Life space limitations in visually impaired older adults.

SIGNIFICANCE: Future work should develop and evaluate interventional strategies to help overcome visual and health-related barriers to travel in visually impaired seniors and mitigate adverse impacts of loneliness for those who do not leave town. PURPOSE: Life space refers to the area in which a person travels within a given time period. We explored whether demographics, vision, and/or health characteristics were related to restrictions in self-reported life space for visually impaired seniors. METHODS: Visually impaired (n = 114) clinical trial participants aged ≥55 years learned visual assistive iPhone apps and completed the following baseline questionnaires: Life Space, 36-Item Short-Form Health Survey, University of California, Los Angeles Loneliness Scale, and New-General Self-efficacy Scale. Multiple logistic regressions evaluated associations between life space and patient factors after accounting for their distance to the next county or state. RESULTS: During 2021 to 2023, 17%, 43%, and 70% of participants had not left their town, county, or state, respectively, in the past 3 months, or planned to in the next 3 months. Those with reduced distance best-corrected visual acuity had greater odds of not leaving the county in these time frames (odds ratio [OR] = 3.5; p=0.04). Minority race was associated with greater odds of not leaving town or the county in the past 2 weeks or future 3 months (OR = 4.3 to 6.4; p=0.009 to 0.049). Increased self-efficacy was associated with reduced odds of not leaving the state in the past 3 months, next 3 months, or past and/or future 3 months (OR = 0.54 to 0.55; p=0.02 to 0.03). Better physical function was associated with reduced odds of not leaving the state in the past 2 weeks or 3 months (OR = 0.96 to 0.98; p=0.01 to 0.04). Increased loneliness was related to greater odds of not leaving town in the past and/or future 3 months (OR = 1.8 to 2.0; p=0.007 to 0.009). CONCLUSIONS: Minority race, reduced vision, self-efficacy, and physical health were related to life space restrictions in this cohort of visually impaired seniors, whereas loneliness was greater among those who were not leaving town.

Factors related to training time and achieving proficiency with visual-assistive mobile applications in visually impaired older adults.

SIGNIFICANCE: A majority of visually impaired older adults were able to learn to proficiently use visual-assistive iPhone applications (apps) following a median 1 hour and/or multiple training sessions, which should be considered when planning vision rehabilitation service delivery, including the option for remote telerehabilitation for those who prefer that modality. PURPOSE: Older adults with low vision are increasingly using technology to improve their visual functioning. We examined whether age-related comorbidities were potential barriers to success in learning to use visual-assistive apps on a smartphone. METHODS: A clinical trial assessed visual-assistive apps in 116 older adults aged 55+ years (mean [standard deviation], 72 [10] years). Subjects were randomized to use an app (SuperVision+, Seeing AI, or Aira) preloaded to a loaner iPhone and completed one-on-one training. App proficiency was measured by the participant's ability to use the iPhone/app without cueing at the end of training sessions. Training time was recorded for the initial session and totaled after subsequent sessions. Multiple regression models explored significant factors associated with training time and proficiency. RESULTS: Median initial and total training times were 45 and 60 minutes, respectively. Increased initial and total training times were both significantly related to increased age (p<0.001), legal blindness (p<0.007), Seeing AI versus SuperVision+ app (p<0.03), and participants from New England versus California (p<0.001). Most (71%) achieved proficiency after the initial training session; those odds were significantly greater among younger participants (p=0.04), those who opted for telerehabilitation (p=0.03), those who had higher cognitive scores (p=0.04), or those who were from New England (p=0.04). The majority (90%) was ultimately proficient with the app; those odds were significantly greater among participants who already had an optical magnifier (p=0.008), but were unrelated to other factors including study site. CONCLUSIONS: Following multiple, extensive training sessions, age, mild cognitive loss, or level of visual impairment did not preclude gaining proficiency with visual-assistive apps by visually impaired seniors, but those factors were associated with longer training times. Telerehabilitation can be a viable option to provide app training remotely for visually impaired seniors who choose that modality.

Outcomes of Telerehabilitation Versus In-Office Training With Magnification Devices for Low Vision: A Randomized Controlled Trial.

Purpose: An evidence basis is lacking but needed to compare reading ability outcomes after magnification device training remotely via telerehabilitation versus in office. Methods: A multicenter randomized controlled trial at academic centers and vision rehabilitation private practices randomized 61 visually impaired adults to telerehabilitation or in-office training 1 to 4 months after dispensing new portable electronic, hand-held, or stand optical magnifiers. Telerehabilitation included loaner equipment for Zoom videoconferencing with remote control access software. Using a multilevel regression model, changes in Activity Inventory responses using Rasch analysis estimated reading ability in dimensionless log odds units (logits) (0.14-logit change corresponds with ability change expected from a one-line change in visual acuity). Results: Across 47 participants who completed the trial, reading ability with new magnifiers improved significantly by 0.61 logits on average (95% confidence interval [CI], 0.36-0.86; P < 0.001) from baseline to 1 month, and by an additional 0.44 logits on average (95% CI, 0.19-0.69; P < 0.001) from 1 to 4months (i.e., after magnifier training), with very similar significant findings for both telerehabilitation (n = 29; mean improvement = 0.44 logits; 95% CI, 0.08-0.80; P = 0.018) and in-office training (n = 18; mean improvement = 0.43 logits; 95% CI, 0.15-0.71; P = .003), and no significant difference between randomized groups across both follow-ups (95% CI, -0.43 to 0.61; P = .73). Vision, demographics, and health factors were nonsignificantly related to reading ability changes from 1 to 4 months. Conclusions: Reading ability improved after the provision of newly dispensed magnifiers, with further improvements following additional magnifier training via either telerehabilitation or in-office usual care. Translational Relevance: These findings provide support for the use of telerehabilitation to enhance reading ability with newly prescribed magnifiers as an alternative modality of care delivery.

