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Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. Recent literature has encapsulated these cognitions in the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. Given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users' satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual's satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users' appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users' satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use-related outcomes.

Digital health interventions (DHIs) have the potential to enable public end users, such as citizens and patients, to manage and improve their health. Although the number of available DHIs is increasing, examples of successfully established DHIs in public health systems are limited. To counteract the nonuse of DHIs, they should be comprehensively evaluated while integrating end users. Unfortunately, there is a wide variability and heterogeneity according to the approaches of evaluation, which creates a methodological challenge. This scoping review aims to provide an overview of the current established processes for evaluating DHIs, including methods, indicators, and end-user involvement. The review is not limited to a specific medical field or type of DHI but offers a holistic overview. This scoping review was conducted following the JBI methodology for scoping reviews based on the framework by Arksey & O'Malley and complies with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Three scientific databases (PubMed, Scopus, and Science Direct) were searched in April 2023. English and German studies between 2008 and 2023 were considered when evaluating DHIs that explicitly address public end users. The process of study selection was carried out by several researchers to avoid reviewer bias. The search strategy identified 9618 publications, of which 160 were included. Among these included articles, 200 evaluations were derived and analyzed. The results showed that there is neither a consensus on the methods to evaluate DHIs nor a commonly agreed definition or usage of the evaluated indicators, which results in a broad variety of evaluation practices. This aligns with observations of the existing literature. It was found that there is a lack of references to existing frameworks for the evaluation of DHIs. The majority of the included studies referred to user-centered approaches and involved end users in the evaluation process. As assistance for people developing and evaluating DHIs and as a basis for thinking about appropriate ways to evaluate DHIs, a results matrix was created where the findings were combined per DHI cluster. Additionally, general recommendations for the evaluators of DHIs were formulated. The findings of this scoping review offer a holistic overview of the variety and heterogeneity according to the approaches of evaluation of DHIs for public end users. Evaluators of these DHIs should be encouraged to reference established frameworks or measurements for justification. This would ease the transferability of the results among similar evaluation studies within the digital health sector, thereby enhancing the coherence and comparability of research in this area.

Introduction Oral pre‐exposure prophylaxis (PrEP) for HIV prevention has been proven to significantly reduce new HIV infections yet scale‐up has been slow. As contexts continue to adjust to make space for PrEP, it is crucial to understand the perspectives and experiences of potential end‐users. In order to inform PrEP and demand creation interventions, this paper examines personal perspectives on adopting and using PrEP among HIV at‐risk populations in sub‐Saharan Africa. Methods Using the principles of a scoping review in July 2018, we explored the extent, range, and nature of published literature regarding PrEP uptake and use among; men who have sex with men, HIV serodiscordant couples, adolescent girls and young women, pregnant and breastfeeding women, women partners of migrant workers; and people who use drugs. Steps included: identification of the research question; identification of relevant studies; study selection; charting the data; and collation – summarizing and reporting results. PubMed and PsycInfo were searched for papers relating to PrEP uptake and use in sub‐Saharan Africa. Resulting papers were reviewed with data extracted and compiled in Excel for analysis. A broad content analysis was conducted and organized into high‐level themes. Results and discussion Thirty‐five papers were included in this review. There was little opposition in general to oral PrEP; however, there were significant nuances in its broader acceptability, applicability, and usability. We identified five themes within which these are discussed. These relate to balancing complexities of personal empowerment and stigma; navigating complex risk environments; influences of relationships and partners; efficacy and side effects; and practicalities of use. This body of research suggests that while product attributes and the logistics of PrEP delivery and use are important topics, it is vital to consider stigma, the interactions of PrEP use with relationships, and the need for broader understanding of ARVs for prevention versus treatment. Conclusions Planning for, programming and promoting the adoption of oral PrEP necessitates a deeper understanding of end‐user priorities in order to ensure successful interventions. This review illustrates the nuances facilitating or deterring PrEP use that may affect the larger effort of PrEP scale‐up.

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.

Introduction Preventing HIV and unintended pregnancies are key global health priorities. To inform product rollout and to understand attributes of future multipurpose prevention technologies (MPT) associated with preference and use, we evaluated three placebo delivery forms: daily oral tablets, a monthly vaginal ring, and two monthly intramuscular injections in TRIO, a five‐month study among young Kenyan and South African women. Methods HIV‐negative, sexually active, non‐pregnant women aged 18 to 30 were enrolled and randomized to use each placebo delivery form for one month (stage 1). Then, participants chose one product to use for two additional months (stage 2). We assessed safety, product ranking, choice, and use. We examined demographic and behavioural correlates of choice and, reciprocally, unwillingness to use in the future with logistic regression models. Results 277 women enrolled, 249 completed stage 1 and 246 completed stage 2. Median age was 23 years, 49% were Kenyan and 51% were South African. Three participants became pregnant during the study and one participant HIV‐seroconverted. There were 18 product‐related adverse events, six tablets‐related, 11 ring‐related, and one injection‐related. After trying each product, 85% preferred a TRIO product over condoms. Injections were chosen most (64%, 95% confidence interval (CI) 58%, 70%; p < 0.001), and by more South Africans than Kenyans (odds ratio (OR) 2.01, 95% CI: 1.17, 3.43; p = 0.01). There was no significant difference in choosing tablets versus ring (21%, 95% CI: 16%, 26% vs. 15%, 95% CI: 11%, 20%; p = 0.11). Tablet and ring adherence, based on direct observations and self‐reports, improved over time. However, participants’ self‐reported use of tablets did not match objective data from the electronic dose monitoring device. Participants were fully compliant with injections. Conclusion In this population at risk for HIV and pregnancy, all participants agreed to choose and use a placebo MPT delivery form. A majority of participants preferred TRIO products to male condoms, an existing MPT. Injections were most liked and best used, however, they are years away from reaching the clinics. In the meantime, expanding the availability of tablets and giving access to rings can begin to fulfill the promise of choice for HIV prevention technologies and inform the development of suitable delivery forms as MPT.

