Nidal Moukaddam, MD, PhD

To help in the prevention of the coronavirus disease 2019 (COVID-19), the Food and Drug Administration (FDA) issued an emergency use authorization (EUA) for the Pfizer-BioNTech vaccine on December 11, 2020. One week later, the same action was issued for the Moderna vaccine (

 against COVID-19 has provided a sense of optimism, as many have endured prolonged isolation and quarantine, the mental health effects of the pandemic, and the morbidity and mortality of the disease. However, for vaccines to achieve the desired affect there must be a minimum number of individuals who accept it. 

 is not a new issue; it has been widely studied. In fact, the World Health Organization identified it as 1 of the top 10 global health threats in 2019.

Table 1. Efficacy and Safety Data for the Pfizer-BioNTech and Moderna Vaccines

A large survey by the Pew Research Center found the intent to get the COVID-19 vaccine dropped from 72% in May 2020 to 51% by September 2020.

 Of those respondents, 77% reported a concern that vaccine swould be prematurely approved without long-term safety data. Among the individuals who did not want the vaccine, 76% expressed concern about its potential adverse effects, whereas 72% wanted to know more about its efficacy before administration. Of the individuals who intended to get the vaccination, 19% expressed a great deal of confidence about safety and efficacy, 45% reported a fair amount of confidence, and 35% reported not much or no confidence at all (

 The intent to get vaccinated increased in December 2020, as 60% of Americans said that they would “definitely” and “probably” get COVID-19 vaccination once it is possible.

Figure 1. Concerns and Confidence About the Vaccines

More recently, the Kaiser Family Foundation (KFF) surveyed 1676 adults in the United States from November 30 to December 8, 2020. They found that 71% of respondents reported they would “definitely” or “probably” get the COVID-19 vaccine, whereas 27% expressed concern and were hesitant to receive it.

 The sample was randomized to represent the population, including Hispanic and non-Hispanic Black adults. Vaccine hesitancy was high in individuals identifying as Republicans (42%), rural residents (35%), and Black adults (35%) (

 For comparison, during the 2009 influenza A H1N1 pandemic, the early estimates of vaccine acceptability were between 50% and 64%.

 To address lagging hesitancy and resistance, vaccine myths and disinformation need to be addressed, and clear and consistent messages should be delivered to the community about the importance of receiving the vaccine. Psychiatrists can and should take time to 

Figure 2. Groups Hesitant to Receive the Vaccine

 In the 2 COVID-19 vaccines currently available, the mRNA carries instructions to make the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attach, or spike, protein to the host cells. After the vaccine is injected, the mRNA is taken up by the macrophages near the injection site and instructs these cells to make the spike protein. This spike protein then appears on the surface of the macrophages inducing an immune response.

 These types of vaccines take another virus and replace its genetic material with the sequence coding for the SARS-CoV-2 spike protein. The virus acts as a delivery system, or vector, for this genetic sequence.

Replication-defective adenovirus vector vaccines.

 The AstraZeneca/Oxford University vaccine uses a simian adenovirus vector, and the Johnson & Johnson vaccine uses a human adenovirus vector to carry the DNA coding for the spike protein to the host cells and deliver it to the nucleus.

 The cell’s machinery produces the spike protein, which then induces the immunologic response.

 The virus contained in this vaccine does not replicate inside cells, as the genes responsible for replication have been deactivated. These vaccines are not authorized in the US.

 Merck’s vaccine uses a recombinant vesicular stomatitis virus (rVSV) vector.

 The researchers replaced part of its RNA sequence with RNA coding for the spike protein. Unlike the adenoviruses, this vector displays the spike protein on its surface. Because rVSV is replication competent, this platform more closely mimics a real viral attack.

 This vaccine is still in clinical trial.

 These include SARS-CoV-2 spike protein but no genetic material. Novavax’s

, NVX-CoV2373, is made from recombinant nanoparticles studded with coronavirus spike proteins.

 It is awaiting results from phase 3 trials from multiple sites.

The mRNA vaccines are classified as reactogenic: They generate a brisk immune response, which is expected to cause some adverse effects in most individuals who receive them. The most common reported reaction has been pain at the injection site.

