Successful weight maintenance after weight loss is challenging. Digital interventions may facilitate behaviour change to prevent weight regain, however little is known about the acceptance and use of digital interventions in support of weight maintenance. This mixed methods study aims to evaluate user experiences, system use, and preliminary efficacy of eCHANGE, an application based self-management intervention for weight loss maintenance. A 3-month multi-site single group feasibility pilot study was conducted among adults (N = 60) with BMI ≥30 kg/m2, aiming to maintain weight after weight loss of ≥8%. User experiences and system use were examined through validated questionnaires, system use log data, and individual interviews (n = 15). Preliminary efficacy testing included body weight and patient reported outcome measures. Participants rated eCHANGE usability and usefulness as good in support of weight maintenance, with variation in usage. Analysis indicated that higher behavioural engagement scores were statistically significantly associated with frequency of technology usage. Weight loss was maintained by 83% of the participants at 3-months (i.e. defined as weight change of <3% of baseline body weight (kg)). Digital interventions, combining persuasive system design principles and behaviour change techniques, supporting self-regulation and maintenance of health behaviours, have the potential to facilitate weight maintenance after weight loss.Trial Registration: ClinicalTrials.gov NCT04537988 https://clinicaltrials.gov/ct2/show/NCT04537988

Current sensors offering passive and continuous monitoring of behavioral patterns potentially enable real-time affective state monitoring. Previous research on affective state prediction with multimodal sensing in daily life has shown only small-to-moderate effects. One reason for this limited success might be the large variability across individuals. Current research is often of short duration, preventing proper within-individual modeling. With an extensive longitudinal data collection of nine months, this research focuses on individual-level predictions of valence and arousal in daily life. Sixteen PhD candidates from The Netherlands provided data about their affective states (self-reported valence and arousal), physiology (Oura rings) and behavioral patterns (AWARE framework for mobile phone data). Supporting our hypothesis, subject-dependent random forest (RF) models significantly outperformed subject-independent leave-one-subject-out (LOSO) models in predicting self-reported valence and arousal. The subject-dependent models achieved an average Spearman's rho correlation of 0.30 [0.14-0.60] for valence and 0.36 [0.16-0.69] for arousal. In many cases, participants’ a priori indicated informative sources matched with the feature importance. Making use of participants’ self-knowledge might thus help to reduce the amount of data to be collected. For future work, longer-term changes in affective state and combinations of features for estimating real behavioral patterns should be explored.

The developments in eHealth technology have driven a significant shift towards increased digitalization in healthcare, transforming eHealth from standalone tools into intricate infrastructures of multiple interconnected data systems. These infrastructures introduce multifaceted challenges in legal, ethical, financial, and technological (LEFT) domains, highlighting the critical need for a comprehensive framework to navigate the complexities within these domains and facilitate successful implementation. This viewpoint paper presents version 1.0 of a roadmap for eHealth implementation concerning LEFT domains and provides a preliminary overview of key preconditions essential for successful implementation. The roadmap is constructed based on the LEFT barriers and facilitators identified in an earlier study, systematically translating these insights into specific preconditions for successful implementation. Legal preconditions emphasize the necessity of effective data privacy and security measures, along with a comprehensive regulatory compliance framework. Ethical preconditions focus on facilitating patient understanding and control, enhancing consent processes, and implementing robust validations mechanisms for eHealth technologies. Financial preconditions advocate for fostering public-private stakeholder collaboration, establishing a financially sustainable business model, and developing eHealth reimbursement strategies. Technological preconditions include promoting interoperability between different data systems, enhancing data and system quality, and improving usability and accessibility of eHealth technologies. While this paper introduces version 1.0 of the roadmap and outlines the LEFT preconditions for eHealth implementation, effective implementation also requires collaborative decision-making among multidisciplinary stakeholders. Recognizing the need for a holistic approach, these preconditions necessitate careful consideration and systematic planning, with the roadmap serving as guidance to address the challenges of creating a successful eHealth infrastructure.

