Abstract
-
Purpose
This study investigated health-related quality of life (HRQoL) and aimed to identify factors influencing HRQoL for patients with rotator cuff tears (RCTs).
-
Methods
We conducted a descriptive correlational survey with 118 outpatients diagnosed with RCTs at a tertiary general hospital in Seoul. Data were collected between February and June 2021 using structured self-report questionnaires, including the numeric rating scale, Korean version of the Shoulder Pain and Disability Index, Verran and Snyder-Halpern Sleep Scale, and the World Health Organization Quality of Life Instrument, Short Form.
-
Results
HRQoL showed significant positive correlations with sleep quality (r=.64, p<.001) and the frequency of shoulder-intensive sports activities (r=.24, p=.008). It was negatively correlated with symptom days per week (r=–.32, p<.001), symptom hours per day (r=–.51, p<.001), pain (r=–.21, p=.025), functional disability (r=–.49, p<.001), and depression (r=–.60, p<.001). Stepwise regression analysis indicated that sleep quality (β=.36, p<.001) was the strongest predictor of HRQoL, followed by occupation (β=.26, p<.001), depression (β=–.24, p=.010), and symptom hours per day (β=–.19, p=.013).
-
Conclusion
Sleep quality was the most influential factor affecting HRQoL in patients with RCTs. These findings underscore the need for comprehensive nursing interventions that address sleep disturbances, provide psychological support for depressive symptoms, consider occupational demands, and promote early management of prolonged symptoms to enhance HRQoL in this population.
-
Key Words: Quality of life; Rotator cuff injuries; Sleep quality
INTRODUCTION
With increased life expectancy and greater participation in sports activities, the incidence of rotator cuff tears (RCTs) is rising sharply. RCTs account for more than 70% of shoulder pain cases [
1] and represent one of the most common musculoskeletal disorders in adults [
2]. In the United States, approximately 4.5 million patient visits are related to shoulder pain each year, with about 250,000 rotator cuff repairs performed annually. These numbers are expected to increase further due to the aging U.S. population [
2]. A similar trend has been observed in South Korea. According to data from the Health Insurance Review and Assessment Service (HIRA), shoulder disorders (M75) ranked 12th among inpatient diagnoses in 2024. The number of patients diagnosed with rotator cuff syndrome (M751), including RCTs, has steadily grown over the past decade, from 589,759 in 2015 to 899,322 in 2024 [
3].
RCTs are primarily caused by degenerative changes. As blood supply to shoulder tendons decreases and muscle elasticity declines, the likelihood of tears increases [
4]. Accordingly, the prevalence of RCTs has significantly increased in adults over the age of 50 years. However, cases in younger individuals are also on the rise, often associated with muscle injury or inflammation from vigorous sports activity [
4].
Patients with RCTs frequently report severe pain that worsens at night and disrupts sleep [
5]. These disturbances include shorter sleep duration, delayed sleep onset, fragmented sleep, and reduced sleep efficiency, all of which are strongly linked to nocturnal pain and limited sleeping positions [
6]. Shoulder pain is also significantly associated with depression [
7], and higher levels of depression correspond to lower quality of life [
8]. Furthermore, both pain and functional impairment from RCTs have been shown to adversely affect quality of life [
9]. Taken together, these findings suggest that patients with RCTs experience a combination of physical and psychological challenges that substantially affect their well-being [
10]. Nonetheless, few comprehensive studies have examined these factors collectively in this population.
