Alison M. Darcy, PhD

In the United States, the Centers for Disease Control and Prevention (CDC) 

that in 2019, 4.7% of adults aged 18 years or older reported regular feelings of depression, and 11.2% reported regular feelings of worry, nervousness, or anxiety.

 Forty percent of Americans with a 12-month history of severe mental disorders 

 Disorders were assessed by structured diagnostic interview using the World Health Organization’s World Mental Health Composite International Diagnostic following DSM-IV criteria. The analyzed disorders included mood (ie, major depressive disorder, dysthymia, or bipolar disorder type I or II), anxiety (ie, generalized anxiety disorder, panic disorder, agoraphobia, social phobia, specific phobia), posttraumatic stress disorder, and substance use disorders (ie, alcohol or drug abuse or dependence). In lower- and middle-income countries, this figure can exceed 75%.

The ongoing COVID-19 pandemic has increased mental health care needs while simultaneously restricting access, with unknown long-term consequences. From August 2020 to February 2021, the CDC 

an increase in the proportion of adults reporting recent symptoms of anxiety or depression from 36.4% to 41.5%, with the fraction reporting unmet mental health care needs increasing from 9.2% to 11.7%.

 Among children and adolescents, the proportion of mental health–related 

visits for those aged 5 to 11 years and 12 to 17 years increased 24% and 31%, respectively, compared with 2019.

is a clinical research psychologist, an adjunct faculty member in the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine, and the founder and president of

is a practicing psychiatrist, an adjunct assistant professor of psychiatry and human behavior at the Warren Alpert Medical School of Brown University, and vice president and medical director of

1. Norris T, Clark TC, Boersma P, Schiller JS. Technical notes for early release of selected estimates based on data from the National Health Interview Survey. National Center for Health Statistics. February 2021. Accessed June 25, 2021. 

2. Wang PS, Aguilar-Gaxiola S, Alonso J, et al. Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys. 

3. Vahratian A, Blumberg SJ, Terlizzi EP, Schiller JS. Symptoms of anxiety or depressive disorder and use of mental health care among adults during the COVID-19 pandemic – United States, August 2020-February 2021. 

4. Leeb RT, Bitsko RH, Radhakrishnan L, et al. Mental health–related emergency department visits among children aged <18 years during the COVID-19 pandemic – United States, January 1-October 17, 2020. 

Articles

What are the greatest unmet mental health needs in the United States today? Recent statistics from the Centers for Disease Control may hold the answers. 

Mental Health in America: A Growing Crisis

Around the world, 5 billion individuals have mobile devices, half of which are 

 It is therefore unsurprising that mobile technology has become a core component of digital therapeutics (DTx). Today’s mobile technology allows mental health DTx to be more than simple remote assessment tools that report patient outcomes like those of the Patient Health Questionnaire-9. Rather, the latest DTx aim to deliver medical interventions directly to patients, using software intended to treat, manage, and potentially prevent disease.

Although care access is a key issue that digital solutions can improve, DTx also create an opportunity for genuine innovation. In fact, DTx are not necessarily diluted versions of in-person psychotherapy. Rather, in some cases they give rise to a mechanism of action enabled by the digital medium itself. For example, Akili’s EndeavorRx uses video game dynamics to train attentional processes in an intervention for children with attention-deficit/hyperactivity disorder. Omada Health delivers a digital version of the evidence-based Diabetes Prevention Program because it is a lifestyle intervention, including a support component enabled by technology and weight monitoring through connected devices. Pear Therapeutics’ reSET and reSET-O are prescription DTx for substance use disorder and opioid use disorder, respectively, intended to provide cognitive behavioral therapy (CBT) as an adjunct to outpatient treatment.

Innovations, however, are not good simply because they exist. Clinicians will need to carefully and thoughtfully consider different DTx offerings, their claimed mechanisms of action, evidence for their efficacy and ease of use, and other relative strengths and weaknesses.

