population: demographic and disease data: age, gender, marital, educational and employment status, disease duration and RA disease-modifying medication was mailed to participants.

The DASH and QuickDASH scoring

The DASH consists of 30 items scored on a 1–5 scale. The scoring instructions indicate that summating all items to a total score is acceptable, given at least 27 items have been completed. An algorithm is available to construct an overall standardized score of 0–100, including coping with missing values. The 30-item scale can be said to assess upper limb functioning, comprising both aspects of pain, and activities of daily living. The QuickDASH is formed from a subset of 11 items, and is scored in a similar fashion. In the current study this was derived from participants’ completed DASH questionnaires.

Construct validity

Construct validity was examined by fit to the Rasch measurement model [17]. Data were fit to the Rasch measurement model using the RUMM2030 software [18]. The Rasch model is widely used in health outcomes to determine the sufficiency of the raw score, unidimensionality, local independence, Differential Item Functioning (DIF), and the threshold ordering of polytomous items. Various published papers explain these aspects in some detail [19, 20]. In this study, fit to the model was determined through a non-significant Chi-Square (Benjamin-Hochberg adjusted p values with 25% false discovery rate) and DIF was evaluated for age and gender. Any breach of the local (response) independence assumption was tested through item residual correlations of ≥0.2 above the average residual correlation [21, 22]. Where this breach occurred, items were summated together into testlets to absorb the local dependency [23] (super items which simply add up the item set into one new item). When these testlets were used to assess the scale, additional indicators were available. Expressed as the value ‘A’ in the RUMM2030 program, this is the proportion of common non-error variance retained in the resulting latent estimate, and where a value of 0.85 and above is considered sufficient for supporting a strong unidimensional general factor, consistent with the Explained Common Variance (ECV) to be found in the bi-factor literature [24, 25].

Precision

Finally, the degree of precision of both the DASH and QuickDASH were considered through their Standard Error of Measurement (SEM), and the Smallest Detectable Difference (SDD) expressed as a percentage of the operational range of the scale. The latter is concerned with that value which is needed to show a meaningful difference above the level of error in the scale, and is represented as 1.96* SEM.

Results

Participants

Three hundred and thirty-seven subjects with confirmed RA completed the DASH questionnaire, with a mean age of 62.0 years (SD12.1), 73.6% (n = 252) were female, 71.7% (n = 241) were married or living with a partner and 10.8% (n = 36) had children living at home. Over half (n = 169;50.8%) were retired, and a further 10.6% (n = 35) had retired early due to ill health.

DASH scores

The median score on the DASH using the standardized scoring system (i.e. range 0–100) was 33 (IQR 17.5–55.0). Only eight participants (2.4%) had more than three missing responses. A significant difference was observed between DASH scores by gender, with females having a higher (worse) score than males (Mann-Whitney U Z score = − 2.609; p = 0.009).

Rasch analysis

Dash-30

The data from the 30 items were fit to the Rasch measurement model. The initial fit to support a unidimensional structure was poor (Chi-square = 518 (df 120) p = < 0.001; item fit residual SD 2.6; person fit residual SD 1.48; reliability (Person Separation Index (PSI) = 0.97). The local independence assumption was breached by 27 pairs of significant residual correlations (> 0.02), indicating a pattern consistent with two potential domains of activity limitations, incl. Mobility and self-care and impairments of functions such as pain. For example, the items “Recreational activities in which you take some force or impact through your arm, shoulder or hand (e.g., golf, hammering, tennis, etc.)” and “Recreational activities in which you move your arm freely (e.g., playing frisbee, badminton, etc.)” had a residual correlation of 0.472. The residual correlation matrix of the DASH-30 and QuickDASH can be found in the Additional file 1: Tables S1 and S2. Thus, taking the domains identified, two testlets (super items) were created to absorb the local dependency. In this instance fit showed a Chi-Square of 7.6 (df 8); p = 0.469; and reliability (Person Separation Index: PSI) of 0.85, which was satisfactory. However, DIF was present for gender across both testlets (but not for age). Both testlets displayed the same pattern, the DIF was present for the first class interval representing a group with least problems, and this magnitude did not exceed 0.11 logits. For higher levels of problems with, for example PF (testlet containing item 1-20), the magnitude of difference did not exceed 0.04 logits. The graphical interpretation of DIF on the PF testlet (items 1–20) is shown in Fig. 1. As such no action was taken for DIF.
Fig. 1

