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.

Mobile clinic LVR is effective at expanding access to care and produces clinically meaningful outcomes comparable to those seen in other outpatient LVR delivery models.

To provide calibrated item measures and rating category thresholds for the Activity Inventory (AI), an adaptive visual function questionnaire, from difficulty ratings obtained from a large sample of new low vision patients at pre-rehabilitation baseline.Methods: Baseline AI (510 items) rating scale data from five previous low vision rehabilitation outcome studies (n = 3623) were combined, and the method of successive dichotomizations was used to estimate calibrated item measures and rating category thresholds. Infit statistics were analyzed to evaluate the fit of the data to the model. Factor analysis was applied to person measures estimated from different subsets of items (e.g., functional domains such as reading, mobility) to evaluate differential person functioning.Results: Estimated item measures were well targeted to the low vision patient population. The distribution of infit statistics confirmed the validity of the estimated measures and the two-factor structure previously observed for the AI. Conclusions:Our calibrated item measures and rating category thresholds enable researchers to estimate changes in visual ability from low vision rehabilitation on the same scale, facilitating comparisons between studies.Translational Relevance: The work described in this paper provides calibrated item measures and rating category thresholds for a visual function questionnaire to measure patient-centered outcomes in low vision clinical research. The calibrated AI also can be used as a patient outcome measure and quality assurance tool in clinical practice.

SIGNIFICANCE:This work validates Rasch analysis of a performance-based low vision outcome measure evaluated in patients' own homes to ensure real-world relevance. Inclusion of sources of variance from the patient's home environment in functional outcome measures introduced nonuniform variance in measurements but did not preclude estimation of valid measures.PURPOSE: This study aimed to validate Rasch analysis of a performance-based outcome measure with real-world relevance.METHODS: Low vision patients (N = 161) receiving services from an occupational therapist performed Timed Instrumental Activity of Daily Living (TIADL) tasks in their homes. Rasch analysis was applied to error count and performance time data. Internal validity was assessed with evaluations of the accuracy and precision of estimated measures. External validity was assessed by comparing TIADL measures with measures estimated from the Activity Inventory (i.e., from self-reported difficulty ratings).RESULTS: Task measures were well targeted to person measures estimated from task performance time but were poorly targeted for measures estimated from task performance errors, for which most task trials (72%) were performed without error at baseline. Error-based person measures had larger standard errors with a smaller pseudo-R 2 than time-based person or task measures and error-based task measures. Person measure infits for time-and error-based estimates conformed to expected values. The linear regressions between time-based person and task measures and corresponding error-based estimates had slopes of approximately 0.5, an observation consistent with larger estimation error variance for error-based measures than for time-based measures. Time-based TIADL person measures (x) and Activity Inventory person measures (estimated from all items, y) were colinear but weakly correlated (R = 0.19).CONCLUSIONS: Functional ability measures estimated from performance times of instrumental activity of daily living tasks in patients' homes demonstrate good internal and external validity. The ceiling effect from the infrequency of task performance errors in our data set limits use of TIADL error data to measure rehabilitation outcomes.