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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: Cogn Behav Ther. 2018 Nov 20;48(5):419–429. doi: 10.1080/16506073.2018.1533580

The development and initial validation of the Fatigue Sensitivity Questionnaire

Brooke Y Kauffman a, Lorra Garey a, Alec Nordan a, Charles Jardin a, Nubia A Mayorga a, Zuzuky Robles b, Michael J Zvolensky a,c,d
PMCID: PMC6527489  NIHMSID: NIHMS1018248  PMID: 30457446

Abstract

Currently, there is poor understanding of fatigue and the possible psychological conditions that may underlie chronic fatigue. Although substantial work has been directed to better clinically address fatigue, no work has explored individual differences in expectations or perceptions of the negative consequences associated with fatigue-related symptoms. The goals of this study were to (a) develop and (b) validate a measure of expectations or perceptions of the negative consequences associated with fatigue-related symptoms (e.g. fatigue sensitivity) across two independent samples (N = 1,827; 73.1% female; Mage = 21.68; SD = 4.54) of young adults. Results supported a 10-item measure of fatigue sensitivity, entitled the Fatigue Sensitivity Questionnaire (FSQ). The FSQ demonstrated unidimensionality, excellent internal consistency, and strong convergent and discriminant validity. Overall, the 10-item scale offers a single score that can be employed to measure fatigue sensitivity. Clinically, the FSQ may be a brief, informative, and easily disseminated measure in better understanding and capturing expectations or perceptions about the negative consequences of fatigue. As a research tool, the use of the FSQ may provide broader understanding of vulnerability factors that may influence fatigue-related health outcomes. Future research is needed to test the validity of the FSQ in other samples.

Keywords: Fatigue sensitivity, measurement, assessment, development, validation, questionnaire

Fatigue is a complex phenomenon experienced in medical, neurologic, and psychiatric disorders as well as in the otherwise healthy general population (Wright & O’Connor, 2014; Yu, Lee, & Man, 2010). Fatigue is often underdiagnosed and undertreated in primary care and related medical settings (Shen, Barbera, & Shapiro, 2006; Yu et al., 2010); an issue likely linked to a poor understanding of fatigue (Yu et al., 2010). Shen et al. (2006) have defined fatigue as an overwhelming sense of tiredness, lack of energy and a feeling of exhaustion, associated with impaired physical and/or cognitive functioning. Fatigue is typically conceptualized along a continuum, with degrees of severity (O’connell & Stokes, 2014). Generally, the experience of “normal” fatigue is related to physical exertion, which is adaptive or protective in nature, and ameliorated by rest (O’connell & Stokes, 2014; Trendall, 2000). In contrast, “abnormal” (or chronic) fatigue persists over time, is less responsive to rest, and may exist independently or because of an underlying psychological or physical condition (O’connell & Stokes, 2014; Trendall, 2000).

Clinical and epidemiological data consistently substantiate a high co-occurrence of fatigue with negative physical and mental health outcomes (Aaron et al., 2001; Bateman et al., 2015). For example, Bateman et al. (2015) found that 84% of adults were diagnosed with a comorbid mental or physical health condition following the onset of chronic fatigue. Epidemiological estimates also indicate that individuals with fatigue are at a greater risk for developing a psychiatric conditions, even after accounting for an array of other factors such as sociodemographic variables (Harvey, Wessely, Kuh, & Hotopf, 2009). There is empirical evidence indicating that clinical or chronic fatigue may even result in pathological changes to neural structure/function (Wang, Trongnetrpunya, Samuel, Ding, & Kluger, 2016). Despite individuals identifying fatigue as a common and primary healthcare complaint (Whitehead, Unahi, Burrell, & Crowe, 2016), fatigue is often overlooked within medical settings (Ream, 2007). As a result, untreated fatigue can exacerbate chronic disease or psychiatric conditions and contribute to significant functional impairment (Goedendorp et al., 2014; Kapella, Larson, Patel, Covey, & Berry, 2006).

