It is nearly impossible to walk down the street, ride the subway, go to a coffee shop, or even have family time at home without being surrounded by people deeply engrossed in the digital worlds of their smartphones. Being digitally wired has become a large part of our everyday lives. Whether we engage in social media, listen to music, watch videos, text with friends, or read the news, there is no doubt that many of us are socially dependent on these devices. In fact, research has shown that not only do we have feelings of anxiety when separated from our smartphones, but we also have negative cognitive and physiological responses to that stress.1 For better or worse, our inseparability from our smartphones will continue to deepen as the technological capabilities of these devices expand and evolve.
But what if we were able to harness the immense power of our digital connections to create tools that can potentially make our lives happier and healthier? That is exactly what is in development.
The growth of health and fitness smartphone apps has been exponential. Research by Flurry Analytics found that in 2013, the overall mobile app industry grew 115% in terms of average daily use.2 In the first half of 2014, health and fitness apps specifically saw a 62% increase in use, an 87% faster growth than the rest of the mobile app industry, which is itself growing at an exponential rate. There are over 6000 health and fitness mobile apps available, with features such as activity monitoring, food diaries, and calorie counting.2
More recently, apps are being developed specifically to monitor behavioral health patterns. The use of smartphone technology allows for collection of sensor data of an individual’s behavior to identify behavioral trends. For instance, Ginger.io uses sensor data as well as self- reported information collected through the phone to help mental health professionals identify problematic behaviors that may indicate the need for additional support.3
The app can monitor movement and communication patterns to determine whether someone has been isolating from others, decreasing activity, or having changes in mood, all of which could be indicative of depression. In a series of studies, research from the MIT Media Lab supports the use of this type of data collection and analysis by demonstrating relationships between mood, sleep, and sociability and showing how these data can be collected through smartphone sensors to help monitor symptoms of depression.4,5
Mental health professionals using the Ginger.io app can invite patients to install the app on their smartphones. The app will monitor and analyze both the patient’s sensor and self-report data. Whether the patient has a low score on a mood survey or a concerning change in behavior patterns, Ginger.io will notify the mental health professional when an intervention may be warranted. For instance, let’s say a patient has not been sleeping well and decides to stay home from work for several days. The patient’s decreased mobility triggers an alert to his mental health professional that the patient’s mood is declining, based on analytics of a population model that shows that mobility is inversely proportional to mood. The mental health professional can then reach out to the patient via phone to check in and provide appropriate support.
The Ginger.io app is currently used by 30 medical centers, including Kaiser Permanente and the University of California, San Francisco. Research and development of other apps are also in progress, with a $2.42 million grant from the NIH given to researchers at the Harvard School of Public Health to develop an app that will analyze factors such as unlocking and locking phones to determine sleep patterns.6
The benefit of using smartphone apps to monitor behavior includes the nature of the continuous, passive collection of data. Not relying completely on self-report will lead to more accuracy of information. Also, using this already ubiquitous device for data collection will not serve as an inconvenience to the user. Physicians will have the benefit of collecting real-time information so they may efficiently serve their patients in need.
However, some patients and health care professionals have expressed concerns about privacy information, questioning how this information can be used in the future by health insurance providers. Furthermore, unless the algorithms for the notification of health care professionals have an ideal sensitivity and specificity, physicians could potentially receive a number of false-positive notifications, ultimately leading to increased workload and costs. Although the technology has great potential to identify those at highest risk while providing more accurate and comprehensive patient care, concerns about privacy as well as effects on workload remain to be seen.
1. Clayton RB, Leshner G, Almond A. The extended iSelf: the impact of iPhone separation on cognition, emotion, and physiology. J Comput Mediat Commun. January 8, 2015. http://onlinelibrary.wiley.com/doi/10.1111/jcc4.12109/full. Accessed February 18, 2015.
2. Khalaf S. Health and fitness apps finally take off, fueled by fitness fanatics. Flurry. June 19, 2014. http://www.flurry.com/blog/flurry-insights/health-and-fitness-apps-final... fanatics. Accessed February 18, 2015.
3. Ginger.io. Big data, better health. Smarter care starts with your smartphone. https://ginger.io. Accessed February 18, 2015.
4. Moturu ST, Khayal I, Aharony N, et al. Using social sensing to understand the links between sleep, mood, and sociability. In: Proceedings of the IEEE International Conference on Social Computing (SocialCom 2011); September 2011; Cambridge, MA. http://hd.media.mit.edu/tech-reports/TR-670.pdf. Accessed February 18, 2015.
5. Moturu ST, Khayal I, Aharony N, et al. Sleep, mood and sociability in a healthy population. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5267-5270.
6. Walker J. Can a smart phone tell if you’re depressed? Wall Street Journal. January 5, 2015. http://www.wsj.com/articles/can-a-smartphone-tell-if-youre-depressed-142.... Accessed February 18, 2015.
7. Hanson CL, Cannon B, Burton S, Giraud-Carrier C. An exploration of social circles and prescription drug abuse through twitter. J Med Internet Res. 2013; 15:e189. September 6, 2013. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785991. Accessed February 18, 2015.
8. Santos JC, Matos S. Analysing Twitter and web queries for flu trend prediction. Theor Biol Med Model. 2014;11(suppl 1):S6. http://www.tbiomed.com/content/11/S1/S6. Accessed February 18, 2015.
9. Google Flu Trends. Explore flu trends—United States. https://www.google.org/flutrends/us/#US. Accessed February 18, 2015.
10. Paul MJ, Dredze M. A Model for Mining Public Health Topics From Twitter. Technical Report. Johns Hopkins University; 2011. http://www.cs.jhu.edu/~mpaul/files/2011.tech.twitter_health.pdf. Accessed February 18, 2015.
11. National Institutes of Health. Using social media to better understand, prevent, and treat substance abuse. October 17, 2014. http://www.nih.gov/news/health/oct2014/nida-17.htm. Accessed February 18, 2015.
12. De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. Microsoft Research. Association for the Advancement of Artificial Intelligence. 2013. http://research.microsoft.com/pubs/192721/icwsm_13.pdf. Accessed February 18, 2015.
13. Singer N. Risks in using social media to spot signs of mental distress. New York Times. Decem-ber 26, 2014. http://www.nytimes.com/2014/12/ 27/technology/risks-in-using-social-posts-to- spot-signs-of-distress.html. Accessed February 18, 2015.