Dopamine does not cause pleasure; it causes "wanting" and is critical to addiction and social attachments

Key Takeaways

  • Dopamine does not cause pleasure. It causes “wanting” and craving.

  • The same dopamine pathways critical to addiction and are also critical to certain social attachments (monogamy and mother-infant bonding)

  • And our dopamine systems are highly influenced by genetics and environment; much of what we “want” may be outside conscious control.


Psychologists sometimes treat all kinds of loss in a similar way. Whether a loss of life, relationship, old habit, or personality trait, I’ve heard it conceptualized by therapists as grief.

Right before quarantine started, my ex informed me it was best if we didn’t talk anymore. For the first few weeks (maybe first few months…) of shelter-in-place, I remember really missing her, especially at night.

I also remember really missing the two cafes I frequented. Early mornings, evenings, and weekends, I’d go for a drink, and bring a book.

One type of missing made me feel ready to cry, the other not as much; but both shared a similar ache-y quality. Like my mind or body was reaching for something that could never be there.

I noticed quarantine give birth to some new habits too, some I didn’t like: a lot more Netflix binging, food binging. I felt especially drawn to these on the weekend. I tried a few times to stop. Again, it felt ache-y.

This led me down a research rabbit hole starting with the search terms “addiction,” “dopamine,” and “withdrawal.”

I still haven’t found direct evidence for dopamine’s role in grief, but I discovered some incredibly interesting research, seemingly related.

The below is an effort to distill the research. Much is included and cited. Key takeaways are at the top and section headers are bolded for easy skipping. If you’re interested in learning more, I’ve included citations, and the two main papers that guided most the research were “Is Social Attachment an Addictive Disorder?” and “The Debate Over Dopamine’s Role in Reward” (Insel 2003, Berridge 2007).

Teaser: A compulsion to act that escapes awareness

For five days a week, over six weeks, subjects sat in an empty room and read the following instructions:

To start the session press the left lever. During the session you are free to press the right lever as often as you like. Only presses on the right lever will have an effect; any responses on the left lever once the session has begun will have no effect. However you need not press either lever. When the red light comes on and stays on the nurse will give you an injection.

Depending on the week, subjects were given either placebo, 3.75mg, 7.5mg, 15mg, or 30mg of morphine (Lamb 1991).

During placebo week, subjects would stop pressing their levers by days four and five. For every morphine dose, however, lever pressing remained constant throughout the week.

At the end of each day subjects were asked “Do you feel the medicine?” and “Did you like the medicine?” Only the two highest morphine doses produced significantly more “yes” responses than placebo.

Morphine is quite addictive and the willingness to press a lever thousands more times for just 3.75mg of the drug speaks to this.

But subjects were unable to consciously differentiate between the feeling of low dose morphine and placebo. This speaks to something potentially haunting about the processes that underlie addiction: they may compel us to act differently, and we don’t even notice, or know why.

Quick overview on the dopamine system and addiction

The American Psychiatric Association classifies addiction as a severe form of “substance use disorder,” which is when “people keep using the substance even when they know it is causing or will cause problems” (APA). Put another way: “Addiction is a form of compulsive behavior with an increasing narrowing of the behavioral repertoire towards drug intake. The essence of addiction is a subjective sense of a loss of control” (Insel 2003).

The NIH and National Institute of Drug Abuse (NIDA) pour over $1.2 billion in research every year to understand and combat the epidemic of addiction. Although we lack great solutions, it is well understood that the brain’s dopamine and dopamine pathways are highly involved in the addiction process.

Most of the brain’s dopamine is released from a structure called the ventral tegmental area (VTA). It sits near the spinal cord and within the inner, older parts of the brain it sends projections to the amygdala (associated with threat processing), hippocampus (associated with memory formation), and nucleus accumbens (NAc, associated with initiating movement). This network within the inner limbic region of the brain is referred to as the mesolimbic dopamine pathway.

The VTA also sends projections to the newer prefrontal cortex (associated with executive and strategic thinking). This network is referred to as the mesocortical dopamine pathway.

Though the specific biological steps that lead to addiction are unknown, it is known that addictive drugs lead to high amounts of dopamine release within both these dopamine networks.

Dopamine was initially known for pleasure and reward

Dopamine was only discovered in 1957. Soon after, two studies solidified its initial reputation as the “pleasure chemical.” Dr. Robert Heath, a scientist with connections to MK-Ultra, implanted electrodes into the brain of “Subject B-19” (Heath 1972). When B-19 stimulated near his dopamine pathways, he expressed “feelings of pleasure, alertness, and warmth.” He also “had feelings of sexual arousal and described a compulsion to masturbate.”

A few years later Dr. Russel Portenoy ran a similar experiment with a female subject and observed similar results (Portenoy 1986). She stimulated so regularly at times, she ended up “neglecting personal hygiene and family commitments.” Heath and Portenoy concluded they discovered the “pleasure” centers of the human brain. 

The belief that dopamine causes or somehow facilitates a sensation of pleasure remains quite pervasive. In 2014 headlines from a medical news site still read “Dopamine Is the Chemical That Mediates Pleasure in the Brain.” 

However, starting in the late 90’s, Dr. Wolfram Schultz and Dr. Kent Berridge separately began compiling evidence that dopamine has very little to do with pleasure.

Berridge directly consulted Heath and Portenoy’s raw transcripts, and found that the supposed pleasure electrodes “did not cause much sensory pleasure after all”; and there was no “clear declaration of exquisite pleasure” (Berridge 2010, Berridge 2003). He also notes that although there was much talk of orgasm, it was never achieved via electrode stimulation.

Dopamine release is less about reward, more about cues and predictions

Dr. Schultz has been name-checked in quite a few bestsellers and podcasts including The Power of Habit, Hooked, and Atomic Habits. He has discovered a reliable and specific mathematical formula that can predict levels of dopamine release. His groundbreaking work has been used to explain how phones and apps keep our attention; and also how we might establish more positive behaviors and habits.

In his most cited study, he gave monkeys different quantities of juice and used electrodes to record dopamine activity (Schultz 1997). Trials began with displays of different colored shapes (each associated with different amounts of juice), then the monkeys were required to press a lever to receive their juice. At first, dopamine activity spiked immediately following the taste of juice. After more trials, greater amounts of dopamine released after the colored shape cue and less following the reward itself. Eventually nearly all the dopamine activity spiked after only the reward cue.

