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 

, 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.

 Beyond sleep difficulties and associated daytime complaints, individuals with insomnia are at increased 

 (eg, depression, anxiety, substance use disorders) and physical health conditions (eg, 

 It is not surprising, then, that the burden of insomnia is significant and individuals with insomnia report a 

 than their healthy-sleeping counterparts.

Cognitive behavioral therapy for insomnia (CBT-I; see 

) 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.

 in a range of populations, including those with 

 to medication for the long-term management of their sleep difficulties.

British Association for Psychopharmacology

, the European Sleep Research Society, and other international bodies recommend CBT-I as first-line therapy 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.

 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

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.

 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.

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.

 (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.

 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 

 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.

 It is likely that the future of dCBT will involve many more products following a similar route.

 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.

 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 

 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 

 in addition to other areas of mental health,

 and that effects are likely mediated by 

 Benefits have also been observed in terms of quality of life, physical health, and 

; effects on physical health and workplace productivity are, again, likely mediated by reductions in insomnia.

 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 

 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 

 and traditional individual and group therapy for insomnia, yielding an estimated net monetary benefit of $681 per individual over 6 months.

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).

 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.

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 

, and those that have been carried out have reported mixed results.

 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.

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.

Epidemiology of insomnia: prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors.

Insomnia and risk of cardiovascular disease.

Risk of type 2 diabetes in patients with insomnia: a population-based historical cohort study.

4. Hertenstein E, Feige B, Gmeiner T, et al. 

Insomnia as a predictor of mental disorders: a systematic review and meta-analysis.

5. Riemann D, Baglioni C, Bassetti C, et al. 

European guideline for the diagnosis and treatment of insomnia.

6. Wu JQ, Appleman ER, Salazar RD, Ong JC. 

Cognitive behavioral therapy for insomnia comorbid with psychiatric and medical conditions: a meta-analysis.

Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis.

Increasing access to and utilization of cognitive behavioral therapy for insomnia (CBT-I): a narrative review.

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.

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.

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.

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.

Digital cognitive behavioral therapy (dCBT) for insomnia: a state-of-the-science review.

Profile of Somryst prescription digital therapeutic for chronic insomnia: overview of safety and efficacy.

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.

19. Ritterband LM, Thorndike FP, Gonder-Frederick LA, et al. 

Efficacy of an Internet-based behavioral intervention for adults with insomnia.

Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. 

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

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.

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.

Internet-based cognitive behavioral therapy for insomnia (ICBT-i) improves comorbid anxiety and depression—a meta-analysis of randomized controlled trials.

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.

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.

Internet-based cognitive behavioral therapy for insomnia: a health economic evaluation. 

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.

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.

Cost-effectiveness of digital cognitive behavioral therapy (Sleepio) for insomnia: a Markov simulation model in the United States.

31. Espie CA, Hames P, McKinstry B. Use of the Internet and mobile media for delivery of cognitive behavioral insomia therapy. 

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.

33. Blom K, Tillgren HT, Wiklund T, et al. 

Internet-vs. group-delivered cognitive behavior therapy for insomnia: a randomized controlled non-inferiority trial.

Articles

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

Advances in Digital Cognitive Behavioral Therapy for the Treatment of Insomnia

As the need grows for equitable, accessible, and affordable mental health treatment, digital therapeutics—app-based treatments—show promise for filling these gaps, particularly to target anxiety. The high prevalence of anxiety disorders “vastly exceeds the capacities of mental health services” to 

Anxiety has risen since the beginning of the COVID-19 pandemic. The US Census Bureau reported that adults were 3 times more likely to screen positive for anxiety disorders in 2020 compared with 2019 (31% vs 8%). A cross-sectional 

of individuals conducted in China February 2020 found the prevalence of generalized anxiety disorder (GAD) to be 35.1%; a meta-analysis found an average anxiety prevalence of 32% during the pandemic (17 studies, n = 63,439).

 A study of patients diagnosed with COVID-19 between January and August 2020 (n = 62,000+) found that 1 in 5 patients with no psychiatric history received a new mental health diagnosis, with the greatest health risk being for anxiety disorders.

Digital therapeutics can be designed to specifically target a behavioral or psychological mechanism of action, and they can be developed with user-centered design principles to maximize engagement. However, to date, very few digital therapeutics have been tested for efficacy, and even fewer have been studied regarding mechanism of action. There seem to be 2 categories of digital therapeutics emerging: those that are commercially developed with little supporting evidence or plans for evaluation and those with academic or government backing (and often not easily available to consumers).

