Skip to main content
Erschienen in: Somnologie 2/2023

Open Access 27.04.2023 | Original studies

Sleep-related metacognitions and cognitive behavioral therapy for insomnia

verfasst von: M.Sc. Anna-Lea Jenewein, apl. Prof. Dr. Michael Schredl, Dr. Claudia Schilling

Erschienen in: Somnologie | Ausgabe 2/2023

Abstract

Background

Cognitive behavioral therapy for insomnia (CBT-I) is the treatment of choice for chronic insomnia. In the metacognitive model of Ong et al., a current model of the pathogenesis of chronic insomnia, sleep-related metacognitions are considered to be an insomnia-promoting factor.

Objective

The objective of this study is to investigate the role of metacognitions in insomnia and CBT‑I. Are metacognitions reduced by CBT-I? Can the pretreatment scores of sleep-related metacognitions predict the effectiveness of the therapy? Does treatment response improve when sleep-related metacognitions are directly addressed?

Materials and methods

A total of 92 persons with chronic insomnia participated in CBT‑I with seven 90-min group sessions. Of these patients, 52 received CBT‑I and 40 received CBT‑I with integrated metacognitive elements (CBT-I + MCE). Sleep-related metacognitions and insomniac complaints were quantified using the Metacognitions Questionnaire—Insomnia (MCQ‑I 20) and the Insomnia Severity Index (ISI).

Results

CBT‑I improved subjective sleep quality (effect sizes d > 2.0) and decreased sleep-related metacognitions (effect sizes d > 0.8). Neither the presence of comorbidities nor the extent of sleep-related metacognitions affected treatment response. Treatment response did not differ between the CBT‑I and CBT-I + MCE groups.

Conclusion

Sleep-related metacognitions seem to play an important role in insomnia etiology but did not predict treatment response. The introduction of elements from metacognitive therapy (MCT) is as effective as the classical CBT‑I and might therefore be considered as an addition to the standard cognitive strategies of CBT‑I.
Hinweise
Scan QR code & read article online
Insomnia is a sleep disorder with high prevalence. Sleep-related metacognitions are cognitions related to interpretation, monitoring, reacting to, or controlling of sleep-related thoughts. One current insomnia model assigns an important role to sleep-related metacognitions. Cognitive behavioral therapy for insomnia (CBT-I), the first-line treatment for insomnia, was shown to also reduce sleep-related metacognitions. In our research, we studied the interplay between CBT‑I and sleep-related metacognitions.
Insomnia is a sleep disorder characterized by problems in sleep initiation, maintenance, duration, and quality—despite adequate opportunity—leading to impaired daytime functioning [1]. Chronic insomnia, with symptoms persisting for over 3 months at least 3 days a week [1], shows a prevalence of between 5% and 15% in the general population [12, 18, 23].
Spielman’s 3P model describes the predisposing, precipitating, and perpetuating factors resulting in difficulties in sleep initiation and maintenance and their improvement by behavioral interventions [25]. Morin’s cognitive behavioral model of insomnia [14] displays the vicious circle of insomnia containing hyperarousal, dysfunctional cognitions, maladaptive behavior, and daytime consequences, suggesting that cognitive strategies be appended to insomnia therapy [14]. The attention–intention–effort (AIE) pathway of Espie et al. [6] proposes that sleep initiates automatically in healthy individuals, whereas in psychophysiological insomnia, giving attention to sleep, actively intending to fall asleep, and putting effort into it result in inhibition of the natural de-arousal [6].
Sleep-related cognitions are unrealistic expectations, erroneous beliefs, or biases about sleep [16]. Sleep-related metacognitions, on the other hand, are the reaction to and interpretation, monitoring, or control of sleep-related cognitions [8, 31]. The metacognitive model by Ong et al. [20] adds these metacognitive processes to the cognitive arousal theories of insomnia [22] as so-called secondary arousal. In conclusion, this approach focuses less on restructuring the content of dysfunctional sleep-related cognitions but rather on changing one’s handling of them [24, 31]. Patients suffering from insomnia show more sleep-related metacognitions than healthy controls [21] or patients with other sleep disorders [21, 24].
Cognitive behavioral therapy for insomnia (CBT-I) is a very effective insomnia treatment [911, 19, 28]. It was also shown to reduce sleep-related metacognitions in a former study [8]. CBT‑I in the study was performed according to Espie [5], consisted of seven group sessions of 90 min each, and reduced sleep-related metacognitions (MCQ-I) in the pretreatment to posttreatment comparison with a large effect size (d = −1.186) [8]. Regarding factors influencing CBT‑I effectiveness, previous reports found no effect of age and gender; however, current psychiatric comorbidity may predict less effectiveness [27].
In our research, we aimed to investigate if and to what extent CBT‑I can reduce sleep-related metacognitions, whether the baseline level of metacognitions can predict the outcome of CBT‑I, and whether a therapy integrating elements of metacognitive therapy into CBT‑I improves its outcome.