Schultz found that when a stimulus (such as the taste of juice, or color of a shape) causes dopamine release, it’s not because the stimulus itself was rewarding, but because the stimulus predicted reward. At first the monkey has not learned that the colored-shape predicts reward. Once the association is formed, the stimulus of the shape releases dopamine, rather than the reward. Hence the importance of cues in addictions and habits. *Note: His later experiments would reveal an important nuance to his mathematical model of dopamine release, which he calls “reward prediction error” (Schultz 2016). Not necessary to understand here, but useful to know

Schultz work fantastically, and with mathematical precision, shows what dopamine release “codes” for and how it’s related to reward prediction rather than the reward itself. But it provides slightly less insight into the subjective state a monkey or human might feel or perceive as a result of dopamine.

Dopamine causes “wanting” and craving, not “liking” or pleasure

“What originally attracted my colleagues and me to study dopamine… [were theories that] dopamine is an essential contributing cause of hedonic messages we experience as pleasure,” writes Berridge.

He continues: “But the data we collected soon forced a change of mind” (Berridge 2007)

Many animals, including human babies, make similar facial expressions when they ‘like’ or ‘enjoy’ something versus dislike or are disgusted by something (see diagram).  When animals like something their mouths tend to open wider and their tongues show slightly; when disliking or disgusted, they tend to wince.

To test whether dopamine caused or enhanced pleasure, Berridge and his colleagues used different chemical and surgical methods to remove and add dopamine to the brains of rats. In some, whole areas of the brain were removed; in others, chemicals were injected to either stimulate or block dopamine receptors. To be sure, he also genetically modified rats to either produce more or less dopamine. He then gave both the rats sugar pellets. Elevating or depleting dopamine did not alter the rats’ ‘liking’ reactions.

He also measured how willing the different rats were to press a lever for their sugar pellets. There were significant differences. Dopamine-elevated rats tripled their lever pressing activity, while dopamine-depleted rats would almost stop pressing at all.

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Genetic and surgical modification of human subjects is not so simple. But Berridge cites a study on Parkinson’s patients, who experience a dramatic decay of dopamine neurons.

L-DOPA is a common treatment and is the closest chemical to dopamine that can be orally ingested and make it to the brain. Dr. Evans and his team gave Parkinson’s patients different amounts of L-DOPA, imaged their brains, and asked,

On a scale of 1-100 “Do you like the effects your feeling right now?” There was no relationship between score and dosage (Evans 2006).

He then asked, on a scale of 1-100, “Do you want more of what you consumed, right now?” A statistically significant difference emerged. Greater doses of L-DOPA were associated with a higher rating of “wanting more” and also more brain activity in dopaminergic regions.

With converging evidence in both animal and human studies, Dr. Berridge concluded that dopamine causes “wanting” but not pleasure or “liking.”

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Berridge calls special attention to his use of quotation marks around both “wanting” and “liking.” They demarcate the non-conscious processes underlying “wanting” and “liking” that are “distinguishable from their subjective feelings.”

For instance, “liking” facial reactions to sugar are relatively automatic and separate from conscious awareness or the subjective feelings of pleasure of babies or animals. And dopamine induced “wanting,” measured by a rat’s increased willingness to press a lever for sugar and a human’s willingness to press a lever for 3.75mg of morphine, is also automatic and not necessarily conscious. Both can operate beneath our awareness.

Dopamine induced “wanting” can compel an organism to act.

*An interesting note on L-DOPA: Eventually, some Parkinson’s patients treated with L-DOPA develop a second disorder, DDS, Dopamine Dysregulation Syndrome, where dopamine levels become hyper-elevated and symptoms include compulsive behaviors such as OCD and gambling.

Dopamine causes mother-infant attachment

Addiction is a brain disorder characterized by pathological “wanting.” It is an obsession that compels us toward some stimulus even if harm and risk stand in the way. It causes injury and death for many. But maybe some addictions, or addiction-like behaviors, help us thrive and survive.

In his review “Is Social Attachment an Addictive Disorder” Dr. Thomas Insel provides evidence of the remarkable similarities in the biology of addiction and certain social attachments (Insel 2003).

When a rat mother is given the choice between access to cocaine or her newborn pups, she prefers her pups (Mattson 2001). After the pups turn 16 days old, she reverses and chooses the cocaine.

To test whether this mother-infant attachment was due to the same dopaminergic system implicated in substance abuse, researchers tested two alternative scenarios: first, they surgically removed parts of the dopaminergic pathway and, second, they used chemicals (including cocaine) to block normal dopamine signaling in that pathway.

Both experimental conditions of reduced dopamine activity significantly reduced the rat mother’s approach behavior to her new pups.

For certain periods of time, a rat mother may find her pups more dopaminergically stimulating than cocaine. And to a significant extent, her attachment is mediated by the same dopaminergic pathways implicated in drug abuse.

Dopamine causes certain male-female attachment

The Prairie vole and Montane vole species are virtually identical except for one important trait: Prairie voles are monogamous and Montane voles are polygamous. A small genetic difference causes female Prairie voles to have more oxytocin receptors and male Prairie voles to have more vasopressin (the male version of oxytocin) receptors in their dopamine pathways (Insel 1998). Importantly, oxytocin and vasopressin tend to be released during affiliative social behaviors, such as sex.

Insel and his colleagues were able to show that by experimentally altering dopamine activity, they could change whether prairie voles were monogamous or polygamous.

First, they tested dopamine receptors directly. They were able to induce pair-bonding in Prairie voles in the absence of mating by stimulating a specific kind of dopamine receptor in the nucleus accumbens (Wang 1999). By blocking the same receptors, they blocked pair-bonding during mating.

Next, they altered dopamine pathway activity by way of oxytocin and vasopressin manipulation. In a similar fashion, they were able to induce and block pair-bonding by injecting oxytocin and oxytocin antagonists, respectively, into the dopamine pathways of female prairie voles (Insel 1995). They replicated this in male prairie voles using vasopressin and vasopressin antagonists (Lim 2002).

As with mother-infant attachment, the same dopamine pathway implicated in addiction disorders, mediates male-female attachment as well, at least for mice and voles. 

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Insel explains the most likely reason addiction and attachment share biological pathways and genes is that “these pathways and genes evolved not for drug abuse but for mediating the motivational aspects of social interaction.” Drugs hijack a natural process that normally serves an evolutionary purpose.

He hypothesizes that drug use sometimes “serves as a substitute for social attachments and that a common neurobiology underlies the major forms of attachment.”

Dopamine functionality and our attachments are sometimes beyond our control: genetics and environment

Due to a combination of genetics and environment, addiction is highly heritable. In identical-twin studies, substance-abuse disorders show a 0.7 factor of heritability (Ducci 2012). And if addiction occurs at least once in a household, it is much more likely to occur again. Specific genetic markers are also being correlated with addiction (Karamitros 2018). 

