Journal List > Yonsei Med J > v.59(5) > 1095124

TOOLS
Moon, Park, Song, Kim, Park, Nam, Kim, Na, Lee, Hwang, and Park: Common Upper Extremity Disorders and Function Affect Upper Extremity-Related Quality of Life: A Community-Based Sample from Rural Areas
Original Article
Orthopedics

Yonsei Medical Journal 2018; 59(5): 669-676.

Published online: 1 June 2018

DOI: https://doi.org/10.3349/ymj.2018.59.5.669

Common Upper Extremity Disorders and Function Affect Upper Extremity-Related Quality of Life: A Community-Based Sample from Rural Areas

Dong Kyu Moon1, Young Jin Park1, Sang-Youn Song1, Mi Ji Kim2, Jin Sung Park1, Dae-Cheol Nam1, Dong-Hee Kim1, Jae-Boem Na3, Sang-Il Lee4, Sun Chul Hwang1, Ki-Soo Park2

1Department of Orthopedic Surgery, Institute of Health Sciences, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea.

2Department of Preventive Medicine, Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju, Korea.

3Department of Radiology, Institute of Health Sciences, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea.

4Department of Internal Medicine, Institute of Health Sciences, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea.

Co-corresponding author: Sun Chul Hwang, MD, PhD, Department of Orthopedic Surgery, Institute of Health Sciences, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, 79 Gangnam-ro, Jinju 52727, Korea. Tel: 82-55-750-8102, Fax: 82-55-754-0477, hscspine@hanmail.net

Co-corresponding author: Ki-Soo Park, MD, PhD, Department of Preventive Medicine, Institute of Health Sciences, Gyeongsang National University School of Medicine, 79 Gangnam-ro, Jinju 52727, Korea. Tel: 82-55-772-8095, Fax: 82-55-772-8099, parkks@gnu.ac.kr

Received 20 February 2018       Revised 3 May 2018       Accepted 3 May 2018

© Copyright: Yonsei University College of Medicine 2018

Yonsei University College of Medicine

(open-access, http://creativecommons.org/licenses/by-nc/4.0/):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

Upper extremity musculoskeletal disorders (UEMDs), such as rotator cuff tear, epicondylitis, and hand osteoarthritis, have a negative impact on quality of life (QOL). In this study, we evaluated the prevalence of rotator cuff tear, lateral and medial epicondylitis, and hand osteoarthritis in the dominant side and the impact of these UEMDs on the disabilities of the arm, shoulder, and hand (DASH) outcome measure, which assesses upper extremity-related QOL.

Materials and Methods

In 2013–2015, 987 participants from rural areas completed a questionnaire and underwent physical examinations, laboratory tests, simple radiographic evaluations of bilateral upper extremities, and magnetic resonance imaging studies of bilateral shoulders. Based on data from these participants, researchers evaluated DASH and performed a functional assessment of each region of the dominant side and related UEMDs.

Results

The prevalences of epicondylitis, rotator cuff tear, and hand osteoarthritis were 33.7%, 53.4%, and 44.6%, respectively. Univariate regression analysis results revealed that epicondylitis, epicondylitis+rotator cuff tear, epicondylitis+hand osteoarthritis, and epicondylitis+rotator cuff tear+hand osteoarthritis were significantly associated with DASH score. Multiple regression analysis, including DASH, UEMD, and regional functional assessments, showed that only epicondylitis and epicondylitis+rotator cuff tear were associated with DASH score.

Conclusion

Epicondylitis significantly affected QOL, while other UEMDs, such as hand osteoarthritis and rotator cuff tear, had no significant impact. When a patient's QOL is affected by a UEMD, there is an increased possibility of the simultaneous presence of other UEMDs.

Keywords: Upper extremities, musculoskeletal disorders, function, quality of life

INTRODUCTION

Upper extremity musculoskeletal disorders (UEMDs) are common in the general population. Point prevalence rates for UEMDs range from 2–53%, while 12-month prevalence rates range from 2–41%, depending on the setting, definition, and classification used.12 Upper extremity pain can cause substantial disability, need for health care, and loss of work time.3 Studies in the general population, as well as those involving workers, report that UEMDs have a negative impact on quality of life (QOL).45
The UEMDs include cervical disc disease, myelopathy, rotator cuff disease, lateral and medial epicondylitis, carpal tunnel syndrome, ulnar nerve entrapment syndrome, osteoarthritis (OA), myofascial pain syndrome, and other conditions.6 In a recently published systematic review, the most frequently identified work-related UEMD was rotator cuff disease.7 A rotator cuff tear not only causes pain, but can lead to declines in muscle strength and shoulder mobility. These symptoms can have a negative impact on activities of daily living, work, and leisure activities.8 Other common UEMDs include lateral and medial epicondylitis and hand OA, which have also been reported to have a negative impact on upper extremity function and activities of daily living.910
However, diagnostic criteria for UEMDs have varied among studies, and inconsistent results have been reported.111 Furthermore, most previous studies have been based on questionnaires, with or without physical examination. There is little research on how the occurrence of simultaneous UEMDs affects upper extremity-related QOL. In addition, there is no literature about the prevalence of UEMD on the dominant side and its effect on upper extremity-related QOL in the general population.
The purpose of this study was to evaluate the prevalence of rotator cuff tear, epicondylitis, and hand OA on the dominant side and to evaluate the impact of these UEMDs on QOL using the disability of arm, shoulder, and hand (DASH) outcome measure for assessing upper extremity-related QOL.