Health-related quality of life (HRQoL) is a multidimensional construct that encompasses perceived physical, psychological, and social functioning, role performance, and general health perception, thereby reflecting subjective well-being and overall life satisfaction [
11]. HRQoL is widely used as a comprehensive indicator for assessing the impact of disease across multiple life domains. It is also a critical parameter in disease evaluation, treatment planning, and the measurement of patient satisfaction with care [
12]. In this regard, an increasing number of studies argue that care for patients with RCTs should extend beyond physical impairment to include psychological factors [
13]. Previous research has demonstrated strong associations between RCTs and diverse symptoms. Pain and physical disability are consistently linked to RCTs [
10], as are depressive symptoms [
14]. Sleep disorders, in particular, are frequently reported as comorbid conditions [
15]. However, most prior studies have investigated individual symptoms in isolation rather than exploring their combined effects on HRQoL. Moreover, earlier research often relied on generalized measures of psychological or functional status, which may not capture the lived experiences of RCT patients. This study aimed to address these gaps by incorporating shoulder-specific measures of functional disability and multidimensional assessments of sleep disturbances, given their high prevalence and clinical significance in this patient population. Specifically, we sought to evaluate HRQoL levels and identify the major factors influencing HRQoL in patients with RCTs through a multivariate approach.
METHODS
1. Study Design
This study used a cross-sectional correlational design. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
2. Setting and Samples
Participants were patients with RCTs. Inclusion criteria were: (1) outpatients aged 18 years or older diagnosed with RCTs and undergoing regular follow-up at a tertiary hospital in South Korea; and (2) individuals who understood the study purpose and voluntarily agreed to participate. Exclusion criteria were: (1) patients who had undergone rotator cuff surgery and were experiencing infection or other complications; (2) those with additional musculoskeletal disorders beyond RCT; and (3) those with mental illnesses or other complex conditions affecting pain.
The required sample size was estimated using G*Power 3.1.7 [
16]. For multiple regression analysis with a significance level of .05, a power of .8, a medium effect size of .20, and 12 predictors (eight disease-related characteristics and four primary variables: pain, functional disability, depression, and sleep quality), a minimum of 98 participants was indicated. Although a previous study involving patients with rheumatoid arthritis applied a 10% dropout rate [
17], we adopted a more conservative 15% rate to account for pain and limited shoulder mobility in RCT patients. This yielded an adjusted target sample size of 116. A total of 118 participants were recruited, and all were included in the final analysis.
Initially, 12 predictors were selected based on clinical relevance and prior studies. Four additional general characteristics were incorporated into the final regression model after demonstrating statistical significance in bivariate analysis. To confirm adequacy for the final model with 13 predictors, a second sample size calculation using the same parameters indicated a requirement of 101 participants. Therefore, the 118 participants recruited were sufficient to support the regression analysis.
3. Instruments
1) Socio-demographic and disease-related characteristics
Socio-demographic variables included age, sex, body mass index (BMI), education level, occupation, marital status, and smoking status. Disease-related variables were identified through literature review and comprised eight items: duration of RCT symptoms, symptom days per week, symptom hours per day, usual shoulder use, frequency of shoulder-intensive sports activities, history of rotator cuff repair surgery, medication use for RCT-related pain, and presence of diabetes mellitus [
15,
18,
19].
2) Pain
Pain intensity was measured using the numeric rating scale (NRS), which is derived from the visual analog scale [
20]. The NRS ranges from 0 (no pain) to 10 (worst imaginable pain), reflecting pain experienced in daily life. It is widely used as a subjective pain assessment tool. Test-retest reliability of the NRS has been reported as strong (r=.73–.88) [
21].
3) Functional disability
The Shoulder Pain and Disability Index (SPADI) includes 13 items divided into two subscales: a 5-item pain subscale assessing pain severity and an 8-item function/disability subscale evaluating difficulty with daily upper-limb activities. Scores are calculated as the mean of all items, with higher scores indicating greater disability. The Korean version of SPADI has established validity and reliability [
22]. Cronbach’s ⍺ was .94 in the original study [
22] and .92 in this study.
4) Depression
Depression was measured using the Center for Epidemiologic Studies Depression Scale (CES-D) [
23], for which the Korean version has well-established reliability and validity [
24]. The tool comprises 20 items rated on a 4-point Likert scale, capturing the frequency of depressive symptoms over the past week. Higher scores indicate greater depression. Cronbach’s ⍺ was .91 in the original study [
24] and .76 in this study.