Why Are Mental Health DTx Primarily Based on CBT?

CBT lends itself to digitization because the way in which individuals interact with their devices—and the devices’ ubiquitous presence—matches, and even facilitates, the core principles of change that underpin the therapy. Hence, most DTx assert that they are based on CBT.

However, in order to claim theoretical foundations in CBT, an intervention must promote behavioral adaptation through 

: context engagement, attention change, and cognitive change.

 Context engagement helps promote adaptive associative learning beyond what is currently perceived by the person. Examples of context engagement strategies include behavioral activation and exposure therapies; in the latter case, virtual reality is being explored as a potential component of DTx for posttraumatic stress disorder.

 Attention change refers to a shift in attention to adaptive, relevant, nondistressing stimuli. This includes therapeutic processes such as attention training, acceptance or tolerance training, and mindfulness techniques. Cognitive change refers to a shift in one’s perspective on an event, which then affects the emotional significance and meaning of that event. This includes metacognitive awareness and cognitive distancing, which are promoted through therapeutic processes such as cognitive reframing or reappraisal.

These 3 principles all have in common the need to draw awareness to, record, and evaluate how the patient experiences and interacts with the world. In this model, anything that facilitates these practices in an ecologically valid way may amplify their therapeutic effect. The therapeutic relationship in CBT is characterized by collaborative empiricism, wherein the therapist encourages symptom self-monitoring with a view to constructing experiments and opportunities to challenge one’s world view. In traditional therapist-delivered CBT, data have shown that the more a patient adheres to these tasks between sessions, the better their 

 Well-designed DTx have the potential to make those homework tasks easy. Beyond being a simple matter of convenience, making homework easy may have particular potency in some diagnoses, like major depressive disorder, in which lack of motivation (anhedonia) and a sense of disconnection are pervasive. Setting up the patient for success with tasks that facilitate therapeutic processes, especially in a way that is achievable and minimizes cognitive burden, can build self-efficacy and could accelerate time to recovery. While many such DTx are still in development, some have demonstrated potential to meaningfully reduce symptoms in just 2 weeks of use compared with a psychoeducation control group.

The overall contribution of these 3 foundational CBT principles to the treatment of an individual will depend on that individual and the issues with which they present. Some patients will have more insight or self-awareness of their emotional state or of their negative automatic thoughts and, therefore, may need to work less on attention change. Similarly, a patient with a specific phobia may need to work more on context engagement, using exposure techniques to relearn and diffuse previously learned negative associations. An experienced CBT therapist will make these modifications instinctively. Although the earlier waves of digital CBT were mostly one-size-fits-all applications, more recent digital solutions can include an innumerable number of skills and techniques from several therapeutic approaches (ie, CBT, interpersonal therapy, and dialectical behavior therapy) and adaptively meet patients where they are clinically.

As artificial intelligence (AI) and machine learning (ML) technology advances, DTx have an opportunity to grow beyond limited and inflexible content streams and become responsive to the patient’s present emotional state and circumstances (

). This is especially important, because maintaining user engagement over time proves challenging for many DTx. One approach is to use a relational agent, which is a specialized type of chatbot purpose-built to replicate some of the unique traits and dynamics of human interpersonal relationships. One such chatbot—Woebot—demonstrated Working Alliance Inventory – Short Revised scores comparable with those reported in the literature for traditional outpatient individual and group CBT. These results demonstrate that DTx can establish a 

Additionally, research has shown that individuals are more willing to disclose personal information to a virtual human, as doing so lowers fear of self-disclosure and impression management.

 This may be particularly important in adolescents, who prefer online or mobile tools over face-to-face interactions and tend to fear the stigma associated with seeking traditional mental health services.

 DTx have even been successfully deployed as an adjunctive treatment in challenging populations such as patients with early psychosis.