Materials and methods

Participants

A total of 92 patients with chronic insomnia disorder diagnosed according to the third edition of the International Classification of Sleep Disorders (ICSD‑3; mean age = 49.4; standard deviation [SD] = 12.01) participated in group therapy for chronic insomnia. Demographic information, comorbidities, and use of sleep medication are displayed in Table 1. Antidepressant medication in the patient group without affective comorbidity was used as sleep-promoting medication. A subgroup analysis was performed for those 69 insomnia patients without any psychiatric or sleep-related comorbidity (mean age [M] = 50.20, SD = 12.16). There was a significant age difference (total sample: t(90) = −2.65, p = 0.010; subsample without comorbidities: t(67) = −2.68, p = 0.009), with women (total sample: M = 51.31, SD = 11.72, N = 68; subsample without comorbidities: M = 52.26, SD = 11.45, N = 53) being older than men (total sample: M = 44.00, SD = 11.37, N = 24; subsample without comorbidities: M = 43.38, SD = 12.29, N = 16).
Table 1
Participant data
Patient data
Total sample (with and without comorbidities)
Subsample without comorbidities
Participants, no.
92
69
Gender, female %
74
77
Age, mean ± SD
49.40 ± 12.01
50.20 ± 12.16
Duration insomnia, mean ± SD
10.77 ± 9.37
10.96 ± 9.49
Non-compliance, no.
1
1
Insomnia patients with comorbiditiesa, no.
23
Comorbid mental disorder, no.
10
Comorbid other sleep disorder, no.
17
Antidepressants and benzodiazepine receptor agonists
46
29
Herbal sleeping aids
8
6
SD standard deviation, no. number of patients
aMultiple diagnoses possible

Therapy

CBT‑I was conducted according to [17]. It consisted of seven 90-minute group sessions. The first four sessions were held on a weekly basis, the fifth and sixths session each after 2 weeks. The last session after 8 weeks of therapy is here referred to as “post visit.” A seventh follow-up visit was offered 3 months after the post visit (here referred to as “follow-up visit”). A licensed specialist in psychiatry and psychotherapy, certified in sleep medicine and with a diploma in metacognitive therapy (MCT) from the MCT Institute, conducted the group sessions. The therapy consisted of the development of a disease model, sleep restriction monitored by sleep diaries, psychoeducation about sleep-related topics, restructuring of dysfunctional sleep-related beliefs, relaxation techniques, and exchange of experiences during therapy between the group members.
For 40 out of 92 patients with insomnia receiving treatment from 2019 onwards, CBT‑I integrated elements MCT. Approximately 25% of the total therapy time was allocated to metacognitive interventions. The allocated time was obtained by omission of restructuring of dysfunctional sleep-related beliefs and reducing time spent on psychoeducation about sleep architecture and relaxation techniques from the original program. Sleep-related cognitions and metacognitions were collected by the group members, and a metacognitive case formulation inspired by the generalized anxiety disorder case formulation from Wells was developed with the group [31]. The metacognitive interventions in subsequent therapy sessions referred to this case formulation. It contained sleep-related cognitions like “If I don’t sleep now, I won’t be able to be active tomorrow,” sleep-related metacognitions like “Thinking in bed means I won’t get to sleep,” and the cognitive, emotional, behavioral, and physiological reactions to them. The technique of “detached mindfulness” was introduced. Behavioral experiments and exercises were used to show how to let go of thoughts without giving them further attention. Metaphors were introduced to facilitate its use. Another technique is postponing worrying and rumination. The purpose of this technique is to reduce dysfunctional thought processes and show that thought processes are controllable, i.e., to challenge the negative metacognitive belief of uncontrollability of excessive thinking [31]. The goal of another technique, the “attention training technique,” is to focus attention outward, away from dysfunctional thought processes [31].