MATERIALS AND METHODS

Selection of subjects

This study was conducted as part of the farmers' UEMD investigation (Namgaram study). Participants in this study were enrolled from members of rural communities in Gyeongsangnam- do, Korea, who were over 40 years of age. After selecting six villages in the rural area, the survey was conducted among villagers who agreed to participate. The study was conducted from July 2013 to November 2015. Researchers visited the six villages and informed the population about the purpose of the study and related processes, and those who agreed to participate were enrolled. Inclusion criteria for the study were 1) literacy level sufficient to complete the questionnaire, 2) unaffected activities of daily living, 3) being socially active, and 4) not receiving medical services for UEMDs within the previous 12 months. Each participant completed a questionnaire and underwent physical examination, laboratory tests, simple radiographic evaluations of bilateral upper extremities, and bilateral shoulder magnetic resonance imaging (MRI) studies. Three research nurses knowledgeable in the purpose of the study and related data collection procedures interviewed each participant using a questionnaire. The total number of participants completing all aspects of the study was 987, including 295 non-agricultural workers (29.88%) and 692 agricultural workers (70.11%). The study was approved by the Institutional Review Board of Gyeongsang National University (IRB No. GNUH 2015-02-001).

Demographic and health-related characteristics

Demographic information collected from study participants included gender, age, marital status (dichotomized as “yes” and “no”), and educational level (categorized as “less than elementary school,” “middle school,” and “more than high school”). The dominant side was categorized as “right,” “left,” or “both.” If the participant was ambidextrous, one side was randomly selected for study regardless of the presence of a UEMD.
Hypertension, diabetes mellitus, waist circumference, depression, and laboratory test findings were also considered in the study. The presence of comorbidities was evaluated through history taking, blood pressure (BP) measurement, and laboratory tests. Hypertension was diagnosed based on medical history and detection of BP above 140 mm Hg systolic or above 90 mm Hg diastolic pressure. BP was measured twice in a seated position after ≥5 minutes of rest using an automatic electronic sphygmomanometer (HBP-1300, Omron Healthcare®, Kyoto, Japan).12 A prior diagnosis of diabetes was accepted for study purposes. New cases of diabetes were also diagnosed upon confirming serum level of HbA1c higher than 6.5%.13 Waist circumference was measured parallel to the midline between the lowest border of the rib cage and the iliac crest at the end of a normal expiration, with participants standing with their feet 25–30 cm apart.14 Waist circumference was measured using a measuring tape. Depressive symptoms were measured with the Patient Health Questionnare-2 (PHQ-2), a two-item questionnaire that uses a four-point scale (0=not at all to 3=nearly every day).15 A higher score indicates greater depression. Laboratory tests performed for study purposes included hemoglobin and total cholesterol.

Clinical evaluation

We evaluated DASH and performed a functional assessment of each region of the dominant side and related UEMDs based on data from study participants. Prior to evaluation of UEMDs, functional assessment of each region was performed. Shoulder joints were assessed with the Constant-Murley score (CMS), elbow joints were assessed with the Mayo elbow performance score (MEPS), and hands were assessed with the Australian/Canadian Hand Osteoarthritis Index (AUSCAN).161718 Upper extremity- related QOL was assessed based on DASH,19 a 30-item questionnaire composed of 21 physical function items, six symptom items, and three social/role function items. Each item is rated on a five-point Likert scale (1=least disability to 5= most disability). All 30 response scores were added together, producing a raw score, which was then transformed into a maximum score of 100. A higher score indicates greater disability. Cronbach's α was 0.95 in this study. CMS, MEPS, and AUSCAN scores were assessed by two experienced orthopedic surgeons, each with more than 10 years of experience.