Objective The aim of this study was to describe the process used to develop a theory‐based, online fall prevention self‐management programme for ambulatory and non‐ambulatory people with multiple sclerosis (pwMS). Methods The development process was guided by the Medical Research Council framework of complex interventions and began with a scoping review of the literature on self‐management of falls in pwMS. Subsequent phases of development were performed through iterative and concurrent processes and were informed by the perspectives of pwMS and healthcare professionals with MS expertise. Results Through a systematic and iterative process in close collaboration with pwMS and healthcare professionals, a theory‐based online fall prevention self‐management programme, Fewer Falls in MS, for ambulatory and non‐ambulatory pwMS was developed. The programme is grounded in theory and pedagogical models and features utilization of action plans to address diverse influences on fall risks. Conclusions A carefully operationalized definition of self‐management and an iterative co‐development process were essential to the creation of the Fewer falls in MS programme. Continuation of the co‐development process and collaboration with end users was needed to refine the programme. Patient or Public Contribution PwMS and healthcare professionals were involved throughout the development process of the programme. The patient organization Neuro Sweden was contacted in the initial phase to discuss the relevance of a self‐management programme to prevent falls in MS. They supported the research group (all authors) in identification of and contact with pwMS with interest to participate. Three members of the research group (S.T.J., M.F. and C.Y.), that is, the operative group, met neuro Sweden and one pwMS to further discuss the relevance of a self‐management programme to prevent falls. To develop the process and content of the fall prevention programme, a co‐design process was performed together with pwMS and healthcare professionals. The results of the co‐design process are presented in this manuscript. In addition to participating in the co‐design process, pwMS and healthcare professionals provided feedback to the research group on programme process and content on several occasions during the subsequent programme development process. In a pretest (Beta version) of the programme, four pwMS acted as test subjects and provided additional feedback on the programme to the research group. Trial Registration NCT04317716.

Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.

Spreadsheets are very common for information processing to support decision making by both professional developers and non-technical end users. Moreover, business intelligence and artificial intelligence are increasingly popular in the industry nowadays, where spreadsheets have been used as, or integrated into, intelligent or expert systems in various application domains. However, it has been repeatedly reported that faults often exist in operational spreadsheets, which could severely compromise the quality of conclusions and decisions based on the spreadsheets. With a view to systematically examining this problem via survey of existing work, we have conducted a comprehensive literature review on the quality issues and related techniques of spreadsheets over a 35.5-year period (from January 1987 to June 2022) for target journals and a 10.5-year period (from January 2012 to June 2022) for target conferences. Among other findings, two major ones are: (a) Spreadsheet quality is best addressed throughout the whole spreadsheet life cycle, rather than just focusing on a few specific stages of the life cycle. (b) Relatively more studies focus on spreadsheet testing and debugging (related to fault detection and removal) when compared with spreadsheet specification, modeling, and design (related to development). As prevention is better than cure, more research should be performed on the early stages of the spreadsheet life cycle. Enlightened by our comprehensive review, we have identified the major research gaps as well as highlighted key research directions for future work in the area.

Background Exergames are playful technology-based exercise programs. They train physical and cognitive functions to preserve independence in older adults (OAs) with disabilities in daily activities and may reduce their risk of falling. This study gathered in-depth knowledge and understanding of three different user groups’ experiences in and relevant needs, worries, preferences, and expectations of technology-based training, to develop an exergame training device for OAs. Methods We conducted a qualitative study using semi-structured focus group interviews of primary (OAs in geriatric or neurological rehabilitation) and secondary (health professionals) end users, as well as expert interviews of tertiary end users (health insurance experts or similar), exploring user perspectives on adjusting an existing exergame to OAs’ needs. Voice-recorded interviews were transcribed by researchers and analyzed using thematic analysis (TA) following an inductive, data-driven, iterative approach. Results We interviewed 24 primary, 18 secondary, and 9 tertiary end users at two rehabilitation centers in Austria and Switzerland. Our TA approach identified five to six themes per user group. Themes in the primary end user group reflected aspects of safety, training goals, individuality, game environment, social interactions, and physical and technical overload. Themes in the secondary end user group comprised facets of meaningfulness, distraction through the game environment, safety, gamification elements, the availability and accessibility of the exergame. Tertiary end users’ themes addressed aspects of financial reimbursement, suitable target populations, professional training for the handling of exergame devices, training goals, and concerns about the use of exergames in geriatric rehabilitation. Conclusions In conclusion, an exergame for OAs must be safe, motivating and fully adaptable to the target group while promoting the return to or preservation of autonomy and independence in daily life. Our findings contribute to developing hard- and software extensions for the ExerG training device. Further research is needed to expand the validity of our findings to larger populations.

Background Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management.