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Metacognitions, anxiety, and distress related to motor fluctuations in Parkinson's disease
Journal of Psychosomatic Research 78 (2015) 143–148
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Journal of Psychosomatic Research
Metacognitions, anxiety, and distress related to motor fluctuations in Parkinson's disease Richard G. Brown a, Bruce A. Fernie a,b,⁎ a b
King's College London, Institute of Psychiatry, Department of Psychology, London, UK CASCAID, South London & Maudsley NHS Foundation Trust, London, UK
a r t i c l e
i n f o
Article history: Received 13 August 2014 Received in revised form 22 September 2014 Accepted 23 September 2014 Keywords: Parkinson's disease Motor-fluctuations Distress Metacognition Off-periods
a b s t r a c t Objective: This study tested the relationship between metacognitive factors, intolerance of uncertainty, anxiety, and the predictability of, and distress associated with, acute fluctuations in symptoms in idiopathic Parkinson's disease (PD), when controlling for disease parameters. Method: 106 adults with idiopathic PD (30 females; Mage = 65.3; 90% white) participated in this study, with 93 of them reported experiencing off-periods. A cross-sectional design was employed that utilised: the Hospital Depression and Anxiety Scale, Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale, the Addenbrooke's Cognitive Examination — Revised, the Intolerance of Uncertainty Scale, and the Metacognitions Questionnaire 30. Correlation analyses, hierarchical regression analysis, and ordinal regression analysis were used to test the experimental hypotheses. Results: Anxiety was not significantly associated with motor symptom severity or cognitive functioning, while metacognitive factors were significantly related to anxiety when controlling for motor experiences of daily living and intolerance of uncertainty, R2 = 0.56, F(1,82) = 15.04, p b 0.001 (adjusted R2 = 0.53). For participants with motor fluctuations, no association was found between predictability of, and distress associated with, off-periods. Metacognitions concerning uncontrollability and danger were significantly related to off-period distress when controlling for motor experiences of daily living, intolerance of uncertainty, and other metacognitive factors, χ2(1) = 20.52, p = 0.001. Conclusion: Metacognitive factors play a role in anxiety and off-period distress in PD and this is discussed in terms of the Self-Regulatory Executive Function model. Interventions from metacognitive therapy are potential means to ameliorate off-period distress and anxiety in PD. © 2014 Elsevier Inc. All rights reserved.
Introduction Parkinson's disease (PD) is a progressive neurological disorder with an estimated population prevalence of approximately 300 per 100,000, increasing to 1%, over the age of 60 years and up to 4% in the oldest age groups [1]. PD is typically considered a disorder of movement with symptoms of slowed and reduced amplitude voluntary action, tremor, and rigidity affecting limb and eye movement, in addition impaired control of balance, swallowing, and speech. A range of disabling non-motor symptoms are also commonly experienced including depression, anxiety, psychosis, cognitive impairment, autonomic dysfunction, fatigue, and pain. Motor and non-motor disability increase with disease progression despite symptomatic treatment using levodopa, dopamine agonists, or other drugs that modify brain dopamine levels. With disease progression and increasing duration of treatment, ⁎ Corresponding author at: Department of Psychology, Institute of Psychiatry, King's College London, Henry Wellcome Building, De Crespigny Park, London SE5 8AF, UK. Tel.: +44 7779 300 427; fax: +44 20 7848 5310. E-mail address: [email protected] (B.A. Fernie).
http://dx.doi.org/10.1016/j.jpsychores.2014.09.021 0022-3999/© 2014 Elsevier Inc. All rights reserved.
effectiveness gradually declines. In those treated with levodopa, fluctuations in symptom severity over the course of the day commonly develop. These include ‘wearing off’ (a relatively predictable re-emergence of symptoms towards the end of a medication dose); ‘on/off fluctuations’ (unpredictable and sudden recurrence of Parkinsonian symptoms); ‘delayed on’ (unpredictably increased time between ingestion of a dose and motor benefit), and ‘dose failure’ (unpredictable failure of a dose to provide usual benefit) [2]. In addition to worsening of motor symptoms during ‘off-periods’, the emergence or exacerbation of distressing non-motor symptoms is also reported by many individuals including pain, fatigue, drenching sweats, depression, and anxiety. Currently, fluctuations and associated symptoms are managed by alterations in drug regimen but this becomes increasingly difficult with disease progression. This offers potential to develop adjunctive psychological approaches to manage individual symptoms (e.g. depressed mood, pain, or fatigue) or reduce the level of associated subjective distress. There is evidence that traditional CBT treatment approaches can be helpful in reducing depressive and anxiety symptoms in PD [3,4] although not specifically in the context of motor fluctuations. One challenge of CBT is the limits on reality-testing of thoughts and beliefs about PD (e.g.
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‘there is no cure for this disease’ or ‘I have no control over my symptoms’) as these may represent accurate appraisals of the disease, and may be challenging to test during ‘in the moment’ distress associated with offperiods. Key to developing a more targeted therapy is an understanding of the cognitive and attentional processes that contribute to the emotional difficulties experienced by some with PD including those associated with off-period distress. We propose that metacognitive therapy (MCT) [5], an effective treatment for depression and anxiety [6] may be particularly well-suited to the management of PD distress. MCT is based on the Self-Regulatory Executive Function model (S-REF) [7] and posits that psychological distress results from perseverative cognitive processes (e.g. rumination and worry) and attentional strategies (e.g. symptom focussing and hypervigilance). These are proposed to be governed by both explicit and implicit metacognitions and form a Cognitive Attentional Syndrome (CAS). Preliminary research has implicated metacognitions in distress in a small sample of people with PD [8]. S-REF proposes that specific CAS configurations are activated in response to inner events such as cognitions (including memories), emotions, and physical states. If an individual with PD experiences symptoms associated with an off-period and endorses positive metabeliefs about worry (e.g. “worry helps me to solve problems”), the response to this off-related symptom will be worry. An individual who holds negative beliefs about worry, such as ‘my worry is uncontrollable’, may be less inclined to make attempts to halt this cognitive process and instead to ‘worry about worry’, increasing distress further and helping to drive more worry. In both instances a stop-signal for this process is not received (i.e. the goal of solving a problem), resulting in worry perseveration and distress. The modifications of the metacognitions that are hypothesized to fuel maladaptive CAS configurations are a key target of MCT interventions. In this study, we test the following hypothesis: that metacognitive factors explain a significant proportion of variance in anxiety and offperiod distress after controlling for disease characteristics, cognitive function, off-period predictability, and trait intolerance of uncertainty. Methods Participants and procedure Participants with a clinical diagnosis of PD were recruited from a cohort of patients involved in a separate longitudinal study (n = 512), PROMS-PD [9]. We approached individuals that had expressed a willingness to engage with additional research, who were judged at their last assessment to have capacity to consent, and had sufficient sensory and
motor function to complete a booklet of questionnaires. Those who had been seen for assessment for the main study in the past three months or due to receive an assessment in the coming three months were not approached to prevent overburdening them with requests. Of 178 eligible individuals, 106 returned completed questionnaires (59.6%). Table 1 provides the participant characteristics. Ethics approval for the study was obtained from the local research ethics committees. All of those returning return questions gave informed consent to participate in the study. Measures: self-report questionnaires Depression, anxiety and distress Depression and anxiety were assessed using the Hospital Anxiety and Depression Scale (HADS) [10]. This 14-item self-report measure provides a total score as well as separate depression (HADS-D) and anxiety (HADS-A) scores, with higher scores representing more severe symptoms. The HADS was originally designed for use in patients with physical health conditions and has been validated for use in patients with PD [11]. A cut-off of eight out of 21 on the depression and anxiety subscales indicates significant symptomatology. A single-item measure was used to measure distress associated with off-periods. Participants reporting motor fluctuations (see below) indicated how distressing they found off-periods on a five-point Likert-type scale: [1] “I have no distress (or I do not experience off-periods)”; [2] “I can feel a little upset during OFF times, but it does not trouble me much”; [3] “I feel mildly distressed during OFF times”; [4] “I feel moderately distressed during OFF times”; and [5] “I feel extremely distressed during OFF times”. Cognitive and metacognitive constructs The English version of the 27-item Intolerance of Uncertainty Scale (IUS) [12] was used to assess trait intolerance of uncertainty. It has been shown to be associated with worry and anxiety and possesses good psychometric properties [12], but has not been reported previously in patients with PD. The Metacognitions Questionnaire 30 (MCQ) [13] is a 30-item self-report measure that assesses five-factors pertaining to metacognition: [1] positive beliefs about worry (MCQ 1; e.g. “Worrying helps me cope”); [2] negative beliefs about thoughts concerning uncontrollability and danger (MCQ 2; e.g. “When I start worrying I cannot stop”); [3] cognitive confidence (MCQ 3; e.g. “My memory can mislead me at times”); [4] beliefs about the need to