Diagnostic criteria

For the purposes of the current study, rotator cuff tears included partial and full thickness tears and were diagnosed by MRI. A 3.0 Tesla MRI system (Ingenia; Philips Medical Systems®, Eindhoven, the Netherlands) was used to obtain MRI images, which included axial, sagittal, and coronal T2-weighted images [repetition time (TR)/echo time (TE)=2800/60]; coronal T1-weighted images (TR/TE=500/20); and coronal fat-saturated fast spin-echo images. Slice thickness was 3 mm without gaps. In MRI findings, a partial-thickness tear of the rotator cuff was diagnosed when a focus of high signal intensity was apparent on T2-weighted images in either the bursa or the joint surface of the tendon that did not involve the entire thickness of the cuff. A full thickness tear of the rotator cuff was diagnosed when focal or diffuse high signal intensity on T2-weighted images extended from the bursa to the joint surface of the tendon or when the tendinous portion of the cuff was not visualized.20 MRI scans were interpreted by a radiologic specialist with 20 years of experience and an orthopedic surgeon with 15 years of experience. Among total participants, 438 (44.4%) had partial thickness tears and 89 (9.0%) had full thickness tears.
Epicondylitis included lateral and medial epicondylitis. Epicondylitis was defined as self-reported pain at either epicondyle area on two or more days in the previous month and one of the following during the exam: presence of pain at the lateral epicondyle with resisted active wrist extension, pain at the medial epicondyle with resisted active wrist flexion, or tenderness to palpation over the medial and lateral epicondyle regions during physical exam.21 Physical examination was performed by an orthopedic surgeon and a rheumatologist with more than 10 years of experience each. Epicondylitis was present in 333 (33.7%) of the total participants.
For diagnosis of hand OA, anterior-posterior plain radiographs of both hands were obtained from all participants. The second to fifth distal interphalangeal, proximal interphalangeal, first to fifth metacarpophalangeal, thumb interphalangeal, and first carpometacarpal joints for each hand were graded for OA using the modified Kellgren-Lawrence (KL) scale. This scale assesses the existence and severity of osteophytes (OPs), joint space narrowing (JSN), sclerosis, and erosion. The modified KL scale was graded from 0 to 4, where 0=no OA; 1=questionable OPs and/or JSN; 2=definite small OPs and/or mild JSN; 3=moderate OPs and/or moderate JSN, sclerosis, and possible presence of erosion; and 4=large OPs and/or severe JSN, sclerosis, and possible presence of erosions.22 Radiological hand OA was defined as a case wherein the result of plain radiography was determined to be higher than KL grade 2 for at least one joint.23 These radiographs were interpreted by a musculoskeletal radiologic specialist with 20 years of experience in radiographic evaluation and a rheumatologist with 10 years of experience in radiographic evaluation. Hand OA was present in 440 (44.6%) of all participants.

Statistical analysis

We assessed differences in each UEMD in terms of demographics and existing comorbidities using the independent-samples t-test for continuous variables. For categorical variables, we used the chi-square test for analysis. Continuous variables are described as mean and standard deviation and categorical variables as percentage.
Differences in DASH score according to UEMDs were evaluated in two ways. First, differences in DASH score according to the presence or absence of epicondylitis, rotator cuff tear, and hand OA was examined. Second, participants were classified as follows: 1) participants without UEMD, 2) participants with only epicondylitis, 3) participants with only rotator cuff tear, 4) participants with only hand OA, 5) participants with epicondylitis and rotator cuff tear (epicondylitis+rotator cuff tear), 6) participants with epicondylitis and hand OA, 7) participants with rotator cuff tear and hand OA, and 8) participants with all three UEMDs (epicondylitis+rotator cuff tear+ hand OA). The significance of differences in DASH scores among these groups was evaluated using t-test or ANOVA, and only significant factors were adjusted. The Tukey method was utilized for post-hoc testing.
Multiple regression analysis was performed to determine the association between UEMDs and regional functional assessment scores. Each group with at least one disease based on the absence of UEMD was dummy variable. Regression analysis was used to identify how each variable was associated with DASH score. All regression analyses were adjusted for demographics and comorbidities that differed in each disease as primitive variables. Univariate analysis was performed to determine the association between DASH and UEMD or regional functional assessment score. Multiple regression analysis was performed to determine the association between DASH and UEMD and each regional functional assessment score. Multiple regression analysis was also used to determine the association between DASH and UEMD and all regional functional assessment scores. All regression analyses were assessed for multicollinearity.
IBM® SPSS Statistics software, version 23.0 (IBM Corp., Armonk, NY, USA) was used to analyze the data, and p<0.05 was considered to indicate statistical significance.

RESULTS

Population demographics and comorbidities

Findings related to study population demographics and comorbidities and differences according to disease are shown in Table 1. Of all participants, 475 (48.1%) were male and 512 (51.9%) were female. The mean age of participants was 59.8±8.4 years.
The prevalences of epicondylitis, rotator cuff tear, and hand OA among all subjects were 33.7%, 53.4%, and 44.6%, respectively. Epicondylitis was significantly associated with gender, mean age, education level, depression, waist circumference, hemoglobin, and HbA1C, while rotator cuff tear was significantly associated with age, education level, hypertension, and waist circumference. Hand OA was significantly associated with gender, age, marital status, education level, depression, hypertension, waist circumference, hemoglobin, and total cholesterol.

UEMD and DASH scores

The DASH scores according to UEMD are shown in Table 2. There were significant DASH score differences according to the presence or absence of epicondylitis, rotator cuff tear, and hand OA. The same results were obtained when adjusting for primitive variables, including age, gender, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and total cholesterol. Each of these factors was associated with significant differences in outcome according to UEMD. DASH scores also differed between participants classified with only one UEMD and those with multiple UEMDs. DASH scores also differed in the four subgroups when adjusted for primitive variables in post-hoc tests (Fig. 1).

Regression analysis

Multiple regression analysis results for the associations between UEMDs and regional functional assessment scores are presented in Table 3. Only rotator cuff tear, epicondylitis+rotator cuff tear, epicondylitis+hand OA, and epicondylitis+rotator cuff tear+hand OA were significantly associated with CMS. Epicondylitis, epicondylitis+rotator cuff tear, epicondylitis+hand OA, and epicondylitis+rotator cuff tear+hand OA were significantly associated with MEPS. Except for rotator cuff tear, all disease categories were associated with AUSCAN. Univariate regression analysis results of the associations between DASH score and UEMD are shown in Table 4. Only epicondylitis, epicondylitis+rotator cuff tear, epicondylitis+ hand OA, and epicondylitis+rotator cuff tear+hand OA were significantly associated with DASH score. All regional functional assessments were significantly associated with DASH score, even when adjusted for primitive variables and each UEMD. Multiple regression analysis results evaluating associations between DASH scores, UEMD, and regional functional assessment scores are shown in Table 5. Only epicondylitis, epicondylitis+rotator cuff tear, CMS, and AUSCAN scores were associated with DASH score.

