Increasingly, we use mobile applications and services in our daily life activities, to support our needs for information, communication or leisure. However, user acceptance of a mobile application depends on at least two conditions: the application's perceived experience, and the appropriateness of the application to the user's context and needs. However, we have a weak understanding of a mobile user's quality of experience (QoE) and the factors influencing it. This article presents a 4-week-long 29-Androidphone- user study, where we collected both QoE and the underlying network's quality of service measurements through a combination of user, application, and network data on the user's phones. We aimed to derive and improve the understanding of users' QoE for a set of widely used mobile applications in users' natural environments and different daily contexts. We present data acquired in the study and discuss implications for mobile applications design.

Humanoid social robots have an increasingly prominent place in today’s world. Their acceptance in social and emotional human–robot interaction (HRI) scenarios depends on their ability to convey well recognized and believable emotional expressions to their human users. In this article, we incorporate recent findings from psychology, neuroscience, human–computer interaction, and HRI, to examine how people recognize and respond to emotions displayed by the body and voice of humanoid robots, with a particular emphasis on the effects of incongruence. In a social HRI laboratory experiment, we investigated contextual incongruence (i.e., the conflict situation where a robot’s reaction is incongrous with the socio-emotional context of the interaction) and cross-modal incongruence (i.e., the conflict situation where an observer receives incongruous emotional information across the auditory (vocal prosody) and visual (whole-body expressions) modalities). Results showed that both contextual incongruence and cross-modal incongruence confused observers and decreased the likelihood that they accurately recognized the emotional expressions of the robot. This, in turn, gives the impression that the robot is unintelligent or unable to express “empathic” behaviour and leads to profoundly harmful effects on likability and believability. Our findings reinforce the need of proper design of emotional expressions for robots that use several channels to communicate their emotional states in a clear and effective way. We offer recommendations regarding design choices and discuss future research areas in the direction of multimodal HRI.

Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones holds for a new generation of mobile phones, smart phones. We conduct a data collection field study of 28 smart phone owners over a period of 4 weeks. We show that in fact this assumption is still false, with the within arm's reach proximity being true close to 50% of the time, similar to the earlier work. However, we also show that smart phone proximity within the same room (arm+room) as the user is true almost 90% of the time. We discuss the reasons for these phone proximities and the implications of this on the development of mobile phone applications, particularly those that collect user and environmental context, and delivering notification to users. We also show that we can accurately predict the proximity at the arm level and arm+room level with 75 and 83% accuracy, respectively, with features simple to collect and model on a mobile phone. Further we show that for several individuals who are almost always within the arm+room level, we can predict this level with over 90% accuracy

Personalized drug delivery systems (PDDS), implying the patient-tailored dose, dosage form, frequency of administration and drug release kinetics, and digital health platforms for diagnosis and treatment monitoring, patient adherence, and traceability of drug products, are emerging scientific areas. Both fields are advancing at a fast pace. However, despite the strong complementary nature of these disciplines, there are only a few successful examples of merging these areas. Therefore, it is important and timely to combine PDDS with an increasing number of high-end digital health solutions to create an interactive feedback loop between the actual needs of each patient and the drug products. This review provides an overview of advanced design solutions for new products such as interactive personalized treatment that would interconnect the pharmaceutical and digital worlds. Furthermore, we discuss the recent advancements in the pharmaceutical supply chain (PSC) management and related limitations of the current mass production model. We summarize the current state of the art and envision future directions and potential development areas.

Background Stress has been shown to impact adversely on multiple facets critical to optimal performance. Advancements in wearable technology can reduce barriers to observing stress during surgery. This study aimed to investigate the association between acute intraoperative mental stress and technical surgical performance. Methods Continuous electrocardiogram data for a single attending surgeon were captured during surgical procedures to obtain heart rate variability (HRV) measures that were used as a proxy for acute mental stress. Two different measures were used: root mean square of successive differences (RMSSD) and standard deviation of RR intervals (SDNN). Technical surgical performance was assessed on the Operating Room Black Box® platform using the Generic Error Rating Tool (GERT). Both HRV recording and procedure video recording were time‐stamped. Surgical procedures were fragmented to non‐overlapping intervals of 1, 2 and 5 min, and subjected to data analysis. An event was defined as any deviation that caused injury to the patient or posed a risk of harm. Results Rates of events were significantly higher (47–66 per cent higher) in the higher stress quantiles than in the lower stress quantiles for all measured interval lengths using both proxy measures for acute mental stress. The strongest association was observed using 1‐min intervals with RMSSD as the HRV measure (P < 0·001). Conclusion There is an association between measures of acute mental stress and worse technical surgical performance. Further study will help delineate the interdependence of these variables and identify triggers for increased stress levels to improve surgical safety.

For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individual’s reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, the primary aim of this study was to describe the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. The secondary aim was to investigate whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles for two hundred thousand users. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at cycle days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to women’s health overall, which has historically been severely understudied.