An examination of the role of intolerance of distress and uncertainty in hoarding symptoms

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ScienceDirect Comprehensive Psychiatry 72 (2017) 121 – 129 www.elsevier.com/locate/comppsych

An examination of the role of intolerance of distress and uncertainty in hoarding symptoms Brittany M. Mathes a , Mary E. Oglesby a , Nicole A. Short a , Amberly K. Portero a , Amanda M. Raines b , Norman B. Schmidt a,⁎ a

Department of Psychology, Florida State University, 1107 W. Call Street, Tallahassee, FL, USA b Southeast Louisiana Veterans Health Care System, 3500 Canal Street, New Orleans, LA, USA

Abstract Background: Hoarding disorder (HD) is a common and debilitating disorder characterized by an accumulation of and failure to discard one's possessions. The identification and examination of underlying factors that may contribute to hoarding symptoms are needed to elucidate the nature of the disorder and refine existing treatments. Two transdiagnostic vulnerability factors that have been associated with hoarding symptoms are distress intolerance (DI) and intolerance of uncertainty (IU). Objectives: This study examined the relationships between DI, IU, and symptoms of hoarding in two samples consisting of outpatients and individuals recruited from Amazon's Mechanical Turk. We hypothesized that DI and IU would show unique and interactive associations with hoarding symptoms. Results: Across both samples, DI and IU were significantly associated with hoarding symptoms. However, DI and IU did not interact in their prediction of symptoms, and only IU remained a significant predictor, when accounting for relevant covariates. Conclusions: Results suggest that IU is a robust predictor of hoarding symptoms and may be a promising and novel treatment target for HD. © 2016 Elsevier Inc. All rights reserved.

1. Introduction Hoarding disorder (HD) is characterized by difficulty discarding one's possessions and/or engaging in excessive acquiring of items, resulting in the accumulation of clutter that limits the use of functional living spaces [1]. HD is thought to affect approximately 2%–6% of the population and is associated with significant impairment, such as the inability to perform necessary household functions, health consequences due to unsafe or unsanitary household environments, social isolation, and even mortality due to dangerous living conditions [2,3]. Cognitive behavioral models of HD suggest that information-processing biases and dysfunctional beliefs about possessions contribute to maladaptive saving behaviors characteristic of the disorder [1,4,5]. Saving behaviors (i.e., acquiring or not discarding) act as avoidance strategies aimed to prevent emotional distress that may be associated with making ⁎ Corresponding author at: Department of Psychology, Florida State University, 1107 W. Call St., Tallahassee, FL 32306-4301, USA. Tel.: +1 850 645 1766; fax: +1 850 644 7739. E-mail address: [email protected] (N.B. Schmidt). http://dx.doi.org/10.1016/j.comppsych.2016.10.007 0010-440X/© 2016 Elsevier Inc. All rights reserved.

decisions about a possession [1,6]. Though avoidance results in short-term reduction of distress, it ultimately exacerbates symptoms by negatively reinforcing the use of maladaptive saving behaviors. As such, identifying underlying factors that may contribute to this cycle is necessary in order to elucidate the nature of the disorder and refine existing treatments. One construct that has been linked to avoidance across psychopathology and has garnered recent attention in the hoarding literature is distress intolerance (DI), defined as the perceived inability to tolerate aversive emotional states [7,8]. Individuals with elevated DI have difficulty tolerating, understanding, and managing distress. DI has been identified as a transdiagnostic vulnerability factor for a number of psychiatric conditions, including substance use [9], eating [10], anxiety [11], and obsessive–compulsive (OC) spectrum disorders [12,13]. A growing body of literature suggests that hoarding symptoms are associated with greater DI [14–18], such that individuals with hoarding symptoms may have difficulty discarding and/or not acquiring possessions due to difficulties tolerating the distress that may accompany such behaviors. Though DI is posited to be an important maintaining factor in hoarding, findings regarding the relationship between