As discussed earlier, predisposition to certain social attachments have clear genetic components too. A similar gene to the AVPR1 vasopressin gene found in prairie voles has been found in humans. Human males with the monogamous-vole variant have slightly more stable relationships through life (Walum 2008).

Attraction to certain social attachment styles is also influenced by early gene-environment interactions. At least with rats, depending on the mothering styles, genes will turn on or off resulting in different levels of production and sensitivity to stress hormones. Baby rats can also learn to become attracted to stimulus that normally induces stress and aversion in normal rats. Professor Robert Sapolsky titled a paper on these effects “Any Kind of Mother in a Storm” (Sapolsky 2009).

It seems much of what we want may be biologically shaped by things outside our control.

The “Dark Side of Addiction”: stress pushes us further toward a substance

The closest line of research I found toward understanding dopamine’s role in grief comes from Dr. George Koob, who publishes continuously on “The Dark Side of Addiction” (Koob 2018).

The Dark Side of Addiction, which Koob also calls the “anti-reward system,” is the strong negative reinforcing effects of addiction.

You are willing to work just as hard to get more of the positive effects of a drug or stimulus (positive reinforcement), as you are to remove the strong painful after-effects of stress and withdrawal-like symptoms (negative reinforcement).

Koob believes a few key chemicals mediate the stress and pain of withdrawal symptoms, specifically corticotropin-releasing factor (CRF) and dynorphin. In rat studies, blocking CRF and dynorphin reduces withdrawal-like symptoms (e.g. anxiety and paw tremors) (Zorrilla 2014).

These chemicals are supposedly normally released after dopamine release to keep the body in homeostasis. But when released in high amounts, as might happen after a drug binge, they lead to withdrawal and withdrawal-like symptoms. And this cycle perpetuates binge-withdrawal, driving toward stronger addiction.

Conclusion

Whether or not the evidence for dopamine’s critical role is overturned by future research, a tremendous amount of human energy is directed toward objects of our “wanting” and craving. And much of what we want and how it influences us may be determined unconsciously by genetics or environment.

The evidence supports dopamine’s role in at least the “wanting” and craving of addictive drugs, mother-infant attachment, male-female pair bonding, and normal pleasurable rewards such as juice and sugar. It seems likely that all other conscious and unconscious wanting is also caused by dopamine. So how much of our actions have we chosen, rather than some combination of gene, environment, and dopamine systems has chosen for us?

Even something seemingly as significant as love or choosing a romantic partner, how much choice do we really have? From spending the last few years living in New York, I’ve noticed that I’ve begun to feel more physically attracted to a certain look and fashion. I don’t think I chose it; I was just surrounded by it.

Every city, family, and culture also has its own moral standards and status symbols; but also preferred colors, shapes, and sounds. What other choices am I not making? What else has dopamine compelled me to do?

[Summary] A Solution-Focused Research Approach to Achieve an Implementable Revolution in Digital Mental Health (Mohr, Riper, Schueller 2017)

TLDR; Digital mental health tools are not working in the real-world mostly due to adherence. We need to rethink our approach and design tools patients will actually use in practice.

https://twin.sci-hub.do/6602/7c3bada72283112462f149667a811e06/mohr2017.pdf?download=true

TLDR; Real-world implementation of digital mental health tools is failing. To bridge the ‘research to practice gap’ we need to rethink how we approach research and design tools that people will use in real life.

Although we have promising research, real-world implementations have mostly failed and are not being used by patients or therapists. We need a paradigm shift in our approach to close the “research to practice gap.”

  • “Numerous health care systems have attempted to implement DMH interventions to address the large burden of mental health. However, these real-world implementation efforts have failed, often because they are not used by patients or therapists. This large research to practice gap suggests failures at many points, including DMH intervention design, research methods, and implementation approaches. The promised revolution in mental health will require a paradigm shift that addresses all 3 components to overcome the design, research, and routine care chasms.”

A “solution-focused approach” prioritizes practical solutions that work in real-world settings over efficacy in the lab by improving on 3 stages: create, trial, and sustain.

  • “A solution-focused approach differs from an efficacy approach by prioritizing the development of a solution to a practical problem over the production of generalizable efficacy knowledge that might be correct in abstract but does not represent or trans- late to any specific real-world setting. We will more likely be successful if we produce a sustainable solution in a real-world setting and then adapt to other contexts. We suggest that solution-focused research can achieve its goals more rapidly by progressing through the 3 stages of create, trial, and sustain rather than the traditional phases of discovery, pilot, efficacy, and effectiveness that are more familiar to clinical scientists.”

Create

Current interventions lack adherence and engagement, with patients failing to complete more than 1 or 2 sessions.

  • “Adherence and sustained engagement with DMH interventions is a long-standing, recognized problem.”

  • “Lack of engagement suggests fundamental problems in the design of DMH interventions, which commonly rely on psychoeducational self-help materials and worksheets delivered via digital media that require significant time and effort on the part of patients. Patients’ enthusiasm for these designs can be gauged by the failure of many to complete  more than 1 or 2 sessions.”

We must be willing to let go of old paradigms (eg internet-based CBT) and be willing to find tools that are designed to fit into the lives of end-users and genuinely address their needs and preferences.

  • “Although our first-generation DMH interventions, translating evidence-based treatments into web-based treatments, have provided useful insights, in creating our next generation of DMH interventions, we must be willing to let go of old paradigms, such as internet-based cognitive behavioral therapy. We must be willing to design new digital experiences that leverage unique affordances of technologies and novel insights they can help deliver. Digital tools need to fit into the fabric of patients’ lives and accommodate practitioners’ workflows. Design approaches have started to be used in DMH interventions, but our goal should not be to design the best internet-based cognitive behavior treatment; instead, we must understand the lives and workflows that these tools are intended to support and address stakeholders’ needs, preferences, and goals.”

We can use technology to design and create tools that are more efficient and engaging.

  • “Technologies will enable new forms of services that can be efficient yet engaging. To achieve this, we must design these new services and the supporting technologies and interfaces.”

We have to think about how DMH tools will be implemented in the real world as we’re making and researching them.

  • “The failure to bridge DMH research and real-world implementation in care settings indicates that implementation cannot be left as a post hoc procedure. Sustainable DMH interventions will require that implementation plans addressing multilevel organizational issues, reimbursement, and business models be defined from the start, along with the design of the services and technologies.”

Trial Methods

New trial methods must allow for product iteration and evolution.

  • “Digital technologies are continuously evolving, requiring rapid evaluation to prevent obsolescence. Thus, DMH trial methods cannot lock down intervention elements; rather, trial methods must allow for learning, iteration, and redesign, which can be achieved by integrating continuous quality improvement methods and principles of iterative design into trial methods.”

