Adapting psychophysiological data collection for COVID-19: The "Virtual Assessment" model

Area of Science:

• Developmental Psychology
• Infant Mental Health Research
• Psychophysiology

Background:

• The COVID-19 pandemic severely disrupted global research, necessitating adaptations for data collection.
• Infant mental health studies, especially those using psychophysiological measures, face unique challenges in remote settings due to required proximity and equipment.
• Maintaining research continuity while ensuring participant and researcher safety is paramount.

Purpose of the Study:

• To describe novel protocols for virtual assessments of infant mental health.
• To evaluate the success of remote data collection, including behavioral and autonomic nervous system (ANS) data.
• To provide a model for other researchers to resume safe data collection during and after the pandemic.

Main Methods:

• Developed and implemented 'virtual assessment' protocols for remote data collection from participants' homes.
• Collected behavioral data through video-recorded parent-child interactions.
• Acquired autonomic nervous system (ANS) data using the Still-Face Paradigm (SFP) adapted for remote administration.

Main Results:

• Successfully adapted traditional lab-based procedures for remote data collection across two research sites.
• Demonstrated the feasibility of collecting both behavioral and psychophysiological (ANS) data in a virtual setting.
• Data quality and participant safety were optimized while minimizing burden.

Conclusions:

• Virtual assessment models offer a viable solution for continuing infant mental health research amidst disruptions.
• The described adaptations facilitate safe and effective remote psychophysiological data collection.
• Dissemination of these methods can aid researchers in resuming critical studies, particularly in infant mental health.