DISCUSSION

In this study, DASH scores were higher in the presence of any UEMD than in the absence of any UEMD. The reported minimal clinically important difference for the DASH outcome measure is 10–10.81.2425 On the basis of this, differences in DASH scores according to the presence or absence of any UEMD were not clinically significant for UEMDs as evaluated in this study. However, DASH scores for epicondylitis+hand OA, epicondylitis+rotator cuff tear, and epicondylitis+rotator cuff tear+hand OA were 9.7–10.9 higher than in normal participants. Furthermore, according to multivariate regression analysis incorporating UEMD and regional functional assessment scores, CMS was more strongly associated with epicondylitis+rotator cuff tear than with only rotator cuff tear, and was most strongly associated with epicondylitis+rotator cuff tear+hand OA. Even in the absence of rotator cuff tear, CMS was associated with epicondylitis+hand OA. MEPS was associated with epicondylitis+rotator cuff tear, epicondylitis+hand OA, and epicondylitis+rotator cuff tear+hand OA, as well as with epicondylitis only. AUSCAN was associated with all UEMDs, with the exception of rotator cuff tear. Constant, et al.26 stated that strength could be underestimated during evaluation of CMS in the presence of elbow disease. Our findings suggest that regional functional assessments can be affected by disease in an adjacent area. This seems clinically significant and suggests that, when clinically significant loss of upper limb function occurs, UEMDs, including epicondylitis, are likely to be present.
All regional functional scores were associated with the DASH outcome measure, even when the analysis was adjusted for UEMDs. Multiple regression analysis showed that epicondylitis and epicondylitis+rotator cuff tear were associated with DASH score. CMS and AUSCAN were also associated with DASH score. The difference in the effects of epicondylitis seen in this study versus in previous studies could be due to differences in diagnostic methods. We diagnosed epicondylitis by physical examination, although rotator cuff tear and hand OA were diagnosed radiologically. Epicondylitis was present in a large number of patients with existing symptoms; however, rotator cuff tear and hand OA were often present in asymptomatic patients. Previous studies reported that rotator cuff tear and hand OA mostly presented with no symptoms.272829 Rotator cuff tear and hand OA are degenerative diseases, and age is the most important risk factor.293031 In conclusion, rotator cuff tear and hand OA are conditions induced by degenerative changes associated with the aging process. Therefore, the progression of rotator cuff tear and hand OA occurs gradually, leading to compensation for other normal tissues, and the disease itself is less likely to immediately affect QOL.3233 For this reason, rotator cuff tear and hand OA do not directly affect DASH. However, the presence of symptoms due to rotator cuff tear or hand OA had a negative impact on CMS and AUSCAN scores, which could affect DASH. In contrast, the primary cause of epicondylitis has been reported to be overuse; thus, the condition is common in the 40- to 50-year-old group.34 Therefore, if epicondylitis is not significantly affected by other factors and is due to overuse, symptoms are likely to be immediately evident and directly affect QOL. In this study, epicondylitis with symptoms was more likely to be selected. Therefore, epicondylitis directly affects DASH and MEPS.
We found that the prevalence of epicondylitis in the dominant arm was 33.7%, which is outside the range of 3.5–20.2% reported in the literature.3536 Our study included 692 agricultural workers, representing 70.11% of total participants. In other studies, the prevalence of epicondylitis was higher in agricultural workers than in factory workers.3536 This suggests that the agricultural work environment induces epicondylitis to a greater extent than a factory work environment. In the current study, the prevalence of rotator cuff tear in the dominant arm was 53.4% (partial thickness rotator cuff tear: 44.4%, full thickness rotator cuff tear: 9.0%), also higher than the 4.7–22.1% reported in the literature.3738 Unlike other studies that evaluated full thickness rotator cuff tears of the bilateral shoulders using ultrasound, we used MRI to evaluate the dominant shoulder only, which could account for the difference in prevalence. Nevertheless, the presence of a partial rotator cuff tear in 44.4% of participants is a surprising result. In this study, prevalence of hand OA in the dominant arm was 44.6%, which is lower than the 47–61.7% reported in the literature.3839 This may be because the mean age of participants in our study is lower than that of participants evaluated in other studies.
This study had several limitations. First, our sample size was fairly small, and because of the cross-sectional nature of the study, we could not assess the long-term effects of UEMD on QOL. Second, we did not evaluate all UEMDs and did not consider the radiologic severity of the disease. Therefore, we could not evaluate the effects of all UEMDs on QOL or if the radiologic severity of disease had an impact on QOL. Nevertheless, the severity of symptoms was reflected in the functional assessment scores, and we also evaluated QOL according to symptoms. Third, the proportion of participants with mild functional impairment was relatively high in our study because participants were residents of rural communities who had not undergone hospital treatment due to UEMDs. Therefore, depending on the UEMD, moderate to severe symptoms might have different effects on upper extremity-related QOL.
The strength of this study is that UEMD evaluation was performed objectively in all participants by questionnaire survey, functional assessment, physical exam, and imaging tests. In particular, evaluation of rotator cuff tear using MRI is more accurate than evaluation using ultrasound, and partial thickness rotator cuff tear was included in the study. Moreover, our study is the first to examine the associations among UEMD, regional functional assessment scores, and upper extremity-related QOL.
In this study, UEMDs other than epicondylitis were predominantly asymptomatic, and epicondylitis was found to significantly affect upper extremity-related QOL, while other UEMDs, such as hand OA and rotator cuff tear, did not. In conclusion, the presence of UEMDs does not often affect upper extremity-related QOL, and it is difficult to detect the presence of disease and receive treatment in advance, even if the patient is affected by the disease. This suggests that it is important to diagnose UEMDs at an early stage to prevent irreversible degenerative changes. When a UEMD impacts upper extremity-related QOL in patients, it is highly likely that another UEMD is also present. Therefore, physicians should meticulously evaluate and diagnose such patients.

