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In 2018 more than 20 million Americans met criteria for substance use disorder, particularly alcohol abuse (73%). During the COVID-19 pandemic, alcohol sales have risen sharply, up 54% from the previous year.

In this Mental Health Minute, Judith J. Prochaska, PhD, MPH points out that the pandemic has made it difficult for patients to seek traditional forms of counseling. She discusses a digital therapeutic that can be an adjunct for traditional therapies, and 

 on the treatment’s efficacy for substance abuse and mood disorders.

 is a professor in the department of medicine at Stanford University with the Stanford Prevention Research Center and a member of the Stanford Cancer Institute.

Would you recommend a digital therapeutic to your patients? 

Share comments with your colleagues by emailing 

. Comments may be shared online pending review and editing for style.

A recent study tested a new digital therapeutic for effectiveness on both substance abuse and depression. 

Could Artificial Intelligence Improve Cognitive Behavioral Therapy?

As smartphone ownership has skyrocketed and internet connectivity has become a necessity, patients and clinicians alike have seen the potential of evidence-based digital interventions. However, mainstream clinical practice has been slow to adopt these new technologies. Reluctance to use digital interventions might be related to attitude/familiarity, knowledge, and systemic/liability concerns.

The slow adoption may also be due to a lack of interdisciplinary dialogue between app users (the consumer, the patient, the physician), app makers/designers (engineers, computer scientists, and artificial intelligence experts), and the clinical practitioners. Currently, app and wearables data do not directly correspond to psychiatric diagnoses. Incorporating technology into mental health treatment plans could allow clinicians to work smarter, with continuously updated information. Clinicians and patients would benefit greatly from novel clinical workflows that incorporate apps and wearables as long as they meet confidentiality/privacy requirements. Innumerable patients have phones and other digital devices that could be gathering relevant information, but the information present in those devices has no formal role in clinical practice yet.

Wearables can include shirts, necklaces, headbands, wristbands, watches, shoes, and eyeglasses. They can be used to monitor physiological parameters such as temperature, heart rate, skin conductance, respiratory amplitude, and body movements, which may help monitor disease status.

Flexible biosensing is a rapidly growing industry and area of research. It aims to produce flexible material that can be worn as close to the skin as possible in a noninvasive and unobtrusive manner. These wearables can include physiological signal-monitoring sensors; customized designs with transmitters, receivers, filters, and circuit tracks—all with reduced size, lighter weight, and lower power consumption.

 Biosensing can also be used to measure chemical levels in the body, such as blood sugar, calcium, lithium, and oxygen saturation.

In the field of psychiatry, only the more basic sensing aspects are eyed as adjuncts for information-gathering and digital therapeutics. Yet, even in their most basic form, sensors have the possibility of changing the way psychiatrists monitor their patients’ symptoms. Instead of relying on subjective information from patients, clinicians could gather objective data on sleep habits, activity levels, food intake, medication adherence, and even substance use. Onset of certain troubling patterns or departure from established baseline patterns serve as diagnostic cues. For instance, increases in motor activity (a proxy for agitation) can be a 

Key to success is the establishment of a clear relationship between the objective measure and the disease or mental health issue. For instance, irregular respiratory amplitude might signal decompensation of asthma. Since the link between shortness of breath and an asthma attack is well understood, a 

 looking at such data could theoretically be implemented.

 Turning to psychiatry, anxiety symptoms can occur in panic attacks and mimic asthma in an individual who has both disorders; in this case, irregular respiratory amplitude could signal worsening in either or both conditions. Interestingly, the wearable itself cannot make this determination and differentiate conditions unless an artificial intelligence algorithm gathers and interprets additional data points. A stepwise approach to this chain of events is portrayed in 

A Closer Look at Smartphone-Provided Information

The data gathered by smartphones are comparable to wearables, because most individuals carry smartphones on them most of the time. Information provided by a phone or a wearable is referred to as passive sensing, requiring no action on behalf of the user. Below is a non-exhaustive summary of phone-measured dimensions. Note that wearable bands used for fitness cover much of the same information but may not share all of it with users.

The most basic data a phone can provide is GPS information. GPS location tracking provides a detailed image of a patient’s routine. Phones can also measure exposure to daylight, which can share information about the patients leaving their home. These 2 data points tell a story of isolation and psychomotor retardation, both potential indicators of worsening depression. Similarly, sleep fluctuations can be detected by examining screen on/off features and accelerometry data.