Perhaps unsurprisingly, the world is inundated by an ever-growing number of DTx—mostly in the form of mental health apps—that make broadly similar claims. However, few of these have chosen the path of clinical validation and regulatory approval. Many have no intention to pursue a regulated pathway given the time and cost involved. Just because a DTx app is available on Google Play and the App Store does not mean that any of its claims are valid. More importantly, a poorly designed app could be unhelpful, or even harmful.

Regulation of DTx brings rigor in terms of clinical efficacy, risk, and safety, as well as to the DTx development process. This is critical as many DTx rely on AI and ML algorithms that must be rigorously, accurately, and fairly trained and tested on clinically validated datasets. Results of such analyses must also be clearly and transparently reported.

 The US Food and Drug Administration has recently proposed a regulatory action plan that would encompass the entire life cycle of software as a medical device, an acknowledgment that AI and ML models often need regular updating, retraining, and retesting to ensure 

Every new treatment modality carries unique difficulties. For example, identification of safety events (ie, suicidal or homicidal ideation, intent, or plan) is a particular challenge for DTx utilizing relational agents where there is no human continuously monitoring interactions. Since DTx allow for 24/7 access and virtual support, they will need to detect crises accurately and provide appropriate assistance. As such, mental health DTx can be seen as a way of augmenting and enhancing rather than replacing human-to-human care delivery.

Any new technology-based treatment should incorporate the needs of all stakeholders and consider the digital divide that could prevent access to those who may most benefit from DTx. Further, despite the fact that prescription-based DTx have been developed from psychological and behavioral sciences, clinical psychologists and their associated professions will not be in a position to prescribe them.

 These concerns are certainly not new to mental health care, but the incorporation of a new technological approach necessitates a nuanced evolution of how we think about and address them as a profession.

When faced with the prospect of recommending DTx to patients, clinicians may struggle to make sense of the seemingly unending parade of options, with widely varying degrees of quality and clinical validity. Several websites offer expert reviews of DTx apps, but these reviews do not always incorporate the essential voice of the patient (

Because many DTx solutions rely on CBT principles, a multidisciplinary approach can be useful. For instance, a psychiatrist considering a DTx app can seek input from clinical psychologist colleagues. Fundamentally, one must exercise common-sense clinical judgment when considering DTx. Are the claims made by the app feasible? Do you envision your patients being able to use and benefit from it? If possible, give the DTx app a test drive before recommending it.

DTx offer unique benefits for meeting patients where they are. However, they must be continually tested, validated, and tried in the real world if they are to become part of a comprehensive system of care that helps us improve mental health across the lifespan.

is a clinical research psychologist, an adjunct faculty member in the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine, and the founder and president of 

is a practicing psychiatrist, an adjunct assistant professor of psychiatry and human behavior at the Warren Alpert Medical School of Brown University, and vice president and medical director of 

1. Silver L. Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center. February 5, 2019. Accessed June 25, 2021. 

2. Mobile fact sheet. Pew Research Center. April 7, 2021. Accessed June 25, 2021. 

3. Mennin DS, Ellard KK, Fresco DM, Gross JJ. United we stand: emphasizing commonalities across cognitive-behavioral therapies. 

4. van’t Wout M, Spofford CM, Unger WS, et al. Skin conductance reactivity to standardized virtual reality combat scenes in veterans with PTSD. 

5. Callan JA, Kazantzis N, Park SY, et al. A propensity score analysis of homework adherence–outcome relations in cognitive behavioral therapy for depression. 

6. Lebeau RT, Davies CD, Culver NC, Craske MG. Homework compliance counts in cognitive-behavioral therapy. 

7. Riley BJ. The role of homework in exposure-based CBT outcome for problem gambling. 

8. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. 

9. Darcy A, Daniels J, Salinger D, et al. Evidence of human-level bonds established with a digital conversational agent: cross-sectional, retrospective observational study. 