A substantial body of work has been developed in order to better understand and clinically address fatigue (Whitehead et al., 2016). Scholars have developed psychometrically sound measures of fatigue from both unidimensional and multidimensional perspectives (Whitehead, 2009). For instance, unidimensional measurements of fatigue have typically focused on the severity of fatigue (Whitehead, 2009) whereas multidimensional approaches have aimed to measure both the intensity of fatigue as well as its impact on cognitive, behavioral, and social functioning (e.g. because of my fatigue I am less motivated to do anything that requires physical effort; John et al., 1994; Whitehead, 2009). Although there is great clinical utility in understanding the experience of fatigue, past work has not explored individual differences in expectations or perceptions about the consequences of fatigue-related symptoms. This limitation is unfortunate, as there are large bodies of empirical work that suggest expectations or perceptions about the negative consequences of physical and mental states can have a profound impact on well-being (Kirsch, 1985, 1990).

Drawing broadly from such influential theoretical and empirical work on expectancies (Kirsch, 1990), we sought to evaluate whether expectations or perceptions about the negative consequences associated with fatigue-related symptoms is a valid and reliable construct. As such, we conceptualize fatigue sensitivity as expectations about the negative consequences associated with fatigue-related symptoms. Specifically, fatigue sensitivity aims to capture individual differences in expectations (or, perceptions) that the experience of fatigue-related symptoms may lead to adverse physical, cognitive, or social consequences. This can be differentiated from fatigue more generally, in that we are interested in the cognitive appraisal (i.e. expectations, perceptions) of fatigue-related symptoms rather than the experience of fatigue itself. For example, a person may believe that experiencing a headache (a symptom of fatigue) may be a sign that something is seriously wrong (cognitive appraisal of fatigue; e.g. “I am developing cancer”). Moreover, a fatigue-related symptom such as yawning in the presence of others may lead to negative judgement (cognitive appraisal of fatigue; e.g. “They believe that I cannot handle life”). To further illustrate, the person may avoid activities known to increase fatigue-related symptoms (e.g. physical activity; socially demanding tasks) to avoid fears related to the thought that their body may “shut-down” if they were to experience fatigue-related symptoms (e.g. exhaustion). Subsequent avoidance behaviors of fatigue-related stimuli may then theoretically contribute to greater fatigue sensitivity, and poorer mental and physical health, and therefore, reinforce fatigue sensitivity.

Together, the current study was designed to validate the construct of fatigue sensitivity through two phases. We first developed a measure of fatigue sensitivity, entitled Fatigue Sensitivity Questionnaire (FSQ), to identify the best fitting model. We then examined the psychometric properties of the measure for replicability as well as construct validity, including internal consistency and discriminant and convergent validity. Specifically, fatigue sensitivity was evaluated for convergent validity with other well-validated measures of mental health including anxiety sensitivity (Taylor et al., 2007), general depression (Watson et al., 2007), social anxiety (Watson et al., 2007), panic (Watson et al., 2007), and negative affectivity (Watson, Clark, & Tellegen, 1988). Conversely, fatigue sensitivity was also assessed for discriminant validity with measures of positive affectivity (Watson et al., 1988) and well-being (Watson et al., 2007). Given the proposed classification of the construct fatigue sensitivity as the expectations or perceptions about the negative consequences associated with the experience of fatigue-related symptoms rather than an assessment of the fatigue itself, convergent, and discriminant measures were chosen to correspond to other assessments of cognitive patterns rather than assessments of fatigue-related symptoms. It was hypothesized that fatigue sensitivity would positively and significantly correlate with anxiety sensitivity, general depression, anxious arousal, and negative affectivity. Furthermore, we hypothesized that fatigue sensitivity would negatively relate to positive affectivity and well-being.

