Ecological Momentary Intervention

Smartphones are becoming more widely adopted by the medical, psychological, and behavioral health professions as tools for data collection. It's also becoming increasingly apparent that they hold the promise of powerful intervention delivery. However, misconceptions about the software development process are slowing down the field.

Mobile apps open the door for ecological momentary interventions (EMI), also know as Just-in-Time Adaptive Interventions (JITAI), where intervention content is sent to participants at just the right time to reduce negative health behaviors and support the development of new behaviors. The mobile apps dynamically adapt to the individual user to change the type and amount of support needed and to deliver it at the time most likely to support positive behavior change.

JITAIs are already being piloted by research groups in diverse areas such as physical activity [1,2], eating disorders [3], alcohol use [4, 5, 6], smoking cessation [7], weight loss [8], and mental illness [9,10]. However, research is still in the early stages and the technical assistance required for the research and development phase is often prohibitively expensive.

The power of any JITAI system lies in its ability to detect when a user is moving towards a situation that is likely to trigger unwanted behavior (e.g. a location where alcohol use has occurred, a trending negative mood) and provide personalized intervention content designed to steer the user towards a new behavior. Mobile apps also allow intervention content to be divided into manageable steps as the user’s individual progress is monitored, and then reflected back to them with content delivery that's tailored to best support them. These mobile apps also provide the potential for recognizing “teachable moments” or natural learning opportunities that arise in an individual’s day and to capitalize on those for greater intervention impact.

Nahum-Shani, et al, (2104) [11] provide a conceptual framework for understanding the essential components of JITAI. They outline four key elements:

  1. Decision Point: This is the point in time when the system makes a “decision” about what, if any, treatment to offer to the user. For example, a randomly prompted self-report that indicates a trend in increased negative mood may trigger the system to send personalized messages designed to encourage a more positive mood for that individual. Or, if the Smartphone’s GPS system detects that a user is within 500 meters of a location that they have previously identified as a place where they have frequently used a substance, the system could trigger a message to the user suggesting they call their support person.
  2. Intervention Type: The content of the intervention offered at the decision point can be classified as either Instrumental or Emotional. Instrumental types of support offer practical activities such as a prompt to monitor food intake, instructions for cognitive reframing techniques, reminders to take medication, etc. Emotional supports are intended to increase self-worth in the moment and may include tailored positive messages. The source of the intervention could be automated, i.e. pre-programmed into the system. This could look like a message or video, i.e. a prompt to connect with another person.
  3. Tailoring Variables: Information about the individual and their momentary needs is used to refine the treatment content provided in any given moment. For instance, a self-report of no smoking today, which the system recognizes as no smoking for the last 7 days, could trigger a different positive support message than that sent on the first day of non-smoking. Passive data from the phone can also be used in tailoring intervention content. For example, if the phone shows no significant acceleration for 1 hour, a reminder could be sent to the user to engage in physical activity.
  4. Decision Rules: Decision points can be operationalized by specifying to the system at what points (e.g. when previous substance location is detected within 500 meters), which intervention should be delivered (e.g. a prompt a call to support person) at what time (e.g. immediately). Or for example, opting to offer a motivational video if a self-report of negative mood is received three times in a 12-hour period.

The challenge many research groups face in creating an EMI or JITAI is the cost of software development. Researchers often assume they need to hire a software development team to build their app, which means many projects never get off the ground due to the high cost of custom mobile software. However, paying for custom software is usually the last step in a multi-stage process of intervention development. Before an intervention is ready to be encapsulated in a mobile app and made available to the public or treatment centers, a lot of work first needs to be done in defining the components listed above: decision points, intervention types, and tailoring variables, so that the decision rules can be operationalized.

The catch-22 is that this work cannot be done without access to mobile apps that allow the various iterations of the intervention to be tested and refined. This is the function provided by the ivu system. It allows for “rapid prototyping” of interventions, allowing refinements to be made easily and at low costs before the final intervention is ready to be produced as a stand-alone app. The ivu system has been in use since 2009. And since then, the bugs have been ironed out and it is agnostic to content. Researchers provide all the content and decision rules, and the ivu system implements them, allowing pilot participants to access the intervention from Apple or Android apps on their own Smartphones. Having access to the ivu system speeds up the intervention development process and drastically cuts costs as there is no need to build custom software.

Contact us to learn more.

  1. Consolvo, S., McDonald, D.W., Toscos, T., Chen, M.Y., Froelich, J., . . . (2008). Activity sensing in the wild: A field trial of UbiFit Garden. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1797-1806).
  2. King, A.C., Hekler, E.B., Grieco, L.A., Winter, S. J., Sheats, J. L., Buman, M.P., . . . Cirimele, J. (2013). Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults, PLoS One, 8(4), e62613.
  3. Bauer, S., de Niet, J., Timman, R., & Kordy, H. (2010). Enhancement of care through self-monitoring and tailored feedback via text messaging and their use in the treatment of childhood overweight. Patient Education and Counseling, 79(3), 315-319.
  4. Gustafson, D. H., McTavish, F. M., Chih, M. Y., Atwood, A. K., Johnson, R. A., Boyle, M. G., . . . Shah, D. (2014). A smartphone application to ssupport recovery from alcoholism: A randomized clinical trial. JAMA psychiatry, 71(5), 566-572.
  5. Suffoletto, B., Callaway, C. W., Kristan, J., Monti, P., & Clark, D. B. (2013). Mobile phone text message intervention to reduce binge drinking among young adults: Study protocol for a randomized controlled trial. Trials, 14(1), 93-93.
  6. Witkiewitz, K., Desai, S. A., Bowen, S., Leigh, B. C., Kirouac, M., & Larimer, M. E. (2014). Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychology of Addictive Behaviors, 28(3), 639-650.
  7. Riley, W. T. , Obermayer, J., & Jean-Mary, J. (2008). Internet and mobile phone text messaging intervention for college smokers. Journal of American College Health, 57(2), 245-248.
  8. Patrick, K., Raab, F., Adams, M. A., Dillon, L., Zabinski, M., Rock, C. L., . . . Norman, G. J. (2009). A text message–based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11(1), e1.
  9. Ben-Zeev, D., Kaiser, S. M., Brenner, C. J., Begale, M., Duffecy, J., & Mohr, D. C. (2013b). Development and usability testing of FOCUS: A smartphone system for self-management of schizophrenia. Psychiatric Rehabilitation Journal, 36(4), 289-296.
  10. Depp, C., Vahia, I. V., & Jeste, D. (2010). Successful aging: focus on cognitive and emotional health. Annual Review of Clinical Psychology, 6, 527-550.
  11. Nahum-Shani, S., Smith, S. N., Tewari, A., Witkiewitz, K., Collins, L. M., Spring, B., & Murphy, S. A. (2014). Just-intime adaptive interventions (JITAIs): An organizing framework for ongoing health behavior support. (Technical Report No. 14-126). University Park, PA: The Methodology Center, Penn State.