Tuesday, March 20, 2018

Downloading Happiness

By Sorab Arora

Sorab Arora is currently a Master’s in Public Health student at Emory University, specializing in Healthcare Management and Policy. He has researched health technology design and strategy focused on behavioral medicine, most recently at Northwestern University’s Center for Behavioral Intervention Technologies. Arora is a graduate of both the University of Chicago (Summer Business Scholar – 2017) and Grinnell College (2016), where he has bridged social entrepreneurship with mobile technologies and medical innovation. 

With median adult smartphone ownership rising to nearly 70% in advanced markets, individuals ranging from wealthy millennials to homeless youth have unprecedented access to mobile technologies (Poushter, 2016; Ben-Zeev et al., 2013). From “swiping” potential soulmates to ordering prescription glasses to one’s door, the proliferation of opportunities for immediate gratification through mobile applications only continues to grow. In what economists have now termed the “Fourth Industrial Revolution,” this period of integrated consumer technologies focuses on human-centered design and improved efficiency across global sectors (Schwab, 2017). In healthcare especially, mobile health (mHealth) platforms offer an innovative new element to how medicine can be conceptualized, delivered, and implemented. 

The melding of mHealth technologies in the field of psychotherapy offers a marriage of tremendous promise. As Fitbits, Apple Watches, and the like have taken center stage as wearables to enhance wellness, data collected from these sources offers a wealth of vital, longitudinal information. Predictive analytics allow healthcare providers (and consumers) to gain a more precise understanding of their health through personalized strategies based on their past health trends (Siegel, 2013). Coupled with artificial intelligence and machine learning, predictive analytics can offer improved differentiations in care by closely analyzing biological and behavioral markers. The value in doing so is a greater understanding of healthcare patterns at both the individual and the societal level, driving more insightful strategies for improvement. In other words, mobile technologies have opened the door to data-mining metrics that were once nearly impossible to quantify– including behaviors, cognitions, and emotions (Mohr, Zhang & Schueller, 2017). 

Image courtesy of Wikimedia Commons.
As it currently stands, smartphones have access to a tremendous amount of personal data ranging from location, movement, circadian rhythms, exercise, diet, and even ambient light. Applying analytics to the world of mental health can have dramatic impacts not as a replacement for current treatments, but by offering more accurate indicators of what and how to treat. Telemedicine -- usage of technology to remotely deliver healthcare – has also played a key role in psychiatry by facilitating innovative symptom tracking and communication between patients and providers (Mermelstein et al., 2017). Aligned with these strides in telepsychiatry, recent studies indicate efficacy in correlating behavioral markers to clinical disorders in efforts to more precisely pinpoint once-overlooked symptomology (Harari et al., 2016). 

Implementing smartphone usage in clinical settings has been a recent focus for The National Center for Telehealth & Technology through their efforts in improving mood and anxiety disorders, especially for veteran populations (Luxton et al., 2011). The move to focusing on everyday individuals using and benefiting from similar mobile apps, however, comes with its own string of unique legal, ethical, and medical concerns. 

Koko is a crowd-sourced platform for providing positive, constructive feedback to others that was based on cognitive-behavioral therapy to change conceptualization of challenging events ranging from teen bullying to work stress in efforts to build resilience. Woebot helps facilitate conversation and track moods through quantitative and qualitative measures utilizing machine learning. Headspace is a popular mobile app across Android and iPhone systems that teaches meditation and mindfulness through short, daily modules. These applications just scratch the surface of how healthtech innovation and human-centered interactions have fused in recent years. But with this plethora of opportunity comes a slew of related questions. What are the related ethical concerns and how is privacy safeguarded? How would one measure adherence and incentivize actual usage of these apps? Would these new technologies yield clinically significant improvements in psychiatric populations? All these questions (and more) beg to be answered, but the issue of efficacy takes center stage. 