Table 1 Participant characteristics Characteristic
Fluctuators
Non-fluctuators
Combined
n Gender Mean age in years (SD; range) Mean MDS-UPDRS 2 (SD; range) Mean MDS-UPDRS 3 (SD; range) Mean ACE-R: total (SD; range) Mean ACE-R: attention and orientation (SD; range) Mean ACE-R: memory (SD; range) Mean ACE-R: fluency (SD; range) Mean ACE-R: language (SD; range) Mean ACE-R: visiospatial (SD; range) Mean MDS-UPDRS — 4: item 4 (SD; range) Mean distress during off-periods (SD; range) Mean HADS A (SD; range) Mean IUS (SD; range) MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
93 63 male; 30 female 65.3 (9.4; 43–85) 17.36 (6.56; 5–36) 34.38 (12.59; 13–78) 88.59 (8.34; 51–100) 17.53 (0.88; 14–18) 21.86 (3.52; 11–26) 10.38 (2.56; 3–14) 24.87 (1.25; 20–26) 15.18 (1.20; 11–16) 2.87 (1.43; 0–4) 1.83 (1.13; 0–4) 9.38 (2.50; 5–16) 52.74 (18.52; 27–113) 9.18 (3.08; 6–20) 10.35 (3.84; 6–20) 13.10 (4.34; 6–24) 10.90 (3.33; 6–24) 12.49 (3.34; 6–24)
13 10 male; 3 female 68.1 (8.3; 50–80) 13.00 (5.64; 6–22) 30.55 (10.81; 8–44) 90.64 (8.39; 70–100) 17.54 (0.78; 16–18) 22.92 (2.90; 16–26) 10.46 (2.07; 7–14) 24.69 (1.32; 22–26) 15.15 (1.57; 11–16) N/A N/A 7.46 (1.39; 5–10) 45.25 (11.01; 31–66) 9.31 (2.63; 6–15) 7.69 (1.97; 6–12) 12.08 (4.01; 6–20) 10.08 (1.93; 8–14) 10.69 (2.87; 6–17)
106 73 male; 33 female 65.6 (9.3; 43–85) 16.85 (6.58; 5–36) 33.94 (12.40; 8–78) 88.83 (8.33; 51–100) 17.50 (0.96; 13–18) 21.93 (3.44; 11–26) 10.37 (2.47; 3–14) 24.83 (1.24; 20–26) 15.15 (1.25; 11–16) N/A N/A 9.17 (2.48; 5–16) 51.86 (17.92; 57–113) 9.19 (3.02; 6–20) 10.02 (3.75; 6–20) 12.97 (4.30; 6–24) 10.80 (3.20; 6–24) 12.27 (3.33; 6–24)
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; ACE-R = Addenbrooke's Cognitive Examination — Revised; HADS A = Hospital Anxiety and Depression Scale (anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30.
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control thoughts (MCQ 4; e.g. “Not being able to control my thoughts is a sign of weakness”); and [5] cognitive self-consciousness (MCQ 5; e.g. “I pay close attention to the way my mind works”). Respondents indicate agreement on a four-point Likert-type scale with higher scores indicating higher levels of unhelpful metacognitions. The MCQ-30 possesses good psychometric properties [13,14] and has been reported in one previous study on people with PD [8]. Measures: disease parameters Physical symptoms and cognitive functioning Self-reported motor fluctuations over the preceding week were assessed using items from Part IVB of the Movement Disorder Society revision of the Unified Parkinson's Disease rating Scale (MDS-UPDRS) [15]. These provided operational definitions of motor fluctuations and off-periods and five or six-point ordinal scales to assess the presence/ absence of motor fluctuations, the amount of time spent per day in the off-period, and the predictability of the off-periods. The latter was measured on a four-point scale from ‘2 — Off-periods predictable all or almost all of the time (more than 75% of the time)’ to ‘5 — Off-periods are rarely predictable (less than 25% of the time)’. A score of 1 indicated that off-periods were not present. As this was a postal survey we did not have the opportunity to conduct face-to-face assessments of primary Parkinsonian motor symptoms or cognitive function. Instead, we estimated function from the most recent measurements taken as part of the PROMS-PD study (3–9 months prior to questionnaire completion). Information used includes the overall motor symptom severity (MDS-UPDRS 3), motor experiences of daily living (MDS-UPDRS 2) and complexity of motor symptoms (MDS-UPDRS 4) [16] with higher scores of each indicating more severe symptoms. Cognitive function was assessed using the Addenbrooke's Cognitive Examination — Revised (ACE-R) [17]. This is a brief cognitive examination assessing attention/orientation, memory, verbal fluency, language, and visuospatial function as subscores with a maximum total score of 100. Statistical analysis Prior to analysis questionnaires were examined for missing data and limited imputation used to avoid loss of power. Where a scale (or subscale) had six or more items, missing data were replaced with
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the scale/subscale median provided no more than two items were missing. For scales/subscales with fewer than six items, only a single missing item was permitted for imputation. 2.20% of HADS responses were missing, as were 2.32% of MCQ responses, 2.41% of IUS responses, 1.89% of responses to the question about the predictability of off-periods (MDS-UPDRS 4), and 0.94% of responses to the questions regarding distress during off-periods (MDS-UPDRS 4). For the whole sample, Spearman rho correlations were calculated to assess the bivariate relationships between anxiety and study variables to identify predictors for multivariate analysis. A hierarchical regression analysis was conducted with anxiety (HADS A) as the outcome variable and experimental variables that were found to be significantly associated with HADS A in the correlation analysis as predictor variables. The next analyses were restricted to those participants who reported experiencing off-periods during the preceding week. Another Spearman rho correlation matrix was generated to identify study variables that were significantly associated with distress during off-periods; these variables were entered as predictor variables in an ordinal regression analysis with off-period distress as the outcome variable. Results Sample and subsample characteristics Table 1 shows the demographic and clinical characteristics of the total sample (n = 106) and the subsamples of participants with (n = 93) and without motor fluctuations (n = 13). Table 2 shows the corresponding characteristics on the measures of distress, anxiety, intolerance of uncertainty, and metacognitions. The ethnicity of the sample was almost exclusively white (96.2%). Disease stage at last face-to-face assessment indicated mild to moderate progression in the majority of the sample with only 11.5% having the most severe disease (Stage 4 or 5). Whole sample Table 2 presents a Spearman's rho coefficients between study variables. The analysis revealed that anxiety (HADS-A) was significantly but very weakly associated with the motor experiences of daily living (MDS-UPDRS 2; r = 0.29), moderately strong with intolerance of uncertainty (IUS; r = 0.55), weakly with positive beliefs about worry (MCQ 1; r = 0.38), moderately strong with beliefs concerning uncontrollability and danger (MCQ 2; r = 0.56), very weakly with metacognitions concerning cognitive confidence (MCQ 3; r = 0.27), weakly with the need to control thoughts (MCQ 4; r = 0.36) and cognitive self-consciousness (MCQ 5; r = 0.35). No significant relationship was found between measured cognitive functioning (ACE-R), either as a total score or subscores, and metacognitions concerning (lack of) cognitive confidence (MCQ 3). The overall severity of Parkinsonian motor symptoms (MDS-UPDRS 3) was very weakly associated with memory (ACE-R; r = 0.23) and activities of daily living (MDS-UPDRS 2) were weakly
Table 2 Spearman's Rho correlation matrix of study variables (n = 106) Measure 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
MDS-UPDRS 2 MDS-UPDRS 3 ACE-R (total) ACE-R (attention and orientation) ACE-R (memory) ACE-R (fluency) ACE-R (language) ACE-R (visuospatial) Predictability of off-periods Distress during off-periods HADS A IUS MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
1 2
3
4
5
6
7
8
9
.51⁎⁎ −.19 −.12 −.23⁎ −.07 −.02 −.16 −.35⁎⁎ −.19 −.20 −.32⁎⁎ −.20⁎ −.32⁎⁎ .41⁎⁎ .77⁎⁎ .76⁎⁎ .57⁎⁎ .48⁎⁎ .15 .28⁎⁎ .23⁎ .31⁎⁎ .32⁎⁎
.22⁎ .48⁎⁎
10
11
.12 .29⁎⁎ .29⁎⁎ .03 .11 .09 .00 −.04 −.14 .07 −.18 −.17
.19⁎ −.05 −.01 .35⁎⁎ .07 .06 .28⁎⁎ −.05 −.05 −.01 −.15
−.08 −.06 .09 −.07
.26⁎⁎ −.03 .53⁎⁎
12
14
15
.35⁎⁎ .03 .08 −.09 .04 .02 .12 .00
.24⁎ −.02 .06 .03
.28⁎⁎ .10 −.04 .12
−.03 .09 −.04 .00 ⁎⁎ .29 .10 .12 −.10
.05 .01 .25⁎ .08
−.05
13
−.23⁎
−.02
16
17
.28⁎⁎ .17 .08 −.02 .04 .17 .00 .14
−.05 −.12 .19 .13
.02 .04 .19 −.05
.17 .09 .21⁎ .08
−.09
−.02
.07
.38⁎⁎
.16
.45⁎⁎
.11
.23⁎
.28⁎⁎
.55⁎⁎
.38⁎⁎ .39⁎⁎
.56⁎⁎ .71⁎⁎ .29⁎⁎
.27⁎⁎ .62⁎⁎ .33⁎⁎ .57⁎⁎
.36⁎⁎ .52⁎⁎ .45⁎⁎ .52⁎⁎ .44⁎⁎
.35⁎⁎ .52⁎⁎ .56⁎⁎ .53⁎⁎ .54⁎⁎ .56⁎⁎
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; ACE-R = Addenbrooke's Cognitive Examination — Revised; HADS A = Hospital Anxiety and Depression Scale (Anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30. ⁎ p b 0.05. ⁎⁎ p b 0.01.
