The influence of anxiety sensitivity on observing in mindfulness among clinical populations with anxiety and/or depressive disorders

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Carpenter, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4125571/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2025 Read the published version in BMC Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Many individuals who engage in mindfulness techniques experience decreased anxiety. Yet some who engage in mindfulness, particularly those with panic disorder (PD) or elevated anxiety sensitivity (AS) note heightened anxiety when focusing on particular sensations. The Five Facet Mindfulness Questionnaire (FFMQ) is the most widely utilized mindfulness questionnaire- however, the Observe facet has shown variability in the literature. This study therefore aimed to determine whether specific aspects of the Observe facet of the FFMQ differ in individuals with PD or elevated anxiety sensitivity (AS). Methods: We examined a clinical sample of 1521 Japanese individuals who completed online self-report questionnaires, including the Five Facet Mindfulness Questionnaire (FFMQ). Results: Several multiple indicator multiple cause (MIMIC) models investigated differential item functioning among the items in the Observe facet of the FFMQ, based on PD and/or AS. This process was repeated to examine the relationship between the Anxiety Sensitivity Index-3 (ASI-3) subscales and particular items of the Observe facet. Increased AS correlated with more observing generally, and increased AS was associated with greater scores on observing internal items and lower scores on observing external items. When PD and AS were analyzed simultaneously, only AS remained significant. The cognitive subscale showed the same pattern of results as the total ASI-3 subscale. Conclusions: The findings of this study reveal that AS may modulate the mindfulness experience. The study sheds light on the importance of understanding where an individual observes in order to tailor mindfulness treatments for individuals. panic anxiety sensitivity mindfulness observe internal external Figures Figure 1 Background Mindfulness techniques have become pervasive in the field of mental health. Many individuals engage in meditation as a mindfulness practice; however, it is important to note that meditation is only one type of mindfulness. Jon Kabat-Zinn, the pioneer of Western mindfulness, defined mindfulness as “awareness that arises through paying attention, on purpose, in the present moment, non-judgmentally.” [1] Mindfulness, in many ways, is the anthesis of anxiety, as anxiety typically involves future-oriented worries. Mindfulness-based programs (MBPs) have developed as an effective treatment for stress, depression and anxiety. As opposed to anxious thoughts about the future or rumination about the past, MBPs emphasize increasing awareness toward present moment sensations and experiences, while maintaining a nonjudgmental attitude [2]. As such, research has found that mindfulness is a prominent protective factor for reducing psychological distress [3]. A meta-analysis by Hofmann and colleagues[4] found that across 39 studies with participants receiving mindfulness-based therapy, anxiety and mood symptoms significantly improved. Given the plethora of evidence that mindfulness can be beneficial for anxiety, the question remains: why do some people struggle with mindfulness? For example, some people find that centering their attention on their breath can be uncomfortable or even increase anxiety. This is often true for those with panic disorder (PD), who are highly in tune with bodily sensations, frequently observing their internal symptoms and finding them uncomfortable [5]. Fear of bodily sensations is not just unique to PD, however. Research has found that individuals with elevated anxiety sensitivity (AS) more generally are likely to notice increased anxiety and may feel more anxious when tuning into physical sensations [6]. AS is the belief that anxiety symptoms are harmful, and AS varies across anxiety disorders. Therefore, it is likely that individuals who are more sensitive to their anxiety may have difficulty with mindfulness techniques that involve focusing on internal sensations. A recent study by Xie and colleagues [7] assigned participants to either an eight-week mindfulness intervention or a control group wait-list. Results suggested that AS is a potential mechanism for the impact of mindfulness training on anxiety and depression. Additionally, mindfulness treatments have also been found effective at improving AS. In a recent study, Khalili and colleagues [8] studied the effects of mindfulness-based cognitive therapy (MBCT) on AS in patients with multiple sclerosis. The researchers found that MBCT helped reduce AS in patients. In line with moving towards individualized treatment, it is important to have a more robust understanding of who may benefit from what type of mindfulness skills and why. In order to reach this goal, we need to better understand the relationship between AS and mindfulness, so we can learn who may or may not benefit from certain types of MBPs. Much of the research thus far on understanding how observation relates to mindfulness has found positive associations between observing and mindfulness. For instance, several studies have investigated the incremental validity of each facet of the Five Facet Mindfulness Questionnaire (FFMQ)[9]- one of the most widely utilizing mindfulness questionnaires, in predicting anxiety, depression and stress [10;11]. The facets of the FFMQ include Act with Awareness, Describe, Observe, Non-judge and Non-react. Observe was the only factor not found to be a component of Baer and colleagues’ [9] overall mindfulness construct, and this finding was replicated in clinical samples [12].In fact, Brown and colleagues[13] found that those who scored highly on the Observe facetexperienced increased stress and anxiety. This finding points to the possibility that observing may actually increase anxiety for some people, and this is something that needs to be better understood before we recommend specific mindfulness treatments to individuals [14]. Diehl and colleagues [15] recently examined the underlying structure of the FFMQ and the relations between the facets and dimensions of psychopathology. The authors noted that all facets except Observe were negatively associated with the internalizing factor, providing further and current evidence of this challenge with the Observe facet. Previous studies have investigated the relationship between PD and observing and have found that those with PD tend to core higher on the Observefacet of the FFMQ compared to individuals with other anxiety disorders [16]. Interpretation of these relationships, however, is complicated by the fact that higher observing scores does not necessarily correlate with higher overall mindfulness scores, as discussed earlier. Another challenge with these findings is the Observe facet of the FFMQ does not differentiate between where one observes (internally on physical sensations/thoughts/emotions or externally on sights/smells/sounds around them). A study by Waszczuk and colleagues [17] found significant relations between mindfulness skills on the Kentucky Inventory of Mindfulness Skills (KIMS)[18] and AS. The researchers found that those with higher AS had a heightened tendency to observe thoughts, sensations and emotions. The Observe facet was found to tap into a form of hypervigilance to threatening interpersonal experiences in those who were fearful of their psychological and physical sensations. An interesting finding was noted by Baer and colleagues [19] which was that higher scores on the Observefacet were associated with stronger psychological adjustment among meditating samples, whereas the relationship in non-meditating individuals was either nonsignificant or in the opposite direction. Thus, it appears that meditation experience may moderate the relationship between observing and anxiety. Given the many potential benefits of mindfulness as treatment for anxiety, it is important researchers and clinicians to have a better understanding of who may benefit from different types of mindfulness skills and why, so that we can work towards disseminating and implementing mindfulness as effective treatment for individuals. As the FFMQ is perhaps the most commonly used mindfulness scale [20]and important gaps in this scale exist, it is important to investigate the aspects of the scale that are less understood. There is increasingly significant evidence that mindfulness is not only multifaceted, but also certain facets of the FFMQ, such as Observe, appear to operate differently for different people [17]. Thus, the goal of this study was to better understand what may be happening with the Observe, to clarify any changes that may need to be made in the psychometric mindfulness literature. Most research on this topic has focused on the relationship between PD and increased observation, due to this idea that those who focus internally may find mindfulness practices, such as meditation, to be aversive. This study wanted to better understand the relationship between AS more generally across a clinical sample related to types of observation. We believe the Observe facet of the FFMQ may be more complicated than we think, and it may be important to note more precisely what people are observing and why. In this way, we can determine which types of mindfulness may work best for which individuals. Finally, the FFMQ in the Japanese population has not been as thoroughly explored as in the American population. Thus, studying mindfulness in a Japanese population can improve cross-cultural knowledge of mindfulness more broadly. Methods Study Aims The goal of this study was to examine the relationship between PD, AS and internal/external items on the Observe facet of the FFMQ. Our first aim was to examine the Observe facet as a single latent variable across a clinical population, and we hypothesized this would reveal a single factor solution, consistent with prior research (See Fig 1 for full item description). The second aim was to investigate the relation between PD and specific items of the Observe facet using MIMIC modelling to examine differential item functioning. We hypothesized those with PD would endorse higher levels of Observe overall, as well as higher internal focus and lower external focus, compared to those without PD. The third aim was to investigate the relation between varying levels of AS and specific items of the Observe facet. We hypothesized that individuals who are more sensitive to their anxiety would observe more overall compared to those lower in AS. We predicted individuals with elevated AS would score higher on items measuring internal focus and lower on items measuring external focus. The final aim of the study was to explore the relationship between the ASI-3 subscales and items of the Observe facet that were either more internally or externally focused. We hypothesized that the physical concern subscale of the ASI-3 would be related more to internal focus and less to external focus, compared to the cognitive or social subscales. Participants The present study included participants 18 years or older with a diagnosis of an anxiety or mood disorder (total n=1521; PD n=198; social anxiety disorder (SAD) n=116; obsessive compulsive disorder (OCD) n=66; major depressive disorder (MDD) n=406; comorbid MDD and any anxiety disorder n=636; comorbid anxiety disorders n=99). The mean age was 42.42 ( SD= 9.49), 51% were female, and all participants were Japanese (see Table 1). Table 1. Demographic and Clinical Descriptive Information Diagnosis Male (N) Female (N) Japanese (N) MDD only 239 167 406 PD only 84 114 198 SAD only 43 73 116 OCD only 29 37 66 Comorbid MDD & any anxiety disorder or OCD 310 326 636 Comorbid anxiety disorder or OCD 41 58 99 Age Mean (years) SD 42.42 9.49 Note. a MDD = major depressive disorder; PD = panic disorder; SAD = social anxiety disorder; OCD = obsessive-compulsive