Back

Efficacy of virtual reality treatment of phantom leg pain: Results of a randomized clinical trial

Ambron, E.; Williamson, R.; Li, J.-S.; Karrenbach, M.; Rombokas, E.; Coslett, H. B.; Buxbaum, L. J.

2026-04-28 pain medicine
10.64898/2026.04.20.26350810 medRxiv
Show abstract

Approximately 90% of individuals with limb amputation experience the persistent sensation of the missing extremity and up to 85% experience debilitating pain in the missing limb, a condition termed phantom limb pain (PLP). In this registered clinical trial (NCT05296265), we tested the efficacy of Virtual Reality (VR) treatment of phantom leg pain in a sample of transtibial and transfemoral amputees with PLP. Adaptive randomization was used to assign 36 participants (19 transfemoral, 17 transtibial) recruited across three study sites to eight sessions of an active or distractor VR treatment. The active VR treatment required leg movements and provided virtual visual feedback. The distractor treatment was a commercially available VR treatment for pain based on the principle of distraction. The primary outcome measures were the comparison of ratings of pain intensity and quality at baseline versus immediately post-treatment and at 1-week and 8-week follow up. The secondary outcome measure, obtained in each session, was average pain intensity since the last treatment. Pain on both intensity and quality measures was significantly reduced with moderate effect sizes for the active treatment only; intensity effects persisted at 1-week follow-up, and quality effects persisted at 8-weeks follow-up. Ratings of pain intensity since the last treatment showed a large effect size for the active treatment and was significant for both treatments. This clinical trial showed significant efficacy of VR treatment for PLP, particularly for an active treatment providing virtual visual feedback of the amputated limb.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.