 The former has the advantage of maximizing curb appeal: Resources can be devoted toward developing engaging user experiences using the latest design techniques, and companies can push a digital therapeutic to market rapidly. Scientists have developed evidence-based digital therapeutics, yet few are publicly available. They appear more slowly than their commercial counterparts, and those that do appear usually fail to attract users and user engagement.

 However, both face the same fate: No matter how shiny an app is, it still takes time to run rigorous clinical trials to determine efficacy (and mechanism).

John Torous, MD, Digital Section Editor for 

 and chair of the American Psychiatric Association’s Smartphone App Evaluation Task Force, put it bluntly when interviewed for an article on digital therapeutics in Nature: “Right now it almost feels like the Wild West of health care.”

How can the busy health care clinician keep up-to-date on the latest trends, determine which digital therapeutics are evidence based, and give informed answers when patients ask which apps to use? Is there enough evidence for prescribers to pull out their proverbial digital prescription pad to recommend an app-based treatment for anxiety?

After considering the recent literature, there are a few questions clinicians should consider.

Why Should There Be an Effect? What Mechanism Is Being Targeted?

A good place to start is to consider if a digital therapeutic is mechanistically based. Although many apps assert that they are scientifically based, very few live up to this claim. The National Institutes of Health established a 

 called the Science of Behavior Change (SOBC) to focus on mechanisms of change in behavioral interventions as a way to both strengthen intervention development and “increase rigor, transparency, and dissemination of common terminology, methods, and measures.”

 The basic idea behind SOBC is to measure if a behavioral intervention engages a hypothesized mechanism of change and if engaging that target leads to change in a health outcome. If this approach sounds similar to that taken for the development of any new drug, it should, because it is. It was modeled after the experimental medicine approach. However, a significant research-practice gap exists in the digital mental health field, especially in the field of anxiety interventions.

Findings of a recent study looking at the frequency of evidence-based treatment elements in apps for anxiety showed that the most commonly included elements were psychoeducation (53%), relaxation (47%), meditation (41%), and mindfulness (29%) (

 Yet, the inclusion of an element does not automatically translate to targeting a specific mechanism or therapeutic benefit. Another recent review found that 67% of apps for anxiety lacked involvement of any health professional in their development, and just 4% had been empirically evaluated.

This unfortunate lack of focus on mechanism is not due to an absence of information on how anxiety can develop and be perpetuated. For example, decades-old research has suggested potential mechanisms of action, such as targeting worry-driven negative reinforcement, yet 

 have these been incorporated into digital therapeutic devopment.

With apps that can reach thousands, and even millions, of potential patients, it is much easier to collect data from a convenience sample, look for reductions in some type of scale, and then claim it to be an evidence-based treatment for anxiety. However, there is a world of difference between someone who is mildly anxious (after all, who is not these days?), and someone whose anxiety is severe enough to land them firmly in the realm of needing treatment for a DSM-diagnosed anxiety disorder. For example, a 

 of randomized controlled trials of mindfulness apps reported significant effect sizes for reductions in anxiety, but every study recruited individuals from app users and the general population and specific nonclinical groups (largely students and employees).

 Of these, only a single study included individuals with mild to moderate anxiety using the General Anxiety Disorder-7 (GAD-7) cutoff of 14 to exclude individuals who reported severe anxiety (GAD-7: 5-9, mild; 10-14, moderate; 15+, severe anxiety).

 of internet-based cognitive behavioral therapy (iCBT) used GAD-7 scores of ≥ 8 as a minimum cutoff to recruit individuals with mild to moderate symptoms of anxiety, individuals in the United Kingdom’s National Health System (NHS) and “who may be eligible and require iCBT” as part of the Improving Access to Psychological Therapies (IAPT) program.

 Additionally, 2 studies of app-based mindfulness training for anxiety (Unwinding Anxiety) have used GAD-7 scores of ≥ 10 as minimum cutoff for inclusion of anxious physicians and individuals with GAD, respectively.

 Taken together, these results suggest that the majority of studies are drawing convenience samples from nonclinical populations, and that can lead to problematic inferences on the efficacy and utility of digital therapeutics in individuals with moderate to severe anxiety—those that would be most likely to seek or be referred to treatment—unless they have been specifically studied in these populations.

What Is the Evidence? Is There a Clinically Relevant Reduction in Symptoms?