disorder Procedures Participants were recruited from panelists registered at Macromill Incorporation, a large internet marketing research company in Japan. The institutional review board at the National Center of Neurology and Psychiatry approved the ethical and scientific validity of this study (approval number: A2013-022). Of the 1,095,443 registered panelists, 389,265 registered as “disease panelists,” reporting a current diagnosis of a clinical disorder that had been diagnosed by a practitioner. For PD, participants were asked “Are you currently diagnosed as having panic disorder and being treated for the problem in a medical setting?” The same questions were asked for other diagnoses [21]. We based our population for this study on the sample from Curtiss and colleagues [22], who randomly extracted a sample of 2,830 participants from this panelist pool based on gender, age, and living area, consisting of both clinical and non-clinical participants. All participants signed informed consent and completed questionnaires online. Measures Five Facet Mindfulness Questionnaire (FFMQ)[9] This self-report questionnaire is comprised of 39 total questions, made up of five subscales. Responses are rated on a 5-point Likert scale, with scores ranging from 0 (“Never or very rarely true”) to 5 (“Very often or always true”). The Observe facet is comprised of 8 questions (1, 6, 11, 15, 20, 26, 31 and 36, see Fig 1). The sections are summed for total scores. This scale has been validated in a Japanese sample and has exhibited good psychometric properties [23]. Several studies have investigated the incremental validity of individual facets of the FFMQ in predicting anxiety [10]. Anxiety Sensitivity Index-3 (ASI-3 )[24] This 18-item self-report questionnaire was developed from the original ASI [25]. The scale measures the extent to which an individual is worried about negative consequences occurring from their anxiety-related symptoms. The ASI-3 is comprised of three subscales (cognitive, physical and social), each consisting of six items. Participants rate responses on a 5-point Likert scale, with possible scores ranging from 0 (“Very little”) to 4 (“Very much”). Responses are summed for a total overall score. The ASI-3 has shown valid assessment of AS, as well as acceptable to good internal consistency [26]. This scale was translated into Japanese to be used in the survey. Statistical Analyses In the first set of analyses, individuals diagnosed with PD were compared to individuals without PD. In the following analyses, all individuals with MDD, PD, SAD and OCD were compared based on varying levels of AS. To examine differential item functioning of the Observe facet of the FFMQ, a multiple indicator multiple cause (MIMIC) model approach was adopted utilizing latent variables. Consistent with the framework posited by Brown [27], an iterative model-building approach was pursued. First, a measurement model of the latent variable of the Observe facet was evaluated. Second, the AS covariate was regressed onto the latent variable and all indicators, yet all pathway coefficients from the covariate to the indicators were fixed to zero. Modification indices were inspected to determine whether there were any salient instances of local model misspecification (i.e., whether freely estimating the regression parameter would substantially improve model fit, as indicated by a modification index exceeding 3.84 and substantive justification). Third, a new latent variable model was specified permitting the covariate to freely predict the latent variable and the indicators that exhibited the largest modification indices in the prior model. After estimation of the MIMIC model, modification indices were re-inspected to inform whether any other regression parameters from the covariate to the indicators should be freed. This process was continued until the modification indices and substantive justification revealed no further instances of poor fit. Then, this process was repeated for individual AS subscales and PD as a covariate. We assessed model fit using four fit indices. The chi-square statistic (χ 2 ) can be construed such that smaller values correspond to better fit. Because this fit index is especially sensitive to sample size and overly stringent, however, three additional fit indices were examined. The non-normed fit index (NNFI) and the Comparative Fit Index (CFI) were utilized as they exact a penalty for adding parameters, which is not the case with the laxer Normed Fit Index (NFI). The Root Mean Square Error of Approximation (RMSEA) is a measure based on the non-centrality parameter. NNFI and CFI values approaching 0.95 and 0.90 indicate good and acceptable model fit, respectively, and values less than 0.10 indicate adequate model fit for RMSEA, with values around 0.06 indicating good or excellent fit [28]. Modification indices were examined to determine the presence of local areas of model strain. All latent variable analyses were conducted using the Lavaan package in using maximum likelihood estimation [29]. Results Means and standard deviations of the FFMQ and ASI subscales are presented in Table 2. Table 2. Means and SD of FFMQ and ASI Subscales Group FFMQ Observe FFMQ Non-react FFMQ Non-judge FFMQ Describe FFMQ Act with Awareness ASI Physical ASI Cognitive ASI Social No PD 20.85 (5.89) 17.14 (4.80) 24.734 (6.47) 20.84 (6.37) 25.89 (6.32) 7.23 (6.28) 8.03 (6.71) 9.6 (6.33) PD 21.67 (6.297) 17.29 (4.88) 24.58 (6.67) 21.79 (6.28) 26.07 (6.42) 11.011 (7.06) 9.17 (6.99) 10.69 (6.29) Measurement Model Results of the measurement model revealed the acceptability of a one-factor solution of the Observe facet. Although the chi-square statistic was significant ( χ 2 (20) =252.57, p <.001), the other indices indicated adequate global fit: CFI =0.92, NNFI =0.89, RMSEA =0.076 (90% CI: 0.078 to 0.097; see Table 3). Table 3. Results from MIMIC model c 2 df CFI NNFI RMSEA (90% CI) Observe Measurement Model 252.57*** 20 0.92 0.89 0.087 (0.078, 0.097) PD to Observe Model 259.98*** 27 0.92 0.89 0.075 (0.067, 0.084) AS to Observe Model 360.06*** 27 0.90 0.86 0.090 (0.082, 0.098) AS MIMIC Model 236.81*** 36 0.93 0.90 0.078 (0.069, 0.087) AS Subscales to Observe Model 409.72*** 41 0.89 0.86 0.078 (0.070, 0.084) AS Subscales MIMIC Model 236.09*** 29 0.94 0.89 0.069 (0.061, 0.077) Note. b MIMIC = multiple indicator multiple cause; PD = panic disorder AS = anxiety sensitivity; c * p < 0.05; ** p < 0.01; *** p <0.001 All factor loadings were positive and significant, with unstandardized coefficients ranging from 0.84 to 1.35. Given this consistency with prior literature [9,12], this specification of the one-factor solution was retained for subsequent analyses. Panic Disorder A MIMIC model was pursued to determine whether PD accounts for differential item functioning in indicators of the latent variable of Observe. A measurement model with PD as a covariate was specified wherein the pathways of PD predicting the item indicators were fixed to zero, yet the pathway to Observe was freely estimated. This model was associated with global fit indices that were between adequate to good fit: CFI =0.92, NNFI =0.89, RMSEA =0.075 (90% CI: 0.067 to 0.084; see Table 3). PD significantly predicted Observe (B =0.08, R 2 =0.06, p <0.007), indicating that participants diagnosed with PD exhibited greater levels of observing than non-PD participants. Factor loadings were all positive and significant, with unstandardized coefficients from 0.84 to 1.35. Inspection of modification indices revealed that each were small and below the recommended cut-offs (≥ 3.84). Thus, there was not substantial justification for pursuing MIMIC modeling to examine differential item functioning. Anxiety Sensitivity To determine whether AS accounts for differential item functioning in indicators of Observe, several MIMIC models were pursued. First, a measurement model with AS as a covariate was specified wherein the pathways of the AS variable predicting the item indicators were fixed to zero, yet the pathway to Observe was freely estimated. This model was associated with global fit indices that were between adequate and good fit: CFI =0.90, NNFI =0.86, RMSEA =0.090 (90% CI: 0.082 to 0.098; see Table 3). AS significantly predicted Observe (B =0.39, R 2 =0.15 p <0.001), indicating that as participants’ AS increased, level of observing increased as well. Again, the factor loadings were all positive and significant, with unstandardized coefficients ranging from 0.82 to 1.31. Inspection of the modification indices revealed that item 1 (“When I’m walking, I deliberately notice the sensations of my body moving”), item 6 (“When I take a shower or bath, I stay alert to the sensations of water on my body”), item 26 (“I notice the smells and aromas of things”), and item 31 (“I notice visual elements in art or nature, such as colors, shapes, textures, or patterns of light and shadow”) were substantially above recommended cut-offs (≥3.84), with modification indices of 28.05, 36.32, 20.83, and 46.31, respectively. Next, a MIMIC model was pursued in which the pathway between the AS covariate and these items (1, 6, 26, and 31) were freely estimated. The fully specified MIMIC model exhibited statistically significant better fit than the model with the covariate pathways fixed to zero, χ 2 diff (4) =123.26, p <0.001; see Table 3). Although the chi-square statistic was significant ( χ 2 (36) =236.81, p <0.001), the other indices indicated adequate to good global fit: CFI =0.93, NNFI =0.90, RMSEA =0.078 (90% CI: 0.069 to 0.087). AS predicted higher levels of item 1 (B =0.68, R 2 =0.322, p <0.001) and item 6 (B =0.685, R 2 =0.315, p <0.001), and lower levels of item 26 (B= 0.685, R 2 =0.315, p <0.001) and item 31 (B =0.695, R 2 =0.305, p <0.001). Factor loadings were all positive and significant, with unstandardized coefficients ranging from 0.93 to 1.47. Inspection of the modification indices revealed no substantial areas of misspecification were substantively justified. Anxiety Sensitivity Subscales For the final step, the same MIMIC procedure was repeated, except this time the individual subscales of the ASI-3 were modeled as exogenous covariates (cognitive, social, and physical). The initial model included freely estimated pathways from the three covariates to Observe, whereas the pathways to the item indicators were fixed to zero. This model was associated with adequate fit. A correlation of r =0.72, p <0.001 was found between cognitive and physical subscales as well as between cognitive and social subscales (see Table 4). A correlation of r =0.63, p <0.001 was found between social and physical subscales (see Table 4). Table 4. FFMQ and ASI Subscale Correlations FFMQ Observe FFMQ Non-react FFMQ Non-judge FFMQ Describe FFMQ Act with Awareness ASI Physical ASI Cognitive FFMQ Non-react 0.33*** FFMQ Non-judge -0.58*** -0.13*** FFMQ Describe 0.16*** 0.41*** 0.09*** FFMQ Act with Awareness -0.46*** -0.10*** 0.55*** 0.28*** ASI Physical 0.28*** -0.08** -0.29*** 0.15*** -0.30*** ASI Cognitive 0.33*** -0.14*** 0.43*** -0.23*** -0.46*** 0.72*** ASI Social 0.29*** -0.14*** -0.41*** -0.22*** -0.36*** 0.63*** 0.72*** The chi-square statistic was significant ( χ 2 (41) =409.72, p <0.001; see Table 3), and the other indices indicated adequate to good global fit: CFI =0.89, NNFI =0.86, RMSEA =0.078 (90% CI: 0.070 to 0.084; see Table 3). All three freely estimated regression pathways were positive and significant (physical ASà observe, B =0.09, p <0.05; cognitive ASà observe, B =0.25, p <0.001; social ASà observe, B =0.098, p <0.05; see Table 3). All the factor loadings were significant and positive, ranging from 0.81 to 1.29 (R 2 =0.153 for Observe with all three predictors). The modification indices were inspected for the same items identified in the prior MIMIC model with the total AS score (items 1, 6, 26 and 31). The modification indices were large (i.e., exceeding a value of 10) for the pathways from the three covariates to items 1, 6 and 31. For item 26, modification indices above a value of 10 were observed for the pathways from the physical and cognitive subscales, yet