The use of text/SMS can establish an individual’s sociability baseline. How large is a patient’s social network? How many people do they interact with on a regular basis? How long do their conversations last, and do they prefer phone to text? Deviations from this baseline can be seen in mood disorders (ie, isolation and withdrawal in depression; extraversion and increased sociability in mania); psychotic disorders (ie, isolation and withdrawal with worsening paranoia or persecutory delusions); and anxiety disorders and substance use disorders (ie, calls at erratic times; increased activity; certain suspicious locations). The length and content of verbal and written communication can also be indicative of pathology or relapses.

Lastly, movement anomalies and tremors can also be detected with smartphone-based monitoring and can be useful in assessing medication adherence and effectiveness.

Symptom Monitoring and Privacy and Confidentiality

The potential of technology to detect deviations in behavioral patterns is vast. However, privacy concerns are legitimate, and every effort needs to be made to protect individuals with mental illness, especially because they are a vulnerable population. For instance, could looking at interpersonal communications violate a patient’s privacy and confidentiality? Linguistic analysis and natural language processing indicate high correlations between in-person clinical ratings and blinded ratings based on social media data posts,

 in discriminating unipolar from bipolar depression.

 On Reddit, sentiment analysis, personal pronouns, and semantic category analysis differentiated belonging to a mental health support group versus other nonmental health groups.

 Social media content has been looked at as a 

 Accessing content of social media posts could be valuable in assessing and following mood disorders.

Looking at an individual’s conversations can be helpful in assessment and diagnosis of mood, psychotic disorders, and brain injuries, but carries risk of privacy invasion. Prosody assessment can help diagnose schizophrenia spectrum disorders by clarifying lexical and syntactic information.

 With privacy-preserving measures and no content analysis, our group has proven that depressive symptom severity 

 with decreased text/SMS and call durations and frequency.

 Privacy preserving analyses is more technically challenging, but may be more acceptable to patients in terms of sharing data and allowing speech recordings.

Ecological momentary assessment (EMA) refers to data reported by an individual in close temporal proximity to an event, which can help avoid recall bias. The use of smartphones has allowed EMA collection of feelings, reactions, and other behavioral measures more frequently than before. Answers in response to a prompt asking about mood are useful, especially in correlation with objective data from wearables or phones. In contrast to passive sensing, EMA cannot be collected continuously.

In the field of mental health, EMA is often centered on reports of mood but can also include questions about suicidality, self-harm impulses, hallucinations, substance or alcohol use, physical activity, self-esteem, etc. Both random and scheduled sampling are options, and the more frequent data collections allow for finer granularity in understanding microprocesses that govern emotional and behavioral experiences. EMA can also play a role in shaping emotions or behaviors by increasing awareness and, thus, needs to be studied carefully for potential placebo-like effect.

Choosing the right EMA is crucial to understanding the patient’s experience, just as choosing the right interview question can facilitate therapeutic alliance. In selecting apps,

 it is important to determine the goals. For example, would an event-contingent sampling (ie, asking a patient to log when they have cravings for a substance or urges to hurt themselves) be helpful? Is time-contingent sampling (ie, examining when things happen to rule out adverse effects or explore life occurrences on mood) a more appropriate option? EMA is also used to train artificial intelligence and deep learning algorithms to predict events, such as resurgence of auditory hallucinations or bad mood.

 Once the algorithm is trained, frequent EMA is no longer needed.

Referred to as digital health, the ability to leverage technology for encouraging positive behaviors is an area that could revolutionize health care, especially in psychiatry.

Mental disorders are heterogenous. A divergence can exist between what a patient reports and what the psychiatrist sees (eg, mixed features in a manic episode). The following tips can help fit this phenomenology within EMA and passive sensing findings.

Carefully evaluate prospective apps.The app evaluation model available on the American Psychiatric Association’s website offers a 

 to clinicians wishing to evaluate such applications.

 Specifically, clinicians should consider:

On which platforms/operating systems does the app work? Does it work on a desktop computer?

Has the app been updated in the past 180 days?

Is there a transparent privacy policy that is clear and accessible before use? Does the patient understand it?

Does the app collect, use, and/or transmit sensitive data? How are the data stored, and is storage claimed to be secure?

