Objectives: Ecological momentary assessment (EMA) methods are known to have validity for capturing momentary changes in variables over time. However, data quality relies on the completion rates, which are influenced by both participants’ characteristics and study designs. This study applied an EMA method using a mobile application to assess momentary moods and stress levels in patients with Moyamoya disease to examine variables associated with EMA completion rates. Methods: Adults with Moyamoya disease were recruited from a tertiary hospital in Seoul. Patients with cognitive impairment were excluded. The EMA survey was loaded as a mobile application onto the participants’ personal smartphones. Notifications were sent at semi-random intervals four times a day for seven consecutive days. Daily completion rates were calculated as the percentage of completed responses per day; overall completion rates were calculated as the proportion of completed responses per total of the 28 scheduled measures in the study and assessed through a descriptive analysis, t-test, ANOVA, and regression analysis, with mixed modeling to identify the point at which the daily completion rate significantly decreased. Results: A total of 98 participants responded (mean age, 41.00 ± 10.30 years; 69.4% female; 75.5% married). The overall completion rate was 70.66%, with no gender or age differences found. The daily completion rate decreased significantly after day 5 (p = 0.029). Conclusions: Obtaining a good completion rate is essential for quality data in EMA methods. Strategic approaches to a study design should be established to encourage participants throughout a study to improve completion rates.
Bibliographical noteFunding Information:
We thank Simone Verhagen, Department of Psychiatry and Neuropsychology at Maastricht University, for sharing her experience using EMA methods. This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A1B03030706).
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Health Informatics
- Health Information Management