JMIR Publications

ABSTRACT

Background:

Engaging participants in intensive longitudinal data (ILD) collection studies over multiple months and ensuring high data quality is crucial but challenging due to the potential burden of repeated measurements. Participants may engage in inattentive responding behavior (IR) to combat burden, but the processes underlying this behavior are unclear.

Objective:

This study was designed to broaden researchers’ knowledge about IR during ILD studies using qualitative analysis and to uncover the underlying IR processes to aid with future hypothesis generation.

Methods:

We explored the process of IR by conducting qualitative exit interviews with 31 young adult participants (ages 18-29) who completed a 12-month ILD study with smartphone-based ecological momentary assessment (EMA). The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions.

Results:

Thematic analysis revealed five overarching themes on factors that influence participant engagement: 1) friends and family also had to tolerate the frequent surveys, 2) participants tried to respond to surveys quickly, 3) the repetitive nature of surveys led to neutral responses, 4) attention check questions helped to combat overly consistent response patterns, and 5) different motivations may have led to different levels of data quality.

Conclusions:

These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, dynamic motivation, attention check questions, and intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multi-month intensive longitudinal data studies through better data collection protocols.