ACKNOWLEDGEMENTS

This study was supported by a grant of the Center for Farmer's Safety and Health, Ministry of Agriculture, Food and Rural Affairs, Republic of Korea.

Notes

The authors have no financial conflicts of interest.

References

1. Huisstede BM, Bierma-Zeinstra SM, Koes BW, Verhaar JA. Incidence and prevalence of upper-extremity musculoskeletal disorders. A systematic appraisal of the literature. BMC Musculoskelet Disord. 2006; 7:7. PMID: 16448572.
crossref
2. Huisstede BM, Wijnhoven HA, Bierma-Zeinstra SM, Koes BW, Verhaar JA, Picavet S. Prevalence and characteristics of complaints of the arm, neck, and/or shoulder (CANS) in the open population. Clin J Pain. 2008; 24:253–259. PMID: 18287832.
crossref
3. Walker-Bone K, Palmer KT, Reading I, Coggon D, Cooper C. Prevalence and impact of musculoskeletal disorders of the upper limb in the general population. Arthritis Rheum. 2004; 51:642–651. PMID: 15334439.
crossref
4. McDonald M, DiBonaventura Md, Ullman S. Musculoskeletal pain in the workforce: the effects of back, arthritis, and fibromyalgia pain on quality of life and work productivity. J Occup Environ Med. 2011; 53:765–770. PMID: 21685799.
5. Spreeuwers D, de Boer AG, Verbeek JH, van Beurden MM, de Wilde NS, Braam I, et al. Work-related upper extremity disorders: one-year follow-up in an occupational diseases registry. Int Arch Occup Environ Health. 2011; 84:789–796. PMID: 21274724.
crossref
6. Boocock MG, Collier JM, McNair PJ, Simmonds M, Larmer PJ, Armstrong B. A framework for the classification and diagnosis of work-related upper extremity conditions: systematic review. Semin Arthritis Rheum. 2009; 38:296–311. PMID: 18280546.
crossref
7. da Costa JT, Baptista JS, Vaz M. Incidence and prevalence of upper-limb work related musculoskeletal disorders: a systematic review. Work. 2015; 51:635–644. PMID: 26409936.
crossref
8. Razmjou H, Bean A, van Osnabrugge V, MacDermid JC, Holtby R. Cross-sectional and longitudinal construct validity of two rotator cuff disease-specific outcome measures. BMC Musculoskelet Disord. 2006; 7:26. PMID: 16533405.
crossref
9. Roquelaure Y, Ha C, Leclerc A, Touranchet A, Sauteron M, Melchior M, et al. Epidemiologic surveillance of upper-extremity musculoskeletal disorders in the working population. Arthritis Rheum. 2006; 55:765–778. PMID: 17013824.
crossref
10. Slatkowsky-Christensen B, Mowinckel P, Loge JH, Kvien TK. Health-related quality of life in women with symptomatic hand osteoarthritis: a comparison with rheumatoid arthritis patients, healthy controls, and normative data. Arthritis Rheum. 2007; 57:1404–1409. PMID: 18050180.
crossref
11. Hegmann KT, Thiese MS, Wood EM, Garg A, Kapellusch JM, Foster J, et al. Impacts of differences in epidemiological case definitions on prevalence for upper-extremity musculoskeletal disorders. Hum Factors. 2014; 56:191–202. PMID: 24669553.
crossref
12. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The Seventh Report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003; 289:2560–2572. PMID: 12748199.
crossref
13. Marathe PH, Gao HX, Close KL. American Diabetes Association Standards of Medical Care in Diabetes 2017. J Diabetes. 2017; 9:320–324. PMID: 28070960.
crossref
14. Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract. 2007; 75:72–80. PMID: 16735075.
crossref
15. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003; 41:1284–1292. PMID: 14583691.
16. Bellamy N, Campbell J, Haraoui B, Buchbinder R, Hobby K, Roth JH, et al. Dimensionality and clinical importance of pain and disability in hand osteoarthritis: Development of the Australian/Canadian (AUSCAN) Osteoarthritis Hand Index. Osteoarthritis Cartilage. 2002; 10:855–862. PMID: 12435330.
crossref
17. Constant CR, Murley AH. A clinical method of functional assessment of the shoulder. Clin Orthop Relat Res. 1987; 214:160–164.
crossref
18. Morrey BF. The elbow and its disorders. 2nd ed. Philadelphia: Saunders;1993.
19. 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; 29:602–608. PMID: 8773720.
20. Reinus WR, Shady KL, Mirowitz SA, Totty WG. MR diagnosis of rotator cuff tears of the shoulder: value of using T2-weighted fatsaturated images. AJR Am J Roentgenol. 1995; 164:1451–1455. PMID: 7754891.