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DI and hoarding symptoms remain mixed when controlling for anxiety and depression symptoms [16–18], suggesting that the influence of DI on hoarding symptoms may be confounded by other factors. Another individual difference variable that may contribute to hoarding behaviors is intolerance of uncertainty (IU), defined as an individual's perceived inability to endure an emotional response that is triggered by the absence of certain information and maintained by the subsequent perceived presence of uncertainty [19]. Though IU was first identified as a risk factor for generalized anxiety disorder (GAD) [20], it has more recently been associated with other anxiety and OC-spectrum disorders [21–24]. However, only two studies to date have examined the role of IU in hoarding symptoms. In the first study, Oglesby and colleagues [25] found that IU was associated with greater hoarding symptoms in an undergraduate sample, when controlling for anxiety and depression symptoms. A more recent study [26] replicated Oglesby's findings in an undergraduate sample, accounting for anxiety and depression symptoms, as well as hoarding-related beliefs. Additionally, Wheaton and colleagues [26] found that individuals with HD reported significantly greater IU when compared to healthy controls, but comparable degrees of IU as compared to individuals with GAD and obsessive–compulsive disorder (OCD). Taken together, individuals with hoarding symptoms may hold on to and/or acquire possessions due to an inability to tolerate unknown outcomes that could accompany their decision to discard and/or not acquire. DI and IU are both characterized by one's inability to tolerate negatively perceived stimuli, and research has shown that they are moderately correlated [27–30]. However, despite the shared association between DI and IU, they are conceptually distinct, such that DI reflects a broad inability to tolerate aversive emotional states, whereas IU reflects a more circumscribed inability to tolerate the unknown [31]. Importantly, theorists suggest that IU may exacerbate the influence of DI on behavioral responses and may be an important component of treatment aimed to reduce DI [19]. Indeed, extant literature suggests that risk factors, in general, may differentially influence symptoms in the presence of other risk factors [32]. Specifically, the interaction between two risk factors may contribute to more severe symptoms, such that the elevation of one risk factor may exacerbate the influence of the other, thereby resulting in greater overall distress and difficulty coping with perceived difficulties [33]. Therefore, it may be that individuals who are intolerant of both emotional distress and uncertainty are at greater risk for developing more severe hoarding symptoms. However, no study to date has examined whether these constructs act independently or synergistically in their influence on symptoms. Elucidating the nature of these constructs may help to clarify the relative importance of each in the maintenance of hoarding symptoms, thereby aiding in the refinement of existing treatments. As such, the current study aimed to examine the relative contribution of and potential interaction between DI and IU in the prediction of hoarding symptoms. We hypothesized

that a) DI and IU would be positively associated with overall and specific hoarding symptoms (i.e., difficulty discarding, acquiring, clutter), and b) DI and IU would interact in their influence on hoarding symptoms, such that individuals with elevated DI and IU would report the most severe symptoms.

2. Study 1 method 2.1. Participants The sample consisted of 254 individuals (57.5% female) presenting for psychological treatment or research at the Anxiety and Behavioral Health Clinic (ABHC) at Florida State University between October 2013 and September 2015. Individuals presenting to the ABHC are referred elsewhere only if there is a presence of a current psychotic and/or bipolar-spectrum disorder or serious suicidal intent. The average age of the sample was 33.85 years (SD = 15.11, range = 18–82). Of the sample, 65.7% self-identified as white/Caucasian, 20.9% as African American, 1.6% as Asian, 0.8% as Pacific Islander, 0.4% as American Indian/Native American, and 10.6% as other (e.g., biracial). Additionally, 11.4% of the sample self-identified as Hispanic. In regards to education background, 55.1% of the sample received some college education, 19.3% graduated from a four-year college, 11.4% received a graduate degree, 10.2% graduated from high school or received an equivalent degree, 2.0% graduated from a trade or technical school, and 2.0% did not graduate from high school. Of the sample, 42.5% met diagnostic criteria for a primary anxiety disorder diagnosis, 29.2% met criteria for a primary mood disorder, 9.1% met criteria for a primary trauma and stressor-related diagnosis, 3.6% met criteria for a primary OC-spectrum disorder diagnosis, 3.2% met criteria for a primary substance use disorder, 3.2% met for other disorders (e.g., eating disorder), and 9.1% did not meet criteria for any psychological disorders. Regardless of primary diagnosis, 1.2% of the sample met criteria for HD. The sample showed significant variability in hoarding symptoms, with scores on the Saving Inventory-Revised (SIR) [34] ranging from 0 to 72. The average SIR score was 21.93 (SD = 18.00), and 17.7% of the sample scored above the clinical cut-off on the SIR (≥41), which is comparable to prior studies utilizing outpatient samples [35]. Of note, the rate of individuals meeting diagnostic criteria for HD was lower than the rate of those endorsing clinically significant hoarding symptoms. This discrepancy is consistent with research suggesting that hoarding symptoms are dimensional in nature [36] and may, therefore, be clinically significant even in the absence of a diagnosis. 2.2. Measures Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I) [37]. The SCID-I is a semi-structured clinical interview that assesses the presence of psychiatric conditions. All interviews were administered by doctoral level clinical

