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Digital Mental Health Apps in Action
Apps in Action: Answering Your Questions About Prescription Digital Mental Health Treatment

Released: November 21, 2024

Expiration: November 20, 2025

Greg W. Mattingly
Greg W. Mattingly, MD
David C Mohr
David C Mohr, PhD
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Key Takeaways
  • Digital therapeutics can be useful for a wide variety of patients, so early communication and shared decision-making are important in determining whether they are right for any given patient.
  • The recently approved prescription digital therapeutic for major depressive disorder is an app that leverages neuroplasticity with an exercise combining cognition and emotion processing.
  • Digital therapeutics have been and continue to be developed for a wide variety of conditions and are a growing set of tools to consider in the HCP toolbox. Considering the evidence and quality of the growing number of DMHTs is important for HCPs to be able to address questions that patients may bring to the table in their care.

What kind of patients would be ideal for the use of these digital therapeutics? Stable patients we have been seeing for a while? New patients? What about disease severity—does that matter? ​
The evidence indicates that depression symptom severity alone does not indicate whether a person will respond to a digital mental health treatment (DMHT). Meta-regression analyses have shown no significant relationship between baseline symptoms and clinical outcomes in either improvement in symptoms or adherence to treatment. Digital mental health interventions have demonstrated effectiveness across different severity levels, with studies showing significant reduction in both depression and anxiety symptoms compared with control groups. Therefore, symptom severity alone should not be the sole factor in referral to DMHTs. Although conclusive evidence is currently lacking, there may be, however, other severity indicators that could indicate a poor fit, such as a history of being refractory to other treatments or a long history of depression. Recent adaptive trial designs have explored various levels of therapist support combined with digital interventions, suggesting that treatment intensity can be adjusted based on individual needs rather than initial symptom severity alone. Currently, DMHTs for depression and other common mental health conditions such as anxiety are being implemented as frontline treatments with studies showing a >75% probability to cost-saving while gaining response or remission for patients presenting with new symptoms in general internal medicine or other medical services.  

The beauty of digital therapies is that they can offer an equitable way to extend mental health services to a wide variety of patients who otherwise may have been unable to receive therapeutic care because of cost, timing, or geographic availability. DMHT has been used to augment standard care, fill gaps in service, and in some instances, as an alternative to standard in-person care (although this is not an ideal scenario). Not only have digital therapies been used to decrease symptom severity and disease burden, they also have been used to detect relapse, enhance adherence, and foster psychoeducation in various mental health conditions. The ideal patient could be new to treatment, or an existing patient looking for on-demand services that can be accessed when they have free time, or to enhance life skills and social engagement, or perhaps for someone for whom traditional models of care have been met with frustration. Imagine on-demand services for a university student away at college, a busy working mom wishing to pursue psychotherapy in the evenings, or an isolated senior who has been unable to access standard in-person care. 

Does the level of education affect the degree of benefit a patient receives from a digital therapeutic?
There is no strong evidence that the level of education affects the degree of benefit a person might receive from a digital therapeutic. User engagement and the effectiveness of digital therapeutics depend on multiple factors. Research shows that intervention design and usability features play crucial roles in patient adoption and continued use. That said, clinical judgment and common sense should be applied. Digital therapeutics that contain features that exceed the individual’s capacity should not be recommended. For example, digital therapeutics that rely heavily on written language should not be recommended for patients with low reading levels. A patient’s knowledge and abilities in using the technology should be assessed before recommending a digital therapeutic, in order to select an appropriate digital therapeutic and to determine whether any training is needed. Studies have shown that proper intervention design and appropriate support features can significantly affect user retention and therapeutic outcomes. For example, the combination of self-monitoring capabilities and learning course, along with customized push notifications and human coaching, has shown promise in maintaining user engagement. 

In general, many digital therapeutics require a fifth-grade to eighth-grade reading level, but digital therapeutics have been developed for those with various cognitive challenges, including attention-deficit/hyperactivity disorder, autism, schizophrenia, and the effects of head trauma or concussion. 

What is EFMT and how does it work?
Emotional Faces Memory Task (EFMT) is a digital therapeutic task intended to help treat depression. In this task, users are shown a series of faces displaying different emotions and then asked to identify the emotions they have seen. A face showing an emotion (eg, happiness, sadness, anger) is displayed and then removed. Users then are asked to name the emotion and to recall whether they have seen the same emotion in an earlier sequence. This activates the amygdala, which processes emotions, and the dorsolateral prefrontal cortex (DLPFC), which is involved in regulating emotion, thereby improving how the brain processes emotional information. Studies using pupillometry have shown that DLPFC stimulation directly affects emotional processing, with left and right DLPFC having distinct roles in how the brain allocates cognitive resources when processing emotional stimuli. The task leverages neuroplasticity, the brain’s ability to form new neural connections and reorganize existing ones, potentially leading to long-lasting changes in emotional processing and regulation. Early trials indicate this process may be helpful in reducing depression symptom severity, with studies showing that EFMT training as monotherapy during a 6-week period was associated with changes in brain connectivity on functional MRI and symptomatic improvement. 