not the social subscale (MI =3.70). In light of these modification indices, a MIMIC model was specified such that all three subscales freely predicted the latent variable, as well as items 1, 6, and 31 (See Fig 1). Figure 1. Mimic Model AS Covariate Note : Dashed lines denote loadings fixed to one to scale the latent variable, whereas solid lines denote loadings that are freely estimated. This figure was created using R package semPlot. With respect to item content: Q1 is "When I’m walking, I deliberately notice the sensations of my body moving;" Q6 is "When I take a shower or bath, I stay alert to the sensations of water on my body;" Q11 is “I notice how foods and drinks affect my thoughts, bodily sensations, and emotions;” Q15 is “I pay attention to sensations, such as the wind in my hair or sun on my face;” Q20 is “I pay attention to sounds, such as clocks ticking, birds chirping, or cars passing;” Q26 is I notice the smells and aromas of things;” Q31 is “I notice visual elements in art or nature, such as colors, shapes, textures, or patterns of light and shadow;” Q36 is “I pay attention to how my emotions affect my thoughts and behavior.” The physical and cognitive subscales were specified to freely predict item 26. Although the chi-square statistic was significant ( χ 2 (29) =236.09, p <0.001; see Table 3), the other indices indicated adequate to good fit global fit: CFI =0.94, NNFI =0.89, RMSEA =0.069 (90% CI: 0.061 to 0.077). The regression pathways of the cognitive subscale exhibited the same pattern of relations as the total ASI-3 scale. Cognitive AS predicted higher levels of item 1 (B =0.196, R 2 =0.33, p <0.001) and item 6 (B =0.168, R 2 =0.355, p <0.001), and lower levels of item 26 (B = -0.13, R 2 =0.326, p <0.001) and item 31 (B = -0.12, R 2 =0.309, p 0.05), and the pathway from the social subscale to item 26 was not specified. All factor loadings were significant and positive, ranging from 0.94 to 1.47. Discussion This study investigated the relationship between types of observation across PD diagnosis and level of AS. PD significantly predicted observing, revealing that participants diagnosed with PD had greater levels of observing than those without PD. This is important to note clinically, as this finding suggests that those with PD are more likely to endorse noticing generally. PD did not appear to predict individual items of the Observe facet, thereby providing no evidence of differential item functioning between internal or external items. Those with higher AS were found to observe more frequently. Differential item functioning did exist in four items as a function of AS: two items related to internal focus (items 1 and 6) and two items related to external focus (items 26 and 31). This finding suggests that AS may influence how people respond to internal and external Observe items, regardless of diagnosis. Individuals with increased AS were found to rate two items related to internal focus higher (items 1 and 6), and two items related to external focus lower (items 26 and item 31). This fits with prior findings that individuals who tend to scan their body for physical sensations likely spend more time worrying about and focusing on internal sensations [5]. Perhaps due to this type of focus, those with increased AS are also less likely to focus on the world around them. Although the proportion of variance accounted for in the differential item functioning by the AS covariate was relatively modest, the significant relationships tell us an important story. Perhaps a reason the Observe facet of the FFMQ continues to show mixed results in the literature is due to the fact that the factor does not differentiate between observing internal or external items. In this way, the scale gives a mixed or inaccurate picture of one’s observing tendencies. A solution for this would be to develop a scale that breaks down Observe into observing internal stimuli and observing external items. When subscales of the ASI-3 were investigated, cognitive concerns showed the same pattern of results as total AS. The physical subscale did not appear to predict any of the items, which was contrary to our expectations. This was particularly interesting because none of the items in the Observe facet that showed differences were cognitively related. Therefore, it appears there may be something about cognitive AS that is driving the influence of AS on internal vs. external indicators of observing. One explanation for why this may be is that individuals who focus on their thoughts may not be as aware when they focus on external stimuli. Or, perhaps someone endorsing item 7 of the ASI-3, “When my chest feels tight, I get scared that I won't be able to breathe properly” observes internal sensations more. The internal Observe items appear to be more tactile in nature, whereas the physical ASI-3 items are more visceral. AS cognitive concerns often reflect catastrophic thinking, which may drive individuals to continue monitoring their interoceptive sensations rather than exteroceptive. As anxiety often impacts what individuals observe, individuals who are more likely to catastrophize may interpret somatic symptoms as dangerous, leading to a vicious cycle of increased worry and narrower focus on physiological sensation [30]. Limitations and Future Research A limitation of this study is that diagnoses were self-reported. Additionally, the results do not shed light on the directionality of observing internal stimuli and its impact on AS. As this study was cross-sectional in nature, future research may benefit from investigating this relationship further, perhaps through longitudinal research. Another limitation is that a priori power analysis was not run on this sample. However, given conventional standards for power analyses, the sample size exceeds most heuristic recommendations [13]. A unique strength of this study is that it includes a fully Japanese sample. It is important to understand how cultural issues may or may not intersect with universal constructs, and we hope that our study can help be part of bridging this gap. As with all research conducted in one area of the world, it is important to keep in mind that cultural factors may shape how respondents answered questions, and thus it may be less feasible to extrapolate findings from a Japanese sample to all Western samples. Additionally, the ASI-3 was translated into Japanese for the first time, so it may be helpful for future studies to test its reliability and validity in Japanese. Future research would benefit from continuing to explore how culture or other identity factors play a role. Of note, several FFMQ subscales were negatively correlated with one another, which is consistent with previous findings obtained by Japanese samples [23]. In Japanese samples, those who score lower on the Observe facet tend to score higher on the Non-judge or Act with Awareness facets because observing appears to tap into a sensitivity to sensations and non-judging and acting with awareness measure the ability to take attention away from cognitions or sensations. Negative correlations were found between the Non-react and Non-judge facets (r = -0.13) and the Non-react and Act with Awareness facet results (r = -0.10) are difficult to interpret as these correlations are very weak (see Table 4). Conclusions This study utilized a transdiagnostic approach to understand differences in core components of anxiety related to observation. It appears AS is important to study across clinical samples, to understand why those who have more internal focus toward bodily sensations perceive certain types of mindfulness as threatening. Future studies may benefit from a more thorough understanding and conceptualization of AS, across clinical and non-clinical samples, as anxious individuals may observe more internally or be more sensitive to their anxiety. As not all facets of mindfulness have been found to neatly correlate with reduced anxiety [9], it is important to continue to conduct transdiagnostic research to identify which types of mindfulness will be most beneficial for each person. Future research may benefit from expanding this work to a non-clinical sample, as observation is an important component of mindfulness that extends to all types of individuals. Based on the findings from this study, it may be beneficial to develop a scale that splits the Observe facet into internal and external observation, including cognitive and emotional observation. As the field is beginning to shift away from a nomothetic approach of treating specific diagnoses and towards an idiographic approach of better understanding symptoms at the individual level, we hope to continue to understand the function of types of observation which may lead us to the ability to implement more accurate personalized treatment. Declarations Ethics approval and consent to participate The institutional review board at the National Center of Neurology and Psychiatry approved the ethical and scientific validity of this study (approval number: A2013-022). All participants signed informed consent and completed questionnaires online. Consent for publication N/A Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests N/A Funding This study was supported by a Grant-in-Aid for Research Activity start-up (24830127) awarded to MI from the Japan Society for the Promotion of Science, National Center of Neurology and Psychiatry Intramural Research Grant (30-2) for Neurological and Psychiatric Disorders. Authors' contributions D. Moskow Diamond initiated the conception of this project and all authors substantially contributed to its design. Material preparation and data collection were performed by M. Ito. The first draft of the manuscript was mostly written by D. Moskow Diamond, and J. Curtiss ran analyses and created the figure included in this paper. All authors commented on previous versions of the manuscript and read and approved the final manuscript. Acknowledgements N/A References Kabat-Zinn J. Wherever you go there you are: Mindfulness meditation in everyday life. New York, NY: Hyperion; 1994. Rodrigues, MF, Nardi, AE., Levitan, M. 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The potential mediating role of anxiety sensitivity in the impact of mindfulness training on anxiety and depression severity and impairment: A randomized controlled trial. Scandinavian Journal of Psychology. 2022;64, 21-29. https://doi.org/10.1111/sjop.12860. Khalili, MD, Makvand Hosseini, S, Sabahi, P. (2023). Effects of Mindfulness-Based Cognitive Therapy on Anxiety Sensitivity in Patients with Multiple Sclerosis. Jundishapur Journal of Chronic Disease Care. 2023;12(1), e132672. https://doi.org/10.5812/jjcdc-132672. Baer, RA, Smith, GT, Hopkins, J, Krietemeyer, J, Toney, L. Using self-report assessment methods to explore facets of mindfulness. 2006;13(1), 27-45. https://doi.org/10.1177/1073191105283504. Barcaccia, B, Baiocco, R, Pozza, A, Pallini, S, Mancini, F, Salvati, M. The more you judge the worse you feel. A judgmental attitude towards one’s inner experience predicts depression and anxiety. Pers Individ Differ. 2019;138(1), 33–39. https://doi.org/10.1016/j.paid.2018.09.012. Petrocchi, N, Ottaviani, C. Mindfulness facets distinctively predict depressive symptoms after two years: the mediating role of rumination. Pers Individ Differ. 2016;93, 92–96. https://doi.org/10.1016/j.paid.2015.08.017. Curtiss, J, Klemanski, DH. Factor analysis of the five facet mindfulness questionnaire in a heterogeneous clinical sample. J Psychopathol Behav. 2014;36(4), 683–694. https://doi.org/10.1007/s10862-014-9429-y. Brown, DB, Bravo, AJ, Roos, CR, Pearson, MR. Five facets of mindfulness and psychological health: evaluating a psychological model of the mechanisms of mindfulness. 2015;6(5), 1021–1032. https://doi.org/10.1007/s12671-014-0349-4. Lecuona, O, García-Rubio, C, de Rivas, S, Moreno-Jiménez, JE, Rodríguez-Carvajal, R. Unraveling heterogeneities in mindfulness profiles: A review and latent profile analysis of the Five Facet Mindfulness Questionnaire Short-Form (FFMQ-SF) in the Spanish population. Mindfulness. 