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

Ashutosh Sabharwal, PhD

Digital therapeutics (DTx) is a new category of medicine that refers to treatments delivered directly to patients via software or apps. The scope of DTx encompasses treatment, management, and prevention of a broad spectrum of diseases and disorders. As such, DTx are part of a larger ecosystem of digital health technologies that provides support for patients, clinicians, and medical institutions. These technologies include patient- and clinician-platforms, as well as the unseen operational components.

DTx differs from the multitude of mental health apps on the market because they are meant to be prescribed or recommended by a health care professional; part of a comprehensive treatment plan; evidence-based; and subject to the same regulatory requirements as traditional medical treatments. The development of DTx is an area that affects patients, clinicians, payors, and policymakers. The 

 has put forth a 10-point list of core principles for DTx products as a first step in operationalizing the industry; these principles also help to distinguish DTx products from unregulated apps.

In order to use DTx effectively, we must understand its relationship to the following categories: patients, clinicians, health care systems, and innovation/discovery, especially as it relates to comorbid disorders.

80% of adults in the US have a smartphone

, and many of them spend hours per day interacting with their devices.

 Although most of that time is likely for entertainment purposes, there are also opportunities to improve their health.

Recent advances in technology have led to the unobtrusive, seamless collection of patient-generated health data from smartphones, which can be used to improve patient care and outcomes. For example, smartphones can collect data on sleep, activity, physiology, and device use, as well as provide an environmental context. Digital phenotyping refers to the “moment-by-moment quantification of the individual-level human phenotype in situ, using data from smartphones and other personal devices,” and DTx takes it to the next level by 

recommending actions based on that phenotyping

The near ubiquity of these devices means that the clinical applications of digital phenotyping and DTx are highly scalable; their use also allows the individuals’ data to be used as their own unique baseline. Research has demonstrated the 

feasibility and acceptability of physiological data

 The hope is that digital markers of behavioral change (ie, observable changes in sleep, physical activity, and social interaction) might become 

sensitive measures of meaningful variation

 in functional status, symptoms, and risk for adverse outcomes in patients, thereby better guiding and personalizing care.

Acceptability of app use, and DTx in general, is an encouraging sign. Scalability will likely depend on age and generational influences (younger individuals may potentially be more amenable to DTx use), comfort with technology in general, and socioeconomic disparities (ie, more affluent individuals with better phones/wearables will be able to access more nuanced DTx options).

A yet unexplored problem is adherence to DTx use once prescribed; as large-scale implementation becomes a reality, this issue will need to be appropriately addressed. “App burnout,” a phenomenon referring to the short-term use of apps, may be relevant to DTx as the prescribed length of time (ie, weeks, months, years) increases. Current studies in mental health, for instance, have looked at DTx with contingency management and cognitive behavioral therapy for 12 to 16 weeks; medication active sensing systems meant to improve adherence, such as Abilify MyCite (aripiprazole tablets with sensor), have been helpful in short-term studies and have been evaluated for a maximum of 26 weeks per the United States Food and Drug Administration (FDA) approval. Thus, although the short-term implementation of DTx has been an attractive option for the management of chronic disorders, much is still unknown about the interaction of DTx with the naturalistic course of a chronic illness, and much remains unknown about the best times to implement DTX measures (

 Extracting Biobehavioral Markers From Sensors for the Development of DTX

DTx have been developed over the past 15 to 20 years to target chronic medical and mental issues, with the assumption that many of these conditions can be improved by shaping behaviors. However, physicians adopting DTx in chronic management face ethical, legal, and practical challenges. In the case of psychotic disorders, ingestible sensors (despite positive data in 

) are seen as potentially decompensating factors in a patient’s delusion, thus limiting adoptability.

 have faced adoptability challenges for depressive and psychotic disorders because of clinician concerns that they may worsen the primary disease.

, a maturity technology timeline that relates technology visibility to time. This concept explains the issues involved in DTx adoptability, including novelty of the new modality, expected value, expectation inflation of opinion related to innovation, and time needed for the technology itself to reach maturity.

Clinicians theoretically support the idea of closing treatment gaps and would likely be open to leveraging concrete treatment planning and adherence tools to reinforce what was discussed in-session. However, despite FDA approval of DTx apps (such as Welldoc’s BlueStar for Type II diabetes management and Pear Therapeutics’ reSET for the treatment of substance use disorder), DTx is still globally an underprescribed and underused modality. In addition, the 

From a clinician perspective, there are a number of hurdles to overcome, including lack of familiarity with DTx, lack of time to introduce and administer DTx, and the lack of DTx integration with electronic health records. Similarly, there are obstacles in billing for DTx. Although they exist, very few physicians are employed in systems that offer digital formularies. Thus, adoption by physicians is an unsolved challenge for DTx implementation. In order to achieve DTx scalability, it is necessary to change the clinical visit microenvironment and make DTx a viable treatment decision.

