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Elysa Marco, MD, talks about positive play—how using digital media can enhance specific abilities for kids, especially those with attention-deficit/hyperactivity disorder. 

is a board-certified cognitive and behavioral pediatric neurologist, neuroscience researcher, and author. She specializes in the evaluation and treatment of children with sensory processing disorder, autism, and other neurodevelopmental conditions at Cortica in San Rafael, California.

Kids will find the screens. Health care providers should join them in order to supply the most helpful digital treatments. 

You Can't Beat the Screens

“There simply aren’t enough of us,” said Lori Raney, MD, in a scientific session, titled “

Unprecedented Access to Psychiatry: Expand Your Impact to Infinity and Beyond! Collaborative Care, eConsults, and ECHO Models

American Psychiatric Association (APA) Annual Meeting

. The session addressed the vast shortage of psychiatrists and proposed alternative models of care to improve outcomes for more patients.

Speakers discussed the ever-widening gap between the shortage of psychiatrists and the overflowing flood of patients needing care. Shannon Kinnan, MD, a member of the APA Integrated Care Committee, spoke about the gravity of the problem of access to psychiatry. “The severity and the incidence of psychiatric illness continues to climb, which has only been magnified by the pandemic,” said Kinnan, assistant professor at Creighton University School of Medicine, Omaha, Nebraska. She worked in psychiatric services that had a wait list of 350 patients.

Moderated by Ron M. Winchel, MD, assistant clinical professor at Columbia University School of Medicine, Department of Psychiatry, presentations centered around 4 main themes: collaborative/integrated care, psychiatric consults, ECHO models, and eConsults.

Collaborative care, also known as integrated care, is ever evolving, informed by decades of research conducted by the APA, Academy of Psychosomatic Medicine, and other organizations. Kinnan directed participants to APA’s 

 to explore current evidence-base for the model, elements of implementation with detailed examples, lessons learned by those in the trenches, as well as recommendations on how to advance the use of the collaborative care model.

According to Kinnan, the collaborative care model is made up of a patient-centered team that targets the special needs of each patient, looks for solutions, and lays out piece by piece ways to educate primary care physicians (PCP). One core feature of the collaborative model includes encouraging PCPs to use measurement-based care with tools such as Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7).

“The patient’s main points of contact are their PCP and a care manager,” she explained. “The psychiatric consultant then reviews cases in a registry with a care manager and offer guidance to the team on the case.”

The integrated psychiatry model attends to the needs of patients with team-based care and systematic regular caseload reviews, which involves psychiatrists, case managers, PCPs, social workers, and other health care providers. It enables delivery of services to patients at most dire need of psychiatric services.

“It is better than traditional care in the primary care setting,” Kinnan told attendees. “Patients get better, faster. And there’s cost savings as well.”

A consult enables those involved in behavioral health to manage multiple cases and communicate with different allied health professionals, with the goal of extending psychiatric services.

According to Lori Raney, MD, a practicing psychiatrist in Colorado and principal consultant with Health Management Associates, curbside consultation educates PCPs about diagnostic dilemmas and medications, helping them with complex patients.

A consult is an opportunity for psychiatrists to educate and collaborate with the general practitioner, especially important since medical training often involves only 2 weeks of psychiatric training. The consult enables the PCP to understand specific needs of patients and provides diagnostic clarification.

“There are definite approaches you can take,” Raney told attendees. “You want to build the PCP’s confidence in prescribing psychotropic medications and diagnosing behavioral health conditions.”

Psychiatrists can get calls for consults on a variety of issues, Raney explained. “The most prevalent, most common behavioral health conditions we are getting called about often involve anxiety disorders, depression, and substance use disorders . . . And then you’ll see other things, patients with multiple somatic symptom personality disorder traits and pain that play out in the clinic,” said Raney.

Another item on the menu of integrated behavioral health is the electronic mental health consultation (eConsult).

Christopher T. Benetez, MD, associate medical director of the Los Angeles County Department of Mental Health and assistant clinical professor at UCLA David Geffen School of Medicine, has conducted more than 5000 eConsults in the LA County project over the past 5 years.

“An eConsult is an asynchronous consultative, provider-to-provider communication within a shared electronic health record [EHR] or web-based program,” Benetez explained.

The eConsult approach enables PCPs to have remote access to psychiatrists, entering questions into an EHR for quicker response than the bottleneck often found in traditional models of care.

As the second largest health system, LA County occupies more than 4000 square miles with 10.5 million residents, and it is both rural and urban. Psychiatrists are also involved in an array of community partnerships, jails, and courts.

The eConsult in LA County and other community health hubs are intended for the Safety Net population, defined by the Institute of Medicine (IOM) as “those providers that organize and deliver a significant level of health care and other needed services to uninsured, Medicaid and other vulnerable patients.”1

The eConsult system has optimized health equity by filtering psychiatric expertise to geographically challenged regions and a greater number of residents. “Our Safety Net takes care of over half a million patients a year with > 400 distinct sites; 4000 submitters; 500 specialty reviewers; and > 65 specialty portals,” said Benetez.