2022;13(8), 2031–2046. https://doi.org/10.1007/s12671-022-01939-y. Diehl, JM, Rodriguez-Seijas, C, Thompson, JS, Dalrymple, K, Chelminski, I, Zimmerman, M. Exploring the optimal factor structure of the Five Facet Mindfulness Questionnaire: Associations between mindfulness facets and dimensions of psychopathology. J Pers Assess. 2021;1–11. https://doi.org/10.1080/00223891.2021.1998080. Hawley, LL, Rogojanski, J, Vorstenbosch, V, Quilty, LC, Laposa, JM, Rector, NA. The structure, correlates, and treatment related changes of mindfulness facets across the anxiety disorders and obsessive compulsive disorder. J Anxiety Disord. 2017;49 , 65-75. https://doi.org/10.1016/j.janxdis.2017.03.003. Waszczuk, MA, Zavos, HM, Antonova, E, Haworth, CM, Plomin, R, Eley, TC. A multivariate twin study of trait mindfulness, depressive symptoms, and anxiety sensitivity. Depress Anxiety. 2015;32(4), 254-261. https://doi.org/1002/da.22326. Baer, RA., Smith, GT, Allen, KB. Assessment of mindfulness by self report: the Kentucky Inventory of Mindfulness Skills. Assessment. 2004;11, 191–206. https://doi.org/10.1177/1073191104268029. Baer, RA, Smith, GT, Lykins, E, Button, D, Krietemeyer, J, Sauer, S, Walsh, E, Duggan, D, Williams, MG. Construct validity of the Five Facet Mindfulness Questionnaire in meditating and nonmeditating samples. 2008;15(3), 329-342. https://doi.org/10.1177/1073191107313003. Goldberg, SB., Wielgosz, J, Dahl, C, Schuyler, B, MacCoon, DS, et al. Does the Five Facet Mindfulness Questionnaire measure what we think it does? Construct validity evidence from an active controlled randomized clinical trial. Psychol Assessment. 2016;28(8), 1009-1014. https://doi.org/10.1037/pas0000233. Ito, M, Oe, Y, Kato, N, Nakajima, S, Fujisato, H, Miyamae, M, Kanie, A, Horikoshi, M, Norman, SB. Validity and clinical interpretability of Overall Anxiety Severity and Impairment Scale (OASIS). J Affect Disord. 2015;170, 217–224. https://doi.org/10.1016/j.jad.2014.08.045. Curtiss, J, Klemanski, DH, Andrews, L, Ito, M, Hofmann, S. G. The conditional process model of mindfulness and emotion regulation: An empirical test. J Affect Disord. 2017; 212, 93-100. https://doi.org/10.1016/j.jad.2017.01.027. Sugiura, Y, Sato, A, Ito, Y, Murakami, H. Development and validation of the Japanese version of the Five Facet Mindfulness Questionnaire. Mindfulness. 2012;3(2), 85-94. https://doi.org/10.1007/s12671-011-0082-1. Taylor, S, Zvolensky, MJ, Cox, BJ, Deacon, B, Heimberg, RG, Ledley, D. R. Robust dimensions of anxiety sensitivity: development and initial validation of the Anxiety Sensitivity Index-3. Psychol Assessment. 2007;19(2), 176–188. https://doi.org/10.1037/1040-3590.19.2.176. Peterson, RA, Reiss, S. Anxiety Sensitivity Index Revised Manual . Worthington, OH: IDS Publishing; 1992. Jardin, C, Paulus, DJ, Garey, L, Kauffman, B, Bakhshaie, J, Manning, K, Mayorga, NA, Zvolensky, MJ. Towards a greater understanding of anxiety sensitivity across groups: The construct validity of the Anxiety Sensitivity Index-3. Psychiatry Res. 2018;268 , 72–81. https://doi.org/10.1016/j.psychres.2018.07.007. Brown, TA. Confirmatory Factor Analysis for Applied Research. (2 nd ) New York, NY: Guilford Press. 2015. Browne, MW, Cudeck, R. Alternative Ways of Assessing Model Fit. In: Bollen K, Long JS, editors. Testing Structural Equation Models. Newbury Park, CA: Sage Publications; 1993. p. 136-162. Rosseel, Y. lavaan: An R package for structural equation modeling. J Stat Softw. 2012;48(2), 1-36. https://doi.org/10.18637/jss.v048.i02. Greeson, J, Brantley, J. Mindfulness and anxiety disorders: Developing a wise relationship with the inner experience of fear. In Didonna F, editor. Clinical handbook of mindfulness. New York: Springer; 2009. p. 171-188. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviews received at journal 19 Sep, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers invited by journal 12 Jul, 2024 Editor assigned by journal 21 Jun, 2024 Editor invited by journal 10 Apr, 2024 Submission checks completed at journal 10 Apr, 2024 First submitted to journal 18 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4125571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290912064,"identity":"88981dc1-0c4c-4858-8ae6-f53da38509bf","order_by":0,"name":"Danielle Moskow Diamond","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDCCAwwMjA0MDHIMDDwkajEmXUtiA9Fa+K4dPvxxRsXh9LX9aw8wfNxTS1iL5O20NMkNZw7nbrvxLoFxxrPjhLUY3M4xY3zYdhuo5YwBM8+BY0RpMf4I1JJuRooWA8mNbbcTzM73gLTUEOmXGWf+G267wZdwcMaBA4S18N1OPvyxpyJN3uz82YMPPhyoI6wFASQSQHF0mBQt/GA3kWTLKBgFo2AUjBAAAGzFSHo13KOdAAAAAElFTkSuQmCC","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Danielle","middleName":"Moskow","lastName":"Diamond","suffix":""},{"id":290912065,"identity":"91d0c9d9-f35f-488b-ae31-14109826a7d1","order_by":1,"name":"Joshua Curtiss","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Curtiss","suffix":""},{"id":290912066,"identity":"d5918aa8-2106-4d2c-a195-f0bee25ee67f","order_by":2,"name":"Joseph K. Carpenter","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"K.","lastName":"Carpenter","suffix":""},{"id":290912067,"identity":"8eb643b3-eb6c-4b30-bacf-38dcf4b67cb3","order_by":3,"name":"Masaya Ito","email":"","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Masaya","middleName":"","lastName":"Ito","suffix":""},{"id":290912068,"identity":"0891aa18-8cf4-475b-9449-d9a276a79a3b","order_by":4,"name":"Stefan G. Hofmann","email":"","orcid":"","institution":"Philipps University Marburg","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"G.","lastName":"Hofmann","suffix":""}],"badges":[],"createdAt":"2024-03-18 19:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4125571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4125571/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-06488-x","type":"published","date":"2025-04-01T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54998513,"identity":"4ce2d5bf-cf28-49bd-a199-13861dc43615","added_by":"auto","created_at":"2024-04-19 18:25:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMimic Model AS Covariate\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4125571/v1/8dfd53f2e5a6debfc0d4386f.jpg"},{"id":80082026,"identity":"5752e6e8-b0f5-47cf-b73a-d73023a431b6","added_by":"auto","created_at":"2025-04-07 16:05:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4125571/v1/ebdbba28-2ac9-46db-a794-35d958970c63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":" The influence of anxiety sensitivity on observing in mindfulness among clinical populations with anxiety and/or depressive disorders","fulltext":[{"header":"Background","content":"\u003cp\u003eMindfulness techniques have become pervasive in the field of mental health. Many individuals engage in meditation as a mindfulness practice; however, it is important to note that meditation is only one type of mindfulness. Jon Kabat-Zinn, the pioneer of Western mindfulness, defined mindfulness as \u0026ldquo;awareness that arises through paying attention, on purpose, in the present moment, non-judgmentally.\u0026rdquo; [1] Mindfulness, in many ways, is the anthesis of anxiety, as anxiety typically involves future-oriented worries. Mindfulness-based programs (MBPs) have developed as an effective treatment for stress, depression and anxiety. As opposed to anxious thoughts about the future or rumination about the past, MBPs emphasize increasing awareness toward present moment sensations and experiences, while maintaining a nonjudgmental attitude [2]. As such, research has found that mindfulness is a prominent protective factor for reducing psychological distress [3]. A meta-analysis by Hofmann and colleagues[4] found that across 39 studies with participants receiving mindfulness-based therapy, anxiety and mood symptoms significantly improved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the plethora of evidence that mindfulness can be beneficial for anxiety, the question remains: why do some people struggle with mindfulness? For example, some people find that centering their attention on their breath can be uncomfortable or even increase anxiety. This is often true for those with panic disorder (PD), who are highly in tune with bodily sensations, frequently observing their internal symptoms and finding them uncomfortable [5]. Fear of bodily sensations is not just unique to PD, however. Research has found that individuals with elevated anxiety sensitivity (AS) more generally are likely to notice increased anxiety and may feel more anxious when tuning into physical sensations [6]. AS is the belief that anxiety symptoms are harmful, and AS varies across anxiety disorders. Therefore, it is likely that individuals who are more sensitive to their anxiety may have difficulty with mindfulness techniques that involve focusing on internal sensations. A recent study by Xie and colleagues [7] assigned participants to either an eight-week mindfulness intervention or a control group wait-list. Results suggested that AS is a potential mechanism for the impact of mindfulness training on anxiety and depression. Additionally, mindfulness treatments have also been found effective at improving AS. In a recent study, Khalili and colleagues [8] studied the effects of mindfulness-based cognitive therapy (MBCT) on AS in patients with multiple sclerosis. The researchers found that MBCT helped reduce AS in patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with moving towards individualized treatment, it is important to have a more robust understanding of who may benefit from what type of mindfulness skills and why. In order to reach this goal, we need to better understand the relationship between AS and mindfulness, so we can learn who may or may not benefit from certain types of MBPs. Much of the research thus far on understanding how observation relates to mindfulness has found positive associations between observing and mindfulness. For instance, several studies have investigated the incremental validity of each facet of the Five Facet Mindfulness Questionnaire (FFMQ)[9]- one of the most widely utilizing mindfulness questionnaires, in predicting anxiety, depression and stress [10;11]. The facets of the FFMQ include Act with Awareness, Describe, Observe, Non-judge and Non-react. Observe was the only factor not found to be a component of Baer and colleagues\u0026rsquo; [9] overall mindfulness construct, and this finding was replicated in clinical samples [12].In fact, Brown and colleagues[13] found that those who scored highly on the Observe facetexperienced increased stress and anxiety. This finding points to the possibility that observing may actually increase anxiety for some people, and this is something that needs to be better understood before we recommend specific mindfulness treatments to individuals [14]. Diehl and colleagues [15] recently examined the underlying structure of the FFMQ and the relations between the facets and dimensions of psychopathology. The authors noted that all facets except Observe were negatively associated with the internalizing factor, providing further and current evidence of this challenge with the Observe facet.\u003c/p\u003e\n\u003cp\u003ePrevious studies have investigated the relationship between PD and observing and have found that those with PD tend to core higher on the Observefacet of the FFMQ compared to individuals with other anxiety disorders [16]. Interpretation of these relationships, however, is complicated by the fact that higher observing scores does not necessarily correlate with higher overall mindfulness scores, as discussed earlier. Another challenge with these findings is the Observe facet of the FFMQ does not differentiate between where one observes (internally on physical sensations/thoughts/emotions or externally on sights/smells/sounds around them). A study by Waszczuk and colleagues [17] found significant relations between mindfulness skills on the Kentucky Inventory of Mindfulness Skills (KIMS)[18] and AS.