Is there evidence of specific benefit from academic institutions, end-user feedback, or research studies? (Note: Most apps do not, hence the difficulty in recommending specific ones.)

Does the app have a clinical/recovery foundation relevant to your intended use?

Does the app seem easy to use? How much battery and data does it consume?

Can data be easily shared and interpreted in a way that is consistent with the stated purpose of the app?

Avoid making a diagnosis based on data points from an app.Psychiatric diagnosis is categorical and relies on specific symptoms for a predetermined duration as dictated by the DSM-5 or the International Classification of Diseases, Eleventh Edition. Wearables do not provide a diagnosis, but rather a set of data points. A symptom of depressed mood does not equate to major depression, nor does a hallucination equate to a formal thought disorder. In incorporating findings in your formulation, use the data as part of a full clinical picture. Discuss app findings with the patient while avoiding diagnostic labels.

Think dimensional, not categorical.Symptom remission is the goal of treatment, but symptoms occur along a continuous spectrum. Findings from an app can be invaluable in helping patients move from partial to full remission. For instance, although a patient with depression may present with an improved affect and grooming suggestive of remission, the curves from a mood app show persisting poor functioning and, therefore, incomplete improvement. In this case, the improvement may have reached a certain threshold, which is not enough for full functional recovery. The dimension of depressed mood is distinct from the construct of a depressive disorder.

Apps and wearables generate an overwhelming amount of data that require specialized processing with deep learning algorithms. Clinicians should start with dimensions in which they have been trained, such as psychomotor retardation to agitation, depressed mood, food intake, and sleep. However, it is clear that othertransdiagnostic dimensions can tell a lot about an individual’s functioning, even if they are not part of traditional psychiatric diagnostic criteria. Functional imaging studies have largely moved in this transdiagnostic, dimensional direction, and these include psychological concepts such as impulsivity, compulsivity, sociability, and other biomarkers chosen based on functional connectivity and biomarker data.

 offers an example of dialogue between the categorical and dimensional approaches in psychiatry as mediated by wearable or app-provided information.

Use data from apps in session to discuss progress with your patients.Data collected outside of appointments offer a glimpse into the patient’s real life. In initiating use of any app or wearable, discuss the expectations and lay the foundation with the patient in terms of what the technology can do and cannot do. Then, during a later appointment, ask the patient to show you their mood graphs, and use this information to initiate a dialogue on noted bad days, triggers, sleep pattern deviation, etc.

There is rapid turnover of apps and technology, with few applications withstanding the test of time. As such, it is difficult to recommend any particular application, but it is useful to know the main ones in the field. Examples include Daylio and T2 Mood Tracker for journaling; Woebot, CBT coach, and CBT-I Coach for cognitive behavioral basics and insomnia; and Calm and Youper for wellness. A clinician familiar with an app can better guide the conversation.

Technology can be useful as adjunct to information gathering, diagnostic clarification, symptom tracking, and teaching/reinforcing behaviors for patients with mental illness. Still in its infancy, this exciting field is rapidly growing and holding much promise.

“Julie” is a young adult with depression. Pharmacological management has helped somewhat, but the patient reveals urges to self-harm and continued intermittent depressive symptoms. However, Julie is not fully open about this issue. Financial considerations and transportation issues preclude the addition of psychotherapy and more frequent visits. Interestingly, Julie wears a fitness band. At one of the appointments, you suggest looking at her sleep results and note a particular Thursday and Friday have a more interrupted pattern than other days. Upon closer inquiry, Julie reports alcohol binging on those days.

With this in mind, you suggest she install a cognitive behavioral therapy app for substance use (reSET, which is FDA-approved), and plan for closer follow-up visits.

is associate professor, Menninger Department of Psychiatry & Behavioral Sciences, Baylor College of Medicine and Ben Taub Adult Outpatient Services Director, Medical Director, Stabilization, Treatment & Rehabilitation (STAR) Program for Psychosis. 

is chair of the Department of Electrical and Computer Engineering and the Earnest Dell Butcher professor of engineering at Rice University.

1. Sreenilayam SP, Ahad IU, Nicolosi V, et al. Advanced materials of printed wearables for physiological parameter monitoring. 

2. Mukhopadhayay SC, Islam T. Wearable sensors for physiological parameters measurement: physics, characteristics, design and applications. In: Mukhopadhayay SC, Islam T, eds. 