 Other commonly reported systemic adverse effects have included chills, fatigue, headache, and joint and muscle pain.

 In most individuals who received the vaccine, these effects resolved within a couple of days.

 In general, the adverse effects are more common in younger vaccine recipients, with the second dose predictably producing more adverse effects than the first one.

Severe allergic reactions (anaphylaxis) have been reported in some recipients shortly after the first dose.

 notes that anaphylaxis is rare, with an estimated rate of 11.1 cases per million doses of the vaccine.

 The likely culprit for these reactions is polyethylene glycol, a compound in the Pfizer-BioNTech and Moderna vaccines.

 Following these reports, post-vaccination observation has been made mandatory: 30 minutes for those with a history of severe allergic reactions of any kind and 15 minutes for everyone else.

 The number of cases of Bell palsy reported in the trials of both mRNA vaccines was small and not more frequent than expected in the general population. Hence, it is not certain whether the vaccine caused the paralysis.

Mental Illness Exacerbation in Context of Vaccine Hesitancy

Mental health professionals can expect anxiety, paranoia, and even 

 in the context of vaccine hesitancy. Widespread conspiracy thinking can make it more difficult to achieve proper control of the pandemic and promote adherence to healthy behaviors. Vigilant fact-checking and challenging irrational thinking are essential. Stressful circumstances could trigger or exacerbate mental illness, and the debunking of vaccine-related myths should include an assessment of whether the beliefs represent mental symptoms that need treatment.

Anxiety about vaccine safety and adverse effects correlates with increasing scores on the Vaccine Conspiracy Belief Scale,

 obtaining information from social media, and lower educational status. 

 are inversely correlated with adhering to safety restrictions and behaviors, and related to pseudoscientific practices and vaccine hesitancy.

 Studies also found that individuals with strong COVID-19 conspiracy beliefs tended to have lower education.

The right framing can present the vaccine as a gain instead of a risk.

 Prospect theory posits that in the context of uncertainty, a behavior expected to prevent a loss is more likely to be selected.

 Anecdotally, individuals who have lost loved ones to the illness are likelier to accept the vaccine. Current conspiracy theories suggest that vaccines are riskier than reality and downplay the risks of COVID-19 illness. Current misinformation suggests the vaccine is related to infertility, poisoning, and other imagined severe effects. This risk-benefit analysis, of course, does not stand up to scientific scrutiny, but careful debunking of misinformation in the context of a positive therapeutic alliance can result in a more realistic and balanced view of the situation.

In individuals with mental illness, the propensity to overinflate threats and avoid risks may contribute to vaccine hesitancy, as in the case in anxiety disorders.

 The same mechanism could apply to psychotic disorders but has not been formally studied (

Table 2. Myths About COVID-19 Vaccination

Confronting and Overcoming Vaccine Myths and Misconceptions

A survey conducted by the Yale New Haven Health System showed that 1 out of 6 health care workers expressed 

 to getting the vaccine in the first wave, with the most common reason being medium- and long-term safety issues.

 Health care staff also expressed concerns about lack of existing research and data transparency, uncertainty about the inclusion of minority groups in the trials, a rushed process, RNA technology, underlying or preexisting conditions not studied, political influence, and length of expected immunity from the vaccine.

 The KFF’s vaccine monitoring system found similar hesitancy trends in the general population.

A probing yet nonconfrontational approach combined with directing individuals toward objective resources may work best in addressing some of the common misconceptions. Motivational interviewing techniques can also tackle vaccine hesitancy.

“The vaccine will alter my DNA/make me infertile.”

 The concept of using mRNA as a novel therapeutic tool dates to the early 1990s.

 The mRNA is not designed to integrate with host DNA, nor can it typically do so given its rapid degradation.

 In the COVID-19 vaccine, once the immune response is initiated against the spike protein, mRNA is degraded by the cells.

“I already had COVID-19, so I do not need the vaccine.” 

The Centers for Disease Control and Prevention recommends that individuals who previously had COVID-19 get 

. Health experts do not know how long the immunity lasts postinfection because this can be variable depending upon the individual.

“Not everyone with COVID-19 dies, so I will be fine without the vaccine.” 

