Vision Quality of Life with Time Survey: Normative Data and Repeatability

Abstract

Purpose

To develop a novel Vision Quality of Life (QoL) survey that emphasizes the amount of time a visual activity can be performed before symptoms occur.

Methods

The Vision Quality of Life with Time (VisQuaL-T) survey was developed with 10 daily activities and a list of common visual symptoms. Participants were recruited from a university campus. Participants were not excluded based on binocular impairments to obtain a normative dataset. Participants were instructed to denote when they first experience symptoms within certain time ranges. If participants did not engage in one of the 10 activities, they were instructed to denote “N/A”. A composite score (range 0–3) was determined by only accounting for the questions that were answered.

Results

The normative data cohort had a sample size of 376 participants and the repeatability cohort had 54 participants. The normative, test, and retest datasets had a mean composite score of 2.47±0.54, 2.69±0.42, and 2.67±0.49 and 95% confidence interval of 2.38–2.71, 2.58–2.81, 2.54–2.80, respectively. There was good reliability and high correlation between the test and retest timepoints with an ICC of 0.825 and a Pearson correlation coefficient of 0.839 in the repeatability cohort. The normative data cohort showed good internal consistency with a Cronbach’s alpha value of 0.803. Test and retest timepoints showed no statistical significance among the individual questions (p > 0.1).

Conclusion

A lower bound score of 2.4 can potentially be used to differentiate visually normal and symptomatic participants. Statistical analysis showed the survey is repeatable and reliable. Using time as a metric for assessing symptomology could be a useful method for identifying patients with QoL issues and for assessing effectiveness of binocular vision, accommodative, and eye movement treatments.

Keywords:

• quality of life
• timed survey
• endurance
• visual symptoms
• patient-reported outcome measure

Acknowledgments

This research was supported by NJIT Startup Funds provided to C. Yaramothu.

The authors thank Joel Rajah, Linda Tawdros, Yasemin Botas, and Fatima Masadeh of the New Jersey Institute of Technology’s Biomedical Engineering Department for their assistance in recruiting participants and collecting data.

Disclosure

The authors have no interest or relationship, financial or otherwise, that might be perceived as influencing the authors’ objectivity which can be considered a potential source of conflict of interest.