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psychology graduate students, who received training in the administration and interpretation of the SCID. Training procedures included watching SCID training videos, observing SCID administrations, and administering interviews with a trained interviewer. All diagnoses were discussed and confirmed at weekly supervision meetings with a licensed clinical psychologist (NBS). Structured Interview for Hoarding Disorder (SIHD) [38]. The SIHD is a semi-structured clinical interview that assesses the presence of HD, based on the DSM-5. All interviews were administered by trained doctoral level clinical psychology graduate students, and all diagnoses were reviewed with a licensed clinical psychologist (NBS). The SIHD was included as a supplement to the SCID-I, given that the SCID-I for DSM-IV does not assess for HD. Saving Inventory-Revised (SIR) [34]. The SIR is a 23-item self-report measure that assesses severity of hoarding behaviors. The measure includes three factor-analytically derived subscales that assess the core features of hoarding: difficulty discarding (e.g., “To what extent do you have difficulty throwing things away?”), acquiring (e.g., “How distressed or uncomfortable would you feel if you could not acquire somewhat you wanted?”), and clutter (e.g., “To what extent does clutter prevent you from using parts of your home?”). Participants are asked to respond to each item using a 5-point Likert scale, with higher scores indicating greater severity of hoarding symptoms. In the present study, the SIR demonstrated excellent internal consistency (α = .95). Distress Intolerance Index (DII) [8]. The DII is a 10-item self-report measure that assesses one's ability to tolerate distressing emotional states. The DII was derived from factor analyses of common measures of DI, including the Anxiety Sensitivity Index [39], Distress Tolerance Scale [40], the Frustration Discomfort Scale [41], and the Discomfort Intolerance Scale [42]. Participants are asked to respond to each item using a 5-point Likert scale, with higher scores indicating greater difficulty tolerating aversive emotional states. In the present study, the DII demonstrated excellent internal consistency (α = .91). Intolerance of Uncertainty Scale-12 (IUS-12) [43]. The IUS-12 is a 12-item self-report measure that assesses the degree to which individuals are unable to tolerate and adaptively respond to uncertain situations. The IUS-12 is a short version of the original 27-item IU Scale [20], and research indicates that it is more refined, psychometrically stable, and consistent with theory, as compared to the original IUS [44,45]. Participants are asked to respond to each item (e.g., “Unforeseen events upset me greatly”) using a 5-point Likert scale, in which higher scores indicate greater difficulty tolerating and adaptively responding to uncertain situations. In the present study, the IUS-12 demonstrated excellent internal consistency (α = .93). Positive Affect Negative Affect Schedule (PANAS) [46]. The PANAS is a 20-item self-report measure that assesses two global dimensions of affect: negative (e.g., distressed, afraid, irritable) and positive (e.g., enthusiastic, proud, inspired).

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Participants are asked to respond to each item using a 5-point Likert scale. In the present study, we only used the negative affect subscale (PANAS-NA), which demonstrated excellent internal consistency (α = .91). 2.3. Procedure Upon arrival to the clinic and prior to engaging in treatment or research activities, participants met with a trained research assistant, who obtained informed consent. Participants then completed semi-structured interviews and a battery of self-report questionnaires. All study procedures were approved by the university's institutional review board. 2.4. Data analytic plan Examination of all variables indicated that there were no outliers and no violations of normality, linearity, and homoscedasticity on all study variables. Variance inflation factors were calculated to test for multicollinearity among predictors and were within acceptable ranges (VIF = 1.02–1.56). Correlation analyses were used to examine relationships among DI, IU, and hoarding symptoms. Hierarchical multiple regression analyses were used to examine the extent to which DI, IU, and the interaction between DI and IU predicted overall hoarding symptom severity. Following the recommendations of Aiken and West [47], DI and IU were centered and then multiplied to generate an interaction term. In step one, age, gender, and negative affect were entered as covariates, given research showing age and gender differences in HD [48,49] and significant overlap between hoarding and affective symptoms [50,51]. In step two, the centered terms for DI and IU were entered. In step three, the centered interaction term of DI and IU was entered. The aforementioned analysis was then repeated in three separate equations predicting the severity of difficulty discarding, acquiring, and clutter symptoms. In addition to the analyses outlined above, we repeated all regressions analyses without covarying for negative affect. Removing negative affect as a covariate is in line with recent research suggesting that IU represents a fundamental fear that underlies higher-order constructs (i.e., negative affect) [52], and as such, controlling for higher-order constructs may result in findings that are inconsistent with this theoretical view [52].

