Measuring Illness Behavior in Patients with Systemic Sclerosis

Abstract

Objective

Illness behaviors (cognitive, affective, and behavioral reactions) among individuals with systemic sclerosis (SSc) are of clinical concern due to relationships between these behaviors and physical and mental-health quality of life such as pain and symptoms of depression. Self-report measures with good psychometric properties can aid in the accurate assessment of illness behavior. The Illness Behavior Questionnaire (IBQ) was designed to measure abnormal illness behaviors; however, despite its long-standing use, there is disagreement regarding its subscales. The goal of the present study was to evaluate the validity of the IBQ in a cohort of patients with SSc.

Methods

Patients with SSc (N = 278) completed the IBQ at enrollment to the Genetics versus ENvironment In Scleroderma Outcome Study (GENISOS). Structural validity of previously derived factor solutions was investigated using confirmatory factor analysis. Exploratory factor analysis was utilized to derive SSc-specific subscales.

Results

None of the previously derived structural models were supported for SSc patients. Exploratory factor analysis supported a SSc-specific factor structure with 5 subscales. Validity analyses suggested that the subscales were generally independent of disease severity, but were correlated with other health outcomes (i.e., fatigue, pain, disability, social support, mental health).

Conclusion

The proposed subscales are recommended for use in SSc, and can be utilized to capture illness behavior that may be of clinical concern.

Systemic sclerosis (SSc) is a chronic, rheumatic condition characterized by the thickening of skin and fibrosis of internal organs. It is most common among women between ages 30 and 50 but is relatively rare, with an overall prevalence of 150 to 300 cases per million [1–2]. There are two subtypes; limited cutaneous SSc is milder and has less severe organ involvement, diffuse cutaneous SSc is characterized by more extensive skin and organ involvement and worse prognosis [3]. Individuals with SSc report problems across multiple domains including fatigue [4], pain [5], disability [6], sleep [7], interpersonal functioning, [8], anxiety, depression [9], and more generally, physical and mental-health related quality of life [10]. There is also an increasing awareness that disease severity is inadequate for discriminating patients who are at risk for poor adjustment, suggesting a need to also emphasize psychosocial variables [6].

Illness behaviors, defined as cognitive, emotional and behavioral reactions [11], can occur in response to chronic diseases such as SSc. Although illness behavior is neutral by definition, some behaviors are more adaptive than others [12]. For example, concerns about health may encourage a patient with SSc to seek necessary medical help, or could lead to excessive doctor’s visits and anxiety. It may be helpful to divulge one’s feelings about their disease to others, but excessive disclosure may lead to social network problems. Such extreme responses, termed abnormal illness behavior, also include actions to maintain the sick role, or a level of disability that exceeds the given pathology [12].The Illness Behavior Questionnaire (IBQ) is a widely used tool that was developed to measure these reactions [13]. The IBQ contains 62 yes/no items, including all 14 items of the Whiteley Index [14]. The history and development of the IBQ have been discussed elsewhere [15]. The IBQ was developed in a relatively small sample (N = 100) of pain clinic patients using principal components analysis with varimax rotation, which yielded 7 subscales¹: General Hypochondriasis (anxious health-related concern), Disease Conviction (belief that a “real” disease is present), Psychological vs. Somatic Functioning (tendency to somaticize), Denial (tendency to attribute life stress to physical problems), Affective Inhibition (inability to express personal feelings to others), Affective Disturbance (anxiety, depression), and Irritability (anger, friction). The IBQ has been associated with physical and psychological quality of life across a variety of conditions such as healthcare utilization and disability [16], post-operative outcomes [17], health-related quality of life [18], psychopathology [19], anxiety [20], depression [21], fatigue [4, 22], pain [23], and social support [24]. Unfortunately the psychometric properties of the IBQ have not been well-established. The original factor structure [13] has been shown to be unstable across studies. Although internal structure is only one consideration when evaluating a measure’s overall performance [25], this does suggest that the interpretability of the IBQ for other disease groups may be uncertain. Several alternate structures have been proposed [26–28], although most researchers utilize the original subscales. The original subscales have been used in patients with cancer [29], gastroesophogeal reflux disease [17], myocardial infarction [30], stroke [16], lupus [31], fibromyalgia [32], osteoarthritis, rheumatoid arthritis [33], chronic fatigue syndrome, multiple sclerosis [34], and back pain [23, 35].