3. Faurholt-Jepsen M, Vinberg M, Frost M, et al. 

Smartphone data as an electronic biomarker of illness activity in bipolar disorder

Wearable respiration monitoring: interpretable inference with context and sensor biomarkers

5. Kuosmanen E, Wolling F, Vega J, et al. Smartphone-based monitoring of Parkinson disease: quasi-experimental study to quantify hand tremor severity and medication effectiveness. 

6. Kelly DL, Spaderna M, Hodzic V, et al. Blinded clinical ratings of social media data are correlated with in-person clinical ratings in participants diagnosed with either depression, schizophrenia, or healthy controls. 

7. Mariani R, Di Trani M, Negri A, Tambelli R. 

Linguistic analysis of autobiographical narratives in unipolar and bipolar mood disorders in light of multiple code theory

8. Low DM, Rumker L, Talkar T, et al. Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.

9. Yoo DW, Birnbaum ML, Van Meter AR, et al. 

Designing a clinician-facing tool for using insights from patients’ social media activity: iterative co-design approach

10. Lucarini V, Grice M, Cangemi F, et al. Speech prosody as a bridge between psychopathology and linguistics: the case of the schizophrenia spectrum. 

11. Cao J, Truong AL, Banu S, et al. Tracking and predicting depressive symptoms of adolescents using smartphone-based self-reports, parental evaluations, and passive phone sensor data: development and usability study. 

12. Moukaddam N, Truong A, Cao J, et al. 

Findings from a trial of the Smartphone and Online Usage-based eValuation for Depression (SOLVD) application: what do apps really tell us about patients with depression? Concordance between app-generated data and standard psychiatric questionnaires for depression and anxiety

13. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. 

14. Doherty K, Balaskas A, Doherty G. The design of ecological momentary assessment technologies. 

15. Li B, Sano A. Early versus late modality fusion of deep wearable sensor features for personalized prediction of tomorrow’s mood, health, and stress. 

16. Tseng VW, Sano A, Ben-Zeev D, et al. Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. 

17. App Advisor. American Psychiatric Association. Accessed January 18, 2021. 

https://www.psychiatry.org/psychiatrists/practice/mental-health-apps

Still in its infancy, the field of technology in psychiatry is rapidly growing and holding much promise in diagnosis, symptom tracking, behaviorial reinforcement, and a host of other useful tools.

Data-Driven: How Technology Can Improve Psychiatric Practice

Against a sea change in real-world practice of technology in psychiatry, John Torous, MD, MBI, shares what clinicians can expect next.

6 Predictions for Digital Psychiatry in 2021

Zoom fatigue” describes the tiredness, worry, or burnout associated with 

 Like other experiences associated with the coronavirus (

) pandemic, Zoom fatigue is widely prevalent, intense, and completely new.

 that may tax the more than 300 million daily participants of Zoom

, experts representing diverse disciplines including acoustics, business, and the social sciences have contributed their explanations. For instance, audio has been proposed as the main reason that video meetings are draining. It turns out that millisecond delays in virtual verbal responses negatively affect our interpersonal perceptions, even without any internet or technical issues.

Other explanations attribute the fatigue to an underlying predisposition painted by the pandemic backdrop, such as increased financial stress and unemployment. Cognitive factors are also likely to contribute to fatigue, as an increased ability to virtually multitask threatens our attentional capacity. Exploring such multifactorial etiologies has delved us into a deeper understanding of the fragile intricacies that make up the precious in-person social interactions we had before COVID-19.

What is the psychological explanation of Zoom fatigue? Wecan start the exploration by revisiting the process of mental fatigue. A core psychological component of fatigue is a rewards-costs tradeoff that happens in our minds unconsciously.

 Basically, at every level of behavior, a tradeoff is made between the likely rewards versus costs of engaging in a certain activity.

 Even minor decisions, such as pressing the “Delete” versus “Backspace” button to erase a typed word, are made on the basis of these unconscious estimates to maximize reward (eg, time) over cost (eg, effort).

 The link between this reward assessment and fatigue then comes here: activation of the 

 in brain structures associated with reward (eg, ventral striatum, anterior cingulate cortex [ACC], amygdala) increases subjective alertness, energy, and motivation

Is a lack of perceived reward relative to cost during videoconferencing a primary psychological mechanism of Zoom fatigue? Indeed, social interactions are very much associated with our reward circuits, as oxytocin—the hormone involved in social bonding—modulates these same dopaminergic pathways involved in reward processing.