As of February 2, 2021, there were 26,034,475 cases of COVID-19 in the US and 439,955 deaths.

 The long-term effects are still unknown, but COVID-19 is suspected to cause dysautonomia, cardiovascular, and neurologic sequelae. Thus, vaccination is recommended not only to protect oneself immediately, but also in the long run and for those who are at high risk.

“By taking the vaccine I will get the COVID-19 infection or will test positive for COVID-19.”

 The COVID-19 vaccine does not contain the live virus, and vaccines are unable to trigger the full disease. An individual who has received the vaccine will not test positive for COVID-19 in the absence of infection.

“Would the vaccine make me psychotic or does it have a microchip?”

 Pfizer-BioNTech and Moderna have made their vaccine ingredients available to the public. They both contain mRNA along with lipids and other ingredients that help to maintain the pH for stability.

 Patients with mental illness, especially those with 

 spectrum disorder, can become paranoid about vaccine contents.

 Initially, direct the individual to the drug companies’ websites and ingredient lists. Address this particular concern with a careful assessment of psychosis in vulnerable patients.

“Vaccines cannot be trusted, as they were rushed in the process of their development.”

 During the clinical trials, the companies used the vaccine in a large sample of volunteers to establish its safety and efficacy. It took a few months for the clinical trials to collect substantial data, which was further scrutinized by the experts in the FDA, and the 2 vaccines received EUA. In addition, many vaccine safety monitoring systems are in place: the V-safe, National Healthcare Safety Network, and others are tasked with identifying adverse events not encountered in clinical trials.

 The only missing piece, in fact, is long-term effectiveness data, as most adverse effects with vaccines tend to occur in the first 6 weeks after administration. To refute this misconception, point out that the pandemic’s impact led to unprecedented 

 between governments and pharmaceutical companies and considerably shortened bureaucratic wait times.

is assistant professor of internal medicine at the Baylor College of Medicine. 

is assistant professor of psychiatry at the Baylor College of Medicine.

is professor and executive vice chair, Barbara & Corbin J. Robertson Jr. Chair in Psychiatry at Menninger, chief of the Division of Community Psychiatry at Baylor College of Medicine. 

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. She also serves on 

1. US Food and Drug Administration. Pfizer-BioNTech COVID-19 Vaccine. January 28, 2021. Accessed February 2, 2021. 

https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/pfizer-biontech-covid-19-vaccine

2. US Food and Drug Administration. Moderna COVID-19 Vaccine. January 28, 2021. Accessed February 2, 2021. 

https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/moderna-covid-19-vaccine

3. Lazarus JV, Ratzan SC, Palayew A, et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat Med. 2020:1–4.

4. Tyson A, Johnson C, Funk C. US public now divided over whether to get COVID-19 vaccine. Pew Center Research. September 17, 2020. Accessed February 2, 2021. 

2020/09/17/u-s-public-now-divided-over-whether-to-get-covid-19-vaccine

5. Funk C, Tyson A. Intent to get a COVID-19 vaccine rises to 60% as confidence in research and development process increases. Pew Research Center. December 3, 2020. Accessed February 11, 2021. 

https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to-60-as-confidence-in-research-and-development-process-increases

6. Hamel L, Kirzinger A, Muñana C, Brodie M. KFF COVID-19 vaccine monitor: December 2020. Kaiser Family Foundation. December 15, 2020. Accessed February 2, 2020. 

https://www.kff.org/coronavirus-covid-19/report/kff-covid-19-vaccine-monitor-december-2020

7. Reiter PL, Pennell ML, Katz ML. Acceptability of a COVID-19 vaccine among adults in the United States: How many people would get vaccinated? Vaccine. 2020;38(42):6500-6507.

8. Baden LR, El Sahly HM, Essink B, et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med. 2021;384:403-416.

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10. O’Callaghan KP, Blatz AM, Offit PA. Developing a SARS-CoV-2 vaccine at warp speed. JAMA. 2020;324(5):437-438.