Acceptability of Telerehabilitation for Magnification Devices for the Visually Impaired Using Various Approaches to Facilitate Accessibility.

Purpose: We examined different methods to reduce the burden of accessing technology for videoconferencing during telerehabilitation for magnification devices for the visually impaired. Methods: During telerehabilitation studies over the past 5 years, vision rehabilitation providers assessed and gave training to visually impaired participants with newly dispensed magnification devices at home who connected to Zoom videoconferencing via loaner tablets or smartphones with assistance from (phase 1; n = 10) investigators by phone, (phase 2; n = 11) local Lions Club volunteers in participants' homes, or (phase 3; n = 24) remote access control software in a randomized controlled trial with 13 usual care controls who received in-office training. All participants completed the same post-telerehabilitation phone survey. Results: A significantly greater proportion of phase 3 subjects indicated they strongly or mostly agreed that the technology did not interfere with the session (96%) compared to phase 1 (60%; 95% confidence interval [CI], 1.2-12.5; P = 0.03) or phase 2 (55%; 95% CI, 1.8-188; P = 0.01). The majority indicated telerehabilitation was as accurate as in person (68%), they were comfortable with telerehabilitation (91%) and interested in a future session (83%), and their magnifier use improved (79%), with no significant differences in these responses between phases (all P > 0.10), including comparisons of participants randomized to telerehabilitation or in-office training in phase 3 who reported similar overall satisfaction levels (P = 0.84). Conclusions: Participants across all phases reported high levels of acceptance for telerehabilitation, with least interference from technology using remote access control in phase 3. Translational Relevance: With accommodations for accessibility to videoconferencing technology, telerehabilitation for magnification devices can be a feasible, acceptable, and valuable option in countries with resources to support the technology.

U.S. Optometrists' Reported Practices and Perceived Barriers for Low Vision Care for Mild Visual Loss.

SIGNIFICANCE: Identification of modifiable barriers to low vision rehabilitation (LVR) can inform efforts to improve practice management of patients with low vision (LV), through, for example, targeted educational programs for optometrists who do not practice LVR. PURPOSE: Mild vision loss (20/25 to 20/70) is increasing in prevalence among the aging population, yet it is unclear whether near-reading complaints (the highest presenting chief complaint) are being addressed. Studies of LVR provision by U.S. optometrists are currently lacking. This study elucidated self-reported optometric practice patterns for patients with mild vision loss. METHODS: Anonymous surveys were completed by 229 actively practicing optometrists across the United States. The survey inquired about the frequency of providing LVR for mild vision loss patients and the top barriers that prevent them from offering LVR management (including optical aids or referral). RESULTS: Compared with those moderately actively practicing LVR, twice as many (2.08×) practitioners who do not practice any LVR reported that they never prescribe near-reading add power of 4 D or greater for mild vision loss (P < .001). Among those who do not practice LVR, 39 and 11% indicated that they never prescribe any LVR management strategies for patients with visual acuity of 20/25 to 20/40 and 20/50 to 20/70, respectively. The two most commonly reported barriers to LVR indicated by about half of respondents were "cost of the LV exam and/or devices" and that "patients are not interested or would not go to an LV exam." Nearly a third of providers reported that "it is not feasible to stock magnifiers in office." CONCLUSIONS: A sizable group of non-LVR providers in the United States may not be addressing the near-vision needs of patients with mild vision loss. Several of the reported top barriers are potentially modifiable through the development of targeted educational programs for providers.

Utilization and Abandonment of Low Vision Devices Prescribed on a Mobile Clinic.