There are several possibilities as to why the IBQ has not been well-replicated in different populations and diseases. The IBQ may have been overfactored [26], which can lead to unreliable or split factors [36]. Because IBQ items are binary, poor factor specification is especially problematic given the high influence of item-level error on a factor [26]. It is also plausible that previous samples were not large enough to reproduce the IBQ’s structure. The original subscales were developed using data from 100 patients, although the structure did later replicate in 1,578 pain and psychiatric patients [37]. Another study [26] also used a relatively small sample (N = 200), but others reported findings from large (N = 675–1,061) samples [27–28]. Another consideration is that the factorial instability is due to a disconnect between methodological and practical considerations, and the challenges inherent to measuring complex psychological constructs [25]. Alternately, it has been suggested that the IBQ’s inconsistent factor structure is due to disease-specific illness behaviors unique to the physical process, treatment, and functional and social implications [15]. Accordingly, some items may be more or less relevant for a given disease. For example, the Disease Conviction subscale may not apply to individuals with an identified pathology; it is reasonable that a person with a diagnosed disease would indeed have a strong belief that they have a disease. Thus, a new research agenda has been proposed [15, 27], which entails investigating the need for disease-specific subscales to best capture the experiential, cognitive, and behavioral aspects of a given illness. Because understanding illness behavior may aid in providing total clinical care, so that patients with maladaptive illness behaviors may be identified and offered additional intervention or referral, it would be beneficial to determine whether the IBQ can be used in patients with SSc.

Objective

The study’s first aim was to evaluate the various IBQ factor structures. If the internal structure is not upheld, which could suggest problems with previously derived solutions for patients with SSc, the second aim of the study was to uncover a plausible factor structure specifically for SSc. The third aim was to establish convergent and divergent validity of the subscales derived from the best fitting model, via correlations of derived subscales with disease severity, and other quality of life variables. We predicted that the dimensions of the IBQ would have little to no correlation with disease severity, as has been shown with other psychosocial variables [6]. We also predicted that greater endorsement of illness behaviors would be related to worse fatigue, pain, disability, social support, and mental health, as has been previously demonstrated [4, 16, 20–24].

Materials and Methods

Participants

This investigation utilized data provided by participants from the Genetics versus ENvironment In Scleroderma Outcome Study (GENISOS), a prospective early-disease (within 5 years of onset) cohort study that represents collaboration among the University of Texas Health Science Center at Houston, the University of Texas Medical Branch at Galveston, and the University of Texas-Health Science Center at San Antonio. Enrollment is ongoing. Data are collected annually via a clinical exam and survey packet during regular outpatient visits, and intermittently as inpatient services (as needed) at the hospitals staffed by the clinician-investigators. Patients with SSc who lived within the geographic catchment area of one of the three centers were recruited from the rheumatology faculty clinics, the county hospital, and chapters of the Scleroderma Foundation [38]. Participants had to be at least 18 years old.

Procedure

Baseline data from the GENISOS study were used [38]. IRB approval was obtained at all participating institutions, including San Diego State University and University of California, San Diego for analysis of archival data. All participants gave written informed consent. Participants received clinical examinations by the physician investigators including evaluations of clinical manifestations (e.g., sclerodactyly, skin thickening, Raynaud’s phenomenon, gastrointestinal involvement), comorbidities, an electrocardiogram, a chest radiograph, and blood samples and completed a packet of psychosocial measures.

Measures

Illness Behavior Questionnaire (IBQ [13])

The IBQ is a 62-item self-report measure designed to measure illness behavior (see the supplementary appendix for basic item data). Using a yes/no format, respondents indicate whether an item describes them, with ‘abnormal’ behaviors being scored 1 point.

Modified Rodnan Skin Score (mRSS [39])

The mRSS total score is an established indicator of skin disease severity in SSc calculated by measuring the extent and severity of skin thickening on 17 body surfaces by palpation on a 4-point scale (0 = uninvolved to 3 = severe thickening). Higher scores indicate greater severity.

