Background The major challenges in Chronic Obstructive Pulmonary Disease (COPD) care are guiding a patient in daily living with the consequences of the disease, reducing the impact of symptoms and improving Health Related Quality of Life (HRQoL). The new nurse-led COPD-Guidance, Research on an Illness Perception (COPD-GRIP) intervention translates the evidence concerning illness perceptions and Health Related Quality of Life (HRQoL) into a practice nurse intervention. The aim is to explore the nurses' experiences with applying the new COPD-GRIP intervention. Method An explanatory mixed-method study nested in a cluster randomized trial in primary care was conducted. Pre-intervention questionnaires were sent to all participating nurses (N = 24) to identify expectations. Post-intervention questionnaires identified experiences after applying the intervention followed by two focus groups to further extend exploration of findings. Questionnaires were analyzed by descriptive analyses. To identify themes the audio-taped and transcribed focus groups were independently coded by two researchers. Results The nurses described the intervention as a useful, structured and individualized tool to guide COPD patients in living with the consequences of COPD. Applying the intervention took less time than the nurses initially expected. The intervention enables to provide patient-centered care and to address patient needs. Barriers were encountered, especially in patients with a lower social economic status, in patients with a lower health literacy and in patients with other cultural backgrounds than the Dutch background. Conclusion Nurses perceived the COPD-GRIP intervention as a feasible, individualized tool. According to the nurses, the intervention is a valuable improvement in the care for COPD patients.
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This study aims to explore the experiences and needs of individuals after a first episode of psychosis with regard to the way in which information about diagnosis, treatment options and prognosis is communicated with them. psychosis, communicating, stigma, needs, individual’s perception
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BACKGROUND: Although enhancing physical activity (PA) is important to improve physical and/or cognitive recovery, little is known about PA of patients admitted to an inpatient rehabilitation setting. Therefore, this study assessed the quantity, nature and context of inpatients PA admitted to a rehabilitation center. METHODOLOGY/PRINICIPAL FINDINGS: Prospective observational study using accelerometry & behavioral mapping. PA of patients admitted to inpatient rehabilitation was measured during one day between 7.00-22.00 by means of 3d-accelerometery (Activ8; percentage of sedentary/active time, number of sedentary/active bouts (continuous period of ≥1 minute), and active/sedentary bout lengths and behavioral mapping. Behavioral mapping consisted of observations (every 20 minutes) to assess: type of activity, body position, social context and physical location. Descriptive statistics were used to describe PA on group and individual level. At median the 15 patients spent 81% (IQR 74%-85%) being sedentary. Patients were most sedentary in the evening (maximum sedentary bout length minutes of 69 (IQR 54-95)). During 54% (IQR 50%-61%) of the observations patients were alone) and in their room (median 50% (IQR 45%-59%)), but individual patterns varied widely. CONCLUSION/SIGNIFICANCE: The results of this study enable a deeper understanding of the daily PA patterns of patients admitted for inpatient rehabilitation treatment. PA patterns of patients differ in both quantity, day structure, social and environmental contexts. This supports the need for individualized strategies to support PA behavior during inpatient rehabilitation treatment.
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The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
