Telemonitoring Tools for Glaucoma Patients: A Systematic Review of Current Trends and Applications

Abstract

Background/Objectives: In 2010, approximately 60.5 million people were affected by glaucoma, making it the leading cause of permanent vision impairment globally. With the rise of telehealth tools and technological advancements in glaucoma care, this review aims to provide an up-to-date analysis regarding remote monitoring systems in glaucoma management. Methods: A systematic literature search (in compliance with PRISMA guidelines) was conducted across six databases (CINAHL, MEDLINE, PsycINFO, Web of Science, Scopus, and Cochrane Library) and one grey literature source (Google Scholar), covering the period from 2000 to 2024. Relevant studies meeting predefined inclusion criteria were identified and analyzed. Results: The search identified 21 eligible studies focusing on various glaucoma telemonitoring tools. Several studies demonstrated the potential for continuous remote intraocular pressure (IOP) monitoring and highlighted the effectiveness of home-based visual field-testing technologies (e.g., Melbourne Rapid Fields, Eyecatcher, and VF-Home), which showed results closely matching in-clinic tests. All 21 studies underwent risk of bias assessment with appropriate tools based on study design, and none showed a high overall risk of bias, indicating robust methodology. Conclusions: Glaucoma telemonitoring tools are feasible and cost-effective, helping to reduce patient travel and waiting times and improving patient satisfaction. However, periodic in-person examinations remain necessary to optimally monitor disease progression and adjust treatments. Future directions should focus on interdisciplinary collaboration and the development of advanced algorithms (including artificial intelligence) to further enhance patient outcomes in teleglaucoma care.

Keywords: digital health; glaucoma; telehealth; telemonitoring; teleophthalmology.

Figure 1

PRISMA flow diagram detailing the identification, screening, and final inclusion of studies.

Figure 2

Analysis of the forest plot for mean IOP reduction. Forest plots illustrating the mean difference (MD) in IOP reduction between home- and clinic-monitored patients. The overall MD is 0.10 mmHg (95% CI: −1.08 to 1.28), indicating no significant difference between the two monitoring methods. High heterogeneity (I² = 85%) was observed, leading to the use of a random-effects model. Individual study results (Hark et al., Rosenfeld et al., Levin et al.) are shown with their respective confidence intervals [14,17,24].

Figure 3

Funnel plot assessing publication bias in the meta-analysis. Each point represents a study. The x-axis represents the mean difference (MD), while the y-axis shows the standard error (SE) of MD. The symmetry of the plot suggests a low likelihood of publication bias, although interpretation should be cautious given the limited number of studies.

Figure 4

Forest plot for meta-analysis of visual field (VF) progression (mean deviation change) in home vs. clinic monitoring groups. A negative mean difference (−1.56 dB, 95% CI: −2.06 to −1.06) indicates that the home-monitored group had greater VF loss than the clinic-monitored group. The result was statistically significant (p < 0.00001). Studies are represented by squares proportional to their weight, and the diamond shows the overall effect. High heterogeneity (I² = 94%) suggests considerable differences between studies [11,12].

Figure 5

Funnel plot for the VF progression meta-analysis. The plot displays each study’s effect size (MD in VF change) versus its standard error. Some asymmetry is observed, but with only two studies, it is difficult to draw conclusions about publication bias. The limited data emphasize the need for more studies on VF outcomes in telemonitoring.

Figure 6

Risk of bias overview for cross-sectional studies (AXIS tool). Most domains are green (low risk), with a few yellow flags (moderate risk) for items like discussion of study limitations and statistical analyses. No high-risk (red) domains were observed, indicating reasonably sound methodology in the cross-sectional studies [14,15].

Figure 7

Risk of bias assessment using the JBI tool for case series. All evaluated domains are low risk (green) for both case series studies. This implies the case series had clear inclusion criteria, appropriate measurements, and complete follow-up, with no notable sources of bias [24,26].

Figure 8

Risk of bias assessment using the CASP tool for qualitative studies. All domains (research design, recruitment strategy, data collection, reflexivity, ethical considerations, analysis, etc.) were judged low risk (green). The qualitative studies were well designed, lending credibility to their patient-reported insights [20,27].

Figure 9

Risk of bias assessment for diagnostic accuracy studies (QUADAS-2). Most domains across the ten studies are low risk (green) in patient selection, index test conduct, and reference standard application. Some domains are marked unclear (yellow) due to insufficient information (e.g., unspecified blinding procedures). Notably, no high-risk issues (red) were identified, meaning no major biases were evident despite a few reporting ambiguities [8,9,11,12,14,21,22,23,25,28].

Figure 10

Summary of risk of bias for diagnostic accuracy studies. This figure aggregates the QUADAS-2 assessments: it shows that the majority of domains are low risk, with a portion unclear and virtually none at high risk. The unclear areas suggest where future studies should report methods more transparently (for example, details on patient flow and test interpretation).

Figure 11

Risk of bias per domain for non-randomized interventional studies (ROBINS-I). This highlights moderate risk (yellow) in specific domains like confounding and missing data for some studies. Other domains (selection of participants, classification of interventions, outcome measurement) are largely low risk (green). There were no high-risk (red) ratings, implying that while these studies have some bias due to study design limitations, their findings are still generally credible [10,13,16,17,19].

Figure 12

Summary of overall risk of bias for non-randomized interventional studies (ROBINS-I). This summary underscores that none of the five studies had a critical or high overall risk; most domains are low risk with a few moderate. The results support the validity of these studies’ conclusions while acknowledging the need to interpret certain outcomes (e.g., those susceptible to confounding) with caution.