Forced Vital Lung Capacity (% predicted FVC)

Percent predicted FVC is a validated measure for severity of SSc-related interstitial lung disease [40]. It indicates the ratio of the volume of air that the study subject can forcibly exhale after a maximum inspiration to the same volume in age, gender, weight, height, and ethnicity matched unaffected controls. All pulmonary measurements met criteria outlined by the American Thoracic Society/European Respiratory Society, and were reviewed by a pulmonologist. Lower scores indicate greater severity of SSc-related interstitial lung disease.

Fatigue Severity Scale (FSS [41])

The FSS is a widely used 29-item self-report questionnaire wherein respondents rate the extent of their agreement with statements regarding their level of fatigue on a Likert scale (1 = completely disagree to 7 = completely agree). It has demonstrated adequate test-retest reliability, discriminant validity, and convergent validity [41]. The FSS yields an overall score and 4 factor-analytically derived subscales. The total score, in which higher total scores represent more severe global fatigue, was used in the current study. Internal consistency (α = .90) was good.

Medical Outcomes Study Short-Form 36 (SF-36 [42])

The SF-36 is a 36-item self-report health-related quality of life measure that yields 8 factor-analytically derived subscales and 2 composite scores of physical and mental health. The questions follow a variety of response formats, scoring algorithms are required for generating the subscales. It is reliable and valid for SSc [43]. The Bodily Pain (α = .87) and Mental Health (α = .79) subscales were utilized. Higher scores indicate better domain-related quality of life.

Modified Health Assessment Questionnaire (mHAQ [44])

The mHAQ is a 8-item self-report index of overall disability. Respondents rate their functional ability to perform tasks on a 4-point scale (0 = without any disability to 3 = unable to do). It has been validated for use in SSc [44–45], and shown to have a one-factor structure [46]. Internal consistency (α = .91) was good in the current sample. Higher scores reflect greater disability.

Interpersonal Support Evaluation List (ISEL [47])

The ISEL is a widely-used 40-item self-report measure of perceived social support wherein respondents rate whether a statement is “probably true” or “probably false” based on their experience. The ISEL yields four subscales and a total score of overall support which has been supported using confirmatory factor analysis [48]. The total score was used for the current study and demonstrated good internal consistency (α = .87). Higher scores indicate better social support.

Data analysis

Factor analysis was conducted to achieve aims one and two. Theory-driven confirmatory factor analysis (CFA) was utilized to evaluate previously derived IBQ factor structures. If CFA models do not provide sufficient fit, it is reasonable to follow up with exploratory factor analysis (EFA [50]). Data-driven EFA was conducted to explore alternate structures by estimating the number of underlying latent variables within the data and thus identifying SSc-specific subscales.

Because the IBQ contains binary data, traditional factor analytic techniques are inappropriate, as the assumptions of linearity and normality are violated [51]. A tetrachoric correlation matrix, wherein it is assumed that a normally distributed continuous latent variable underlies the “truncated” binary items should therefore be used [51]. Moreover, ordinary least-squares and maximum likelihood estimation approaches are not recommended due to dependencies and systematic residuals among observed variables [52]. Consequently, a tetrachoric correlation matrix with a weighted least-squares means and variance adjusted (WLSMV) estimation procedure in MPlus 6.1 [53] that is robust to non-normal and non-independent data was used. Internal consistency for all factors in all models was evaluating using the Kuder-Richardson-20 formula.

Evaluation of model fit

For CFA and EFA, it is recommended that samples are at least 200[54], although samples greater than 250 are preferred for binary data [55]. The current sample is near the low end of this desired range, but does meet these recommendations. Because χ² tests may not be suitable to determine model fit, descriptive fit indices were also calculated [56]. The Comparative Fit Index (CFI; [57]) and Root Mean Square Error of Approximation (RMSEA; [58]) were used, as other descriptors (e.g., Root Mean Square Residual [59]) are unfit for binary data [55]. A model fit well if CFI values were ≥ .95 and RMSEA values were ≤ .05 based on widely accepted guidelines [55].