3. Results 3.1. Preliminary analyses The means, standard deviations, and zero-order correlations for all variables are displayed in Table 1. As expected, hoarding symptoms were significantly correlated with IU (r = .38, p b .001) and DI (r = .28, p b .001). Additionally, IU and DI were significantly correlated (r = .58, p b .001).

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Table 1 Pearson correlations, means, and standard deviations for all variables in study 1.

1. 2. 3. 4. 5. 6. 7.

SIR-T SIR-D SIR-A SIR-C DII IUS-12 PANAS-NA

1

2

______ .91⁎⁎ .88⁎⁎ .86⁎⁎ .28⁎⁎ .38⁎⁎ .40⁎⁎

______ .78⁎⁎ .65⁎⁎ .25⁎⁎ .37⁎⁎ .33⁎⁎

3

______ .60⁎⁎ .29⁎⁎ .37⁎⁎ .33⁎⁎

4

5

______ .21⁎ .28⁎⁎ .39⁎⁎

______ .58⁎⁎ .60⁎⁎

6

______ .61⁎⁎

7

M (SD)

______

21.93 (18.00) 7.47 (6.70) 7.98 (6.16) 6.48 (7.50) 22.07 (9.88) 33.01 (12.38) 26.64 (9.93)

SIR-T: Saving Inventory Revised-total; SIR-D: Saving Inventory Revised-difficulty discarding subscale; SIR-A: Saving Inventory Revised-acquiring subscale; SIR-C: Saving Inventory Revised-clutter subscale; DII: Distress Intolerance Index; IUS-12: Intolerance of Uncertainty Scale-12; PANAS-NA: Positive and Negative Affect Schedule-negative affect subscale. ⁎ p b .01. ⁎⁎ p b .001.

3.2. Primary analyses Hierarchical multiple regression was used to evaluate the main and interactive effects of DI (as measured by the DII) and IU (as measured by the IUS-12) on hoarding symptoms (as measured by the SIR), when covarying for age, gender, and negative affect (as measured by the PANAS-NA). In the first model, our dependent variable was overall hoarding symptoms. At step 1, age, gender, and negative affect were entered, which accounted for 18.8% of the variance, p b .001. At step 2, DI and IU were entered, which accounted for an additional 3.2% of the variance, p = .01. At step 3, the interaction term for DI and IU was entered, which did not add significant variance, p = .67. In the full model, age (β = .16, t = 2.71, p = .01, sr 2 = .02), negative affect (β = .27, t = 3.57, p b .001, sr 2 = .04), and IU (β = .24, t = 3.12, p = .002, sr 2 = .03) emerged as significant predictors. The full model accounted for 22.1% of the variance in hoarding symptoms, F(6,247) = 11.65, p b .001. Next, a series of hierarchical multiple regressions were used to evaluate the main and interactive effects of DI and IU on specific hoarding symptoms (difficulty discarding, acquiring, clutter), when accounting for age, gender, and negative affect. For each symptom domain, age, gender, and negative affect were entered into the first step of the model. In the second step, DI and IU were entered. In the third step, the interaction term for DI and IU was entered. IU was significantly associated with difficulty discarding (β = .29, t = 3.82, p b .001, sr 2 = .05) and acquiring (β = .26, t = 3.34, p = .001, sr 2 = .04) symptoms. DI and the interaction between DI and IU were not significant predictors of any symptoms, ps N 30. See Table 2 for full regression statistics. When removing negative affect from all analyses, results remained largely the same, such that only IU was a significant predictor of overall hoarding symptoms (β = .32, t = 4.47, p b .001, sr 2 = .07), difficulty discarding (β = .34, t = 4.76, p b .001, sr 2 = .08), and acquiring symptoms (β = .30, t = 4.09, p b .001, sr 2 = .06). IU was also a significant predictor of clutter symptoms (β = .22, t = 2.98, p = .003, sr 2 = .03) when removing negative affect as a covariate. DI and the

