Advances in Digital Cognitive Behavioral Therapy for the Treatment of Insomnia

Psychiatric TimesVol 38, Issue 8 Digital Supplement

Close to a third of adults in Western countries report trouble with sleep. Digital cognitive behavioral therapy may be a treatment solution...



According to some estimates, close to one-third of adults in Western countries regularly experience difficulties with sleep. Additionally, approximately 10% of adults meet DSM-5 diagnostic criteria for insomnia disorder, which is characterized by difficulties falling or staying asleep at least 3 days per week for at least 3 months, along with impairment in daytime functioning or well-being.1 Beyond sleep difficulties and associated daytime complaints, individuals with insomnia are at increased risk of developing mental disorders (eg, depression, anxiety, substance use disorders) and physical health conditions (eg, type 2 diabetes, cardiovascular disease, and hypertension).2-4 It is not surprising, then, that the burden of insomnia is significant and individuals with insomnia report a lower quality of life than their healthy-sleeping counterparts.5

Treating Insomnia

Cognitive behavioral therapy for insomnia (CBT-I; see Table) and medication are 2 approaches to insomnia treatment. Medication is the most widely available treatment, despite CBT-I’s sustained sleep improvements and fewer side effects than pharmacotherapy.5 Decades of research show that CBT-I is effective in a range of populations, including those with psychiatric and medical comorbidities.6,7 Importantly, patients prefer CBT-I to medication for the long-term management of their sleep difficulties.8 As a result, the American Academy of Sleep Medicine, American College of Physicians, British Association for Psychopharmacology, the European Sleep Research Society, and other international bodies recommend CBT-I as first-line therapy for insomnia.5,9-11

Components of CBT for Insomnia

Table. Components of CBT for Insomnia

Unfortunately, the need for CBT-I far exceeds the capacity of our health care systems. Colleagues once calculated that the required capacity to extend 3 hours of CBT-I to half of the 50 million individuals who use sleep medication in the United States and United Kingdom would be about 2 million clinicians. This is greater than 10 times the number of licensed psychologists in both countries combined.12 This problem is magnified internationally, given that 88% of sleep specialists are based in the United States, meaning that most countries do not have any sleep specialists to deliver CBT-I.13

This limitation, coupled with other barriers including stigma, a lack of CBT-I awareness among patients and providers, and the perception of insomnia treatment as a low priority, prevents widespread access to this recommended treatment.6 Although telehealth delivery has been proposed as a solution, trained therapists are still required. Involving therapists, however efficiently, is not scalable to meet the volume of need. Therefore, we have an urgent need to raise awareness of CBT-I and develop modes of delivery that are more accessible and easier to disseminate.

The Potential of DTx

Internet- and smartphone-delivered treatments, termed “digital therapeutics” (DTx), are ideally situated to provide a solution for overcoming the dissemination and access barriers. Chiefly, DTx decouple treatment from the requirement to work directly with a mental health care professional. It also makes treatment flexible and scalable, as access to the internet and devices are its only limiting factor (and even that may one day be an obsolete consideration). Plus, many patients with insomnia turn to self-help more readily than to professionals,1 making DTx an ideal access point.

Digitally-delivered CBT (dCBT) can take 1 of 3 formats: 1) web- and smartphone-based tools to support ongoing psychotherapy with a clinician; 2) dCBT packages implemented by the consumer outside of a therapy context but guided by regular input from a clinician, ie, guided dCBT; and 3) fully automated dCBT requiring no support from clinicians.14 Of these, fully automated CBT is the most promising option for addressing access to CBT-I; these programs can completely transcend the limitations in provider availability, offer immediate access, and negate any reluctance patients may have to working with a therapist. For these reasons, it is the only form of dCBT that could be scaled to implement treatment guideline recommendations to use CBT-I as first-line therapy.

To fully realize the ambitious aim of digital solutions, attention to technical expertise and the infrastructure of big tech collaborators is required. DTx are successful and scalable only if they 1) guarantee an intuitive, engaging, and adaptive user experience; 2) can be updated, monitored, and repaired easily as needed; and 3) are capable of managing tremendous amounts of data securely. If the aim is to make CBT as accessible and acceptable as pharmacotherapy, clinicians and patients need not (and should not) be satisfied with digital solutions that fall short of this goal. Technological rigor must be matched by empirical rigor, ensuring only those treatments with the very strongest evidence base are carefully translated to a digital format.