Questionnaires

The Insomnia Severity Index (ISI) [2, 14, 15] consists of seven items covering the nature and severity of insomniac complaints, both at nighttime and during the daytime, during the previous 2 weeks on a five-point Likert scale ranging from 0 to 4, with 4 indicating a very severe problem. A sum score is calculated reaching values between 0 and 28, with higher values representing more insomnia symptoms: a score of 0–7 indicates no clinically significant insomnia, 8–14 subthreshold insomnia, 15–21 clinical insomnia (moderate severity), and 22–28 clinical insomnia (severe) [2, 15]. The German version was used [4]. In the current study the reliability coefficients (Cronbach’s alpha) for the three measurement points were 0.711 (pretest), 0.798 (posttest), and 0.809 (follow-up).
The Metacognitions Questionnaire—Insomnia was originally created by Waine et al. [30] and includes 60 questions about sleep-related metacognitions [30]. Each question is answered on a four-point Likert scale from “do not agree” (1) to “agree very much” (4). A sum score of all items is calculated, with higher values representing more maladaptive sleep-related metacognitions [30]. A short form of the questionnaire, the MCQ-I 20, was developed by Schredl [24]. Three certified MCT therapists independently agreed on 20 translated items that unambiguously captured sleep-related metacognitions, e.g., “Before I fall asleep, I must get things sorted in my mind.” The MCQ-I 20 showed high internal consistency (rtt = 0.906) and high test–retest reliability (r = 0.916). The sum scores were previously shown to be higher for patients diagnosed with insomnia disorder as well as for those with nightmare disorder and depression disorder [24]. In the current study, the reliability coefficients (Cronbach’s alpha) for the two measurement points were 0.885 (pretest) and 0.818 (posttest).
Two questions related to the uncontrollability of excessive thinking were presented at the beginning and after the intervention. These questions were adapted from the CAS module “cognitive attentional syndrome” published in [31]. The questions used were 1) “Nocturnal chains of thoughts are uncontrollable” and 2) “Thought circles happen automatically.” Participants rated their level of conviction in these statements on a scale of 0 to 100, with 0 representing “I don’t believe in this belief at all” and 100 representing “I am absolutely convinced that this belief is true.”

Procedures

The CBT‑I program was offered to patients who underwent clinical diagnostics in the sleep laboratory of the Central Institute of Mental Health Mannheim, Germany, and who had received a diagnosis of chronic insomnia disorder. Each group consisted of 4 to 9 patients. The therapy was carried out between March 2015 and November 2020, whereby 52 patients received standard CBT‑I therapy and 40 patients received CBT‑I with integrated metacognitive elements.
Patients completed paper–pencil self-assessment questionnaires at the beginning of the therapy (pre), after 8 weeks (post), and after 20 weeks (follow-up visit; Fig. 1). If the patients were not able to join a meeting, they were asked to send the completed questionnaires. Not all participants completed the ISI at all timepoints. Some completed it only at the pre visit (total sample n = 11; sample without comorbidities n = 6), at the pre and post visits (total sample n = 23; sample without comorbidities n = 20), at the pre and follow-up visits (total sample n = 22; sample without comorbidities n = 19), and some completed the ISI at pre, post, and follow-up visits (total sample n = 21; sample without comorbidities n = 13). The effect of metacognitive interventions was evaluated by presenting two questions related to the uncontrollability of excessive thinking at the beginning and after the intervention (only in the CBT-I + MCE group).
The Ethics Committee II of the Medical Faculty Mannheim/University Heidelberg approved the retrospective analysis of the clinical data.

Statistical analysis

Data analysis was performed using the SPSS statistical software package, version 27 (IBM Corp., Armonk, NY, USA). Paired t-tests were used to test differences in mean scores comparing the pre, post, and follow-up timepoints. For the ISI, Bonferroni adjustment for multiple comparisons was performed. Cohen’s d was calculated between the three timepoints [3].
Associations between patient-reported psychometric measures (ISI, MCQ-I 20) and the demographic data (age, gender, comorbidities) at the pre visit were calculated using the Pearson correlation coefficient.
Mixed linear models were used to assess interactions of demographics or the pretreatment MCQ-I 20 score and therapy effectiveness based on the ISI scores over time (ISI score as dependent variable and time as the fixed factor, i.e., pre, post, follow-up). Demographic data (age, gender, comorbidities) and the pretreatment MCQ-I 20 score were set as covariates. The random factor was the subjects ID. We ran multiple linear models with one covariate in each model. For the treatment comparison of the CBT‑I (CBT-I) with the CBT‑I with integrated metacognitive elements (CBT-I+MCE), mixed linear models were calculated.

Results

Associations between ISI, MCQ-I, and other variables at baseline

The ISI showed a medium-size statistically significant correlation with the MCQ-I 20 score in the total sample (patients with and without comorbidities; r(54) = 0.46, p = < 0.001) and a high correlation in the subsample without comorbidities (r(41) = 0.57, p = < 0.001). There was no correlation between the baseline ISI or MCQ-I 20 with demographic data (age, gender, comorbidities). Women were slightly older than men (small correlation in the total sample, medium correlation in the subsample without comorbidities).

