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Abstract: INTRODUCTION: Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation.
Abstract: INTRODUCTION: Higher body mass index (BMI) is associated with lower mortality in mechanically ventilated critically ill patients. However, it is yet unclear which body component is responsible for this relationship.
Abstract: INTRODUCTION: Early protein and energy feeding in critically ill patients is heavily debated and early protein feeding hardly studied.
In het WHEELS-project wordt de eerste leefstijlapp voor rolstoelgebruikers met een dwarslaesie of beenamputatie ontwikkeld. Doel is dat zij ook ná de revalidatiefase kunnen werken aan hun vitaliteit en een gezonde leefstijl. In dit artikel wordt beschreven hoe de app in 6 stappen is ontwikkeld en worden de eerste resultaten van een gebruikersstudie samengevat.
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De publicatielijst bevat alle publicaties waar Marije Deutekom aan bijgedragen heeft in de periode 2016 - 2020
Inleiding Van praktijkondersteuners wordt verwacht dat zij samen met chronische zieke patiënten doelen en actieplannen formuleren. Dit vraagt een verandering van hun rol: van medisch expert naar coach. Wij onderzochten de ervaringen van praktijkondersteuners en patiënten met COACH, een nieuwe aanpak voor gezamenlijke besluitvorming, en hun mening over de implementatiemogelijkheden van deze aanpak. Methode Vijftien praktijkondersteuners in Noord-Limburg kregen een training in de nieuwe aanpak; 23 patiënten deden mee aan het onderzoek. De kwantitatieve en kwalitatieve procesevaluatie omvatte individuele interviews (n = 15), een focusgroep (n = 9) en vragenlijstonderzoek bij de praktijkondersteuners, interviews met patiënten (n = 10) en dertien audio-opnamen van een consult. Resultaten De praktijkondersteuners vonden COACH waardevol om tot persoonsgerichte doelen te komen, maar moeilijk te integreren in de bestaande werkroutines. Ze ervoeren een rolconflict ten aanzien van het medisch protocol en voelden zich daarin weinig ondersteund door de huisartsen. De helft van de geïnterviewde patiënten merkte geen verschil in de werkwijze van de praktijkondersteuner; de anderen meldden dat de praktijkondersteuner meer vragen had gesteld en dat zij meer inzicht in hun situatie hadden gekregen. Conclusie Om praktijkondersteuners daadwerkelijk te kunnen inschakelen bij gezamenlijke besluitvorming, zullen praktijkondersteuners en huisartsen samen moeten nadenken over een gezamenlijke rolopvatting.
Background & aims: In dietary practice, it is common to estimate protein requirements on actual bodyweight, but corrected bodyweight (in cases with BMI <20 kg/m2 and BMI ≥30 kg/m2) and fat free mass (FFM) are also used. Large differences on individual level are noticed in protein requirements using these different approaches. To continue this discussion, the answer is sought in a large population to the following question: Will choosing actual bodyweight, corrected bodyweight or FFM to calculate protein requirements result in clinically relevant differences? Methods: This retrospective database study, used data from healthy persons ≥55 years of age and in- and outpatients ≥18 years of age. FFM was measured by air displacement plethysmography technology or bioelectrical impedance analysis. Protein requirements were calculated as 1) 1.2 g (g) per kilogram (kg) actual bodyweight or 2) corrected bodyweight or 3) 1.5 g per kg FFM. To compare these three approaches, the approach in which protein requirement is based on FFM, was used as reference method. Bland–Altman plots with limits of agreement were used to determine differences, analyses were performed for both populations separately and stratified by BMI category and gender. Results: In total 2291 subjects were included. In the population with relatively healthy persons (n = 506, ≥55 years of age) mean weight is 86.5 ± 18.2 kg, FFM is 51 ± 12 kg and in the population with adult in- and outpatients (n = 1785, ≥18 years of age) mean weight is 72.5 ± 18.4 kg, FFM is 51 ± 11 kg. Clinically relevant differences were found in protein requirement between actual bodyweight and FFM in most of the participants with overweight, obesity or severe obesity (78–100%). Using corrected bodyweight, an overestimation in 48–92% of the participants with underweight, healthy weight and overweight is found. Only in the Amsterdam UMC population, protein requirement is underestimated when using the approach of corrected bodyweight in participants with severe obesity. Conclusion: The three approaches in estimation of protein requirement show large differences. In the majority of the population protein requirement based on FFM is lower compared to actual or corrected bodyweight. Correction of bodyweight reduces the differences, but remain unacceptably large. It is yet unknown which method is the best for estimation of protein requirement. Since differences vary by gender due to differences in body composition, it seems more accurate to estimate protein requirement based on FFM. Therefore, we would like to advocate for more frequent measurement of FFM to determine protein requirements, especially when a deviating body composition is to be expected, for instance in elderly and persons with overweight, obesity or severe obesity.
Dit boek beschrijft hoe beleidsmedewerkers en bestuurders van gemeenten en non-profitorganisaties kunnen bijdragen aan een sociaal domein dat de krachten van mensen beter benut. Het thema is outreachend werken: het vergroten van de kansen op preventie, herstel, sociale stijging en zelfredzaamheid van specifieke groepen mensen in zorgwekkende omstandigheden. Het onderzoek beschrijft vijf beloftevolle outreachende praktijken met/voor mensen in kwetsbare omstandigheden. De auteurs beschrijven de transitie van systeem- naar leefwereld, van top-down naar bottom-up aansturing van de sociale sector, van deductief naar inductief leren en ontwikkelen.
Augmented Play Spaces (APS) are (semi-) public environments where playful interaction isfacilitated by enriching the existing environment with interactive technology. APS canpotentially facilitate social interaction and physical activity in (semi-)public environments. Incontrolled settings APS show promising effects. However, people’s willingness to engagewith APSin situ, depends on many factors that do not occur in aforementioned controlledsettings (where participation is obvious). To be able to achieve and demonstrate thepositive effects of APS when implemented in (semi-)public environments, it is important togain more insight in how to motivate people to engage with them and better understandwhen and how those decisions can be influenced by certain (design) factors. TheParticipant Journey Map (PJM) was developed following multiple iterations. First,based on related work, and insights gained from previously developed andimplemented APS, a concept of the PJM was developed. Next, to validate and refinethe PJM, interviews with 6 experts with extensive experience with developing andimplementing APS were conducted. Thefirst part of these interviews focused oninfluential (design) factors for engaging people into APS. In the second part, expertswere asked to provide feedback on thefirst concept of the PJM. Based on the insightsfrom the expert interviews, the PJM was adjusted and refined. The Participant JourneyMap consists of four layers: Phases, States, Transitions and Influential Factors. There aretwo overarchingphases:‘Onboarding’and‘Participation’and 6statesa (potential)participant goes through when engaging with an APS:‘Transit,’‘Awareness,’‘Interest,’‘Intention,’‘Participation,’‘Finishing.’Transitionsindicate movements between states.Influential factorsare the factors that influence these transitions. The PJM supportsdirections for further research and the design and implementation of APS. Itcontributes to previous work by providing a detailed overview of a participant journeyand the factors that influence motivation to engage with APS. Notable additions are thedetailed overview of influential factors, the introduction of the states‘Awareness,’‘Intention’and‘Finishing’and the non-linear approach. This will support taking intoaccount these often overlooked, key moments in future APS research and designprojects. Additionally, suggestions for future research into the design of APS are given.