Sustainable Implementation

We argue that the end goal of research should be sustainable implementation. Solution-focused research can support sustainment by slowly withdrawing research support and transferring critical knowledge and skills to the organization in which the DMH has been designed and evaluated and has demonstrated effectiveness.

Conclusion

“To truly revolutionize mental health services, we must let go of preconceptions and explore new ways of developing, providing, and evaluating DMH interventions that result in sustainable implementation in the real world.”

[Draft] Summary of Insel's Latest Papers

TLDR; We need to "close the gap" between mental health needs and what we can treat. To do this we need to focus on incorporating modern brain science and new technology into mental healthcare.

TLDR; We need to close the huge growing gap between mental health needs and what we can treat. One way to do this is to incorporate modern brain science into mental health. Specifically: call mental disorders brain disorders; train clinicians in neuroscience (clinical neuroscientists); use smartphones/data for more specific/realistic diagnoses (digital phenotyping); shed old DSM categories for new categories based off specific biological mechanisms (RDoC).

Main Ideas

We need to train clinicians on neuroscience. Even though neuroscience is not fully actionable, psychiatry residents need to learn it because brain science will yield much more effective diagnoses and treatments in the future. Psychiatry will shift away from using only subjective, observable signs and classifications (eg DSM) toward more objective and biologically based diagnoses and treatments.

  • “Just the formulation of mental disorders as brain disorders will be an important shift in perspective. When the residents of today are the seasoned clinicians of midcentury, they may find it difficult to believe we ever divorced psychiatry from brain science. What is exciting is to realize that residents today can be the vanguard of change to create a future with a far more scientific and more effective discipline.” (Insel 2015)

  • “Before research on the convergence of biology and behavior can deliver on the promise of precision medicine for mental disorders, the field must address the imprecise concepts that constrain both research and practice. Labels like “behavioral health disorders” or “mental disorders” or the awkwardly euphemistic “mental health conditions,” when juxtaposed against brain science, invite continual recapitulation of the fruitless “mind-body” and “nature-nurture” debates that have impeded a deep understanding of psychopathology. The brain continually rewires itself and changes gene expression as a function of learning and life events. And the brain is organized around tightly regulated circuits that subserve perception, motivation, cognition, emotion, and social behavior. Thus, it is imperative to include measures of both brain and behavior to understand the various aspects of dysfunction associated with disorders. Shifting from the language of “mental disorders” to “brain disorders” or “neural circuit disorders” may seem premature, but recognizing the need to incorporate more than subjective reports or observable behavior in our diagnosis of these illnesses is long overdue.” (Insel, Cuthbert 2015)

We already have effective treatments based in evidence, and yet people don’t have access. “Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care”

  • “Perhaps the most important role for public health is less obvious. If one of the inconvenient truths about mental health care in the United States is that we have a fragmented and chaotic system, one of the inconspicuous truths is that we have effective treatments. In this field, perhaps more than any other area of health care, we need to close the unconscionable gap between what we know and what we do. We know how to help people recover from mood and anxiety disorders as well as psychotic illnesses. We have medications, psychotherapies, and forms of rehabilitation that have efficacy comparable to that of interventions in the rest of medicine. We have the evidence base and the experience to bend the curve on morbidity and mortality. Yet we have not translated this knowledge into an effect on public health. Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care. This is the challenge and the opportunity for public health to bend the curve for mental health. Technology can help.”

“We need to close the gap between population health needs and mental health care reach.” (Insel 2019) Many more people need help than seek it, and the current institutions with traditional clinicians and providers are not enough.

  • “High rates of mental illness are found in our criminal justice system, school system, homeless shelters, primary care clinics, and emergency departments. Sometimes it seems that people with mental illness are everywhere except the mental health care system. Indeed, most population-based epidemiological studies find that more than half of the population with mental illness is not receiving mental health care.4 We can improve mental health care for the fraction being seen by psychiatrists, psychologists, social workers, and other mental health providers, but this will not bend the curve on population health unless we expand our scope of care to the millions who are outside the mental health tent. We need to close the gap between population health needs and mental health care reach.” (Insel 2019)

Brain health is one of the only areas of healthcare without accessible, actionable, objective measurements of disease. Currently, clinicians mostly use a flawed DSM-5; they hardly measure at all (if they do it can only be in-session), and further practically constrained by physician time and patient motivation.

  • “Just as the thermometer provided a standardized, objective measurement for detecting fever, tools to quantify health and disease parameters have transformed medicine in almost every major disease area—electrocardiograms for heart disease, blood glucose for diabetes, and, recently, genetic diagnostic tests for cancer. But when it comes to brain health, and in the case of mental illness especially, progress has been uneven. Although direct brain imaging instruments exist, most (MRI, PET, MEG) are expensive, inaccessible to many, rarely useful for deciding the treatment of an individual patient, and time-intensive to administer. While they can identify brain lesions in multiple sclerosis or dementia, they are less useful in mental disorders. This lack of measurement matters because, to borrow a truism from business, “we don’t manage well what we don’t measure well.”” (Chauvin, Insel 2018)

The smartphone enables 3 innovations that can close the gap: (1) digital phenotyping (data monitoring for better treatment/diagnoses), (2) digital interventions or support (CBT online, or peer-to-peer support), and (3) better care management tools for clinicians.

  • “Three innovations can close the gap between population needs and mental health care reach. First, with data from sensors, speech analytics, and keyboard use, scientists are learning how to measure cognition, mood, and behavior passively from the smartphone. This field, called “digital phenotyping,” offers a sort of digital smoke alarm for mental health issues. With appropriate consent and transparency, digital phenotyping can provide ethical and effective biomarkers that predict relapse or recovery, much the way we monitor progress in diabetes or hypertension.”

  • “Second, smartphone applications have been developed for interventions ranging from crisis services and peer support to evidence-based psychotherapies. These online interventions have been shown to be equivalent in efficacy to face-to-face treatments. Moreover, online treatments are more convenient and, for many people, more acceptable than brick-and-mortar care.”

  • “Finally, smartphones can improve care management by collecting data on service use and coordinating care in real time. Together, these three functions—measurement, intervention, and care management—can generate a learning engine that improves with experience.” (Insel 2019)

Articles and TLDRs

2019

Bending the Curve for Mental Health: Technology for a Public Health Approach (Insel 2019)

TLDR; Mental health is a large public health problem and technology can close the gap between what traditional clinicians and institutions can do and the growing number of people who aren’t being served. We have evidence for what works but it is not accessible to the public: technology can take existing knowledge to create better diagnostics (digital phenotyping), make interventions more accessible, and help provide valuable data/insights to care teams.

