In this study two currently relevant aspects of using medical assistive technologies were addressed—security and privacy. In a two-step empirical approach that used focus groups (n = 19) and a survey (n = 104), users’ requirements for the use of medical technologies were collected and evaluated. Specifically, we focused on the perceived importance of data security and privacy issues. Outcomes showed that both security and privacy aspects play an important role in the successful adoption of medical assistive technologies in the home environment. In particular, analysis of data with respect to gender, health-status and age (young, middle-aged and old users) revealed that females and healthy adults require, and insist on, the highest security and privacy standards compared with males and the ailing elderly.

Background: Mobile health (mHealth) care apps are a promising technology to monitor and control health individually and cost-effectively with a technology that is widely used, affordable, and ubiquitous in many people’s lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened health care systems worldwide may directly profit from the use of therapy apps by those patients already in need of medical treatment and monitoring. Objective: We aimed to compare the factors influencing the acceptance of lifestyle and therapy apps to better understand what drives and hinders the use of mHealth apps. Methods: We applied the established unified theory of acceptance and use of technology 2 (UTAUT2) technology acceptance model to evaluate mHealth apps via an online questionnaire with 707 German participants. Moreover, trust and privacy concerns were added to the model and, in a between-subject study design, the influence of these predictors on behavioral intention to use apps was compared between lifestyle and therapy apps. Results: The results show that the model only weakly predicted the intention to use mHealth apps (R2=0.019). Only hedonic motivation was a significant predictor of behavioral intentions regarding both app types, as determined by path coefficients of the model (lifestyle: 0.196, P=.004; therapy: 0.344, P<.001). Habit influenced the behavioral intention to use lifestyle apps (0.272, P<.001), while social influence (0.185, P<.001) and trust (0.273, P<.001) predicted the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity showed the strongest correlation to the intention to use (r=0.469, P<.001), stressing the importance of experience. Also, age (r=–0.15, P=.004), gender (r=–0.075, P=.048), education level (r=0.088, P=.02), app familiarity (r=0.142, P=.007), digital health literacy (r=0.215, P<.001), privacy disposition (r=–0.194, P>.001), and the propensity to trust apps (r=0.191, P>.001) correlated weakly with behavioral intention to use mHealth apps. Conclusions: The results indicate that, rather than by utilitarian factors like usefulness, mHealth app acceptance is influenced by emotional factors like hedonic motivation and partly by habit, social influence, and trust. Overall, the findings give evidence that for the health care context, new and extended acceptance models need to be developed with an integration of user diversity, especially individuals’ prior experience with apps and mHealth.

The role of display resolution in visual information processing was investigated in 2 experiments. Experiment 1 examined the effects of 2 CRT conditions (resolutions of 60 and 120 dots per inch [dpi]) and a paper control condition (255 dpi) on proofreading speed and accuracy. The results showed that reading performance was significantly better in the paper condition than in the 2 CRT conditions. Experiment 2 examined the effects of 3 different CRT resolutions (62, 69, and 89 dpi) on eye movement parameters (fixation duration and number of fixations) in a visual search task. Further, search reaction times (RTs) and observers′ ratings of visual fatigue were analyzed. RTs and fixation durations were significantly increased in the low-resolution condition (62 dpi) as compared with the high-resolution condition (89 dpi). The significant interaction between display resolution and time both for search RTs and fixation durations was taken as evidence for stronger fatigue in the low-resolution conditions. Additionally, the extent of visual fatigue correlates both with search RTs and eye movement parameters. This finding gives rise to the assumption that observers′ responsiveness to effects of display resolution in terms of visual fatigue differs markedly. Actual or potential applications of this research include recommendations for the use of high-resolution displays (90 dpi and greater) to optimize visual performance, to make prolonged on-screen viewing more comfortable, and to avoid visual fatigue.

In this experiment, older and younger novice mobile phone users were examined when using handsets of different complexity. The independent variables were user age (young: 20 – 35 years; old: 50 – 64 years) and cognitive complexity of two mobile phones (Nokia 3210; Siemens C35i). The cognitive complexity of the phones was defined by the number of production rules applied when processing the four experimental tasks. Dependent variables were effectiveness and efficiency as taken from user protocols and the reported ease of use. The less complex phone (Nokia) performed significantly better than the complex one (Siemens). However, the benefit from the lower complexity was much larger than theoretically predicted. Thus, defining cognitive complexity of technical devices by the number of production rules does not account for the real difficulties which users experience. Older users had a lower navigation performance than younger users, however their performance matched younger users' when using mobiles with low complexity.