An investigation and replication of sleep-related cognitions, acceptance and behaviours as predictors of short- and long-term outcome in cognitive behavioural therapy for insomnia

Prognostic models for predicting insomnia treatment outcomes: A systematic review

OBJECTIVE

To identify and critically evaluate models predicting insomnia treatment response in adult populations.

METHODS

Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain.

CONCLUSION

Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.

Mechanisms of cognitive behavioural therapy for insomnia

Although much is known now about behavioural, cognitive and physiological consequences of insomnia, little is known about changes after cognitive behavioural therapy for insomnia on these particular factors. We here report baseline findings on each of these factors in insomnia, after which we address findings on their changes after cognitive behavioural therapy. Sleep restriction remains the strongest determinant of insomnia treatment success. Cognitive interventions addressing dysfunctional beliefs and attitudes about sleep, sleep-related selective attention, worry and rumination further drive effectiveness of cognitive behavioural therapy for insomnia. Future studies should focus on physiological changes after cognitive behavioural therapy for insomnia, such as changes in hyperarousal and brain activity, as literature on these changes is sparse. We introduce a detailed clinical research agenda on how to address this topic.

Improving mental health and daytime function in adult insomnia patients predict cognitive behavioral therapy for insomnia effectiveness: A case-control study

Objective

This study investigated demographic, sleep related symptoms and mental health status as predictors of clinically significant treatment responses to cognitive behavioral therapy in adults who have good adherence for the cognitive behavioral therapy for insomnia (CBT-I) program in primary insomnia.

Methods

A total of 42 adults with primary insomnia disorder were treated with CBT-I at a university hospital from June 2020 to January 2021. Demographic variables were surveyed and sleep-related symptoms were measured using self-reported questionnaires before and after the intervention, comprising a 6-week interval. The treatment responder group was defined as patients with an Insomnia Severity Index change score >7 compared to baseline. Logistic regression and paired t-test examined whether these factors predicted treatment outcomes for CBT-I.

Results

Demographic variables did not predict treatment outcomes. Higher levels of anxiety were associated with a higher likelihood of treatment response (odds ratio [OR] = 1.234; confidence interval [CI]: 1.008-1.511). More severe insomnia at baseline was associated with a greater likelihood of treatment response (OR = 1.450; CI: 1.121-1.875). The lesser the dysfunctional beliefs and attitudes about sleep, the more effective the treatment response (OR = 0.943; CI: 0.904-0.984). Unlike the group of treatment responders, daytime function, depressive mood, and anxiety status did not improve in the group of treatment non-responders after CBT-I intervention.

Conclusions

Patients with severe insomnia and anxiety at baseline should be treated more aggressively with CBT-I. During treatment, patients' mental health problems and daytime activities should be continuously monitored, in order to help improve these problems which might strengthen the effectiveness of CBT-I.