 Moreover, how that social interaction happens seems to matter. For instance, functional MRI data reveal that live face-to-face interactions, compared to viewing recordings, are associated with greater activation in the same brain regions involved in reward (ie, ACC, ventral striatum, amygdala).

 So, more active social connection is associated with more perceived reward, which in turn affects the very neurological pathways modulating alertness versus fatigue.

This neuropathophysiology may explain Zoom fatigue. For example, if the audio delays inherent intechnology are associated with more negative perceptions and distrust between people

, there is likely decreased reward perceived when those people are videoconferencing. Another example is direct mutual gaze. There is robust 

 on how eye contact improves connection—faster responses

, and increased likeability and attractiveness.

 These tools that make interactions organically rewarding are compromised over video. On video, gaze must be directed at the camera to appear like making eye contact with an observer, and during conferences with 3 or more people, it can be impossible to distinguish mutual gaze between any 2 people.

Not only are rewards lessened via these social disconnections during videoconferencing, but there are also elevated costs in the form of cognitive effort. Much of communication is actually unconscious and nonverbal, as emotional content is rapidly processed through social cues like touch, joint attention, and body posture.

 These nonverbal cues are not only used to acquire information about others, but are also directly used to prepare an adaptive response and engage in reciprocal communication, all in a matter of milliseconds.

 However, on video, most of these cues are difficult to visualize, since the same environment is not shared (limiting joint attention) and both subtle facial expressions and full bodily gestures may not be captured. Without the help of these unconscious cues on which we have relied since infancy to socioemotionally assess each other and bond, compensatory cognitive and emotional effort is required. In addition, this increased cost competes for people’s attention with acutely elevated distractions such as multitasking, the home environment (eg, family, lack of privacy), and their mirror image on the screen. Simply put, videoconferences can be associated with low reward and high cost.

Further yet, there are undoubtedly many other factors contributing to Zoom fatigue. The biopsychosocial model commits our field of psychiatry to holistically consider all the hidden influences that make up our experiences. Biologically, videoconferencing is confounded by a more sedentary daily rhythm experienced during the pandemic. Notably, physical activity is associated with about a 40% reduced risk of fatigue.

 Psychologically, a filtering of our stimuli through a fundamental rewards-costs analysis may explain how our dopaminergic pathways contribute. Socially, a perfect storm of vast unemployment, school shutdowns, racial injustices, political divisions, physical distancing, and loneliness predisposes everyone in our nation vulnerably.

The purpose of the biopsychosocial formulation, however, is to guide the treatment plan. Although the factors contributing to Zoom fatigue are complicated and multidimensional, a closer examination can lead to hope. The contributing factors, depending on their adjustability, serve as potential therapeutic targets to alleviate fatigue and salvage the aspects of social interaction that were once unconscious and taken for granted. Exploring alternative and more explicit ways to improve perceived reward psychologically during virtual communication may be a therapeutic approach for not only Zoom fatigue, but the mental and physical toll that come with it.

 is Assistant Professor at the David Geffen School of Medicine at University of California at Los Angeles and the Directorof the Pediatric Consult-Liaison Service and Pediatric Emergency Psychiatry at UCLA Mattel Children's Hospital. She has nothing to disclose regarding this article.

This article was originally posted on June 27, 2020, under the title

"A Psychological Exploration of Zoom Fatigue," and has since been updated. -Ed

1. Wolf CR. Virtual platforms are helpful tools but can add to our stress. Psychology Today. May 14, 2020. Accessed October 19, 2020. 

https://www.psychologytoday.com/us/blog/the-desk-the-mental-health-lawyer/202005/virtual-platforms-are-helpful-tools-can-add-our-stress

2. Patnaik S, Malara N, Mukherjee S. Zoom says it has 300 million daily meeting participants, not users. Reuters. April 30, 2020. Accessed October 19, 2020. 

https://www.reuters.com/article/us-zoom-video-commn-encryption/zoom-says-it-has-300-million-daily-meeting-participants-not-users-idUSKBN22C1T4

3. Zoom Company News. 90-Day Security Plan Progress Report: April 22. Accessed October 19, 2020. 

https://blog.zoom.us/90-day-security-plan-progress-report-april-22/

4. Roberts F, Francis AL. Identifying a temporal threshold of tolerance for silent gaps after requests. 

5. Johnson. Why zoom meetings are so dissatisfying. 

May 16, 2020. Accessed October 19, 2020. 

https://www.economist.com/books-and-arts/2020/05/16/why-zoom-meetings-are-so-dissatisfying

6. Boksem MA, Tops M. Mental fatigue: costs and benefits. 

7. van der Linden, D. The urge to stop: The cognitive and biological nature of acute mental fatigue. In: Ackerman PL, ed. Decade of Behavior/Science Conference. Cognitive fatigue: Multidisciplinary perspectives on current research and future applications. American Psychological Association; 2011: 149-164.

