What Affects the Completion of Ecological Momentary Assessments in Chronic Pain Research? An Individual Patient Data...

Ecological momentary assessment (EMA) involves repeated sampling of people's current experiences in real time in their natural environments, which offers a granular perspective on patients' experience of pain and other symptoms. However, EMA can be burdensome to patients, and its benefits depend upon patients' engagement in the assessments.

The goal of this study was to investigate factors affecting EMA-completion rates among patients with chronic pain.

This individual patient data meta-analysis was based on 12 EMA datasets that examined patients with chronic noncancer-related pain (n=701). The EMA-completion rates were calculated on a daily basis for each patient. Multilevel models were used to test the following predictors of completion rates at different levels: within-patient factors (days into the study and daily pain level), between-patient factors (age, sex, pain diagnosis, and average pain level per person), and between-study EMA design factors (study duration, sampling density, and survey length).

Across datasets, an EMA-completion rate of 85% was observed. The strongest results were found for the between-patient factor age: Younger respondents reported lower completion rates than older respondents (P=.002). One within-patient factor, study day, was associated with completion rates (P<.001): over the course of the studies, the completion rates declined. The two abovementioned factors interacted with each other (P=.02) in that younger participants showed a more rapid decline in EMA completion over time. In addition, none of the other hypothesized factors including gender, chronic pain diagnoses, pain intensity levels, or measures of study burden showed any significant effects.

Many factors thought to influence the EMA-completion rates in chronic pain studies were not confirmed. However, future EMA research in chronic pain should note that study length and young age can impact the quality of the momentary data and devise strategies to maximize completion rates across different age groups and study days.