\u0026nbsp;The researchers found that those with higher AS had a heightened tendency to observe thoughts, sensations and emotions. The Observe facet was found to tap into a form of hypervigilance to threatening interpersonal experiences in those who were fearful of their psychological and physical sensations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn interesting finding was noted by Baer and colleagues [19] which was that higher scores on the Observefacet were associated with stronger psychological adjustment among meditating samples, whereas the relationship in non-meditating individuals was either nonsignificant or in the opposite direction. Thus, it appears that meditation experience may moderate the relationship between observing and anxiety. Given the many potential benefits of mindfulness as treatment for anxiety, it is important researchers and clinicians to have a better understanding of who may benefit from different types of mindfulness skills and why, so that we can work towards disseminating and implementing mindfulness as effective treatment for individuals. As the FFMQ is perhaps the most commonly used mindfulness scale [20]and important gaps in this scale exist, it is important to investigate the aspects of the scale that are less understood. There is increasingly significant evidence that mindfulness is not only multifaceted, but also certain facets of the FFMQ, such as Observe, appear to operate differently for different people [17]. Thus, the goal of this study was to better understand what may be happening with the Observe, to clarify any changes that may need to be made in the psychometric mindfulness literature.\u003c/p\u003e\n\u003cp\u003eMost research on this topic has focused on the relationship between PD and increased observation, due to this idea that those who focus internally may find mindfulness practices, such as meditation, to be aversive. This study wanted to better understand the relationship between AS more generally across a clinical sample related to types of observation. We believe the Observe facet of the FFMQ may be more complicated than we think, and it may be important to note more precisely what people are observing and why. In this way, we can determine which types of mindfulness may work best for which individuals. Finally, the FFMQ in the Japanese population has not been as thoroughly explored as in the American population. Thus, studying mindfulness in a Japanese population can improve cross-cultural knowledge of mindfulness more broadly.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Aims\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of this study was to examine the relationship between PD, AS and internal/external items on the Observe facet of the FFMQ. Our first aim was to examine the Observe facet as a single latent variable across a clinical population, and we hypothesized this would reveal a single factor solution, consistent with prior research (See Fig 1 for full item description). The second aim was to investigate the relation between PD and specific items of the Observe facet using MIMIC modelling to examine differential item functioning. We hypothesized those with PD would endorse higher levels of Observe overall, as well as higher internal focus and lower external focus, compared to those without PD. The third aim was to investigate the relation between varying levels of AS and specific items of the Observe facet. We hypothesized that individuals who are more sensitive to their anxiety would observe more overall compared to those lower in AS. We predicted individuals with elevated AS would score higher on items measuring internal focus and lower on items measuring external focus. The final aim of the study was to explore the relationship between the ASI-3 subscales and items of the Observe facet that were either more internally or externally focused. We hypothesized that the physical concern subscale of the ASI-3 would be related more to internal focus and less to external focus, compared to the cognitive or social subscales.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe present study included participants 18 years or older with a diagnosis of an anxiety or mood disorder (total n=1521; PD n=198; social anxiety disorder (SAD) n=116; obsessive compulsive disorder (OCD) n=66; major depressive disorder (MDD) n=406; comorbid MDD and any anxiety disorder n=636; comorbid anxiety disorders n=99). The mean age was 42.42 (\u003cem\u003eSD=\u003c/em\u003e9.49), 51% were female, and all participants were Japanese (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Demographic and Clinical Descriptive Information\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDiagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFemale (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.807692307692307%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eJapanese (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; MDD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.807692307692307%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; PD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.807692307692307%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; SAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.807692307692307%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; OCD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.41025641025641%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.2435897435897436%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Comorbid MDD \u0026amp; any\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; anxiety disorder or OCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.41025641025641%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.2435897435897436%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Comorbid anxiety disorder\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; or OCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.41025641025641%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.2435897435897436%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eMean (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.41025641025641%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.2435897435897436%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;42.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.423076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.41025641025641%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.2435897435897436%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003eMDD = major depressive disorder; PD = \u0026nbsp;panic disorder; SAD = social anxiety disorder; OCD = \u0026nbsp;obsessive-compulsive disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProcedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited from panelists registered at Macromill Incorporation, a large internet marketing research company in Japan. The institutional review board at the National Center of Neurology and Psychiatry approved the ethical and scientific validity of this study (approval number: A2013-022). Of the 1,095,443 registered panelists, 389,265 registered as \u0026ldquo;disease panelists,\u0026rdquo; reporting a current diagnosis of a clinical disorder that had been diagnosed by a practitioner. For PD, participants were asked \u0026ldquo;Are you currently diagnosed as having panic disorder and being treated for the problem in a medical setting?\u0026rdquo; The same questions were asked for other diagnoses [21]. We based our population for this study on the sample from Curtiss and colleagues [22], who randomly extracted a sample of 2,830 participants from this panelist pool based on gender, age, and living area, consisting of both clinical and non-clinical participants. All participants signed informed consent and completed questionnaires online.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFive Facet Mindfulness Questionnaire (FFMQ)[9]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis self-report questionnaire is comprised of 39 total questions, made up of five subscales. Responses are rated on a 5-point Likert scale, with scores ranging from 0 (\u0026ldquo;Never or very rarely true\u0026rdquo;) to 5 (\u0026ldquo;Very often or always true\u0026rdquo;). The Observe\u003cem\u003e\u0026nbsp;\u003c/em\u003efacet is comprised of 8 questions (1, 6, 11, 15, 20, 26, 31 and 36, see Fig 1). The sections are summed for total scores. This scale has been validated in a Japanese sample and has exhibited good psychometric properties [23]. Several studies have investigated the incremental validity of individual facets of the FFMQ in predicting anxiety [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnxiety Sensitivity Index-3 (ASI-3\u003c/em\u003e)[24]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eThis 18-item self-report questionnaire was developed from the original ASI [25]. The scale measures the extent to which an individual is worried about negative consequences occurring from their anxiety-related symptoms. The ASI-3 is comprised of three subscales (cognitive, physical and social), each consisting of six items. Participants rate responses on a 5-point Likert scale, with possible scores ranging from 0 (\u0026ldquo;Very little\u0026rdquo;) to 4 (\u0026ldquo;Very much\u0026rdquo;). Responses are summed for a total overall score. The ASI-3 has shown valid assessment of AS, as well as acceptable to good internal consistency [26]. This scale was translated into Japanese to be used in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the first set of analyses, individuals diagnosed with PD were compared to individuals without PD. In the following analyses, all individuals with MDD, PD, SAD and OCD were compared based on varying levels of AS. To examine differential item functioning of the Observe\u003cem\u003e\u0026nbsp;\u003c/em\u003efacet of the FFMQ, a multiple indicator multiple cause (MIMIC) model approach was adopted utilizing latent variables. Consistent with the framework posited by Brown [27], an iterative model-building approach was pursued. First, a measurement model of the latent variable of the Observe\u003cem\u003e\u0026nbsp;\u003c/em\u003efacet was evaluated. Second, the AS covariate was regressed onto the latent variable and all indicators, yet all pathway coefficients from the covariate to the indicators were fixed to zero. Modification indices were inspected to determine whether there were any salient instances of local model misspecification (i.e., whether freely estimating the regression parameter would substantially improve model fit, as indicated by a modification index exceeding 3.84 and substantive justification). Third, a new latent variable model was specified permitting the covariate to freely predict the latent variable and the indicators that exhibited the largest modification indices in the prior model. After estimation of the MIMIC model, modification indices were re-inspected to inform whether any other regression parameters from the covariate to the indicators should be freed. This process was continued until the modification indices and substantive justification revealed no further instances of poor fit. Then, this process was repeated for individual AS subscales and PD as a covariate.\u003c/p\u003e\n\u003cp\u003eWe assessed model fit using four fit indices. The chi-square statistic (\u0026chi;\u003csup\u003e2\u003c/sup\u003e) can be construed such that smaller values correspond to better fit. Because this fit index is especially sensitive to sample size and overly stringent, however, three additional fit indices were examined. The non-normed fit index (NNFI) and the Comparative Fit Index (CFI) were utilized as they exact a penalty for adding parameters, which is not the case with the laxer Normed Fit Index (NFI). The Root Mean Square Error of Approximation (RMSEA) is a measure based on the non-centrality parameter. NNFI and CFI values approaching 0.95 and 0.90 indicate good and acceptable model fit, respectively, and values less than 0.10 indicate adequate model fit for RMSEA, with values around 0.06 indicating good or excellent fit [28]. Modification indices were examined to determine the presence of local areas of model strain. All latent variable analyses were conducted using the Lavaan package in using maximum likelihood estimation [29].