crossref
21. Sluiter JK, Rest KM, Frings-Dresen MH. Criteria document for evaluating the work-relatedness of upper-extremity musculoskeletal disorders. Scand J Work Environ Health. 2001; 27(Suppl 1):1–102.
22. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957; 16:494–502. PMID: 13498604.
crossref
23. Haugen IK, Englund M, Aliabadi P, Niu J, Clancy M, Kvien TK, et al. Prevalence, incidence and progression of hand osteoarthritis in the general population: the Framingham Osteoarthritis Study. Ann Rheum Dis. 2011; 70:1581–1586. PMID: 21622766.
crossref
24. Franchignoni F, Vercelli S, Giordano A, Sartorio F, Bravini E, Ferriero G. Minimal clinically important difference of the disabilities of the arm, shoulder and hand outcome measure (DASH) and its shortened version (QuickDASH). J Orthop Sports Phys Ther. 2014; 44:30–39. PMID: 24175606.
crossref
25. Sorensen AA, Howard D, Tan WH, Ketchersid J, Calfee RP. Minimal clinically important differences of 3 patient-rated outcomes instruments. J Hand Surg Am. 2013; 38:641–649. PMID: 23481405.
crossref
26. Constant CR, Gerber C, Emery RJ, Søjbjerg JO, Gohlke F, Boileau P. A review of the Constant score: modifications and guidelines for its use. J Shoulder Elbow Surg. 2008; 17:355–361. PMID: 18218327.
crossref
27. Hochberg MC, Vignon E, Maheu E. Session 2: clinical aspects. Clinical assessment of hand OA. Osteoarthritis Cartilage. 2000; 8(Suppl A):S38–S40. PMID: 11156493.
crossref
28. Lawrence JS, Bremner JM, Bier F. Osteo-arthrosis. Prevalence in the population and relationship between symptoms and x-ray changes. Ann Rheum Dis. 1966; 25:1–24. PMID: 5905334.
29. Minagawa H, Yamamoto N, Abe H, Fukuda M, Seki N, Kikuchi K, et al. Prevalence of symptomatic and asymptomatic rotator cuff tears in the general population: from mass-screening in one village. J Orthop. 2013; 10:8–12. PMID: 24403741.
crossref
30. Andrianakos AA, Kontelis LK, Karamitsos DG, Aslanidis SI, Georgountzos AI, Kaziolas GO, et al. Prevalence of symptomatic knee, hand, and hip osteoarthritis in Greece. The ESORDIG study. J Rheumatol. 2006; 33:2507–2513. PMID: 17143985.
31. Gumina S, Carbone S, Campagna V, Candela V, Sacchetti FM, Giannicola G. The impact of aging on rotator cuff tear size. Musculoskelet Surg. 2013; 97(Suppl 1):69–72. PMID: 23588834.
crossref
32. Kloppenburg M, Kwok WY. Hand osteoarthritis--a heterogeneous disorder. Nat Rev Rheumatol. 2011; 8:22–31. PMID: 22105244.
crossref
33. Parsons IM, Apreleva M, Fu FH, Woo SL. The effect of rotator cuff tears on reaction forces at the glenohumeral joint. J Orthop Res. 2002; 20:439–446. PMID: 12038616.
crossref
34. Descatha A, Albo F, Leclerc A, Carton M, Godeau D, Roquelaure Y, et al. Lateral epicondylitis and physical exposure at work? A review of prospective studies and meta-analysis. Arthritis Care Res (Hoboken). 2016; 68:1681–1687. PMID: 26946473.
crossref
35. Mora DC, Miles CM, Chen H, Quandt SA, Summers P, Arcury TA. Prevalence of musculoskeletal disorders among immigrant Latino farmworkers and non-farmworkers in North Carolina. Arch Environ Occup Health. 2016; 71:136–143. PMID: 25454715.
crossref
36. Pullopdissakul S, Ekpanyaskul C, Taptagaporn S, Bundhukul A, Thepchatri A. Upper extremities musculoskeletal disorders: prevalence and associated ergonomic factors in an electronic assembly factory. Int J Occup Med Environ Health. 2013; 26:751–761. PMID: 24264438.
crossref
37. Jeong J, Shin DC, Kim TH, Kim K. Prevalence of asymptomatic rotator cuff tear and their related factors in the Korean population. J Shoulder Elbow Surg. 2017; 26:30–35. PMID: 27497789.
crossref
38. Zhang Y, Xu L, Nevitt MC, Niu J, Goggins JP, Aliabadi P, et al. Lower prevalence of hand osteoarthritis among Chinese subjects in Beijing compared with white subjects in the United States: the Beijing Osteoarthritis Study. Arthritis Rheum. 2003; 48:1034–1040. PMID: 12687546.
crossref
39. Dahaghin S, Bierma-Zeinstra SM, Hazes JM, Koes BW. Clinical burden of radiographic hand osteoarthritis: a systematic appraisal. Arthritis Rheum. 2006; 55:636–647. PMID: 16874787.
crossref
Fig. 1

