OBJECTIVES: This study sought to determine the benefits to sponsor, developer, and translation provider of monitored cognitive debriefing interviews with patient questionnaires. Monitored cognitive debriefing (CD) was theorized to be particularly suitable for newly-developed questionnaires with minimal or no previous linguistic validation (LV), and for adapted questionnaires (i.e., a disease-specific instrument adapted to a new disease). METHODS: Thirty (30) CD interviews were carried out on a newly-developed patient instrument in six languages. Of those, sixteen (16) interviews were monitored by the sponsor. All languages were selected by the sponsor and a percentage of those interviews were monitored, the first languages to undergo LV. The translation provider recruited patients, provided interpreters, secured facilities with two-way mirrors and audio-visual recording capabilities, and performed the interviews. The monitor noted any sub-threshold issues that were outside of typical LV data capture rubric. Upon noting such issues in more than one country, the test authors would convene a meeting to consider if the PRO linguistic validation protocol or PRO source instrument required revision. RESULTS: The monitor suggested follow-up questions as needed during the CD interviews, tailoring the process to target specific dimensions of the PRO. Issues were tracked across languages by the monitor, and the interviewer asked subjects for their preference for alternative translations, when suggested by the monitor. Compared to those interviews which were unmonitored, on average, subjects made more comments overall, and more comments leading to a translation revision. CONCLUSIONS: Monitored interviews are beneficial for the validation of newly-developed or adapted instruments. This augmentation to CD provides a means of vendor management and early identification of issues during LV that are outside of the typical data capture rubric. The process enables the sponsor to suggest follow-up questions or modifications on-site, thus correcting issues preemptively that may negatively impact understanding and subsequent data pooling.