5) Sleep quality
Sleep quality was measured with the Verran and Snyder-Halpern (VSH) sleep scale, consisting of eight items that assess waking during sleep, tossing and turning, total sleep time, depth of sleep, sleep latency, mood upon awakening, method of awakening, and sleep satisfaction [
25]. Higher total scores reflect better sleep quality. The Korean version of the VSH has validated psychometric properties [
26]. Cronbach’s ⍺ was .83 in the original study [
26] and .94 in this study.
6) Health-related quality of life
The Korean version of the World Health Organization Quality of Life Instrument, Short Form (WHOQOL-BREF) was used to measure HRQoL [
27]. This tool includes 26 items: 24 items across four domains (physical health, psychological health, social relationships, and environment) and two items measuring overall quality of life. Items are rated on a 5-point scale and converted to a 100-point scale, with higher scores indicating better HRQoL [
28]. Cronbach’s ⍺ was .96 in the original study [
27] and .95 in this study.
Data collection was conducted from February 25, 2021, to June 29, 2021, at one tertiary general hospital in Seoul, South Korea. With permission from the orthopedic and nursing departments, participants were recruited from outpatients diagnosed with RCTs. While waiting for consultations, the principal investigator explained the study purpose and procedures. Those who agreed provided written informed consent before completing the survey. Each survey required 10 to 20 minutes to complete, and participants received a small gift as a token of appreciation.
5. Ethical Considerations
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. H-2101-124-1190). The principal investigator explained the purpose and methods of the research. Participants were also assured that they could withdraw from the study at any point without any disadvantage.
6. Data Analysis
Data analysis was conducted using IBM SPSS ver. 25.0 (IBM Corp., Armonk, NY, USA), with a significance level of .05. Descriptive statistics were used to summarize socio-demographic and disease-related characteristics, as well as pain, functional disability, depression, sleep quality, and HRQoL. Group differences were examined using the independent t-test for normally distributed variables and the Mann–Whitney U test for non-normal distributions. One-way analysis of variance with the Scheffe post-hoc test was also conducted. Associations among variables were analyzed using Pearson correlation coefficients, and the factors influencing HRQoL were identified through stepwise multiple regression.
RESULTS
1. Participant Characteristics
The mean age of participants was 62.05±8.86 years, with most in their 50s or 60s (76.2%). A majority were female (56.8%), and 47.5% were classified as obese (BMI≥25 kg/m2). Overall, 44.1% had a college-level education or higher, and 91.5% were married. Regarding occupation, 25.4% were office workers or professionals. Additionally, 28.0% reported current or past smoking history.
In terms of disease-related characteristics, the average duration of rotator cuff-related symptoms was 32.71 months, with 55.1% reporting a duration of 1–3 years. Participants experienced symptoms on average 5.34 days per week and 10.00 hours per day. The mean level of usual shoulder use was 3.22 out of 5, and the mean frequency of shoulder-intensive sports activities was 2.28 out of 5. Furthermore, 26.3% had undergone rotator cuff surgery, 19.5% used medication for rotator cuff pain, and 20.3% had diabetes (
Table 1).
Participants’ scores for pain, functional disability, depression, sleep quality, and HRQoL are summarized in
Table 2. The mean pain score was 5.40±2.37. The overall functional disability score was 55.86±17.24, with pain and function/disability subscale scores of 67.73±19.83 and 48.64±18.19, respectively. The mean depression score was 33.84±6.33, and the mean sleep quality score was 43.79±18.32. The total HRQoL score was 53.50±11.73, with subdomain scores of 11.85±2.48 for physical health, 12.31±2.27 for psychological health, 13.47±2.09 for social relationships, and 12.62±1.87 for environmental health.
HRQoL scores by socio-demographic and disease-related characteristics are presented in
Table 3. In terms of socio-demographic characteristics, individuals younger than 65 years had significantly higher HRQoL scores than those aged 65 or older (t=3.76,
p<.001). Participants with a college education or higher reported greater HRQoL than those with a high school diploma or lower (F=11.24,
p<.001). Post-hoc analysis indicated that both the middle school or lower and high school groups had significantly lower scores compared to the college or above group. Married participants reported higher HRQoL than unmarried individuals (z=1.97,
p=.049). In terms of occupation, office workers or professionals had significantly higher HRQoL than those in other occupations (t=4.59,
p<.001).