Exploratory analysis

Previous researchers have used different combinations of items in their exploratory factor analyses of the IBQ. In the original study, items 1–52 were included in the analysis, and items 53–62 were added afterwards to increase the number of items per subscale and to improve internal consistency [13]. Prior and Bond [27] used a similar strategy by including items 1–52 in their analysis, and later adding items 54 and 59, based on face validity and internal consistency. Zonderman, Heft and Costa [28] found that the solutions for two analyses (the first on items 1–52, the second on items 1–63) were identical and reported the latter solution. Main and Waddell [26] removed 25 items due to poor reliability and/or incidence, leaving 37 items for the analyses. Given the heterogeneity of approaches, and Pilowsky’s [37] suggestion that the IBQ may be particularly useful as an item pool, all 62 items were analyzed in the EFA so that results were not reliant on face validity. Models with 1–7 factors were tested to reflect the various numbers of dimensions found in previous studies. A factor needed at least 3 items (preferably 4) to reduce the likelihood of over-factoring [26]. In EFA, items with loadings of the strict criterion of >.40 was used to inhibit errors in factor estimation. Cross-loadings were determined as loadings greater than half of the primary loading. Although underfactoring (i.e., including too few factors in a model) has not typically been a criticism of the IBQ, it can lead to problems, such as the combination of multiple factors [36]; therefore, the pattern matrix was also inspected for interpretability. Items derived from the factor analysis were further evaluated for their contribution to the internal consistency of their subscale. Based on recommendations for decreasing redundancy among subscale items, items were retained if their removal from a subscale resulted in decreased internal consistency, and eliminated if internal consistency was unchanged upon removal [49]. Subscale intercorrelations were then evaluated; models with intercorrelations with high multicollinearity (r >.7) were considered unsuitable.

Results

Descriptive characteristics are available in Table 1. Skin thickening (t [274] = −13.79; diffuse M = 22.03 [11.10]; limited M = 6.74 [5.39]) and forced vital lung capacity (t [262] = 2.65; diffuse M = 80.09 [20.71]; limited M = 87.16 [22.36]) indicated greater disease severity in the diffuse subtype (ps < .001 and <.01, respectively).

Table 1.

Sample characteristics.

Variable	M (SD) or n (%)
Age1	49.05 (12.92)
Age of disease onset1	46.42 (13.03)
Modified Rodnan Skin Score1	15.49 (11.84)
Forced Vital Lung Capacity1	83.06 (21.66)
Sex2
Women	233 (83.8%)
Men	45 (16.2%)
Ethnicity2
White	135 (48.6%)
Hispanic	82 (29.5%)
Black	53 (19.1%)
Asian	7 (2.5%)
American Indian	1 (0.3%)
Marital status2
Married/Partnered	159 (58.2%)
Never married	30 (11.0%)
Divorced/Separated	72 (26.4%)
Widowed	12 (4.4%)
Education2
Less than high school	44 (16.1%)
High school diploma	143 (52.4%)
Associate’s degree	26 (9.5%)
Bachelor’s degree	38 (13.9%)
Post-graduate	22 (8.1%)
Family income2
< $14,999	67 (24.1%)
$15,000 – $29,999	65 (23.4%)
$30,000–$49,999	56 (20.1%)
$50,000–$99,999	51 (18.3%)
≥ $100,000	29 (10.4%)
Disease subtype2
Diffuse	160 (57.6%)
Limited	118 (42.4%)

Note.

¹M (SD);

²n(%)

CFAs of original and alternate models

First, CFA was used to examine the model fit of the 7 dimensions comprised of 40 items, as suggested by Pilowsky and Spence [13]. Internal consistencies were poor (.200 – .697); only Affective Disturbance (.759) was reliable. Model fit was poor statistically, χ² [719, N = 278] = 1048.04, p < .001, and descriptively, CFI = .893, RMSEA = .041. Interfactor correlations ranged from |.20–1.06²|, suggesting high redundancy among factors. Because internal consistency and solution were both poor, most dimensions were inadmissible, thus alternate structures were considered.

The 6 dimensions comprised of 47 IBQ items as suggested by Zonderman and colleagues [28] were tested first. Internal consistency was better (.632 – .796). Model fit was poor statistically, χ² [1019, N = 278] = 1538.46, p < .001, and descriptively, CFI = .871, RMSEA = .043. Interfactor correlations ranged from |.21 – .81|.