Method

Participants

A sample of 1,827 (73.1% female; Mage = 21.68; SD = 4.54) undergraduate students enrolled in a psychology course were recruited via flyers and the psychology subject pool of a large, ethnically diverse southwestern university between January 2014 and April 2016. Participants received extra credit toward their psychology course as compensation. Exclusion criteria included being younger than age 18 and non-proficiency in English (to ensure comprehension of study questions). Participants identified themselves as follows: 28% non-White Hispanic, 24% non-Hispanic White, 29% Asian, 11% non-Hispanic African American, 3% Hispanic African American, and 5% Other/Mixed race. The overall sample of 1,827 participants was randomly split in two subsamples. Subsample 1 (n = 887; 74.0% female; Mage = 21.64; SD = 4.68) was used to develop the FSQ (Phase I). Subsample 2 (n = 940; 72.2% female; Mage = 21.71; SD = 4.41) was used to validate the FSQ (Phase II).

Procedure

Phase I focused on item creation reduction. Two independent doctoral-level experts on stress and physical health evaluated a pool of items for inclusion in data analysis. Each expert identified items that most strongly aligned with the agreed upon measure definition, contained clear language, and were unambiguously related to fatigue sensitivity. A third doctoral-level expert reviewed the overlapping items identified by the two original experts. The three experts agreed on thirteen items to capture fatigue sensitivity. Several models were tested and compared to identify the best fitting FSQ structure.

Phase II focused on measure validation. The best fitting structure for FSQ items was evaluated for replicability and construct validity. Construct validity was assessed by internal consistency of the FSQ measures as well as its convergent and discriminant validity.

Study procedures complied with the Institutional Review Board at the university in which the study was conducted. Each participant completed online informed consent before proceeding to an internet-based self-report survey. All study measures were completed online. Students were compensated with extra credit toward psychology coursework. No identifying information was retained linking participants to survey responses.

PHASE I

Measures

Demographics questionnaire

A demographic questionnaire assessed participant sex (male = 1, female = 2), age, and race. Items from this measure were used to describe the sample.

Fatigue sensitivity questionnaire (FSQ)

We created an initial pool of 63 items that assessed physical, cognitive, and social concerns in response to fatigue-related symptoms and experiences. Two independent raters evaluated the pool of items for inclusion in data analysis. Ultimately, 13 fatigue sensitivity items were examined in a factor analysis. Instructions for the scale stated, “Please circle the number that best corresponds to how much you agree with each item.” Item responses included: 0 (Very little), 1 (A little), 2 (Some), 3 (Much), and 4 (Very much). Examination of responses across response options indicated an unbalanced distribution and a low response rate for 4 (Very much) across items (range: 1.6–6.7%); thus, response 3 and 4 were collapse into one score.

Analytic strategy

Confirmatory factor analysis (CFA) was conducted to evaluate the structure of the proposed 13-item FSQ measure. A model-fitting (CFA) approach was used instead of a purely exploratory factor analytic approach because of the theory-driven model being evaluated. Several structures were evaluated to identify the best fitting model, including a correlated factors model; a unidimensional model; and a bifactor model (see Reise, Moore, & Haviland, 2010 for thorough description of these models). Analyses were conducted using Mplus 8 (Muthén & Muthén, 2012). Robust weighted least squares estimator (WLSMV in Mplus) was employed. Overall model fit was assessed using the χ2 statistic and several fit indices. A nonsignificant χ2 test statistic indicates good model fit. Other model fit statistics and association criteria included: root mean square error of approximation (RMSEA), with values less than 0.06 indicating excellent fit, values less than .08 indicating acceptable fit, and values above 0.10 suggesting poor fit; and the Comparative Fit Index (CFI), with values between 0.95 and 1.00 indicating excellent fit and values between 0.90 and 0.94 indicating acceptable fit (Awang, 2012; Hu & Bentler, 1999). Model comparisons were evaluated using change is χ2 wherein a significant change provided statistical evidence for the less restrictive model.

Results

Item evaluation

Strong inter-item correlations emerged (r’s range: 0.53–0.85). Based on extant research (Campbell et al., 2013), items that correlated higher than 0.80 were evaluated and removed to reduce redundancy. Using this criterion, three items were removed. The remaining 10 items evinced a moderate average inter-item correlation (r = 0.55). The final 10 items were subjected to confirmatory factor analyses.

