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).
For people with early-dementia (PwD), it can be challenging to remember to eat and drink regularly and maintain a healthy independent living. Existing intelligent home technologies primarily focus on activity recognition but lack adaptive support. This research addresses this gap by developing an AI system inspired by the Just-in-Time Adaptive Intervention (JITAI) concept. It adapts to individual behaviors and provides personalized interventions within the home environment, reminding and encouraging PwD to manage their eating and drinking routines. Considering the cognitive impairment of PwD, we design a human-centered AI system based on healthcare theories and caregivers’ insights. It employs reinforcement learning (RL) techniques to deliver personalized interventions. To avoid overwhelming interaction with PwD, we develop an RL-based simulation protocol. This allows us to evaluate different RL algorithms in various simulation scenarios, not only finding the most effective and efficient approach but also validating the robustness of our system before implementation in real-world human experiments. The simulation experimental results demonstrate the promising potential of the adaptive RL for building a human-centered AI system with perceived expressions of empathy to improve dementia care. To further evaluate the system, we plan to conduct real-world user studies.
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.
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.
