This study demonstrates how playing a well-designed multitasking motion video game in a virtual reality (VR) environment can positively impact the cognitive and physical health of older players. We developed a video game that combines cognitive and physical training in a VR environment. The impact of playing the game was measured through a four-week longitudinal experiment. Twenty healthy older adults were randomly assigned to either an intervention group (i.e., game training) or a control group (i.e., no contact). Participants played three 45-min sessions per week completing cognitive tests for attention, working memory, reasoning and a test for physical balance before and after the intervention. Results showed that compared to the control group, the game group showed significant improvements in working memory and a potential for enhancing reasoning and balance ability. Furthermore, while the older adults enjoyed playing the video game, ability enhancements were associated with their intrinsic motivation to play. Overall, cognitive training with multitasking VR motion video games has positive impacts on the cognitive and physical health of older adults.

The steering law is an excellent performance model for trajectory-based tasks, such as drawing and writing in GUIs. Current studies on steering tasks focus on the effect of system factors (i.e. path width and amplitude) on the movement time and steering law's related applications. We conducted a series of experiments to further explore the effect of different operational biases (bias speed or accuracy) on steering completion time and standard deviation for two steering trajectory shapes, i.e. a straight steering task and a circular steering task, and then establish a new model accommodating system and subjective factor in steering tasks. Empirical results showed that the new model is more predictive and robust than the traditional steering law.

Many handheld devices with stylus pens are available in the market; however, there have been few studies which examine the effects of the size of the stylus pen on user performance and subjective preferences for handheld device interfaces for various age groups. Two experiments (pen-length experiment and pen-tip width/pen-width experiment) were conducted to determine the most suitable dimensions (pen-length, pen-tip width and pen-width) for a stylus pen among young adult, child and older users. In pen-length experiment, five pen-lengths (7, 9, 11, 13 and 15 cm) were evaluated for young adult, child and older users, respectively. In pen-tip width/pen-width experiment, six combinations of three pen-tip widths (0.5, 1.0 and 1.5 mm) and two pen-widths (4 and 7 mm) were compared for young adult, child and older users, respectively. In both experiments, subjects conducted pointing, steering and writing tasks on a PDA. The results were assessed in terms of user performance and subjective evaluations for all three pointing, steering and writing tasks. We determined that the most suitable dimensions of the pen are as follows: pen-length 11–15 cm, pen-tip width 1.0–1.5 mm and pen-width 7 mm for older users; for child users, pen-length 7–13 cm, pen-tip width 1.0–1.5 mm and pen-width 4 mm; and for young adult users, pen-length 11 cm, pen-tip width 0.5 mm, and pen-width 7 mm.

A thought experiment is proposed that reveals a difference between Fitts’ index of difficulty and Shannon’s entropy, in the quantification of the information content of a series of rapid aimed movements. This implies that the contemporary Shannon formulation of the index of difficulty is similar to, but not identical to, entropy. Preliminary work is reported toward developing a model that resolves the problem. Starting from first principles (information theory), a formulation for the entropy of a Fitts’ law style rapid aimed movement is derived, that is similar in form to the traditional formulation. Empirical data from Fitts’ 1954 paper are analysed, demonstrating that the new model fits empirical data as well as the current standard approach. The novel formulation is promising because it accurately describes human movement data, while also being derived from first principles (using information theory), thus providing insight into the underlying cause of Fitts’ law.