A number of dCBT programs have been developed; the 2 most widely known and fully automated programs are Sleepio15 and Somryst (previously called SHUTi).16 Both deliver content and exercises across 6 sessions over a flexible timeline, and content remains available to suit the patient’s needs. Sleepio is delivered via their website and has an accompanying smartphone application; Somryst was historically delivered via their website and is now available via a smartphone application. Both Sleepio and Somryst use dynamic, user-friendly interfaces to keep patients engaged (eg, animations that illustrate treatment content), and both use patient input (eg, sleep diaries and in-program questions) to personalize the intervention. The US Food and Drug Administration recently cleared Somryst as a prescription digital therapeutic (PDT), which can be prescribed much like pharmacological interventions.17 It is likely that the future of dCBT will involve many more products following a similar route.

Examining Effectiveness

The efficacy of dCBT-I is supported by a large and growing evidence base. In randomized controlled trials, both guided and fully automated dCBT interventions have demonstrated medium to large effect sizes on a range of outcomes including insomnia severity, sleep efficiency, total sleep time, and sleep onset latency. Additionally, dCBT interventions consistently outperform the behavioral therapy equivalent of placebo (imagery-based procedures), wait-list, sleep hygiene, and usual care controls.18-21 dCBT has also been shown to improve sleep in a wide variety of populations, including individuals with subclinical sleep complaints, pregnant women, and individuals with comorbid medical and psychiatric disorders. The durability of effects for dCBT is promising, with these improvements being maintained up to a year and beyond.21-23 Based on these findings, there is an emerging consensus that dCBT-I produces effects comparable to face-to-face therapy.

Notably, the effects of dCBT-I extend beyond better sleep. Studies have consistently shown reductions in anxiety and depression symptoms in addition to other areas of mental health,24 and that effects are likely mediated by improvements in insomnia symptoms.25,26 Benefits have also been observed in terms of quality of life, physical health, and workplace productivity27,28; effects on physical health and workplace productivity are, again, likely mediated by reductions in insomnia.23 It is critical, therefore, to continue exploring how dCBT could be integrated in behavioral medicine, rehabilitation, and other nonpsychiatric health care settings in order to best meet the needs of those seeking insomnia therapy and for whom insomnia may not be their presenting complaint but who could nonetheless benefit from intervention.

Scaling dCBT has the potential to be significantly more affordable than scaling in-person services. Even guided dCBT is more cost-effective than traditional CBT-I,27,29 and we can expect that fully automated dCBT would offer yet more cost efficiency. To illustrate, my colleagues and I recently evaluated the estimated monetary benefits of Sleepio in terms of treatment costs and downstream effects of treating insomnia, such as improved workplace productivity. We found fully automated dCBT to be more cost-effective than pharmacotherapy and traditional individual and group therapy for insomnia, yielding an estimated net monetary benefit of $681 per individual over 6 months.30

Consider, finally, another methodological advantage of dCBT for insomnia, or any digital therapeutic for that matter: the ease of disseminating digital interventions permits conducting research with sample sizes at least an order of magnitude larger than face-to-face RCTs (eg, recent trials of Sleepio randomized 1711 and 3755 participants respectively).20,25 Researchers have the freedom to ensure that study populations are broadly representative and generalizable or narrowly specified to meet the scientific need. This means that the evidence base for dCBT may be able to grow faster, and with greater rigor and granularity, than that of medication or face-to-face therapy.

It is worth noting, however, that dCBT will not be a panacea for all. Some individuals will still require face-to-face CBT-I with a trained clinician or sleep medicine specialist, and some will require medication management. Nevertheless, dCBT has the advantage of providing access at scale to evidence-based CBT-I and, therefore, is well-positioned to be part of a stepped-care model of insomnia treatment that is consistent with treatment guidelines.31

Future Research Directions

To responsibly harness the tremendous potential of dCBT for insomnia, it is important to recognize where more research is needed. Although we can confidently say that dCBT is advantageous in a general sense, the picture must be sharpened in a number of ways. To date, there have been very few noninferiority trials comparing dCBT to in-person CBT-I, and those that have been carried out have reported mixed results.28,32,33 In addition, a systematic exploration of the variables moderating and mediating response to dCBT is important, especially in figuring out how best to fine-tune treatment delivery to meet the needs of patients with insomnia. Factors likely to affect treatment effectiveness include facility with digital applications in general, inclination to access treatment or self-help generally, and the tendency to have difficulty engaging with treatments and adhering to treatment regimens.

