Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Nurs Res. 2021 Jan;70(5):334–343. doi: 10.1097/NNR.0000000000000528

Pain and Self-Efficacy Among Patients with Systemic Sclerosis: A Scleroderma Patient-centered Intervention Network (SPIN) Cohort Study

Robyn K Wojeck 1, Susan G Silva 2, Donald E Bailey Jr 3, Mitchell R Knisely 4, Linda Kwakkenbos 5, Marie-Eve Carrier 6, Warren R Nielson 7, Susan J Bartlett 8, Janet Pope 9, Brett D Thombs 10
PMCID: PMC8543734  NIHMSID: NIHMS1715188  PMID: 34173376

Abstract

Background:

Pain is one of the most common symptoms affecting patients with systemic sclerosis, however, little is known about the relationship between self-efficacy and pain and changes in pain over time.

Objectives:

The purpose of this study was to describe the relationships between self-efficacy and pain in patients with systemic sclerosis, as well as determine whether changes in self-efficacy mediate changes in pain.

Methods:

A prospective longitudinal study was conducted using data from the Scleroderma Patient-centered Intervention Network Cohort. The baseline sample included 1903 adults, with a trajectory subsample of 427 who completed three-month assessments across three-years. Hierarchical (sequential) forward multivariable regression, covarying for participant characteristics, was conducted to determine the association between self-efficacy and patient characteristics on pain outcomes. Trajectory models, covarying for participant characteristics, were used to examine changes in self-efficacy and pain outcomes across time, and whether self-efficacy mediated the pain trajectories.

Results:

Mean time since diagnosis was 9.5 years with 39.2% diagnosed with diffuse cutaneous systemic sclerosis. Greater self-efficacy was associated with less pain interference and intensity. Increasing age, female gender, finger ulcers, and small joint contractures were related to greater pain interference and intensity. Esophageal gastrointestinal symptoms were associated with more pain interference. Self-efficacy and pain trajectories remained stable across time, and self-efficacy did not mediate the pain trajectories.

Discussion:

This study identified self-efficacy, age, gender, finger ulcers, small joint contractures, and esophageal gastrointestinal symptoms as important correlates associated with pain in patients with systemic sclerosis. Additionally, this study found that self-efficacy and pain outcomes remained stable over time, providing important insights into the longitudinal pain experiences of patients with systemic sclerosis.

Keywords: pain, self-efficacy, systemic sclerosis, scleroderma

Systemic sclerosis (SSc; scleroderma) is a rare, progressive, multisystem autoimmune disease characterized by immune dysfunction, vasculopathy, and fibrosis of the skin and internal organs (Van Den Hoogen et al., 2013). While prevalence rates vary worldwide (7 to 489 cases per million; Chifflot et al., 2008), SSc is more common in women with a peak disease onset often between 20 to 50 years old (Alba et al., 2014). SSc is associated with significant morbidity and mortality, as well as high health care costs and reduced productivity resulting in a total cost of $1.9 billion per year in North America (Fischer et al., 2017). Patients with SSc exhibit a variety of clinical manifestations, but are often divided into two main subsets, limited (lcSSc) or diffuse cutaneous SSc (dcSSc), based on the extent of skin involvement (Denton & Khanna, 2017). There is no cure for SSc and major goals of treatment are aimed at ameliorating symptoms, reducing functional disability, and improving health-related quality of life (Almeida et al., 2015).

Pain is one of the most common and debilitating symptoms experienced by patients with SSc, leading to functional disability and reduced health-related quality of life (Haythornthwaite et al., 2003). Pain is experienced by up to 83% of patients with SSc with more than a third of patients rating their pain as moderate to severe (Schieir et al., 2010). The most common source of pain is joint pain (Ostojic et al., 2019); other sources of pain include finger ulcers, joint contractures, gastrointestinal symptoms, synovitis, joint tenderness, and Raynaud’s phenomenon (Malcarne et al., 2007; Schieir et al., 2010). Patients with SSc have described their pain as excruciating, debilitating, and draining (Suarez-Almazor et al., 2007; Sumpton et al., 2017). In addition, nearly half of patients with SSc experience pain on a daily basis (Ostojic et al., 2019), underscoring the importance of effective pain management in this population. Pain management in SSc includes a variety of treatments such as pharmacologic therapies, exercise and rehabilitation programs, and psychosocial interventions, however, additional emphasis on psychosocial correlates, such as self-efficacy, on pain in patients with SSc is needed to better understand and treat pain this population (Merz et al., 2018).

While SSc is a chronic and progressive disease in which patients are challenged with managing their symptoms on a daily basis, patients with SSc report less confidence in their ability to perform self-care tasks related to managing their pain (Thombs et al., 2017). More severe symptoms, such as higher levels of pain, have also been associated with lower levels of self-efficacy, or one’s perceived confidence in performing a specific behavior or task (Bandura, 1997), in patients with SSc (Buck et al., 2010; Thombs et al., 2017). Self-efficacy is an essential component of chronic disease self-management (Moore et al., 2016), and gaining a better understanding of the relationship between pain and self-efficacy is essential for future development of self-management interventions in this population.

Additionally, little is known about changes in self-efficacy and pain over time. To our knowledge, only two studies have explored changes in pain over time (Merz et al., 2017; Sekhon et al., 2010). Sekhon et al. (2010) found little change in pain among 109 patients with SSc who were evaluated at two consecutive visits (ranging from 4.2 to 10.9 months between visits), and Merz et al. (2017) found a slight improvement in pain among patients with early disease who were followed for three annual visits. However, these studies were limited by small sample sizes and limited data collection points (i.e., two consecutive visits or three annual visits). Additionally, these studies did not explore self-efficacy and its potential mediating effect on pain which has been supported in other rheumatic diseases (Somers et al., 2010). As such, the aims of this study were to (a) describe the relationship between self-efficacy for managing pain and pain outcomes (i.e., pain interference and pain intensity), (b) examine self-efficacy for managing pain and pain trajectories across three years, and (c) determine whether changes in self-efficacy for managing pain mediate changes in pain outcomes across three years in patients with SSc.

Methods

Design

This study utilized prospective longitudinal cohort data collected at enrollment and each subsequent three-month follow-up assessment across three years (baseline to 36 months) from patients with SSc enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort. Self-efficacy for managing pain, pain interference, and pain intensity scores were evaluated at each assessment time point. The baseline assessment included patient data collected upon enrollment in the SPIN Cohort. This study was approved by the Institutional Review Board of Duke University Health System; the SPIN Cohort study was approved by the Research Ethics Committee of the Jewish General Hospital, Montreal, Canada and by the Institutional Review Boards of each participating SPIN center.

Cross-sectional analyses were conducted to determine whether self-efficacy was associated with pain outcomes at baseline. Trajectory analyses were performed to examine change in self-efficacy, pain interference, and pain intensity across 36-months. Finally, we explored whether change in self-efficacy was a mechanism that influenced pain trajectories across 36-months. Patient characteristics associated with pain scores based on published findings were included as covariates in the analyses. A 36-month period was selected based on two considerations: (1) examining a time period that was long enough to capture change or flares in pain outcomes and (2) 78% of those enrolled had not completed a 36-month assessment at the time of the analysis.

