Are we leveraging digital technology in addiction treatment?

The Center for Health Enhancement Systems Studies at the University of Wisconsin-Madison, developed A-CHESS, a smartphone app for recovery support, based in part on the ideas that emerged from experts in diverse fields and people suffering from addiction or affected by it. In 2009, in a Behavioral Healthcare article, I talked about key ingredients that a technology-based addiction treatment system such as A-CHESS would need.

A lot of the identified, necessary ingredients weren’t yet available when we first started thinking about a mobile technology app to assist those in recovery. But mobile health has changed so fast that what was visionary a few years ago is considered outdated today. And new features have emerged that we couldn’t have imagined then. 

Today, A-CHESS continues to evolve as a smartphone app for recovery that provides nearly constant access to peer support, information, activities, counseling and other services. It has been proven to reduce risky drinking and improve abstinence.¹

In any case, here’s an update of how close we are to our original vision, on a scale of 1 to 5, with 1 meaning we still have a long way to go, and 5 meaning, we’re there, but don’t unbuckle your seatbelt just yet.

Integrated systems and health records

Are We There Yet Score = 4

This is one place where we’re on the cusp of a big change. There now exist some electronic medical record systems that allow you to take data from a system like A-CHESS and enter it into an EHR, but that’s not true across the board.  In many cases, data can’t be entered into an EHR from an outside device.

A transition seems to be underway with systems such as Apple’s Health Net and its collaboration with Epic EHR systems, which is allowing one to integrate these systems together in ways we haven’t been able to in the past.  We’re pretty close to “there” with this one.

Wearable devices to capture moods, triggers and other risk factors for those with addiction

Are We There Yet Score = 3

There’s a lot of work being done on this. Many devices like FitBit and others that are more sophisticated have sensors that are collecting data. But few devices can collect all the information needed to establish a person’s mood. They collect some data, but not enough to have a major impact.  And very few of these systems on the market have been tested properly, so we don’t know how accurate they are.

Another factor to consider once the data is collected is how to integrate it to reach a conclusion on a person’s mood. For example, we can collect data on galvanic skin response and heart rate. But we are not sure yet how best to combine that information to reach a conclusion about stress level.

Another example: There are sensors that measure aspects of a person’s gait. Weaving back and forth could mean they’re under the influence or they might be having a stroke. For a sensor to really measure gait accurately to yield information about a condition or mood, a person would need to walk for five minutes in a confined location. Only then could the data be pulled together to reach any conclusions. So we have a ways to go to use these tools in the real world. Things look promising, but a lot more needs to happen.

Virtual experiences

Are We There Yet Score = 2

Virtual reality is being used today in some really interesting ways. The military is using it in some instances to help returning vets with PTSD. For example, the University of Southern California Institute for Creative Technologies creates a virtual city where patients are exposed to a sniper attack or experience an explosion.  There is evidence that these kinds of virtual reality tools can make a difference in terms of dealing with PTSD. Yet we don’t know how to use those tools to replicate, for example, verbal abuse that might lead to trauma or risk for substance abuse. On the other hand, virtual reality can be used to simulate the influence of some drugs. Virtual reality tools for dealing with addiction are limited, but the ones in development are promising.  

Treatment access and “one-stop shopping”

Are We There Yet Score = 4

There are automated programs for SBIRT, for teaching CBT, and for relapse prevention. A lot of improvements can still be made by integrating the monitoring devices, but there’s been a lot of progress in this area.

Networks enabling those with addiction to connect on digital platforms

Are We There Yet Score = 4.5

Things have come a long way in this area, with new options coming out regularly. You can now find platforms that are “walled gardens” that allow only a limited number of people to participate. This has been a popular feature of the A-CHESS app.  Then there are fully open programs that allow anybody to join. The challenge, of course, lies in ensuring privacy and honesty.

Tailored digital messaging to thwart risk for relapse

Are We There Yet Score = 3

One of the nice things about today’s smartphones is the number of sensors available, including the increasingly sophisticated cameras and GPS functions. These sensors allow you to detect important information and tailor messages. With mobile health apps like A-CHESS, a GPS feature can alert a person walking through or driving near a high-risk area, such as an entertainment complex with lots of bars, to change directions. With other apps, an accelerometer that detects quick stops and swerving when a person is driving might give an indication of drunk driving.  Important technologies are available but are not yet widely used. We have a long way to go before we take full advantage of what these systems have to offer, but we are making good progress.

Diagnostic tools

Are We There Yet Score = 1.5

Some diagnostic tools are emerging that can help clinicians detect whether a person has an SUD, as well as select the most appropriate treatment or intervention. The data that’s being collected by surveys or sensors offers a lot of potential for improving and expanding a clinician’s diagnostic skills.  A-CHESS, for example, has a feature called The Weekly Check-in, a self-monitoring service that tracks patients progress in recovery. One study of that feature suggests that clinicians could use the data collected to predict and possibly prevent relapse by providing targeted support at the right time.²  However, given current levels of activity in mHealth, we have a long way to go.

Help for family members

Are We There Yet Score = 2.5

One thing that’s abundantly clear is that families need a lot of support when a loved one is dealing with an addiction. And it’s also clear that families can make a big difference in recovery. Virtual reality and video are showing potential for training family members in skills such as how to have the difficult conversation that might get someone started in treatment. But the question remains of how to pull this together into a technology-based system. The pieces are there, but the puzzle hasn’t been assembled. I think we could, however, make a lot of progress on this one in a short period of time if we turn our attention to it.

The last thing that I would add is that technology could be incredibly helpful in efforts to integrate SUD treatment and primary care—all the way from analyzing tweets and other big data sources, to using computers to design different models of integration, to accelerating the exchange of information between addiction providers and primary care.³ If integration is going to succeed, it’s because the technological support systems are in place to make it happen.

Dave Gustafson is director of the Center for Health Enhancement Systems Studies and NIATx, based at the University of Wisconsin.  Gustafson leads a team that has developed Addiction CHESS, or A-CHESS, a smartphone app for people in recovery. 

References

1 Gustafson, D. H., McTavish, F. M., Chih, M.-Y., Atwood, A. K., A. Johnson, R., G. Boyle, M., … Shah, D. (2014). A smartphone application to support recovery from alcoholism: A randomized controlled trial. JAMA Psychiatry, 71(5), 566–572. https://doi.org/10.1001/jamapsychiatry.2013.4642

2 Chih, M.-Y., Patton, T., McTavish, F. M., Isham, A., Judkins-Fisher, C. L., Atwood, A. K., & Gustafson, D. H. (2014). Predictive Modeling of Addiction Lapses in a Mobile Health Application. Journal of Substance Abuse Treatment, 46(1), 29–35. https://doi.org/10.1016/j.jsat.2013.08.004

3 Awoyinka, L., Gustafson, D., & Johnson, R. (2014). Using technology to integrate behavioral health into primary care. In L. Marsch, S. Lord, & J. Dalerie (Eds.), Transforming behavioral health: State of the science. New York, NY: Peter Lang Publishing.