Many real world mobile device interactions occur in context-rich environments. However, the majority of empirical studies on mobile computing are conducted in static or idealized conditions, resulting in a deficit of understanding of how changes in context impact users’ abilities to perform effectively. This paper attempts to address the disconnect between the actual use and the evaluation of mobile devices by varying contextual conditions and recording changes in behavior. A study was performed to investigate the specific effects of changes in motion, lighting, and task type on user performance and workload. The results indicate that common contextual variations can lead to dramatic changes in behavior and that interactions between contextual factors are also important to consider.

This paper examines factors that affect performance on a basic menu selection task by users who are visually healthy and users with Diabetic Retinopathy (DR) in order to inform better interface design. Linear and logistic regression models were used to examine various contextual factors that influenced task efficiency ( time) and accuracy ( errors). Interface characteristics such as multimodal feedback, Windows(R) accessibility settings, and menu item location were investigated along with various visual function and participant characteristics. Results indicated that Windows(R) accessibility settings and other factors, including age, computer experience, visual acuity, contrast sensitivity, and menu item location, were significant predictors of task performance.

This study investigates the effectiveness of two design interventions, the Microsoft® Windows® accessibility settings and multimodal feedback, aimed at the enhancement of a menu selection task, for users with diabetic retinopathy (DR) with stratified levels of visual dysfunction. Several menu selection task performance measures, both time- and accuracy-based, were explored across different interface conditions and across groups of participants stratified by different degrees of vision loss. The results showed that the Windows® accessibility settings had a significant positive impact on performance for participants with DR. Moreover, multimodal feedback had a negligible effect for all participants. Strategies for applying multimodal feedback to menu selection are discussed, as well as the potential benefits and drawbacks of the Windows® accessibility settings.

This study examines effects of the most common cause of blindness in persons over the age of 55 in the United States, age-related macular degeneration (AMD), on the performance of older adults when completing a simple computer-based task. Older users with normal vision (n = 6) and with AMD (n = 6) performed a series of drag-and-drop tasks that incorporated a variety of different feedback modalities. The user groups were equivalent with respect to traditional visual function parameters (i.e., visual acuity, contrast sensitivity, and color vision) and measured subject cofactors, aside from the presence or absence of AMD (i.e., drusen and retinal pigment epithelial mottling). Task performance was assessed with measures of time (trial time and feedback exposure time) and accuracy (error frequency). Results indicate that users with AMD exhibited decreased performance with respect to required feedback exposure time, total trial time, and errors committed. Some nonvisual and multimodal feedback forms show potential as solutions for enhanced performance, for those with AMD as well as for visually healthy older adults.

There is a clear need for evaluation methods that are specifically suited to mobile device evaluation, largely due to the vast differences between traditional desktop computing and mobile computing. One difference of particular interest that needs to be accounted for is that mobile computing devices are frequently used while the user is in motion, in contrast to desktop computing. This study aims to validate the appropriateness of two evaluation methods that vary in representativeness of mobility, one that uses a treadmill to simulate motion and another that uses a controlled walking scenario. The results lead to preliminary guidelines based on study objectives for researchers wishing to use more appropriate evaluation methodologies for empirical, data-driven mobile computing studies. The guidelines indicate that using a treadmill for mobile evaluation can yield representative performance measures, whereas a controlled walking scenario is more likely to adequately simulate the actual user experience.

This experiment examines the effect that computer experience and various combinations of feedback (auditory, haptic, and/or visual) have on the performance of older adults completing a drag-and-drop task on a computer. Participants were divided into three computer experience groups, based on their frequency of use and breadth of computer knowledge. Each participant completed a series of drag-and-drop tasks under each of seven feedback conditions (three unimodal, three bimodal, one trimodal). Performance was assessed using measures of efficiency and accuracy. Experienced users responded well to all multimodal feedback while users without experience responded well to auditory-haptic bimodal, but poorly to haptic-visual bimodal feedback. Based on performance benefits for older adults seen in this experiment, future research should extend investigations to effectively integrate multimodal feedback into GUI interfaces in order to improve usability for this growing and diverse user group.

This study examines the effects of multimodal feedback on the performance of older adults with an ocular disease, Age-Related Macular Degeneration (AMD), when completing a simple computer-based task. Visually healthy older users (n = 6) and older users with AMD (n = 6) performed a series of drag-and-drop tasks that incorporated a variety of different feedback modalities. The user groups were equivalent with respect to traditional visual function metrics and measured subject cofactors, aside from the presence or absence of AMD. Results indicate that users with AMD exhibited decreased performance, with respect to required feedback exposure time. Some non-visual and multimodal feedback forms show potential as solutions to enhance performance, for those with AMD as well as for visually healthy older adults.

This paper examines factors that affect performance of a basic menu selection task by users who are visually healthy and users with Diabetic Retinopathy (DR) in order to inform better interface design. Interface characteristics such as multimodal feedback, Windows® accessibility settings, and menu item location were investigated. Analyses of Variance (ANOVA) were employed to examine the effects of interface features on task performance. Linear regression was used to further examine and model various contextual factors that influenced task performance. Results indicated that Windows® accessibility settings significantly improved performance of participants with more progressed DR. Additionally, other factors, including age, computer experience, visual acuity, and menu location were significant predictors of the time required for subjects to complete the task.

This study examines the effects of multimodal feedback on the performance of older adults with different visual abilities. Older adults possessing normal vision (n=29) and those who have been diagnosed with Age-Related Macular Degeneration (n=30) performed a series of drag-and-drop tasks under varying forms of feedback. User performance was assessed with measures of feedback exposure times and accuracy. Results indicated that for some cases, non-visual (e.g. auditory or haptic) and multimodal (bi- and trimodal) feedback forms demonstrated significant performance gains over the visual feedback form, for both AMD and normally sighted users. In addition to visual acuity, effects of manual dexterity and computer experience are considered.

This experiment examines the effect of combinations of feedback (auditory, haptic, and/or visual) on the performance of older adults completing a drag-and-drop computer task. Participants completed a series of drag-and-drop tasks under each of seven feedback conditions (3 unimodal, 3 bimodal, 1 trimodal). Performance was assessed using measures of efficiency and accuracy. For analyses of results, participants were grouped based on their level of computer experience. All users performed well under auditory-haptic bimodal feedback and experienced users responded well to all multimodal feedback. Based on performance benefits for older adults seen in this experiment, future research should extend investigations to effectively integrate multimodal feedback into GUI interfaces in order to improve usability for this growing and diverse user group.