Popularity does not equal evidence for efficacy. A number of app-based programs have put a primary focus on increasing their user base rather than developing digital therapeutics for anxiety. Although there is nothing inherently wrong with this approach—it is just a different focus—seeing a lot of individuals use an app can lead to a false sense of efficacy. Most studies to date are small pilot studies, often run by the app developers rather than by independent researchers, and the results have yet to be replicated. Additionally, many studies are run relative to wait-list control groups. Although this is better than no control group, it does not assess basic confounding components that are covered by a treatment as usual control group, such as receiving some type of treatment and expectancy effects (which, if performed well, will include the usual clinical care delivered by their therapist or doctor).

As previously mentioned, only a few published studies have examined digital therapeutics in individuals with moderate to severe anxiety: 1 in the general population (mainly recruited from users who were already signed up for the app Pacifica) with baseline GAD-7 scores of between 5 and 14; 1 (iCBT) in the UK’s NHS in individuals with baseline GAD-7 scores of ≥ 8; 1 (Unwinding Anxiety) in anxious physicians with baseline GAD-7 scores of ≥ 10; and 1 (Unwinding Anxiety) in individuals with GAD with baseline GAD-7 scores of ≥ 10.

In the first study, a randomized controlled trial that compared the Pacifica app to a wait-list control, only 35% of the app and 47% of wait-list participants completed the study. A composite anxiety score, using an average score of standardized GAD-7 and Depression and Anxiety Stress Scale-21 (anxiety component) scores, showed a statistically significant drop in both groups after 4 weeks (wait-list control, 0.23 composite point; Pacifica, 0.66 composite point), which was reported as statistically significantly different. In the second study, a randomized controlled trial of iCBT (Silvercloud) vs wait-list control, individuals in the iCBT group showed a statistically significant 35% reduction in GAD-7 scores at 8 weeks (baseline, 12.7; 8-week follow-up, 8.2) relative to a 15% reduction in the control group (baseline, 12.7; 8-week follow-up, 10.8). Retention was 82% and 76%, respectively.

In the third study, a single-arm trial of the Unwinding Anxiety app, anxious physicians demonstrated a statistically significant reduction in GAD-7 scores of 48% and 57% after 1 and 3 months, respectively (baseline, 11.5; 1-month, follow-up, 6.0; 3-month follow-up, 5.0). Retention was 60%. In the fourth randomized controlled trial looking at Unwinding Anxiety vs treatment as usual (TAU), individuals randomized to Unwinding Anxiety showed a statistically significant 67% reduction in GAD-7 scores (baseline, 12.0; 2-month follow-up, 3.0) relative to a 14% reduction in the TAU group at 2 months (baseline, 13.0; 2-month follow-up, 10.0). Retention was 88% for the Unwinding Anxiety group and 100% for the TAU group.

When prescribing a medication or suggesting that a patient try a behavioral treatment, we fall back on the ideals of the Hippocratic oath that we took at the beginning of medical school: First, do no harm. Although digital therapeutics do not carry the traditional adverse effect risk profile of a medication, it is important to approach information in a parallel manner: Ingesting information is not free of unintended consequences. For example, 

 for bipolar disorder found the presentation of information that could lead to harm (eg, encouraging individuals to drink liquor to help them sleep during manic episodes).

 Additionally, privacy is a concern, as apps can easily collect personal information ranging from symptoms to geolocation.

Digital therapeutics have the potential to live up to their promise: Mechanistic and evidence-based treatments that can be widely disseminated at low cost. To date, very few have demonstrated efficacy, and these studies would benefit from replication and extension of their findings. As clinicians consider recommending app-based treatments, it is important to stay abreast of emerging study results, and as they evaluate apps for their patients, they must keep those aforementioned important questions in mind (

is an associate professor of psychiatry at the Warren Alpert Medical School of Brown University, executive medical director of behavioral health at Sharecare Inc, and author of Unwinding Anxiety. Twitter: @judbrewer. He owns stock in, and serves as a paid consultant for, Sharecare Inc, the company that owns the Unwinding Anxiety app described in this article. The financial interest has been disclosed to and is being managed by his institution, Brown University, in accordance with its Conflict of Interest and Conflict of Commitment policies.

1. Firth J, Torous J, Carney R, et al. Digital technologies in the treatment of anxiety: recent innovations and future directions. 

2. Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. 