Differential item functioning by gender for PF testlet

The unidimensional latent estimate so derived accounted for 88% of the total non-error variance and was thus consistent with an ECV indicative of a strong general factor upon which all items load, supporting the unidimensionality of the total set, having accounted for the local dependency in the data. The t-test of the two independent estimates derived from the testlets showed just 2.2% of estimates were significantly different, supporting unidimensionality. This was further supported by the latent correlation of the two testlets being 0.97. No individual showed a positive fit residual > 0.124, (where 2.5 an above would indicate misfit) suggesting that the total score adequately represents the profile of responses in all cases.

QuickDASH

Fit of the 11 items of the QuickDASH to the Rasch model was poor (Chi-Square 221.4 (44); p < 0.001; Item fit residual SD 2.62; Person fit residual SD 1.12; reliability (PSI) = 0.92). Again, the pattern of residual correlations indicated two domains. Applying the two testlet solution, resulted in satisfactory fit (Chi-Square 10.9 (8); p = 0.205; reliability (PSI = 0.84; Cronbach’s α = 0.85). The t-test of the differences between the two testlet derived estimates showed that just 1.6% of estimates were different. The latent estimate indicated an ECV of 0.9. No person had a positive residual greater than 1.0, indicating that the raw score from all 11 items reflects the pattern of responses in all cases.

Measurement precision

The SEM of the DASH (on a score range of 30–150) was 10.47, and that of the QuickDASH (on a score range of 11–55) was 3.95. The SDD of each was 29.0 and 10.9 respectively. This amounted to 24.2% of the operational range of the DASH, and 24.9% of the operational range of the QuickDASH. If the published figures on scale reliability were used, instead of those adjusted to avoid the bias of the local independence assumption, then the SEM of the DASH was 5.03, and that of the QuickDASH was 2.50. Consequently, the SDD would be 13.9 and 6.9, respectively, representing 11.6 and 15.7% of the scale widths.

Exchange between DASH-30 and QuickDASH

Given fit to the Rasch model, any subset of items should give a valid estimate of the person’s upper-limb functioning. Consequently, two new testlets were created, one with the QuickDASH set of 11 items, the second with the remaining 19 items. Fit to the model was excellent (Chi-Square = 6.71 (df 8) p = 0.569; reliability (PSI = 0.96; Cronbach’s α = 0.89). the t-test of the differences between the two testlet derived estimates showed that just 5.3% (95%CI: 2.5–8.2) of estimates were different. The latent estimate indicated an ECV of 0.99, and the latent correlation between the two tests was 1.0. There was no DIF by age or gender. As such a transformation between the two versions of the scale was available. Table 1 gives the raw score (using the 0–4 range giving a total range of 0–120 (add 30 to create the 30–150 range), the standardized score for each scale (range 0–100), and the associated interval scale Rasch metric (range 0–100) through a simple linear rescaling of the ordinal raw score. The latter can be used for parametric analyses, assuming appropriate distributions.
Table 1

Raw score to interval scale Rasch Metric for DASH and QUICKDASH

DASH Raw

DASH

DASH

QuickDASH Raw

QuickDASH

DASH

(0–4)

Standard

Rasch

(0–4)

