With the increasing availability of sophisticated technologies and portable health monitoring devices, a new wave of data collection methods have become accessible to researchers. One such method is Ecological Momentary Assessment (EMA), which brings valuable opportunities to study individuals in more naturalistic settings. Gone are the days of having to collect data in artificial laboratories or relying on retrospective self-report methods, as we can now study people as they go about their daily lives.
The term EMA refers to the method of collecting patient data in real-time, through the use of digital devices such as smartphones or electronic diaries that can record (and transmit) data. For example, EMA methods are suited to examining cravings, as they are a highly dynamic phenomena best measured in a participant’s natural environment. Studies have used a variety of EMA data collection methods to measure cravings, such as mobile phones, electronic diaries or interactive voice response (IVR). Typically craving is measured multiple times per day over 3-4 week periods; an alert will be sent to the participant via the device, prompting them to answer questions on their craving level. This data then provides an accurate picture of an individual’s craving levels as they occur.
EMA has allowed researchers to overcome traditional barriers and biases associated with self-report questionnaires. Standard data collection methods rely on retrospective memory; patients may have to recall the quantity and frequency of alcohol/illicit drug use over extended time periods, or recall the intensity of their cravings at specific past time-points. Expecting drug and alcohol users to remember very specific details about their substance use, mood, or behaviour over protracted time periods is unrealistic and potentially subject to bias. EMA overcomes this barrier and allows data to be collected in the real-world as a participant experiences it.
EMA also brings the opportunity to provide instantaneous feedback and intervention to an individual, a process is termed Ecological Momentary Intervention (EMI). An example of EMI would be asking a participant to rate the intensity of their cravings, and then providing a tailored intervention offering personalised support or feedback based on the results. EMI has demonstrated research efficacy in non-addiction settings for physical activity (1) and HIV support (2).
EMI methods have had limited research evaluation within the addictions field, but have shown promise in alcohol harm reduction (3), motivational interventions for reducing marijuana use (4) and smoking cessation (5). Existing methods include smartphone apps or text messaging-based interventions that provide motivational enhancement, feedback and psycho-education. As technologies develop, there is potential for mobile health (mhealth) devices to play a greater part in the day-to-day treatment of patients, although this is a relatively new area of research.
EMA and EMI methods demonstrate promise for both the study and treatment of people with substance use disorders. Mobile phones in particular have the capability to assess and provide intervention to a client ‘in-the-moment’, outside of clinic hours, at crisis times when traditional support structures are unavailable. Furthermore the data collected from EMA is substantially more valid and unbiased than existing methods. Whilst more evaluation is needed, research is clearly moving away from stark laboratory settings and paper and pencil questionnaires towards real-time technology based measurement.
1. King AC, Ahn DK, Oliveira BM, Atienza AA, Castro CM, Gardner CD. Promoting physical activity through hand-held computer technology. American journal of preventive medicine. 2008;34(2):138-42.
2. Lester R, Karanja S. Mobile phones: exceptional tools for HIV/AIDS, health, and crisis management. The Lancet infectious diseases. 2008;8(12):738-9.
3. Cohn AM, Hunter‐Reel D, Hagman BT, Mitchell J. Promoting behavior change from alcohol use through mobile technology: the future of ecological momentary assessment. Alcoholism: Clinical and Experimental Research. 2011;35(12):2209-15.
4. Shrier LA, Rhoads A, Burke P, Walls C, Blood EA. Real-time, contextual intervention using mobile technology to reduce marijuana use among youth: A Pilot Study. Addictive behaviors. 2014;39(1):173-80.
5. Rodgers A, Corbett T, Bramley D, Riddell T, Wills M, Lin R-B, et al. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tobacco control. 2005;14(4):255-61.
Joanna Milward, King’s College London
The opinions expressed in this commentary reflect the views of the author(s) and do not necessarily represent the opinions or official positions of the Society for the Study of Addiction.