Regarding disease-related characteristics, participants not taking medication for RCT-related pain had significantly higher HRQoL scores than those who were (z=–4.13, p<.001). Likewise, participants without diabetes mellitus reported higher HRQoL scores than those with diabetes (z=–3.23, p<.001).
4. Correlations among Key Variables
Correlations among major variables are presented in
Table 4. Sleep quality (r=.64,
p<.001) and frequency of shoulder-intensive sports activities (r=.24,
p=.008) showed a positive correlation with HRQoL. Meanwhile, symptom days per week (r=–.32,
p<.001), symptom hours per day (r=–.51,
p<.001), pain (r=–.21,
p=.025), functional disability (r=–.49,
p<.001), and depression (r=–.60,
p<.001) were negatively correlated with HRQoL.
Stepwise regression analysis was conducted to identify predictors of HRQoL in patients with RCTs. Independent variables included age, education level, marital status, occupation, medication use for RCT-related pain, diabetes mellitus, symptom days per week, symptom hours per day, frequency of shoulder-intensive sports activities, pain, functional disability, depression, and sleep quality. Categorical variables were dummy-coded as follows: education (high school or lower=0, college or higher=1), marital status (others=0, married=1), occupation (others=0, office worker/professional=1), medication use for RCT-related pain (no=0, yes=1), and diabetes mellitus (no=0, yes=1).
The assumptions of regression analysis were tested and met. Tolerance values ranged from .48 to .97, and the variance inflation factor ranged from 1.03 to 2.10, both indicating no concerns with multicollinearity. The Durbin–Watson statistic was 1.85, suggesting no autocorrelation of residuals. Additionally, the residuals met the assumption of normality.
Stepwise regression results showed that Model 1, which included only sleep quality, explained 40.2% of the variance in HRQoL (F=79.71, p<.001), establishing sleep quality as the strongest predictor. In Model 2, the addition of occupation increased explanatory power to .48 (F=17.16, p<.001), highlighting the independent contribution of social factors. Model 3 included depression, further increasing the adjusted R² to .52 (F=10.47, p=.002), supporting the importance of psychological factors. In Model 4, symptom hours per day was added, resulting in an adjusted R² of .54 (F=6.38, p=.013), producing a model that integrates physical, psychological, and social domains.
In the final model (Model 4), the significant predictors of HRQoL were sleep quality (β=.36,
p<.001), occupation (β=.26,
p<.001), depression (β=–.24,
p=.010), and symptom hours per day (β=–.19,
p=.013), with sleep quality emerging as the strongest predictor of HRQoL (
Table 5).
DISCUSSION
This study aimed to identify the factors influencing HRQoL in adults with RCTs. Analysis revealed that sleep quality, occupation, depression, and symptom hours per day were significant predictors. These findings suggest that functional, emotional, and physiological dimensions are interrelated in shaping overall quality of life. By examining multidimensional variables such as socio-demographic characteristics, physical function, and psychological factors, this study provides a more comprehensive understanding of the determinants of HRQoL in this population. The results highlight the interrelationships among these factors and emphasize the importance of a comprehensive, multidisciplinary approach when planning treatment and care for patients with RCTs.
In this study, the overall HRQoL score of participants was 53.50, which is comparable to the preoperative HRQoL score of 58.8 reported in a previous study of RCT patients [
29]. This finding indicates that the HRQoL of individuals with RCTs remains low. Therefore, interventions to improve HRQoL in this patient group should focus on identifying and actively addressing the contributing factors.
Sleep quality was identified as the most influential predictor in the regression model. This demonstrates that sleep is not simply a measure of rest or daily functioning but a multifaceted indicator encompassing pain perception, emotional well-being, and the ability to engage in everyday activities [
30]. Sleep disturbances are prevalent in RCT patients and are frequently worsened by nocturnal pain, which hinders physical recovery and heightens emotional distress [
31]. These results underscore that sleep quality is a central determinant of overall well-being, rather than a secondary health indicator.