interaction between DI and IU were not significant predictors of any symptoms, ps N .31. 4. Study 2 method 4.1. Participants The sample consisted of 526 individuals (69.2% female) recruited through an online crowdsourcing marketplace. Eligibility included living in the United States, being at least 18 years of age, and demonstrating high-quality work on previous marketplace tasks, as evidenced by rating of at least 90% on a Human Intelligence Task. The average age of the sample was 34.87 years (SD = 12.41; range = 18–72). Of the sample, 84.2% self-identified as white/Caucasian, 8.0% as African American, 4.2% as Asian, 1.1% as American Indian or Alaskan Native, and 2.5% as other (e.g., biracial). Additionally, 6.5% of the sample self-identified as Hispanic. In regards to education background, 35.7% of the sample received some college education, 35.2% graduated from a four-year college, 12.9% graduated from high school or received an equivalent degree, 11.4% received a graduate degree, 3.6% graduated from a trade or technical school, and 1.1% received some high school education. The sample showed significant variability in hoarding symptoms, with scores on the SIR ranging from 0 to 85. The average SIR score was 21.25 (SD = 18.65), and 18.4% of the sample scored above the clinical cut-off on the SIR (≥41), which is comparable to prior studies utilizing community samples [34]. 4.2. Measures As in study 1, participants completed the SIR, DII, and IUS-12. All three measures demonstrated good to excellent internal consistency in the sample (SIR: α = .96; DII: α = .85; IUS-12: α = .94). Big Five Inventory (BFI) [53]. The BFI is a 44-item self-report measure that assesses the Big Five personality domains: neuroticism, openness, conscientiousness, agreeableness, and extraversion [53]. Participants are asked to respond to

B.M. Mathes et al. / Comprehensive Psychiatry 72 (2017) 121–129 Table 2 Hierarchical regression equations predicting SIR total and subscale scores in study 1. R2 Predicting SIR-T Age Gender PANAS-NA DII IUS-12 DII × IUS-12 SIR-D Age Gender PANAS-NA DII IUS-12 DII × IUS-12 SIR-A Age Gender PANAS-NA DII IUS-12 DII × IUS-12 SIR-C Age Gender PANAS-NA DII IUS-12 DII × IUS-12

.22

.21

β

sr 2

2.71⁎⁎ 1.68 3.57⁎⁎⁎ −.48 3.12⁎⁎ −.42

.16 .10 .27 −.04 .24 −.02

.02 .01 .04 b.001 .03 b.001

3.77⁎⁎⁎ 1.56 2.24⁎ −.55 3.82⁎⁎⁎ −.01

.22 .09 .17 −.04 .29 b.001

.05 .01 .02 b.001 .05 b.001

1.63 .52 1.96 .50 3.34⁎⁎ −.84

.10 .03 .15 .04 .26 −.05

.01 b.001 .01 b.001 .04 .002

1.69 2.15⁎ 4.76⁎⁎⁎ −1.06 1.17 −.29

.10 .13 .37 −.08 .09 −.02

.01 .02 .08 .004 .01 b.001

t

.17

.18

SIR-T: Saving Inventory Revised-total; SIR-D: Saving Inventory Reviseddifficulty discarding subscale; SIR-A: Saving Inventory Revised-acquiring subscale; SIR-C: Saving Inventory Revised-clutter subscale; PANAS-NA: Positive and Negative Affect Schedule-negative affect subscale; DII: Distress Intolerance Index; IUS-12: Intolerance of Uncertainty Scale-12; DII × IUS-12: interaction term between DII and IUS-12. ⁎⁎⁎ p b .001. ⁎⁎ p b .01. ⁎ p b .05.

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4.3. Procedure Participants were recruited through Amazon's Mechanical Turk to complete online surveys assessing risk factors of anxiety and related pathology. In order to check for participant random responding, two validity check items were included in the surveys (e.g., “Are you reading this questionnaire?”). If participants did not correctly respond to both items, they were excluded; however, no participants fulfilled these criteria, so there were no exclusions. Participation in the study took approximately one hour, and participants were compensated $1.00 per hour of participation. Prior studies have shown that data collected via Mechanical Turk are high quality and appropriate for studying clinical phenomena [54–56]. Informed consent was obtained electronically prior to participant's completion of questionnaires. All study procedures were approved by the university's institutional review board. 4.4. Data analytic plan Examination of all variables indicated that there were no outliers and no violations of normality, linearity, and homoscedasticity on all study variables. Variance inflation factors were calculated to test for multicollinearity among predictors and were within acceptable ranges (VIF = 1.01–1.24). All analyses were consistent with those used in study 1, with the exception of neuroticism replacing negative affect as a covariate. Neuroticism has been associated with hoarding symptoms [14,57]. However, as in study 1, we repeated all regression analyses without covarying for neuroticism, as research suggests that controlling for neuroticism may not be theoretically sound when considering the relationship between IU and symptoms [52]. 5. Results 5.1. Preliminary analyses

each item using a 5-point Likert scale. In the present study, we only used the neuroticism subscale (BFI-N; e.g., “I see myself as someone who is depressed, blue,” “I see myself as someone who worries a lot”), which demonstrated good internal consistency (α = .88).