10. Lucas GM, Gratch J, King A, Morency L-P. It’s only a computer: virtual humans increase willingness to disclose. 

11. Kretzschmar K, Tyroll H, Pavarini G, et al. Can your phone be your therapist? Young people’s ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. 

12. D’Alfonso S, Santesteban-Echarri O, Rice S, et al. Artificial intelligence–assisted online social therapy for youth mental health. 

13. Stevens LM, Mortazavi BJ, Deo RC, et al. Recommendations for reporting machine learning analyses in clinical research. 

14. Artificial intelligence and machine learning in software as a medical device. US Food and Drug Administration. Updated January 12, 2021. Accessed June 25, 2021. 

15. Carl JR, Jones DJ, Lindhiem OJ, et al. Regulating digital therapeutics for mental health: opportunities, challenges, and the essential role of psychologists. 

Why are mental health digital therapeutics primarily based on CBT?

DTx and Cognitive Behavioral Therapy: Opportunities, Benefits, and Challenges of an Evolving Landscape

Eating disorders as classified by DSM-5 include diagnostic categories for anorexia nervosa, bulimia nervosa, binge eating disorder, avoidant restrictive feeding intake disorder, and feeding and eating conditions not otherwise specified. These are serious illnesses associated with numerous psychological and medical comorbidities, including increased risk of death.

Patients with eating disorders are notoriously difficult to treat and are also known to have high relapse rates. Resistance in eating disorders is common and can help explain why treatments often fail and why patients drop out. In general, resistance can refer to the conscious and unconscious factors that prevent a patient from engaging fully in the treatment process. Importantly, patient resistance is not uniform across the different eating disorder diagnoses, and how it is addressed also depends on the age of the individual.

Why patients with eating disorders resist treatment

 Patients with eating disorders are typically terrified of weight gain and will go to great lengths to avoid this outcome. To decrease anxiety about weight gain, patients sometimes adopt behaviors that they believe will “protect” them from gaining weight-eg, scrutinizing body parts, daily weighing, following strict rules about when and how much to eat, and overexercising. Patients become highly invested in these safety behaviors, which results in decreased willingness to change them and increases therapeutic resistance.

 Another key factor that affects resistance to change is whether patients view the disorder as ego-dystonic (ie, part of themselves) or ego-syntonic (ie, separate from themselves). Individuals who experience the behaviors associated with the eating disorder as problematic and incongruent with how they see themselves view their eating disorder as ego-dystonic; this view is typically endorsed in bulimia nervosa and binge eating disorder. As a result, these patients often want help because they are frustrated with unsuccessful weight loss attempts and are ashamed of their binge eating/purging behaviors. This desire for help leads to an improved therapeutic alliance and motivation to change.

In contrast, individuals with eating disorders who experience their behaviors as congruent with their personality and have a certain amount of pride in the ability to diet and exercise to extremes view the eating disorder as ego-syntonic. This ego-syntonic view is most often seen in anorexia nervosa. In most cases, when the eating disorder is experienced as ego-syntonic, there will be little or no motivation to change the behaviors, which results in high levels of treatment resistance that increases with time.

Ego-syntonic patients typically avoid therapy and will often attend only because of outside pressure (eg, to please a loved one). In these cases, it is extremely difficult to form a therapeutic alliance or to agree on goals for treatment. For example, these patients will be opposed to weight gain, although it is necessary in underweight patients with anorexia nervosa. This discrepancy in treatment goals between patient and clinician leads many patients to drop out of therapy, a common problem in anorexia nervosa.

In general, individuals with eating disorders resist treatment for many reasons. It can be helpful to both patient and clinician to understand and distinguish potential sources of process resistance and outcome resistance as an early step in the therapeutic process.

