Training in retinoscopy: learning curves using a standardized method

Abstract

Background: Retinoscopy is one of the most effective objective techniques for evaluating refractive status, especially in non-cooperative patients. However, it presents a slow learning curve that often leads to student frustration. With the current Covid-19 pandemic and the need for social isolation, clinical education based on simulation has become more relevant. Therefore, we implemented retinoscopy laboratories and learning protocols to reduce student stress and learning time.

Methods: We conducted a study to evaluate the retinoscopy learning curve using a new training protocol proposal. One hundred trainees were assessed in four stages, corresponding to 08, 12, 16, and 20 hours of training. Six different refractive defects were used trying to reproduce frequent conditions of care. The time spent on the assessment was not considered as additional training time. To analyze the data, we used non-parametric statistics and linear regression to assess the variables associated with training time and performance rate.

Results: The mean performance score at 08 hrs was 32.49% (±16.69 SD); at 12 hrs was 59.75% (±18.80 SD); at 16 hrs was 70.83% (±18.53 SD) and at 20 hrs was 84.26% (±13.18 SD). Performance at 12 hrs was significative higher than 08 hrs of training, but did not show significant differences with the performance rate at 16 and 20 hrs. We found a strong positive correlation between performance and training time in retinoscopy (R = 0.9773, CI: 0.2678 - 0.9995 p = 0.0227).

Conclusion: This study showed that an increasing number of hours of practice positively correlates with performance in retinoscopy. The elaboration of a protocol and standardization of performance per hour also allowed us to estimate that a minimum of 13.4 hrs of practice is required to achieve 60% performance. Using the resulting formula, it is possible to determine the number of hours of retinoscopy practice are necessary to reach a certain level of performance.

Keywords: Learning curve; Optometry; Refractive errors; Retinoscopy; Simulation training; Standardization; Teaching.

Fig. 1

Frequency distribution of the performance rate to different retinoscopy practice times. Was evaluated the performance of the group at 08, 12, 16 and 20 hrs. The histogram shows the data as the relative frequency of the percentage of success by participants

Fig. 2

Effects of retinoscopy practice time on the performance rate. A Analysis for differences in the complete group, B Analysis for differences in the Undergraduate group, C Analysis for differences in the Graduate group. For each time (08, 12, 16 and 20 hrs). Data are presented as mean and (± SD), using Mann-Whitney U test or Kruskal-Wallis test. A p-value less than 0.05 was considered statistically significant. Significance is indicated by the following symbols: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.00001, ns = not significant. Post hoc analyses were applied (Dunn's test, p < 0.05)

Fig. 3

Comparison of performance in undergraduate and graduate trainees. The data shows an analysis of the performance rate at 08 and 12 hrs between UG and G groups. Data are presented as mean and (± SD), compared using Kruskal-Wallis test. A p-value less than 0.05 was considered statistically significant. Significance is indicated by the following symbols: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.00001, ns = not significant. Post hoc analyses were applied (Dunn's test, p < 0.05)

Fig. 4

Linear Regression of performance rate as a function of retinoscopy practice time. The data were strongly adjustable to linear regression (R² = 0.5015, p < 0.0001). The graph shows the average value for each training time