Virtual Clinical Trials

“EMA is poised not only to replace clinician-administered rating scales in research settings but also to increase accessibility of EMA measures to the patients and health care providers in clinical settings, ultimately allowing real-world clinical settings to contribute meaningful data to research and development of new interventions.”
When All Else Fails, Listen to the Patient, 2019.

Benefits of Ecological Momentary Assessment in Clinical Trials

Reduces Bias

  • PROs more effective than clinician ratings
  • Reduces recall bias
  • Reduces social desirability bias

Frequent Sampling = Increased Sensitivity

  • Capture symptom fluctuation throughout the day and over treatment course
  • Less complex measures are more sensitive to change

Leads to Precision Medicine

  • Discover temporal dynamics of symptom changes for responders and non-responders
  • Develop personalized just-in-time interventions to support drug treatment

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EMA Reduces Bias

EMA relies on patient-reported outcomes rather than clinician ratings. Historically it has been assumed that clinician ratings are more objective than patient-reported outcomes but Uher et al (2012) found that self-report measures contributed more to the prediction of clinician-reported measures than vice versa and concluded that if only one form of measurement can be used that self-report is the best choice. Additionally, a large meta-analysis found that clinician-administered scales were associated with a higher placebo response than Patient-Reported Outcomes (PROs) which they attributed to the social desirability bias – i.e. the patients’ desire to provide the answer they think the clinician wants to hear (Mora et al., 2011).

Recall bias is a major problem and well documented. People don’t remember very accurately what happened in the past – especially when it comes to how they felt. Ecological Momentary Assessment has been shown to be more effective than retrospective reports in pain (Stone et al., 2005), affect (Parkinson et al., 1995), and major depressive disorder (van Rijsbergen et al., 2014). 

More Frequent Sampling Leads to Increased Sensitivity

The infrequent sampling of traditional clinical trial methods (pre- and post-treatment) is not only impacted by recall bias but flattens response effects. It assumes that we know how a disorder behaves over time, which we don’t (Mofsen et al., 2019). Symptoms of depression have been shown to fluctuate throughout the day by using EMA (Peeters, et al., 2006) as has impulsivity in bipolar patients (Depp et al., 2015).  EAM allows researchers to track the temporal fluctuation in symptoms in relation to medication ingestion. 

EMA allows for frequent sampling (often multiple times a day) which provides a data granularity unprecedented in mental health drug trials. In addition to tracking temporal fluctuation, the high sampling rate requires that measures be simplified – to reduce patient burden. Simpler measures, that target core symptoms only, have been found to be more sensitive to detecting assay response than traditional scales. For instance, EMA has found treatment effects that the traditional scales in major depressive disorder were unable to detect (Barge- Schaapveld & Nicolson, 2020).

In a direct comparison of traditional paper-and-pencil methods of tracking anxiety and depression symptoms and EMA with older adults participating in a Mindfulness-Based Stress Reduction intervention, EMA was found to be much more sensitive in detecting change. The Number-to-Treat (NTT) scores from EMA were 15-50% lower than scores from pencil-and-paper methods (Moore et al., 2016).

Precision Medicine and Just-in-Time Intervention

Baseline scores in clinical trials are often inflated (due to bias as discussed above) and then return to “normal” over the course of treatment, thus reducing the observed treatment effect. EMA methods can be used to determine a more ecologically valid baseline, and thus increase sensitivity to change, than traditional methods. EMA can be used to take repeated measures throughout the baseline period in addition to tracking context (e.g. at work, at home, daily stressors, interpersonal conflict, menstrual cycle, weather, time of day, etc.) to provide a baseline mean, range, and standard deviation that is more reflective of the patient’s experience. Variance from this baseline can then be tracked continuously throughout the treatment period to understand better how the drug interacts with symptoms. This can be done for each individual patient leading us to precision medicine

In the wider mental health field, EMA has been used in the development of EMI – Ecological Momentary Interventions- whereby intervention content is delivered via Smartphone to the patient throughout their daily life. When combined with machine learning algorithms that make decisions about which piece of content to deliver at which time EMIs become “just-in-time adaptive interventions” – highly precise behavioral interventions designed to change behavior in the moment. Used in combination with drug treatments JITAI’s hold the promise of dramatically increasing positive outcomes for many struggling with mental health.