Data Source

The SPIN Cohort was established in 2013 to collect patient-reported data to better understand the problems faced by patients with SSc, and to develop and test interventions to improve quality of life for patients with SSc (Kwakkenbos et al., 2019). The SPIN Cohort includes adult patients with SSc from 45 SPIN Centers in the United States, Canada, the United Kingdom, France, Spain, Mexico, and Australia. The SPIN sample is a convenience sample in which eligible participants are invited to participate by a local SPIN physician or supervised nurse coordinator. Written informed consent is obtained and the SPIN physician or supervised nurse coordinator complete and submit an online medical data form, which initiates patient registration in the SPIN Cohort. After completion of online registration, participants receive an automated welcoming email with instructions on how to activate their online SPIN account and how to complete the SPIN Cohort online patient-reported measures. Participants complete the online patient-reported measures at enrollment and every three months.

Eligibility criteria for inclusion in the Cohort were: (1) a diagnosis of SSc according to the 2013 American College of Rheumatology/European League Against Rheumatism classification criteria (Van Den Hoogen et al., 2013) confirmed by a SPIN-affiliated physician, (2) 18 years of age or older, (3) ability to give informed consent, and (4) fluent in English, French, or Spanish. Exclusion criteria included not having access to the internet or otherwise not being able to respond to the patient-reported measures via the internet.

Selected Sample

This study included a baseline sample of 1903 patients with SSc who completed an enrollment assessment from January 2013 through December 2018, and a trajectory subsample comprised of 427 who completed the enrollment and month-36 assessments. From the baseline sample, participants with self-efficacy and pain (interference and intensity) scores at enrollment were included in the trajectory subsample. Of those, participants without self-efficacy and pain scores at month-36 were omitted from the trajectory subsample. Trajectory subsample participants, however, were not required to have self-efficacy and pain scores at each interim assessment. A sensitivity analysis was conducted to compare baseline participants included and excluded from the trajectory subsample (see data analysis plan and results). Supplemental Figure 1 provides a flow diagram of the final analysis samples for this study.

Data Collection

Participants completed patient-reported measures at enrollment (baseline) and every three months thereafter via the internet. Each online assessment included items to measure self-reported outcomes.

Baseline Patient Characteristics

Participants provided demographic data upon enrollment. The SPIN-affiliated physician reported age and gender as well as clinical data, such as the SSc subset (lcSSc or dcSSc), time since SSc diagnosis, and the presence or absence of disease manifestations.

Self-Efficacy for Managing Pain

One item from the Self-Efficacy for Managing Chronic Disease (SEMCD) scale (Ritter & Lorig, 2014) was used to measure self-efficacy for managing pain. The SEMCD scale consisted of six items measuring one’s confidence in performing certain activities regularly at the present time. The single item related to self-efficacy for managing physical discomfort or pain was used to measure self-efficacy for managing pain. More specifically, this item examined: “how confident do you feel that you can keep the physical discomfort or pain of your disease from interfering with things you want to do” (Ritter & Lorig, 2014). This item was rated on a scale from 1 (not confident at all) to 10 (totally confident), with higher scores indicating greater self-efficacy. The SEMCD scale is a valid and reliable measure in patients with SSc with established convergent validity and high internal consistency (Cronbach’s α = .93; Riehm et al., 2016) however, the reliability and validity of the single item related to pain has not been tested. For the participants in the trajectory subsample, self-efficacy was assessed at baseline and every three-months, except for months 3, 9, and 15. The trajectory subsample was comprised of participants who had been enrolled in the cohort for at least 36 months, which was before the inclusion of self-efficacy in the assessment packet at months 3, 9, and 15.

Pain Interference and Intensity

The pain interference domain and pain intensity item of the Patient-Reported Outcomes Measurement Information System-29 version 2.0 (PROMIS-29v2; Hays et al., 2018) were used to evaluate pain in the past seven days. The pain interference domain score was comprised of four items rated on a scale of 1 (not at all) to 5 (very much), with higher scores indicating greater pain interference. Raw domain scores, calculated by adding the ratings for the four pain interference items, had a possible range of 4 to 20. These scores were converted to T-scores standardized for the general U.S. population (Mean=50, Standard Deviation=10). The single pain intensity item was measured on an 11-point scale, ranging from 0 (no pain) to 10 (worst imaginable pain). Psychometric studies demonstrated that the latter provides valid and reliable measures of these pain constructs in patients with SSc, with demonstrated convergent validity and Cronbach’s α values ranging from .86 to .96 for domain scores (Kwakkenbos et al., 2017). Pain interference and pain intensity were assessed at enrollment and every three months.

Data Analysis

Non-directional tests were performed with significance set at .05 for all tests. The significance level was not adjusted for multiple testing due to the exploratory nature of this study. Effect sizes were calculated to address clinical significance. Analyses were conducted using SAS version 9.4 software.

Baseline Patient Characteristics

Descriptive statistics were used to summarize participant characteristics of the baseline sample and trajectory subsample. A sensitivity analysis using General Linear Models for scalar measures and chi-square tests for categorical data were performed to compare the baseline characteristics of those included in the trajectory subsample to those excluded from the subsample.

Baseline Covariates

Eight patient characteristics were selected a priori as covariates in the baseline and trajectory analysis. The three demographic covariates were age (at completion of baseline assessment), female gender, and married/living with a partner. The five clinical covariates were the presence of: (1) finger ulcers (i.e., digital pulp ulcers distal to the distal interphalangeal joints (DIPs) or anywhere else on the finger); (2) moderate or severe small joint contractures (i.e., to the DIP, proximal interphalangeal joints (PIPs), metacarpophalangeal joints (MCPs), and/or wrists); (3) esophageal gastrointestinal symptoms (i.e., dysphagia, heartburn, and/or reflux, due to SSc at any time, now or in the past); (4) time since diagnosis (i.e., time since diagnosis to completion of baseline assessment); and (5) diffuse SSc subset (i.e., defined as skin sclerosis involving the limbs proximal to the elbows and knees and/or the chest and/or trunk, at any time). These covariates were examined in previous studies exploring pain and have been associated with pain outcomes (Malcarne et al., 2007; Merz et al., 2017; Ostojic et al., 2019; Schieir et al., 2010). Although considered, the Modified Rodnan Skin Score (MRSS) was not included due to the 18% missing rate and its high overlap with disease subset.

Baseline Sample: Self-Efficacy and Pain Relationships

Bivariate regression was used to examine the relationship of (a) self-efficacy for managing pain with pain interference and intensity scores and (b) eight participant characteristics with these pain scores at enrollment in the baseline sample. Next, hierarchical (sequential) forward multivariable regression models were conducted to determine the influence of self-efficacy on pain scores, covarying for the eight participant characteristics. Variance inflation factors (VIF) scores were used to check for multicollinearity (VIF ≥ 5.0 indicating concern) among the predictors in each model. For this hierarchical approach, Model 1 included self-efficacy, Model 2 included self-efficacy and demographic covariates, and Model 3 (final model) included self-efficacy, demographic covariates, and clinical covariates. Adjusted R2 (aR2, adjusting for the number of predictors in the model) was used as an indicator of effect size and clinical significance. Small, medium, and large effects were indicated by an aR2 of .02, .13, and .26, respectively. These aR2 values are equivalent to Cohen’s f2 values of .02 (small), .15 (medium), and .35 (large) effects (Lenhard & Lenhard, 2015).