\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMeans and standard deviations of the FFMQ and ASI subscales are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Means and SD of FFMQ and ASI Subscales\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.174311926605505%\" valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eObserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-react\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-judge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eDescribe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eAct with Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.926605504587156%\" valign=\"top\"\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.091743119266056%\" valign=\"top\"\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.174311926605505%\" valign=\"top\"\u003e\n \u003cp\u003eNo PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e20.85\u003c/p\u003e\n \u003cp\u003e(5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e17.14 (4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e24.734\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(6.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e20.84\u003c/p\u003e\n \u003cp\u003e(6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\" valign=\"top\"\u003e\n \u003cp\u003e25.89 \u0026nbsp;(6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003cp\u003e(6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.926605504587156%\" valign=\"top\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003cp\u003e(6.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.091743119266056%\" valign=\"top\"\u003e\n \u003cp\u003e9.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.174311926605505%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e21.67 (6.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e17.29 (4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e24.58\u003c/p\u003e\n \u003cp\u003e(6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e21.79\u003c/p\u003e\n \u003cp\u003e(6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.761467889908257%\" valign=\"top\"\u003e\n \u003cp\u003e26.07 \u0026nbsp;(6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.009174311926605%\" valign=\"top\"\u003e\n \u003cp\u003e11.011\u003c/p\u003e\n \u003cp\u003e(7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.926605504587156%\" valign=\"top\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003cp\u003e(6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.091743119266056%\" valign=\"top\"\u003e\n \u003cp\u003e10.69\u003c/p\u003e\n \u003cp\u003e(6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMeasurement Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults of the measurement model revealed the acceptability of a one-factor solution of the Observe facet. Although the chi-square statistic was significant (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(20) =252.57, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001), the other indices indicated adequate global fit: CFI =0.92, NNFI =0.89, RMSEA =0.076 (90% CI: 0.078 to 0.097; see Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Results from MIMIC model\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003eNNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003eRMSEA (90% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003eObserve Measurement Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e252.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.087 (0.078, 0.097)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003ePD to Observe Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e259.98***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.075 (0.067, 0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003eAS to Observe Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e360.06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.090 (0.082, 0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003eAS MIMIC Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e236.81***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.078 (0.069, 0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003eAS Subscales to Observe Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e409.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.078 (0.070, 0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.049180327868854%\" valign=\"top\"\u003e\n \u003cp\u003eAS Subscales MIMIC Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.60655737704918%\" valign=\"top\"\u003e\n \u003cp\u003e236.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.081967213114754%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.049180327868853%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.540983606557376%\" valign=\"top\"\u003e\n \u003cp\u003e0.069 (0.061, 0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003eMIMIC = \u0026nbsp; multiple indicator multiple cause; PD = \u0026nbsp; panic disorder AS = anxiety sensitivity;\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003ep\u003c/em\u003e \u0026lt;0.001\u003c/p\u003e\n\u003cp\u003eAll factor loadings were positive and significant, with unstandardized coefficients ranging from 0.84 to 1.35. Given this consistency with prior literature [9,12], this specification of the one-factor solution was retained for subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePanic Disorder\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA MIMIC model was pursued to determine whether PD accounts for differential item functioning in indicators of the latent variable of Observe. A measurement model with PD as a covariate was specified wherein the pathways of PD predicting the item indicators were fixed to zero, yet the pathway to Observe was freely estimated. This model was associated with global fit indices that were between adequate to good fit: CFI =0.92, NNFI =0.89, RMSEA =0.075 (90% CI: 0.067 to 0.084; see Table 3). PD significantly predicted Observe (B =0.08, R\u003csup\u003e2\u003c/sup\u003e =0.06, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.007), indicating that participants diagnosed with PD exhibited greater levels of observing than non-PD participants. Factor loadings were all positive and significant, with unstandardized coefficients from 0.84 to 1.35. Inspection of modification indices revealed that each were small and below the recommended cut-offs (\u0026ge; 3.84). Thus, there was not substantial justification for pursuing MIMIC modeling to examine differential item functioning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnxiety Sensitivity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether AS accounts for differential item functioning in indicators of Observe, several MIMIC models were pursued. First, a measurement model with AS as a covariate was specified wherein the pathways of the AS variable predicting the item indicators were fixed to zero, yet the pathway to Observe was freely estimated. This model was associated with global fit indices that were between adequate and good fit: CFI =0.90, NNFI =0.86, RMSEA =0.090 (90% CI: 0.082 to 0.098; see Table 3). AS significantly predicted Observe (B =0.39, R\u003csup\u003e2\u003c/sup\u003e =0.15 \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), indicating that as participants\u0026rsquo; AS increased, level of observing increased as well. Again, the factor loadings were all positive and significant, with unstandardized coefficients ranging from 0.82 to 1.31. Inspection of the modification indices revealed that item 1 (\u0026ldquo;When I\u0026rsquo;m walking, I deliberately notice the sensations of my body moving\u0026rdquo;), item 6 (\u0026ldquo;When I take a shower or bath, I stay alert to the sensations of water on my body\u0026rdquo;), item 26 (\u0026ldquo;I notice the smells and aromas of things\u0026rdquo;), and item 31 (\u0026ldquo;I notice visual elements in art or nature, such as colors, shapes, textures, or patterns of light and shadow\u0026rdquo;) were substantially above recommended cut-offs (\u0026ge;3.84), with modification indices of 28.05, 36.32, 20.83, and 46.31, respectively. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, a MIMIC model was pursued in which the pathway between the AS covariate and these items (1, 6, 26, and 31) were freely estimated. The fully specified MIMIC model exhibited statistically significant better fit than the model with the covariate pathways fixed to zero, \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ediff\u003c/sub\u003e (4) =123.26, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; see Table 3). Although the chi-square statistic was significant (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (36) =236.81, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), the other indices indicated\u0026nbsp;adequate to good\u0026nbsp;global fit: CFI =0.93, NNFI =0.90, RMSEA =0.078 (90% CI: 0.069 to 0.087). AS predicted higher levels of item 1 (B =0.68, R\u003csup\u003e2\u003c/sup\u003e =0.322, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and item 6 (B =0.685, R\u003csup\u003e2\u003c/sup\u003e =0.315, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and lower levels of item 26 (B= 0.685, R\u003csup\u003e2\u003c/sup\u003e =0.315, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and item 31 (B =0.695, R\u003csup\u003e2\u003c/sup\u003e =0.305, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Factor loadings were all positive and significant, with unstandardized coefficients ranging from 0.93 to 1.47. Inspection of the modification indices revealed no substantial areas of misspecification were substantively justified.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnxiety Sensitivity Subscales\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the final step, the same MIMIC procedure was repeated, except this time the individual subscales of the ASI-3 were modeled as exogenous covariates (cognitive, social, and physical). The initial model included freely estimated pathways from the three covariates to Observe, whereas the pathways to the item indicators were fixed to zero. This model was associated with adequate fit. A correlation of r =0.72, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001 was found between cognitive and physical subscales as well as between cognitive and social subscales (see Table 4). A correlation of r =0.63,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u0026lt;0.001 was found between social and physical subscales (see Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4. FFMQ and ASI Subscale Correlations\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eObserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-react\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-judge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eDescribe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eAct with Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.157894736842104%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-react\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eNon-judge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eDescribe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFFMQ\u003c/p\u003e\n \u003cp\u003eAct with Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003ePhysical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e0.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003eCognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.912280701754385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eASI\u003c/p\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe chi-square statistic was significant (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (41) =409.72, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; see Table 3), and the other indices indicated adequate to good global fit: CFI =0.89, NNFI =0.86, RMSEA =0.078 (90% CI: 0.070 to 0.084; see Table 3). All three freely estimated regression pathways were positive and significant (physical AS\u0026agrave; observe, B =0.09, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; cognitive AS\u0026agrave; observe, B =0.25, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; social AS\u0026agrave; observe, B =0.098, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; see Table 3). All the factor loadings were significant and positive, ranging from 0.81 to 1.29 (R\u003csup\u003e2\u003c/sup\u003e =0.153 for Observe with all three predictors).