DASH scores according to UEMD. The model was adjusted for primitive variables (age, sex, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and cholesterol) that produced significant difference in outcomes. a, b, c, d: post-hoc analysis. UEMD, upper extremity musculoskeletal disorder; DASH, disabilities of the arm, shoulder, and hand; Epi, epicondylitis; RCT, rotator cuff tear; HOA, hand osteoarthritis.

ymj-59-669-g001
Table 1

Study Population Demographics and Comorbidities

ymj-59-669-i001
Variable Total Epicondylitis Rotator cuff tear Hand osteoarthritis
No Yes p value No Yes p value No Yes p value
Total, n (%) 987 (100.0) 654 (66.3) 333 (33.7) 460  46.6 527 (53.4) 547 (55.4) 440 (44.6)
Gender, n (%)
 Female 512 (51.9) 297 (58.0) 215 (42.0) <0.001 253 (49.4) 259 (50.6) 0.066 262 (51.2) 250 (48.8) 0.005
 Male 475 (48.1) 357 (75.2) 118 (24.8) 207 (43.6) 268 (56.4) 285 (60.0) 190 (40.0)
Age (yr)
 Mean±Std. 59.8±8.4) 60.2±8.4 58.9±8.4 0.017 57.7±8.3 61.7±8.0 <0.001 57.1±8.3 63.1±7.3 <0.001
 ≤49, n (%) 111 (11.2) 71 (64.0) 40 (36.0) 0.172 73.0 (65.8) 38 (34.2) <0.001 96.0 (86.5) 15.0 (13.5) <0.001
 50–59, n (%) 366 (37.1) 231 (63.1) 135 (36.9) 196 (53.6) 170 (46.4) 245 (66.9) 121 (33.1)
 60–69, n (%) 376 (38.1) 254 (67.6) 122 (32.4) 155 (41.2) 221 (58.8) 169 (44.9) 207 (55.1)
 ≥70, n (%) 134 (13.6) 98 (73.1) 36 (26.9) 36 (26.9) 98 (73.1) 37 (27.6) 97 (72.4)
Marital status, n (%)
 No 101 (10.2) 67 (66.3) 34 (33.7) 0.980 39 (38.6) 62 (61.4) 0.087 46 (45.5) 55 (54.5) 0.034
 Yes 885 (89.7) 586 (66.2) 299 (33.8) 421 (47.6) 464 (52.4) 501 (56.6) 384 (43.4)
Educational level, n (%)
 Less than elementary school 283 (28.7) 166 (58.7) 117 (41.3) 0.005 92 (32.5) 191 (67.5) <0.001 73 (25.8) 210 (74.2) <0.001
 Middle school 190 (19.3) 127 (66.8) 63 (33.2) 74 (38.9) 116 (61.1) 82 (43.2) 108 (56.8)
 More than high school 512 (51.9) 359 (70.1) 153 (29.9) 294 (57.4) 218 (42.6) 392 (76.6) 120 (23.4)
Depression, n (%)
 No 883 (89.5) 600 (68.0) 283 (32.0) 0.001 419 (47.5) 464 (52.5) 0.121 500 (56.6) 383 (43.4) 0.027
 Yes 104 (10.5) 54 (51.9) 50 (48.1) 41.0 (39.4) 63 (60.6) 47.0 (45.2) 57.0 (54.8)
Hypertension, n (%)
 No 701 (71.0) 461 (65.8) 240 (34.2) 0.604 346 (49.4) 355 (50.6) 0.007 417 (59.5) 284 (40.5) <0.001
 Yes 286 (29.0) 193 (67.5) 93 (32.5) 114 (39.9) 172 (60.1) 130 (45.5) 156 (54.5)
Waist circumference (cm)
 Mean±Std. 84.67±8.67 85.11±8.80 83.82±8.35 0.027 83.51±9.06 85.68±8.18 <0.001 84.08±8.98 85.41±8.22 0.016
Hemoglobin (g/dL)
 Mean±Std. 14.03±1.40 14.11±1.44 13.85±1.32 0.006 14.05±1.41 14.00±1.40 0.592 14.13±1.42 13.89±1.38 0.009
HbA1c (%)
 Mean±Std. 5.95±0.83 5.99±0.90 5.87±0.68 0.016 5.91±0.76 5.98±0.89 0.213 5.91±0.84 5.99±0.82 0.136
Cholesterol (mg/dL)
 Mean±Std. 194.11±38.01 193.25±37.92 195.81±38.19 0.318 191.95±37.21 196.00±38.64 0.094 191.60±37.53 197.24±38.42 0.020

Std, standard deviation.

Table 2

DASH Score for Each UEMD

ymj-59-669-i002
UEMD DASH score
Mean Std. p value Adj. mean* Adj. Std.* p value
Total 13.3 13.1 13.2 4.8
Epicondylitis No 11.0 11.6 <0.001 10.9 4.1 <0.001
Yes 17.8 14.7 17.7 4.5
Rotator cuff tear No 12.1 12.6 0.007 12.0 4.5 <0.001
Yes 14.4 13.5 14.2 4.8
Hand osteoarthritis No 12.1 12.6 0.001 12.1 4.4 <0.001
Yes 14.8 13.6 14.7 4.8

DASH, disability of arm, shoulder, and hand; UEMD, upper extremity musculoskeletal disorder; Std, standard deviation; Adj, adjusted.