We also need to compare different means of disseminating and implementing dCBT at scale (eg, as PDTs vs self-help, with and without the involvement of providers known to the patient, and across different settings). Demonstrating real-world cost-effectiveness will be critical.

Even with the overarching aim of maximizing automation, it will nonetheless be essential to identify conditions under which judicious use of sleep experts is incrementally beneficial. It is also important to consider that some individuals who pursue dCBT treatment may have conditions that require evaluation and treatment by a health care provider. While this consideration applies to a wide range of medical conditions, a few notable examples include the following: obstructive sleep apnea, narcolepsy, major depressive disorder, anxiety disorders, mania, substance use disorders, and endocrine disorders associated with sleep disturbance. It will be important to identify ways to incorporate some means of identifying individuals with such conditions and motivating them to engage in appropriate evaluation/treatment into digital platforms. Finally, recognizing that pharmacotherapy remains the most widespread intervention for insomnia to date, it is important to explore how dCBT-I might be implemented in combination with pharmacotherapy, or as a means of reducing reliance on medication. Only with these questions in mind can we meaningfully leverage the power of dCBT to bring clinical practice in line with the best available guidelines for treating insomnia.

Dr Krystal is the Ray and Dagmar Dolby Distinguished Professor in the Department of Psychiatry and Behavioral Sciences at University of California San Francisco. He is also Director of the Clinical and Translational Sleep Research Laboratory, Director of the Dolby Family Center for Mood Disorders, and Vice-Chair for Research. Dr Krystal receives research grant support and/or consulting fees from Janssen Pharmaceuticals, Axsome Therapeutics, Reveal Biosensors, the Ray and Dagmar Dolby Family Fund, the National Institutes of Health, Adare Pharmaceuticals, Big Health, Eisai, Evecxia Therapeutics, Ferring Pharmaceuticals, Galderma, Harmony Biosciences, Idorsia Pharmaceuticals, Jazz Pharmaceuticals, Millenium Pharmaceuticals, Merck, Neurocrine Biosciences, NeuraWell Therapeutics, Pernix Therapeutics, Otsuka Pharmaceuticals, Sage Therapeutics, and Takeda. He also has options ownership at Big Health.


1. Morin CM, LeBlanc M, Daley M, et al. Epidemiology of insomnia: prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors. Sleep Med. 2006;7(2):123-130.

2. Javaheri S, Redline S. Insomnia and risk of cardiovascular disease. Chest. 2017;152(2):435-444.

3. Lin CL, Chien WC, Chung CH, Wu FL. Risk of type 2 diabetes in patients with insomnia: a population-based historical cohort study. Diabetes Metab Res Rev. 2018;34(1).

4. Hertenstein E, Feige B, Gmeiner T, et al. Insomnia as a predictor of mental disorders: a systematic review and meta-analysis. Sleep Med Rev. 2019;43:96-105.

5. Riemann D, Baglioni C, Bassetti C, et al. European guideline for the diagnosis and treatment of insomnia. J Sleep Res. 2017;26(6):675-700.

6. Wu JQ, Appleman ER, Salazar RD, Ong JC. Cognitive behavioral therapy for insomnia comorbid with psychiatric and medical conditions: a meta-analysis. JAMA Intern Med. 2015;175(9):1461-1472.

7. Trauer JM, Qian MY, Doyle JS, et al. Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis. Ann Intern Med. 2015;163(3):191-204.

8. Koffel E, Bramoweth AD, Ulmer CS. Increasing access to and utilization of cognitive behavioral therapy for insomnia (CBT-I): a narrative review. J Gen Intern Med. 2018;33(6):955-962.

9. Qaseem A, Kansagara D, Forciea MA, et al. Management of chronic insomnia disorder in adults: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133.

10. Edinger JD, Arnedt JT, Bertisch SM, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine systematic review, meta-analysis, and GRADE assessment. J Clin Sleep Med. 2021;17(2):263-298.

11. Wilson S, Anderson K, Baldwin D, et al. British Association for Psychopharmacology consensus statement on evidence-based treatment of insomnia, parasomnias and circadian rhythm disorders: an update. J Psychopharmacol. 2019;33(8):923-947.