Confirmatory factor analysis

The three-factor correlated model demonstrated acceptable model fit (X2[32] = 186.40, p < .001; RMSEA = .07 [90% CI: .06, .08], p < .001; CFI = .992). The unidimensional model also demonstrated acceptable model fit (X2[35] = 190.63, p < .001; RMSEA = .07 [90% CI: .06, .08], p < .001; CFI = .992). Finally, the bifactor model evinced a nonconverging solution with negative residual variances. Attempts to assist with convergence though model modifications were unsuccessful. Thus, the bifactor model was deemed inappropriate for the current data. Nested model comparison across the three-factor correlated model and unidimensional model favored the unidimensional model (Δχ2[3] = 5.67, p = .13). See Table 1 for items and standardized factor loadings.

Table 1.

Standardized factor loadings.

Study 1
 Study 2
Item Factor Loadings Factor Loadings
7. When I have headaches, I worry that something may be seriously wrong with me. 0.77 0.77
27. When I cannot stay awake, I fear I might be dying. 0.88 0.90
52. I fear that my body will shut down when I begin to feel Exhausted 0.89 0.88
2. When I cannot stay awake, I become concerned that I might become depressed. 0.74 0.74
10. When I feel tired, I worry that I will become very irritable. 0.70 0.66
49. I worry that I will not be able to think when I am drained. 0.87 0.85
54. I cannot handle feeling tired or drained. 0.83 0.84
6. When I yawn in the presence of others, I fear what people might think of me. 0.84 0.84
39. When I cannot concentrate on something because I am fatigued, I become concerned that other people will think I am dumb. 0.86 0.87
62. When I feel sluggish, I am afraid that people will judge me negatively. 0.85 0.87
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N for Study 1 = 887; N for study 2 = 940; Standardized factor loadings presented. All factor loading significant at p < 0.001.

Phase II

Measures

Fatigue sensitivity questionnaire (FSQ)

The FSQ is a 10-item self-report measure developed in Phase I of the present report. The FSQ assesses the tendency for individuals to misappraise fatigue-related symptoms and sensations as having harmful physical, social, and/or cognitive consequences. Each item is assessed on a 4-point Likert scale ranging from 0 (Very Little) to 3 (Much/Very Much). A total score was calculated to summarize this measure.

Anxiety sensitivity index-3 (ASI-3; Taylor et al., 2007)

The ASI-3 is an 18-item measure developed based upon the original Anxiety Sensitivity Index (Reise et al., 2010). Respondents indicate the extent to which they are concerned about possible negative consequences of anxiety-related symptoms (e.g. “It scares me when my heart beats rapidly”). Responses are rated on a 5-point Likert scale ranging from 0 (Very Little) to 4 (Very Much) and summed to create a total score. The ASI-3 has three subscales: physical concerns, cognitive concerns, and social concerns. In past work, the ASI-3 has demonstrated sound psychometric properties as a valid assessment of anxiety sensitivity, with the total score and each of the subscales also exhibiting acceptable to good internal consistency (Taylor et al., 2007). In the present study, we utilized the total ASI-3 score (α = 0.93).

Inventory of depression and anxiety symptoms (IDAS; Watson et al., 2007)

The IDAS is a 64-item self-report measure of depression and anxiety symptoms experienced during the previous two weeks. The IDAS contains 12 subscales: general depression (20 items), dysphoria (10 items), well-being (8 items), anxious arousal (8 items), lassitude (6 items), insomnia (6 items), suicidality (6 items), social anxiety (5 items), ill temper (5 items), traumatic intrusions (4 items), appetite loss (3 items), and appetite gain (3 items). In previous work, the IDAS subscales have shown good internal reliability (α = 0.80–0.89) and convergent validity with other measures of depression and anxiety among university students (Watson et al., 2007) and among a diverse sample of community adults (Watson et al., 2012). In the present study, we employed the general depression (α = 0.92), social anxiety (α = 0.87), panic (α = .90), and well-being (α = 0.90) subscales.