Changes in insomnia severity and metacognitions between pre, post, and follow-up visits

Statistical analyses are summarized in Tables 2 and 3. Compared to the pre visit, the ISI scores were significantly lower at the post visit and at the follow-up visit (large effect sizes). Comparing the post to the follow-up visit, the ISI score also became significantly lower over time, with medium (total sample) or large (without comorbidities) effect sizes. The MCQ-I 20 scores also displayed a significant reduction between the pre and the post visits with large effect sizes. The degree of conviction regarding the uncontrollability of excessive thinking decreased significantly upon comparing measures assessed prior to and after the metacognitive intervention (“Uncontrollable nocturnal chains of thoughts”: M = 49.17, SD = 24.66 at pre and M = 22.50, SD = 20.27 at post, Wilcoxon test: z = −3.7; p < 0.0001; “Automatic thought circles”: M = 58.75, SD = 32.21 at pre and M = 29.58, SD = 22.36 at post, Wilcoxon test: z = −3.8; p < 0.0001).
Table 2
Insomnia Severity Index—change over time
 
Mean ± SD
t-tests
Cohen’s d
 
Pre
Post
Follow-upa
Pre–post
Pre–follow-upa
Post–follow-upa
Pre–post
Pre–follow-upa
Post–follow-upa
    
N
t
p-value
N
t
p
N
t
p-value
   
Insomnia Severity Index
Total sample (with and without comorbidities)
17.32 ± 4.45
(N = 77)
7.93 ± 3.99
(N = 45)
7.01 ± 4.38
(N = 44)
44
−13.74
< 0.001
43
−14.5
< 0.001
21
−2.78
0.03
−2.07
(N = 44)
−2.21
(N = 43)
−0.61
(N = 21)
Subsample without comorbidities
17.22 ± 4.24
(N = 58)
7.94 ± 3.69
(N = 34)
6.90 ± 3.81
(N = 33)
33
−12.28
< 0.001
32
−12.66
< 0.001
13
−3.12
0.03
−2.14
(N = 33)
−2.24
(N = 32)
−0.87
(N = 13)
SD standard deviation
aFollow-up visit: 3 months after end of therapy
Table 3
Short version Metacognitions Questionnaire-Insomnia—change over time
 
Mean ± SD
t-tests
Cohen’s d
 
Pre
Post
Pre–post
Pre–post
   
N
t
p-value
 
Short version Metacognitions Questionnaire—Insomnia
Total sample (with and without comorbidities)
38.42 ± 10.11
(N = 57)
31.08 ± 7.02
(N = 53)
53
−6.05
< 0.001
−0.83
(N = 53)
Subsample without comorbidities
37.71 ± 10.25
(N = 42)
30.20 ± 6.03
(N = 41)
41
−5.53
< 0.001
−0.86
(N = 41)
SD standard deviation
In addition to the abovementioned improvements in questionnaire data, several participants stopped their sleep medication (total sample: n = 2; subsample without comorbidities: n = 1) or their sedative antidepressants (total sample: n = 8; subsample without comorbidities: n = 6) during the course of therapy.

Predictors of therapy effectiveness

Interactions between time and the covariates determine if therapy effectiveness is based on a given predictor. There were no significant interactions (Table 4). On the other hand, the fixed factor time was significant in all calculated models except for the MCQ-I 20 in the subsample without comorbidities. In both samples, the covariate pretreatment MCQ-I 20 was statistically significant.
Table 4
Predictors of therapy effectiveness based change in the Insomnia Severity Index over time
 
Time
Covariate
Interaction
 
F
p-value
F
p-value
F
p-value
Total sample (with and without comorbidities)
Gender
50.5
< 0.001
2.9
0.095
0.1
0.884
Age
16.4
< 0.001
3.4
0.068
1.3
0.279
Comorbidities
153.3
< 0.001
0.1
0.833
0.0
0.710
Short version Metacognitions Questionnaire—Insomnia (pre visit)
5.4
0.006
11.5
0.001
1.2
0.302
Subsample without comorbidities
Gender
39.9
< 0.001
0.5
0.501
0.3
0.739
Age
9.0
< 0.001
2.9
0.093
0.2
0.799
Short version Metacognitions Questionnaire—Insomnia (pre visit)
2.3
0.106
11.8
0.001
2.4
0.102
Mixed linear models calculating the interactions between the covariates demographics (age, gender, comorbidities) and pretreatment MCQ-I 20 score with the therapy effectiveness based on the ISI scores over time (pre, post, follow-up)

Comparison of CBT-I and CBT-I+MCE

Therapy outcome did not differ between the treatment conditions CBT‑I only and CBT‑I with metacognitive elements (Table 5). The fixed factor time was significant in all calculated models, replicating the findings that the therapy worked. The summed-up data between the two compared treatment groups did not differ.
Table 5
Comparison of treatment effects of patients undergoing CBT‑I or CBT-I + MCE
 
