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[Summary] Digital phenotyping: a global tool for psychiatry (Insel 2018)
TLDR; Digital phenotyping uses smart phone sensors and interactions to provide continuous, objective measurements of mental health for better diagnoses and treatment.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6127813/
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.
Technology and information science may prove just as significant for mental health as revolutions in genomics and neuroscience.
“In 2050, when psychiatrists look back at the first two decades of the 21st century, what will they recognize as having the greatest impact? No doubt the revolution in genomics, which has given us new insights into the risk architecture of mental illness, and the revolution in neuroscience, which has given us a new view of mental illnesses as circuit disorders, will be considered important. But perhaps the revolution in technology and information science will prove more consequential for global mental health.”
Digital phenotyping uses sensors, voice and speech, and human-computer interaction monitoring via smartphones to create continuous and objective measurements for psychiatry.
“What specific problems can be addressed by the smartphone? Our lack of objective measurement has handicapped both diagnosis and treatment in psychiatry. As just one example, our assessment of depression depends largely on self‐reports of sleep, appetite and emotional state, although we recognize that people with depression are biased in their assessments. The smartphone offers us an objective and ecological source of measurement. This approach, now called digital phenotyping, is based on sensors (activity and location), voice and speech (sentiment and prosody), and, perhaps most important, human‐computer interaction3.”
Just as current neuropsychological tests measure cognitive traits and affective states, measuring typing, scrolling, and clicking on smartphones can do the same, but it may be better because the measurement is continuous.
“Human‐computer interaction measures not what you type but how you type. Subtle aspects of typing and scrolling, such as the latency between space and character or the interval between scroll and click, are surprisingly good surrogates for cognitive traits and affective states4. If this seems improbable, remember that many of our neuropsychological tests, such as the Trails A and B tests or the Digit Symbol Substitution, are not substantially different from the psychomotor requirements of operating a smartphone. In a sense, those gold standard tests of cognitive control and information processing are attempting to assess how we function. In a world where we spend so much of our lives on our smartphones, could it become possible to assess how we function directly and continuously rather than using laboratory measures at a single point in time?”
Since digital phenotyping is continuous instead of point-in-time at a clinic, it can help track moments or issues that would otherwise not be reported and help those who might not seek help or would experience a significant delay.
“The promise of digital phenotyping is that this objective measure happens in the context of a patient's lived experience, reflecting how he/she functions in his/her world, not in our clinic. Signals from a new mother struggling with depression may look quite different during a 3 am feeding compared to what she reports to her clinician the next day. This kind of ecological and continuous measurement addresses some of the central issues that challenge our field. We know that most people with a mental illness do not seek help, and those who do seek help usually arrive after considerable delay. For populations at risk, such as post‐partum women or victims of trauma, could digital phenotyping signal the transition from risk to the need for care? For people in care, too often we fail to preempt relapse. For patients in treatment, could digital phenotyping serve as a “smoke alarm” providing early signals of relapse or recovery?”
Though still being developed, early signs show it can yield clinically relevant signals.
“Digital phenotyping is still being developed as a clinical tool. It seems clear from the early results that, although activity and geolocation data are non‐specific and noisy, for some people changes in activity can be an early sign of mania or depression5. Speech and voice may also yield clinically relevant signals. We have known for decades that when people are depressed their pronouns shift to first person singular6. But again, the sensitivity and specificity of these findings still need to be defined. Putting sensor data, speech and voice data, and human‐computer interaction together might provide a digital phenotype that could do for psychiatry what HgbA1c or serum cholesterol has done for other areas of medicine, giving precision to diagnosis and accuracy to outcomes.”
However there are still ethical issues around data that need to be resolved.
“The opportunity of this new approach to measurement is matched by an ethical challenge. When does measurement become surveillance?”
“To be clear, digital phenotyping is still a research project conducted on small numbers of consented volunteers. While researchers hope this approach will solve global mental health problems, the scientific and ethical issues need to be resolved before digital phenotyping becomes a tool for population health.”
“Some of the most vexing issues may have technical solutions. For instance, human‐computer interaction is “content‐free”. This approach collects how you type, not what you type and, therefore, might be less intrusive than monitoring geolocation or search history. Tools that can analyze smartphone signals on the phone rather than sending data to the cloud have the advantage of keeping raw data local and private.”