SIGNIFICANCE: Device utilization and abandonment for patients seen on a mobile clinic are explored. Findings are informative for resource allocation in a novel low vision rehabilitation (LVR) delivery model. This study also explores the relationships between device abandonment and LVR patient-reported functional outcomes. PURPOSE: This prospective cohort study investigated low vision device utilization and abandonment in a novel mobile clinic delivery model. METHODS: A device abandonment questionnaire was administered by telephone 3 months and 1 year after mobile clinic LVR. Participants (n = 65) had previously met the U.S. definition of legal blindness and were prescribed a total of 154 devices at their low vision consultative visits. Trends in device utilization and correlations with clinical and demographic participant characteristics, as well as functional outcomes as assessed by Massof Activity Inventory, are explored. RESULTS: An average of 2.6 device recommendations were made per participant. Digital magnification, optical magnifiers, and filters were most frequently recommended. At 3 months, 29% of participants abandoned at least one device, although only 17% of received devices were abandoned. There was no significant difference in the number of devices used, abandoned, or not received at 3 months versus 1 year after LVR. Devices prescribed for reading goals were most frequently used and least often abandoned, whereas glare control and distance magnification devices were more frequently abandoned. Neither patient characteristics nor Massof Activity Inventory change score was predictive of device abandonment. There was no significant difference in the odds of device abandonment in comparison with a previous study that assessed academic outpatient LVR clinics using the same questionnaire. CONCLUSIONS: Although more device recommendations are given per patient on the mobile clinic, there is no significant difference in device abandonment for patients seen on the mobile clinic versus other outpatient LVR delivery models.

Multivariable Regression Model of the EuroQol 5-Dimension Questionnaire in Patients Seeking Outpatient Low Vision Rehabilitation.

PURPOSE: To understand the source of between-person variance in baseline health utilities estimated from EuroQol 5-dimension questionnaire (EQ-5D) responses of a representative sample of the US low vision outpatient population prior to rehabilitation. METHODS: A prospective, observational study of 779 new low vision patients at 28 clinic centers in the US. The EQ-5D, Activity Inventory (AI), Telephone Interview for Cognitive Status (TICS), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) physical functioning component, and Geriatric Depression Scale (GDS) were administered by telephone interview prior to rehabilitation. EQ-5D responses were transformed into health utilities, which served as the dependent variable in all analyses. Data were then analyzed to determine how much overall visual ability, functional domains of visual ability, and comorbidities (e.g. physical functioning, depression, cognition) independently contribute to the EQ-5D-based health utility index. RESULTS: Multivariable regression analyses showed that the GDS and SF-36 physical account for nearly 40% of the variance observed in health utilities estimated from EQ-5D responses of low vision patients. Age was also a significant predictor of health utilities, but accounted for very little variance. None of the other variables were significant predictors. CONCLUSIONS: Health utilities of low vision patients estimated from the EQ-5D primarily are associated with comorbid factors that are not likely to be responsive to low vision rehabilitation, thereby rendering the EQ-5D an unsuitable outcome measure for this population. However, because the EQ-5D is responsive to comorbid states, it could be a useful tool for evaluating the impact of comorbidities on low vision patient quality of life.

Responsiveness of the EQ-5D to the effects of low vision rehabilitation.

PURPOSE: This study is an evaluation of the responsiveness of preference-based outcome measures to the effects of low vision rehabilitation (LVR). It assesses LVR-related changes in EQ-5D utilities in patients who exhibit changes in Activity Inventory (AI) measures of visual ability. METHODS: Telephone interviews were conducted on 77 low-vision patients out of a total of 764 patients in the parent study of "usual care" in LVR. Activity Inventory results were filtered for each patient to include only goals and tasks that would be targeted by LVR. RESULTS: The EQ-5D utilities have weak correlations with all AI measures but correlate best with AI goal scores at baseline (r = 0.48). Baseline goal scores are approximately normally distributed for the AI, but EQ-5D utilities at baseline are skewed toward the ceiling (median, 0.77). Effect size for EQ-5D utility change scores from pre- to post-LVR was not significantly different from zero. The AI visual function ability change scores corresponded to a moderate effect size for all functional domains and a large effect size for visual ability measures estimated from AI goal ratings. CONCLUSIONS: This study found that the EQ-5D is unresponsive as an outcome measure for LVR and has poor sensitivity for discriminating low vision patients with different levels of ability.

Interpretation of health and vision utilities in low vision patients.

PURPOSE: The purpose of the study is to evaluate the relationship between time trade off (TTO) and standard gamble (SG) estimates of health and vision utilities in a low vision patient sample. METHODS: Telephone surveys were conducted on 74 low vision patients. All study participants were administered utility questionnaires that used the TTO and SG methods as they relate to health and vision. RESULTS: There is high between-person variability in the relationship of TTO- to SG-estimated utilities for both vision and health. However, when transformed to logits, differences between TTO and SG utilities for health are equal to differences between TTO and SG utilities for vision. These differences are symmetrically distributed around the origin. The data were consistent with a model that includes both health or vision state and personal response criteria. The model explains between-person variability in the relationship of TTO to SG utilities as idiosyncratic differences within people between response criteria for making TTO and SG judgments. CONCLUSIONS: The large between-person variability in the relation of utilities estimated from TTO to those estimated from SG can be explained by large between- and within-person variability in personal TTO and SG response criteria. However, within each person, the response criteria used to judge health state are the same as the response criteria used to judge vision state. This observation leads to the conclusion that health and vision states are in the same units when estimated from utilities. A meta-analysis of published studies that compared TTO with SG utilities for different health states confirms the conclusion of the model that average utilities across people are criterion-free estimates of average health-related states on a common logit scale.