Reimbursement and oversight are the 2 big areas of concern in health care systems, yet the 

 The first question that must be answered is who is responsible for regulating DTx. The second is, what is the relation of DTx to machine learning and whether changes in software or updated machine learning algorithms would require reapproval or new approvals. The 

 approach currently under consideration by the FDA could address some of these concerns.

Currently, approved or cleared products have received their acceptance from the FDA Center for Devices and Radiological Health, some using the Breakthrough Therapy designation. The aim, ideally, would be a path for software-as-a-medical-device (SaMD) products, with predetermined steps through the lifecycle of the software. This will require modernization of the FDA paths for medical devices, which were written in 1976.

System-level challenges of DTx also include cybersecurity. DTx interfaces with, and is reliant on, multiple nonmedical entities, including internet, phone, and cloud storage service providers. There are currently no global answers to these issues, but patients and clinicians alike will be reluctant to transmit sensitive health data over unsecured channels. Moreover, reimbursement for DTx is moving slowly but surely. Some private insurers pay for DTx as prescribed, but nationwide reimbursement codes remain uncommon.

Thus far, the main DTx developments have been the delivery of already proven treatments via electronic/software means. However, the future of DTx will likely include higher-order constructs and products that address comorbid disorders. Digital innovation in mental health already exists, and is accelerating rapidly, which is a good thing. Digital innovation serves 3 broad purposes.

First, digital innovation can increase access to mental health care. With the currently available technologies, there has been a continual improvement in the delivery of mental health services via telemedicine/telepsychiatry. Telepsychiatry has been important in facilitating patient follow-up by reducing travel, thereby helping longitudinal engagement. This, in turn, has improved adherence and reduces loneliness and misinformation.

 may not always be optimal for patient engagement and patient-physician rapport, studies have shown it is not inferior to face-to-face care.

 Feasibility and success in delivering therapeutic interventions, such as cognitive behavioral or supportive therapy, and sleep hygiene education via the internet/phone/apps have been demonstrated.

Second, digital innovations provide a new arsenal of tools to measure aspects of patient biology and behavior in ways that were not previously possible. This objective data collection, in turn, will likely allow better diagnosis and will allow clinicians to adjust care, as necessary.

Third, the fast-expanding repertoire of artificial intelligence tools enables the discovery of complex relationships between new forms of data and mental health conditions. As a result, novel ideas are emerging to quantify complex concepts like loneliness, mood swings, and cravings/binge eating that may underlie worsening mental health symptoms. Future DTx developments are expected to go beyond already proven treatments.

Mobile and Wearable Technologies in Mental Health

 reviewed mobile phone/wearable sensor–based mobile studies (N = 33) for mental health conditions between 2009 and 2018. The majority of the studies targeted unipolar depression or anxiety in healthy participants, with fewer studies focusing on bipolar and psychotic disorders. The Global Positioning System (GPS) was the most common sensor used. Features such as reduced mobility patterns and time spent at home during specific time intervals of the day were associated with scores indicative of depression or anxiety, as measured by questionnaires such as the Patient Health Questionnaire (PHQ)-9, Generalized Anxiety Disorder (GAD)-7, or by Ecological Momentary Assessment (EMA) self-reported mood.

, measured with an accelerometer/gyroscope; this was followed by phone call logs.

Higher-level clinically relevant constructs, such as behavioral regularity, may be useful to better understand behavior patterns. Data from 

 showed that 2 behavioral regularity indices

 were correlated with perceived stress scale scores, the mental component scale of the 12-Item Short Form Health Survey (SF-12), and daily self-reported mood.

 The indices included a sleep regularity index that quantified the regularity of sleep time, wake time, and sleep duration, and a daily mobility index that detailed routines based on GPS data. Using behavioral rhythm markers (eg, ultradian, circadian, and infradian rhythms) estimated from mobile phone sensors, these data predicted self-reported symptoms from individuals with schizophrenia (the machine learning algorithm is able to predict when patients will be reporting certain symptoms based on behavioral patterns collected as above).

 based on mobile phone sensors were used to predict symptom resurgence in patients with schizophrenia.

higher-level construct of social ambiance

 can likewise be obtained by unconstrained day-long recordings from wrist-worn audio-bands that estimate the number of simultaneous speakers.

 The number of simultaneous speakers was used as a proxy for an individual’s sociability, as social isolation is often a symptom of mental illness. By classifying audio into 4 levels (ie, quiet, low, mid, and high social ambiance), researchers found that individuals with depression or psychosis spent less time in diverse environments with higher social ambiance levels. Moreover, social ambiance patterns are associated with the severity of self-reported depression and anxiety symptoms, and personality traits such as neuroticism and agreeableness.

Despite strong preliminary evidence associating sensor data with mental health conditions, many challenges lie ahead in data-driven modeling and inference. This issue is a major challenge associated with the scalability of DTx development, as it requires industry and academic collaborations. For instance, computational models need to learn associations between individual patients’ biomarkers and mental health constructs (eg, symptoms, critical events). Every patient has different behavioral patterns and physiological responses, so the challenge becomes, how to determine regular/irregular patterns for each patient, or each group of patients.