Image courtesy of Wikimedia Commons.
As mental health and neurotechnologies are brought to market, there have been minimal barriers to entry in creating mobile apps with apparent face value. From ideation stages to actual product launch, several healthtech designs are relatively unregulated and untested regarding their actual validity as medical devices or supplements (Mohr, Zhang & Schueller, 2017). Because of this lack of quality control, individuals may be managing their stress and mental health disorders in ways that do more harm than good. 

Calling for evidence-based mental health apps and screening related technologies through more efficacious standards paves the path for creating clinically significant improvements long-term for at-risk patients (Lui, Marcus & Barry, 2017). While resources like PsyberGuide evaluations, the American Psychiatric Associations (APA) App Evaluation Model, and similar criteria have helped equip individuals with skills to discern between mental health apps by providing holistic evaluation criterion, a fundamental issue remains. Too many apps lack efficacy to be hailed as breakthroughs in the current climate of healthtech design and innovation– especially those in the fields of mental health and neuroscience. 

With privacy concerns as a major player in healthcare data storage, requirements for novel healthtech apps, wearables, and software go beyond simply achieving gold standards for randomized clinical trials or patient satisfaction. These technologies handle highly sensitive personal information, making bioethics and legal considerations key factors in data storage and analysis. Concerns over GPS and raw audio data already introduce a design challenge for individuals who cannot allow these systems to run in workplace settings or confidential meetings, reminiscent of “Big Brother” collecting too much information of daily activities (Klasjna et al., 2009). 

As the ability to collect and synthesize more intimate data emerges, the process of ensuring data security through deep learning software must draw on psychologists to effectively collaborate with colleagues across healthcare, computer science, and engineering. If used as a medical technology supplement to face-to-face therapy or psychotherapeutic interventions - compliant with The Health Information Technology for Economic and Clinical Health Act (HITECH Act) – the need for ensuring patient confidentiality and privacy becomes the responsibility of more than just the provider (Luxton et al., 2011). 

Image courtesy of Wikimedia Commons.
As wellness based health-tech products are launched to market, evaluating their efficacies from a more rigorous clinical and legal standpoint becomes crucial. Because advanced technologies have significantly altered daily interactions at a personal and professional level, leveraging human-computer interactions in the field of behavioral medicine offers tremendous potential for enhancing short- and long-term treatment strategies. Tracking emotional states through novel frameworks can therefore serve as a tool for “downloading” a healthier state of mind – given adherence to these applications as if they were tangible medication itself. The potential benefits of improved patient-provider relationships, decreased per capita cost, and increased access to care make discussing cross-disciplinary strategy, limitations, and directions invaluable with regards to emerging neurotechnologies. 

References
 
Ben-Zeev, D., Davis, K. E., Kaiser, S., Krzsos, I., & Drake, R. E. (2013). Mobile technologies among people with serious mental illness: opportunities for future services. Administration and Policy in Mental Health and Mental Health Services Research, 40(4), 340-343. 

Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838-854. 

Klasnja, P., Consolvo, S., Choudhury, T., Beckwith, R., & Hightower, J. (2009). Exploring privacy concerns about personal sensing. Pervasive Computing, 176-183. 

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. 

Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42(6), 505. 

Madan, A., Cebrian, M., Lazer, D., & Pentland, A. (2010, September). Social sensing for epidemiological behavior change. In Proceedings of the 12th ACM international conference on Ubiquitous computing (pp. 291-300). ACM. 

Mermelstein, H., Guzman, E., Rabinowitz, T., Krupinski, E., & Hilty, D. (2017). The Application of technology to health: The evolution of telephone to telemedicine and telepsychiatry: A historical review and look at human factors. Journal of Technology in Behavioral Science, 1-16. 

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. 

Poushter, J. (2016). Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center, 22. 

Schwab, K. (2017). The fourth industrial revolution. Crown Business. 

Siegel, E. (2013). Predictive analytics. Hoboken: Wiley.


Want to cite this post?

Arora, S. (2018). Downloading Happiness. The Neuroethics Blog. Retrieved on , from http://www.theneuroethicsblog.com/2018/03/downloading-happiness.html

No comments: