The Development of a Short Form of the DePaul Symptom Questionnaire

Abstract

Purpose/Objective:

The DePaul Symptom Questionnaire (DSQ) is a widely-used instrument that assesses common symptoms of myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). The DSQ has strong psychometric properties; however, it consists of 99 items, and the energy limitations and cognitive difficulties experienced by individuals with ME and CFS may hinder their ability to easily complete the questionnaire

Method:

The current study examined symptom prevalence and discriminative ability to develop a short form of the DSQ (DSQ-SF).

Results:

The resulting short-form questionnaire consists of 14 items that were highly prevalent among individuals with ME and CFS. Additionally, the items demonstrated the ability to differentiate individuals with ME and CFS from adult controls and, to a lesser extent, individuals with multiple sclerosis

Conclusions/Implications:

The DSQ-SF may serve as an effective, brief screening tool for symptoms of ME and CFS.

Myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) are the names of debilitating illnesses (IOM, 2015) whose diagnostic criteria remain elusive, as over twenty different case definitions exist (Brurberg, F0nhus, Larun, Flottorp, & Malterud, 2014). The Fukuda et al. (1994) criteria for CFS have historically been the most widely applied in research (Brurberg et al., 2014). This case definition requires the presence of six or more months of fatigue, a substantial reduction in functioning, and four of the following eight symptoms: post-exertional malaise, memory and concentration problems, unrefreshing sleep, headaches, muscle pain, joint pain, sore throat, or tender lymph nodes. In spite of its ubiquitous use, the case definition’s polythetic criteria have been criticized, as individuals could meet symptom requirements without experiencing pathognomonic symptoms of the illness, such as post- exertional malaise (Jason et al., 1999). In response to this critique, the Canadian Consensus Criteria for ME/CFS (Carruthers et al., 2003) were constructed to require several core symptoms of the illness: fatigue, post-exertional malaise, sleep dysfunction, and neurocognitive problems. Additionally, these criteria require symptoms that implicate at least two of the following bodily systems: autonomic, neuroendocrine, or immune. In 2011, an international group of researchers and clinicians further refined criteria for ME (Carruthers et al., 2011). Their resulting case definition, the ME International Consensus Criteria (ME-ICC), defined the illness as the concurrence of eight symptoms across the following dimensions: post-exertional neuroimmune exhaustion, three neurological impairments, three immune, gastro-intestinal, or genitourinary impairments, and one energy metabolism / ion transport impairment. While this case definition required characteristic symptoms of the illness, the number of symptoms necessary for diagnosis also increased. Previous research has cautioned against case definitions with greater symptom requirements, as they are associated with increased psychiatric comorbidity among individuals who fulfill criteria (e.g., Katon & Russo, 1992; Brown, Jason, Evans, & Flores, 2013). Most recently, the Institute of Medicine (2015) developed a clinical case definition with a reduced, four-symptom requirement: a substantial reduction in functioning, post-exertional malaise, unrefreshing sleep, and either cognitive impairment or orthostatic intolerance. This IOM report (2015) further underscored the need for consistently-applied diagnostic processes in research and clinical practice, as the use of varied diagnostic criteria can result in heterogeneous research samples and hinder the search for biological markers and treatments.

The DePaul Symptom Questionnaire (DSQ) was developed (Jason et al., 2010) in an effort to standardize symptom assessment and directly compare and contrast individuals who meet various ME and CFS case definitions. The instrument was originally constructed to assess the symptom requirements of the Fukuda et al. (1994) CFS and Canadian ME/CFS (Carruthers et al., 2003) criteria. Items were later appended such that the instrument could operationalize the concurrently-released ME-ICC (Carruthers et al., 2011). The DSQ consists of 99 items related to ME and CFS symptoms, illness onset and duration, medical and psychiatric history, and energy availability and expenditure. The DSQ’s items have demonstrated strong test-retest reliability (Jason, So, Brown, Sunnquist, & Evans, 2015), content validity (Jason, Sunnquist, Brown, Furst, et al., 2015), internal consistency reliability (Jason, Sunnquist, Brown, Furst, et al., 2015), and the ability to distinguish individuals with ME and CFS from healthy controls, as well as from individuals with other chronic illnesses (Jason et al., 2014; Ohanian et al., 2016; Murdock et al., 2017). Strand et al. (2016) found a sensitivity of 98% when comparing the agreement between a physicians’ diagnosis of ME and CFS using the Canadian Consensus Criteria and the DSQ’s assessment of this case definition. Furthermore, when compared to other instruments that assess fatigue and health-related functioning, the DSQ’s symptom rating system was not limited by ceiling effects (Murdock et al., 2017). Consistent with the questionnaire’s original purpose, several studies have successfully utilized the DSQ to operationalize various ME and CFS case definitions in order to compare the symptom profiles and functional status associated with different criteria (e.g., Jason et al., 2013; Jason, Sunnquist, Brown, Furst, et al., 2015; Jason, Sunnquist, Brown, Newton, et al., 2015). These studies illustrated that approximately 90% of individuals diagnosed with ME and CFS meet the Fukuda et al. (1994) criteria (Jason et al., 2013); approximately 75% meet the Canadian ME/CFS criteria (Jason et al., 2013); and approximately 88% meet the IOM criteria (Jason, Sunnquist, Brown, Newton, et al., 2015).