Cognitive behavioral therapy for insomnia (N = 37)
Cognitive behavioral therapy for insomnia with integrated metacognitive elements (N = 40)
Time
Treatment condition
Time × treatment condition interaction
 
Pre
Post
Follow-upa
Pre
Post
Follow-upa
F
p-value
F
p-value
F
p-value
 
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
      
Total sample (with and without comorbidities)
Insomnia Severity Index
17.53 ± 3.16
(N = 37)
6.57 ± 4.72
(N = 7)
7.51 ± 3.86
(N = 25)
17.13 ± 5.42
(N = 40)
8.18 ± 3.85
(N = 38)
6.35 ± 5.00
(N = 19)
88.3
<0.001
0.1
0.812
0.6
0.541
Short version Metacognitions Questionnaire—Insomnia
39.88 ± 9.21
(N = 17)
30.13 ± 4.72
(N = 15)
37.80 ± 10.52
(N = 40)
31.45 ± 7.77
(N = 38)
18.4
<0.001
0.1
0.746
1.2
0.275
Subsample without comorbidities
Insomnia Severity Index
17.05 ± 2.76
(N = 28)
7.20 ± 5.59
(N = 5)
7.94 ± 4.02
(N = 21)
17.37 ± 5.31
(N = 30)
8.07 ± 3.39
(N = 29)
5.07 ± 2.67
(N = 12)
62.1
<0.001
0.3
0.558
1.7
0.187
Short version Metacognitions Questionnaire—Insomnia
36.83 ± 6.86
(N = 12)
29.33 ± 4.87
(N = 12)
38.07 ± 11.42
(N = 30)
30.55 ± 6.50
(N = 29)
9.9
0.003
0.3
0.578
0.0
0.874
SD standard deviation
a Follow-up visit: 3 months after end of therapy

Discussion

Our study shows that CBT‑I results in a decrease in sleep-related metacognitions. Therapy outcome was not dependent on the level of pretreatment sleep-related metacognitions. Also, CBT‑I and CBT‑I with integrated metacognitive elements were equally effective.
Prior to discussing the findings in detail, a few methodological issues will be addressed. The study was carried out in a clinical setting over several years. Therefore, not all questionnaires were completed at all timepoints, as there were changes in the procedures over time. On the other hand, the real-world character of the data offered the possibility to control for comorbidities, and the decrease in ISI scores due to therapy showed large effect sizes—fitting very well with a previous meta-analysis regarding the efficacy of CBT‑I [9, 28].
In addition to the reduction of insomnia symptoms measured by the ISI, CBT‑I also reduced the MCQ‑I scores with large effect sizes. Our effect size of about d = 0.80 was somewhat higher than the effect size (d = −0.525) reported by Galbiati et al. [8]. Thus, the improvement in sleep quality (the primary aim of CBT-I) is paralleled by a reduction in dysfunctional sleep-related metacognitions. As no difference between classical CBT‑I and CBT‑I including metacognitive elements was observed, one might speculate that the effect on dysfunctional sleep-related metacognitions may be explained by a general improvement in sleep. However, repeated ratings on the conviction about uncontrollability of excessive thought processes showed that the metacognitive interventions worked in the sleep therapy setting. It would be very interesting to test whether purely metacognitive approaches to insomnia without applying techniques like sleep restriction would also yield large effect sizes regarding subjective sleep quality. According to the metacognitive model of Ong et al., a high level of dysfunctional sleep-related metacognitions can cause secondary arousal, which can result in a perpetuation of negative behavior and emotions and therefore perpetuate the vicious cycle of chronic insomnia. Within this framework, an effect of metacognitive therapy [31] would be expected.
The literature regarding demographic predictors of CBT‑I effectiveness shows no effect of gender or age [7, 26, 29], but current psychiatric conditions seem to predict lower therapy effectiveness [27]. This was not confirmed by our study. In our clinical setting, CBT‑I was highly effective in patients suffering from insomnia disorder only, as well as in patients with insomnia disorder plus comorbid mental disorders. We did not see an effect of the level of pretreatment sleep-related metacognitions on therapy outcome. This finding is interesting in the context of the model of Ong et al., showing that even though sleep-related metacognitions play a role in insomnia etiology, their baseline level had no effect on therapy outcome.
For 40 out of 92 participants, classical CBT‑I was complemented by metacognitive strategies. The rationale behind this was that the classical cognitive restructuring of thoughts such as “If my insomnia continues like this, I will get sick” [17] was not seen as satisfactory, especially as recent research [13] has shown that chronic insomnia has many negative health consequences. That is, the content of the thought is correct, and therefore, it could be beneficial for patients to focus on dealing with sleep-related cognitions in a constructive way instead of questioning the plausibility of sleep-related cognitions. As stated above, investigating the effects of MCT alone and comparing it to classical CBT‑I would give us a better understanding about the interplay between dysfunctional sleep-related metacognitions and general sleep quality, and provide support for the metacognitive model of Ong et al. [20]. It would also be interesting to study whether changes in sleep-related metacognitions during therapy predict long-term improvements. In this study, the introduction of MCT elements was as effective as classical CBT‑I; thus, the findings might stimulate a “modernization” of CBT‑I regarding the techniques dealing with sleep-related cognitions and metacognitions.