The advantages of the system set in place is centralized and coordinated systems and greater access to routine care, as well as patient-centered care and patient education. Limitations include measuring data, reimbursement, and concerns over liability.

Another way to improve access to psychiatric expertise has been through a learning community. Mark Duncan, MD, spoke about one alternative model at the University of Washington (UW).

Duncan is assistant professor at the Department of Psychiatry and director, UW Psychiatry and Addictions Case Conference (UW PACC), a 

 initiative. ECHO stands for Extension for Community Healthcare Outcomes.

 is made up of a didactic and case presentations. “The goal is to increase patient access across the state to effective and evidence based psychiatric, psychological, and addiction care through increasing the knowledge and self-efficacy of all interested health providers within an intentionally supportive virtual community,” said Duncan.

Using a collaborative/educational model, UW PACC is in a position to help guide care across disciplines, including psychology, psychiatry, and primary care. The net result has been to help change practice, build community, and identify providers needing the most assistance.

“It’s a great service because we’re interacting with the provider and asking for quick follow-up questions across many other specialties, so we can get a well-rounded summary of recommendations for people.”

America’s Health Care Safety Net: Intact But Endangered.

 Washington, DC: National Academy Press; 2000.

Collaborative care guides care across disciplines, including psychology, psychiatry, and primary care. The goal is to change practice, build community, and identify health professionals needing the most assistance.

Serving the Needs of the Many: Identifying Strategies for Better Care

The last year has seen an unprecedented shift in the medical landscape, and mental health care is no exception. Experts look at the evidence and risks of digital tools, apps, telehealth, and other technologies for the treatment of serious mental illness. 

In this video, John Torous, MD, and Sherin Khan, LCSW, briefly talk about their presentation at the 

American Psychiatric Association Annual Meeting

Three New Tools to Augment Care for People With Serious Mental Illness

. Speakers are Sherin Khan, LCSW; John Torous, MD, MBI; Amy Cohen, PhD; Tristan Gorrindo, MD, and Moderator, John Luo, MD. 

Dr Torous is director of the Digital Psychiatry Division, Department of Psychiatry at Beth Israel Deaconess Medical Center, Boston; editor in chief of 

​. Sherin Khan, LCSW, is the Senior Director of Workforce and Organizational Development at 

, Illinois' oldest and largest provider of community mental health services; and Social Work Expert for 

The last year has seen an unprecedented shift in the medical landscape, and mental health care is no exception. Experts look at the evidence and risks of digital tools, apps, telehealth, and other technologies for the treatment of serious mental illness.

New Digital Psychiatry Tools to Improve Care

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?

The first study to use social media listening (SML) analysis of unsolicited patient and caregiver insights might give a better look into the perspective of patients with tardive dyskinesia (TD).

TD, a difficult movement disorder, is commonly associated with prolonged exposure to dopamine receptor blocking agents such as antipsychotics. Based on gathered data, it also has a number of patients left feeling unheard.

“Because the emotions and sentiments of patients (and sometimes caregivers) on social media platforms are unsolicited and unfiltered, SML methods… offer a more comprehensive context for understanding how patients experience TD and the unmet needs in this therapeutic space,” Farrar et al. wrote in their discussion. “As indicated in the recent FDA guidance on patient engagement for drug development,

 social media can allow access to difficult-to-reach populations, provide accurate and automatic capture of data, and possibly result in greater self-disclosure by patients.”

For this pilot study, researchers collected patient insights from 261 social media posts made by patients and caregivers, mainly on blog forums and Twitter, using predefined search terms. Of the 261 posts, 107 were used for analysis. Overall, the sentiments leaned negative, with 64% responses being negative, 33% neutral, and only 3% positive.

Three themes emerged: anger, insecurity, and symptoms. Common sentiments showed patients were angry about developing TD from a medication used to treat another of their conditions, insecure about feeling unaccepted by society, and afraid of being judged.

All posts were gathered from publicly available, nonpassword protected sites. In order to protect the identity of users, all personal information was excluded prior to any manual review of the content and verbatim quotes were not included in the final report.

While researchers admitted that the study might be limited by a small sample size and reliance on self-reporting, they concluded social media may help convey unmet patient needs that do not get addressed in clinical discussions.

1. Farrar M, Lundt L, Franey E, Yonan C. Patient perspective of tardive dyskinesia: results from a social media listening study. 

2. US Food and Drug Administration. Patient-focused drug development: collecting comprehensive and representative input guidance for industry, Food and Drug Administration staff, and other stakeholders. Center for Drug Evaluation and Research; 2020.

The first study to gather unsolicited patient responses via social media finds 3 negative themes. 

Patients With Tardive Dyskinesia Feel Unheard, Social Media Listens

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

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May 16, 2020. Accessed October 19, 2020. 

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