Despite the strong psychometric properties of the DSQ, the 99-item questionnaire may require substantial time to complete (approximately 30–50 minutes), particularly due to the energy limitations and cognitive difficulties associated with ME and CFS. Additionally, researchers and physicians have expressed an interest in a shorter symptom screen for use in clinical practice and time-constrained research protocols. The current study sought to develop a short form of the DSQ (DSQ-SF) through examining each item’s prevalence among patients, discriminative ability, and utility in case definition classification.

Method

Participants

The current study examined data from two samples: a Multisite Sample [composed of individuals with ME or CFS (n = 948) and controls (n = 47)], and a Chronic Illness Sample [composed of individuals with ME or CFS (n = 353) and a control group of individuals with multiple sclerosis (MS, n = 135)]. Both samples were analyzed due to their different control groups: a general control group of adults without ME or CFS (in the Multisite Sample), and a control group of individuals with MS (in the Chronic Illness Sample).

Multisite sample.

Participants were recruited from four different research sites as part of a larger effort to understand the symptomatology of ME and CFS. Full sample descriptions are included in Sunnquist and Jason (2018); each site’s recruitment process is briefly described below. All participants completed a written, informed consent process.

DePaul University.

An international convenience sample was recruited through contacting ME and CFS support groups and past participants who had indicated interest in future studies. Additionally, study information was posted on social media pages. To be eligible, individuals needed to be at least 18 years old and have a self-reported, current diagnosis of ME or CFS.

Solve ME/CFS Initiative BioBank.

Individuals were recruited through physician referral, the Solve ME/CFS Initiative website, and the Solve ME/CFS Initiative social media accounts. Participants were required to be 18 years of age or older and have a diagnosis of ME or CFS from a licensed physician who specializes in the illness. Additionally, adults without ME or CFS were recruited for inclusion in a control group.

Newcastle.

Participants in the Newcastle sample were referred for a medical assessment at the Newcastle-upon-Tyne Royal Victoria Infirmary clinic due to a suspected diagnosis of CFS. An experienced physician performed a comprehensive medical history and examination.

Norway 1.

Individuals with CFS who had been diagnosed by a physician or medical specialist were invited to participate in a randomized controlled trial of a CFS self-management program. Participants were recruited from Oslo and surrounding communities through healthcare professionals, the waiting list for a patient education program, and CFS patient organizations. Study measures were completed prior to the intervention’s commencement.

Norway 2.

Participants, aged 18 to 65, were recruited from an inpatient medical ward for severely ill patients, as well as from the outpatient clinic at a multidisciplinary CFS/ME Center. All participants took part in a comprehensive medical history interview and a detailed medical examination conducted by experienced consultant physicians and a psychologist.

Chronic illness sample.

As part of a larger chronic illness study (Jason et al., 2017), participants with several illnesses (ME or CFS, MS, Lupus, Cancer, Post-Polio Syndrome, and HIV/AIDS) were recruited through support group websites, national foundations, research forums, and social media outlets. The current study included only the ME or CFS and MS groups, as these groups were large enough to be analyzed (353 individuals with ME or CFS and 135 individuals with MS). Furthermore, MS presents with similar symptoms to ME and CFS, including fatigue, cognitive impairment, pain, muscle weakness, vertigo, genitourinary issues, and gastrointestinal distress (Kister et al., 2013). Previous research has also directly compared the symptom profiles of individuals with ME and CFS to those with MS; results suggest that individuals with ME and CFS generally report higher frequency and severity ratings across symptom domains (Jason et al., 2017), and they are more likely to endorse immune symptoms than individuals with MS (Jason et al., 2016).

To participate in this sample, individuals were required to be 18 years or older and able to comprehend written English. Study measures were completed online. This sample was analyzed separately from the Multisite Sample, as it would have been possible for the same individuals to participate in both studies.