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associated with the total score of the ACE-R, fluency, language, and visuospatial subscores of the ACE-R, but not to any of the other psychological variables. There was no evidence of multicollinearity (r N 0.9) between the predictor variables. Additionally, the ranges of the Tolerance Index (0.38–0.83) and the Variance Inflation Factor (1.20–2.65) for all predictor variables were inspected, revealing no further support for the presence of multicollinearity. Histograms and normality plots suggested that the residuals were normally distributed. Plots of the regression-standardized residuals against the regression standardized predicted values suggested that the assumptions of linearity and homoscedasticity were met. Additionally, the Durbin–Watson test suggested that the assumption of independent errors is tenable. Table 3 presents a three-step hierarchical regression analysis with anxiety (HADS-A) as the outcome variable. The model used motor experiences of daily living (MDS-UPDRS 2) on the first step and added intolerance of uncertainty (IUS) on the second. The third step added all five metacognitive factors as predictor variables. In the final step of the regression model, MDS-UPDRS 2, IUS, MCQ 1, MCQ 2, and MCQ 3 were significant predictors of anxiety. The predictor variables in the final step of the equation explained 56% of the variance in anxiety scores (adjusted R2 = 0.53). Subgroup analysis — associations and predictors of off-period distress Off-period distress was very weakly related to the motor experiences of daily living (MDS-UPDRS 2; r = 0.22, p = 0.04) and weakly to intolerance of uncertainty (IUS; r = 0.36, p = b 0.001), as well as metacognitions concerning uncontrollability and danger (MCQ 2; r = 0.38, p b 0.001), very weakly to the need to control thoughts (MCQ 4; r = 0.21, p = 0.04), and cognitive consciousness (MCQ 5; r = 0.22, p = 0.04). Table 4 presents an ordinal regression analysis with off-period distress as the outcome variable. Motor experiences of daily living (MDS-UPDRS 2) and intolerance of uncertainty (IUS), as well as metacognitions concerning uncontrollability and danger (MCQ 2), the need to control thoughts (MCQ 4), and cognitive consciousness (MCQ 5), were entered into the model as covariate predictor variables. This model revealed that only metacognitions concerning uncontrollability and danger were a significant predictor of off-period distress. The model explained 24% of the variation of off-period distress, as indicated by Nagelkerke pseudo R2. Tests of parallel lines led us to accept the assumption of proportional odds.
Discussion This study found that activity of daily living, intolerance of uncertainty, positive beliefs about worry, metacognitions concerning uncontrollability, and (lack of) cognitive confidence predicted anxiety in PD, while metacognitions concerning uncontrollability and danger alone predicted off-period distress when controlling for disease parameters, intolerance of uncertainty, and predictability of off-periods. Furthermore, the results suggest that off-period distress is not associated with the unpredictability of the off-periods. These findings provide further
Table 3 Hierarchical regression model with HADS A as the outcome variable Predictor
R2
Adjusted R2
B
SE
β
95% confidence interval LL
Step 1 MDS-UPDRS 2
Step 2 MDS-UPDRS 2 IUS
Step 3 MDS-UPDRS 2 IUS MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
.10⁎⁎
.40⁎⁎
0.56⁎⁎
.09⁎⁎
.39⁎⁎
0.53⁎⁎
UL
0.12
0.67
.32⁎⁎
0.04
0.19
0.44 0.08
0.03 0.01
0.12⁎ .31⁎⁎
−0.02 0.05
0.11 0.10
0.07 0.04 0.19 0.29 −0.16 0.03 −0.10
0.03 0.02 0.07 0.08 0.06 0.08 0.08
0.19⁎ 0.31⁎⁎ 0.25⁎⁎ 0.45⁎⁎ 0.27⁎⁎
0.01 0.01 0.05 0.14 −0.27 −0.13 −0.25
0.13 0.07 0.33 0.45 −0.05 0.19 0.05
.04 .14
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; HADS A = Hospital Anxiety and Depression Scale (Anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30; SE = standard error; UL = upper limit; LL = lower limit; n = 113. ⁎ p b 0.05. ⁎⁎ p b 0.01.
Table 4 Ordinal regression analysis with off-period distress as the outcome variable Measure
MDS-UPDRS 2 IUS MCQ 2 MCQ 4 MCQ 5 -2 log likelihood = 214.89 Chi-square = 20.52⁎⁎
Estimate
0.01 0.02 0.22 0.02 -0.07
SE
0.03 0.02 0.09 0.09 0.08
Wald
0.07 1.00 6.06⁎ 0.04 0.82
95% confidence interval LL
UL
-0.06 -0.02 0.05 -0.17 -0.23
0.08 0.05 0.40 0.20 0.09
Note. MDS-UPDRS = Movement Disorders Society – Unified Parkinson's Disease Rating Scale; IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30; SE = standard error; UL = upper limit; LL = lower limit; n = 93. ⁎ p b 0.05. ⁎⁎ p b 0.01.