Conclusion

To summarize, a better understanding of the role of sleep-related metacognitions within the insomnia model might be useful to adapt the well-established therapeutic techniques for treating insomnia. One possible option is incorporation of metacognitive elements. In addition to studying the efficacy of these new approaches, it would also be very interesting to see whether a metacognitive approach to sleep-related cognitions is perceived as more positive by the patients than the classical technique of cognitive restructuring.

Practical conclusion

  • Cognitive behavioral therapy for insomnia (CBT-I) decreases sleep-related metacognitions.
  • The therapy outcome did not depend on the level of pretreatment sleep-related metacognitions.
  • Introducing elements of metacognitive therapy (MCT) was as effective as classical CBT‑I.
  • The introduction of MCT elements might be a valuable addition to the standard cognitive strategies of CBT‑I.

Declarations

Conflict of interest

A.-L. Jenewein, M. Schredl and C. Schilling declare that they have no competing interests.
All procedures followed were in accordance with the ethical standard of the responsible ethics committee and the Helsinki Declaration of 1975 (in its most recently amended version). The local ethics committee approved the retrospective analyses of the clinical data.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

Unsere Produktempfehlungen

Somnologie

Print-Titel

  • Aktuelles, gesichertes Fachwissen zu Ätiologie, Pathophysiologie, Differentialdiagnostik und Therapie von Schlafstörungen

• Multidisziplinärer Ansatz

e.Med Interdisziplinär

Kombi-Abonnement

Jetzt e.Med zum Sonderpreis bestellen!

Für Ihren Erfolg in Klinik und Praxis - Die beste Hilfe in Ihrem Arbeitsalltag

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de.

Jetzt bestellen und 100 € sparen!

e.Dent – Das Online-Abo der Zahnmedizin

Online-Abonnement

Mit e.Dent erhalten Sie Zugang zu allen zahnmedizinischen Fortbildungen und unseren zahnmedizinischen und ausgesuchten medizinischen Zeitschriften.