2018

Building the Thermometer for Mental Health (Chauvin, Insel 2018)

TLDR; Digital phenotyping offers an objective measure of mental health that can be continuous (as opposed to once or less per week), precise, and capture real-world interactions. Early studies show capabilities in detecting depression, mania, PTSD, and other strong correlations with traditional cognitive and mood assessments.

Digital phenotyping: a global tool for psychiatry (Insel 2018)

TLDR; Instead of standard subjective measurements of mental health, digital phenotyping promises objectivity; and instead of delayed or at-best weekly check-ins, it promises continuous, real world measurement of how someone is feeling. Digital phenotyping uses data from smartphone sensors and interactions (eg scrolling/typing speed). It is being developed and already finding clinically relevant signals.

Digital Technologies in Psychiatry: Present and Future (Hirschtritt, Insel 2018)

TLDR; Tech has the ability to address 5 major issues: measurement, access, care delay, fragmented care, and stigma. Digital phenotyping is showing promise for objective measurement and digital interventions are showing promise for greater access; one main challenge for both is real world adherence and usage is low so far.

Other promising uses of tech include chat bots, combining tech to support clinician or lay-clinician work, and anonymous peer support.

Preparing Physician-Scientists for an Evolving Research Ecosystem (Hirschtritt, Heaton, Insel 2018)

TLDR; Growing research opportunities funded by private companies can help us develop new diagnostics and therapeutics. Many physicians gravitate away from research due to financial barriers, but private company support of research may help increase research opportunities and have it applied toward tech-enabled new therapeutics.

“Science needs physician-scientists who understand how to take foundational knowledge and apply it to develop novel therapeutics or policy decisions. Increasingly, their research, especially high-risk research in academia, may be supported by private sources, including philanthropy. Although positions in industry may be less stable and more subject to market forces than academic medical center–based research careers, physician-scientists in the private sector may explore diverse projects in multiple roles (including product development, regulatory affairs, bioassay design, marketing strategy, and policy drafting), particularly in larger organizations. The technology industry, a significant new entrant in the biomedical research ecosystem, offers biomedical researchers massive data sets with an opportunity to explore new diagnostics and therapeutics at a scale that is usually not approachable by academia.”

Join the disruptors of health science (Insel 2018)

TLDR; There are significant opportunities to improve healthcare with access to new technology and data, but we need deep partnerships between tech companies, clinicians, and researchers to act on them. Along with billions of dollars in cash, tech companies bring product and data science expertise to the traditional health care system focused on treatment and fundamental science.

“These companies have transformed the worlds of information, entertainment and commerce. But by moving into health care, they face some formidable challenges. In my view, solving them will require deep partnerships between technology companies, clinical experts, patient advocates and academic scientists.”

2017

Digital Phenotyping: Technology for a New Science of Behavior (Insel 2017)

TLDR; In a field that hardly uses tools for measurement; and where clinicians are too busy and patients are unmotivated; we need an objective, passive, ubiquitous device to capture actionable information. Digital phenotyping (prediction and diagnosis via smartphone usage) is already showing promising results, but we need additional clinical and real world research and validation.

“What the field needs is an objective, passive, ubiquitous device to capture behavioral and cognitive information continuously. Ideally, this device would transmit actionable formation to the patient and the clinician, improving the precision of diagnosis and enabling measurement-based care at scale.”

2016

Translating Oxytocin Neuroscience to the Clinic: A National Institute of Mental Health Perspective (Insel 2016)

TLDR; Although we’ve learned a lot from animal studies, and early human oxytocin studies are promising, we have to learn more about what oxytocin specifically targets in humans, and what doses are effective. NIMH now requires demonstrating “target engagement” in studies in order to create more informative studies and results.

2015

Integrating neuroscience into psychiatric residency training (Insel 2015)

TLDR; Even though neuroscience is not fully actionable, psychiatry residents need to learn it because brain science will yield much more effective diagnoses and treatments in the future. Psychiatry will shift away from using only subjective, observable signs and classifications (eg DSM) toward more objective and biologically based diagnoses and treatments.

“When the residents of today are the seasoned clinicians of midcentury, they may find it difficult to believe we ever divorced psychiatry from brain science.”

Brain disorders? Precisely. Precision medicine comes to psychiatry (Insel, Cuthbert 2015)

TLDR; To create more effective treatments we need to re-name “mental” disorders as “_brain_” disorders, shift away from old diagnostic categories (DSM), and research more precise diagnosis and treatment (based in biology).

2014

Mind the Gap: Neuroscience Literacy and the Next Generation of Psychiatrists (Chung, Insel 2014)

TLDR; Psychiatry training is outdated and needs to incorporate new scientific understandings of the brain. NIMH will encourage more clinician-researchers and more neuroscience literacy in new clinicians in order to facilitate development and informed use of new therapeutic approaches and technologies.

National Institute of Mental Health Clinical Trials: New Opportunities, New Expectations (Insel, Gogtay 2014)

TLDR; NIMH is setting guidelines to make mental health studies more scientifically rigorous in order to make more effective treatments in a timely manner. The focus is finding more specific disease mechanisms (eg what are the brain circuits and genes behind depression?) by requiring measuring "target engagement” (eg did the experimental treatment activate the specific brain circuit?). This will build toward more informative studies and more effective treatments.

Some quotes

“Perhaps the most important role for public health is less obvious. If one of the inconvenient truths about mental health care in the United States is that we have a fragmented and chaotic system, one of the inconspicuous truths is that we have effective treatments. In this field, perhaps more than any other area of health care, we need to close the unconscionable gap between what we know and what we do. We know how to help people recover from mood and anxiety disorders as well as psychotic illnesses. We have medications, psychotherapies, and forms of rehabilitation that have efficacy comparable to that of interventions in the rest of medicine. We have the evidence base and the experience to bend the curve on morbidity and mortality. Yet we have not translated this knowledge into an effect on public health. Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care. This is the challenge and the opportunity for public health to bend the curve for mental health. Technology can help.” (Insel 2019)

“By expanding our field of view to examine population needs rather than only those in the mental health care system, we can reduce morbidity and mortality among most of the people affected, including those in our jails, our schools, and our homeless shelters. By focusing on the diverse needs of people with serious mental illness, a public health approach can build out the framework for recovery, including psychosocial supports and the need for inclusion and equity.” (Insel 2019)