evidence of the role of metacognitions in chronic health conditions (e.g. Chronic Fatigue Syndrome) [18,19,20] and align themselves with those of Allott, Wells [8], who found that maladaptive metacognitive style predicted distress when controlling for disease parameters and medication regime. The findings regarding the role of metacognitive factors in predicting anxiety and off-period distress in PD are consistent with MCT and the CAS. MCT postulates that it is not the characteristics of the activating event (i.e. symptom worsening during the off-periods or its predictability) that result in psychological distress, but an individual's cognitive response to them, which is governed by the CAS. For example, an individual who holds positive metacognitions about the value of worry may respond to an off-period with worry, increasing distress and anxiety. Similarly, beliefs concerning uncontrollability and danger may inhibit attempts to reduce worry (contributing to its perseveration) and exacerbate anxiety. Beliefs regarding (a lack of) cognitive confidence may lead to an inhibition of adaptive coping strategies during an off-period. A theoretical model of off-period distress based on the CAS is presented in Fig. 1. The non-significance of intolerance of uncertainty and predictability of motor-fluctuations in the off-period distress model can be understood in terms of the framework of MCT: i.e. it seems that it is not the characteristics of the activating event that results in distress, instead it is the cognitive and attentional response, governed by metacognitive beliefs. The significance of metacognitions concerning the lack of cognitive confidence in predicting off-period distress could be attributed to increasing cognitive dysfunction as PD progresses. However, the absence of a significant relationship between cognitive function (ACE-R) and metacognitions about cognitive confidence (MCQ 3) provides some evidence against this hypothesis, suggesting that cognitive confidence and cognitive functioning are different concepts. It might be assumed that an adaptive metacognitive style is dependent on high cognitive performance and, as such, MCT interventions are likely only to be beneficial to individuals without executive dysfunction. In the present study there is no significant relationship between anxiety, off-period distress, and the measures of cognitive functioning (ACE-R — total and subscores) although previous studies have found evidence of executive impairment associated with anxiety [21,22]. The ACE-R may have lacked sensitivity to detect subtle changes in executive function and more detailed assessment may identify associations between cognitive function and metacognitions. However, even if anxious patients are more likely to have executive impairment, this does not rule out MCT. The CAS model assumes non-optimal attentional processes and these are in turn a core treatment target of MCT, with the Attentional Training Technique specifically seeking to enhance selective and divided attention and switching. These findings suggest that metacognitions play a role in both anxiety in general and off-period distress in PD, and intimate interventions
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147
Fig. 1. Theoretical model of off-period distress.
from MCT, such as Detached Mindfulness, Attention Training Technique, Situational Attentional Refocussing, and the modification of metacognitive beliefs may be helpful in ameliorating these issues. This study is subject to several limitations that will have to be addressed by future research. First, social desirability, self-report biases, context effects, and poor recall may have contributed to errors in the self-report measurements. Future studies should address this vulnerability to respective recall bias by employing ecological momentary assessment designs. Second, a cross-sectional design was adopted, and this does not allow causal inferences. Third, this study utilizes selfreport measures to assess subjective experience and meta-awareness and as such, like much cognitive research, there is always doubt whether we are measuring the constructs we intend. Fourth, the study utilised the MCQ 30 to assess metacognitive beliefs. While this measure was designed to assess metacognitions involved in worry, it may be more appropriate to use the Metacognitions about Symptom Control Scale (MaSCS) in future research as this has been designed to assess metabeliefs involved in physical health conditions [5]. Fifth, disease parameters were not assessed concomitantly with the other study variables, meaning that participants' PD symptoms may have progressed by the time emotional and psychological constructs were measured. Sixth, as mentioned, the use of the ACE-R may have precluded the identification of key cognitive factors. Finally, there were issues with the sample characteristics: it was moderate in size and this impacted on the power of the statistical analyses; the majority of participants were male, despite this reflecting the gender distribution of the disorder; and participants predominately ethnically identified themselves as ‘white’, which impacts on our ability to generalize these findings to other ethnicities. However, despite these limitations, we believe that these findings pave the way to an understanding of the role of metacognitive factors in off-period distress and anxiety in PD. Traditional CBT is an efficacious treatment for anxiety [23], though there is some evidence that suggests that MCT may be a superior treatment for anxiety in populations without comorbid physical health problems [6]. The application of traditional CBT interventions to individuals with chronic physical health conditions may be limited due to disease impairments (e.g. restricting behavioural activation) and difficulties associated with challenging the validity of some beliefs or thoughts about disease (e.g. “there is no cure for Parkinson's disease”). Interventions from MCT aimed at modifying metacognitive knowledge and experiences (i.e. Attention Training
Technique and Detached Mindfulness) are likely to be less limited by the physical limitations resulting from chronic conditions and may be more appropriate than those from traditional CBT as they do not require challenging the veracity of cognitions that may relate to valid appraisals of disease. Interventions from MCT have potential to treat off-period distress and anxiety in PD. Acknowledgements The authors acknowledge the members of the PROMS-PD Study Group who were responsible for the cohort from which the study participants were recruited. London KR Chaudhuri, King's College Hospital NHS Foundation Trust, London (participant recruitment) C Clough, King's College Hospital NHS Foundation Trust, London (participant recruitment) B Gorelick, Parkinson's Disease Society, London (member of the study management group) A Simpson, Institute of Psychiatry, King's College London, London (data collection) R Weeks, King's College Hospital NHS Foundation Trust, London (participant recruitment) Liverpool and North Wale M Bracewell, Ysbyty Gwynedd, Bangor (participant recruitment, data collection) M Jones, University of Wales Bangor, Bangor (participant recruitment, data collection) L Moss, Wythenshawe Hospital, Manchester (participant recruitment, data collection) P Ohri, Eryri Hospital, Caernarfon (participant recruitment) L Owen, Wythenshawe Hospital, Manchester (participant recruitment, data collection) G Scott, Royal Liverpool University Hospital, Liverpool (participant recruitment) C Turnbull, Wirral Hospitals NHS Trust, Wirral (participant recruitment) Newcastle
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S Dodd, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne (participant recruitment, data collection) R Lawson, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne (participant recruitment, data collection) Authors RGB and BAF receive salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Research Unit at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. References [1] de Lau LM, Breteler M. Epidemiology of Parkinson's disease. Lancet Neurol 2006;5: 525–35. [2] Lyons KE, Pahwa R. The impact and management of nonmotor symptoms of Parkinson's disease. Am J Manag Care 2011;17:S308–14. [3] Charidimou A, Seamons J, Selai C, Schrag A. The role of cognitive–behavioural therapy for patients with depression in Parkinson's disease. Parkinsons Dis 2011;2011. [4] Dobkin RD, Menza M, Allen LA, Gara MA, Mark MH, Tiu J, et al. Cognitive–behavioral therapy for depression in Parkinson's disease: a randomized, controlled trial. Am J Psychiatry 2011;168:1066–74. [5] Wells A. Metacognitive therapy for anxiety and depression. New York: The Guildford Press; 2008. [6] Normann N, van Emmerik AA, Morina N. The efficacy of metacognitive therapy for anxiety and depression: a meta-analytic review. Depress Anxiety 2014;31:402–11. [7] Wells A, Matthews G. Attention and emotion: a clinical perspective. Taylor & Francis Group; 1994. [8] Allott R, Wells A, Morrison AP, Walker R. Distress in Parkinson's disease: contributions of disease factors and metacognitive style. Br J Psychiatry 2005;187:182–3. [9] Brown RG, Landau S, Hindle JV, Playfer J, Samuel M, Wilson KC, et al. Depression and anxiety related subtypes in Parkinson's disease. J Neurol Neurosurg Psychiatry 2011; 82:803–9. [10] Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–70.
[11] Marinus J, Leentjens AF, Visser M, Stiggelbout AM, van Hilten JJ. Evaluation of the hospital anxiety and depression scale in patients with Parkinson's disease. Clin Neuropharmacol 2002;25:318–24. [12] Buhr K, Dugas MJ. The intolerance of uncertainty scale: psychometric properties of the English version. Behav Res Ther 2002;40:931–45. [13] Cartwright-Hatton S, Wells A. Beliefs about worry and intrusions: the metacognitions questionnaire and its correlates. J Anxiety Disord 1997;11:279–96. [14] Spada MM, Mohiyeddini C, Wells A. Measuring metacognitions associated with emotional distress: factor structure and predictive validity of the metacognitions questionnaire 30. Personal Individ Differ 2008;45:238–42. [15] Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez‐Martin P. Movement disorder society‐sponsored revision of the unified Parkinson's disease rating scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Mov Disord 2008;23:2129–70. [16] Fahn S, Elton RL. The unified Parkinson's disease rating scale. In: S. F, Marsden CD, Goldstein MA, Calne DB, editors. Recent developments in Parkinsons disease, 2. Florham Park, NJ: Macmillan Healthcare Information; 1987. p. 153–63. [17] Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges JR. The Addenbrooke's cognitive examination revised (ACE-R): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry 2006;21:1078–85. [18] Fernie BA, Maher-Edwards L, Murphy G, Nikcevic AV, Spada MM. The metacognitions about symptom control scale: development and concurrent validity. Clin Psychol Psychother 2014. http://dx.doi.org/10.1002/cpp.1906 (Advance online publication). [19] Maher-Edwards L, Fernie BA, Murphy G, Wells A, Spada MM. Metacognitions and negative emotions as predictors of symptom severity in chronic fatigue syndrome. J Psychosom Res 2011;70:311–7. [20] Maher‐Edwards L, Fernie BA, Murphy G, Nikcevic AV, Spada MM. Metacognitive factors in chronic fatigue syndrome. Clin Psychol Psychother 2012;19:552–7. [21] Fonoff FC, Fonoff ET, Barbosa ER, Quaranta T, Machado RB, Andrade DC, et al. Correlation between impulsivity and executive function in patients with Parkinson disease experiencing depression and anxiety symptoms. J Geriatr Psychiatry Neurol 2014. http://dx.doi.org/10.1177/0891988714541870 (Advance online publication). [22] Bogdanova Y, Cronin-Golomb A. Neurocognitive correlates of apathy and anxiety in Parkinson's disease. Parkinsons Dis 2012;2012:793076. [23] Hofmann SG, Smits JA. Cognitive–behavioral therapy for adult anxiety disorders: a meta-analysis of randomized placebo-controlled trials. J Clin Psychiatry 2008;69: 621.