The 6 dimensions comprised of 33 IBQ items as suggested by Main and Waddell [26] were tested next. Internal consistencies ranged from .566 to .814. Model fit was poor statistically, χ² [492, N = 278] = 1093.12, p < .001, and descriptively, CFI = .782, RMSEA = .066. Interfactor correlations ranged from |.32 – .72|.

Finally, the 3 dimensions comprised of 31 IBQ items as suggested by Prior and Bond [27] were tested. Internal consistency was good (.733 – .805); however, model fit was poor statistically, χ² [431, N = 278] = 804.70, p < .001, and descriptively, CFI = .893, RMSEA = .056. Interfactor correlations ranged from |.69 – .74|.

Exploratory analysis of IBQ items³

Because none of the models fit adequately, EFA was utilized to determine if a better model could be derived (Table 2). The 4-factor model was the first to meet the descriptive fit criteria, therefore models 4–7 were evaluated for interpretability. Inspection of the simple structure of these models showed an adequate number of items on the 4- and 5-factor models. For the 6- and 7-factor models, several dimensions yielded only 2 to 3 items. Given the issues of over-factoring [26], these models were not evaluated further.

Table 2.

Weighted least-squares means and variance adjusted (WLMSV) exploratory factor analysis on 62 items of the IBQ

Model	RMSEA	CFI	WLMSV χ2	df	p
1-factor	.041	.815	2695.99	1829	<.001
2-factor	.031	.897	2250.30	1768	<.001
3-factor	.026	.930	2033.10	1708	<.001
4- factor	.022	.955	1860.94	1649	<.001
5- factor	.018	.970	1732.58	1591	.007
6- factor	.014	.983	1614.28	1534	.075
7- factor	.012	.987	1536.84	1478	.140

Both the 4- and 5-factor models were reviewed on the basis of simple structure and interpretability. Both contained 3 identical factors. However, the largest factor from the 4-factor model was split into 2 meaningful factors in the 5-factor model, suggesting that the 4-factor model was underfactored. At this point, 33 items were removed due to insufficient loadings or cross-loadings. Each factor was then further refined based on internal consistency, as described above. The final solution used 23 items. The factor loadings are shown in Table 3.

Table 3.

Summary of loadings for 5 rotated factors

	1	2	3	4	5
IBQ3	.632	.159	−.017	.192	.038
IBQ16	.988	−.295	.016	−.003	.077
IBQ26	.970	−.270	−.011	−.020	−.076
IBQ41	.727	.036	.068	.114	−.100
IBQ50	.613	.014	.068	.053	−.074
IBQ1	.180	.796	−.129	−.083	−.040
IBQ8	.036	−.647	−.110	.042	−.114
IBQ21	.040	.613	.012	−.007	.088
IBQ24	.128	.533	.130	−.008	−.071
IBQ34	.267	.745	−.179	−.076	.033
IBQ4	−.152	.077	−.647	.038	.145
IBQ48	.154	.051	.534	.205	.030
IBQ51	−.196	.200	.729	.043	−.082
IBQ56	.001	−.021	.743	.044	−.019
IBQ61	−.128	.096	.730	.129	−.035
IBQ27	.083	−.122	.032	.875	−.011
IBQ43	−.017	−.217	.099	.735	.045
IBQ55	.408	.146	.086	−.705	.059
IBQ60	.072	.018	−.008	.790	−.004
IBQ22	−.024	.110	−.197	.038	−.785
IBQ36	.143	−.016	−.011	.214	.551
IBQ53	.137	.046	−.191	.229	.650
IBQ62	−.105	.041	.006	.165	.768

SSc-specific subscales of the IBQ

Table 4 describes the subscales, and items shared with subscales from previous solutions. Intercorrelations among the SSc subscales (rs = .00 to .38) were reasonable.

Table 4.