Standard

Rasch

0

0.0

0.0

0

0.0

0.0

1

0.8

5.5

1

2.3

6.4

2

1.7

9.1

2

4.5

11.5

3

2.5

11.5

3

6.8

15.6

4

3.3

13.3

4

9.1

19.4

5

4.2

14.9

5

11.4

23.0

6

5.0

16.3

6

13.6

26.4

7

5.8

17.6

7

15.9

29.6

8

6.7

18.9

8

18.2

32.7

9

7.5

20.1

9

20.5

35.5

10

8.3

21.3

10

22.7

38.2

11

9.2

22.5

11

25.0

40.7

12

10.0

23.6

12

27.3

43.0

13

10.8

24.7

13

29.5

45.2

14

11.7

25.8

14

31.8

47.3

15

12.5

26.8

15

34.1

49.2

16

13.3

27.9

16

36.4

51.0

17

14.2

28.9

17

38.6

52.8

18

15.0

29.9

18

40.9

54.4

19

15.8

30.8

19

43.2

55.9

20

16.7

31.8

20

45.5

57.4

21

17.5

32.7

21

47.7

58.8

22

18.3

33.6

22

50.0

60.1

23

19.2

34.5

23

52.3

61.4

24

20.0

35.4

24

54.5

62.7

25

20.8

36.2

25

56.8

63.9

26

21.7

37.0

26

59.1

65.1

27

22.5

37.8

27

61.4

66.4

28

23.3

38.6

28

63.6

67.6

29

24.2

39.4

29

65.9

68.9

30

25.0

40.2

30

68.2

70.1

31

25.8

40.9

31

70.5

71.4

32

26.7

41.6

32

72.7

72.8

33

27.5

42.3

33

75.0

74.3

34

28.3

43.0

34

77.3

75.8

35

29.2

43.7

35

79.5

77.3

36

30.0

44.3

36

81.8

79.0

37

30.8

45.0

37

84.1

80.7

38

31.7

45.6

38

86.4

82.6

39

32.5

46.2

39

88.6

84.5

40

33.3

46.8

40

90.9

86.6

41

34.2

47.4

41

93.2

88.9

42

35.0

48.0

42

95.5

91.5

43

35.8

48.6

43

97.7

95.1

44

36.7

49.1

44

100.0

100.0

45

37.5

49.7

   

46

38.3

50.2

   

47

39.2

50.7

   

48

40.0

51.3

   

49

40.8

51.8

   

50

41.7

52.3

   

51

42.5

52.8

   

52

43.3

53.2

   

53

44.2

53.7

   

54

45.0

54.2

   

55

45.8

54.7

   

56

46.7

55.1

   

57

47.5

55.6

   

58

48.3

56.0

   

59

49.2

56.5

   

60

50.0

56.9

   

61

50.8

57.3

   

62

51.7

57.8

   

63

52.5

58.2

   

64

53.3

58.6

   

65

54.2

59.1

   

66

55.0

59.5

   

67

55.8

59.9

   

68

56.7

60.3

   

69

57.5

60.7

   

70

58.3

61.2

   

71

59.2

61.6

   

72

60.0

62.0

   

73

60.8

62.4

   

74

61.7

62.9

   

75

62.5

63.3

   

76

63.3

63.7

   

77

64.2

64.1

   

78

65.0

64.6

   

79

65.8

65.0

   

80

66.7

65.5

   

81

67.5

65.9

   

82

68.3

66.3

   

83

69.2

66.8

   

84

70.0

67.3

   

85

70.8

67.7

   

86

71.7

68.2

   

87

72.5

68.7

   

88

73.3

69.2

   

89

74.2

69.6

   

90

75.0

70.2

   

91

75.8

70.7

   

92

76.7

71.2

   

93

77.5

71.7

   

94

78.3

72.2

   

95

79.2

72.8

   

96

80.0

73.3

   

97

80.8

73.9

   

98

81.7

74.5

   

99

82.5

75.1

   

100

83.3

75.7

   

101

84.2

76.3

   

