The Reliability of Fitts’s Law as a Movement Model for People with and without Limited Fine Motor Function

For over six decades, Fitts’s law (1954) has been utilized by researchers to quantify human pointing performance in terms of “throughput,” a combined speed-accuracy measure of aimed movement efficiency. Throughput measurements are commonly used to evaluate pointing techniques and devices, helping to inform software and hardware developments. Although Fitts’s law has been used extensively in HCI and beyond, its test-retest reliability, both in terms of throughput and model fit, from one session to the next, is still unexplored. Additionally, despite the fact that prior work has shown that Fitts’s law provides good model fits, with Pearson correlation coefficients commonly at r=.90 or above, the model fitness of Fitts’s law has not been thoroughly investigated for people who exhibit limited fine motor function in their dominant hand. To fill these gaps, we conducted a study with 21 participants with limited fine motor function and 34 participants without such limitations. Each participant performed a classic reciprocal pointing task comprising vertical ribbons in a 1-D layout in two sessions, which were at least four hours and at most 48 hours apart. Our findings indicate that the throughput values between the two sessions were statistically significantly different, both for people with and without limited fine motor function, suggesting that Fitts’s law provides low test-retest reliability. Importantly, the test-retest reliability of Fitts’s throughput metric was 4.7% lower for people with limited fine motor function. Additionally, we found that the model fitness of Fitts’s law as measured by Pearson correlation coefficient, r, was .89 (SD=0.08) for people without limited fine motor function, and .81 (SD=0.09) for people with limited fine motor function. Taken together, these results indicate that Fitts’s law should be used with caution and, if possible, over multiple sessions, especially when used in assistive technology evaluations.