"The second issue is related to access to mental health services. In many parts of the developed world and most parts of the developing world, evidence-based mental health care is not available. The Substance Abuse and Mental Health Services Administration reports that 55% of U.S. counties lack a mental health professional (9). A recent interagency report on the state of treatment for serious mental illness reported that about 2% of patients receive evidence-based treatments such as assertive community treatment, supported employment, and family psychoeducation (10).” (Hirschtritt, Insel 2018)

“In recent years, multiple studies have addressed the efficacy—a measure of how well an intervention leads to the expected results in an ideal or controlled circumstance—of digital mental health interventions. However, fewer studies have directly measured the effectiveness—the degree to which an intervention performs under real-world conditions—of these rapidly evolving technologies. Nevertheless, available evidence suggests the value of digital interventions relative to face-to-face interventions as a means of improving access to evidence-based treatments…If the target is access, then the litmus test will be the shift from efficacy to effectiveness, which will require adoption of digital interventions at scale and sustained engagement.” (Hirschtritt, Insel 2018)

“The gap between efficacy and effectiveness is nontrivial. In fact, efforts to move digital interventions from research into practice have generally failed (33). Outside of research protocols, patients and providers either do not adopt or do not adhere to online treatments. Mohr et al. (34) remind us that, going forward, we need to consider digital interventions as technology-enabled services rather than stand-alone products, integrating them into the fabric of a patient’s life and the workflow of a provider’s practice. To improve adoption and adherence, the next generation of digital interventions will likely look less like Internet-based manualized treatment and more like the video games for ADHD or immersive VR.” (Hirschtritt, Insel 2018)

“There are few areas of medicine undergoing the profound changes we see in psychiatry today. Over the past decade, the fundamental sciences underlying psychiatry have begun to shift from psychology and pharmacology to neuroscience and cognitive science. As the tools of neuroscience have progressed, we can begin to understand the disorders of the mind by studying the brain. The two related disciplines of systems neuroscience and cognitive science hold particular promise for revealing how brain activity is converted to mental activity and behavior.” (Insel 2015)

“First, we will see major changes in diagnosis. Psychiatric diagnosis, in contrast to diagnosis in most areas of medicine, relies solely on observable signs and subjective symptoms. Our diagnostic criteria are consensus definitions of symptoms that cluster together. While this approach offers reliability and clear communication, it lacks biological validity and therefore cannot provide the necessary precision for selecting treatments. Over the next five years, data from genomics, systems neuroscience, and cognitive science should help us to transform diagnostics by augmenting subjective reports with objective measures.” (Insel 2015)

“How will we tune neural circuits? Both invasive (deep brain stimulation) and non-invasive (trans-cranial magnetic stimulation) tools have been developed for neuromodulation. It seems likely that psychotherapy that involves learning and skill building also alters regional brain function, tuning circuits through the brain’s remarkable neuroplasticity.” (Insel 2015)

“Just the formulation of mental disorders as brain disorders will be an important shift in perspective. When the residents of today are the seasoned clinicians of midcentury, they may find it difficult to believe we ever divorced psychiatry from brain science. What is exciting is to realize that residents today can be the vanguard of change to create a future with a far more scientific and more effective discipline.” (Insel 2015)

Diagnosis in psychiatry, in contrast to most of medicine, remains restricted to subjective symptoms and observable signs. Clinicians rightly pride themselves on their empathic listening and well-honed observational skills. But recently psychiatry has undergone a tectonic shift as the intellectual foundation of the discipline begins to incorporate the concepts of modern biology, especially contemporary cognitive, affective, and social neuroscience. As these rapidly evolving sciences yield new insights into the neural basis of normal and abnormal behavior, syndromes once considered exclusively as “mental” are being reconsidered as “brain” disorders—or, to be more precise, as syndromes of disrupted neural, cognitive, and behavioral systems.” (Insel, Cuthbert 2015)

“Before research on the convergence of biology and behavior can deliver on the promise of precision medicine for mental disorders, the field must address the imprecise concepts that constrain both research and practice. Labels like “behavioral health disorders” or “mental disorders” or the awkwardly euphemistic “mental health conditions,” when juxtaposed against brain science, invite continual recapitulation of the fruitless “mind-body” and “nature-nurture” debates that have impeded a deep understanding of psychopathology. The brain continually rewires itself and changes gene expression as a function of learning and life events. And the brain is organized around tightly regulated circuits that subserve perception, motivation, cognition, emotion, and social behavior. Thus, it is imperative to include measures of both brain and behavior to understand the various aspects of dysfunction associated with disorders. Shifting from the language of “mental disorders” to “brain disorders” or “neural circuit disorders” may seem premature, but recognizing the need to incorporate more than subjective reports or observable behavior in our diagnosis of these illnesses is long overdue.” (Insel, Cuthbert 2015)

“Even more vexing is the separation of psychiatry from modern neuroscience, especially because neuroscience has transformed our understanding and approaches to that organ, the brain [1]. Despite the revolution in brain research of the past few decades, psychiatry continues to train its newest practitioners according to an outdated model of how the brain works or, in many cases, just ignores it. Fewer than half of US psychiatry residency programs provide any education in modern systems neuroscience [2, 3].” (Chung and Insel 2014)

“This trend not withstanding, psychiatry continues to face a workforce shortage. We see this shortage in two general areas. More psychiatrists are required to address the unmet treatment needs of the public [5], and we need a next generation of scientific leaders who will transform our diagnostics and therapeutics using neuroscience tools to address these needs. Given these twin goals, the National Institute of Mental Health (NIMH) is taking a two-pronged approach that includes next-generation efforts to identify and support trainees who will redefine psychiatry as “clinical neuroscience,” and neuroscience literacy efforts that encourage the integration of neuroscience into the training of psychiatry residents.”

[Summary] Bending the Curve for Mental Health: Technology for a Public Health Approach (Insel 2019)

TLDR; Mental health is a public health problem and current institutions are not enough. Tech can "close the unconscionable gap between what we know and what we do" and ensure the public is served.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595506/#__ffn_sectitle

TLDR; Mental health is a large public health problem and technology can close the gap between what traditional clinicians and institutions can do and the growing number of people who aren’t being served. We have evidence for what works but it is not accessible to the public: technology can take existing knowledge to create better diagnostics (digital phenotyping), make interventions more accessible, and help provide valuable data/insights to care teams.

In this field, perhaps more than any other area of health care, we need to close the unconscionable gap between what we know and what we do… We have the evidence base and the experience to bend the curve on morbidity and mortality. Yet we have not translated this knowledge into an effect on public health. Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care.”

Mental health issues are clearly a public health crisis and we need to change how we approach it.