12. Luik A, Espie CA. Digital CBT for insomnia. In Bassetti C, McNicholas W, Paunio T, Peigneux P, eds. Sleep Medicine Textbook. Eurpean Sleep Research Society; In press.

13. Thomas A, Grandner M, Nowakowski S, et al. Where are the behavioral sleep medicine providers and where are they needed? A geographic assessment. Behav Sleep Med. 2016;14(6):687-698.

14. Luik AI, Kyle SD, Espie CA. Digital cognitive behavioral therapy (dCBT) for insomnia: a state-of-the-science review. Curr Sleep Med Rep. 2017;3(2):48-56.

15. Sleepio. Accessed June 21, 2021.

16. Somryst. Accessed June 21, 2021.

17. Morin CM. Profile of Somryst prescription digital therapeutic for chronic insomnia: overview of safety and efficacy. Expert Rev Med Devices. 2020;17(12);1239-1248.

18. Espie CA, Kyle SD, Williams C, et al. A randomized, placebo-controlled trial of online cognitive behavioral therapy for chronic insomnia disorder delivered via an automated media-rich web application. Sleep. 2012;35(6):769-781.

19. Ritterband LM, Thorndike FP, Gonder-Frederick LA, et al. Efficacy of an Internet-based behavioral intervention for adults with insomnia. Arch Gen Psychiatry. 2009;66(7):692-698.

20. Espie CA, Emsley R, Kyle SD, et al. Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. JAMA Psychiatry. 2019;76(1):21-30.

21. Ritterband LM, Thorndike FP, Ingersoll KS, et al. Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: a randomized clinical trial. JAMA Psychiatry. 2017;74(1):68-75.

22. Luik AI, Marsden A, Emsley R, et al. Long-term benefits of digital cognitive behavioural therapy for insomnia: follow-up report from a randomized clinical trial. J Sleep Res. 2020;29(4):e13018.

23. Blom K, Jernelöv S, Rück C, et al. Three-year follow-up comparing cognitive behavioral therapy for depression to cognitive behavioral therapy for insomnia, for patients with both diagnoses. Sleep. 2017;40(8).

24. Ye YY, Zhang YF, Chen J, et al. Internet-based cognitive behavioral therapy for insomnia (ICBT-i) improves comorbid anxiety and depression—a meta-analysis of randomized controlled trials. PLoS One. 2015;10(11):e0142258.

25. Freeman D, Sheaves B, Goodwin GM, et al. The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis. Lancet Psychiatry. 2017;4(10):749-758.

26. Henry AL, Miller CB, Emsley R, et al. Insomnia as a mediating therapeutic target for depressive symptoms: a sub-analysis of participant data from two large randomized controlled trials of a digital sleep intervention. J Sleep Res. 2021;30(1):e13140.

27. Thiart H, Ebert DD, Lehr D, et al. Internet-based cognitive behavioral therapy for insomnia: a health economic evaluation. Sleep. 2016;39(10):1769-1778.

28. Shaffer KM, Finkelstein EA, Camacho F, et al. Effects of an Internet-based cognitive behavioral therapy for insomnia program on work productivity: a secondary analysis. Ann Behav Med. 2021;55(6):592-599.

29. De Bruin EJ, van Steensel FJ, Meijer AM. Cost-effectiveness of group and internet cognitive behavioral therapy for insomnia in adolescents: results from a randomized controlled trial. Sleep. 2016;39(8):1571-1581.

30. Darden M, Espie CA, Carl JR, et al. Cost-effectiveness of digital cognitive behavioral therapy (Sleepio) for insomnia: a Markov simulation model in the United States. Sleep. 2021;44(4):zsaa223.

31. Espie CA, Hames P, McKinstry B. Use of the Internet and mobile media for delivery of cognitive behavioral insomia therapy. Sleep Med Clin. 2013;8:407-419.

32. Lancee J, van Straten A, Morina N, et al. Guided online or face-to-face cognitive behavioral treatment for insomnia: a randomized wait-list controlled trial. Sleep. 2016;39(1):183-191.

33. Blom K, Tillgren HT, Wiklund T, et al. Internet-vs. group-delivered cognitive behavior therapy for insomnia: a randomized controlled non-inferiority trial. Behav Res Ther. 2015;70:47-55.

Related Videos
© 2024 MJH Life Sciences

All rights reserved.