Trajectory Analyses

Random coefficients regression models for longitudinal data, a type of multi-level, mixed-effects model for repeated measures, were employed to determine the trajectories of change in self-efficacy and pain outcomes across 36 months in the trajectory subsample, covarying for patient characteristics. A hierarchical forward (sequential) modeling approach was used to build towards a final trajectory model for each outcome that included time and the eight patient characteristics. Model 1 included time, Model 2 included time and demographic covariates, and Model 3 (final model) included time, demographic covariates, and clinical covariates. Fixed effects were time (months) and covariates (baseline patient characteristics), while random effects were participant and participant-by-time. Baseline was defined as month-0. As needed, trajectories were fitted for a non-linear pattern of change. The assumption of data missing at random was evaluated.

Trajectory analyses, covarying for patient characteristics, were applied to test for the mediating effect of change in self-efficacy on pain trajectories across 36 months, in accordance with recommended guidelines and path criteria for establishing mediation (Baron & Kenny, 1986; Bennett, 2000).

Statistical Power

The baseline sample of 1903 provided greater than 80% statistical power to examine the relationship between self-efficacy and patient characteristics with the pain outcomes using a hierarchical forward regression, assuming nine explanatory variables (self-efficacy and eight covariates), small effect sizes (aR2 = .02), and two-tailed tests with significance set at .05 per test for the final and most complex models (Model 3). Based on these assumptions, it was estimated that a sample size of 788 would be required to achieve 80% power (G*Power3 software; Faul et al., 2007). The trajectory subsample of 427 with time and eight covariates in the model did not provide 80% power, assuming two-tailed tests, significance set at .05 per test, and the smallest clinical meaningful effect size for the time would be small effect sizes (aR2 = .01), as determined using SuperMix software for longitudinal analyses (Hedeker & Gibbons, 2021).

Results

Baseline Patient Characteristics

The baseline sample was comprised of 1903 adults with SSc. Of those, the 427 who completed a month-36 outcome assessment were included in the trajectory subsample. Table 1 presents the characteristics of the baseline sample and trajectory subsample at enrollment. The mean age of the baseline sample was 54.8 years (range: 18.3 to 88.6), with a high proportion of the sample being female (87.5%) and White (83.6%) which is consistent with other large SSc cohorts (Dougherty et al., 2018). The mean time from diagnosis was 9.5 years (range: 0.0 to 55.8) and 39.2% had dcSSc. The patients who performed serial measures over three years were comparable to the overall sample.

Table 1.

Patient Characteristics

Characteristics Baseline Sample
(n=1903)		 Trajectory Subsample
(n=427)
Age, in years 54.8 (12.6) 56.7 (11.7)
Female 1666 (87.5%) 377 (88.3%)
Race
 White 1589 (83.6%) 379 (89.0%)
 Black 115 (6.1%) 20 (4.7%)
 Other 197 (10.4%) 27 (6.3%)
Married/Living with partner 1358 (71.4%) 326 (76.4%)
Time since diagnosis, in years 9.5 (8.0) 10.0 (8.3)
Diffuse subset 739 (39.2%) 159 (37.8%)
Raynaud’s phenomenon 1853 (98.0%) 416 (98.4%)
Modified Rodnan Skin Score 7.2 (8.2) 7.3 (7.7)
Distal digital tip ulcers 655 (35.0%) 144 (33.7%)
Digital tip ulcers anywhere 306 (16.7%) 62 (15.0%)
Tendon friction rubs 395 (23.7%) 83 (21.8%)
Moderate - severe small joint contractures 472 (26.2%) 97 (24.1%)
Moderate - severe large joint contractures 227 (12.9%) 48 (11.9%)
Esophageal GI symptoms 1602 (85.0%) 366 (85.9%)
Stomach GI symptoms 572 (31.1%) 119 (28.8%)
Intestinal GI symptoms 727 (39.0%) 157 (37.6%)
Open in a new tab

Note. n (%) reported for categorical measures; mean (standard deviation) provided for scalar measures; GI, gastrointestinal.

Table 2 details self-efficacy for managing pain and pain scores at enrollment for the baseline sample and trajectory subsample. Pain interference scores for the baseline sample were converted to T-scores and revealed that the mean pain interference T-score of 55.5 (SD = 9.7) was significantly higher than the mean of 50 (SD = 10, z = 24.0, p < .001) estimated for the general U.S. population. A sensitivity analysis did not reveal significant differences in the baseline patient characteristics, self-efficacy scores, and pain scores for those included in the trajectory subsample (N = 427) compared to those excluded from the subsample (N = 1476).

Table 2.

Baseline Self-Efficacy for Managing Pain and Pain Scores

Baseline Measure Baseline Sample
Mean (SD)
(n=1903)		 Trajectory Subsample
Mean (SD)
(n=427)
Self-efficacy scores 6.1 (2.7) 6.3 (2.7)
Pain interference scores 9.4 (4.7) 9.2 (4.8)
Pain interference T-scores 55.5 (9.7) 55.1 (9.8)
Pain intensity scores 3.6 (2.6) 3.5 (2.7)
Open in a new tab

Note. SD = Standard Deviation; Self-efficacy range was 1 to 10, with higher scores indicating greater self-efficacy; Pain interference range was 4 to 20, with higher scores indicating greater pain interference; Pain intensity range was 0 to 10, with higher scores representing greater pain intensity.

Baseline Analysis: Bivariate Relationships

Bivariate regression indicated that self-efficacy for managing pain was significantly related to pain interference (β = −.60, p < .001, aR2 = .36) and intensity (β = −.55, p < .001, aR2 = .30), with an aR2 ≥ .26 indicating large effects (Table 3, Model 1). As self-efficacy increased (greater confidence), pain interference and pain intensity scores significantly decreased (less pain). As expected, pain interference and pain intensity scores were positively correlated (β = +.85, p < .001, aR2 = .72).

Table 3.

Baseline Analysis: Hierarchical Forward Multivariable Regression Models

Model 1 (n=1903) Model 2 (n=1899) Model 3 (n=1689)
Pain Outcome Block Explanatory Variable aR2 = .36 aR2 = .36 aR2 = .38
b SE β b SE β b SE β
Interference 1 Self-efficacy −1.05 *** .03 −.60 −1.05*** .03 −.60 −1.01*** .03 −.58
2 Age, in years +.01 .01 +.03 +.02* .01 +.04
Female +.88 *** .26 +.06 +.89*** .28 +.06
Married/living with partner −.50** .19 −.05 −.36 .20 −.03
3 Finger ulcers +.85*** .27 +.07
Small joint contractures +.50* .23 +.05
Esophageal GI symptoms +.82*** .25 +.06
Time since diagnosis +.01 .01 +.12
Diffuse subset +.37 .20 +.04
Pain Outcome Block Explanatory Variable aR2 = .30 aR2 = .31 aR2 = .32
b SE β b SE β b SE β
Intensity 1 Self-efficacy −.53 *** .02 −.55 −.54*** .02 −.55 −.51 *** .02 −.53
2 Age +.01* .00 +.04 +.01* .00 +.04
Female +.58*** .15 +.07 +.58*** .16 +.07
Married/living with partner −.33** .11 −.06 −.23* .12 −.04
3 Finger ulcers +.42** .15 +.06
Small joint contractures +.39** .13 +.07
Esophageal GI symptoms +.28 .15 +.04
Time since diagnosis +.01 .01 +.02
Diffuse subset +.10 .12 +.02
Open in a new tab

Note. aR2=adjusted R2; b=unstandardized regression coefficient; SE=standard error; β =standard regression coefficient;

*

p≤.05;

**

p≤.01;

***

p≤.001;

Block 1=self-efficacy; Block 2=baseline demographic characteristics; Block 3=baseline clinical characteristics; Self-efficacy: higher scores=greater self-efficacy for managing pain; Pain interference: higher scores=greater pain interference; Pain intensity: higher scores=greater pain intensity. Self-efficacy and age were continuous variables; Remaining characteristics coded 0=absent and 1=present; aR2: .02=small, .13=medium, .26=large effect sizes.