*Adjusted for primitive variables (age, sex, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and cholesterol) that were associated with a significant difference in outcome.

Table 3

Multiple Regression Analysis Results of the Associations between UEMDs and Regional Functional Assessment Scores

ymj-59-669-i003
UEMD CMS MEPS AUSCAN
B Standard error p value B Standard error p value B Standard error p value
Only Epi −2.005 1.260 0.112 −7.512 1.211 <0.001 3.887 1.215 0.001
Only RCT −2.464 1.099 0.025 −1.688 1.056 0.110 1.292 1.060 0.223
Only HOA −0.007 1.338 0.996 −0.400 1.286 0.756 3.453 1.290 0.008
Epi+RCT −4.451 1.420 0.002 −7.909 1.364 <0.001 7.956 1.369 <0.001
Epi+HOA −4.618 1.681 0.006 −11.530 1.615 <0.001 8.619 1.621 <0.001
RCT+HOA −2.428 1.161 0.037 −0.013 1.116 0.991 2.764 1.120 0.014
Epi+RCT+HOA −6.509 1.332 <0.001 −6.656 1.280 <0.001 7.298 1.284 <0.001

UEMD, upper extremity musculoskeletal disorder; Epi, epicondylitis; HOA, hand osteoarthritis; CMS, Constant-Murley score; MEPS, Mayo elbow performance score; AUSCAN, Australian/Canadian Hand Osteoarthritis Index.

Reference group comprised individuals with no UEMDs. Primitive variables (age, sex, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and cholesterol) that produced a significant difference in outcome were adjusted in the model.

Table 4

Results of Univariate and Multivariate Regression Analyses to Evaluate the Associations between UEMDs and Regional Functional Assessment Scores and DASH Scores

ymj-59-669-i004
UEMD Univariate regression Multiple regression
B Standard error p value B Standard error p value B Standard error p value B Standard error p value
Only Epi 5.860 1.479 <0.001 4.829 1.332 <0.001 4.791 1.500 0.001 3.510 1.292 0.007
Only RCT 2.393 1.290 0.064 1.126 1.163 0.333 2.153 1.284 0.094 1.612 1.121 0.151
Only HOA 2.217 1.571 0.158 2.213 1.413 0.117 2.166 1.561 0.166 0 .129 1.369 0.925
Epi+RCT 9.302 1.667 <0.001 7.012 1.507 <0.001 8.174 1.686 <0.001 4.492 1.473 0.002
Epi+HOA 5.116 1.973 0.010 2.741 1.782 0.124 3.483 2.013 0.084 −0.095 1.739 0.957
RCT+HOA 0.923 1.363 0.498 −0.325 1.229 0.791 0.893 1.356 0.510 −0.748 1.188 0.529
Epi+RCT+HOA 5.599 1.564 <0.001 2.252 1.424 0.114 4.663 1.576 0.003 1.187 1.381 0.390
CMS −0.531 0.034 <0.001 −0.514 0.034 <0.001
MEPS −0.204 0.037 <0.001 −0.142 0.039 <0.001
AUSCAN 0.623 0.033 <0.001 0.605 0.034 <0.001

UEMD, upper extremity musculoskeletal disorders; DASH, disability of arm, shoulder, and hand; Epi, epicondylitis; RCT, rotator cuff tear; HOA, hand osteoarthritis; CMS, Constant-Murley score; MEPS, Mayo elbow performance score; AUSCAN, Australian/Canadian Hand Osteoarthritis Index.

Reference group comprised individuals with no UEMDs. The model was adjusted for primitive variables (age, sex, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and cholesterol), which produced significant difference in outcomes.

Table 5

Results of Multiple Regression Analysis to Evaluate the Association between UEMDs and All Regional Functional Assessment Scores and DASH Score

ymj-59-669-i005
UEMD B Standard error p value
Only Epi 3.096 1.230 0.012
Only RCT 0.811 1.052 0.441
Only HOA 0.518 1.282 0.686
Epi+RCT 3.610 1.397 0.010
Epi+HOA| −1.001 1.662 0.547
RCT+HOA −1.381 1.113 0.215
EPI+RCT+HOA −0.508 1.310 0.698
CMS −0.375 0.033 <0.001
MEPS −0.014 0.033 0.679
AUSCAN 0.492 0.034 <0.001

UEMD, upper extremity musculoskeletal disorders; DASH, disability of arm, shoulder, and hand; Epi, epicondylitis; RCT, rotator cuff tear; HOA, hand osteoarthritis; CMS, Constant-Murley score; MEPS, Mayo elbow performance score; AUSCAN, Australian/Canadian Hand Osteoarthritis Index.

Reference group comprised individuals with no UEMDs. The model was adjusted for primitive variables (age, sex, educational level, marital status, depression, hypertension, waist circumference, hemoglobin, HbA1c, and cholesterol) that produced significant difference in outcomes.

TOOLS
Similar articles