Contents lists available at ScienceDirect
Journal of Psychosomatic Research
Metacognitions, anxiety, and distress related to motor fluctuations in Parkinson's disease Richard G. Brown a, Bruce A. Fernie a,b,⁎ a b
King's College London, Institute of Psychiatry, Department of Psychology, London, UK CASCAID, South London & Maudsley NHS Foundation Trust, London, UK
a r t i c l e
i n f o
Article history: Received 13 August 2014 Received in revised form 22 September 2014 Accepted 23 September 2014 Keywords: Parkinson's disease Motor-fluctuations Distress Metacognition Off-periods
a b s t r a c t Objective: This study tested the relationship between metacognitive factors, intolerance of uncertainty, anxiety, and the predictability of, and distress associated with, acute fluctuations in symptoms in idiopathic Parkinson's disease (PD), when controlling for disease parameters. Method: 106 adults with idiopathic PD (30 females; Mage = 65.3; 90% white) participated in this study, with 93 of them reported experiencing off-periods. A cross-sectional design was employed that utilised: the Hospital Depression and Anxiety Scale, Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale, the Addenbrooke's Cognitive Examination — Revised, the Intolerance of Uncertainty Scale, and the Metacognitions Questionnaire 30. Correlation analyses, hierarchical regression analysis, and ordinal regression analysis were used to test the experimental hypotheses. Results: Anxiety was not significantly associated with motor symptom severity or cognitive functioning, while metacognitive factors were significantly related to anxiety when controlling for motor experiences of daily living and intolerance of uncertainty, R2 = 0.56, F(1,82) = 15.04, p b 0.001 (adjusted R2 = 0.53). For participants with motor fluctuations, no association was found between predictability of, and distress associated with, off-periods. Metacognitions concerning uncontrollability and danger were significantly related to off-period distress when controlling for motor experiences of daily living, intolerance of uncertainty, and other metacognitive factors, χ2(1) = 20.52, p = 0.001. Conclusion: Metacognitive factors play a role in anxiety and off-period distress in PD and this is discussed in terms of the Self-Regulatory Executive Function model. Interventions from metacognitive therapy are potential means to ameliorate off-period distress and anxiety in PD. © 2014 Elsevier Inc. All rights reserved.
Introduction Parkinson's disease (PD) is a progressive neurological disorder with an estimated population prevalence of approximately 300 per 100,000, increasing to 1%, over the age of 60 years and up to 4% in the oldest age groups [1]. PD is typically considered a disorder of movement with symptoms of slowed and reduced amplitude voluntary action, tremor, and rigidity affecting limb and eye movement, in addition impaired control of balance, swallowing, and speech. A range of disabling non-motor symptoms are also commonly experienced including depression, anxiety, psychosis, cognitive impairment, autonomic dysfunction, fatigue, and pain. Motor and non-motor disability increase with disease progression despite symptomatic treatment using levodopa, dopamine agonists, or other drugs that modify brain dopamine levels. With disease progression and increasing duration of treatment, ⁎ Corresponding author at: Department of Psychology, Institute of Psychiatry, King's College London, Henry Wellcome Building, De Crespigny Park, London SE5 8AF, UK. Tel.: +44 7779 300 427; fax: +44 20 7848 5310. E-mail address: [email protected] (B.A. Fernie).
http://dx.doi.org/10.1016/j.jpsychores.2014.09.021 0022-3999/© 2014 Elsevier Inc. All rights reserved.
effectiveness gradually declines. In those treated with levodopa, fluctuations in symptom severity over the course of the day commonly develop. These include ‘wearing off’ (a relatively predictable re-emergence of symptoms towards the end of a medication dose); ‘on/off fluctuations’ (unpredictable and sudden recurrence of Parkinsonian symptoms); ‘delayed on’ (unpredictably increased time between ingestion of a dose and motor benefit), and ‘dose failure’ (unpredictable failure of a dose to provide usual benefit) [2]. In addition to worsening of motor symptoms during ‘off-periods’, the emergence or exacerbation of distressing non-motor symptoms is also reported by many individuals including pain, fatigue, drenching sweats, depression, and anxiety. Currently, fluctuations and associated symptoms are managed by alterations in drug regimen but this becomes increasingly difficult with disease progression. This offers potential to develop adjunctive psychological approaches to manage individual symptoms (e.g. depressed mood, pain, or fatigue) or reduce the level of associated subjective distress. There is evidence that traditional CBT treatment approaches can be helpful in reducing depressive and anxiety symptoms in PD [3,4] although not specifically in the context of motor fluctuations. One challenge of CBT is the limits on reality-testing of thoughts and beliefs about PD (e.g.