Patient characteristics were significantly associated with both pain interference and intensity (p ≤ .05). Exceptions were age and time since diagnosis. Younger age was significantly related to greater pain interference (β = −.05, p = .034, aR2 = .01); however, age was not associated with intensity (β = −.04, p = .093, aR2 = .00). Time since diagnosis was not significantly related to pain interference (β = +.03, p = .188, aR2 = .00) or intensity (β = +.04, p = .124, aR2 = .00). Finger ulcers explained 3% of variability of pain interference and intensity (both β = +.17, p < .001, aR2 = .03, small effects). Moderate or severe small joint contractures also explained 3% of variability of both pain interference and intensity (both β = +.18, p < .001, aR2 = .03, small effects). Although statistically significant, the remaining characteristics individually explained 1% or less of the variability of the pain scores (aR2 ≤ .01, weak effects).

Baseline Analysis: Multivariable Relationships

Table 3 provides the hierarchical forward multivariable regression results. The final full covariate-adjusted model (Model 3) indicated that self-efficacy was significantly related to both pain interference (β = −.58, p < .001) and intensity (β = −.53, p < .001), after adjusting for all eight patient characteristics. Specifically, increasing self-efficacy was associated with less pain interference and intensity. This final model explained 38% of the variability of pain interference (aR2 = .38) and 32% of the variability of intensity (aR2 = .32), which was a 2% improvement in aR2 for both pain outcomes compared to the initial model that included only self-efficacy (Model 1). VIF scores were <1.2 for the final model, indicating that multicollinearity was not a concern.

The final model indicated the following statistically significant relationships between each covariate and the pain outcomes, after adjusting for self-efficacy and other patient characteristics: (1) increasing age was associated with greater pain interference and intensity; (2) females reported greater pain interference and intensity; (3) adults married/living with a partner reported less pain intensity, but this covariate was not related to pain interference; (4) finger ulcers were associated with greater pain interference and intensity; (5) moderate or severe small joint contractures were related to greater pain interference and intensity; and (6) esophageal gastrointestinal symptoms were associated with greater pain interference, but not intensity. Neither time since diagnosis nor diffuse subset were significantly related to pain outcomes. In contrast to the bivariate results, the results from the full regression model indicated (a) older age was significantly associated with both pain outcomes; (b) married/living with a partner was only related to pain intensity; (c) esophageal gastrointestinal symptoms were associated with pain interference only; and (d) dcSSc was not related to pain interference or intensity.

Trajectories Analysis: Change in Self-Efficacy and Pain Outcomes

Supplemental Table 1 presents descriptive statistics and the completion rate for self-efficacy, pain interference, and pain intensity at each assessment across the 36 months. All 427 patients in the trajectory subsample completed the baseline (Month-0) and final (Month-36) assessment. The completion rate was 85% or higher for most of the other assessments included in each analysis. For each outcome, a linear trajectory model best fitted the longitudinal data.

Table 4 presents the trajectory model results for self-efficacy and pain outcomes, applying a forward model building approach. The time effect in the initial model (Model 1) and two covariate-adjusted models (Models 2 and 3) for each outcome was not statistically significant, indicating no significant change across the 36 months in self-efficacy, pain interference, or pain intensity. With regard to magnitude of change across the 36 months, minimal to very small effect sizes were observed for each outcome (intraclass correlation coefficients < .15). Figure 1 presents the adjusted mean trajectory for each outcome, covarying for the patient characteristics (Model 3).

Table 4.

Trajectory Analysis Hierarchical Forward Mixed-effects Models

Outcome Block Explanatory Variable Model 1 (n=427) Model 2 (n=424) Model 3 (n=377)
p p p
Interference 1 Time, in months .382 .548 .719
2 Age, in years .215 .591
Female .832 .956
Married/living with partner .044 .215
3 Finger ulcers .005
Small joint contractures .007
Esophageal GI symptoms .001
Time since diagnosis .195
Diffuse subset .844
Intensity 1 Time, in months .298 .323 .158
2 Age, in years .400 .824
Female .942 .761
Married/living with partner .033 .181
3 Finger ulcers .001
Small joint contractures .001
Esophageal GI symptoms .006
Time since diagnosis .147
Diffuse subset .126
Self-efficacy 1 Time, in months .101 .217 .984
2 Age, in years .033 .135
Female .110 .158
Married/living with partner .039 .107
3 Finger ulcers .042
Small joint contractures .012
Esophageal GI symptoms .001
Time since diagnosis .125
Diffuse subset .444
Open in a new tab

Note. Mixed-effects trajectory models with time and baseline patient characteristics (covariate) as fixed effects; patient and patient-by-time interaction as random effects; Block 1=time, in months; Block 2=baseline demographic characteristics; Block 3=baseline clinical characteristics; Significant results are bolded.

Three baseline covariates were significantly related to all three outcomes in the final model (Model 3). Finger ulcers were associated with greater pain interference (unstandardized regression coefficient (b) = +1.81, SE = .63, p = .005), greater pain intensity (b = +1.21, SE = .37, p = .001), and less self-efficacy (b = −.75, SE = .37, p = .042). Moderate-to-severe small joint contractures were associated with greater pain interference (b = +1.43, SE = .52, p = .007), greater pain intensity (b = +1.00, SE = .31, p = .001), and less self-efficacy (b = −.75, SE = .30, p = .012). Finally, esophageal gastrointestinal symptoms were associated with greater pain interference (b = +1.91, SE = .57, p = .001), greater pain intensity (b = +.93, SE = .33, p = .006), and less self-efficacy (b = −1.13, SE = .33, p = .001). Thus, finger ulcers, small joint contractures, and esophageal gastrointestinal symptoms were related to higher levels of pain and less self-efficacy for managing pain.

Change in Self-Efficacy as a Mediator of Pain

Change in self-efficacy over 36 months did not mediate the trajectories of pain interference or intensity, covarying for patient characteristics. Not all four criteria (paths) for establishing mediation were met. Path A (impact of time on self-efficacy) was not met due to the minimal, non-significant change in self-efficacy over time (noted above). Path B (association between self-efficacy and pain outcomes) was met for improvements in self-efficacy and were significantly associated with reductions in pain interference (b = −.43, SE = .07, p < .001, aR2 = .09, small effect) and intensity (b = −.24, SE = .04, p < .001, aR2 = .08, small effect). Path C (impact of time on pain outcomes) and Path C’ (impact of time on pain outcomes, covarying for self-efficacy over time) were not met due to the minimal, non-significant change in pain interference and intensity over time (noted above).