Among socio-demographic characteristics, occupation also showed a significant association with HRQoL. In this study, occupations were classified into office-based and other categories according to functional demands related to shoulder use. Office workers generally engaged in less physically demanding tasks, whereas self-employed individuals, manual laborers, and housewives were more likely to experience repetitive or prolonged shoulder use, which increased physical strain and exacerbated pain [
32]. These occupational characteristics may contribute to activity limitations and higher physical burden, underscoring the need for tailored intervention strategies that account for job-related demands.
Depression emerged as another significant predictor, negatively associated with HRQoL. Although strongly correlated with sleep quality, its independent retention in the regression model suggests a distinct contribution. Chronic physical restrictions caused by pain can lead to emotional withdrawal and negative self-perceptions [
14]. These findings indicate that psychological distress in RCT patients is not merely a secondary response to physical symptoms but may represent a primary factor reducing HRQoL [
13]. Therefore, routine assessment of psychological status and provision of emotional support should be integral components of nursing care.
Among disease-related characteristics, symptom hours per day also significantly influenced HRQoL. This finding suggests that symptom chronicity adversely affects patients’ subjective health perceptions, functional performance, and recovery expectations [
33]. Thus, early intervention at the onset of symptoms may be essential for preserving HRQoL in patients with RCTs.
Although variables such as pain, functional disability, and certain disease-related characteristics showed significant correlations with HRQoL in the bivariate analyses, they were excluded from the final regression model. This may be due to overlapping explanatory contributions from other variables in the model. It is possible that these factors influence quality of life indirectly through more immediate variables such as sleep quality or depressive symptoms. This interpretation is supported by previous studies demonstrating that psychological factors—including depression, anxiety, and sleep disorders—mediate the relationship between physical symptoms and quality of life [
34]. These findings indicate that interventions focusing solely on alleviating pain or improving physical function may have a limited impact on overall quality of life, underscoring the need for comprehensive approaches that address emotional regulation and sleep recovery simultaneously. Furthermore, traditional clinical indicators such as surgical history, medication use, smoking, and diabetes mellitus did not significantly affect HRQoL in this population. These results suggest that subjective experiences, including emotional well-being and the chronicity of symptoms, may be more directly associated with quality of life than medical history or comorbidities [
13].
In conclusion, this study is meaningful in that it identified key factors influencing HRQoL in patients with RCTs. The findings suggest a shift in clinical priorities from conventional pain- and function-based interventions toward more targeted strategies addressing sleep quality as a core determinant of HRQoL. Sleep status, which strongly affects physical recovery, emotional stability, and daily functioning, should be routinely assessed and managed in nursing practice. Additionally, categorizing occupations according to functional demands rather than descriptive titles offers new insight into evaluating patients’ physical requirements and recovery potential [
32]. This functional approach may help guide the development of customized nursing strategies that consider patients’ daily environments and lived experiences. The simplified presentation of key predictors in this study also provides practical evidence for designing nursing interventions and educational programs. In particular, sleep and emotional health represent domains in which nurses can play an active role through education, counseling, and supportive care. Since the final regression model explained approximately 53% of the variance in HRQoL, future research should incorporate additional variables such as overall health status, comorbidities, coping strategies, and social support to yield a more comprehensive understanding. Differentiating general pain from night pain, which may have an immediate impact on sleep quality, could improve the precision of symptom assessment. Moreover, future studies may benefit from incorporating objective measures such as wearable sleep trackers to complement self-reported data and enhance the accuracy of sleep assessment.