 Process resistance refers to resistance related to necessary changes for improvement and is associated with high levels of anxiety and discomfort. Examples of process resistance include gaining weight, eating more regularly, and eating larger amounts and/or foods that have been avoided. Outcome resistance, on the other hand, refers to concerns about the ultimate changes that will result once the behaviors and cognitions maintaining the eating disorder are no longer available to use as a coping strategy (eg, cannot binge as a coping strategy and may have to start attending to other unpleasant responsibilities in life that have been avoided because of the eating disorder).

While ultimately not helpful, these eating disorder behaviors and cognitions function as a temporizing measure in ways that the patient may or may not be aware of. For example, the individual with anorexia nervosa may not feel negative emotions as intensely while in a starvation state and thus, when eating normally, will need to learn to tolerate these emotional experiences. Most patients with an eating disorder struggle with both process and outcome resistance.

How a clinician can address treatment resistance

The options of how to proceed when encountering resistance will depend on the age of the individual and the type of disorder. Generally with 

, if there is not a strong desire to change the behavior (ie, the eating disorder is ego-syntonic) we recommend using a family-based approach.

 In this model, the adolescent’s resistance to change is circumvented because this treatment targets changing parental behaviors related to promoting weight restoration rather than directly confronting the adolescent. However, the therapist must overcome parental resistance and avoidance by facilitating learning and teaching strategies to change the behaviors that maintain their child’s eating disorder.

A pivotal intervention in the first session that is repeatedly modeled throughout the treatment process is externalization. Externalization allows parents to view their child’s firm resistance as part of the illness and not their child’s bad character. This separation allows the parents to focus on the task at hand (ie, re-nourishment) without getting sidetracked by the child’s verbal and nonverbal protests or by their guilt at making their child do something that is so distressing to him or her.

 When working with an adolescent or adult who is somewhat motivated to change her eating disorder behavior, individual therapy can be effective. However, recognition of the need to change behaviors and the ability and willingness to do the hard work to change them can be quite different. For example, patients with bulimia nervosa will often want to stop binge eating but are resistant to giving up the restrictive eating habits that lead to the binge eating episodes.

One helpful strategy to deal with this type of resistance is to begin by working with the patient to identify what she wants to change and/or work on. This step may seem basic, but it can be overlooked by therapists and replaced with a presumption that a patient with an eating disorder agrees with the therapist’s perspective and valuation. Achieving initial consensus on the goals of therapy and agreement to work on a particular problem/ behavior introduces a dialogue about how to achieve those goals.

In treatments such as cognitive-behavioral therapy, the first session is spent building a model illustrating how the eating disorder is functioning for the patient. The model includes behavioral, emotional, and cognitive maintaining processes and illustrates the cyclical and dysfunctional nature of the eating disorder. The model is a working document and can be revisited during every session. As a result, it can be used to help the patient learn more about her eating disorder cycle, evaluate the effects of her behaviors, and address resistance as it emerges. As treatment progresses, the model can be refined and elaborated, highlighting emerging targets for treatment.

One way to use this model is to ask the patient if the eating disorder is helping her accomplish the original goal-often this is related to improving self-esteem. In most cases, the co-created formulation will reflect that the behaviors and cognitions related to the eating disorder are not working and the model illustrates instead that the eating disorder is making her feel worse. This use of the model may be the first time that a patient is able to step back and see the difference between what she is doing and what she is trying to achieve.

Another strategy to help overcome resistance is Socratic questioning (ie, successive questions that progressively challenge the logic in a patient’s statements) about why the patient would want to continue to engage in eating disorder behaviors that are dysfunctional. For example, a patient may focus on the fact that vomiting makes her feel better immediately after a binge, although this is obviously a dysfunctional strategy because it undermines her self-esteem.

 provides an example of a typical model that uses a cognitive-behavioral framework. If the patient wants to stop binge eating, the formulation created by the therapist and patient would clearly show the cause behind the desire to binge (eg, rigid rules around food) and what is required (eg, adopting flexible guidelines around meals instead of rules) to change the behavior. Once the patient understands this dilemma-it is up to her to determine how much she wants to stop binge eating. Is it worth changing the restrictive rules she’s created around food?