102

85.0

76.9

   

103

85.8

77.6

   

104

86.7

78.2

   

105

87.5

78.9

   

106

88.3

79.6

   

107

89.2

80.3

   

108

90.0

81.0

   

109

90.8

81.8

   

110

91.7

82.5

   

111

92.5

83.3

   

112

93.3

84.1

   

113

94.2

85.0

   

114

95.0

86.0

   

115

95.8

87.1

   

116

96.7

88.3

   

117

97.5

89.8

   

118

98.3

91.8

   

119

99.2

95.0

   

120

100.0

100.0

   

An exchange between the two scales can be obtained by looking at either the raw score or the standardized score, and the associated Rasch metric. For example, consider a standardized score on the DASH of 40.0, and its associated Rasch metric of 51.3. The nearest equivalent Rasch value on the QuickDASH is 51.0, and this is associated with a QuickDASH standardized score of 36.4. Thus, a DASH standardized score of 40.0 is equal to a QuickDASH standardized score of 36.4.

Discussion

Taking the basic 30-item (DASH) and 11-item (QuickDASH) scales, and applying modern test theory analyses showed that neither versions of the DASH satisfied model expectations. Multidimensionality, and a breach of the local (response) independent assumptions, were evident in both cases. Thus, the initial findings are consistent with the previously published psychometric evidence for the scales [8, 9, 10, 11, 12, 13, 14, 15, 16, 26]. Yet in both scales, clues exist as to why these findings may misinterpret the construct validity. This lies in the breach of the local independence assumption, whereby clusters of items can be found with varying degrees of residual correlation. In clinical scales this is quite common as, for example, in rehabilitation, health professionals may need to know whether or not a patient can dress their upper body, and their lower body, despite psychometric evidence showing a high residual correlation between the two activities. This type of effect of the breach of the local independence solution has been shown to both inflate classical reliability and to be corrosive for interpretation of the Rasch model [27]. Historically this has led to potential misinterpretation of the construct validity of well-known scales [28]. For example, it can make thresholds appeared disordered, and in the current analysis, of the 5 thresholds that were disordered, four were locally dependent with one or more other items. Local dependency can also drive multidimensionality, which was also the case here. Thus, the absence of applying suitable methods to accommodate this dependency may address some of the challenges identified in previously published findings about the scales.

Consequently, having applied the testlet approach to accommodating local response dependency in the DASH and QuickDASH item sets, within their frame of reference of rheumatoid arthritis, their total (ordinal) scores are shown to be valid given they both satisfy the Rasch model assumptions. It is possible that the frame of reference is crucial, as the DASH and QuickDASH are generic scales, and applied across a wide range of conditions. Thus previous findings in other conditions may reflect differences in how the scale works across different conditions [29]. However, a proper comparison cannot be made until the local dependency issue is firmly dealt with in those other conditions. Failure to take this into account may lead to erroneous conclusions about the construct validity of the scale, including the interpretation of multidimensionality driven by the local dependency. It may also, as shown above, lead to erroneous conclusions about the SEM and SDD of a scale. An inflated reliability will make a scale look much better than it really is, with a lower SEM and consequent SDD.