This study has several limitations. First, generalizability is restricted, as the sample consisted of 118 RCT patients recruited from the orthopedic outpatient clinic of a single tertiary hospital in Seoul. Caution is warranted in interpretation because the study population included post-surgical acute-phase patients, and the purpose of outpatient visits was not distinguished. Second, although the sample size was initially calculated based on disease-related characteristics and primary variables, several general characteristics reached statistical significance during bivariate analysis and were subsequently included in the final regression model. While the sample of 118 participants satisfied the power requirements for the expanded model with 13 predictors, larger and more diverse samples are needed to enhance generalizability. Third, night pain is a characteristic symptom of RCTs, yet the survey items did not explicitly distinguish it. Consequently, respondents may have incorporated night pain into their general pain responses, which likely contributed to sleep quality emerging as the most significant factor. Future studies should specifically assess the correlation between night pain and sleep quality. Fourth, the WHOQOL-BREF, as a generic HRQoL instrument, may not fully capture the unique quality of life issues faced by RCT patients due to its broad scope. Finally, this study considered only diabetes mellitus as a chronic disease variable, although other comorbidities such as osteoarthritis and previous shoulder injuries may also influence the progression and lived experience of RCTs. The exclusion of these conditions may have limited the assessment of comorbidities on HRQoL.
CONCLUSION
This study found significant correlations between HRQoL and factors including age, education level, marital status, occupation, medication use for RCT-related pain, presence of diabetes mellitus, symptom days per week, symptom hours per day, frequency of shoulder-intensive sports activities, pain, functional disability, depression, and sleep quality. Stepwise regression analysis identified sleep quality, occupation, depression, and symptom hours per day as the most influential predictors of HRQoL.
Based on these findings, comprehensive quality of life improvement programs should be developed and implemented for patients with RCTs. Such programs should include targeted interventions to improve sleep, psychological support for depressive symptoms, consideration of occupational demands, and early management of prolonged symptoms.
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CONFLICTS OF INTEREST
The authors declared no conflict of interest.
-
AUTHORSHIP
Study conception and design acquisition - HJH and YHP; analysis - HJH; interpretation of the data - HJH; and drafting or critical revision of the manuscript for important intellectual content - HJH and YHP.
-
FUNDING
None.
-
ACKNOWLEDGEMENT
This article is a revision of the Hyo Jung Han’s master’s thesis (dissertation) from Seoul National University.
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DATA AVAILABILITY STATEMENT