Discussion

SSc is a chronic and progressive disease in which patients frequently experience pain and are challenged with self-managing their symptoms throughout the disease course. In this study, self-efficacy for managing pain and pain interference and intensity did not significantly change over the three-year period. One potential explanation for the stable pain trajectories found in our study is that our sample consisted of patients who had lived with their disease for approximately nine years. Previous research found that patients experience higher levels of disease activity, more rapid progression, and worsening skin thickening within the first five years of symptom onset, after which the overall disease course remains relatively stable (Medsger, 2003). Given patients had stable disease, the occurrence of chronic pain is unsurprising. Chronic pain, or pain that occurs for more than three months (Treede et al., 2015), has been reported in up to 75% of patients with SSc and is often refractory to treatment (Thombs et al., 2008). While our trajectories supported the chronicity of pain, patients have also described their disease as progressive in nature with superimposed flares lasting for three days to three months (Suarez-Almazor et al., 2007). While qualitative studies have provided descriptions of symptom flares in patients with SSc, quantitative studies have yet to quantify or characterize such flares. One potential explanation for this is that the longitudinal data used to establish trajectories in SSc does not include time points frequent enough to capture periods of intense symptoms, subsequently resulting in relatively stable trajectories as seen in this study. Future studies that measure these distinct periods of symptom flares are needed to guide the timing and delivery of pain management interventions in this population.

In our study, the presence of finger ulcers, small joint contractures, and esophageal gastrointestinal symptoms were associated with baseline pain outcomes. While our study underscored the association between finger ulcers and pain, we did not explore the frequency or chronicity of finger ulcers. This is an important consideration given that previous research has identified that patients who experience more frequent digital ulcers, such as those with recurrent or chronic ulcers, often have a higher disease burden and a greater need for intervention (Matucci-Cerinic et al., 2016).

Additionally, we found that small joint contractures and esophageal gastrointestinal symptoms (i.e., dysphagia, heartburn, and/or reflux) were significantly associated with pain interference and intensity. Previous studies have also described an association between joint contractures and esophageal gastrointestinal symptoms (Ashida et al., 2007; Johnson et al., 2006), however, these studies did not explore differences in pain outcomes separately among small and large joint contractures, or explore the extent and severity of gastrointestinal symptoms. This is an important consideration given that small joint contractures of the hand often do not improve with pharmacologic therapy (Young et al., 2016) and many patients do not respond to existing treatments for esophageal gastrointestinal symptoms (Denaxas et al., 2018). Future studies might explore differences in pain outcomes in patients who experience more frequent and/or chronic finger ulcers, and differences in the extent and severity of gastrointestinal symptoms extending beyond esophageal symptoms to inform future pain management interventions in patients with SSc.

Greater self-efficacy for managing pain was associated with less pain interference and lower pain intensity. Patients with SSc report worse self-efficacy than patients with other chronic diseases (i.e., multiple sclerosis, cardiovascular disease, and breast cancer), particularly related to performing self-care tasks for pain (Thombs et al., 2017). Despite the correlation between self-efficacy and pain in this population (Buck et al., 2010), self-management interventions aimed at improving self-efficacy have shown mixed results. For example, there was no significant difference in self-efficacy in a randomized controlled trial comparing an internet-based self-management program with a patient-focused educational book (Khanna et al., 2019). However, online self-management programs for patients with chronic diseases (i.e., arthritis, diabetes, hypertension, lung and heart disease) such as the Chronic Disease Self-Management Program (CDSMP) have been associated with improvements in self-efficacy and health outcomes (Lorig et al., 2006), with sustained improvements in outcomes over time (Barlow et al., 2005). While self-efficacy for managing pain did not change over time and was not a mediator of the pain trajectories in our study, future studies might explore the timing and effect of self-management interventions on pain outcomes, such as the delivery of a self-management intervention during a period of high disease activity (i.e., within the first five years of disease onset).

Limitations

Our study had many strengths, including a large sample, validated SSc diagnosis by a health care provider, inclusion of patient-reported measures, and the use of longitudinal, multicenter data. The SPIN Cohort is a convenience sample of patients receiving care at a SPIN Center with access to the internet and the ability to complete online measures, which may limit the generalizability of findings. Additionally, participants in this study had a mean disease duration of 9.5 years, and results may differ for patients with a shorter or longer disease duration. While patient-reported measures were used, our study only used a single item of the Self-Efficacy for Managing Chronic Disease scale; future studies should consider use of the full scale. Finally, while the baseline sample size provided power to detect statistically significant relationships with small, non-clinically meaningful effects, such findings should be interpreted in terms of their clinical relevance. The sample size for the trajectory analysis did not provide adequate power for the very small time-effects observed. Thus, this analysis was exploratory in nature.

Conclusion

This study examined the relationship between self-efficacy for managing pain and pain outcomes upon enrollment and over three years in adults with SSc. Our findings indicate that self-efficacy for managing pain was strongly and inversely related to pain outcomes and that self-efficacy for managing pain and pain outcomes remained stable over a three-year period. We identified important correlates (i.e., self-efficacy for managing pain, age, gender, finger ulcers, small joint contractures, and esophageal gastrointestinal symptoms) associated with pain outcomes in adults with SSc that may serve as important factors to consider in the development of pain management interventions. Additionally, our findings provide important insights into the longitudinal pain experiences of patients with SSc and can inform the assessment and management of pain in this population.