\u003c/p\u003e\n\u003cp\u003eThe modification indices were inspected for the same items identified in the prior MIMIC model with the total AS score (items 1, 6, 26 and 31). The modification indices were large (i.e., exceeding a value of 10) for the pathways from the three covariates to items 1, 6 and 31. For item 26, modification indices above a value of 10 were observed for the pathways from the physical and cognitive subscales, yet not the social subscale (MI =3.70).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn light of these modification indices, a MIMIC model was specified such that all three subscales freely predicted the latent variable, as well as items 1, 6, and 31 (See Fig 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 1. Mimic Model AS Covariate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Dashed lines denote loadings fixed to one to scale the latent variable, whereas solid lines denote loadings that are freely estimated. This figure was created using R package semPlot.\u003c/p\u003e\n\u003cp\u003eWith respect to item content:\u003c/p\u003e\n\u003cp\u003eQ1 is \u0026quot;When I\u0026rsquo;m walking, I deliberately notice the sensations of my body moving;\u0026quot;\u003c/p\u003e\n\u003cp\u003eQ6 is \u0026quot;When I take a shower or bath, I stay alert to the sensations of water on my body;\u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ11 is \u0026ldquo;I notice how foods and drinks affect my thoughts, bodily sensations, and emotions;\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eQ15 is \u0026ldquo;I pay attention to sensations, such as the wind in my hair or sun on my face;\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ20 is \u0026ldquo;I pay attention to sounds, such as clocks ticking, birds chirping, or cars passing;\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eQ26 is I notice the smells and aromas of things;\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eQ31 is \u0026ldquo;I notice visual elements in art or nature, such as colors, shapes, textures, or patterns of light and shadow;\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eQ36 is \u0026ldquo;I pay attention to how my emotions affect my thoughts and behavior.\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe physical and cognitive subscales were specified to freely predict item 26. Although the chi-square statistic was significant (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (29) =236.09, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; see Table 3), the other indices indicated\u0026nbsp;adequate to good fit\u0026nbsp;global fit: CFI =0.94, NNFI =0.89, RMSEA =0.069 (90% CI: 0.061 to 0.077). The regression pathways of the cognitive subscale exhibited the same pattern of relations as the total ASI-3 scale. Cognitive AS predicted higher levels of item 1 (B =0.196, R\u003csup\u003e2\u003c/sup\u003e =0.33, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and item 6 (B =0.168, R\u003csup\u003e2\u003c/sup\u003e =0.355, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and lower levels of item 26 (B = -0.13, R\u003csup\u003e2\u003c/sup\u003e =0.326, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and item 31 (B = -0.12, \u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e =0.309, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). The pathways from the social and physical ASI-3 subscales to all items were not significant (\u003cem\u003ep\u003c/em\u003e \u0026gt;0.05), and the pathway from the social subscale to item 26 was not specified. All factor loadings were significant and positive, ranging from 0.94 to 1.47.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the relationship between types of observation across PD diagnosis and level of AS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePD significantly predicted observing, revealing that participants diagnosed with PD had greater levels of observing than those without PD. This is important to note clinically, as this finding suggests that those with PD are more likely to endorse noticing generally. PD did not appear to predict individual items of the Observe facet, thereby providing no evidence of differential item functioning between internal or external items.\u003c/p\u003e\n\u003cp\u003eThose with higher AS were found to observe more frequently. Differential item functioning did exist in four items as a function of AS: two items related to internal focus (items 1 and 6) and two items related to external focus (items 26 and 31). This finding suggests that AS may influence how people respond to internal and external Observe items, regardless of diagnosis. Individuals with increased AS were found to rate two items related to internal focus higher (items 1 and 6), and two items related to external focus lower (items 26 and item 31). This fits with prior findings that individuals who tend to scan their body for physical sensations likely spend more time worrying about and focusing on internal sensations [5]. Perhaps due to this type of focus, those with increased AS are also less likely to focus on the world around them. Although the proportion of variance accounted for in the differential item functioning by the AS covariate was relatively modest, the significant relationships tell us an important story. Perhaps a reason the Observe facet of the FFMQ continues to show mixed results in the literature is due to the fact that the factor does not differentiate between observing internal or external items. In this way, the scale gives a mixed or inaccurate picture of one\u0026rsquo;s observing tendencies. A solution for this would be to develop a scale that breaks down Observe into observing internal stimuli and observing external items.\u003c/p\u003e\n\u003cp\u003eWhen subscales of the ASI-3 were investigated, cognitive concerns showed the same pattern of results as total AS. The physical subscale did not appear to predict any of the items, which was contrary to our expectations. This was particularly interesting because none of the items in the Observe facet that showed differences were cognitively related. Therefore, it appears there may be something about cognitive AS that is driving the influence of AS on internal vs. external indicators of observing. One explanation for why this may be is that individuals who focus on their thoughts may not be as aware when they focus on external stimuli. Or, perhaps someone endorsing item 7 of the ASI-3, \u0026ldquo;When my chest feels tight, I get scared that I won\u0026apos;t be able to breathe properly\u0026rdquo; observes internal sensations more. The internal Observe items appear to be more tactile in nature, whereas the physical ASI-3 items are more visceral.\u0026nbsp;AS cognitive concerns often reflect catastrophic thinking, which may drive individuals to continue monitoring their interoceptive sensations rather than exteroceptive. As anxiety often impacts what individuals observe, individuals who are more likely to catastrophize may interpret somatic symptoms as dangerous, leading to a vicious cycle of increased worry and narrower focus on physiological sensation [30].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations and Future Research\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA limitation of this study is that diagnoses were self-reported. Additionally, the results do not shed light on the directionality of observing internal stimuli and its impact on AS. As this study was cross-sectional in nature, future research may benefit from investigating this relationship further, perhaps through longitudinal research. Another limitation is that a priori power analysis was not run on this sample. However, given conventional standards for power analyses, the sample size exceeds most heuristic recommendations [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA unique strength of this study is that it includes a fully Japanese sample. It is important to understand how cultural issues may or may not intersect with universal constructs, and we hope that our study can help be part of bridging this gap. As with all research conducted in one area of the world, it is important to keep in mind that\u0026nbsp;cultural factors may shape how respondents answered questions, and thus it may be less feasible to extrapolate findings from a Japanese sample to all Western samples. Additionally, the ASI-3 was translated into Japanese for the first time, so it may be helpful for future studies to test its reliability and validity in Japanese. Future research would benefit from continuing to explore how culture or other identity factors play a role.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf note, several FFMQ subscales were negatively correlated with one another, which is consistent with previous findings obtained by Japanese samples [23]. In Japanese samples, those who score lower on the Observe facet tend to score higher on the Non-judge or Act with Awareness facets because observing appears to tap into a sensitivity to sensations and non-judging and acting with awareness measure the ability to take attention away from cognitions or sensations. Negative correlations were found between the Non-react and Non-judge facets (r = -0.13) and the Non-react and Act with Awareness facet results (r = -0.10) are difficult to interpret as these correlations are very weak (see Table 4).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study utilized a transdiagnostic approach to understand\u0026nbsp;differences in core components of anxiety related to observation. It appears AS is important to study across clinical samples, to understand why those who have more internal focus toward bodily sensations perceive certain types of mindfulness as threatening. Future studies may benefit from a more thorough understanding and conceptualization of AS, across clinical and non-clinical samples, as anxious individuals may observe more internally or be more sensitive to their anxiety.\u0026nbsp;As not all facets of mindfulness have been found to neatly correlate with reduced anxiety [9], it is important to continue to conduct transdiagnostic research to identify which types of mindfulness will be most beneficial for each person. Future research may benefit from expanding this work to a non-clinical sample, as observation is an important component of mindfulness that extends to all types of individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the findings from this study, it\u0026nbsp;may be beneficial to develop a scale that splits the Observe facet into internal and external observation, including cognitive and emotional observation.\u0026nbsp;As the field is beginning to shift away from a nomothetic approach of treating specific diagnoses and towards an idiographic approach of better understanding symptoms at the individual level, we hope to continue to understand the function of types of observation which may lead us to the ability to implement more accurate personalized treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review board at the National Center of Neurology and Psychiatry approved the ethical and scientific validity of this study (approval number: A2013-022). All participants signed informed consent and completed questionnaires online.