11. National Institutes of Health. Phase 3 trial of novavax investigational COVID-19 vaccine opens. National Institutes of Health (NIH) press release. 

https://www.nih.gov/news-events/news-releases/phase-3-trial-novavax-investigational-covid-19-vaccine-opens

12. Shimabukuro T, Nair N. Allergic reactions including anaphylaxis after receipt of the first dose of Pfizer-BioNTech COVID-19 vaccine. JAMA. 2021.

13. Castells MC, Phillips EJ. Maintaining safety with SARS-CoV-2 vaccines. N Engl J Med. 2020:NEJMra2035343.

14. Centers for Disease Control and Prevention. Interim consideration: preparing for the potential management of anaphylaxis after COVID-19 vaccination. December 31, 2020. Accessed February 2, 2021. 

https://www.cdc.gov/vaccines/covid-19/clinical-considerations/managing-anaphylaxis.html

15. Centers for Disease Control and Prevention. Vaccination considerations for people with underlying medical conditions. December 29, 2020. Accessed February 2, 2021. 

https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/underlying-conditions.html

16. Sallam M, Dababseh D, Eid H, et al. High rates of COVID-19 vaccine hesitancy and its association with conspiracy beliefs: a study in Jordan and Kuwait among other Arab countries. Vaccines (Basel). 2021;9(1):42.

17. Teovanović P, Lukić P, Zupan Z, et al. Irrational beliefs differentially predict adherence to guidelines and pseudoscientific practices during the COVID-19 pandemic. Appl Cogn Psychol. 2020:10.1002/acp.3770.

18. Georgiou N, Delfabbro P, Balzan R. COVID-19-related conspiracy beliefs and their relationship with perceived stress and pre-existing conspiracy beliefs. Pers Individ Dif. 2020;166:110201.

19. Glare P, Fridman I, Ashton-James CE. Choose your words wisely: the impact of message framing on patients’ responses to treatment advice. Int Rev Neurobiol. 2018;139:159-190.

20. Abhyankar P, O’Connor DB, Lawton R. The role of message framing in promoting MMR vaccination: evidence of a loss-frame advantage. Psychol Health Med. 2008;13(1):1-16.

21. Exner C, Zetsche U, Lincoln TM, Rief W. Imminent danger? Probabilistic classification learning of threat-related information in obsessive-compulsive disorder. Behav Ther. 2014;45(2):157-67.

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https://health.clevelandclinic.org/8-common-covid-19-vaccine-myths-explained/

23. Roy B, Kumar V, Venkatesh A. Health care workers’ reluctance to take the Covid-19 vaccine: a consumer-marketing approach to identifying and overcoming hesitancy. NEJM Catal Innov Care Deliv. 2020;10.1056/CAT.20.0676.

24. Gagneur A. Motivational interviewing: a powerful tool to address vaccine hesitancy. Can Commun Dis Rep. 2020;46(4):93-97.

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https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety.html

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28. Gramigna J. Schizophrenia diagnosis among top risk factors for mortality in COVID-19 patients. Healio. January 28, 2021. Accessed February 2, 2021. https://www.healio.com/news/psychiatry/20210
128/schizophrenia-diagnosis-among-top-risk-factors-for-mortality-in-covid19-patients ❒

Articles

The vaccine needs to be appropriately framed: as a benefit, not a risk. 

Breaking the Spell: Fighting Myths About COVID-19 Vaccination

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 

Figure 1. Ideal Scheme of Detection and Change-Triggered Action From Wearables

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.

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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

Nidal Moukaddam, MD, PhD, discusses 3 main aspects of burnout. 

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.

Videos

Employment plays a vital role in a patient's mental health.  

Nidal Moukaddam, MD, PhD

Articles

Breaking the Spell: Fighting Myths About COVID-19 Vaccination

Data-Driven: How Technology Can Improve Psychiatric Practice

Exploring Different Aspects of Burnout

Tumbling Into Telepsychiatry in the COVID-19 Era: Challenges and Hope

The New Game of Microbiology Clue: The Who, When, Where, and Why of the Novel Coronavirus

Psychiatrists Beware! The Impact of COVID-19 and Pandemics on Mental Health

Mini Quiz: Coronavirus Outbreak and Mental Illness

Psychiatric Sequelae of Pandemics

Fears, Outbreaks, and Pandemics: Lessons Learned