Materials

The DePaul Symptom Questionnaire (DSQ) is a self-report measure of demographic characteristics, ME and CFS symptomatology, and medical, occupational, and social history (Jason, Evans, et al., 2010). The DSQ includes 99 items, of which 54 assess the frequency and severity of the ME and CFS symptoms required by several case definitions. The DSQ-SF is to serve as a brief screener for symptoms required by ME and CFS case definitions. Only the 54 symptom items were considered for inclusion in the DSQ-SF, as the remaining 45 items relate to information tangential to the DSQ-SF’s primary purpose, such as demographics and illness history. Participants reference the past six months to rate each symptom on 5-point Likert-type scales. Frequency scores are associated with the following descriptors: 0 = none of the time, 1 = a little of the time, 2 = about half of the time, 3 = most of the time, and 4 = all of the time. Severity scores are defined as: 0 = symptom not present, 1 = mild, 2 = moderate, 3 = severe, 4 = very severe. The DSQ has shown strong psychometric properties, as reviewed in the introduction.

Procedure

To ensure a diverse representation of symptoms in the DSQ-SF, the authors determined that symptoms would be selected from each of the domains identified in the Canadian ME/CFS case definition (fatigue, post-exertional malaise, sleep, neurocognitive impairment, pain, autonomic, neuroendocrine, and immune; Carruthers et al., 2003), as the DSQ was originally developed to measure these criteria, and these criteria involve all of the symptoms assessed in the DSQ (see Table 1 for a summary of the DSQ items included in each case definition). Given this guiding principle, the procedure to select items for the DSQ-SF followed a five-step process: (1) calculate the prevalence of each DSQ symptom among patients to determine which items are most representative of each symptom domain (construct validity); (2) conduct data mining analyses (classification and regression trees) to identify the combination of symptoms that best differentiate individuals with ME and CFS from controls and individuals with MS (discriminative validity); (3) select symptoms from each domain with the highest prevalence and best discriminatory power for inclusion in the DSQ-SF; (4) develop modified case definition algorithms for the DSQ-SF to determine which participants would meet common ME and CFS case definitions; and (5) examine the DSQ-SF’s accuracy in case definition classification (predictive validity). The samples utilized in each step are specified in Table 2.

Table 1.

Symptoms Included in Case Definition Algorithms

Symptom	Fukuda	Canadian	IOM
Fatigue	X	X	X
Minimum exercise makes you physically tired	X	X	X
Next-day soreness/fatigue after everyday activities	X	X	X
Physically drained or sick after mild activity	X	X	X
Dead, heavy feeling after starting to exercise	X	X	X
Mentally tired after the slightest effort	X	X	X
Unrefreshed after you wake up in the morning	X	X	X
Problems staying asleep		X	X
Problems falling asleep		X	X
Need to nap daily		X	X
Waking up early in the morning (e.g. 3am)		X
Sleeping all day and staying awake all night		X	X
Pain or aching in your muscles	X	X
Pain/stiffness/tenderness in >1 joint	X	X
Headaches	X	X
Bloating		X
Abdomen/Stomach pain		X
Eye pain		X
Chest pain		X
Difficulty paying attention for a long period of time	X	X	X
Problems remembering things	X	X	X
Only able to focus on one thing at a time		X	X
Difficulty expressing thoughts	X	X	X
Muscle weakness		X
Absent-mindedness or forgetfulness	X	X	X
Sensitivity to noise		X
Slowness of thought	X	X	X
Sensitivity to bright lights		X
Difficulty understanding things	X	X	X
Unable to focus vision and/or attention		X
Muscle twitches		X
Loss of depth perception		X
Irritable bowel problems		X
Feeling unsteady on your feet, like you might fall		X	X
Shortness of breath or trouble catching your breath		X	X
Dizziness or fainting		X	X
Bladder problems		X
Nausea		X
Irregular heart beats		X	X
Cold limbs		X
Feeling hot or cold for no reason		X
Losing or gaining weight without trying		X
Chills or shivers		X
Night sweats		X
Alcohol intolerance		X
Feeling like you have a high temperature		X
Feeling like you have a low temperature		X
No appetite		X
Sweating hands		X
Flu-like symptoms		X
Some smells/foods/meds/chemicals make you feel sick		X
Tender/Sore lymph nodes	X	X
Sore throat	X	X
Fever		X

X: Indicates that items are included in the original, full-form DSQ case definition algorithms

Shaded boxes: Indicate that the item is included in the DSQ-SF case definition algorithms

Table 2.

Samples Used by Step

	Multisite Sample		Chronic Illness Sample
	ME and CFS	Control	ME and CFS	MS
Step 1. Prevalence	X
Step 2. Decision Trees	X	X	X	X
Step 3. Item Selection	X	X	X	X
Step 4. Case Definition Modification	This step did not involve quantitative analysis
Step 5. Case Definition Comparison	X	X

Step 1.

