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Efficacy of eyelid warming devices as first-step treatment in meibomian gland dysfunction: A systematic review with meta-analysis

Ballesteros-Sánchez A, Rocha-de-Lossada C, Sánchez-González JM

The ocular surface, 2025

367 participants · 5 citations

Abstract

Purpose: To investigate the efficacy of eyelid warming devices as first-step treatment in patients with meibomian gland dysfunction (MGD).

Methods: A systematic review with meta-analysis of RCTs, reporting the effects eyelid warming devices in 3 databases, PubMed, Scopus and Web of Science, was performed according to the PRISMA statement.

Results: Seven studies including 367 patients, and 440 eyes were analysed. The overall efficacy did not significantly favor either group when comparing eyelid warming devices to the control groups or warm towel compresses, nor between moisture chamber devices and warm compress devices. However, the change in OSDI questionnaire (SMD 0.91; 95 % CI: 0.44 to 1.39; P = 0.0002) and NIBUT (SMD 1.10; 95 % CI: 0.61 to 1.59; P 2 = 49 %) and NIBUT (MD 2.24; 95 % CI: 1.20 to 3.28; P 2 = 71 %) were significantly favorable for eyelid warming devices. When comparing moisture chamber devices and warm compress devices, only the change in TBUT (SMD 0.75; 95 % CI: 0.23 to 1.28; P = 0.005; I2 = 30 %) were significantly favorable for moisture chamber goggles.

Conclusions: Despite their limited overall efficacy, eyelid warming devices significantly reduce OSDI questionnaire and improve NIBUT compared to controls or warm towel compress groups. Evidence remains insufficient to confirm whether moisture chamber devices provide better outcomes than warm compress devices. Further well-designed RCTs are needed to confirm these findings.

How strong is the evidence?

Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 367 participants or records.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

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