While the scale or level of detail is reduced in the testlet-based analysis, although this is consistent with how the scales are used in practice, examination at the item level in diagnostic mode is also informative. There is little doubt that the item ‘Tingling’ is a major threat to fit within Rasch model framework. In both the DASH and QuickDASH Rasch analyses, this item at the individual item level, showed a magnitude of misfit much greater than all other items, consistent with the findings of earlier studies [8]. Furthermore, unlike most other items which showed local dependency with one or more other items, ‘tingling’ showed no such dependency at all. Yet when absorbed into the more abstract ‘Impairment of Functions’ testlet, along with other items such as pain, the testlet itself shows no signs of misfit. So, perhaps the effect of the ‘tingling’ item is mitigated at this level, as indeed it must be at the whole test level. Thus, given the focus of use is always on the whole scale score (or subscales when present), the question arises as to the appropriate focus for analysis. The two testlet solution, as applied here, is almost at the whole scale level, similar to the classical test theory approach. Indeed, recent approaches in scale banking, whereby full scale scores for a particular construct are used as items, suggest a different approach to understanding of scale construct validity, albeit still from the Rasch Measurement Theory perspective [30]. Here the emphasis is upon the scale score as a whole, co-calibrated with other total scores from other scales measuring the same construct, which technically does not even need the items, as the total score becomes the item in such an approach. As such the focus of analysis becomes crucial. Where existing scales are being considered, their total (or subscale) score can be the initial focus, although if this fails, then the analysis at the item level sharpens as the reason for failure is explored. When new scales are being developed, then the focus should be reversed, and each individual item considered in the context of all others, and considered for inclusion in the final version of the scale.

While classical test theory approaches have failed to confirm the factor structure of the scales, it is possible that a bi-factor solution may have worked, as the ‘A’ value in the RUMM2030 software is equivalent to the ECV in the bi-factor literature, having been derived from the difference in the reliability estimates between the item- and testlet-based solutions [24]. As in the current study the ECV was below 1, it does suggest that some (although not much) of the variance was discarded in the latent estimate, which would populate the secondary factors in the classical bi-factor model. Nevertheless, both the DASH and QuickDASH had ECV values consistent with a dominant unidimensional first factor, and the associated t-test confirmed that the latent estimates so derived were unidimensional [31].

One caveat to the above analysis is the fact that a two testlet solution is in fact just two items, and this may affect the power of the test of fit to the model. However, these two ‘items’ have a considerable score range and the resulting indicator on the RUMM2030 program showed that the power of the test of fit was ‘excellent’.

Conclusion

The DASH and QuickDASH have been shown to satisfy Rasch model expectations in a RA population, having accounted for local dependency of items through a testlet approach. Consequently, their raw (and standardized) scores can be deemed a sufficient statistic at the ordinal level for upper limb functioning. Rasch-transformed interval scaled estimates are available for the calculation of change score and other mathematical and parametric procedures. Data from other conditions will need to be re-analyzed to ensure that the breach of the local independence solution is adequately dealt with before other aspects are considered. Calculations of SEM and SDD for PROMs should always be based upon an unbiased estimate of reliability, having taken care of the inflation caused by a breach of the local independence assumption associated with all summative scales.

Notes

Acknowledgements

The authors wish to thank: all the study participants for their time in completing questionnaires; the expert panel members for their time in supporting the translation process: John Grogan (translator), Kris Hollands (Canadian-English speaking health researcher, University of Salford); Angela Jackson (Rheumatology occupational therapist, Stepping Hill Hospital, Stockport); Graham McLeish (English language expert, Services for Export and Language, University of Salford); Robert Peet and Kate Woodward-Nutt, Centre for Health Sciences, University of Salford, for assistance with data collection and data entry; and all the Principal Investigators, rheumatology and research nurses assisting with participant recruitment and study support at the participating sites: Prof Terence O’Neill, Ann McGovern, Jennifer Green, Angharad Walker (Salford Royal Hospital); Prof Ian Bruce, Lindsey Barnes, Elizabeth Beswick, Sarah Evans (Manchester Royal Infirmary); Dr. Leena Dass, Dr. Sophia Naz, Lorraine Lock (North Manchester General Hospital); Dr. Chris Deighton, Alison Booth, Jo Morris (Royal Derby Hospital); Prof David Walsh, Debbie Wilson, Jayne Smith (Kings Mill Hospital); Dr. Chetan Mukhtyer, Loretta Dean, Susan Rowell (Norfolk and Norwich Hospitals); Dr. Bela Szenbenyi, Carol Gray (Diana Princess of Wales, Grimsby); Dr. Mike Green, Anne Gill, Lisa Carr (York Hospital); Dr. Kirsten Mackay, Julie Easterbrook, Liz Burnett (Torbay Hospital); Dr. Mike Green, Alison Miernik, Rachel Bailey-Hague (Harrogate District Hospital); Dr. Atheer Al-Ansari, Jayne Edwards, Julia Nicholas (Robert Jones & Agnes Hunt Hospital, Oswestry); Dr. Wendy Holden, Janet Cushnaghan, Angie Dempster, Hayley Paterson (Basingstoke and North Hampshire Hospital); Mr. David Johnson, Lindsey Barber, Jan Smith (Stepping Hill Hospital); Dr. Karen Douglas, Lucy Kadiki, Chitra Ramful, Daljit Kaur (Russell Hall Hospital, Dudley); Dr. Anca Ghiurlic, Christine Graver (Royal Hampshire Hospital, Winchester); Dr. Frank McKenna, Jane McConiffe (Trafford Hospitals); Dr. Sophia Naz and Lorraine Lock (Fairfield Hospital).

