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Written by Vael Gates
Technology Meets Psychotherapy: A Wild West
How psychotherapy aims to harness the power of the digital age
“Hello, I’m melodious_elk123 and looking forward to talking with you today. How are you feeling?”
“Not so good. It’s been months, and I’m still upset over this relationship.”
These could be the opening lines of a conversation in 7cups, a website and app that connects paying customers with trained therapists or volunteer listeners. Anyone who needs help can pull out their smartphone, send a quick text to a licensed therapist, and get a reply back later in the day. Therapy today can look very different from the traditional image of a psychiatrist’s couch. Like in so many other fields, technology has careened into the established field of clinical psychology, and clinicians and patients are exploring the new potentials and limitations of the results. It’s a new frontier, and the verdict is still out on the big question: will technology improve mental health?
Integrating texting and online resources is a clear first step for enhancing psychotherapy with technology. There are now websites and mobile apps aimed at connecting people with licensed and unlicensed therapists, often for much cheaper than in-person visits (e.g. betterhelp.com, talkspace.com, 7cups.com, etc.) These apps can be used standalone, or in between in-person therapist visits. Another approach is psychoeducation: the development of apps that provide training or information on different therapeutic approaches, for example teaching the basics of cognitive behavioral therapy with examples and comprehension questions. Mobile apps are also important for building online communities, where people can support each other and share experiences. The mental health mobile app age is well underway: typing in “depression” in an app store will produce thousands of results.
At the cutting edge of technological integration into psychotherapy, people are seeking to harness data generated by smartphones and social media sites for therapeutic aims (e.g. Stewart and Davis, 2016; Torous et al., 2015; Calvo et al., 2017). For example, clinicians could use active data collection, in which patients are instructed to record their thoughts or feelings throughout the day to be reviewed at their next visit. As another example, smartphones could be used to integrate and analyze the reams of data our pocket-computers are well-equipped to record. This might include data on how often a user engages with others on social media, and location-based tracking of whether a person has left their house. That information could be used to infer whether a patient is having a depressive episode. This suggestive data could then be tied to automated responses; for example, if a social media user begins publicly writing about suicide, that user could be automatically redirected to suicide hotlines and resources (Singer, 2018). Industry startups are springing up around each of these potential improvements, as many of these approaches are still in their infancy.
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Then there’s the end game everyone’s chasing: do these technology-psychotherapy fusions help people? It’s a tough question, because on the face of it, people continue to use these services and many report them valuable. However, some of the improvements people are experiencing may simply be coincidences: maybe people feel better having spent time on any app at all, or maybe they feel better because of the passage of time, and the fact that they just happened to be using a psychotherapy app at the same time is irrelevant. Academic researchers have thus been asking the following question: do patients’ symptoms significantly decrease when using an online intervention (such as a mobile app, alone or in addition to in-person care) compared to a control group? Ultimately this control group would be using an app that is quite similar to the tested app, but without the psychotherapeutic intervention.
The good news is that the research results from randomized control trials (testing mobile apps, primarily) are generally positive: patients’ symptoms generally decrease across mental health conditions like depression, stress, anxiety, and substance use. The bad news is that many of the studies have not yet been independently replicated, and did not have control groups that provided adequate comparisons for the intervention—some studies did not have control groups at all, and many studies used patients from the intervention waitlist as controls (Donker et al., 2013; Firth et al., 2017; Firth et al., 2017; Lui et al., 2017; Stratton et al., 2017). Yet study methodology seems to be improving over time, as the size of these studies continues to grow, and more informative, active control conditions are employed.
However, the mental health app space is a wild west, with few rules or regulations dictating standard and effective practices. Consumers are left to their own devices to determine which apps are supported by scientific research (Bakker et al., 2016). Sometimes individual apps have been tested. Sometimes apps use evidence-based methodologies like cognitive-behavioral therapies, without specifically evaluating mobile-based CBT. But most apps do neither, offering untested methods without any verification, making the choice in apps for both patients and clinicians harder.
And there are ethical concerns, especially for the more powerful paradigm-shifters. Consider the looming question of privacy: if someone is writing about suicide online, should their friends be notified to provide social support? Which friends? Since national regulations around digital therapeutics are not well-established, data security and privacy are unsolved problems (Kotz et al., 2018; Wykes and Schueller, 2018). Additionally, consider passive data collection, in which companies could collect data about where a person walks or how long they talk to different people to infer a patient’s mood. This data could be sent over insecure data streams to companies, and these companies could have permission to send outside assistance (like police or an ambulance) to a patient’s house, without warning them beforehand. Passive data collection could be the story of a dystopian or utopian future, depending on how it’s managed.
It is widely recognized that technology has the potential to improve mental health outcomes. Academics and industry professionals are trying many different approaches, including online or text-based interventions and diving into myriad types of behavioral data using computational methods. Potential and initial results are good, but much remains to be discovered, especially as this blending of fields must balance the strengths and weaknesses of many different players in academia, industry, and policy. What will be the end result for patients, and our capacity to improve mental health? The answer will likely be multifaceted and context-dependent: to be determined.
Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016). Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR mental health, 3(1).
Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649-685.
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: a systematic review. Journal of medical Internet research, 15(11).
Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. Journal of affective disorders, 218, 15-22.
Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials. World Psychiatry, 16(3), 287-298.
Lui, J. H., Marcus, D. K., & Barry, C. T. (2017). Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Professional Psychology: Research and Practice, 48(3), 199.
Kotz, D., Lord, S. E., O'Malley, A. J., Stark, L., & Marsch, L. A. (2018). Workshop on Emerging Technology and Data Analytics for Behavioral Health. JMIR research protocols, 7(6).
Wykes, T. and Schueller, S. (2018). Why reviewing apps is not enough: Transparency for Trust (T4T): The Principles of Responsible Mental Health App Marketplaces.
Singer, Natasha. “In Screening for Suicide Risk, Facebook Takes On Tricky Public Health Role.” The New York Times, The New York Times, 31 Dec. 2018, www.nytimes.com/2018/12/31/technology/facebook-suicide-screening-algorithm.html.
Stewart, R., & Davis, K. (2016). ‘Big data’ in mental health research: current status and emerging possibilities. Social psychiatry and psychiatric epidemiology, 51(8), 1055-1072.
Stratton, E., Lampit, A., Choi, I., Calvo, R. A., Harvey, S. B., & Glozier, N. (2017). Effectiveness of eHealth interventions for reducing mental health conditions in employees: A systematic review and meta-analysis. PloS one, 12(12), e0189904.
Torous, J., Staples, P., & Onnela, J. P. (2015). Realizing the potential of mobile mental health: new methods for new data in psychiatry. Current psychiatry reports, 17(8), 61.
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