EFMT combines N-back, a well-established therapy for “stretching” and enhancing working memory, with the ability to recognize facial emotions and process the response. We are essentially working cognitive pathways in the prefrontal cortex and anterior cingulate and “stretching” them to more accurately monitor and respond to a variety of facial emotions. 

We know that individuals with major depression process information approximately 40% more slowly than when not depressed and we also know their brains tend to cue primarily to negative emotional valence. The EFMT tackles both of these problems by progressively stretching working memory while primed by facial recognition of both negative and positive emotional valence.  

When in the therapeutic relationship or process do you bring up digital therapeutics as an option, and how do you do so?
There are no current guidelines for when or how to bring up digital therapeutics in the context of mental health treatment. When to bring it up should be determined in part by the role the digital therapeutic will play in treatment. It may be better to use digital therapeutics proactively, before a problem has developed, rather than reactively after a problem has developed, as the experience of failure may make it less likely that the digital therapeutic will be helpful. For example, an app that supports medication adherence might be brought up at the beginning of pharmacotherapy as part of how treatment is delivered when the patient is feeling hopeful, rather than after adherence problems have been detected, which may then associate the task of using the digital therapeutic with nonadherence or failure. Similarly, a mindfulness app would likely be better recommended when the intervention is first introduced in treatment, rather than waiting until after the patient has had difficulty with it. 

When recommending a digital therapeutic, there are several points that I make to enhance the likelihood of sustained engagement:

    1. Evidence: Explain that that the digital therapeutic has been demonstrated to be effective for the intended purpose.
    2. Describe the recommended digital therapeutic and what the patient’s experience with the program might be.
    3. Standard care: Present the option as part of the standard treatment for the patient’s condition.
    4. Describe the benefit of the digital therapeutic for the specific patient, such as that it may be easier to fit into the patient’s lifestyle than psychotherapy, as it does not require regular visits that can be difficult to schedule.
    5. Use expectations: Describe the expected use (eg, the app works best if you use it X times per week).
    6. Provider’s personal observations: Explain how the digital therapeutic has been helpful and effective for other, similar patients.
    7. Balance: Credibility may be enhanced by also mentioning any limitations, such as requiring some time commitment to engage with the digital therapeutic.
    8. Integration into care: Let the patient know that you will follow up with the patient to see how it is going. Knowing that the healthcare professional (HCP) will stay involved can increase the patient’s motivation to use the digital therapeutic. 

Digital therapies open the door to an entirely new way to expand on traditional models of care. They can be used to increase access for early intervention, to bolster traditional in-person psychotherapy, to address relapses in symptoms, and to enhance medication response. 

Once a patient is using a digital therapeutic, how do you incorporate their use of it into your sessions with them?
Integrating the digital therapeutic into processes of care improves sustained engagement, as demonstrated in primary care settings that actively monitor patient progress. If HCPs can see engagement from the digital therapeutic (eg, through a dashboard), those data should be mentioned and discussed at follow-up appointments or contacts, with studies showing improved treatment adherence when usage data are reviewed with the patient. If the digital therapeutic does not provide data back to the provider, the HCP can ask about its use, inquire about ways it has been helpful, and assess for any difficulties. Offer assistance in managing any difficulties that may interfere with consistent use. And it is important to positively reinforce any usage. Research shows that addressing difficulties promptly and providing positive reinforcement for engagement can significantly affect long-term adherence, with one study demonstrating that HCP support and encouragement led to sustained use of digital therapeutics during an 11-month period. The integration of face-to-face support with digital interventions has proven particularly effective, creating a hybrid model that maintains therapeutic alliance while maximizing the benefits of digital tools. 

Conclusions
Digital therapeutics are one more tool in the toolbox. As with any intervention, they should be incorporated into a personalized holistic treatment plan. As we all know, we live in an increasingly digital world where cognitive capital and brain health have never been more important. Digital therapeutics offer tremendous potential to provide care that is affordable, available, and equitable for our patients. 

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How likely are you to encourage the use of DMHTs in your clinical practice in the next 12 months? Answer the question below and join in the conversation by adding a comment.

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