Supplementary Material

Supplemental Data File (doc, pdf, etc.)_2

Supplemental Figure 1. SPIN = Scleroderma Patient-centered Intervention Network

Acknowledgements

We would like to acknowledge and thank the SPIN Investigators: Murray Baron, McGill University, Montreal, Quebec, Canada; Daniel E. Furst, Division of Rheumatology, Geffen School of Medicine, University of California, Los Angeles, California, USA; Karen Gottesman, Scleroderma Foundation, Los Angeles, California, USA; Vanessa Malcarne, San Diego State University, San Diego, California, USA; Maureen D. Mayes, University of Texas McGovern School of Medicine, Houston, Texas, USA; Luc Mouthon, Université Paris Descartes, Paris, France; Karen Nielsen, Scleroderma Society of Ontario, Ottawa, Ontario, Canada; Robert Riggs, Scleroderma Foundation, Danvers, Massachusetts, USA; Maureen Sauve, Scleroderma Society of Ontario, Hamilton, Ontario; Fredrick Wigley, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Shervin Assassi, University of Texas McGovern School of Medicine, Houston, Texas, USA; Andrea Benedetti, McGill University, Montreal, Quebec, Canada; Isabelle Boutron, Université Paris Descartes, and Assistance Publique - Hôpitaux de Paris, Paris, France; Ghassan El-Baalbaki, Université du Québec à Montréal, Montreal, Quebec, Canada; Carolyn Ells, McGill University, Montreal, Quebec, Canada; Cornelia van den Ende, Sint Maartenskliniek, Nijmegen, The Netherlands; Kim Fligelstone, Scleroderma & Raynaud’s UK, London, UK; Catherine Fortune, Scleroderma Society of Ontario, Hamilton, Ontario, Canada; Tracy Frech, University of Utah, Salt Lake City, Utah, USA; Amy Gietzen, Scleroderma Foundation, Tri-State Chapter, Binghamton, New York, USA; Dominique Godard, Association des Sclérodermiques de France, Sorel-Moussel, France; Geneviève Guillot, Sclérodermie Québec, Montreal, Quebec, Canada; Daphna Harel, New York University, New York, New York, USA; Monique Hinchcliff, Yale School of Medicine, New Haven, Connecticut, USA; Marie Hudson, McGill University, Montreal, Quebec, Canada; Sindhu R. Johnson, Toronto Scleroderma Program, Mount Sinai Hospital, Toronto Western Hospital, and University of Toronto, Toronto, Ontario, Canada; Maggie Larche, McMaster University, Hamilton, Ontario, Canada; Catarina Leite, University of Minho, Braga, Portugal; Christelle Nguyen, Université Paris Descartes, and Assistance Publique - Hôpitaux de Paris, Paris, France; Gema Cutillas, Asociación Española de Esclerodermia, Madrid, Spain; François Rannou, Université Paris Descartes, and Assistance Publique - Hôpitaux de Paris, Paris, France; Michelle Richard, Scleroderma Society of Nova Scotia, Halifax, Nova Scotia, Canada; Tatiana Sofia Rodriguez Reyna, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Anne A. Schouffoer, Leiden University Medical Center, Leiden, The Netherlands; Maria E. Suarez-Almazor, University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Joep Welling, NVLE Dutch patient organization for systemic autoimmune diseases, Utrecht, The Netherlands, and Federation of European Scleroderma Associations, Brussel, Belgium; Christian Agard, Centre Hospitalier Universitaire - Hôtel-Dieu de Nantes, Nantes, France; Alexandra Albert, Université Laval, Quebec, Quebec, Canada; Marc André, Centre Hospitalier Universitaire Gabriel-Montpied, Clermont-Ferrand, France; Guylaine Arsenault, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Ilham Benzidia, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Elana J. Bernstein, Columbia University, New York, New York, USA; Sabine Berthier, Centre Hospitalier Universitaire Dijon Bourgogne, Dijon, France; Lyne Bissonnette, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Gilles Boire, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Alessandra Bruns, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Patricia Carreira, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Marion Casadevall, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Benjamin Chaigne, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Lorinda Chung, Stanford University, Stanford, California, USA; Pascal Cohen, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Chase Correia, Northwestern University, Chicago, Illinois, USA; Pierre Dagenais, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Christopher Denton, Royal Free London Hospital, London, UK; Robyn Domsic, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Sandrine Dubois, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; James V. Dunne, St. Paul’s Hospital and University of British Columbia, Vancouver, British Columbia, Canada; Bertrand Dunogue, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Regina Fare, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Dominique Farge-Bancel, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Paul R. Fortin, CHU de Québec - Université Laval, Quebec, Quebec, Canada; Anna Gill, Royal Free London Hospital, London, UK; Jessica Gordon, Hospital for Special Surgery, New York City, New York, USA; Brigitte Granel-Rey, Aix Marseille Université, and Assistance Publique - Hôpitaux de Marseille, Hôpital Nord, Marseille, France; Genevieve Gyger, Jewish General Hospital and McGill University, Montreal, Quebec, Canada; Eric Hachulla, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Pierre-Yves Hatron, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Ariane L Herrick, University of Manchester, Salford Royal NHS Foundation Trust, Manchester, UK; Adrian Hij, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Sabrina Hoa, Centre hospitalier de l’université de Montréal – CHUM, Montreal, Quebec, Canada; Alena Ikic, Université Laval, Quebec, Quebec, Canada; Niall Jones, University of Alberta, Edmonton, Alberta, Canada; Artur Jose de B. Fernandes, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Suzanne Kafaja, University of California, Los Angeles, California, USA; Nader Khalidi, McMaster University, Hamilton, Ontario, Canada; Marc Lambert, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; David Launay, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Patrick Liang, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Hélène Maillard, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Nancy Maltez, University of Ottawa, Ottawa, Ontario, Canada; Joanne Manning, Salford Royal NHS Foundation Trust, Salford, UK; Isabelle Marie, CHU Rouen, Hôpital de Bois-Guillaume, Rouen, France; Maria Martin, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Thierry Martin, Les Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France; Ariel Masetto, Université de Sherbrooke, Sherbrooke, Quebec, Canada; François Maurier, Hôpitaux Privés de Metz, Hôpital Belle-Isle, Metz, France; Arsene Mekinian, Assistance Publique - Hôpitaux de Paris, Hôpital St-Antoine, Paris, France; Sheila Melchor, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Mandana Nikpour, St Vincent’s Hospital and University of Melbourne, Melbourne, Victoria, Australia; Louis Olagne, Centre Hospitalier Universitaire Gabriel-Montpied, Clermont-Ferrand, France; Vincent Poindron, Les Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France; Susanna Proudman, Royal Adelaide Hospital and University of Adelaide, Adelaide, South Australia, Australia; Alexis Régent, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Sébastien Rivière, Assistance Publique - Hôpitaux de Paris, Hôpital St-Antoine, Paris, France; David Robinson, University of Manitoba, Winnipeg, Manitoba, Canada; Esther Rodriguez, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Sophie Roux, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Perrine Smets, Centre Hospitalier Universitaire Gabriel-Montpied, Clermont-Ferrand, France; Doug Smith, University of Ottawa, Ottawa, Ontario, Canada; Vincent Sobanski, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Robert Spiera, Hospital for Special Surgery, New York City, New York, USA; Virginia Steen, Georgetown University, Washington, DC, USA; Wendy Stevens, St Vincent’s Hospital and University of Melbourne, Melbourne, Victoria, Australia; Evelyn Sutton, Dalhousie University, Halifax, Nova Scotia, Canada; Benjamin Terrier, Assistance Publique -Hôpitaux de Paris, Hôpital Cochin, Paris, France; Carter Thorne, Southlake Regional Health Centre, Newmarket, Ontario, Canada; John Varga, Northwestern University, Chicago, Illinois, USA; Pearce Wilcox, St. Paul’s Hospital and University of British Columbia, Vancouver, British Columbia, Canada; Angelica Bourgeault, Jewish General Hospital, Montreal, Quebec, Canada; Andrea Carboni Jiménez, Jewish General Hospital, Montreal, Quebec, Canada; Maria Gagarine, Jewish General Hospital, Montreal, Quebec, Canada, Sami Harb, Jewish General Hospital, Montreal, Quebec, Canada; Richard S. Henry, Jewish General Hospital, Montreal, Quebec, Canada; Nora Østbø, Jewish General Hospital, Montreal, Quebec, Canada, Lydia Tao, Jewish General Hospital, Montreal, Quebec, Canada.

Funding:

Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number F31NR019007, and the Jonas Nurse Scholars Initiative. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Scleroderma Patient-centered Intervention Network has been funded by the Canadian Institutes of Health Research, the Canadian Initiative for Outcomes in Rheumatology Care, the Arthritis Society, the Lady Davis Institute of the Jewish General Hospital, Montreal, Canada, and from McGill University, Montreal, Canada. SPIN has also received support from the Scleroderma Society of Ontario, Scleroderma Canada, Sclérodermie Québec, Scleroderma Manitoba, Scleroderma Atlantic, the Scleroderma Association of British Columbia, and the Scleroderma Association of Saskatchewan.

Footnotes

Conflict of Interest: The authors do not have any conflicts of interest to disclose.

Ethical Conduct of Research: This study was approved by the Institutional Review Board of Duke University Health System.

Contributor Information

Robyn K. Wojeck, College of Nursing University of Rhode Island Kingston, RI, USA.

Susan G. Silva, Duke University School of Nursing, Durham, NC, USA.

Donald E. Bailey, Jr., Duke University School of Nursing, Durham, NC, USA.

Mitchell R. Knisely, Duke University School of Nursing, Durham, NC, USA.

Linda Kwakkenbos, Department of Clinical Psychology, Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands.

Marie-Eve Carrier, Lady Davis Institute of Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.

Warren R. Nielson, Adjunct Professor, Department of Psychology, Western University; Associate Scientist, Lawson Research Institute, London, Ontario, Canada.

Susan J. Bartlett, Department of Medicine, McGill University; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.