Rigid rules around food often serve as safety behaviors that theoretically “protect” from weight gain. If patients are unsure about wanting to change their rules around food, the therapist might help the patient explore this by generating a list of the costs and benefits associated with this behavior change. Finally, negative thinking about oneself often perpetuates disordered eating behaviors. A useful strategy to reduce resistance is to provide positive feedback and reinforcement (especially for younger patients) for any effort toward homework and attempts at behavior change. By modeling this consistently (ie, distinguishing between effort and outcome), patients can learn how to think more positively about themselves, which can give them the confidence to push forward.

Adult patients with low or no motivation to change.

, there is sometimes little to no motivation to recover from the disorder, although there is often a recognition of other problems that anorexia nervosa is causing for them (eg, isolation, poor physical health). This lack of motivation is largely due to the ego-syntonic nature of the disorder coupled with chronicity. The combination causes the disorder to become engrained and drastically reduces the likelihood of weight gain, even when the patient appears motivated for treatment.

Given the poor remission rates for patients with chronic anorexia nervosa, novel approaches sometimes focus on minimizing the burden of illness rather than attempting to bring about full weight and cognitive recovery. An example of a therapeutic goal that targets functional improvement that both patient and therapist can agree on would be minimizing harmful exercise practices that lead to injury.

Another example of a functional improvement target is cognitive inefficiencies that contribute to poor quality of life-for example, excessively rigid and detail-oriented thinking. Cognitive remediation therapy (CRT), a novel approach, focuses on improving meta-cognition (thinking about how thinking style affects everyday living), which increases flexibility and the ability to see “the bigger picture” while establishing a strong therapeutic alliance. CRT is intentionally designed to be nonthreatening and, thus, it is not focused on the eating disorder.

The goals of treatment are achieved by playing different games that challenge the particular inefficiencies common in anorexia nervosa. CRT can be used as an adjunctive treatment, and data suggest that when used in this way, it can lead to lower levels of treatment discontinuation.

 By using nonthreatening methods (ie, CRT), agreeing on the same therapeutic goals that do not directly challenge safety behaviors, and by working collaboratively, treatment can be moderated even in the most challenging group.

Treatment resistance in eating disorders is complex and manifests differently depending on diagnosis, patient age, and duration of illness. These factors can inform the clinician about the types of resistance they are likely to encounter and on potential strategies and treatment options that may be useful in overcoming these barriers. However, the role and content of resistance will vary in each case; it is important to understand how each patient views her symptoms and the perceived function they offer in the context of her life. This understanding will help the clinician conceptualize why the patient is resisting and how this resistance is likely to interact with specific symptoms.

Dr Aspen is a Postdoctoral Research Fellow and Dr Darcy is an Instructor in behavioral medicine; Dr Lock is Professor of child psychiatry and pediatrics and Director of the Eating Disorder Program for Children and Adolescents in the department of psychiatry and behavioral sciences at Stanford University School of Medicine, Stanford, Calif. The authors report no conflicts of interest concerning the subject matter of this article.

Tools, Not Schools, of Therapy: Integrating Twelve Treatment Models

. May 2013. http:// jackhiroseresources.com/post-one. Accessed August 6, 2014.

Treatment Manual for Anorexia Nervosa: A Family-Based Approach

. 2nd ed. New York: Guilford Press; 2013.

 Lock J, Agras WS, Fitzpatrick kk, et al. Is outpatient cognitive remediation therapy feasible to use in randomized clinical trials for anorexia nervosa? 

Why do patients with eating disorders resist treatment? How can the clinician address resistance? 

Alison M. Darcy, PhD

Articles

Mental Health in America: A Growing Crisis

DTx and Cognitive Behavioral Therapy: Opportunities, Benefits, and Challenges of an Evolving Landscape

Patient Resistance in Eating Disorders