The data can be obtained from the corresponding author.
Table 1.Socio-demographic and Disease-Related Characteristics (N=118)
|
Variables |
Categories |
n (%) |
M±SD |
Min–Max |
|
Age (year) |
30–39 |
3 (2.5) |
62.05±8.86 |
36–83 |
|
40–49 |
8 (6.8) |
|
|
|
50–59 |
28 (23.7) |
|
|
|
60–69 |
62 (52.5) |
|
|
|
70–79 |
14 (11.9) |
|
|
|
80–89 |
3 (2.5) |
|
|
|
Sex |
Male |
51 (43.2) |
|
|
|
Female |
67 (56.8) |
|
|
|
BMI (kg/m2) |
Underweight and normal weight (≤22.9) |
26 (22.0) |
24.77±2.63 |
18.4–32.5 |
|
Overweight (23.0–24.9) |
36 (30.5) |
|
|
|
Obesity (≥25.0) |
56 (47.5) |
|
|
|
Education level |
Middle or lower |
24 (20.3) |
|
|
|
High |
42 (35.6) |
|
|
|
College or above |
52 (44.1) |
|
|
|
Marital status |
Married |
108 (91.5) |
|
|
|
Others |
10 (8.5) |
|
|
|
Occupation |
Office worker, professional |
30 (25.4) |
|
|
|
Non-office worker |
36 (30.5) |
|
|
|
Housewife |
42 (35.6) |
|
|
|
Unemployed or retired |
10 (8.5) |
|
|
|
Smoking |
Ever smoker |
33 (28.0) |
|
|
|
Non-smoker |
85 (72.0) |
|
|
|
Symptom duration (month) |
<6 |
12 (10.2) |
32.71±42.80 |
1.5–240.0 |
|
6–11 |
18 (15.3) |
|
|
|
12–36 |
65 (55.1) |
|
|
|
>36 |
23 (19.5) |
|
|
|
Symptom days per week |
1–2 |
7 (5.9) |
5.34±1.67 |
2–7 |
|
3–5 |
59 (50.0) |
|
|
|
6–7 |
52 (44.1) |
|
|
|
Symptom hours per day |
Within an hour |
10 (8.5) |
10.00±8.01 |
0.1–24.0 |
|
1–5 |
32 (27.1) |
|
|
|
6–11 |
30 (25.4) |
|
|
|
12–23 |
26 (22.0) |
|
|
|
24 |
20 (16.9) |
|
|
|
Usual level of shoulder use |
|
|
3.22±1.12 |
1–5 |
|
Frequency of shoulder-intensive sports activities |
|
|
2.28±1.28 |
1–5 |
|
History of rotator cuff repair surgery |
Yes |
31 (26.3) |
|
|
|
No |
87 (73.7) |
|
|
|
Medication use for rotator cuff-related pain |
Yes |
23 (19.5) |
|
|
|
No |
95 (80.5) |
|
|
|
Diabetes mellitus |
Yes |
24 (20.3) |
|
|
|
No |
94 (79.7) |
|
|
Table 2.Pain, Functional Disability, Depression, Sleep Quality and HRQoL (N=118)
|
Variables |
Subscale |
M±SD |
Range |
Min |
Max |
|
Pain |
|
5.40±2.37 |
0–10 |
0 |
10 |
|
Functional disability |
Total |
55.86±17.24 |
0–100 |
5.38 |
100 |
|
Pain |
67.73±19.83 |
|
8.00 |
100 |
|
Function disability |
48.64±18.19 |
|
3.75 |
100 |
|
Depression |
|
33.84±6.33 |
0–60 |
24.00 |
53.00 |
|
Sleep quality |
|
43.79±18.32 |
0–80 |
8.60 |
71.20 |
|
HRQoL |
Total |
53.50±11.73 |
0–100 |
28.16 |
75.30 |
|
Physical health |
11.85±2.48 |
4–20 |
6.29 |
17.14 |
|
Psychological health |
12.31±2.27 |
4–20 |
7.33 |
17.33 |
|
Social relationship |
13.47±2.09 |
4–20 |
6.66 |
18.66 |
|
Environment |
12.62±1.87 |
4–20 |
7.50 |
17.00 |
Table 3.Differences in HRQoL According to Participant Characteristics (N=118)
|
Variables |
Categories |
n (%) |
HRQoL |
|
M±SD |
t/z or F (p) Scheffe |
|
Age (year) |
<65 |
66 (55.9) |
56.92±9.43 |
3.76 (<.001) |
|
≥ 65 |
52 (44.1) |
49.17±12.97 |
|
Sex |
Male |
51 (43.2) |
54.91±11.91 |
1.13 (.259) |
|
Female |
67 (56.8) |
52.44±11.57 |
|
BMI (kg/m2) |
Underweight & normal weight (≤22.9) |
26 (22.0) |
52.26±12.24 |
0.66 (.517) |
|
Overweight (23.0–24.9) |
36 (30.5) |
52.36±11.78 |
|
Obesity (≥25) |
56 (47.5) |
54.82±11.54 |
|
Education level |
Middle or lowera