Janet Pope, Division of Rheumatology, University of Western Ontario, London, Ontario, Canada.

Brett D. Thombs, Lady Davis Institute of Medical Research, Jewish General Hospital; Department of Medicine, McGill University; Departments of Psychiatry, Epidemiology, Biostatistics and Occupational Health, Psychology, Educational and Counselling Psychology, and Biomedical Ethics Unit, McGill University, Montreal, Quebec, Canada.

REFERENCES

  1. Alba MA, Velasco C, Simeón CP, Fonollosa V, Trapiella L, Egurbide MV, Sáez L, Castillo MJ, Callejas JL, Camps MT, Tolosa C, Ríos JJ, Freire M, Vargas JA, Espinosa G, Alonso M, Bernardino J, Bernardo J, Eguiluz S, … Velilla J (2014). Early-versus late-onset systemic sclerosis: Differences in clinical presentation and outcome in 1037 patients. Medicine, 93(2), 73–81. 10.1097/MD.0000000000000018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Almeida C, Almeida I, & Vasconcelos C (2015). Quality of life in systemic sclerosis. Autoimmunity Reviews, 14(12), 1087–1096. 10.1016/j.autrev.2015.07.012 [DOI] [PubMed] [Google Scholar]
  3. Ashida R, Ihn H, Mimura Y, Jinnin M, Asano Y, Kubo M, & Tamaki K (2007). Clinical features of scleroderma patients with contracture of phalanges. Clinical Rheumatology, 26, 1275–1277. 10.1007/s10067-006-0490-0 [DOI] [PubMed] [Google Scholar]
  4. Bandura A (1997). Self-efficacy: The exercise of control. W.H. Freeman. [Google Scholar]
  5. Barlow JH, Wright CC, Turner AP, & Bancroft GV (2005). A 12-month follow-up study of self-management training for people with chronic disease: Are changes maintained over time? British Journal of Health Psychology, 10, 589–599. 10.1348/135910705X26317 [DOI] [PubMed] [Google Scholar]
  6. Baron RM, & Kenny DA (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. 10.1037//0022-3514.51.6.1173 [DOI] [PubMed] [Google Scholar]
  7. Bennett JA (2000). Mediator and moderator variables in nursing research: Conceptual and statistical differences. Research in Nursing and Health, 23, 415–420. [DOI] [PubMed] [Google Scholar]
  8. Buck U, Poole J, & Mendelson C (2010). Factors related to self-efficacy in persons with scleroderma. Musculoskeletal Care, 8, 197–203. 10.1002/msc.181 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chifflot H, Fautrel B, Sordet C, Chatelus E, & Sibilia J (2008). Incidence and prevalence of systemic sclerosis: A systematic literature review. Seminars in Arthritis and Rheumatism, 37, 223–235. 10.1016/j.semarthrit.2007.05.003 [DOI] [PubMed] [Google Scholar]
  10. Denaxas K, Ladas SD, & Karamanolis GP (2018). Evaluation and management of esophageal manifestations in systemic sclerosis. Annals of Gastroenterology, 31, 165–170. 10.20524/aog.2018.0228 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Denton CP, & Khanna D (2017). Systemic sclerosis. The Lancet, 390(10103), 1685–1699. 10.1016/S0140-6736(17)30933-9 [DOI] [PubMed] [Google Scholar]
  12. Dougherty DH, Kwakkenbos L, Carrier ME, Salazar G, Assassi S, Baron M, Bartlett SJ, Furst DE, Gottesman K, Van Den Hoogen F, Malcarne VL, Mouthon L, Nielson WR, Poiraudeau S, Sauvé M, Boire G, Bruns A, Chung L, Denton C, … Turner K (2018). The Scleroderma Patient-centered Intervention Network Cohort: Baseline clinical features and comparison with other large scleroderma cohorts. Rheumatology, 57, 1623–1631. 10.1093/rheumatology/key139 [DOI] [PubMed] [Google Scholar]
  13. Faul F, Erdfelder E, Lang AG, & Buchner A (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. 10.3758/BF03193146 [DOI] [PubMed] [Google Scholar]
  14. Fischer A, Zimovetz E, Ling C, Esser D, & Schoof N (2017). Humanistic and cost burden of systemic sclerosis: A review of the literature. Autoimmunity Reviews, 16, 1147–1154. 10.1016/j.autrev.2017.09.010 [DOI] [PubMed] [Google Scholar]
  15. Hays RD, Spritzer KL, Schalet BD, & Cella D (2018). PROMIS-29 v2.0 profile physical and mental health summary scores. Quality of Life Research, 27u, 1885–1891. 10.1007/s11136-018-1842-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Haythornthwaite JA, Heinberg LJ, & McGuire L (2003). Psychologic factors in scleroderma. Rheumatic Disease Clinics of North America, 29, 427–439. 10.1016/S0889-857X(03)00020-6 [DOI] [PubMed] [Google Scholar]
  17. Hedeker D, & Gibbons R (2021). SuperMix 3.1: Mixed effects models. Scientific Software International, Inc. [Google Scholar]
  18. Johnson SR, Gladman DD, Schentag CT, & Lee P (2006). Quality of life and functional status in systemic sclerosis compared to other rheumatic diseases. The Journal of Rheumatology, 33, 1117–1122. [PubMed] [Google Scholar]
  19. Khanna D, Serrano J, Berrocal VJ, Silver RM, Cuencas P, Newbill SL, Battyany J, Maxwell C, Alore M, Dyas L, Riggs R, Connolly K, Kellner S, Fisher JJ, Bush E, Sachdeva A, Evnin L, Raisch DW, & Poole JL (2019). Randomized controlled trial to evaluate an internet-based self-management program in systemic sclerosis. Arthritis Care and Research, 71, 435–447. 10.1002/acr.23595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kwakkenbos L, Cumin J, Carrier ME, Bartlett SJ, Malcarne VL, Mouthon L, Nielson WR, Rannou F, Welling J, & Thombs BD (2019). Factors associated with patient-reported likelihood of using online self-care interventions: A Scleroderma Patient-centered Intervention Network (SPIN) Cohort study. BMJ Open, 9(10), e029542. 10.1136/bmjopen-2019-029542 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kwakkenbos L, Thombs BD, Khanna D, Carrier ME, Baron M, Furst DE, Gottesman K, Van Den Hoogen F, Malcarne VL, Mayes MD, Mouthon L, Nielson WR, Poiraudeau S, Riggs R, Sauvé M, Wigley F, Hudson M, & Bartlett SJ (2017). Performance of the Patient-Reported Outcomes Measurement Information System-29 in scleroderma: A Scleroderma Patient-centered Intervention Network Cohort study. Rheumatology, 56, 1302–1311. 10.1093/rheumatology/kex055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lenhard W, & Lenhard A (2015). Calculation of effect sizes. Psychometrica. http://www.psychometrica.de/effect_size.html. [Google Scholar]
  23. Lorig KR, Ritter PL, Laurent DD, & Plant K (2006). Internet-based chronic disease self-management: A randomized trial. Medical Care, 44, 964–971. 10.1097/01.mlr.0000233678.80203.c1 [DOI] [PubMed] [Google Scholar]
  24. Malcarne VL, Hansdottir I, McKinney A, Upchurch R, Greenbergs HL, Henstorf GH, Furst DE, Clements PJ, & Weisman MH (2007). Medical signs and symptoms associated with disability, pain, and psychosocial adjustment in systemic sclerosis. The Journal of Rheumatology, 34, 359–367. [PubMed] [Google Scholar]