Due to its larger sample size, the Multisite sample was analyzed to determine the prevalence of each DSQ symptom among individuals with ME and CFS. Past research identified a “2–2 threshold” (i.e., a frequency rating of 2, about half of the time, or greater, and a severity rating of 2, moderate, or greater) as optimal in determining whether a symptom is present (Jason et al., 2014). In this step, the percentage of patients who met the “2–2” threshold was computed.

Step 2.

Decision tree analyses, a form of data mining, were used to identify the items within each symptom domain that best predict an ME or CFS diagnosis. Decision trees consist of a series of binary choices (termed “branches”) that aim to accurately classify participants into their correct group (i.e., CFS or MS). At each branch, the analysis identifies which symptom most accurately categorizes participants. The process continues to identify additional symptoms (branches) until all individuals are classified correctly (Lemon, Roy, Clark, Friedmann, & Rakowski, 2003). Decision trees were selected for use in this study due to their lack of assumptions about data distribution and ease of interpretation (Henrard, Speybroeck, & Hermans, 2015).

Two sets of decision tree analyses were conducted. In the Multisite sample, decision trees were utilized to determine which symptoms (when present at the “2–2 threshold”) best differentiated individuals with ME or CFS from control participants. In the Chronic Illness sample, decision trees attempted to distinguish individuals with ME or CFS from those with MS.

The SPSS (version 24) Decision Tree statistical package and the Classification and Regression Tree (CRT) algorithm were selected for these analyses. As decision trees select items based upon overall classification accuracy, diagnostic groups must be of relatively equal size. If one diagnostic group is larger than the second, symptoms that are most prevalent in the larger group will, mathematically, have a larger impact on classification accuracy, and the decision trees will prioritize these symptoms over symptoms that may be more prevalent in the smaller group. Because the ME and CFS samples were larger than the control and MS samples, random, approximately equal subsamples of each group were generated using the UNIFORM function in SPSS. For the first set of decision trees (using the Multisite sample), the UNIFORM function randomly selected 5% of the ME and CFS group (approximately 45 individuals) and 99% of the Control group (approximately 45 individuals). After decision trees were conducted on this subsample, the UNIFORM filter was removed, and the entire process was repeated 99 more times (i.e., random sampling with replacement, a process analogous to bootstrapping). The same process was utilized for the second set of decision trees (using the Chronic Illness sample); however, the UNIFORM function selected 28.3% of the ME and CFS group (approximately 100 individuals) and 73.5% of the MS group (approximately 100 individuals). If a participant did not provide a response for an independent variable used in a decision tree, SPSS utilized surrogates (participants with similar responses to other independent variables) to predict the participant’s response (Henrard et al., 2015). Missing values were limited in these datasets, ranging from 0.0% to 5.1% by variable (median = 0.8%).

Items selected for the first branches of the decision tree are most important in differentiating the groups (i.e., result in the highest accuracy in separating individuals with ME or CFS from the comparison group); therefore, the first two items selected for each tree indicate the two-item combination that best differentiates the diagnostic groups. As the current study sought to identify two items from each domain, items were tallied to compare the number of times they appeared in the first two branches of the 100 decision trees.

Step 3.

The results of the previous analyses were examined to identify the symptoms from each domain with the highest prevalence and discriminatory power. The following guidelines were utilized to select items for the DSQ-SF: (1) Two items were selected from each symptom domain (exceptions are described in the Results section); (2) Within each symptom domain, the two items that appeared most frequently in both sets of decision tree analyses (Multisite sample and Chronic Illness sample) were considered for inclusion in the DSQ-SF; and (3) If the results of the Multisite sample (ME and CFS vs. Control) decision trees differed from those of the Chronic Illness sample (ME and CFS vs. MS) decision trees, symptom prevalence among patients was utilized to make the final item selections.

Step 4.

The DSQ has been utilized to operationalize several case definitions for ME and CFS, including the Fukuda et al. (1994) CFS criteria (Jason et al., 2013), the Canadian ME/CFS criteria (Jason et al., 2013), and the IOM criteria (Jason, Sunnquist, Brown, Newton, et al.,2015). These case definition algorithms were modified such that only items included in the DSQ-SF were used (See Table 1). Of note, all case definition algorithms for the full-form DSQ also use data from the SF-36, a separate questionnaire, to operationalize the case definitions’ requirement that individuals report a substantial reduction in functioning (SF-36 scoring rules can be found in Jason et al., 2013). Information on whether individuals met this substantial reduction criterion was incorporated into the DSQ-SF algorithms in order to directly compare results with the full-form DSQ algorithms.

Step 5.

