A network approach of Insomnia and Dysfunctional Beliefs and Attitudes About Sleep

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# A network approach of Insomnia and Dysfunctional Beliefs and Attitudes About Sleep
## VII Congresso Clínica Psiquiátrica 2022
### Marwin M I B Carmo
### Renatha El Rafihi Ferreira
### Department of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil
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<a href="https://marwincarmo.github.io">Marwin M I B Carmo</a> (<a href="mailto:marwin@usp.br">marwin@usp.br</a>) -- A network approach of Insomnia and Dysfunctional Beliefs and Attitudes About Sleep.
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# Introduction
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Negatively toned cognitive activity triggers arousal and distress, channeling attention and monitoring to sleep, and creating distorted perceptions ([Harvey, 2002](https://doi.org/fwxq35)).

Challenging unhelpful beliefs about sleep is a crucial element of cognitive-behavioral therapy for insomnia.

Understanding how this network of beliefs connects to insomnia severity may provide leads to help targeted interventions.
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# Introduction
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A **network** structure is represented by **nodes** (variables), connected by **edges** (strength of associations) ([Burger et al., 2022](https://doi.org/10.1037/met0000471)).

Measures of centrality ([Borsboom et al., 2021](https://doi.org/10.1038/s43586-021-00055-w)):

- **Node strenght**: sums the absolute edge weights of edges per node;  
- **Closeness**: quantifies the distance between the node and all other nodes;  
- **Betweenness**: quantifies how often a node lies on the shortest path connecting any two other nodes.

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# Objective
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To use a network approach to explore interactions between dysfunctional beliefs and attitudes about sleep and insomnia severity.

To identify specific nodes within dysfunctional beliefs and attitudes about sleep that may play a pivotal role in maintaining insomnia.
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# Method
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**Participants**: 1166 adults, aged 18 to 59 years, both with and without insomnia symptoms.

**Instruments**:

- Insomnia Severity Index (ISI);

- Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16). 
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# Method
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We fitted Gaussian graphical models using graphical LASSO regularization in combination with Extended Bayesian Information Criterion (EBIC) model selection, with a tuning hyperparameter set to 0.5.

- A network describing the associations among overall insomnia severity and the four **factors** of DBAS-16

- A network describing the associations among overall insomnia severity and each of the individual **items** of DBAS-16.

The stability of the network structures was estimated using a case dropping bootstrap procedure (1000 iterations). Node centrality was assessed by strength, closeness, and betweenness.
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# Sample Characteristics

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- **Age**: 38.28 (9.76)

- **Gender (Female)**: 927 (79.5%)

- **Insomnia symptoms**: 935 (80.19%)

- **Race/ethnicity**:
  - Asian: 41 (3.52%)
  - Black: 279 (23.93%)
  - White: 835 (71.61%)
  - Other/Not informed: 11 (0.94%)
  
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- **Education**:
  - College degree or higher: 891 (76.42%)
  - Some college: 166 (14.24%)
  - Less than 12th grade: 109 (9.35%)

- **Region of origin**:

- North: 24 (2.06%)
  - Northeast: 85 (7.29%)
  - Central-West: 51 (4.37%)
  - Southeast: 905 (77.62%)
  - South: 101 (8.66%)
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# Results

<div class="figure" style="text-align: center">
<img src="dbas-network_slides_files/figure-html/factors-plot-1.png" alt="isi = Insomnia Severity Index, worry = Worry about sleep, exp = Sleep expectations, cons = Consequences of insomnia, med = Medication." width="80%" />
<p class="caption">isi = Insomnia Severity Index, worry = Worry about sleep, exp = Sleep expectations, cons = Consequences of insomnia, med = Medication.</p>
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# Results

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# Results

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# Results

<img src="dbas-network_slides_files/figure-html/network-items-1.png" width="100%" style="display: block; margin: auto;" />
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# Results

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# Results

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# Conclusion
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- We used Network analysis as an exploratory technique to identify patterns that may help interpreting empirical phenomena.

- **Worry about sleep** is a factor that can directly influence other nodes.

- *Belief that a poor night of sleep would ruin functioning on the next day* and *feelings that insomnia is ruining ability to enjoy life* are possible targets to for improving CBT-I.

- These findings may benefit the efficiency of future intervention studies by identifying priority symptoms for treatment.
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# References

Harvey, A. G. (2002). A cognitive model of insomnia. *Behaviour Research and Therapy, 40*(8), 869–893. https://doi.org/10/fwxq35

Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. *Nature Reviews Methods Primers, 1*(1). https://doi.org/10.1038/s43586-021-00055-w

Burger, J., Isvoranu, A.-M., Lunansky, G., Haslbeck, J. M. B., Epskamp, S., Hoekstra, R. H. A., Fried, E. I., Borsboom, D., & Blanken, T. F. (2022). Reporting standards for psychological network analyses in cross-sectional data. *Psychological Methods*. https://doi.org/10.1037/met0000471

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# Thank you

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Contact:
  - Email: [marwin@usp.br](mailto:marwin@usp.br)
  - Webpage: [https://marwincarmo.github.io/](https://marwincarmo.github.io/)
  - Github: [marwincarmo](https://github.com/marwincarmo)
  - Twitter: [marwincarmo](https://twitter.com/marwincarmo)
  - Linkedin: [Marwin Carmo](https://www.linkedin.com/in/marwin-carmo/)

This presentation was created using `xaringanthemer` package for R. Code available at [bit.ly/viiccp-code](https://bit.ly/viiccp-code)
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Access the slides at

<a href="https://bit.ly/viicp-dbas">bit.ly/viicp-dbas</a>

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