3. Salari N, Hosseinian-Far A, Jalali R, et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. 

4. Taquet M, Luciano S, Geddes JR, Harrison PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. 

5. Bowland S, Hensley M, Johnson B, Fleming A. Consumer focus groups: a key to transforming behavioral health systems?

6. Anthes E. Mental health: there’s an app for that. 

7. Keller C, Ferrer RA, King RB, Collier E. Future directions of the National Institutes of Health Science of Behavior Change Program. 

8. Wasil AR, Venturo-Conerly KE, Shingleton RM, Weisz JR. A review of popular smartphone apps for depression and anxiety: assessing the inclusion of evidence-based content. 

9. Sucala M, Cuijpers P, Muench F, et al. Anxiety: there is an app for that. A systematic review of anxiety apps. 

10. Brewer JA. Why targeting entrenched habits can treat anxiety. 

 March 19, 2021. Accessed June 21, 2021. 

11. Brewer JA, Roy A, Deluty A, et al. Can mindfulness mechanistically target worry to improve sleep disturbances? Theory and study protocol for app-based anxiety program. 

Unwinding Anxiety: New Science Shows How to Break the Cycles of Worry and Fear to Heal Your Mind

13. Janes AC, Datko M, Roy A, et al. Clinical efficacy and psychological mechanisms of an app-based digital therapeutic for generalized anxiety disorder. Under review.

14. Gál É, Ştefan S, Cristea IA. The efficacy of mindfulness meditation apps in enhancing users’ well-being and mental health related outcomes: a meta-analysis of randomized controlled trials. 

15. Richards D, Enrique A, Eilert N, et al. A pragmatic randomized waitlist-controlled effectiveness and cost-effectiveness trial of digital interventions for depression and anxiety. 

16. Roy A, Druker S, Hoge EA, Brewer JA. Physician anxiety and burnout: symptom correlates and a prospective pilot study of app-delivered mindfulness training. 

17. Moberg C, Niles A, Beermann D. Guided self-help works: randomized waitlist controlled trial of Pacifica, a mobile app integrating cognitive behavioral therapy and mindfulness for stress, anxiety, and depression. 

18. Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar disorder: a systematic review of features and content quality. 

Key questions to consider when evaluating a digital therapeutic for anxiety. 

Anxiety Treatment at Your Fingertips

 remains limited by time constraints, affordability, stigma, and other factors.

 (DTx) represent a valuable addition to psychiatrists’ toolkits. But before they recommend DTx, psychiatrists should consider how these programs are being evaluated by regulators and delivered to patients.

Although existing DTx target a range of physical illnesses, those targeting mental health issues have received 

more funding in 2020 and 2021 than all other types

 This development will have a direct impact on the practice of psychiatry. The scope of this impact depends in large part on how regulatory oversight evolves, since regulation determines: which types of DTx are subject to oversight; whether a particular DTx is available via prescription or over the counter (OTC), and the required level of adjunctive care associated with it; and the evidence required for commercialization and reimbursement.

Given the unique set of applied uses and different risk profiles associated with DTx, oversight will require an updated regulatory model. The US Food and Drug Administration (FDA) has recognized this need as well, given the recent launch of their new Digital Health Center of Excellence (DHCoE). One of the 3 

 is to “innovate regulatory approaches to provide efficient and least burdensome oversight while meeting the FDA standards for safe and effective products.”

 As progress on this front unfolds, 3 aspects of DTx are particularly important for psychiatrists and other mental health providers to consider: evaluation, delivery, and reimbursement.

The necessary amounts and types of evidence required by the FDA for DTx are controversial. Most commercially available DTx for behavioral health conditions qualify as wellness interventions (ie, they do not make treatment claims), and the FDA has stated that these DTx do not need regulatory clearance to be on market because they are low risk. In contrast, DTx that make treatment claims for specific psychiatric disorders do require traditional FDA clearance.

DTx cleared by the FDA have gone through both de novo classification and premarket notification (510k) regulatory pathways. The de novo classification permits device clearance when no marketed predicate device in the space exists and empirical evidence provides reasonable assurance of safety and efficacy for the intended use. Despite these standards, the 

clinical significance of FDA-cleared devices for mental health

 Moreover, DTx pursuing the 510k pathway are required only to show equivalence to previously cleared devices. This has 

raised concerns that DTx will not have clinically significant effects

, or that the results will not generalize to real-world psychiatric patients.

 noted that “breakthrough devices were approved primarily on the basis of studies that used short-term, surrogate end points for effectiveness, which may not translate into clinical benefits.”