  • “I usually started my seminars with a quick quiz about morbidity and mortality rates for mental illness, global burden of disease statistics, and questions about access to care. Invariably, my audience, the next generation of the mental health workforce, would look at me dumbfounded. No part of their training had addressed mental health as a public health crisis. During these years, the suicide mortality rate increased 33%, mental illnesses became the group of disorders with the highest years lost to disability, and depression surpassed all other chronic illnesses as a source of global disability.1–3 Yet our training model continued to address mental health care from a classic medical model without the broader scope of public health.”

Public health approach

“We need to close the gap between population health needs and mental health care reach.” Many more people need help than seek it, and the current institutions with traditional clinicians and providers are not enough.

  • “High rates of mental illness are found in our criminal justice system, school system, homeless shelters, primary care clinics, and emergency departments. Sometimes it seems that people with mental illness are everywhere except the mental health care system. Indeed, most population-based epidemiological studies find that more than half of the population with mental illness is not receiving mental health care.4 We can improve mental health care for the fraction being seen by psychiatrists, psychologists, social workers, and other mental health providers, but this will not bend the curve on population health unless we expand our scope of care to the millions who are outside the mental health tent. We need to close the gap between population health needs and mental health care reach.”

Technology: Problem or Solution

Even though there are concerns about smartphones’ negative effects on mental health, they offer a nearly ubiquitous platform to serve others instead of waiting for them to come to clinicians.

  • “But how can we serve the millions of people who are not receiving care? The simple answer is to go to them rather than waiting for them to come to us. Technology provides an unprecedented tool for outreach. With three billion smartphones in circulation and people increasingly living through these powerful pocket computers, we now have a public health platform that is more ubiquitous than clean water or stable electricity.”

  • “True, for most of us, smartphones may seem to be more of a problem than a solution for mental health. Serious concerns exist about their addictive potential and what is now called “surveillance capitalism”—using the data from our smartphones for marketing, often without our consent or knowledge.7”

The smartphone enables 3 innovations that can close the gap: (1) digital phenotyping (data monitoring for better treatment/diagnoses), (2) digital interventions or support (CBT online, or peer-to-peer support), and (3) better care management tools for clinicians.

  • “Three innovations can close the gap between population needs and mental health care reach. First, with data from sensors, speech analytics, and keyboard use, scientists are learning how to measure cognition, mood, and behavior passively from the smartphone. This field, called “digital phenotyping,” offers a sort of digital smoke alarm for mental health issues. With appropriate consent and transparency, digital phenotyping can provide ethical and effective biomarkers that predict relapse or recovery, much the way we monitor progress in diabetes or hypertension.”

  • “Second, smartphone applications have been developed for interventions ranging from crisis services and peer support to evidence-based psychotherapies. These online interventions have been shown to be equivalent in efficacy to face-to-face treatments. Moreover, online treatments are more convenient and, for many people, more acceptable than brick-and-mortar care.”

  • “Finally, smartphones can improve care management by collecting data on service use and coordinating care in real time. Together, these three functions—measurement, intervention, and care management—can generate a learning engine that improves with experience.”

Closing the Gap

To successfully serve the whole population of those who would benefit (eg underprivileged, jail system, school system), and not just those currently in the system, technology must still be combined with “high touch” and compassionate care. 

  • “To be clear, the use of digital technology for mental health care is still in its infancy. There are great promises for reducing suicide and preempting relapse, but the clinical utility of smartphone applications remains to be seen. Although I doubt that technology alone can solve the mental health crisis, I believe that by combining high technology and “high touch,” we can begin to close the population health–mental health care gap.”

  • “By expanding our field of view to examine population needs rather than only those in the mental health care system, we can reduce morbidity and mortality among most of the people affected, including those in our jails, our schools, and our homeless shelters. By focusing on the diverse needs of people with serious mental illness, a public health approach can build out the framework for recovery, including psychosocial supports and the need for inclusion and equity. By moving upstream to reduce social determinants of disability and relapse, a public health approach can improve outcomes and lower costs for the care of people with serious mental illness. In each of these areas, technology will yield the data, the interventions, and the connectivity, at scale. But high touch will be critical to understand the context of the data and to turn the connectivity into compassion.”

We already have effective treatments based in evidence, and yet people don’t have access. “Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care”

  • “Perhaps the most important role for public health is less obvious. If one of the inconvenient truths about mental health care in the United States is that we have a fragmented and chaotic system, one of the inconspicuous truths is that we have effective treatments. In this field, perhaps more than any other area of health care, we need to close the unconscionable gap between what we know and what we do. We know how to help people recover from mood and anxiety disorders as well as psychotic illnesses. We have medications, psychotherapies, and forms of rehabilitation that have efficacy comparable to that of interventions in the rest of medicine. We have the evidence base and the experience to bend the curve on morbidity and mortality. Yet we have not translated this knowledge into an effect on public health. Closing the gap between what we know and what we do is the most urgent and tractable problem we face in the world of mental health care. This is the challenge and the opportunity for public health to bend the curve for mental health. Technology can help.”

[Summary] Building the Thermometer for Mental Health (Chauvin, Insel 2018)

TLDR; Early studies of monitoring smartphone data show capabilities in detecting depression, mania, PTSD, and other strong correlations with traditional cognitive and mood assessments.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6353119/

TLDR; Digital phenotyping offers an objective measure of mental health that can be continuous (as opposed to once or less per week), precise, and capture real-world interactions. Early studies show capabilities in detecting depression, mania, PTSD, and other strong correlations with traditional cognitive and mood assessments.

Lack of Objective Measurement in Psychiatry

Brain health is one of the only areas of healthcare without accessible, actionable, objective measurements of disease.

  • “Just as the thermometer provided a standardized, objective measurement for detecting fever, tools to quantify health and disease parameters have transformed medicine in almost every major disease area—electrocardiograms for heart disease, blood glucose for diabetes, and, recently, genetic diagnostic tests for cancer. But when it comes to brain health, and in the case of mental illness especially, progress has been uneven. Although direct brain imaging instruments exist, most (MRI, PET, MEG) are expensive, inaccessible to many, rarely useful for deciding the treatment of an individual patient, and time-intensive to administer. While they can identify brain lesions in multiple sclerosis or dementia, they are less useful in mental disorders. This lack of measurement matters because, to borrow a truism from business, “we don’t manage well what we don’t measure well.””

Clinicians currently measure mental illness using the DSM-5 which is very subjective.

  • “In the absence of reliable instruments, clinicians treating mental illness use indirect, intuition-based measures. The DSM-5, the principal schema for classifying mental disorders, requires clinicians to form diagnoses based on their subjective judgments and arbitrary cut-off points.”

Very few clinicians use scales to measure patient improvement over time.