The case definition algorithms for the full-form DSQ and the modified algorithms for the DSQ-SF were applied to individuals in the Multisite sample (this sample was selected due to its larger size), including individuals with ME or CFS and controls. The classification results of the DSQ-SF algorithms were compared to the results of the full-form DSQ algorithms in order to examine the accuracy, sensitivity, and specificity of the DSQ-SF in identifying which patients meet case definitions. It is important to note that individuals in the ME and CFS group may not have met the case definition criteria examined in this article. This discrepancy could occur for many reasons, including: the participant’s physician used different criteria to make a diagnosis; the participant is able to manage certain symptoms (that are required by a case definition) with medication or other treatments; or the participant may have been misdiagnosed. As such, inclusion in the ME and CFS group is not synonymous with fulfillment of case definition criteria.

Results

Demographic Comparison

Table 3 displays the demographic characteristics of each sample and diagnostic group. In the Multisite sample, individuals with ME or CFS, in contrast to Controls, were significantly younger [t(972) = −3.29, p = .001], and there was a higher proportion of females [X²(1, N = 933) = 10.54, p = .001], individuals without a college or advanced degree [X²(1, N = 923) = 14.48, p < .001], and individuals on disability [X²(3, N = 930) = 87.73, p < .001]. In the Chronic Illness sample, individuals with ME or CFS, in contrast to those with MS, were significantly older [t(403) = −4.15, p = .001], and a higher proportion identified as White [X²(1, N = 484) = 5.73, p =.02] and were on disability [X²(3, N = 482) = 65.90, p < .001]. Although mean ages were significantly different, the difference (5–6 years) was not considered to be clinically significant. While slightly fewer individuals with MS than individuals with ME and CFS identified as White in the Chronic Illness sample, both groups were predominantly (over 90%) White. Furthermore, differences in educational attainment and work status were expected due to the educational and occupational impacts of ME and CFS (Jason et al., 2008).

Table 3.

Demographics by Sample

	Multisite Sample			Chronic Illness Sample
	ME or CFS	Control		ME or CFS	MS
	(n = 948)	(n = 47)		(n = 353)	(n = 135)
	M (SD)	M (SD)	Sig.	M (SD)	M (SD)	Sig.
Age	50.1 (13.5)	56.7 (12.7)	**	49.3 (13.2)	43.4 (11.1)	***
	% (n)	% (n)	Sig.	% (n)	% (n)	Sig.
Gender
Female	80.6 (752)	60.9 (28)	*	88.7 (307)	83.3 (110)
Male	19.4 (181)	39.1 (18)		11.3 (39)	16.7 (22)
Race
White	98.3 (914)	100 (46)		96.9 (339)	91.8 (123)	*
Other Race	1.7 (16)	0.0 (0)		3.1 (11)	8.2 (11)
Education Level
High School or Less	36.1 (333)	8.7 (4)	***	29.6 (104)	34.6 (47)
College or Graduate Degree	63.9 (590)	91.3 (42)		70.4 (247)	65.4 (89)
Work Status
On Disability	57.2 (532)	0.0 (0)	***	45.5 (158)	27.4 (37)	***
Student / Homemaker	4.9 (46)	4.4 (2)		7.8 (27)	4.4 (6)
Retired / Unemployed	21.2 (197)	26.7 (12)		22.8 (79)	5.9 (8)
Working	16.7 (155)	68.9 (31)		23.9 (83)	62.2 (84)

^*p < 0.05

^**p < 0.01

^***p < 0.001

Symptom Prevalence and Decision Trees

Table 4 displays the percentage of individuals with ME and CFS (in the Multisite sample) who endorsed symptoms at the “2–2 threshold” (i.e., symptoms of at least moderate severity that occur at least half of the time). This table also displays the number of times each item appeared within the first two branches of 100 decision trees. Items marked with an asterisk were selected for inclusion in the DSQ-SF, as they appeared frequently in decision trees and had the highest prevalence among individuals with ME and CFS.

Table 4.