Funding

This project was funded by Versus Arthritis (formerly Arthritis Research UK) [Grant No: 20031]. NHS service support costs were secured from the Greater Manchester Comprehensive Local Research Network (the Lead CLRN). The views expressed of the authors are not necessarily of those of the NHS or Versus Arthritis (formerly Arthritis Research UK). The Versus Arthritis (formerly Arthritis Research UK) had no role in the design of this study, its execution, analyses, interpretation of the data, or decision to submit results, apart from study oversight.

Availability of the data and materials

Data and materials can be accessed through a request to the lead author.

Authors’ contributions

AH, AT and ST designed the study. AH and YP were responsible for data acquisition. BP and AT conducted the statistical analyses and interpretation. BP, AT and AH led the writing up and all authors approved the final manuscript.

Ethics approval and consent to participate

Ethical approval was obtained from the NRES Committee North West (Greater Manchester North) [12/NW/0841] and the University of Salford Health Sciences Research Ethics Panel prior to the start of the study. Approvals from the Research and Development departments were obtained at each site. All participants provided informed, written consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12891_2019_2414_MOESM1_ESM.docx (21 kb)
Additional file 1: Table S1. Local dependency amongst items of the DASH-30. Table S2. Local dependency amongst items of the QuickDASH. (DOCX 21 kb)