A single computational model might not be able to fit the heterogeneity of different patients. Similarly, patients’ symptoms are based on their self-reports, which are subject to variabilities and biased perceptions. Current technology has a limited ability to detect biased self-reports, thereby limiting the usability of deep learning. Deep learning is a popular and powerful approach to developing complex neural network models; however, it is hard to interpret why certain models work well, and model-building requires a significant amount of data per patient.

Although the overlap between depressive disorders and diabetes is not well understood, it has been hypothesized to be potentially linked to immune system overactivity/systemic inflammation, psychoneuroendocrine dysregulation, cognitive functioning, and 

 This type of comorbidity could be effectively targeted with DTx if models are built to understand this disease overlap,

 especially as the field undergoes a paradigm shift.

One of the biggest conceptual ongoing shifts in psychiatry is characterizing mental illnesses dimensionally, in contrast to the current practice of categorical classification. Taking major depressive disorder (MDD) as an example, a shift in both diagnosis and management can be achieved by leveraging technology and by adopting a more modern, innovative approach to diagnosis. In the current diagnostic paradigm, MDD is defined in the DSM-5 as having 5 of 9 symptoms (with necessary inclusion of depressed mood and/or anhedonia). Thus, it is possible that 2 patients with the same MDD diagnosis have no overlapping symptoms: one has depressed mood, the other anhedonia; one eats too much, one eats too little; one sleeps too much, the other too little; one is suicidal, the other is not, etc.

The National Institute of Mental Health has established the Research Domain Criteria (RDoC) program, which recommends that mental health research be focused on symptoms instead of diagnoses, and studies should be done not categorically, but dimensionally, in a manner that allows overlap with tech-based objective information gathering. The RDoC highlights the following neurobiological transdiagnostic research dimensions: negative valence, positive valence, cognitive processes, systems for social processes, and arousal/regulatory systems. The current state of DTx is patient-centered/patient-driven information collection, but most DTx structures, thus far, do not fit categorical or dimensional psychiatric diagnostic frameworks; instead, they focus on individual symptoms and may be the simplest level of dimensional classification.

In the dimensional paradigm, a symptom like low energy can be translated into low physical activity/low mobility/low socialization by sensor detection. Those, in turn, can be addressed with a focused intervention, such as behavioral activation. This approach has measurable outcomes, is easy to explain to patients, and is more amenable to process improvement based on real-time feedback. The current approach to MDD treatment, which usually starts with the addition of a selective serotonin reuptake inhibitor (SSRI), aims to improve feelings of depression/anhedonia generating the low activity/low energy.

 provides some improvement in survival in the 

, but not the expected protective effect that is hoped for in diabetes and depression.

 Intuitively, this makes sense as a lifting mood may not fully translate into the desired energy increase, unless the patient specifically learns the needed skills to effect this change.

The field has turned to dimensions that may impact behavioral change, and those in turn will need to be measured and relayed back to the patient in a feedback loop. A good illustration of this is self-efficacy, or the belief that one has control over health behaviors. Self-efficacy tends to be diminished in patients with chronic illnesses, who may accept many symptoms as an inevitable part of the disease process, and lack the understanding/ability to take control of their biological processes. Poor self-efficacy may overlap with the social determinants of health and health care disparities but, in general, studies on self-efficacy and specific diabetes-related mitigating behaviors are promising.

 Taking care of oneself would help to improve mobility/energy and, by extension, mood. At this point, this stepped process lacks clarity and established protocols, but it could be an excellent DTx goal for further development.

Leveraging DTx to improve mental and physical health is likely to be the biggest, paradigm-shifting change that medicine has known since the invention of antibiotics. However, how to apply these tools remains an area that needs operationalization. DTx is at the nexus of digital innovation and scalability/wide-scale use of digital interventions, with more exciting developments on the horizon.

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 an Assistant Professor at Rice University, Department of Electrical and Computer Engineering, Computer Science,

and Bioengineering. She also directs the Computational Wellbeing Group.

is an associate professor of Psychiatry Research at Baylor College of Medicine.

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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The hype cycle model: a review and future directions

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Beyond validation: getting health apps into clinical practice

Characteristics and challenges of the clinical pipeline of digital therapeutics

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Are videoconferenced mental and behavioral health services just as good as in-person? A meta-analysis of a fast-growing practice

Feasibility of a simple and scalable cognitive-behavioral intervention to treat problem substance use

A review of digital cognitive behavioral therapy for insomnia (CBT-I Apps): are they designed for engagement?

Usability across health information technology systems: searching for commonalities and consistency

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Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions

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Patient-independent schizophrenia relapse prediction using mobile sensor based daily behavioral rhythm changes

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Articles

Digital therapeutics may be the biggest paradigm-shifting change medicine has seen yet... 

From Theory to Real-World Practice: Clinical Integration of Digital Therapeutics

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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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.

Ashutosh Sabharwal, PhD

Articles

From Theory to Real-World Practice: Clinical Integration of Digital Therapeutics

Data-Driven: How Technology Can Improve Psychiatric Practice