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[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.
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.”=
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.”