BACKGROUND: Combining increased dietary protein intake and resistance exercise training for elderly people is a promising strategy to prevent or counteract the loss of muscle mass and decrease the risk of disabilities. Using findings from controlled interventions in a real-life setting requires adaptations to the intervention and working procedures of healthcare professionals (HCPs). The aim of this study is to adapt an efficacious intervention for elderly people to a real-life setting (phase one) and test the feasibility and potential impact of this prototype intervention in practice in a pilot study (phase two).METHODS: The Intervention Mapping approach was used to guide the adaptation in phase one. Qualitative data were collected from the original researchers, target group, and HCPs, and information was used to decide whether and how specified intervention elements needed to be adapted. In phase two, a one-group pre-test post-test pilot study was conducted (n = 25 community-dwelling elderly), to elicit further improvements to the prototype intervention. The evaluation included participant questionnaires and measurements at baseline (T0) and follow-up (T1), registration forms, interviews, and focus group discussions (T1). Qualitative data for both phases were analysed using an inductive approach. Outcome measures included physical functioning, strength, body composition, and dietary intake. Change in outcomes was assessed using Wilcoxon signed-rank tests.RESULTS: The most important adaptations to the original intervention were the design of HCP training and extending the original protein supplementation with a broader nutrition programme aimed at increasing protein intake, facilitated by a dietician. Although the prototype intervention was appreciated by participants and professionals, and perceived applicable for implementation, the pilot study process evaluation resulted in further adaptations, mostly concerning recruitment, training session guidance, and the nutrition programme. Pilot study outcome measures showed significant improvements in muscle strength and functioning, but no change in lean body mass.CONCLUSION: The combined nutrition and exercise intervention was successfully adapted to the real-life setting and seems to have included the most important effective intervention elements. After adaptation of the intervention using insights from the pilot study, a larger, controlled trial should be conducted to assess cost-effectiveness.TRIAL REGISTRATION: Trial registration number: ClinicalTrials.gov NL51834.081.14 (April 22, 2015).
Abstract: Sedentary behaviour in children, four years of age and older, has increased over the last decades. These children become physically less skilled, which demotivates them for regular sports activities. They become susceptible to health risks such as obesity and have a heightened chance to develop depression and a lower self-esteem. Sports professionals acknowledge that these children in time become unable to keep up with the sports education pace, leaving them prone to social exclusion as well.Exergames seem promising in their potential to increase the amount and quality of physical exercise in this group. Moreover, they offer strategies to motivate children to a more active and healthier lifestyle. However, some issues remain unclear regarding their applicability and individual fittingness. For one thing sports professionals have little to no experience using exergames in physical education, let alone understand which games could be appropriate to structurally activate said children. In addition, existing exergames regularly lack a suitable degree of adaptivity regarding what a child is physically capable of, which psychological needs should be addressed, and to what inactive children find appealing in terms of gameplay.The aim of our research project is to build a first prototype of an adaptive platform for exergames to motivate inactive children to structurally engage in physical exercise more, and better. The participative design method we used in our preliminary qualitative research led to a better understanding of the barriers to move and the psychological needs children have when it comes to physical exercise. We made a first global list of requirements for the adaptive platform and an overview of necessary design directions.Future pursuits in this project include a participative design research study amongst both children and sports professionals, and a thorough review of the literature and state of the art knowledge. We will use this knowledge to create a first prototype of an adaptive platform in collaboration with a serious game company and an organisation of sport professionals. After user testing we will use the evaluation findings as a baseline for future measurements regarding the adaptation of suggested exergames and to formalize and disseminate found design guidelines.
Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Het probleem dat deze projectaanvraag adresseert is de hoge werkdruk van zorgprofessionals in de dementiezorg. Door een stijging in het aantal ouderen met dementie, stijgt de zorgvraag, terwijl het tekort aan zorgprofessionals groeit. Door de inzet van slimme technologische innovaties zoals een Intelligente Zorgomgeving kan deze werkdruk sterk verminderd worden. Een Intelligente Zorgomgeving maakt gebruik van sensortechnieken en gebruikt Artificiële Intelligentie (AI) om gepersonaliseerde zorg te leveren door de zorgbehoefte in kaart te brengen en daarop te reageren. De Intelligente Zorgomgeving werkt daarbij samen met de zorgprofessional. Deze oplossingsrichting wordt in dit project verder uitgewerkt samen met vier zorgpartijen en drie innovatieve MKB. Aan de hand van de casus “Ondersteuning bij eten en drinken” worden Just-in-time adaptive interventions (JITAI) ontwikkeld zodat de zorgprofessional de zorgprofessional ondersteund wordt in het uitvoeren van bepaalde zorgtaken. Een voorbeeld van een interventie is het op het juiste moment geven van op de persoon aangepaste zintuigelijke prikkels (geluiden, lichten en projecties) die senioren stimuleren om te eten. Door dergelijke interventies wordt de druk op de zorgprofessional verminderd en neemt de kwaliteit van de zorg toe. Niet alleen de integratie van de AI-modules is van belang maar ook hoe de AI ‘getoond’ wordt aan de zorgprofessional. Daarom wordt er in dit project ook extra aandacht besteed aan de interactie tussen zorgprofessional en de Intelligente Zorgomgeving waardoor het gebruiksgemak wordt verhoogd en zowel cliënt als zorgprofessional een hogere mate van autonomie kunnen ervaren. Door het prototype van de Intelligente Zorgomgeving verder te ontwikkelen in zorginstellingen in samenwerking met verschillende zorgprofessionals en aandacht te besteden aan het ontwikkelen van AI en Interactie met het systeem kunnen de wensen en behoeften van de zorgprofessionals worden geïntegreerd in de Intelligente Zorgomgeving. Dit gebeurt in drie iteraties waarbij de drie opeenvolgende beschikbare living labs in toenemende mate complex en realistisch zijn.