The means, standard deviations, and zero-order correlations for all variables are displayed in Table 3. As expected, hoarding symptoms were significantly correlated with IU (r = .42, p b .001) and DI (r = .24, p b .001). Additionally, IU and DI were significantly correlated (r = .41, p b .001).

Table 3 Pearson correlations, means, and standard deviations for all variables in study 2.

1. SIR-T 2. SIR-D 3. SIR-A 4. SIR-C 5. DII 6. IUS-12 7. BFI-N

1

2

______ .91⁎ .89⁎ .92⁎ .24⁎ .42⁎ .31⁎

______ .75⁎ .75⁎ .21⁎ .38⁎ .27⁎

3

______ .72⁎ .27⁎ .44⁎ .32⁎

4

______ .18⁎ .36⁎ .27⁎

5

______ .41⁎ .45⁎

6

______ .54⁎

7

M (SD)

______

21.25 (18.65) 7.01 (6.54) 6.81 (6.00) 7.43 (7.95) 16.63 (8.08) 31.04 (11.38) 3.01 (.93)

SIR-T: Saving Inventory Revised-total; SIR-D: Saving Inventory Revised-difficulty discarding subscale; SIR-A: Saving Inventory Revised-acquiring subscale; SIR-C: Saving Inventory Revised-clutter subscale; DII: Distress Intolerance Index; IUS-12: Intolerance of Uncertainty Scale-12; BFI-N: Big Five Inventory-neuroticism subscale. ⁎ p b .001.

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5.2. Primary analyses Hierarchical multiple regression was used to evaluate the main and interactive effects of DI (as measured by the DII) and IU (as measured by the IUS-12) on hoarding symptoms (as measured by the SIR), when covarying for age, gender, and neuroticism (as measured by the BFI-N). In the first model, our dependent variable was overall hoarding symptoms. At step 1, age, gender, and neuroticism were entered, which accounted for 11.4% of the variance, p b .001. At step 2, DI and IU were entered, which accounted for an additional 9.0% of the variance, p b .001. At step 3, the interaction term for DI and IU was entered, which did not add significant variance, p = .07. In the full model, neuroticism (β = .10, t = 2.01, p = .045, sr 2 = .01) and IU (β = .36, t = 7.40, p b .001, sr 2 = .08) emerged as significant predictors. The full model accounted for 20.9% of the variance in hoarding symptoms, F(6,519) = 22.89, p b .001. Next, a series of hierarchical multiple regressions were used to evaluate the main and interactive effects of DI and IU on specific hoarding symptoms (difficulty discarding, acquiring, clutter), when accounting for age, gender, and neuroticism. For each symptom domain, age, gender, and neuroticism were entered into the first step of the model. In the second step, DI and IU were entered. In the third step, the interaction term for DI and IU was entered. IU was significantly associated with difficulty discarding (β = .32, t = 6.49, p b .001, sr 2 = .07), acquiring (β = .36, t = 7.47, p b .001, sr 2 = .08), and clutter symptoms (β = .30, t = 6.08, p b .001, sr 2 = .06). DI was not a significant predictor of any symptoms, ps N .10. The interaction between DI and IU was significantly associated with clutter symptoms, β = −.10, t = −2.36, p = .02, sr 2 = .01. We probed this finding using recommendations from Aiken and West (1991), such that we examined the main effect of IU at high and low (±1 standard deviation from the mean) levels of DI. IU was associated with clutter symptoms at both high (β = .21, t = 3.58, p b .001, sr 2 = .02) and low (β = .39, t = 5.87, p b .001, sr 2 = .06) levels of DI, though this effect was stronger for individuals with low DI. Therefore, individuals with elevated IU and decreased DI reported the highest clutter symptoms. See Table 4 for full regression statistics. When removing neuroticism from all analyses, results were largely consistent. IU was a significant predictor of overall hoarding symptoms (β = .40, t = 9.19, p b .001, sr 2 = .13), difficulty discarding (β = .36, t = 8.01, p b .001, sr 2 = .10), acquiring (β = .39, t = 9.11, p b .001, sr 2 = .12), and clutter symptoms (β = .21, t = 3.58, p b .001, sr 2 = .02). The interaction between DI and IU was significantly associated with clutter symptoms (β = −.10, t = −2.40, p = .02, sr 2 = .01), such that IU was associated with clutter symptoms at high and low levels of DI. Though DI was not a significant predictor of overall, difficulty discarding, and clutter symptoms (ps N .19), it was a significant predictor of acquiring symptoms (β = .09, t = 2.14, p = .03, sr 2 = .01) when we removed neuroticism as a covariate.