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‘there is no cure for this disease’ or ‘I have no control over my symptoms’) as these may represent accurate appraisals of the disease, and may be challenging to test during ‘in the moment’ distress associated with offperiods. Key to developing a more targeted therapy is an understanding of the cognitive and attentional processes that contribute to the emotional difficulties experienced by some with PD including those associated with off-period distress. We propose that metacognitive therapy (MCT) [5], an effective treatment for depression and anxiety [6] may be particularly well-suited to the management of PD distress. MCT is based on the Self-Regulatory Executive Function model (S-REF) [7] and posits that psychological distress results from perseverative cognitive processes (e.g. rumination and worry) and attentional strategies (e.g. symptom focussing and hypervigilance). These are proposed to be governed by both explicit and implicit metacognitions and form a Cognitive Attentional Syndrome (CAS). Preliminary research has implicated metacognitions in distress in a small sample of people with PD [8]. S-REF proposes that specific CAS configurations are activated in response to inner events such as cognitions (including memories), emotions, and physical states. If an individual with PD experiences symptoms associated with an off-period and endorses positive metabeliefs about worry (e.g. “worry helps me to solve problems”), the response to this off-related symptom will be worry. An individual who holds negative beliefs about worry, such as ‘my worry is uncontrollable’, may be less inclined to make attempts to halt this cognitive process and instead to ‘worry about worry’, increasing distress further and helping to drive more worry. In both instances a stop-signal for this process is not received (i.e. the goal of solving a problem), resulting in worry perseveration and distress. The modifications of the metacognitions that are hypothesized to fuel maladaptive CAS configurations are a key target of MCT interventions. In this study, we test the following hypothesis: that metacognitive factors explain a significant proportion of variance in anxiety and offperiod distress after controlling for disease characteristics, cognitive function, off-period predictability, and trait intolerance of uncertainty. Methods Participants and procedure Participants with a clinical diagnosis of PD were recruited from a cohort of patients involved in a separate longitudinal study (n = 512), PROMS-PD [9]. We approached individuals that had expressed a willingness to engage with additional research, who were judged at their last assessment to have capacity to consent, and had sufficient sensory and
motor function to complete a booklet of questionnaires. Those who had been seen for assessment for the main study in the past three months or due to receive an assessment in the coming three months were not approached to prevent overburdening them with requests. Of 178 eligible individuals, 106 returned completed questionnaires (59.6%). Table 1 provides the participant characteristics. Ethics approval for the study was obtained from the local research ethics committees. All of those returning return questions gave informed consent to participate in the study. Measures: self-report questionnaires Depression, anxiety and distress Depression and anxiety were assessed using the Hospital Anxiety and Depression Scale (HADS) [10]. This 14-item self-report measure provides a total score as well as separate depression (HADS-D) and anxiety (HADS-A) scores, with higher scores representing more severe symptoms. The HADS was originally designed for use in patients with physical health conditions and has been validated for use in patients with PD [11]. A cut-off of eight out of 21 on the depression and anxiety subscales indicates significant symptomatology. A single-item measure was used to measure distress associated with off-periods. Participants reporting motor fluctuations (see below) indicated how distressing they found off-periods on a five-point Likert-type scale: [1] “I have no distress (or I do not experience off-periods)”; [2] “I can feel a little upset during OFF times, but it does not trouble me much”; [3] “I feel mildly distressed during OFF times”; [4] “I feel moderately distressed during OFF times”; and [5] “I feel extremely distressed during OFF times”. Cognitive and metacognitive constructs The English version of the 27-item Intolerance of Uncertainty Scale (IUS) [12] was used to assess trait intolerance of uncertainty. It has been shown to be associated with worry and anxiety and possesses good psychometric properties [12], but has not been reported previously in patients with PD. The Metacognitions Questionnaire 30 (MCQ) [13] is a 30-item self-report measure that assesses five-factors pertaining to metacognition: [1] positive beliefs about worry (MCQ 1; e.g. “Worrying helps me cope”); [2] negative beliefs about thoughts concerning uncontrollability and danger (MCQ 2; e.g. “When I start worrying I cannot stop”); [3] cognitive confidence (MCQ 3; e.g. “My memory can mislead me at times”); [4] beliefs about the need to
Table 1 Participant characteristics Characteristic
Fluctuators
Non-fluctuators
Combined
n Gender Mean age in years (SD; range) Mean MDS-UPDRS 2 (SD; range) Mean MDS-UPDRS 3 (SD; range) Mean ACE-R: total (SD; range) Mean ACE-R: attention and orientation (SD; range) Mean ACE-R: memory (SD; range) Mean ACE-R: fluency (SD; range) Mean ACE-R: language (SD; range) Mean ACE-R: visiospatial (SD; range) Mean MDS-UPDRS — 4: item 4 (SD; range) Mean distress during off-periods (SD; range) Mean HADS A (SD; range) Mean IUS (SD; range) MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
93 63 male; 30 female 65.3 (9.4; 43–85) 17.36 (6.56; 5–36) 34.38 (12.59; 13–78) 88.59 (8.34; 51–100) 17.53 (0.88; 14–18) 21.86 (3.52; 11–26) 10.38 (2.56; 3–14) 24.87 (1.25; 20–26) 15.18 (1.20; 11–16) 2.87 (1.43; 0–4) 1.83 (1.13; 0–4) 9.38 (2.50; 5–16) 52.74 (18.52; 27–113) 9.18 (3.08; 6–20) 10.35 (3.84; 6–20) 13.10 (4.34; 6–24) 10.90 (3.33; 6–24) 12.49 (3.34; 6–24)
13 10 male; 3 female 68.1 (8.3; 50–80) 13.00 (5.64; 6–22) 30.55 (10.81; 8–44) 90.64 (8.39; 70–100) 17.54 (0.78; 16–18) 22.92 (2.90; 16–26) 10.46 (2.07; 7–14) 24.69 (1.32; 22–26) 15.15 (1.57; 11–16) N/A N/A 7.46 (1.39; 5–10) 45.25 (11.01; 31–66) 9.31 (2.63; 6–15) 7.69 (1.97; 6–12) 12.08 (4.01; 6–20) 10.08 (1.93; 8–14) 10.69 (2.87; 6–17)
106 73 male; 33 female 65.6 (9.3; 43–85) 16.85 (6.58; 5–36) 33.94 (12.40; 8–78) 88.83 (8.33; 51–100) 17.50 (0.96; 13–18) 21.93 (3.44; 11–26) 10.37 (2.47; 3–14) 24.83 (1.24; 20–26) 15.15 (1.25; 11–16) N/A N/A 9.17 (2.48; 5–16) 51.86 (17.92; 57–113) 9.19 (3.02; 6–20) 10.02 (3.75; 6–20) 12.97 (4.30; 6–24) 10.80 (3.20; 6–24) 12.27 (3.33; 6–24)
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; ACE-R = Addenbrooke's Cognitive Examination — Revised; HADS A = Hospital Anxiety and Depression Scale (anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30.
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control thoughts (MCQ 4; e.g. “Not being able to control my thoughts is a sign of weakness”); and [5] cognitive self-consciousness (MCQ 5; e.g. “I pay close attention to the way my mind works”). Respondents indicate agreement on a four-point Likert-type scale with higher scores indicating higher levels of unhelpful metacognitions. The MCQ-30 possesses good psychometric properties [13,14] and has been reported in one previous study on people with PD [8]. Measures: disease parameters Physical symptoms and cognitive functioning Self-reported motor fluctuations over the preceding week were assessed using items from Part IVB of the Movement Disorder Society revision of the Unified Parkinson's Disease rating Scale (MDS-UPDRS) [15]. These provided operational definitions of motor fluctuations and off-periods and five or six-point ordinal scales to assess the presence/ absence of motor fluctuations, the amount of time spent per day in the off-period, and the predictability of the off-periods. The latter was measured on a four-point scale from ‘2 — Off-periods predictable all or almost all of the time (more than 75% of the time)’ to ‘5 — Off-periods are rarely predictable (less than 25% of the time)’. A score of 1 indicated that off-periods were not present. As this was a postal survey we did not have the opportunity to conduct face-to-face assessments of primary Parkinsonian motor symptoms or cognitive function. Instead, we estimated function from the most recent measurements taken as part of the PROMS-PD study (3–9 months prior to questionnaire completion). Information used includes the overall motor symptom severity (MDS-UPDRS 3), motor experiences of daily living (MDS-UPDRS 2) and complexity of motor symptoms (MDS-UPDRS 4) [16] with higher scores of each indicating more severe symptoms. Cognitive function was assessed using the Addenbrooke's Cognitive Examination — Revised (ACE-R) [17]. This is a brief cognitive examination assessing attention/orientation, memory, verbal fluency, language, and visuospatial function as subscores with a maximum total score of 100. Statistical analysis Prior to analysis questionnaires were examined for missing data and limited imputation used to avoid loss of power. Where a scale (or subscale) had six or more items, missing data were replaced with
