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Background Sensorimotor abnormalities precede and predict the onset of psychosis. Despite the practical utility of sensorimotor abnormalities for early identification, prediction, and individualized medicine applications, there is currently no dedicated self-report instrument designed to capture these important behaviors. The current study assessed and validated a questionnaire designed for use in individuals at clinical high-risk for psychosis (CHR). Methods The current study included both exploratory (n = 3009) and validation (n = 439) analytic datasets—that included individuals identified as meeting criteria for a CHR syndrome (n = 84)—who completed the novel Sensorimotor Abnormalities and Psychosis-Risk (SMAP-R) Scale, clinical interviews and a finger-tapping task. The structure of the scale and reliability of items were consistent across 2 analytic datasets. The resulting scales were assessed for discriminant validity across CHR, community sample non-psychiatric volunteer, and clinical groups. Results The scale showed a consistent structure across 2 analytic datasets subscale structure. The resultant subscale structure was consistent with conceptual models of sensorimotor pathology in psychosis (coordination and dyskinesia) in both the exploratory and the validation analytic dataset. Further, these subscales showed discriminant, predictive, and convergent validity. The sensorimotor abnormality scales discriminated CHR from community sample non-psychiatric controls and clinical samples. Finally, these subscales predicted to risk calculator scores and showed convergent validity with sensorimotor performance on a finger-tapping task. Conclusion The SMAP-R scale demonstrated good internal, discriminant, predictive, and convergent validity, and subscales mapped on to conceptually relevant sensorimotor circuits. Features of the scale may facilitate widespread incorporation of sensorimotor screening into psychosis-risk research and practice.

Sensorimotor and Activity Psychosis-Risk (SMAP-R) Scale: An Exploration of Scale Structure With Replication and Validation