Item Prevalence and Decision Tree Results

			# Times in Branch 1 or 2
			of 100 Decision Trees
	Item	Prevalence	Multisite Sample	Chronic Illness Sample
	*Fatigue	93.1	--	--
PEM	*Minimum exercise makes you physically tired	86.4	83	5
	*Next-day soreness/fatigue after everyday activities	85.1	6	85
	Physically drained or sick after mild activity	83.0	16	29
	Dead, heavy feeling after starting to exercise	78.1	1	12
	Mentally tired after the slightest effort	76.4	2	69
Sleep	*Unrefreshed after you wake up in the morning	90.7	100	97
	Problems staying asleep	63.6	8	2
	Problems falling asleep	59.8	0	75
	Need to nap daily	57.9	85	6
	Waking up early in the morning (e.g. 3am)	47.8	0	6
	Sleeping all day and staying awake all night	11.6	0	14
Pain	*Pain or aching in your muscles	73.9	90	35
	Pain/stiffness/tenderness in >1 joint	62.7	13	6
	Headaches	49.4	7	7
	*Bloating	47.9	77	28
	Abdomen/Stomach pain	37.4	3	76
	Eye pain	28.6	4	19
	Chest pain	18.0	0	27
Neurocognitive	* Difficulty paying attention for a long period of time	80.1	82	54
	* Problems remembering things	76.3	42	3
	Only able to focus on one thing at a time	71.3	14	29
	Difficulty expressing thoughts	70.8	5	0
	Muscle weakness	67.1	12	0
	Absent-mindedness or forgetfulness	66.7	5	3
	Sensitivity to noise	65.2	1	38
	Slowness of thought	64.2	2	2
	Sensitivity to bright lights	57.1	0	53
	Difficulty understanding things	48.6	0	0
	Unable to focus vision and or/attention	47.7	0	0
	Muscle twitches	25.1	0	18
	Loss of depth perception	19.7	0	0
Autonomic	*Irritable bowel problems	47.7	72	51
	*Feeling unsteady on your feet, like you might fall	35.9	45	3
	Shortness of breath or trouble catching your breath	35.4	24	60
	Dizziness or fainting	33.3	23	12
	Bladder problems	30.6	18	5
	Nausea	25.0	1	31
	Irregular heart beats	23.4	0	38
Neuroendocrine	*Cold limbs	57.3	86	1
	*Feeling hot or cold for no reason	54.7	78	6
	Losing or gaining weight without trying	37.2	11	1
	Chills or shivers	35.7	4	19
	Night sweats	32.6	6	0
	Alcohol intolerance	30.6	10	90
	Feeling like you have a high temperature	29.9	1	1
	Feeling like you have a low temperature	25.5	0	81
	No appetite	18.1	0	0
	Sweating hands	10.5	0	0
Immune	*Flu-like symptoms	54.3	98	100
	*Some smells/foods/meds/chemicals make you feel sick	42.4	35	78
	Tender/Sore lymph nodes	32.5	3	11
	Sore throat	30.4	4	9
	Fever	19.7	0	2

At least one DSQ-SF item within each symptom domain appeared frequently in both sets of decision tree analyses, with the exception of the Neuroendocrine domain. The selected items appeared most frequently when the Multisite sample (which includes individuals with ME and CFS, as well as controls) was analyzed, while different symptoms appeared in the Chronic Illness sample (which includes individuals with ME and CFS, as well as individuals with MS) analysis. Item selection for the DSQ-SF prioritized the results of the Multisite site sample, as these symptoms were most prevalent among participants with ME and CFS. Moreover, the item that appeared most frequently in the Chronic Illness sample analyses (alcohol intolerance) has been shown to be problematic among individuals with ME and CFS who refrain from alcohol consumption (Jason & Sunnquist, 2018). Furthermore, the item that appeared second-most-frequently in the Chronic Illness sample analysis (feeling like you have a low temperature) and the items selected for the SF-DSQ cold (cold limbs; feeling hot or cold for no reason) are all related to feeling.

DSQ Short Form Items.

Based upon the symptom prevalence and decision tree guidelines described in the Method section, the following 14 items were selected for inclusion in the DSQ-SF: fatigue (fatigue domain), next-day soreness after non-strenuous activities (post-exertional malaise domain), minimum exercise makes you physically tired (post-exertional malaise domain), unrefreshing sleep (sleep domain), muscle pain (pain domain), bloating (pain domain), problems remembering things (neurocognitive domain), difficulty paying attention for a long period of time (neurocognitive domain), irritable bowel problems (autonomic domain), feeling unsteady on your feet, like you might fall (autonomic domain), cold limbs (neuroendocrine domain), feeling hot or cold for no reason (neuroendocrine domain), flu-like symptoms (immune domain), and some smells, foods, medications, or chemicals make you feel sick (immune domain). The DSQ-SF can be viewed here: https://redcap.is.depaul.edu/surveys/?s=D8WEPJDXA7

Two items were selected from each symptom domain, with the exception of fatigue and unrefreshing sleep. Fatigue is represented by just one item in the DSQ, and unrefreshing sleep had the highest prevalence (over 90%) and appeared significantly more frequently than all other sleep-related items in both sets of decision trees. Unrefreshing sleep appeared as the first branch in all 100 Multisite sample decision trees and in 97 of 100 Chronic Illness sample decision trees. Although flu-like symptoms also appeared in almost all Immune decision trees in both samples, this symptom was less prevalent (54.3%) and the second-most-included symptom in decision trees, some smells, foods, medications, or chemicals make you feel sick, assesses a qualitatively different type of immune issue. Both of these items were retained in the DSQ-SF.

