Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC.81 and.78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.
|Original language||English (US)|
|Journal||Behavior Research Methods|
|State||Accepted/In press - 2022|
Bibliographical noteFunding Information:
This work has been supported in part by The William K. Warren Foundation, by the Veterans Health Administration Clinical Sciences Research and Development Service Career Development Award # IK2 CX001887 (to JRH), the National Institute of Mental Health (1RF1MH116987-01; to KOL), the National Institute on Drug Abuse (U01 DA041089; to MPH), and the National Institute of General Medical Sciences Center Grant Award Number 1P20GM121312 (to MPH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
- Computational psychiatry
- Inhibitory control
- Mobile assessment
- Motor control