SSc-specific subscales from GENISOS data and membership in previous scales

New subscale	Original IBQ item		Original scale membership
			Pilowsky & Spence	Zonderman, Heft, & Costa	Main & Waddell	Prior & Bond
Symptom Bother	IBQ3	Does your illness interfere with your life a great deal?	Disease Conviction	Illness Disruption	Life Disruption	Affirmation of Illness
	IBQ16	Are you bothered by many pains and aches?	Psychological vs. Somatic Functioning (R)	Illness Disruption	Life Disruption	Affirmation of Illness
	IBQ26	Do you experience a lot of pain with your illness?		Illness Disruption		Affirmation of Illness
	IBQ41	Do you find that you are bothered by many different symptoms?	Disease Conviction		Affective and Hypochondriacal Disturbance	Affirmation of Illness
	IBQ50	Do you often have the symptoms of a very serious disease?				Affirmation of Illness
Health Worry	IBQ1	Do you worry a lot about your health?		Health Worry		Concern for Health
	IBQ8	Is it easy for you to forget about yourself and think about all sorts of other things? (R)		Illness Disruption (R)	Affective and Hypochondriacal Disturbance (R)
	IBQ21	Are you afraid of illness?	General Hypochondriasis	Health Worry		Concern for Health
	IBQ24	Do you think that you worry about health more than other people?	General Hypochondriasis	Health Worry		Concern for Health
	IBQ34	Do you often worry about the possibility that you have got a serious illness?		Health Worry	Affective and Hypochondriacal Disturbance	Concern for Health
Interpersonal Functioning	IBQ4	Are you easy to get along with when you are ill?	Irritability (R)	Irritability (R)	Affective and Hypochondriacal Disturbance (R)
	IBQ48	Do you worry or fuss over small details that seem unimportant to others?		Irritability	Affective and Hypochondriacal Disturbance	General Affective State
	IBQ51	Do you find that you get angry easily?	Irritability	Irritability	Affective and Hypochondriacal Disturbance	General Affective State
	IBQ56	Are you more irritable towards other people?	Irritability	Irritability	Affective and Hypochondriacal Disturbance
	IBQ61	Do you often find that you lose patience with other people?	Irritability	Irritability	Affective and Hypochondriacal Disturbance
Other Life Worries	IBQ27	Except for your illness, do you have any problems in your life?	Denial (R)	Absence of Life Problems	Life Disruption (R)	General Affective State
	IBQ43	Do you have any family problems?	Denial (R)	Absence of Life Problems	Life Disruption (R)	General Affective State
	IBQ55	Would all your worries be over if you were physically healthy?	Denial	Absence of Life Problems (R)	Life Disruption
	IBQ60	Do you have personal worries which are not caused by physical illness?	Denial (R)	Absence of Life Problems	Life Disruption (R)
Affective Inhibition	IBQ22	Can you express your personal feelings easily to other people?	Affective Inhibition (R)	Affective Inhibition (R)	Social Inhibition (R)
	IBQ36	When you are angry, do you tend to bottle up your feelings?	Affective Inhibition	Affective Inhibition	Social Inhibition
	IBQ53	Do you prefer to keep your feelings to yourself?	Affective Inhibition	Affective Inhibition	Social Inhibition
	IBQ62	Is it hard for you to show people your personal feelings?	Affective Inhibition	Affective Inhibition	Social Inhibition

Note. (R) = reverse-scored.

Symptom Bother

Three items that loaded onto this subscale were removed as they did not improve internal consistency. Thus, the first subscale retained the 5 best items out of the 8 that met the interpretability criteria. Higher scores indicate greater intensity and life interference of disease-related symptoms. Internal consistency (.778) was adequate.

Health Worry

One item that loaded onto the second subscale was removed as it did not improve internal consistency. Thus, the second subscale retained the 5 best items out of the 6 that met interpretability criteria. Higher scores indicate that a respondent is more preoccupied with health in general. Internal consistency (.725) was adequate.

Interpersonal Functioning

Two items that loaded onto the third subscale were removed as they did not improve internal consistency. Thus, the third subscale retained the 5 best items out of the 7 that met interpretability criteria. Higher scores indicate more interpersonal problems. Internal consistency (.720) was adequate.

Other Life Worries

Four items loaded onto the fourth subscale. Higher scores indicate a greater number of non-illness problems. Internal consistency (.715) was adequate.

Affective Inhibition

Four items loaded onto the fifth subscale. Higher scores reflect greater difficulty expressing emotion to others. Internal consistency (.662) for this subscale was weaker.

Relationships of subscales to health outcomes

Correlations between the subscales and other measures were performed to establish convergent and divergent validity (Table 5). As predicted, the proposed subscales were not generally associated with disease severity. As predicted, the subscales were related to fatigue, pain, disability, social support, and mental health in the expected directions. Higher scores on the subscales were associated with worse outcomes, with stronger relationships among related domains (e.g., relationships between symptom bother and pain, or between affective inhibition and social support).