Table 4 Hierarchical regression equations predicting SIR total and subscale scores in study 2. R2 Predicting SIR-T Age Gender BFI-N DII IUS-12 DII × IUS-12 SIR-D Age Gender BFI-N DII IUS-12 DII × IUS-12 SIR-A Age Gender BFI-N DII IUS-12 DII × IUS-12 SIR-C Age Gender BFI-N DII IUS-12 DII × IUS-12

t

β

sr 2

−1.96 −1.66 2.01⁎ .72 7.40⁎⁎⁎ −1.83

−.08 −.07 .10 .03 .36 −.07

.01 .004 .01 b.001 .08 .01

−.97 −1.45 1.66 .62 6.49⁎⁎⁎ −1.57

−.04 −.06 .08 .03 .32 −.06

.002 .003 .01 b.001 .07 .004

−2.90⁎⁎ −1.86 1.67 1.59 7.47⁎⁎⁎ −.70

−.12 −.07 .08 .07 .36 −.03

.01 .01 .004 .004 .08 b.001

−1.56 −1.24 2.00⁎ −.03 6.08⁎⁎⁎ −2.36⁎

−.06 −.05 .10 −.001 .30 −.10

.004 .003 .01 b.001 .06 .01

.21

.16

.23

.15

SIR-T: Saving Inventory Revised-total; SIR-D: Saving Inventory Reviseddifficulty discarding subscale; SIR-A: Saving Inventory Revised-acquiring subscale; SIR-C: Saving Inventory Revised-clutter subscale; BFI-N: Big Five Inventory-neuroticism subscale; DII: Distress Intolerance Index; IUS-12: Intolerance of Uncertainty Scale-12; DII × IUS-12: interaction term between DII and IUS-12. ⁎⁎⁎ p b .001. ⁎⁎ p b .01. ⁎ p b .05.

6. Discussion The current study aimed to examine the relationships between DI, IU, and hoarding symptoms across two large, but clinically distinct, samples. As hypothesized, DI and IU were both associated with overall and specific hoarding symptoms (i.e., difficulty discarding, acquiring, clutter), which is consistent with previous studies indicating that individuals who have difficulty tolerating distress and uncertainty report greater hoarding symptoms [15,16,25,26]. However, contrary to predictions, DI and IU did not interact in their influence on hoarding symptoms, and only IU was a significant predictor. These results are consistent with previous research demonstrating that DI does not contribute unique variance to anxiety and OC symptoms when accounting for other related risk factors, including IU [27,29,30]. Taken together, though DI and IU are both associated with increased hoarding symptoms, IU may be a particularly salient risk and maintaining factor. A closer examination of our results showed that IU was a significant and consistent predictor of difficulty discarding