References

  1. 1.
    Zeng SY, Gong Y, Zhang YP, Chen SB, Chen JY, Lin CQ, Peng JH, Hou ZD, Zhong JQ, Liang HJ, Huang GH, Wang DM, Lai HY, Li LP, Zeng QY. Changes in the Prevalence of Rheumatic Diseases in Shantou, China, in the Past Three Decades: A COPCORD Study. PLoS One. 2015;10(9):e0138492.CrossRefGoogle Scholar
  2. 2.
    Wright AR, Shi XA, Busby-Whitehead J, Jordan JM, Nelson AE. The Prevalence of Neck and Shoulder Symptoms and Associations with Comorbidities and Disability: The Johnston County Osteoarthritis Project. Myopain. 2015;23(1–2):34–44.CrossRefGoogle Scholar
  3. 3.
    Badley EM, Tennant A. The changing profile of joint troubles with age: findings from a postal survey of the population. Ann Rheum Dis. 1992;51:366–71.CrossRefGoogle Scholar
  4. 4.
    Penta M, Tesio L, Arnould C, Zancan A, Thonnard JL. The ABILHAND questionnaire as a measure of manual ability in chronic stroke patients: Rasch-based validation and relationship to upper limb impairment. Stroke. 2001;32(7):1627–34.CrossRefGoogle Scholar
  5. 5.
    Preston N, Horton M, Levesley M, Mon-Williams M, O'Connor RJ. Development of a parent-reported questionnaire evaluating upper limb activity limitation in children with cerebral palsy. Physiother Res Int. 2017.  https://doi.org/10.1002/pri.1684 [Epub ahead of print].
  6. 6.
    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(6):602–8.CrossRefGoogle Scholar
  7. 7.
    Kennedy CA, Beaton DE. A user’s survey of the clinical application and content validity of the DASH (Disabilities of the Arm, Shoulder and Hand) outcome measure. J Hand Ther. 2017;30:30–40.CrossRefGoogle Scholar
  8. 8.
    Franchignoni F, Giordano A, Sartorio F, Vercelli S, Pascariello B, Ferriero G. Suggestions for refinement of the Disabilities of the Arm, Shoulder and Hand Outcome Measure (DASH): a factor analysis and Rasch validation study. Arch Phys Med Rehabil. 2010;91(9):1370–7.CrossRefGoogle Scholar
  9. 9.
    Lehman LA, Woodbury M, Velozo CA. Examination of the factor structure of the Disabilities of the Arm, Shoulder, and Hand questionnaire. Am J Occup Ther. 2011;65(2):169–78.CrossRefGoogle Scholar
  10. 10.
    Dalton E, Lannin NA, Laver K, Ross L, Ashford S, McCluskey A, Cusick A. Validity, reliability and ease of use of the disabilities of arm, shoulder and hand questionnaire in adults following stroke. Disabil Rehabil. 2016;39:1–8 [Epub ahead of print].Google Scholar
  11. 11.
    Forget NJ, Jerosch-Herold C, Shepstone L, Higgins J. Psychometric evaluation of the Disabilities of the Arm, Shoulder and Hand (DASH) with Dupuytren's contracture: validity evidence using Rasch modeling. BMC Musculoskelet Disord. 2014;15:361.CrossRefGoogle Scholar
  12. 12.
    Baker K, Barrett L, Playford ED, Aspden T, Riazi A, Hobart J. Measuring arm function early after stroke: is the DASH good enough? J Neurol Neurosurg Psychiatry. 2016;87(6):604–10.CrossRefGoogle Scholar
  13. 13.
    Braitmayer K, Dereskewitz C, Oberhauser C, Rudolf KD, Coenen M. Examination of the Applicability of the Disabilities of the Arm, Shoulder and Hand (DASH) Questionnaire to Patients with Hand Injuries and Diseases Using Rasch Analysis. Patient. 2016.  https://doi.org/10.1007/s40271-016-0212-x. [Epub ahead of print]
  14. 14.
    Gabel CP, Yelland M, Melloh M, Burkett B. A modified QuickDASH-9 provides a valid outcome instrument for upper limb function. BMC Musculoskelet Disord. 2009;10:161.CrossRefGoogle Scholar
  15. 15.
    Fayad F, Lefevre-Colau MM, Gautheron V, Macé Y, Fermanian J, Mayoux-Benhamou A, Roren A, Rannou F, Roby-Brami A, Revel M, Poiraudeau S. Reliability, validity and responsiveness of the French version of the questionnaire Quick Disability of the Arm, Shoulder and Hand in shoulder disorders. Man Ther. 2009;14(2):206–12.CrossRefGoogle Scholar
  16. 16.