Weitere Produktempfehlungen anzeigen
Literatur
1.
Zurück zum Zitat American Academy of Sleep Medicine (2014) International classification of sleep disorders. (ICSD-3). American Academy of Sleep Medicine, Darien American Academy of Sleep Medicine (2014) International classification of sleep disorders. (ICSD-3). American Academy of Sleep Medicine, Darien
2.
Zurück zum Zitat Bastien C (2001) Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med 4:297–307CrossRef Bastien C (2001) Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med 4:297–307CrossRef
3.
Zurück zum Zitat Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale, NJ Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale, NJ
4.
Zurück zum Zitat Dieck A, Morin CM, Backhaus J (2018) A German version of the insomnia severity index. Somnologie 1:27–35CrossRef Dieck A, Morin CM, Backhaus J (2018) A German version of the insomnia severity index. Somnologie 1:27–35CrossRef
5.
Zurück zum Zitat Espie CA (1991) The psychological treatment of insomnia. Wiley, Chichester Espie CA (1991) The psychological treatment of insomnia. Wiley, Chichester
6.
Zurück zum Zitat Espie CA, Broomfield NM, MacMahon KMA et al (2006) The attention-intention-effort pathway in the development of psychophysiologic insomnia: a theoretical review. Sleep Med Rev 4:215–245CrossRef Espie CA, Broomfield NM, MacMahon KMA et al (2006) The attention-intention-effort pathway in the development of psychophysiologic insomnia: a theoretical review. Sleep Med Rev 4:215–245CrossRef
7.
Zurück zum Zitat Espie CA, Inglis SJ, Harvey L (2001) Predicting clinically significant response to cognitive behavior therapy for chronic insomnia in general medical practice: analysis of outcome data at 12 months posttreatment. J Consult Clin Psychol 1:58–66CrossRef Espie CA, Inglis SJ, Harvey L (2001) Predicting clinically significant response to cognitive behavior therapy for chronic insomnia in general medical practice: analysis of outcome data at 12 months posttreatment. J Consult Clin Psychol 1:58–66CrossRef
10.
Zurück zum Zitat Irwin MR, Cole JC, Nicassio PM (2006) Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychol 1:3–14CrossRef Irwin MR, Cole JC, Nicassio PM (2006) Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychol 1:3–14CrossRef
12.
Zurück zum Zitat LeBlanc M, Mérette C, Savard J et al (2009) Incidence and risk factors of insomnia in a population-based sample. Sleep 8:1027–1037CrossRef LeBlanc M, Mérette C, Savard J et al (2009) Incidence and risk factors of insomnia in a population-based sample. Sleep 8:1027–1037CrossRef
14.
Zurück zum Zitat Morin CM (1993) Insomnia. Psychological assessment and management, 1st edn. Guilford, New York Morin CM (1993) Insomnia. Psychological assessment and management, 1st edn. Guilford, New York
15.
Zurück zum Zitat Morin CM, Belleville G, Bélanger L et al (2011) The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 5:601–608CrossRef Morin CM, Belleville G, Bélanger L et al (2011) The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 5:601–608CrossRef
16.
Zurück zum Zitat Morin CM, Vallières A, Ivers H (2007) Dysfunctional beliefs and attitudes about sleep (DBAS): validation of a brief version (DBAS-16). Sleep 11:1547–1554CrossRef Morin CM, Vallières A, Ivers H (2007) Dysfunctional beliefs and attitudes about sleep (DBAS): validation of a brief version (DBAS-16). Sleep 11:1547–1554CrossRef
17.
Zurück zum Zitat Müller TH, Paterok B (2010) Schlaftraining. Ein Therapiemanual zur Behandlung von Schlafstörungen, 2nd edn. Hogrefe, Göttingen, Bern, Wien Müller TH, Paterok B (2010) Schlaftraining. Ein Therapiemanual zur Behandlung von Schlafstörungen, 2nd edn. Hogrefe, Göttingen, Bern, Wien
18.
Zurück zum Zitat Ohayon MM (2002) Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev 2:97–111CrossRef Ohayon MM (2002) Epidemiology of insomnia: what we know and what we still need to learn. Sleep Med Rev 2:97–111CrossRef
19.
Zurück zum Zitat Okajima I, Komada Y, Inoue Y (2011) A meta-analysis on the treatment effectiveness of cognitive behavioral therapy for primary insomnia. Sleep Biol Rhythms 1:24–34CrossRef Okajima I, Komada Y, Inoue Y (2011) A meta-analysis on the treatment effectiveness of cognitive behavioral therapy for primary insomnia. Sleep Biol Rhythms 1:24–34CrossRef
20.
Zurück zum Zitat Ong JC, Ulmer CS, Manber R (2012) Improving sleep with mindfulness and acceptance: a metacognitive model of insomnia. Behav Res Ther 11:651–660CrossRef Ong JC, Ulmer CS, Manber R (2012) Improving sleep with mindfulness and acceptance: a metacognitive model of insomnia. Behav Res Ther 11:651–660CrossRef
22.
Zurück zum Zitat Riemann D, Spiegelhalder K, Feige B et al (2010) The hyperarousal model of insomnia: a review of the concept and its evidence. Sleep Med Rev 1:19–31CrossRef Riemann D, Spiegelhalder K, Feige B et al (2010) The hyperarousal model of insomnia: a review of the concept and its evidence. Sleep Med Rev 1:19–31CrossRef
23.
Zurück zum Zitat Schlack R, Hapke U, Maske U et al (2013) Häufigkeit und Verteilung von Schlafproblemen und Insomnie in der deutschen Erwachsenenbevölkerung: Ergebnisse der Studie zur Gesundheit Erwachsener in Deutschland (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 5–6:740–748CrossRef Schlack R, Hapke U, Maske U et al (2013) Häufigkeit und Verteilung von Schlafproblemen und Insomnie in der deutschen Erwachsenenbevölkerung: Ergebnisse der Studie zur Gesundheit Erwachsener in Deutschland (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 5–6:740–748CrossRef
24.
Zurück zum Zitat Schredl M, Schackert M, Feld GB et al (2021) Ein Fragebogen zur Erfassung von schlafbezogenen Metakognitionen: Deutsche Kurzform des MCQ‑I. Somnologie 3:205–211CrossRef Schredl M, Schackert M, Feld GB et al (2021) Ein Fragebogen zur Erfassung von schlafbezogenen Metakognitionen: Deutsche Kurzform des MCQ‑I. Somnologie 3:205–211CrossRef
25.
Zurück zum Zitat Spielman AJ, Caruso LS, Glovinsky PB (1987) A behavioral perspective on insomnia treatment. Psychiatr Clin North Am 4:541–553CrossRef Spielman AJ, Caruso LS, Glovinsky PB (1987) A behavioral perspective on insomnia treatment. Psychiatr Clin North Am 4:541–553CrossRef
26.
Zurück zum Zitat Troxel WM, Conrad TS, Germain A et al (2013) Predictors of treatment response to brief behavioral treatment of insomnia (BBTI) in older adults. J Clin Sleep Med 12:1281–1289CrossRef Troxel WM, Conrad TS, Germain A et al (2013) Predictors of treatment response to brief behavioral treatment of insomnia (BBTI) in older adults. J Clin Sleep Med 12:1281–1289CrossRef
27.
Zurück zum Zitat van de Laar M, Pevernagie D, van Mierlo P et al (2015) Psychiatric comorbidity and aspects of cognitive coping negatively predict outcome in cognitive behavioral treatment of psychophysiological insomnia. Behav Sleep Med 2:140–156CrossRef van de Laar M, Pevernagie D, van Mierlo P et al (2015) Psychiatric comorbidity and aspects of cognitive coping negatively predict outcome in cognitive behavioral treatment of psychophysiological insomnia. Behav Sleep Med 2:140–156CrossRef
29.
Zurück zum Zitat Van Houdenhove L, Buyse B, Gabriëls L et al (2011) Treating primary insomnia: clinical effectiveness and predictors of outcomes on sleep, daytime function and health-related quality of life. J Clin Psychol Med Settings 3:312–321CrossRef Van Houdenhove L, Buyse B, Gabriëls L et al (2011) Treating primary insomnia: clinical effectiveness and predictors of outcomes on sleep, daytime function and health-related quality of life. J Clin Psychol Med Settings 3:312–321CrossRef
30.
Zurück zum Zitat Waine J, Broomfield NM, Banham S et al (2009) Metacognitive beliefs in primary insomnia: developing and validating the metacognitions questionnaire—insomnia (MCQ-I). J Behav Ther Exp Psychiatry 1:15–23CrossRef Waine J, Broomfield NM, Banham S et al (2009) Metacognitive beliefs in primary insomnia: developing and validating the metacognitions questionnaire—insomnia (MCQ-I). J Behav Ther Exp Psychiatry 1:15–23CrossRef
31.
Zurück zum Zitat Wells A (2011) Metakognitive Therapie bei Angststörungen und Depression, 1st edn. Beltz, Weinheim, Basel Wells A (2011) Metakognitive Therapie bei Angststörungen und Depression, 1st edn. Beltz, Weinheim, Basel
Metadaten
Titel
Sleep-related metacognitions and cognitive behavioral therapy for insomnia
verfasst von
M.Sc. Anna-Lea Jenewein
apl. Prof. Dr. Michael Schredl
Dr. Claudia Schilling
Publikationsdatum
27.04.2023
Verlag
Springer Medizin
Erschienen in
Somnologie / Ausgabe 2/2023
Print ISSN: 1432-9123
Elektronische ISSN: 1439-054X
DOI
https://doi.org/10.1007/s11818-023-00404-9