Case Definition Analyses

Fukuda et al. Criteria.

The full DSQ classification algorithm for the Fukuda et al. (1994) criteria is described by Jason and colleagues (2013).

The DSQ-SF Fukuda et al. (1994) algorithm requires fatigue and four of the following symptoms: post-exertional malaise (either next-day soreness after non-strenuous activity or minimum exercise makes you physical tired), unrefreshing sleep, cognitive impairment (either problems remembering things or difficulty paying attention for a long period of time), muscle pain, or flu-like symptoms. As the Fukuda et al. (1994) criteria do not specify frequency or severity requirements for these symptoms, symptoms were classified as present when frequency and severity ratings were 1 or greater.

Among individuals who self-reported a diagnosis of ME and CFS in the Multisite sample, 92.9% met the Fukuda et al. (1994) criteria, as measured by the original, full-form DSQ algorithm. In comparison, 90.9% of patients met criteria when the DSQ-SF algorithm was used. The DSQ-SF algorithm resulted in 97.4% accuracy in classifying patients when compared to the full-form DSQ algorithm. Furthermore, the algorithm demonstrated strong sensitivity (accurately detecting 97.5% of patients who met the Fukuda et al. criteria) and specificity (accurately classifying 95.5% of patients who did not meet the Fukuda et al. criteria, including those with diagnoses ME and CFS and those in the control group).

Canadian ME/CFS Criteria.

The full DSQ classification algorithm for the Canadian ME/CFS criteria (Carruthers et al., 2003) can be found in Jason and colleagues (2013). This algorithm was modified for the DSQ-SF to require: fatigue, post-exertional malaise (either next-day soreness after non-strenuous activity or minimum exercise makes you physical tired), unrefreshing sleep, neurocognitive impairment (either problems remembering things or difficulty paying attention for a long period of time), and one symptom from at least two of the following domains: pain (either muscle pain or bloating), autonomic (either irritable bowel problems or feeling unsteady on your feet, like you might fall), neuroendocrine (either cold limbs or feeling hot or cold for no reason), and immune (either flu-like symptoms or some smells, foods, medications, or chemicals make you feel sick). Symptoms were required to meet the “2–2 threshold.”

The DSQ-SF algorithm for the Canadian ME/CFS criteria (2003) reduced symptom requirements from the original DSQ algorithm due to its limited item bank. Specifically, the DSQ-SF algorithm requires just one, instead of two, neurocognitive symptoms, as the short-form contains only two neurocognitive symptoms in total. Additionally, the original Canadian ME/CFS definition requires pain and symptoms from at least two of the autonomic, neuroendocrine, and immune domains; however, due to the reduced number of DSQ-SF items, the modified algorithm requires symptoms from just two of these domains.

Among individuals who self-reported a diagnosis of ME or CFS in the Multisite sample, 69.7% met the Canadian ME/CFS criteria (Carruthers et al., 2003), as measured by the original, full-form DSQ algorithm, and 65.8% met the DSQ-SF algorithm for the Canadian ME/CFS criteria. The DSQ-SF algorithm, when compared to the full-form DSQ algorithm, resulted in 86.8% accuracy (87.7% sensitivity; 84.7% specificity) in classifying individuals by the Canadian ME/CFS criteria.

IOM Criteria.

The full DSQ classification algorithm for the IOM criteria (Institute of Medicine, 2015) can be found in Jason, Sunnquist, Brown, Newton, and colleagues (2015). The DSQ-SF algorithm for the IOM criteria was modified to require: fatigue, post-exertional malaise (either next-day soreness after non-strenuous activity; minimum exercise makes you physical tired), unrefreshing sleep, and either one neurocognitive (problems remembering things or difficulty paying attention for a long period of time) or orthostatic intolerance (feeling unsteady on your feet, like you might fall) symptom. Symptoms were required to meet the “2–2 threshold.”

Among individuals who self-reported a diagnosis of ME or CFS in the Multisite sample, 84.5% met the IOM criteria (2015), as measured by the full-form DSQ algorithm, and 74.8% met criteria when using the DSQ-SF algorithm. When compared to the full-form DSQ algorithm for the IOM criteria (2015), the DSQ-SF algorithm resulted in 87.6% accuracy (86.9% sensitivity; 91.2% specificity).