Positive and negative affect schedule (PANAS; Watson et al., 1988)

The PANAS is a self-report measure that assesses the degree to which participants typically experience 20 different positive (e.g. excited, proud) or negative affective states (e.g. afraid, distressed). Responses are based on a Likert scale ranging from 1 (Very Slightly or Not at All) to 5 (Extremely). The PANAS yields two subscales, positive affect (PA) and negative affect (NA), which have shown good internal consistency (PA: α = 0.86; NA: α = 0.90) and validity (Watson et al., 1988). Both factors were utilized in the current study (NA α = 0.92; PA α = 0.90).

Analytic strategy

To corroborate findings from Phase I, a CFA was conducted to evaluate the proposed unidimensional FSQ structure using the identified 10-items. Analyses were conducted using Mplus 8 (Muthén & Muthén, 2012). Robust weighted least squares estimator (WLSMV in Mplus) was employed. Overall model fit was evaluated using the sample criteria outlined for Phase I. Cronbach’s alpha was computed to assess the internal reliability of the FSQ measure.

A series of zero-order correlations between baseline variables were conducted to demonstrate convergent and discriminant validity. Variables to evaluate convergent and discriminant validity included measure of anxiety sensitivity (ASI-3), general depression (IDAS), anxious arousal (IDAS), negative affectivity (PANAS), positive affectivity (PANAS), and well-being (IDAS). We hypothesized that the FSQ total score would positively and significantly correlate with anxiety sensitivity, general depression, anxious arousal, and negative affectivity. Furthermore, we hypothesized that the FSQ total score would negatively relate to positive affectivity and well-being.

Results

Confirmatory factor analysis

The unidimensional factor structure found for the FSQ in Phase I of the present report was evaluated in an independent sample. The 10-item FSQ measure demonstrated acceptable model fit in a CFA (X2[35] = 349.92, p < 0.001; RMSEA = 0.10 [90% CI: 0.09, 0.11]; CFI = 0.98). Standardized factor loadings ranged from 0.66 to 0.90. See Table 2 for standardized factor loadings. The FSQ demonstrated excellent internal consistency (α = 0.92).

Table 2.

Fatigue Sensitivity Questionnaire.

Please circle the number that best corresponds to how much you agree with
each item.		 Very
little		 A
little		 Some Much/Very
Much
1. When I cannot stay awake, I become concerned that I might become depressed. 0 1 2 3
2. When I yawn in the presence of others, I fear what people might think of 0 1 2 3
3. When I have headaches, I worry that something may be seriously wrong with me. 0 1 2 3
4. When I feel tired, I worry that I will become very irritable. 0 1 2 3
5. When I cannot stay awake, I fear I might be dying. 0 1 2 3
6. When I cannot concentrate on something because I am fatigued, I become concerned that other people will think I am dumb. 0 1 2 3
7. I worry that I will not be able to think when I am drained. 0 1 2 3
8. I fear that my body will shut down when I begin to feel exhausted. 0 1 2 3
9. I cannot handle feeling tired or drained. 0 1 2 3
10. When I feel sluggish, I am afraid that people will judge me negatively. 0 1 2 3
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Scoring: Physical concerns = sum of Items 3, 5, 8. Cognitive concerns = sum of Items 1, 4, 7, 9. Social concerns = sum of Items 2, 6, 10.

Construct validity

The FSQ total score was positively associated with anxiety sensitivity (r = 0.49, p < 0.001; 24% shared variance), general depression (r = 0.37, p < 0.001; 14% shared variance), social anxiety (r = 0.40, p < 0.001; 16% shared variance), panic (r = 0.43, p < 0.001; 18% shared variance), and negative affectivity (r = 0.37, p < 0.001; 14% shared variance). Additionally, the FSQ total score negatively correlated with positive affectivity (r = −0.14, p < 0.001; 2% shared variance) and well-being (r = −0.16, p < 0.001; 3% shared variance).

