Quality of Life (QoL) in Glaucoma Patients

Labiris, Georgios; Giarmoukakis, Athanassios; Kozobolis, Vassilios

doi:10.5772/25122

Cited by 7 publications

(9 citation statements)

References 54 publications

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“…For VRQoL, on average, the patients reported a composite score of 88.7 ± 12.4; thus, majority had a fairly good VRQoL. Findings from previous studies suggest that glaucoma patients with visual field loss report lower VRQoL scores . Among the study population, about 71% were unwilling to give up any years of life for perfect vision; thus, majority had a good preference‐based HRQoL.…”

Section: Resultsmentioning

confidence: 87%

Estimating patient‐reported outcomes for glaucoma management: A cross‐sectional study

Uruthiramoorthy

Hutnik

Speechley

et al. 2020

J Evidence Based Medicine

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Aim To identify important explanatory variables of four patient‐reported outcomes (PROs): vision‐related quality of life (VRQoL), preference‐based health‐related quality of life (HRQoL), social support and community integration and depressive symptoms. Methods Cross‐sectional study conducted at one ophthalmic practice in a hospital setting. Patients with a diagnosis of glaucoma or glaucoma suspect (n = 250) were sequentially recruited. Patients with language restrictions were excluded. Data were collected through medical chart reviews and face‐to‐face interviews. The PROs were measured using validated tools. Candidate models for predicting PROs from explanatory variables were constructed using linear and logistic regression, as well as classification and regression trees. Through leave‐one‐out cross‐validation, the performance of each model was assessed in terms of mean absolute error. Results Use of mobility aids, best corrected visual acuity (BCVA), income, and living arrangements were most predictive of VRQoL, social support, and community integration. Use of mobility aids was also most predictive of the presence of depressive symptoms, and BCVA with preference‐based HRQoL. Conclusion Although promising associations were discovered, the models based on commonly collected clinical variables had limited ability to accurately predict individual patient PROs. Thus, although this study identifies clinical and demographic variables that are most predictive of PROs, routine collection of PROs in clinical practice may be necessary to obtain a complete picture of the quality of life of glaucoma patients.

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Section: Resultsmentioning

confidence: 87%

Estimating patient‐reported outcomes for glaucoma management: A cross‐sectional study

Uruthiramoorthy

Hutnik

Speechley

et al. 2020

J Evidence Based Medicine

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show abstract

“…The SVM signal is smoothed using a moving average filter shown in (2), where t indicates sample point in the time series and S(t) refers to the corresponding signal amplitude at that sample point. In this study, we set n to 6 to trade off the information accuracy for signal fluency.…”

Section: A Signal Pre-processingmentioning

confidence: 99%

“…Such peripheral visual field loss results in a low self-reported quality of life, due to its major role in facilitating postural control based on its sensitivity to motion [4]. As a result, a number of studies report that glaucoma patients walk more slowly [2], bump into objects more often, have increased postural sway [5], and are more prone to fall [6] compared to non-glaucoma subjects.…”

Section: Introductionmentioning

confidence: 96%

Glaucoma-Specific Gait Pattern Assessment Using Body-Worn Sensors

Fallahzadeh²,

Ghasemzadeh³

2016

IEEE Sensors J.

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Many studies have reported that glaucoma patients experience mobility issues such as walking slowly and bumping into obstacles frequently. However, little is known to date about how a person's gait is impacted due to glaucoma. This article presents design and development of a gait analysis approach using a shoe-integrated sensing system and accompanying machine learning techniques to quantitatively examine gait patterns in glaucoma patients. The customized sensor platform is utilized in a clinical trial conducted with 9 glaucoma patients and 10 agematched healthy participants. The signal processing and machine learning algorithms automatically detect effective gait cycles and extract both steady-state and spatio-temporal gait features from the signal segments. We perform machine learning algorithms to distinguish glaucoma patients from healthy controls, and identify several prominent features with high discriminability between the two groups. The results demonstrate that classification algorithms can be used to identify gait patterns of glaucoma patients with an accuracy higher than 94% in a 10-meter-walk test. It is also demonstrated that gait features such as evenness of the sway speed along medio-lateral direction between the two feet are significantly different (p-value < 0.001) between older adults with and without glaucoma. These results suggest that emerging solutions, such as wearable sensing technologies, can be used for continuous and real-time assessment of gait and mobility problems in individuals with low vision, and may open new avenues for using changes in gait patterns for preventing life threatening situations such as falls.

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“…In particular, because the patients with glaucoma are suffered from the reduction of the visual field, avoiding obstacles would be important for safety following the glaucoma. Previous studies have reported that patients with glaucoma have a lower level of gait function under various conditions, including stair, obstacle, and curve conditions [1,22] . By contrast, some studies have reported no differences in gait speed or gait parameters, such as cadence, step length, and base of support, under straight-line locomotion conditions between patients with glaucoma and normal subjects [12,14,27] .…”

Section: Introductionmentioning

confidence: 98%

Gait characteristics during crossing over obstacle in patients with glaucoma using insole foot pressure

Lee

Kim

et al. 2021

Medicine

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Background: Glaucoma, is the most common cause of irreversible visual deficits, presents as an injury to the optic nerve and it is mainly associated with elevated intraocular pressure. The main symptom of glaucoma is a reduction of the visual field, which is usually a source of complaint at the advanced stage of disease. Because of visual deficit, gait dysfunctions, including low gait speed and increased bumping into objects, postural sway, and falling are occurred. Many studies have used stopwatch or motion-sensing devices to report on gait function following glaucoma. However, there are few reports on gait dysfunction assessed by examining foot pressure. This study investigated gait ability following glaucoma according to different gait conditions by assessing foot pressure.Methods: Thirty older adults (15 in the sex-and age-matched normal group and 15 in the glaucoma group) were recruited for this study. All participants were walked under 2 different gait conditions in an F-scan system and the subject' assessments were randomly assigned to rule out the order effect. Conditions included: gait over an obstacle in a straight 6 m path, gait in a straight path without an obstacle in the 6 m path. Gait variables included cadence, gait cycle, stance time, center of force (COF) deviation, and COF excursion. About 10 minutes were taken for gait evaluation.Results: When walking without an obstacle on a 6 m path, there were significant differences between the 2 groups in gait speed, cadence, gait cycle, and stance time (P < .05). There were significant differences when walking with an obstacle on a 6 m path (P < .05). Two-way analysis of variance showed significant effects associated with "glaucoma" not gait condition on all outcomes except for COF deviation and excursion. Also, there was no the interaction effect between "glaucoma" and "gait condition." Conclusion:We demonstrated that glaucoma patients selected the gait strategy such as lower gait function in both gait conditions particularly, slower gait speed and cadence and longer gait cycle and stance time, as determined by examining foot pressure. We believe that our results could help to improve the quality of life of patients with glaucoma.

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Quality of Life (QoL) in Glaucoma Patients

Cited by 7 publications

References 54 publications

Estimating patient‐reported outcomes for glaucoma management: A cross‐sectional study

Estimating patient‐reported outcomes for glaucoma management: A cross‐sectional study

Glaucoma-Specific Gait Pattern Assessment Using Body-Worn Sensors

Gait characteristics during crossing over obstacle in patients with glaucoma using insole foot pressure

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