OBJECTIVE: Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. METHODS: Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. RESULTS: In total, 88 topics were identified in 158,616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. CONCLUSION: This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.

Self-care support is a key cornerstone of treatment for patients with a cardiovascular disease. The success of any supportive intervention requires adaptation to the distinct needs of individuals. This requirement also applies to eHealth interventions. This study investigates how experts from multiple fields of science assess the potential success of different eHealth design strategies when matched to key self-care needs. An online vignette survey experiment was conducted. Nine vignettes representing different combinations of self-care needs (maintenance, monitoring, management) and eHealth persuasive design strategies (primary task support, dialogue support, social support) were evaluated. In total, 118 experts from 18 different countries participated in the survey. Their evaluations show primary task support as a promising design strategy across all self-care needs. In contrast, dialogue support and social support showed more promise for specific self-care needs. Above all, according to experts, the success of design strategies could be enhanced by (i) personalising the pacing of the intervention and (ii) tailoring the information to the literacy and culture of the person. Adding to that, self-care support should distinctly (iii) support the three self-care needs, be (iv) patient-centered, (v) support the collaboration with caregivers, and (vi) be aligned to the life goals and values of individuals.

Smartphones are powerful tools for reaching the user when it is most needed, i.e. Just-In-Time (JIT). In the context of stress management in professionals, self-tracking can create awareness about stress and eCoaching can provide personalised JIT coping suggestions. Employees should also be receptive to take in or act upon the JIT-messages. Therefore, this study aims to explore what factors (emotional state, events or conditions, and content of the message) affect the employees’ receptivity to JIT-messages. 17 participants were invited to use a prototype of the Resilience Navigator app for two weeks. The mixed-methods approach consisted of mixed effects models analysis on data collected via the app (receptivity and the factors of interest) and qualitative analysis on semi-structured interview data collected after the study period. The overall finding was that the participants’ receptivity in the context of stress management often mismatches with the most relevant moments for JIT-messages. For example, emotions with a negative valence seemed to influence the receptivity towards JIT-messages negatively, although the perceived relevance was high. As technology can pinpoint the most receptive and relevant moment for sending JIT-messages, we advocate to further study this topic with more robust quantitative data.

In development, implementation and evaluation of eHealth it is essential to account for stakeholders' perspectives, opinions and values, which are statements that specify what stakeholders want to achieve or improve via a technology. The use of values enables developers to systematically include stakeholders' perspectives and the context of use in an eHealth development process. However, there are relatively few papers that explain how to use values in technology development. Consequently, in this paper we show how we formulated values during the multi-method, interdisciplinary and iterative development process of a VR application for a complex setting: forensic mental healthcare. We report the main foundations for these values: the outcomes of an online questionnaire with patients, therapists and other stakeholders (n=146) and interviews with patients and therapists (n=18). We show how a multidisciplinary project team used these qualitative results to formulate and adapt values and create lo-fi prototypes of a VR application. We discuss the importance of a systematic development process with multiple formative evaluations for eHealth and reflect on the role of values within this.

While technology based health interventions can be effective, high attrition rates are commonly observed in research and practice and are a major issue in eHealth. Research on adherence has recently gained some scientific attention, but little has been done as to how technology itself engages users. It seems plausible that technology plays a role in persuading people to use an eHealth intervention and keep on using it. The present study seeks to apply the PSD-model with regard to this major issue in eHealth. A comprehensive literature search was conducted using the databases Web of Knowledge, EBSCOHOST, Picarta, Scopus and ScienceDirect. The following characteristics were coded: Health care domain, Study level, Sample size, Intended usage, Usage web-based intervention, Adherence, Predictors of adherence, Intervention, Interaction, Persuasive technology in the intervention. The search yielded 4939 unique titles, of which 460 articles were deemed relevant. After title, abstract and full text screening by two researchers, 109 articles were included. Concluding, we can say that it seems that when designing web-based interventions, most attention is given to support the primary task. Dialogue support and social support appear neglected. Taking into account that more extensive use of persuasive technology seems to be positively related to adherence, more attention should be paid to all forms of support through technology.