Self-management is widely seen as a viable contribution to sustainable health care as it allows to promote physical and mental well-being. A promising approach to promoting a healthy lifestyle is the deployment of personalized virtual coaches, especially in combination with the latest developments in the fields of Data Science and Artificial Intelligence. This paper presents a framework for a virtual coaching system, as well as a use case in which parts of this framework are applied. The virtual coach in the use case aims to encourage customer contact center employees to protect their mental health. This article outlines one part of the use-case in particular, viz. how to promote employee autonomy and supervisor support by, inter alia, monitoring employees’ levels of emotional exhaustion. Current systems focus on providing users with insight in their health status or behavior, the authors developed the functional architecture for a system that can be implemented for different goals and generates personalized, real-time advice based on the combination of user preferences, motivational success and predicted user behavior.
Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs.
Insufficient physical activity presents a significant hazard to overall health, with sedentary lifestyles linked to a variety of health issues. Monitoring physical activity levels allows the recognition of patterns of sedentary behavior and the provision of coaching to meet the recommended physical activity standards. In this paper, we aim to address the problem of reducing the time consuming process of fitting classifiers when generating personalized models for a coaching application. The proposed approach consists of evaluating the effects of clustering participants based on their walking patterns and then recommending a unique model for each group. Each model consists of a random forest classifier with a different number of estimators each. The resulting approach reduces the fitting time considerably while keeping nearly the same classification performance as personalized models.
Organs-on-chips (OoCs) worden steeds belangrijker voor geneesmiddelonderzoek. Het kweken van miniatuurorganen in microfluïdische chips creëert een systeem waarmee geneesmiddelonderzoekers efficiënt geneesmiddelen kunnen testen. OoCs kunnen in de toekomst een belangrijk instrument voor personalized medicine worden: door het kweken van patiëntmateriaal in OoCs kan dan worden bepaald welke interventies voor specifieke patiënten werken en veilig zijn. In de huidige praktijk worden cellulaire veranderingen in OoCs na blootstelling aan een geneesmiddel doorgaans gevolgd met visualisatietechnieken, waarmee alleen effecten van geneesmiddelen kunnen worden waargenomen. Voor bepaling van de voor geneesmiddelonderzoek cruciale parameters absorptie, distributie, metabolisme en excretie (ADME) is het noodzakelijk om de concentraties van geneesmiddelen en hun relevante metabolieten te meten. Het doel van AC/OC is dit mogelijk te maken door het ontwikkelen van analytisch-chemische technieken, gebaseerd op vloeistofchromatografie gekoppeld met massaspectrometrie (LC-MS). Hiermee kunnen ontwikkelaars van OoCs (de eindgebruikers van AC/OC) de voordelen van hun producten voor geneesmiddelonderzoek beter onderbouwen. Dit project bouwt voort op twee KIEM-projecten, waarin enkele veelbelovende analytisch-chemische technieken succesvol zijn verkend. In AC/OC zullen wij: 1. analytisch-chemische methodes ontwikkelen die geschikt zijn om een breed scala aan geneesmiddelen en metabolieten te bepalen in meerdere types OoCs; 2. deze methodes verbeteren, zodat de analyse geautomatiseerd, sneller en gevoeliger wordt; 3. de potentie van deze methodes voor geneesmiddelonderzoek met OoCs demonsteren door ze toe te passen op enkele praktijkvraagstukken. Het OoC-veld ontwikkelt zich razendsnel en Nederland (georganiseerd binnen OoC-consortium hDMT) speelt daarin een belangrijke rol. AC/OC verbindt kennis en expertise op het gebied van analytische chemie, OoCs, celkweek en geneesmiddelonderzoek. Hierdoor kan AC/OC een bijdrage leveren aan sneller en betrouwbaarder geneesmiddelonderzoek. Met de ontwikkeling van een minor ‘OoC-Technology’, waarin we de onderzoeksresultaten vertalen naar onderwijs, spelen we in op de behoefte aan professionals met kennis, ervaring en belangstelling op het gebied van OoCs.
Alcohol use disorder (AUD) is a major problem. In the USA alone there are 15 million people with an AUD and more than 950,000 Dutch people drink excessively. Worldwide, 3-8% of all deaths and 5% of all illnesses and injuries are attributable to AUD. Care faces challenges. For example, more than half of AUD patients relapse within a year of treatment. A solution for this is the use of Cue-Exposure-Therapy (CET). Clients are exposed to triggers through objects, people and environments that arouse craving. Virtual Reality (VRET) is used to experience these triggers in a realistic, safe, and personalized way. In this way, coping skills are trained to counteract alcohol cravings. The effectiveness of VRET has been (clinically) proven. However, the advent of AR technologies raises the question of exploring possibilities of Augmented-Reality-Exposure-Therapy (ARET). ARET enjoys the same benefits as VRET (such as a realistic safe experience). But because AR integrates virtual components into the real environment, with the body visible, it presumably evokes a different type of experience. This may increase the ecological validity of CET in treatment. In addition, ARET is cheaper to develop (fewer virtual elements) and clients/clinics have easier access to AR (via smartphone/tablet). In addition, new AR glasses are being developed, which solve disadvantages such as a smartphone screen that is too small. Despite the demand from practitioners, ARET has never been developed and researched around addiction. In this project, the first ARET prototype is developed around AUD in the treatment of alcohol addiction. The prototype is being developed based on Volumetric-Captured-Digital-Humans and made accessible for AR glasses, tablets and smartphones. The prototype will be based on RECOVRY, a VRET around AUD developed by the consortium. A prototype test among (ex)AUD clients will provide insight into needs and points for improvement from patient and care provider and into the effect of ARET compared to VRET.
Alcohol Use Disorder (AUD) involves uncontrollable drinking despite negative consequences, a challenge amplified in festivals. ARise is a project using Augmented Reality (AR) to prevent AUD by helping festival visitors refuse alcohol and other substances. Based on the first Augmented Reality Exposure Therapy (ARET) for clinical AUD treatment, ARise uses a smartphone app with AR glasses to project virtual humans that tempt visitors to drink alcohol. Users interact in a safe and personalized way with these virtual humans through phone, voice, and gesture interactions. The project gathers festival feedback on user experience, awareness, usability, and potential expansion to other substances.Societal issueHelping treatment of addiction and stimulate social inclusion.Benefit to societyMore people less patients: decrease health cost and increase in inclusion and social happiness.Collaborative partnersNovadic-Kentron, Thalamusa