  25. Matucci-Cerinic M, Krieg T, Guillevin L, Schwierin B, Rosenberg D, Cornelisse P, & Denton CP (2016). Elucidating the burden of recurrent and chronic digital ulcers in systemic sclerosis: Long-term results from the DUO Registry. Annals of the Rheumatic Diseases, 75(, 1770–1776. 10.1136/annrheumdis-2015-208121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Medsger TA (2003). Natural history of systemic sclerosis and the assessment of disease activity, severity, functional status, and psychologic well-being. Rheumatic Disease Clinics of North America, 29, 255–273. 10.1016/S0889-857X(03)00023-1 [DOI] [PubMed] [Google Scholar]
  27. Merz EL, Assassi S, & Malcarne VL (2018). Pain and Its Management in Systemic Sclerosis. Current Treatment Options in Rheumatology, 4, 255–267. 10.1007/s40674-018-0104-4 [DOI] [Google Scholar]
  28. Merz EL, Malcarne VL, Roesch SC, Nair DK, Salazar G, Assassi S, & Mayes MD (2017). Longitudinal patterns of pain in patients with diffuse and limited systemic sclerosis: Integrating medical, psychological, and social characteristics. Quality of Life Research, 26, 85–94. 10.1007/s11136-016-1370-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Moore SM, Schiffman R, Waldrop-Valverde D, Redeker NS, McCloskey DJ, Kim MT, Heitkemper MM, Guthrie BJ, Dorsey SG, Docherty SL, Barton D, Bailey DE, Austin JK, & Grady P (2016). Recommendations of common data elements to advance the science of self-management of chronic conditions. Journal of Nursing Scholarship, 48, 437–447. 10.1111/jnu.12233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ostojic P, Jankovic K, Djurovic N, Stojic B, Knezevic-Apostolski S, & Bartolovic D (2019). Common causes of pain in systemic sclerosis: Frequency, severity, and relationship to disease status, depression, and quality of life. Pain Management Nursing, 20, 331–336. 10.1016/j.pmn.2019.02.006 [DOI] [PubMed] [Google Scholar]
  31. Riehm KE, Kwakkenbos L, Carrier ME, Bartlett SJ, Malcarne VL, Mouthon L, Nielson WR, Poiraudeau S, Nielsen K, Baron M, Frech T, Hudson M, Pope J, Sauve M, Suarez-Almazor ME, Wigley FM, Thombs BD, Furst D, Gottesman K, … Mills SD (2016). Validation of the Self-Efficacy for Managing Chronic Disease Scale: A Scleroderma Patient-Centered Intervention Network Cohort study. Arthritis Care and Research, 68, 1195–1200. 10.1002/acr.22807 [DOI] [PubMed] [Google Scholar]
  32. Ritter PL, & Lorig K (2014). The English and Spanish Self-Efficacy to Manage Chronic Disease Scale measures were validated using multiple studies. Journal of Clinical Epidemiology, 67, 1265–1273. 10.1016/j.jclinepi.2014.06.009 [DOI] [PubMed] [Google Scholar]
  33. Schieir O, Thombs BD, Hudson M, Boivin JF, Steele R, Bernatsky S, Hanley J, Baron M, Pope J, Markland J, Khalidi N, Robinson D, Jones N, Masetto A, Sutton E, Kaminska E, Docherty P, Mathieu JP, Abu-Hakima M, … Mittoo S (2010). Prevalence, severity, and clinical correlates of pain in patients with systemic sclerosis. Arthritis Care and Research, 62, 409–417. 10.1002/acr.20108 [DOI] [PubMed] [Google Scholar]
  34. Sekhon S, Pope J, Baron M, Hudson M, Mathieu JP, Ligier S, Markland J, Robinson D, Jones N, Khalidi N, Kaminska E, Docherty P, Leclercq S, Abu-Hakima M, Masetto A, Douglas Smith C, Sutton E, & Fritzler M (2010). The minimally important difference in clinical practice for patient-centered outcomes including health assessment questionnaire, fatigue, pain, sleep, global visual analog scale, and SF-36 in scleroderma. The Journal of Rheumatology, 37, 591–598. 10.3899/jrheum.090375 [DOI] [PubMed] [Google Scholar]
  35. Somers TJ, Shelby RA, Keefe FJ, Godiwala N, Lumley MA, Mosley-Williams A, Rice JR, & Caldwell D (2010). Disease severity and domain-specific arthritis self-efficacy: Relationships to pain and functioning in patients with rheumatoid arthritis. Arthritis Care and Research, 62, 848–856. 10.1002/acr.20127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Suarez-Almazor ME, Kallen MA, Roundtree AK, & Mayes M (2007). Disease and symptom burden in systemic sclerosis: A patient perspective. The Journal of Rheumatology, 34, 1718–1726. http://www.jrheum.org/content/34/8/1718.abstract [PubMed] [Google Scholar]
  37. Sumpton D, Thakkar V, O’Neill S, Singh-Grewal D, Craig JC, & Tong A (2017). “It’s Not Me, It’s Not Really Me.” Insights from patients on living with systemic sclerosis: An interview study. Arthritis Care and Research, 69, 1733–1742. 10.1002/acr.23207 [DOI] [PubMed] [Google Scholar]
  38. Thombs BD, Hudson M, Taillefer SS, Baron M, Markland J, Pope J, Robinson D, Jones N, Docherty P, Abu-Hakima M, LeClercq S, Khalidi NA, Kaminska E, Sutton E, Smith CD, Mathieu JP, Ligier S, & Rahman P (2008). Prevalence and clinical correlates of symptoms of depression in patients with systemic sclerosis. Arthritis Care and Research, 59, 504–509. 10.1002/art.23524 [DOI] [PubMed] [Google Scholar]
  39. Thombs BD, Kwakkenbos L, Riehm KE, Saadat N, & Fedoruk C (2017). Comparison of Self-Efficacy for Managing Chronic Disease between patients with systemic sclerosis and other chronic conditions: A systematic review. Rheumatology International, 37, 281–292. 10.1007/s00296-016-3602-4 [DOI] [PubMed] [Google Scholar]
  40. Treede RD, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Kosek E, Lavand’homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, … Wang SJ (2015). A classification of chronic pain for ICD-11. Pain, 156, 1003–1007. 10.1097/j.pain.0000000000000160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Van Den Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A, Matucci-Cerinic M, Naden RP, Medsger TA, Carreira PE, Riemekasten G, Clements PJ, Denton CP, Distler O, Allanore Y, Furst DE, Gabrielli A, Mayes MD, Van Laar JM, … Pope JE (2013). 2013 classification criteria for systemic sclerosis: An American College of Rheumatology/European League against Rheumatism collaborative initiative. Arthritis and Rheumatism, 72, 1747–1755. 10.1002/art.38098 [DOI] [PubMed] [Google Scholar]
  42. Young A, Namas R, Dodge C, & Khanna D (2016). Hand impairment in systemic sclerosis: Various manifestations and currently available treatment. Current Treatment Options in Rheumatology, 2, 252–269. 10.1007/s40674-016-0052-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Data File (doc, pdf, etc.)_2

Supplemental Figure 1. SPIN = Scleroderma Patient-centered Intervention Network

NIHMS1715188-supplement-Supplemental_Data_File__doc__pdf__etc___2.docx (46.7KB, docx)

RESOURCES