Discussion

Fatigue is a common and primary healthcare complaint associated with a host of negative consequences (Aaron et al., 2001; Bateman et al., 2015; Whitehead et al., 2016). Thus, there is great clinical utility in more fully understanding the construct of fatigue. To more elucidate compositional aspects of fatigue, the present study utilized recommendations for scale development (see DeVillis, 2012) to derive a theoretically informed and clinically valid measure of fatigue sensitivity. We conceptualized fatigue sensitivity as expectations or perceptions about the negative consequences associated with fatigue-related symptoms. The 10-item FSQ was constructed across two independent samples to capture three domains of fatigue sensitivity including concerns related to physical, cognitive, and social consequences. The results favored a unidimensional model rather than three-factor correlated model in a nested model comparison. Furthermore, examination of responses across response options indicated an unbalanced distribution and a low response rate for 4 (Very much) across items. As a result, response 3 and 4 were collapse into one score (Much/Very much).

The FSQ demonstrated excellent internal consistency and robust associations with variables of interest. Specifically, FSQ scores were positively associated with anxiety sensitivity, general depression, social anxiety, panic, and negative affectivity (r’s = 0.37–0.49). Evidence of discriminant validity was provided by analysis showing that the FSQ total score was negatively correlated with positive affectivity and well-being with small effects (r’s = −0.14 to −0.16). The present results suggest that the FSQ may be a valid and promising additive approach in better understanding and capturing the implications of fatigue for use in real world contexts (e.g. primary care).

Although the current study was examined among college students, the findings offer preliminary empirical support for the potential clinical relevance of the fatigue sensitivity construct and highlight the possible value in the routine assessment of fatigue sensitivity. Theoretically, the use of the FQI has the potential to help identify individuals at risk for poor mental and physical health through a mechanism that, until now, has been largely overlooked and not considered. Thus, fatigue sensitivity may be a potential mechanism in which practitioners may target to improve treatment outcomes. For example, an individual may experience sensitivity to fatigue-related symptoms because of the feared expected consequences of such sensations. As a result, the individual may engage in escape behaviors to avoid experiencing fatigue. As such, in theory, avoidance of fatigue-related symptoms may potentially contribute to a host of negative outcomes including social withdrawal, exercise avoidance, and further perpetuate negative mental (e.g. depressive symptoms) and physical (e.g. weight gain) health outcomes. Finally, given that longer measures are less likely to be utilized in clinical settings (Mark, Johnson, Fortner, & Ryan, 2008), the FSQ provides a brief, practical, easily disseminated measure for use in a broad array of clinical settings.

Although our findings provide important preliminary support for the FSQ, several aspects of the scale require further psychometric evaluation. First, the current sample studied was comprised largely of female college students. Future work could benefit from examining the generalizability of the current model to a larger percentage of males. Additionally, future work may benefit from validating this measure in the general population, as well as within a clinical setting among individuals with psychiatric illness (e.g. panic disorder) and chronic medical conditions (e.g. obesity). Furthermore, the current study did not examine convergent validity related to other measures of fatigue. Thus, future work could benefit from examining FSQ total score with other well-validated measures of fatigue (e.g. Fatigue Symptom Inventory; Whitehead, 2009). Moreover, the current study did not examine the predictive validity of the FSQ total score and other mental and physical health outcomes. Thus, it is recommended that future work examine whether FSQ can identify individuals at risk for these outcomes. Finally, the current study did not examine test-retest reliability. Thus, the reliability of the scale over time is presently unknown. Future work is needed to examine the reliability of the FSQ over time.

Overall, the present study provides initial support for the FSQ. Future work is needed to empirically examine the impact of FSQ on mental and physical health outcomes. As a research tool, the FSQ may provide broader understanding of vulnerability factors that my influence health outcomes. Within a clinical context, future work should determine if fatigue sensitivity as measured by the FSQ is malleable to treatment change.

Acknowledgments

Funding

Work on the present paper was partially supported by an award from the National Institute of Drug Abuse (1F31DA046127-01 to Brooke Y. Kauffman).

Footnotes

Declarations of Interest

All authors report no financial relationships with commercial interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, [MJZ], upon reasonable request.

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