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and acquiring symptoms across both samples, further supporting previous findings [25,26]. Our results suggest that individuals with hoarding symptoms may have difficulty discarding and/or may excessively acquire possessions due to difficulties tolerating the uncertainty associated with making decisions about possessions (e.g., “What if I get rid of this and need it again?” “What if I don't purchase this and miss an opportunity to do so again?”). Indeed, previous research has shown that hoarding symptoms are associated with indecisiveness and doubt [58,59]. Therefore, IU may be one mechanism underlying such indecision, though research is needed to evaluate this relationship. From a broader perspective, individuals with hoarding symptoms may interpret uncertainty as being both negative and positive [60]. Specifically, uncertainty may be interpreted as a threat when considering the perceived negative consequences of discarding and/or not acquiring a desired possession (e.g., needing a previously discarded item). However, it may also be interpreted as positive when considering a possession's potential utility or purpose (e.g., I could likely use this possession in many different ways). Indeed, some theorists suggest that uncertainty can be interpreted as positive when the individual believes he/she can effectively cope with or make sense of the uncertainty [52]. Therefore, individuals with hoarding symptoms may perceive the potential future uses for the possession as being uncertain in a manageable way, given their intention to effectively use that possession. Taken together, uncertainty may not be inherently negative for individuals with hoarding symptoms, and as such, an individual's inability to tolerate uncertainty may interact with negative, but not positive, interpretations of possessions. Of note, there were discrepancies in our results worth mentioning. Though DI and IU interacted in their influence on clutter symptoms in study 2, this result was not replicated in study 1, as the interaction between DI and IU was not significantly associated with clutter symptoms. Furthermore, when removing negative affect as a covariate in study 1, IU emerged as a significant predictor of clutter symptoms, though it was not significantly associated when accounting for negative affect. Taken together, further research is needed to elucidate the impact of IU on clutter, as results were somewhat mixed in our samples. Additionally, DI emerged as a significant predictor of acquiring symptoms when removing neuroticism as a covariate in study 2, though this finding was not replicated in study 1. As such, future research should further examine the relationship between DI and acquiring behaviors, when accounting for higher-order constructs, such as negative affect and neuroticism. Importantly, despite the discrepant findings outlined above, our results were largely replicated across two independent samples, which provides further support for the validity of these findings and extends previous studies that have used undergraduate samples [15,16,25,26]. Though our outpatient sample primarily consisted of individuals with anxiety and mood disorders, research has shown that hoarding symptoms are significantly associated with symptoms of anxiety and

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depression [50,51], and clinically significant hoarding symptoms are often present in individuals with anxiety [35,61] and mood disorders [61]. Furthermore, symptoms of hoarding disorder are dimensional in nature [36], so the examination of risk and maintaining factors along a continuum is important for understanding the nature of the disorder. Though future research should aim to replicate these findings in a sample of individuals with HD, our findings provide important information regarding the role of these constructs in hoarding symptoms. Taken together with the extant literature, our results suggest that theoretical models of HD may benefit from placing a greater emphasis on the role of IU in the development and maintenance of symptoms. Existing models of HD suggest that saving behaviors function as avoidance strategies aimed to reduce distress that may accompany making decisions about possessions [1,4,5]. However, given a growing body of research regarding the relevance of IU in hoarding, fear of uncertainty may be a more prominent mechanism underlying these behaviors. Indeed, research suggests that IU may be an important core feature of emotional disorders that is exacerbated by disorder-specific cognitive distortions [62]. As such, individuals with hoarding symptoms may interpret the unknown outcomes associated with discarding an item as a threat that causes significant distress that cannot be tolerated. Alternatively, as discussed above, it may also be that individuals with hoarding symptoms interpret the unknown possibilities of a possession's utility as being positive and exciting. Therefore, further research should examine the specific ways in which IU may contribute to hoarding symptoms. Our results may also have implications for treatment of HD. Given the malleable nature of IU [63–65], as well as the poor treatment outcomes associated with HD [48,66], IU may be a promising and novel treatment target for HD. Specifically, existing cognitive behavioral treatment protocols for HD may benefit from the inclusion of techniques directly aimed at reducing IU. For example, targeting cognitive distortions associated with IU (e.g., uncertainty is threatening and catastrophic) and teaching skills to increase an individual's ability to cope with uncertainty and/or the aversive emotional responses associated with such uncertainty may be advantageous in the treatment of HD. Indeed, reductions in IU are associated with reductions in symptoms of anxiety disorders, including GAD [63–65] and social phobia [67]. Therefore, research is needed to examine the potential role of IU as an underlying mechanism of treatment for HD. Our results should be interpreted in light of limitations. First, our samples consisted of outpatients and individuals recruited from Amazon's Mechanical Turk who reported relatively low rates of clinically significant hoarding symptoms. Though research shows that hoarding symptoms are dimensional in nature [36], future studies should aim to replicate our findings in a sample of individuals with HD. Second, our studies were cross-sectional, so we are unable to make causal conclusions regarding the nature of the relationship between DI, IU, and hoarding symptoms. Therefore,

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further research is needed to determine the temporal precedence of these constructs. Taken together, the current studies examined the relationship between DI, IU, and hoarding symptoms. Results showed that elevated DI and IU were significantly associated with greater hoarding symptoms. However, DI and IU did not interact in their prediction of symptoms, and only IU remained a significant predictor, when accounting for relevant covariates. As such, IU may be particularly relevant to the etiology and maintenance of hoarding symptoms and may, therefore, be an important and novel treatment target. Author disclosure – declaration of interests The authors of this manuscript do not have any actual or potential conflicts of interest to report or disclose.

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