8. Dix A, Finlay J, Abowd G, Beale R. Human-computer interaction. Prentice Hall; 1993.

9. Lorist MM, Tops M. Caffeine, fatigue, and cognition. 

10. Lorist MM, Boksem MAS, Ridderinkhof KR. Impaired cognitive control and reduced cingulate activity during mental fatigue. 

11. Donaldson ZR, Young LJ. Oxytocin, vasopressin, and the neurogenetics of sociality. 

12. Redcay E, Dodell-Feder D, Pearrow MJ, et al. Live face-to-face interaction during fMRI: A new tool for social cognitive neuroscience. 

The detection of gaze direction: a stare-in-the-crowd effect

14. Mason MF, Hood BM, Macrae CN. Look into my eyes: gaze direction and person memory. 

15. Nurmsoo E, Einav S, Hood BM. Best friends: children use mutual gaze to identify friendships in others. 

16. Conty L, Dezecache G, et al. Early binding of gaze, gesture, and emotion: neural time course and correlates. 

17. Schilbach L. Eye to eye, face to face and brain to brain: novel approaches to study the behavioral dynamics and neural mechanisms of social interactions. 

18. Puetz TW. Physical activity and feeling of energy and fatigue. 

What is Zoom fatigue and is there a biopsychosocial explanation for this COVID-inspired phenomenon? The answer might surprise—and comfort—you.

A Neuropsychological Exploration of Zoom Fatigue

Experts discuss the latest in digital mental health smartphone apps, digital clinics, and online therapies and telehealth, topics that are especially relevant as we are experiencing a radical transformation of health care.

Most people have access to both the internet and devices that can connect to providers, but do patients have tools to make digital health work for them? We are in a pivotal time now with COVID-19 where we have seen wider access being provided at least in highly populated areas. We are still at the beginning stages as we strive to provide wide access to a lot of different people. 

 can be properly converted to a new platform in the form of digital technology apps. Additionally, some interventions might be a easier than others. Cognitive behavioral therapy and dialectical behavior therapy are more formulaic and provide homework for patients. Psychodynamic approaches may be more nuanced than some of the shorter term interventions that we are seeing. 

For some patients, it may be unclear exactly what happens in that translation. One useful analogy is comparing a book with a movie. Sometimes, the book is better than the movie; sometimes vice versa. But there is still a lot that we don't know as to why in-person therapy works for some and not for others. The principles are there but we are still learning about the evidence.

Sometimes, the book is better than the movie; sometimes vice versa. But there is a lot we don't know as to why in-person therapy works for some and why digital works for others. In this video, experts discuss the evidence.


Digital Technologies in Mental Health Care

The US Food and Drug Administration (FDA) has 

the first-ever video game to treat ADHD in children.

 EndeavorRxTM (AKL-T01) is a prescription therapeutic gaming 

designed to improve inattention or combined-type ADHD in youths between the ages of 8 and 12.

FDA approval was based on 5 clinical studies involving over 600 participants who had ADHD, including one 

 This prospective, randomized, controlled STARS-ADHD study showed that AKL-T01 improved objective measures of attention in children with ADHD. Of 857 children screened for the study, a total of 348 patients with confirmed ADHD were randomly assigned to receive AKL-T01.

The video game-like interface was used 25 minutes a day, 5 days a week, for 4 weeks and was found to improve attentional functioning significantly as reported by both patients and parents. After one month of use, roughly half of parents reported clinically meaningful improvement in their child’s daily impairments. “Moreover, this increased to 68% after a second month of treatment. Following a month of treatment, improvements in ADHD impairments were maintained for up to 1 month,” according to the Akili news release.

 A small percentage of participants experienced minor adverse effects that included frustration, headache, dizziness, emotional reaction, nausea, or aggression.