    Franchignoni F, Ferriero G, Giordano A, Sartorio F, Vercelli S, Brigatti E. Psychometric properties of QuickDASH - a classical test theory and Rasch analysis study. Man Ther. 2011;16(2):177–82.CrossRefGoogle Scholar
  17. 17.
    Rasch G. Probabilistic models for some intelligence and attainment tests, (Copenhagen, Danish Institute for Educational Research). [Expanded edition (1980) with foreword and afterword by B.D. Wright]. Chicago: The University of Chicago Press; 1960.Google Scholar
  18. 18.
    Andrich D, Sheridan BS, Luo G. RUMM2030: an MS Windows computer program for the analysis of data according to Rasch unidimensional models for measurement. Perth: RUMM Laboratory; 2015.Google Scholar
  19. 19.
    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? Arthr Rheum. 2007;57:1358–62.CrossRefGoogle Scholar
  20. 20.
    da Rocha NS, Chachamovich E, de Almeida Fleck MP, Tennant A. An introduction to Rasch analysis for Psychiatric practice and research. J Psychiatr Res. 2013;47(2):141–8.CrossRefGoogle Scholar
  21. 21.
    Marais I, Andrich D. Formalizing Dimension and Response Violations of Local Independence in the Unidimensional Rasch Model. J Appl Meas. 2008;9:200–15.PubMedGoogle Scholar
  22. 22.
    Christensen K-B, Makransky G, Horton M. Critical Values for Yen’s Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations. Appl Psychol Meas. 2016;41:1–17.Google Scholar
  23. 23.
    Wainer H, Kiely G. Item clusters and computer adaptive testing: A case for testlets. J Educ Measurement. 1987;24:185–202.CrossRefGoogle Scholar
  24. 24.
    Andrich D. Components of variance of scales with a bifactor subscale structure from two calculations of alpha. Educ Meas Issues Pract. 2016;35:25–30.CrossRefGoogle Scholar
  25. 25.
    Rodriguez A, Reise SP, Haviland MG. Evaluating Bifactor Models: Calculating and Interpreting Statistical Indices. Psychol Methods. 2016;21(2):137–50.CrossRefGoogle Scholar
  26. 26.
    Rodrigues J, Zhang W, Scammell B, Russell P, Chakrabarti I, Fullilove S, Davidson D, Davis T. Validity of the Disabilities of the Arm, Shoulder and Hand patient-reported outcome measure (DASH) and the QuickDASH when used in Dupuytren’s disease. J Hand Surg Eur Vol. 2016;41(6):589–99.CrossRefGoogle Scholar
  27. 27.
    Lipscomb J, Gotay CC, Snyder C, editors. Outcome assessment in Cancer. Cambridge: Cambridge University Press; 2004.Google Scholar
  28. 28.
    Lundgren Nilsson Å, Tennant A. Past and present issues in Rasch analysis: the functional independence measure (FIM™) revisited. J Rehabil Med. 2011;43(10):884–91.CrossRefGoogle Scholar
  29. 29.
    Jerosch-Herold C, Chester R, Shepstone L. Rasch Model Analysis Gives New Insights Into the Structural Validity of the Quick-DASH in Patients With Musculoskeletal Shoulder Pain. J Orthop Sports Phys Ther. 2017:1–20. doi:  https://doi.org/10.2519/jospt.2017.7288 . [Epub ahead of print]
  30. 30.
    Andrich D. The polytomous Rasch model and the equating of two instruments. In: Christensen K_B, Kreiner S, Mesbah M, editors. Rasch Models in Health. New York: John Wiley & sons; 2013. p. 163–96.CrossRefGoogle Scholar
  31. 31.
    Reise SP. The rediscovery of bifactor measurement models. Mulltivariate Behav Res. 2012;47(5):667–96.CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Swiss Paraplegic ResearchNottwilSwitzerland
  2. 2.Department of Health Sciences and Health PolicyUniversity of LucerneLucerneSwitzerland
  3. 3.ICF Research Branch, a cooperation partner within the WHO Collaborating Centre for the Family of International Classifications in Germany (at DIMDI)NottwilSwitzerland
  4. 4.Faculty of Applied Health and Social SciencesTechnical University of Applied Sciences RosenheimRosenheimGermany
  5. 5.Centre for Health Sciences Research, School of Health and SocietyUniversity of SalfordSalfordUK
  6. 6.Division of Nursing, Midwifery & Social WorkUniversity of ManchesterManchesterUK
  7. 7.Mid Cheshire NHS Trust Foundation HospitalsLeighton HospitalCreweUK

Personalised recommendations