Table 5.

Associations between IBQ scales for SSc and disease-related outcomes

IBQ	Modified Rodnan Skin Score	Forced Vital Lung Capacity	Fatigue	Pain	Disability	Social support	Mental health
Symptom Bother	.14*	−.05	.42***	−.57***	.42***	−.15*	−.31***
Health Worry	.11	−.01	.10	−.17**	.21**	−.25***	−.42***
Interpersonal Functioning	−.05	.06	.22***	−.14*	.16**	−.19**	−.33***
Other Life Worries	−.09	.00	.08	−.00	.00	−.13*	−.21**
Affective Inhibition	.05	−.14*	.09	−.15*	.15*	−.32***	−.30***

Note.

^***p < .001;

^**p < .01;

^*p < .05

Discussion

The current study expands on efforts to create a useful measure that characterizes illness behaviors by examining the psychometric properties of the IBQ [13] in patients with SSc. None of the previous solutions adequately fit data from patients with SSc. Failing to replicate the factor structure of a measure is one element that may call its performance into question, thus, the approach became exploratory. The physiological and psychological aspects of specific diseases vary widely, thus it is reasonable for different diseases to have different factor structures and resultant subscales for the IBQ [15, 55]. Thus, only items that were meaningful for SSc patients were included to ensure sharper measurement of the relevant aspects of illness behavior for SSc. On the basis of a number of statistical and theoretical decisions, a SSc-specific structure was derived. The proposed subscales comprised illness-related (Symptom Bother, Health Worry), social (Interpersonal Functioning), and affective (Other Life Worries, Affective Inhibition) domains.

Internal consistency of the subscales was acceptable; although Affective Inhibition was lower but satisfactory, given the small number of items and exploratory nature of the study [60]. Although higher internal consistencies have been reported for longer subscales [27], this is unsurprising given that items were added after factor analysis based on face validity, with the specific intention of increasing internal reliability. Shorter forms that are sufficiently valid and reliable to achieve measurement objectives are generally preferable in clinical contexts.

The validity analyses suggested that SSc-specific subscale scores were generally unrelated to skin thickness and pulmonary function. This suggests that disease severity only partially explains illness behavior. Fatigue, pain, disability, social support, and mental health were generally associated with the subscales, such that greater endorsement of the illness behavior domains was predictive of poorer outcomes. Taken together, these findings suggest that these subscales provide an acceptable assessment of illness behavior in SSc. However, score interpretation should be considered in the larger context of a patient’s current physical status and psychological comorbidities.

Given the rarity of SSc, a notable strength of the current study is the large, representative sample of patients. However, there were some limitations. Only cross-sectional data were utilized. The sample size was on the low end of recommendations for latent variable analyses. Despite these limitations, this study provides preliminary support for the utility of the IBQ for patients with SSc. Future work should focus on confirming this factor structure in a different sample of patients with SSc, and on comparing the measurement model between diffuse and limited subtypes. Additionally, researchers and clinicians should begin building more integrative models of illness behavior, with attention to the physical, psychological and social aspects of SSc to enhance total patient care. Within such a framework, clinicians will be better equipped to identify at-risk patients to implement appropriate interventions to target problematic illness behaviors [61], underscoring the need for a reliable and valid screening tool.

In sum, this study evaluated the factorial validity of the IBQ in a sample of patients with SSc derived from the GENISOS cohort. The original factor structure of the IBQ was not supported among, providing one piece of evidence that may call the factor structure into question. Therefore, a SSc-specific factor structure was uncovered, which demonstrated convergent and divergent validity. These subscales offer clinicians a relatively concise way to identify patients who may benefit from additional intervention.

Significance and Innovations.

• Illness behaviors may be associated with quality of life outcomes in SSc; such behaviors have been measured in other disease populations using the Illness Behavior Questionnaire (IBQ).

• The psychometric properties of the IBQ have not been evaluated in SSc.

• Results support the use of the IBQ in SSc, and that behaviors that may be most relevant to SSc quality of life are symptom bother, health worry, interpersonal functioning, other life worries, and affective inhibition.