145
the scale/subscale median provided no more than two items were missing. For scales/subscales with fewer than six items, only a single missing item was permitted for imputation. 2.20% of HADS responses were missing, as were 2.32% of MCQ responses, 2.41% of IUS responses, 1.89% of responses to the question about the predictability of off-periods (MDS-UPDRS 4), and 0.94% of responses to the questions regarding distress during off-periods (MDS-UPDRS 4). For the whole sample, Spearman rho correlations were calculated to assess the bivariate relationships between anxiety and study variables to identify predictors for multivariate analysis. A hierarchical regression analysis was conducted with anxiety (HADS A) as the outcome variable and experimental variables that were found to be significantly associated with HADS A in the correlation analysis as predictor variables. The next analyses were restricted to those participants who reported experiencing off-periods during the preceding week. Another Spearman rho correlation matrix was generated to identify study variables that were significantly associated with distress during off-periods; these variables were entered as predictor variables in an ordinal regression analysis with off-period distress as the outcome variable. Results Sample and subsample characteristics Table 1 shows the demographic and clinical characteristics of the total sample (n = 106) and the subsamples of participants with (n = 93) and without motor fluctuations (n = 13). Table 2 shows the corresponding characteristics on the measures of distress, anxiety, intolerance of uncertainty, and metacognitions. The ethnicity of the sample was almost exclusively white (96.2%). Disease stage at last face-to-face assessment indicated mild to moderate progression in the majority of the sample with only 11.5% having the most severe disease (Stage 4 or 5). Whole sample Table 2 presents a Spearman's rho coefficients between study variables. The analysis revealed that anxiety (HADS-A) was significantly but very weakly associated with the motor experiences of daily living (MDS-UPDRS 2; r = 0.29), moderately strong with intolerance of uncertainty (IUS; r = 0.55), weakly with positive beliefs about worry (MCQ 1; r = 0.38), moderately strong with beliefs concerning uncontrollability and danger (MCQ 2; r = 0.56), very weakly with metacognitions concerning cognitive confidence (MCQ 3; r = 0.27), weakly with the need to control thoughts (MCQ 4; r = 0.36) and cognitive self-consciousness (MCQ 5; r = 0.35). No significant relationship was found between measured cognitive functioning (ACE-R), either as a total score or subscores, and metacognitions concerning (lack of) cognitive confidence (MCQ 3). The overall severity of Parkinsonian motor symptoms (MDS-UPDRS 3) was very weakly associated with memory (ACE-R; r = 0.23) and activities of daily living (MDS-UPDRS 2) were weakly
Table 2 Spearman's Rho correlation matrix of study variables (n = 106) Measure 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
MDS-UPDRS 2 MDS-UPDRS 3 ACE-R (total) ACE-R (attention and orientation) ACE-R (memory) ACE-R (fluency) ACE-R (language) ACE-R (visuospatial) Predictability of off-periods Distress during off-periods HADS A IUS MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
1 2
3
4
5
6
7
8
9
.51⁎⁎ −.19 −.12 −.23⁎ −.07 −.02 −.16 −.35⁎⁎ −.19 −.20 −.32⁎⁎ −.20⁎ −.32⁎⁎ .41⁎⁎ .77⁎⁎ .76⁎⁎ .57⁎⁎ .48⁎⁎ .15 .28⁎⁎ .23⁎ .31⁎⁎ .32⁎⁎
.22⁎ .48⁎⁎
10
11
.12 .29⁎⁎ .29⁎⁎ .03 .11 .09 .00 −.04 −.14 .07 −.18 −.17
.19⁎ −.05 −.01 .35⁎⁎ .07 .06 .28⁎⁎ −.05 −.05 −.01 −.15
−.08 −.06 .09 −.07
.26⁎⁎ −.03 .53⁎⁎
12
14
15
.35⁎⁎ .03 .08 −.09 .04 .02 .12 .00
.24⁎ −.02 .06 .03
.28⁎⁎ .10 −.04 .12
−.03 .09 −.04 .00 ⁎⁎ .29 .10 .12 −.10
.05 .01 .25⁎ .08
−.05
13
−.23⁎
−.02
16
17
.28⁎⁎ .17 .08 −.02 .04 .17 .00 .14
−.05 −.12 .19 .13
.02 .04 .19 −.05
.17 .09 .21⁎ .08
−.09
−.02
.07
.38⁎⁎
.16
.45⁎⁎
.11
.23⁎
.28⁎⁎
.55⁎⁎
.38⁎⁎ .39⁎⁎
.56⁎⁎ .71⁎⁎ .29⁎⁎
.27⁎⁎ .62⁎⁎ .33⁎⁎ .57⁎⁎
.36⁎⁎ .52⁎⁎ .45⁎⁎ .52⁎⁎ .44⁎⁎
.35⁎⁎ .52⁎⁎ .56⁎⁎ .53⁎⁎ .54⁎⁎ .56⁎⁎
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; ACE-R = Addenbrooke's Cognitive Examination — Revised; HADS A = Hospital Anxiety and Depression Scale (Anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30. ⁎ p b 0.05. ⁎⁎ p b 0.01.
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associated with the total score of the ACE-R, fluency, language, and visuospatial subscores of the ACE-R, but not to any of the other psychological variables. There was no evidence of multicollinearity (r N 0.9) between the predictor variables. Additionally, the ranges of the Tolerance Index (0.38–0.83) and the Variance Inflation Factor (1.20–2.65) for all predictor variables were inspected, revealing no further support for the presence of multicollinearity. Histograms and normality plots suggested that the residuals were normally distributed. Plots of the regression-standardized residuals against the regression standardized predicted values suggested that the assumptions of linearity and homoscedasticity were met. Additionally, the Durbin–Watson test suggested that the assumption of independent errors is tenable. Table 3 presents a three-step hierarchical regression analysis with anxiety (HADS-A) as the outcome variable. The model used motor experiences of daily living (MDS-UPDRS 2) on the first step and added intolerance of uncertainty (IUS) on the second. The third step added all five metacognitive factors as predictor variables. In the final step of the regression model, MDS-UPDRS 2, IUS, MCQ 1, MCQ 2, and MCQ 3 were significant predictors of anxiety. The predictor variables in the final step of the equation explained 56% of the variance in anxiety scores (adjusted R2 = 0.53). Subgroup analysis — associations and predictors of off-period distress Off-period distress was very weakly related to the motor experiences of daily living (MDS-UPDRS 2; r = 0.22, p = 0.04) and weakly to intolerance of uncertainty (IUS; r = 0.36, p = b 0.001), as well as metacognitions concerning uncontrollability and danger (MCQ 2; r = 0.38, p b 0.001), very weakly to the need to control thoughts (MCQ 4; r = 0.21, p = 0.04), and cognitive consciousness (MCQ 5; r = 0.22, p = 0.04). Table 4 presents an ordinal regression analysis with off-period distress as the outcome variable. Motor experiences of daily living (MDS-UPDRS 2) and intolerance of uncertainty (IUS), as well as metacognitions concerning uncontrollability and danger (MCQ 2), the need to control thoughts (MCQ 4), and cognitive consciousness (MCQ 5), were entered into the model as covariate predictor variables. This model revealed that only metacognitions concerning uncontrollability and danger were a significant predictor of off-period distress. The model explained 24% of the variation of off-period distress, as indicated by Nagelkerke pseudo R2. Tests of parallel lines led us to accept the assumption of proportional odds.
Discussion This study found that activity of daily living, intolerance of uncertainty, positive beliefs about worry, metacognitions concerning uncontrollability, and (lack of) cognitive confidence predicted anxiety in PD, while metacognitions concerning uncontrollability and danger alone predicted off-period distress when controlling for disease parameters, intolerance of uncertainty, and predictability of off-periods. Furthermore, the results suggest that off-period distress is not associated with the unpredictability of the off-periods. These findings provide further
Table 3 Hierarchical regression model with HADS A as the outcome variable Predictor
R2
Adjusted R2
B
SE
β
95% confidence interval LL
Step 1 MDS-UPDRS 2
Step 2 MDS-UPDRS 2 IUS
Step 3 MDS-UPDRS 2 IUS MCQ 1 MCQ 2 MCQ 3 MCQ 4 MCQ 5
.10⁎⁎
.40⁎⁎
0.56⁎⁎
.09⁎⁎
.39⁎⁎
0.53⁎⁎
UL
0.12
0.67
.32⁎⁎
0.04
0.19
0.44 0.08
0.03 0.01
0.12⁎ .31⁎⁎
−0.02 0.05
0.11 0.10
0.07 0.04 0.19 0.29 −0.16 0.03 −0.10
0.03 0.02 0.07 0.08 0.06 0.08 0.08
0.19⁎ 0.31⁎⁎ 0.25⁎⁎ 0.45⁎⁎ 0.27⁎⁎
0.01 0.01 0.05 0.14 −0.27 −0.13 −0.25
0.13 0.07 0.33 0.45 −0.05 0.19 0.05
.04 .14
Note. MDS-UPDRS = Movement Disorders Society — Unified Parkinson's Disease Rating Scale; HADS A = Hospital Anxiety and Depression Scale (Anxiety subscale); IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30; SE = standard error; UL = upper limit; LL = lower limit; n = 113. ⁎ p b 0.05. ⁎⁎ p b 0.01.