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Grant-in-Aid for Research Activity start-up (24830127) awarded to MI from the Japan Society for the Promotion of Science, National Center of Neurology and\u0026nbsp;Psychiatry Intramural Research Grant (30-2) for Neurological and Psychiatric\u0026nbsp;Disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eD. Moskow Diamond initiated the conception of this project and all authors substantially contributed to its design. Material preparation and data collection were performed by M. Ito. The first draft of the manuscript was mostly written by D. Moskow Diamond, and J. Curtiss ran analyses and created the figure included in this paper. All authors commented on previous versions of the manuscript and read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKabat-Zinn J. Wherever you go there you are: Mindfulness meditation in everyday life. New\u003c/li\u003e\n\u003cli\u003eYork, NY: Hyperion; 1994.\u003c/li\u003e\n\u003cli\u003eRodrigues, MF, Nardi, AE., Levitan, M. Mindfulness in mood and anxiety disorders: a review of the literature. TrendsPsychiatry Psychother. 2017;39(3), 207-215. https://doi.org/10.1590/2237-6089-2016-0051.\u003c/li\u003e\n\u003cli\u003eBrown KW, Ryan RM, Creswell JD. Mindfulness: theoretical foundations and evidence for its salutary effects. Psychol Inq. 2007;18(4), 211\u0026ndash;237. https://doi.org/10.1080/10478400701598298.\u003c/li\u003e\n\u003cli\u003eHofmann, SG, Sawyer, AT, Witt, AA, Oh, D. The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. J Consult Psychol. 2010;78, 169\u0026ndash;183. https://doi.org/10.1037/a0018555.\u003c/li\u003e\n\u003cli\u003eKraemer, KM., McLeish, AC, Johnson, AL. Associations between mindfulness and panic symptoms among young adults with asthma. Psychol Health Med. 2015;20(3), 322-331. https://doi.org/10.1080/13548506.2014.936888.\u003c/li\u003e\n\u003cli\u003eMcNally, RJ. Anxiety sensitivity and panic disorder. Biol Psychiatry. 2002;52(10), 938\u0026ndash;946. https://doi.org/10.1016/s0006-3223(02)01475-0.\u003c/li\u003e\n\u003cli\u003eXie, Q, Guan, Y, Hofmann, SG, Jiang, T and Liu, X. The potential mediating role of anxiety sensitivity in the impact of mindfulness training on anxiety and depression severity and impairment: A randomized controlled trial. Scandinavian Journal of Psychology. 2022;64, 21-29. https://doi.org/10.1111/sjop.12860.\u003c/li\u003e\n\u003cli\u003eKhalili, MD, Makvand Hosseini, S, Sabahi, P. (2023). Effects of Mindfulness-Based\u003c/li\u003e\n\u003cli\u003eCognitive Therapy on Anxiety Sensitivity in Patients with Multiple Sclerosis. Jundishapur Journal of Chronic Disease Care. 2023;12(1), e132672. https://doi.org/10.5812/jjcdc-132672.\u003c/li\u003e\n\u003cli\u003eBaer, RA, Smith, GT, Hopkins, J, Krietemeyer, J, Toney, L. Using self-report assessment methods to explore facets of mindfulness. 2006;13(1), 27-45. https://doi.org/10.1177/1073191105283504.\u003c/li\u003e\n\u003cli\u003eBarcaccia, B, Baiocco, R, Pozza, A, Pallini, S, Mancini, F, Salvati, M. The more you judge the worse you feel. A judgmental attitude towards one\u0026rsquo;s inner experience predicts depression and anxiety. Pers Individ Differ. 2019;138(1), 33\u0026ndash;39. https://doi.org/10.1016/j.paid.2018.09.012.\u003c/li\u003e\n\u003cli\u003ePetrocchi, N, Ottaviani, C. Mindfulness facets distinctively predict depressive symptoms after two years: the mediating role of rumination. Pers Individ Differ. 2016;93, 92\u0026ndash;96. https://doi.org/10.1016/j.paid.2015.08.017.\u003c/li\u003e\n\u003cli\u003eCurtiss, J, Klemanski, DH. Factor analysis of the five facet mindfulness questionnaire in a heterogeneous clinical sample. J Psychopathol Behav. 2014;36(4), 683\u0026ndash;694. https://doi.org/10.1007/s10862-014-9429-y.\u003c/li\u003e\n\u003cli\u003eBrown, DB, Bravo, AJ, Roos, CR, Pearson, MR. Five facets of mindfulness and psychological health: evaluating a psychological model of the mechanisms of mindfulness. 2015;6(5), 1021\u0026ndash;1032. https://doi.org/10.1007/s12671-014-0349-4.\u003c/li\u003e\n\u003cli\u003eLecuona, O, Garc\u0026iacute;a-Rubio, C, de Rivas, S, Moreno-Jim\u0026eacute;nez, JE, Rodr\u0026iacute;guez-Carvajal, R. Unraveling heterogeneities in mindfulness profiles: A review and latent profile analysis of the Five Facet Mindfulness Questionnaire Short-Form (FFMQ-SF) in the Spanish population. Mindfulness. 2022;13(8), 2031\u0026ndash;2046. https://doi.org/10.1007/s12671-022-01939-y.\u003c/li\u003e\n\u003cli\u003eDiehl, JM, Rodriguez-Seijas, C, Thompson, JS, Dalrymple, K, Chelminski, I, Zimmerman, M. Exploring the optimal factor structure of the Five Facet Mindfulness Questionnaire: Associations between mindfulness facets and dimensions of psychopathology. J Pers Assess. 2021;1\u0026ndash;11. https://doi.org/10.1080/00223891.2021.1998080.\u003c/li\u003e\n\u003cli\u003eHawley, LL, Rogojanski, J, Vorstenbosch, V, Quilty, LC, Laposa, JM, Rector, NA. The structure, correlates, and treatment related changes of mindfulness facets across the anxiety disorders and obsessive compulsive disorder. J Anxiety Disord. 2017;49\u003cem\u003e,\u003c/em\u003e 65-75. https://doi.org/10.1016/j.janxdis.2017.03.003.\u003c/li\u003e\n\u003cli\u003eWaszczuk, MA, Zavos, HM, Antonova, E, Haworth, CM, Plomin, R, Eley, TC. A multivariate twin study of trait mindfulness, depressive symptoms, and anxiety sensitivity. Depress Anxiety. 2015;32(4), 254-261. https://doi.org/1002/da.22326.\u003c/li\u003e\n\u003cli\u003eBaer, RA., Smith, GT, Allen, KB. Assessment of mindfulness by self report: the Kentucky Inventory of Mindfulness Skills. Assessment. 2004;11, 191\u0026ndash;206. https://doi.org/10.1177/1073191104268029.\u003c/li\u003e\n\u003cli\u003eBaer, RA, Smith, GT, Lykins, E, Button, D, Krietemeyer, J, Sauer, S, Walsh, E, Duggan, D, Williams, MG. Construct validity of the Five Facet Mindfulness Questionnaire in meditating and nonmeditating samples. 2008;15(3), 329-342. https://doi.org/10.1177/1073191107313003.\u003c/li\u003e\n\u003cli\u003eGoldberg, SB., Wielgosz, J, Dahl, C, Schuyler, B, MacCoon, DS, et al. Does the Five Facet Mindfulness Questionnaire measure what we think it does? Construct validity evidence from an active controlled randomized clinical trial. Psychol Assessment. 2016;28(8), 1009-1014. https://doi.org/10.1037/pas0000233.\u003c/li\u003e\n\u003cli\u003eIto, M, Oe, Y, Kato, N, Nakajima, S, Fujisato, H, Miyamae, M, Kanie, A, Horikoshi, M, Norman, SB. Validity and clinical interpretability of Overall Anxiety Severity and Impairment Scale (OASIS). J Affect Disord. 2015;170, 217\u0026ndash;224. https://doi.org/10.1016/j.jad.2014.08.045.\u003c/li\u003e\n\u003cli\u003eCurtiss, J, Klemanski, DH, Andrews, L, Ito, M, Hofmann, S. G. The conditional process model of mindfulness and emotion regulation: An empirical test. J Affect Disord. 2017; 212, 93-100. https://doi.org/10.1016/j.jad.2017.01.027.\u003c/li\u003e\n\u003cli\u003eSugiura, Y, Sato, A, Ito, Y, Murakami, H. Development and validation of the Japanese version of the Five Facet Mindfulness Questionnaire. Mindfulness. 2012;3(2), 85-94. https://doi.org/10.1007/s12671-011-0082-1.\u003c/li\u003e\n\u003cli\u003eTaylor, S, Zvolensky, MJ, Cox, BJ, Deacon, B, Heimberg, RG, Ledley, D. R. Robust dimensions of anxiety sensitivity: development and initial validation of the Anxiety Sensitivity Index-3. Psychol Assessment. 2007;19(2), 176\u0026ndash;188. https://doi.org/10.1037/1040-3590.19.2.176.\u003c/li\u003e\n\u003cli\u003ePeterson, RA, Reiss, S. Anxiety Sensitivity Index Revised Manual\u003cem\u003e.\u003c/em\u003e Worthington, OH: IDS Publishing; 1992.\u003c/li\u003e\n\u003cli\u003eJardin, C, Paulus, DJ, Garey, L, Kauffman, B, Bakhshaie, J, Manning, K, Mayorga, NA, Zvolensky, MJ. Towards a greater understanding of anxiety sensitivity across groups: The construct validity of the Anxiety Sensitivity Index-3. Psychiatry Res. 2018;268\u003cem\u003e,\u003c/em\u003e 72\u0026ndash;81. https://doi.org/10.1016/j.psychres.2018.07.007.\u003c/li\u003e\n\u003cli\u003eBrown, TA. Confirmatory Factor Analysis for Applied Research. (2\u003csup\u003end\u003c/sup\u003e) New York, NY: Guilford Press. 2015.\u003c/li\u003e\n\u003cli\u003eBrowne, MW, Cudeck, R. Alternative Ways of Assessing Model Fit. In: Bollen K, Long JS, editors. Testing Structural Equation Models. Newbury Park, CA: Sage Publications; 1993. p. 136-162.\u003c/li\u003e\n\u003cli\u003eRosseel, Y. lavaan: An R package for structural equation modeling. J Stat Softw. 2012;48(2), 1-36. https://doi.org/10.18637/jss.v048.i02.\u003c/li\u003e\n\u003cli\u003eGreeson, J, Brantley, J. Mindfulness and anxiety disorders: Developing a wise\u003c/li\u003e\n\u003cli\u003erelationship with the inner experience of fear. In Didonna F, editor. Clinical handbook of mindfulness. New York: Springer; 2009. p. 171-188.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"panic, anxiety sensitivity, mindfulness, observe, internal, external","lastPublishedDoi":"10.21203/rs.3.rs-4125571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4125571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Many individuals who engage in mindfulness techniques experience decreased anxiety. Yet some who engage in mindfulness, particularly those with panic disorder (PD) or elevated anxiety sensitivity (AS) note heightened anxiety when focusing on particular sensations. The Five Facet Mindfulness Questionnaire (FFMQ) is the most widely utilized mindfulness questionnaire- however, the Observe facet has shown variability in the literature. This study therefore aimed to determine whether specific aspects of the Observe facet of the FFMQ differ in individuals with PD or elevated anxiety sensitivity (AS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We examined a clinical sample of 1521 Japanese individuals who completed online self-report questionnaires, including the Five Facet Mindfulness Questionnaire (FFMQ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Several multiple indicator multiple cause (MIMIC) models investigated differential item functioning among the items in the Observe facet of the FFMQ, based on PD and/or AS. This process was repeated to examine the relationship between the Anxiety Sensitivity Index-3 (ASI-3) subscales and particular items of the Observe facet. Increased AS correlated with more observing generally, and increased AS was associated with greater scores on observing internal items and lower scores on observing external items. When PD and AS were analyzed simultaneously, only AS remained significant. The cognitive subscale showed the same pattern of results as the total ASI-3 subscale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: The findings of this study reveal that AS may modulate the mindfulness experience. The study sheds light on the importance of understanding where an individual observes in order to tailor mindfulness treatments for individuals.\u003c/p\u003e","manuscriptTitle":" The influence of anxiety sensitivity on observing in mindfulness among clinical populations with anxiety and/or depressive disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:25:10","doi":"10.21203/rs.3.rs-4125571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-11T19:15:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T16:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-10-11T19:35:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T21:51:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30764629920237043950326340237821709280","date":"2024-09-19T21:14:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-12T10:18:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-21T07:25:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-10T12:42:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-10T12:37:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-03-18T19:31:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"783c207e-6f2b-4e16-a774-521b9f4fbc97","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T16:00:42+00:00","versionOfRecord":{"articleIdentity":"rs-4125571","link":"https://doi.org/10.1186/s12888-025-06488-x","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-04-01 15:57:23","publishedOnDateReadable":"April 1st, 2025"},"versionCreatedAt":"2024-04-19 18:25:10","video":"","vorDoi":"10.1186/s12888-025-06488-x","vorDoiUrl":"https://doi.org/10.1186/s12888-025-06488-x","workflowStages":[]},"version":"v1","identity":"rs-4125571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4125571","identity":"rs-4125571","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}