|
24 (20.3) |
47.27±12.23 |
11.24 (<.001) a, b<c |
|
Highb
|
42 (35.6) |
50.71±10.56 |
|
College or abovec
|
52 (44.1) |
56.64±10.35 |
|
Marital status†
|
Married |
108 (91.5) |
54.19±11.53 |
1.97 (.049) |
|
Others |
10 (8.5) |
46.09±12.00 |
|
Occupation |
Office worker & professional |
30 (25.4) |
60.35±8.50 |
4.59 (<.001) |
|
Others |
88 (74.6) |
51.17±11.81 |
|
Smoking |
Ever smoker |
33 (28.0) |
53.84±12.52 |
0.18 (.854) |
|
Non-smoker |
85 (72.0) |
53.37±11.49 |
|
History of rotator cuff repair surgery |
Yes |
31 (26.3) |
57.04±10.71 |
1.98 (.050) |
|
No |
87 (73.7) |
52.24±11.88 |
|
Medication use for rotator cuff-related pain†
|
Yes |
23 (19.5) |
44.28±11.40 |
–4.13 (<.001) |
|
No |
95 (80.5) |
55.74±10.73 |
|
Diabetes mellitus†
|
Yes |
24 (20.3) |
46.18±12.18 |
–3.23 (<.001) |
|
No |
94 (79.7) |
55.38±10.91 |
Table 4.Correlations among Variables Related to HRQoL (N=118)
|
Variables |
r (p) |
|
A |
B |
C |
D |
E |
F |
G |
H |
I |
J |
|
A. Symptom duration |
1 |
|
|
|
|
|
|
|
|
|
|
B. Symptom days per week |
.14 (.119) |
1 |
|
|
|
|
|
|
|
|
|
C. Symptom hours per day |
.16 (.086) |
.56 (<.001) |
1 |
|
|
|
|
|
|
|
|
D. Usual level of shoulder use |
.28 (.002) |
.21 (.021) |
.16 (.087) |
1 |
|
|
|
|
|
|
|
E. Frequency of shoulder-intensive sports activities |
.02 (.855) |
–.07 (.458) |
–.15 (.111) |
.02 (.861) |
1 |
|
|
|
|
|
|
F. Pain |
.05 (.592) |
.21 (.024) |
.31 (<.001) |
.08 (.374) |
–.05 (.625) |
1 |
|
|
|
|
|
G. Functional Disability |
.13 (.170) |
.27 (.003) |
.49 (<.001) |
.01 (.881) |
–.20 (.030) |
.58 (<.001) |
1 |
|
|
|
|
H. Depression |
.02 (.827) |
.30 (.001) |
.48 (<.001) |
.04 (.676) |
–.14 (.143) |
.32 (<.001) |
.43 (<.001) |
1 |
|
|
|
I. Sleep quality |
–.11 (.259) |
–.33 (<.001) |
–.47 (<.001) |
–.06 (.506) |
.03 (.734) |
–.34 (<.001) |
.49 (<.001) |
–.71 (<.001) |
1 |
|
|
J. HRQoL |
–.08 (.418) |
–.32 (<.001) |
–.51 (<.001) |
–.14 (.123) |
.24 (.008) |
–.21 (.025) |
–.49 (<.001) |
–.60 (<.001) |
.64 (<.001) |
1 |
Table 5.Factors Influencing the Health-Related Quality of Life (N=118)
|
Variables |
Model 1 |
Model 2 |
Model 3 |
Model 4 |
|
B |
S.E. |
β |
t |
p
|
B |
S.E. |
β |
t |
p
|
B |
S.E. |
β |
t |
p
|
B |
S.E. |
β |
t |
p
|
|
(Constant) |
35.60 |
2.17 |
|
16.39 |
<.001 |
34.51 |
2.05 |
|
16.82 |
<.001 |
58.73 |
7.74 |
|
7.59 |
<.001 |
59.54 |
7.57 |
|
7.87 |
<.001 |
|
Sleep quality |
0.41 |
0.05 |
.64 |
8.93 |
<.001 |
0.39 |
0.04 |
.61 |
9.05 |
<.001 |
0.26 |
0.06 |
.40 |
4.42 |
<.001 |
0.23 |
0.06 |
.36 |
3.91 |
<.001 |
|
Occupation (office, professional=ref.) |
|
|
|
|
|
7.49 |
1.81 |
.28 |
4.14 |
<.001 |
7.50 |
1.74 |
.28 |
4.32 |
<.001 |
6.92 |
1.71 |
.26 |
4.04 |
<.001 |
|
Depression |
|
|
|
|
|
|
|
|
|
|
–0.54 |
0.17 |
–.29 |
–3.24 |
.002 |
–0.44 |
0.17 |
–.24 |
–2.62 |
.010 |
|
Symptom hours per day |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
–0.27 |
0.11 |
–.19 |
–2.53 |
.013 |
|
R2
|
.41 |
.48 |
.53 |
.55 |
|
Adjusted R2
|
.40 |
.48 |
.52 |
.54 |
|
△R2
|
.41 |
.08 |
.04 |
.03 |
|
F change (p) |
79.71 (<.001) |
17.16 (<.001) |
10.47 (.002) |
6.38 (.013) |
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