Table 4 Ordinal regression analysis with off-period distress as the outcome variable Measure
MDS-UPDRS 2 IUS MCQ 2 MCQ 4 MCQ 5 -2 log likelihood = 214.89 Chi-square = 20.52⁎⁎
Estimate
0.01 0.02 0.22 0.02 -0.07
SE
0.03 0.02 0.09 0.09 0.08
Wald
0.07 1.00 6.06⁎ 0.04 0.82
95% confidence interval LL
UL
-0.06 -0.02 0.05 -0.17 -0.23
0.08 0.05 0.40 0.20 0.09
Note. MDS-UPDRS = Movement Disorders Society – Unified Parkinson's Disease Rating Scale; IUS = Intolerance of Uncertainty Scale; MCQ = Metacognitions Questionnaire 30; SE = standard error; UL = upper limit; LL = lower limit; n = 93. ⁎ p b 0.05. ⁎⁎ p b 0.01.
evidence of the role of metacognitions in chronic health conditions (e.g. Chronic Fatigue Syndrome) [18,19,20] and align themselves with those of Allott, Wells [8], who found that maladaptive metacognitive style predicted distress when controlling for disease parameters and medication regime. The findings regarding the role of metacognitive factors in predicting anxiety and off-period distress in PD are consistent with MCT and the CAS. MCT postulates that it is not the characteristics of the activating event (i.e. symptom worsening during the off-periods or its predictability) that result in psychological distress, but an individual's cognitive response to them, which is governed by the CAS. For example, an individual who holds positive metacognitions about the value of worry may respond to an off-period with worry, increasing distress and anxiety. Similarly, beliefs concerning uncontrollability and danger may inhibit attempts to reduce worry (contributing to its perseveration) and exacerbate anxiety. Beliefs regarding (a lack of) cognitive confidence may lead to an inhibition of adaptive coping strategies during an off-period. A theoretical model of off-period distress based on the CAS is presented in Fig. 1. The non-significance of intolerance of uncertainty and predictability of motor-fluctuations in the off-period distress model can be understood in terms of the framework of MCT: i.e. it seems that it is not the characteristics of the activating event that results in distress, instead it is the cognitive and attentional response, governed by metacognitive beliefs. The significance of metacognitions concerning the lack of cognitive confidence in predicting off-period distress could be attributed to increasing cognitive dysfunction as PD progresses. However, the absence of a significant relationship between cognitive function (ACE-R) and metacognitions about cognitive confidence (MCQ 3) provides some evidence against this hypothesis, suggesting that cognitive confidence and cognitive functioning are different concepts. It might be assumed that an adaptive metacognitive style is dependent on high cognitive performance and, as such, MCT interventions are likely only to be beneficial to individuals without executive dysfunction. In the present study there is no significant relationship between anxiety, off-period distress, and the measures of cognitive functioning (ACE-R — total and subscores) although previous studies have found evidence of executive impairment associated with anxiety [21,22]. The ACE-R may have lacked sensitivity to detect subtle changes in executive function and more detailed assessment may identify associations between cognitive function and metacognitions. However, even if anxious patients are more likely to have executive impairment, this does not rule out MCT. The CAS model assumes non-optimal attentional processes and these are in turn a core treatment target of MCT, with the Attentional Training Technique specifically seeking to enhance selective and divided attention and switching. These findings suggest that metacognitions play a role in both anxiety in general and off-period distress in PD, and intimate interventions
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Fig. 1. Theoretical model of off-period distress.
from MCT, such as Detached Mindfulness, Attention Training Technique, Situational Attentional Refocussing, and the modification of metacognitive beliefs may be helpful in ameliorating these issues. This study is subject to several limitations that will have to be addressed by future research. First, social desirability, self-report biases, context effects, and poor recall may have contributed to errors in the self-report measurements. Future studies should address this vulnerability to respective recall bias by employing ecological momentary assessment designs. Second, a cross-sectional design was adopted, and this does not allow causal inferences. Third, this study utilizes selfreport measures to assess subjective experience and meta-awareness and as such, like much cognitive research, there is always doubt whether we are measuring the constructs we intend. Fourth, the study utilised the MCQ 30 to assess metacognitive beliefs. While this measure was designed to assess metacognitions involved in worry, it may be more appropriate to use the Metacognitions about Symptom Control Scale (MaSCS) in future research as this has been designed to assess metabeliefs involved in physical health conditions [5]. Fifth, disease parameters were not assessed concomitantly with the other study variables, meaning that participants' PD symptoms may have progressed by the time emotional and psychological constructs were measured. Sixth, as mentioned, the use of the ACE-R may have precluded the identification of key cognitive factors. Finally, there were issues with the sample characteristics: it was moderate in size and this impacted on the power of the statistical analyses; the majority of participants were male, despite this reflecting the gender distribution of the disorder; and participants predominately ethnically identified themselves as ‘white’, which impacts on our ability to generalize these findings to other ethnicities. However, despite these limitations, we believe that these findings pave the way to an understanding of the role of metacognitive factors in off-period distress and anxiety in PD. Traditional CBT is an efficacious treatment for anxiety [23], though there is some evidence that suggests that MCT may be a superior treatment for anxiety in populations without comorbid physical health problems [6]. The application of traditional CBT interventions to individuals with chronic physical health conditions may be limited due to disease impairments (e.g. restricting behavioural activation) and difficulties associated with challenging the validity of some beliefs or thoughts about disease (e.g. “there is no cure for Parkinson's disease”). Interventions from MCT aimed at modifying metacognitive knowledge and experiences (i.e. Attention Training
Technique and Detached Mindfulness) are likely to be less limited by the physical limitations resulting from chronic conditions and may be more appropriate than those from traditional CBT as they do not require challenging the veracity of cognitions that may relate to valid appraisals of disease. Interventions from MCT have potential to treat off-period distress and anxiety in PD. Acknowledgements The authors acknowledge the members of the PROMS-PD Study Group who were responsible for the cohort from which the study participants were recruited. London KR Chaudhuri, King's College Hospital NHS Foundation Trust, London (participant recruitment) C Clough, King's College Hospital NHS Foundation Trust, London (participant recruitment) B Gorelick, Parkinson's Disease Society, London (member of the study management group) A Simpson, Institute of Psychiatry, King's College London, London (data collection) R Weeks, King's College Hospital NHS Foundation Trust, London (participant recruitment) Liverpool and North Wale M Bracewell, Ysbyty Gwynedd, Bangor (participant recruitment, data collection) M Jones, University of Wales Bangor, Bangor (participant recruitment, data collection) L Moss, Wythenshawe Hospital, Manchester (participant recruitment, data collection) P Ohri, Eryri Hospital, Caernarfon (participant recruitment) L Owen, Wythenshawe Hospital, Manchester (participant recruitment, data collection) G Scott, Royal Liverpool University Hospital, Liverpool (participant recruitment) C Turnbull, Wirral Hospitals NHS Trust, Wirral (participant recruitment) Newcastle
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S Dodd, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne (participant recruitment, data collection) R Lawson, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne (participant recruitment, data collection) Authors RGB and BAF receive salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Research Unit at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. References [1] de Lau LM, Breteler M. Epidemiology of Parkinson's disease. Lancet Neurol 2006;5: 525–35. [2] Lyons KE, Pahwa R. The impact and management of nonmotor symptoms of Parkinson's disease. Am J Manag Care 2011;17:S308–14. [3] Charidimou A, Seamons J, Selai C, Schrag A. The role of cognitive–behavioural therapy for patients with depression in Parkinson's disease. Parkinsons Dis 2011;2011. [4] Dobkin RD, Menza M, Allen LA, Gara MA, Mark MH, Tiu J, et al. Cognitive–behavioral therapy for depression in Parkinson's disease: a randomized, controlled trial. Am J Psychiatry 2011;168:1066–74. [5] Wells A. Metacognitive therapy for anxiety and depression. New York: The Guildford Press; 2008. [6] Normann N, van Emmerik AA, Morina N. The efficacy of metacognitive therapy for anxiety and depression: a meta-analytic review. Depress Anxiety 2014;31:402–11. [7] Wells A, Matthews G. Attention and emotion: a clinical perspective. Taylor & Francis Group; 1994. [8] Allott R, Wells A, Morrison AP, Walker R. Distress in Parkinson's disease: contributions of disease factors and metacognitive style. Br J Psychiatry 2005;187:182–3. [9] Brown RG, Landau S, Hindle JV, Playfer J, Samuel M, Wilson KC, et al. Depression and anxiety related subtypes in Parkinson's disease. J Neurol Neurosurg Psychiatry 2011; 82:803–9. [10] Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–70.
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