  • “Moreover, only 18 percent of US psychiatrists and 11 percent of psychologists routinely use symptom-rating scales or Patient-Reported Outcome Measures (PROMs) to monitor patient improvement.3,4 Thus, for the vast majority of patients with a mental illness, measurement often comes down to “How are you feeling?” during sporadic, brief visits in primary care”

And while there is more interest in measurement-based care, its practical implementation is limited by clinician resources (time/money) and could only be infrequently captured during visits.

  • “There has been a push toward using “measurement-based care” that relies on standard rating scales and patient-reported outcomes, and good evidence that it can improve clinical outcomes.5 But this approach has its limitations. Practically speaking, standard assessments can be difficult to implement, held back by a lack of financial support and limited personnel to administer the tests. They increase paperwork, which can burden stretched clinicians.6 Perhaps most problematic, these measurement tools are necessarily brief and can capture only a narrow spectrum of a patient’s overall state (e.g., general depression symptoms). And since they are administered infrequently, usually in the clinic, they of necessity collect one-time, or “snapshot,” impressions of a person’s mental health.7”

How can we move beyond this state of affairs?

The ideal form of measurement in mental health would be objective, continuous, precise, collected in the real world, actionable, and passively measured. Interventions would be more timely and effective.

  • “Let’s imagine the ideal form of measurement for mental health. In addition to being objective, it would be continuous (assessing symptoms frequently) and precise (both sensitive and specific) and collected in the “real world” (outside the context of the clinical encounter). It should give clinicians access to summarized and up-to-date patient data (e.g. on symptom severity), easily interpretable to provide meaningful, clinically actionable information.8,9 Such information would enable clinicians to measure response to treatment in real-time on an ongoing basis and to adjust treatment plans based on the patient’s preference and response. Finally, to be effective and scale to global populations, the measurement should be passive—done without asking individuals to change their behavior or do anything on top of what they are already doing. Taken together, the combination of attributes would help to ensure early and timely intervention”

The Hope: Advent of Digital Phenotyping

Digital phenotyping applies machine learning to data collected from all our smart devices, then finds patterns to correlate with clinical states.

  • “The increasing ubiquity of smartphones and advent of technologies, such as home devices (Amazon Echo and Google Home) and wearables (FitBit, Apple Watch), that can act as a reliable source of measurement, combined with advances in analyzing continuous data, presents us for the first time with an opportunity to monitor brain function at population scale. This approach, called digital phenotyping, is a two-step process that works by applying machine learning to data collected from digital devices such as wearables and smartphones.16 Obtaining the signals from the phone or wearable device is the first step. Making sense of these signals by finding the patterns that correlate with clinical state is often the more difficult second step.“

There are already studies that show how smart device data can detect mania, depression, suicide ideation, PTSD, and other aspects of mental health.

  • “Today, studies continue to demonstrate that patterns of activity and geolocation can herald mania or depression20 and sleep actigraphy can predict suicidal ideation;21 moreover, other biosensors have shown that heart rate variability can help predict Post Traumatic Stress Disorder diagnosis22 and speech and voice, which can reveal important aspects of our emotional, social and psychological worlds, may be able to provide insight into depression.23”

Digital biomarkers based off typing and scrolling patterns (“human-computer interaction” data) have already been strongly correlated with performance on standard cognitive tests and mood ratings.

  • “One particularly promising approach to developing digital phenotypes of cognition that might help to move the field beyond these concerns involves data from human-computer interaction (HCI). HCI-based digital biomarkers can be generated from passively-collected, content-free interactions, like typing and scrolling patterns on a smartphone, measuring the latency between space and character in a text or the interval between scroll and a click. This approach was originally developed in cybersecurity to track hackers with what was called “digital fingerprinting.” (Based on an individual’s pattern of activity, every individual who spends time online leaves a unique trace, which can be used to create identifiers for individual users—hence the notion of a digital fingerprint.) Applying this concept to mental health, scientists have developed digital biomarkers that strongly correlate with performance on traditional cognitive tests and with mood ratings.24 With the average user’s output of over 2,600 smartphone touches a day,25 these ubiquitous computer interactions can reveal a lot about how we think and how we feel and when combined with other measures like sleep, activity, and speech, create a digital phenotype.”=

The Challenges

To be clinically effective, digital phenotyping needs to identify both biomarkers but also clinically relevant/actionable signals.

  • “While physicians had a reliable instrument for measuring body heat, they didn’t know what a normal temperature range was. It was only with the discoveries made by Carl Wunderlich (1868), a psychiatrist who collated nearly 100,000 observations, that data could define normal and abnormal body temperature. At that point, with the clinical utility of the thermometer evident, it was routinely adopted in clinical practice as part of a complete medical evaluation. Temperature could be used as a biomarker for disease.26 To gain widespread clinical use, digital phenotyping will need to overcome similar challenges, and a few contemporary hurdles as well.”

There are already ongoing large-scale trials to help validate digital phenotyping and the results are promising.

  • “As with body temperature, digital phenotyping needs to be tested in large, diverse populations to identify the digital biomarkers that matter. This means validating digital parameters against standard (if imperfect) measures of cognition and mood to determine which, if any, reliably give accurate, actionable data. The good news? There are already many ongoing large-scale clinical trials helping to validate this technology, and so far, the results have been promising.”

Patients must want to use new digital health tools. There is relatively low usage by patients and clinicians of current available digital mental health tools.

  • “Patients must want to engage with the new digital health tools. Of the more than 300,000 digital health apps currently on the market, a mere 41 account for the bulk of all downloads, while 85 percent have fewer than 5,000 installs.31 Clinicians must likewise be won over. According to an American Medical Association survey, current levels of use of digital health tools by clinicians remain low, with only 26 percent currently using patient engagement technologies (i.e., solutions for chronic conditions designed to promote patient wellness and active participation in their care, for example, through promoting adherence to treatment) and 13 percent using remote patient monitoring technologies designed for daily measurement”

Even with better diagnostics, we’ll also need to find ways to deliver more effective treatments, possibly via the same smartphone.

  • “Finally, we must recognize that digital phenotyping is only one piece of the puzzle. Improved health outcomes require more than detection: if the smartphone becomes a digital smoke alarm, how do we put out the fire? For mental health, many of the best treatments involve communication, skill building, and a therapeutic relationship. All of these can be done on a phone, allowing a “closed loop” approach to mental healthcare, where digital phenotyping identifies a need and the treatment is delivered immediately by a remote clinician. The same phone can also monitor the impact of the treatment, making measurement-based care for an individual with depression or psychosis the equivalent of both the thermometer and the antibiotic for a patient with a fever.”

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