EndeavorRx, built on the Akili Selective Stimulus Management engine (SSMETM), is intended for clinical use in concert with therapy, pharmaceutical treatment, and patient education. Playing the game targets neural networks to improve inattention, a central component of ADHD. While hyperactivity is not particularly addressed by the platform, the video game experience activates parts of the brain that compromise cognitive function.

“With EndeavorRx, we’re using technology to help treat a condition in an entirely new way as we directly target neurological function through medicine that feels like entertainment,” said Eddie Martucci, PhD, Chief Executive Officer of Akili.

Elysa Marco, MD, Assistant Clinical Professor of Neurology and Director of Research, Autism and Neurodevelopmental Center, at UCSF, and Executive Director of Neurodevelopmental Medicine at Cortica Healthcare, added, “Unlike traditional ADHD medications, EndeavorRx is designed to specifically target inattention. Based on the benefits of my research participants and patients have experienced, I am thrilled that EndeavorRx is moving from the lab to the clinic to play an essential role as part of a comprehensive treatment plan for children with ADHD.”

Digital therapeutics for neuropsychiatric disorders have gained momentum since at least 2010, but this is the first-ever device approved by the FDA to be marketed for any condition.

 “Cognitive neuroscience research has the potential to inform the development of novel interventions for specific disorders, such as ADHD, by linking implicated brain systems with specifically-targeted digital interventions, which can be optimally designed to operate within game interfaces,” according to 

 Since the COVID-19 pandemic, early approvals of treatments have been made by the FDA, and this is no exception—perhaps because families are looking for alternatives to traditional medications that may or may not work.

The EndeavorRx treatment will be available with a prescription in the near future, according to Akili. The device will be part of the Endeavor Care Program, which includes the EndeavorRx treatment, to be used in conjunction with a mobile app (Akili Care). Information can be found at 

1. FDA Permits Marketing of First Game-Based Digital Therapeutic to Improve Attention Function in Children with ADHD. Press Release. Federal Drug Administration. June 15, 2020. Accessed June 17, 2020. 

https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-first-game-based-digital-therapeutic-improve-attention-function-children-adhd

2. Akili Announces FDA Clearance of EndeavorRx for Children with ADHD, the First Prescription Treatment Delivered Through a Video Game. Press Release. Akili Interactive; June 15, 2020. Accessed June 17, 2020. 

https://www.akiliinteractive.com/news-collection/akili-announces-endeavortm-attention-treatment-is-now-available-for-children-with-attention-deficit-hyperactivity-disorder-adhd-al3pw

3. Kollins SH; DeLoss, PhD; Cañadas E; Lutz J. A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. 

 2020;4:e168-e178. Accessed June 19, 2020. 

https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30017-0/fulltext

4. Robbins R. In a landmark decision, FDA greenlights a video game for kids with ADHD. Stat News. June 15, 2020. 

https://www.statnews.com/2020/06/15/fda-akili-adhd-endeavorrx

5. Davis NO, Bower J, Kollins SH. Proof-of-concept study of an at-home, engaging, digital intervention for pediatric ADHD. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5764249

FDA approves video game as treatment for ADHD.

The Video Game Will See You Now

There is a massive effort in the US to curb the spread of 

. But the US has been behind other countries in part because of lack of testing capacity. As testing capacity accelerates, the number of confirmed cases has grown. Singapore and South Korea have flattened the curve more quickly through efficiently identifying cases and exposures and promoting quarantine. This public health strategy, contact tracing, is old but their method is novel. They use phones.

Countries are using Bluetooth or mobility data (eg, Global Positioning System [GPS], cell tower) collected by the phone for contact tracing. One method of contact tracing involves scanning and recording Bluetooth signals from other nearby phones, usually within a 30 feet diameter. The real time data offer a novel means to track proximity and potential interaction with others. Another method for contact tracing involves use of mobility data (often GPS) collected by the phone that allows a digital trail of timestamped locations visited to be easily recreated.

Public health officials can leverage either approach, Bluetooth or mobility, to determine who an infected person may have been in close contact with—and then notify those exposed of possible risk. Countries have taken different approaches in what information they share with individuals identified as being at risk.