 For example, a DTx product that uses the Apple Watch to send vibrations to interrupt nightmares in those with posttraumatic stress disorder was given 

breakthrough designation and permission to market

 Rathi and colleagues continued: “The FDA authorized some breakthrough devices without supporting effectiveness data (ie, they were approved on the basis of safety data alone) and authorized other devices that carried important known safety risks.”

Medicare Coverage of Innovative Technology (MCIT) rule

, designed to grant an immediate Medicare coverage pathway for DTx once they are designated as a breakthrough by the FDA, has been delayed multiple times.

To improve the utility of DTx for psychiatric practice, regulatory agencies need to reach greater consensus and clarity on what constitutes sufficient evidence for approvals—both for wellness and the treatment of psychiatric conditions. In the interim, it will be important for psychiatrists to evaluate the evidence for the DTx they would like to leverage in their practice, whether or not the FDA has cleared them.

FDA regulatory classifications of DTx for psychiatric disorders have specified that these products should be distributed by prescription only. However, restricting access to DTx with particularly safe profiles contradicts many of the central goals behind DTx development. It is critical to consider which providers will be able to offer DTx to patients. With few exceptions (eg, the US Army), the only providers with prescription privileges are medical doctors and nurse practitioners. However, mental health care is provided by a broader range of professionals.

Most FDA-cleared DTx have been designated as adjunctive rather than stand-alone treatment, despite the fact that many DTx have been developed as stand-alone interventions and have shown clinical benefits without serving as an adjunct to in-person care. Requiring adjunctive care may be appropriate for some DTx, but denying individuals with low or moderate levels of psychiatric symptoms the opportunity to benefit from stand-alone DTx when risks are minimal seems misguided.

The majority of DTx have not been cleared by the FDA because they fall under other categories of oversight that do not require premarket clearance. In many cases, 

digital tools marketed as wellness products leverage the same clinical interventions (eg, CBT)

 that are simultaneously cleared by the FDA as prescription-only products. Given the large number of individuals who do not meet formal criteria for a diagnosis but experience clinical levels of distress nonetheless, a more granular approach to evaluation may be appropriate.

To provide additional guidance for psychiatrists and patients, it would be helpful for the FDA to establish a clearance status for DTx that is similar to an OTC designation. Although some DTx carry inherent risks, unnecessary restrictions on access to low-risk DTx are a bigger risk to consumers.

Broadly speaking, there are 2 central challenges impacting the development of routine reimbursement pathways for DTx: reimbursement for the DTx itself and reimbursement for coordinating care.

There is currently no standard pathway for reimbursement for DTx; instead, 

DTx tend to be purchased by patients directly

 If providers or patients cannot be reimbursed for the cost of DTx via the insurer, then the reach of DTx will be limited to patients who can pay out of pocket. The most comprehensive solution would be to establish a new DTx benefit category under Medicare; however, this change would require an act of Congress.

Reimbursement for coordinating care surrounding DTx is not straightforward. Under the current system, 4 

Current Procedural Terminology (CPT) codes

 for remote physiologic monitoring treatment management services (99453-99458) have been used for some DTx. However, the October 2020 editorial meeting of the American Medical Association (AMA) seems to have established a new group of codes titled remote therapeutic monitoring services and remote therapeutic monitoring treatment management services. The change in a key word, from physiologicto therapeutic, may make these codes available to a broader scope of DTx, including those that target mental health and substance abuse.

As we move into a new era of DTx for mental health, regulatory and reimbursement structures and processes must be modernized to reflect the treatments they oversee. As psychiatrists and other mental health providers learn to 

, it is critical to advocate for policies that broaden the accessibility and acceptability of these innovative and effective tools.

is a professor of psychology at the University of Miami. 

is chief executive officer of Audacious Digital Health. 

is an associate professor of psychiatry at the University of Pittsburgh. 

is an assistant professor of psychology at Florida International University. 

is a professor in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. 

is a professor of psychology at the Center for Children and Families at Florida International University.

is vice president of clinical development & medical affairs at Big Health Inc.

The occasional case against broad dissemination and implementation: retaining a role for specialty care in the delivery of psychological treatments.

We need policies that make digital therapeutics accessible and acceptable to a broad patient audience. What barriers are there to reaching that goal? 

Vol 38, Issue 8 Digital Supplement

Advances in Digital Cognitive Behavioral Therapy for the Treatment of Insomnia

Anxiety Treatment at Your Fingertips

Issues in Regulating DTx

Technology-Enabled Care: The Future of Our Field