Discussion

The current study identified 14 items for inclusion in a short form of the DePaul Symptom Questionnaire (DSQ-SF). These items demonstrated the highest prevalence among individuals with ME and CFS, as well as the ability to differentiate participants with ME and CFS from adult controls and, to a lesser extent, individuals with MS. Excluding fatigue (as it represents a one-item domain), 12 of the remaining 13 items (with the exception of bloating) were most prevalent among individuals with ME and CFS, and 12 items (with the exception of next day soreness/fatigue after everyday activities) appeared most frequently in the Multisite sample (individuals with ME or CFS and controls) decision trees. Within the Chronic Illness sample (individuals with ME and CFS and individuals with MS), 7 of the 13 items appeared most frequently in the decision trees.

The DSQ-SF also demonstrated good, though imperfect, case definition classification accuracy when compared to the full-form DSQ. Although the DSQ-SF includes just 14 of the original 54 symptoms (26%), its case definition classification accuracy ranged from 86.8% to 97.4%. While these results were positive, the DSQ-SF should primarily be used as a screening instrument, as diagnosis would require a full medical evaluation.

The original DSQ takes 30–50 minutes to complete, while the DSQ-SF can be completed in approximately 5–10 minutes. Given the instrument’s brevity, sensitivity, and discriminative validity, the DSQ-SF may serve as an effective, brief symptom screen for use in time-limited research studies and clinical practice. Clinicians, including rehabilitation psychologists, may wish to utilize the DSQ-SF to objectively measure and compare patients’ symptom intensity over time to evaluate treatment effectiveness in a standardized manner. Likewise, the DSQ-SF may be beneficial in longitudinal research as a brief symptom assessment at each study phase.

As no curative treatment exists for ME and CFS (IOM, 2015), patients often utilize rehabilitative strategies to maximize their functioning, such as the energy envelope intervention, a technique that encourages individuals to maximize their activity level while avoiding overexertion (Jason, Brown, Brown, et al., 2013). Individuals with ME and CFS may benefit from support from rehabilitation psychologists when implementing these strategies. In this scenario, the DSQ-SF could be of use in conducting a baseline assessment of core symptoms and measuring changes in symptom intensity throughout treatment. Furthermore, this brief measure would allow for the examination of antecedents to symptom exacerbation or improvement. Additionally, future research could evaluate the utility of the DSQ-SF to rehabilitation psychologists who work with individuals who have other chronic illnesses, such as MS. Although the DSQ-SF currently assesses the past six months of symptoms, as this time period was found to produce the most reliable symptom measurements (Evans & Jason, 2015), the use of this timeframe could attenuate the DSQ-SF’s ability to quickly detect changes in symptoms. Additional research may be warranted to determine whether shorter timeframes could produce reliable, useful data for repeated measurement of symptom intensity.

The results of the current study should be interpreted within the context of several methodological limitations. The use of convenience samples resulted in groups of participants that were not demographically representative of the general population. Additionally, physician confirmation of diagnosis was not required to participate in certain samples. While over 95% of participants reported that they were diagnosed by a physician, over 20 case definitions exist for the diagnosis of ME and CFS (Brurberg et al., 2014), so physicians may have used varying diagnostic criteria. Furthermore, while combining samples from multiple sites can increase the generalizability of results, this procedure could also introduce error into the analyses, as recruitment processes differed by site. Finally, the size of the control group was relatively small; however, the addition of additional controls is unlikely to be impactful given their generally homogenous, non-symptomatic presentation. Despite these limitations, the large samples and bootstrapping techniques likely resulted in relatively robust findings.

Future research should continue to examine the DSQ-SF’s ability to distinguish ME and CFS from other chronic illnesses to continue to evaluate its utility. Additionally, to address the questionnaire’s case definition classification limitations, future work could translate the DSQ into a dynamic form. Through a dynamc questionnaire, individuals who do not meet case definition criteria based upon the 14 DSQ-SF items would be presented with additional items from the symptom domains that they did not endorse. For example, if an individual’s DSQ-SF responses indicated that they did not meet the post-exertional malaise requirement of the Canadian ME/CFS criteria, they would be presented with additional post-exertional malaise items until (a) they endorsed an item (thus meeting criteria) or (b) all three remaining items had been presented (thus not meeting criteria). In this example, the participant would be presented with 17 items in total, a large reduction from original 54 items, while achieving the same diagnostic accuracy as the full-form DSQ algorithm.

Impact:

• The 2015 National Institutes of Health Pathways to Prevention report on ME and CFS highlighted the need for standardized instruments to assess symptoms of ME and CFS.

• The DePaul Symptom Questionnaire (DSQ) is a validated and widely-used questionnaire that assesses ME and CFS symptoms; however, its length precludes frequent use.

• The current study analyzed psychometric properties of DSQ items to develop a 14-item short-form DSQ for use in research and clinical practice.

• This short form could be used by rehabilitation psychologists to expediently track patients’ symptoms over time and in response to interventions.