, where the age, gender, and location history of those infected are disclosed to those who may have been exposed. On the other end of the spectrum is Singapore, where people are only informed that they may have been exposed. Both South Korea and 

 have been successful in flattening the cure and their use of phone-based contact tracing offers clear potential to better understand how COVID-19 spreads and can be stopped. With increasing interest in using contact tracing in other countries, it is important to understand the potential risks and benefits of use in the US.


During these unprecedented times it is necessary to consider the benefits of disclosure to exposed third parties against the risks of not respecting individual privacy—everyone’s right to control their health information. People are reluctant to share their health information for various reasons, such us harm to one’s reputation, employment problems, and access to insurance.

In the US, health care professionals do not break confidentiality to disclose a patient’s disease status even for communicable diseases like COVID-19 that present a risk to others. One exception is HIV where many state laws encourage or mandate disclosure to public health identities and/or sexual partners. This HIV status disclosure is justified because public health benefits are thought to outweigh the risks to the individual, though this is not without controversy. For COVID-19, one’s personal data (such as age, gender, location, and health status) is shared with others in a public fashion. Yet a discussion about COVID-19 status disclosure does not appear to be happening in many countries where phone-based contract tracing is deployed. In other words, personal data is being shared without the input of people whose information is being shared—in this case those infected.

The current pandemic-related disclosed data can be very sensitive, especially if linked to GPS mobility data collected by phones. Intimate details like where a person sleeps, shops, and even eats can be easily inferred. Later secondary uses of this data can have numerous unintended consequences around employment, social standing, and health insurance coverage. People have already been assaulted because of suspected or confirmed COVID-19—now imagine their home address being shared too. While efforts to deidentify data can be undertaken, history seems to always show that in the near future reidentification is possible. It is assumed that the data are encrypted and secure. But is it? We do not know, key details are lacking.


The legal implications cannot be ignored either; will there be criminal consequences for spreading coronavirus? It may sound fantastical, but in the US there are criminal laws against certain high-risk behaviors that could spread HIV. In Singapore, refusal to share COVID-19 related data requested by the Ministry of Health can lead to prosecution. These consequences have likely led to people avoiding testing or care.

Ethically based solutions offer a path forward. One pathway is to simply ask for 

 to collect data. This could be done in multiple ways including an opt-in system (Singapore) or even opt-out in some circumstances. Although Iranian officials used an opt-in system, the consent was displayed in a non-native language or not at all for many users. Regardless of the system, to respect privacy, it is important that the data collected, shared, and where data are stored is clearly explained to users. Alternatively, it could be an opt-out consent, which may be more coercive because people are less likely to change from the default. If an infected person’s consent is required before his or her data are shared, an opt-in system could be more palatable. Another solution is to aggregate data at the community level to examine trends rather than an individual’s data. This does not necessarily preclude obtaining consent. Although less advantageous for contact tracing, storing contact data on the phone of an infected person rather than a centralized database lets that individual decide whether he or she want to share the data with public health officials.


COVID-19 has spurred many changes leading to wider adoption of various technologies to support public health efforts. But even data collected for the greater good such as phone-based contact tracing can have unintended negative or punitive consequences. There is always a risk when sensitive information is collected. Ultimately, US public health officials must weigh these risks against potential benefits in an informed fashion.

is a Child and Adolescent Psychiatry Fellow, University of California Los Angeles Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA.

is Director, Digital Psychiatry Division, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. He is Digital Psychiatry Section Editor for 

COVID-19 has spurred many changes leading to wider adoption of various technologies to support public health efforts. But even data collected for the greater good can have unintended negative or punitive consequences.

A Novel Approach to Contact Tracing

How can telepsychiatry improve quality and access to care as well as reduce physician burnout? Telepsychiatry pioneers Peter Yellowlees, MMBS, MD, Chief Wellness Officer at UC Davis Health and Professor of General Psychiatry at the University of California, and Jay Shore, MD, MPH, Professor at the Department of Psychiatry and Family Medicine at Colorado School of Public Health, joined moderator Chris Aiken, MD, Director of the Mood Treatment Center and 

 Section Editor for Bipolar Disorder, to discuss tips and tricks in making telepsychiatry work in this era of COVID and beyond. The webinar, which was recorded live on May 18, 2020, is available online at 

https://event.on24.com/wcc/r/2359555/FAB61DFA865C680B16BE9C42AF7BAF30

How can telepsychiatry improve quality and access to care as well as reduce physician burnout? Telepsychiatry pioneers

Digital Psychiatry