Weitere Artikel der Ausgabe 2/2023

Somnologie 2/2023 Zur Ausgabe

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Weniger aneurysmenbedingte Subarachnoidalblutungen unter fünf Arzneien

Lisinopril, Amlodipin, Simvastatin, Metformin und Tamsulosin – unter diesen fünf Medikamenten ist das Risiko für Subarachnoidalblutungen durch Aneurysmen deutlich reduziert. Sie könnten sich möglicherweise zur Prävention solcher Blutungen eignen.

Myasthenia gravis durch Krebsimmuntherapie

13.06.2024 Myasthenia gravis Nachrichten

Sie sind sehr selten, können aber schwerwiegende Folgen haben: immunologische Nebenwirkungen unter Checkpointhemmern. Ein Behandlungsteam berichtet nun über einen Mann, der während einer Darmkrebsbehandlung eine Myasthenia gravis entwickelt.

Auch nach schwerem Schlaganfall lohnt sich frühe Antikoagulation

06.06.2024 Apoplex Nachrichten

Personen mit Vorhofflimmern haben nach einem schweren ischämischen Schlaganfall keine Nachteile, wenn sie etwas früher als empfohlen eine orale Antikoagulation erhalten. Tendenziell kommt es dann sogar seltener zu vaskulären Ereignissen.

Alice-im-Wunderland-Syndrom gehäuft bei Migräne mit Aura

06.06.2024 Migräne Nachrichten

Eine verzerrte Wahrnehmung von Raum, Zeit oder Körper tritt vor allem bei Migräne mit Aura auf: 20% dieser Migränekranken haben solche Veränderungen schon einmal erlebt, meist während einer Migräne-Attacke. Möglicherweise handelt es sich hier um eine Aura-Variante.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.