Purpose Worldwide, there are 30 million people with dementia (PWD) in 2009 and 100 million in 2050, respectively.These numbers show the need for a change in care for PWD. Leisure is one of these care aspects. Leisure activities can support PWD in several ways: meeting basic needs, providing comfort and social interaction, and reducing boredom, agitation, and isolation. An exemplary activity targeted at meeting these needs is ‘De Klessebessers (KB)’ (The Chitchatters), which aims to stimulate social interaction among PWD and provide comfort with supporting technology. This is innovative since technology for PWD generally concentrates on safety and monitoring activities. The activity comprises a radio, television, telephone, and treasure box. Method This study’s focus follows from the original aim of the KB-designers; to stimulate social interaction. In a nursing home and day care centre, the KB game was played with different groups of PWD (n=21: 12 females, 9 males, mean MMSE=17, range 3-28). In the morning KB (with technology), and in the afternoon an activity called ‘Questiongame’ (without technology) were played for 45 minutes. These activities were played twice in a two-month period, and outcomes were compared in terms of impact on social interaction. Group sizes ranged from 3 to 8 PWD assisted by 1 or 2 activity therapists. Two researchers observed the players during the activity with the Oshkosh Social Behavior Coding (OSBC) scale, which encompasses both verbal and nonverbal social and nonsocial behaviour. These behaviours can have a person-initiated and otherinitiated character (quantitative study). A total of 6 activity therapists were interviewed on the KB afterwards (qualitative study). Results & Discussion The quantitative results showed significantly higher scores for KB for the total of social interaction compared to Questiongame. Most of the behaviour is other-initiated (activity therapist). PWD with a lower MMSE score showed more non-verbal behaviour. For PWD with a MMSE score below 7, there was no difference in social interaction between the two activities. According to the qualitative research, KB triggered more social interaction, since the movies and music were stimulating the players to initiate a conversation, to which other players responded. The results of this research correspond with earlier research, which concludes that leisure activities with technology can show positive results on well-being.
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This paper presents the results of an evaluation of a technology-supported leisure game for people with dementia in relation to the stimulation of social behavior.
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Background Movement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time. Objectives 1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke. Methods Accelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified. Results The composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers. Conclusions The composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.
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Background and purpose The aim of this study is to investigate changes in movement behaviors, sedentary behavior and physical activity, and to identify potential movement behavior trajectory subgroups within the first two months after discharge from the hospital to the home setting in first-time stroke patients. Methods A total of 140 participants were included. Within three weeks after discharge, participants received an accelerometer, which they wore continuously for five weeks to objectively measure movement behavior outcomes. The movement behavior outcomes of interest were the mean time spent in sedentary behavior (SB), light physical activity (LPA) and moderate to vigorous physical activity (MVPA); the mean time spent in MVPA bouts ≥ 10 minutes; and the weighted median sedentary bout. Generalized estimation equation analyses were performed to investigate overall changes in movement behavior outcomes. Latent class growth analyses were performed to identify patient subgroups of movement behavior outcome trajectories. Results In the first week, the participants spent an average, of 9.22 hours (67.03%) per day in SB, 3.87 hours (27.95%) per day in LPA and 0.70 hours (5.02%) per day in MVPA. Within the entire sample, a small but significant decrease in SB and increase in LPA were found in the first weeks in the home setting. For each movement behavior outcome variable, two or three distinctive subgroup trajectories were found. Although subgroup trajectories for each movement behavior outcome were identified, no relevant changes over time were found. Conclusion Overall, the majority of stroke survivors are highly sedentary and a substantial part is inactive in the period immediately after discharge from hospital care. Movement behavior outcomes remain fairly stable during this period, although distinctive subgroup trajectories were found for each movement behavior outcome. Future research should investigate whether movement behavior outcomes cluster in patterns.
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Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.
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Introduction Physical activity levels of children with disabilities are low, as these children and their parents face a wide variety of both personal and environmental barriers. Behavior change techniques support pediatric physical therapists to address these barriers together with parents and children. We developed the What Moves You?! intervention Toolkit (WMY Toolkit) filled with behavioral change tools for use in pediatric physical therapy practice. Objective To evaluate the feasibility of using the WMY Toolkit in daily pediatric physical therapy practice. Methods We conducted a feasibility study with a qualitative approach using semi-structured interviews with pediatric physical therapists (n = 11). After one day of training, the pediatric physical therapists used the WMY Toolkit for a period of 9 weeks, when facilitating physical activity in children with disabilities. We analyzed the transcripts using an inductive thematic analysis followed by a deductive analysis using a feasibility framework. Results For acceptability, pediatric physical therapists found that the toolkit facilitated conversation about physical activity in a creative and playful manner. The working mechanisms identified were in line with the intended working mechanisms during development of the WMY Toolkit, such as focusing on problem solving, self-efficacy and independence. For demand, the pediatric physical therapists mentioned that they were able to use the WMY Toolkit in children with and without disabilities with a broad range of physical activity goals. For implementation, education is important as pediatric physical therapists expressed the need to have sufficient knowledge and to feel confident using the toolkit. For practicality, pediatric physical therapists were positive about the ease of which tools could be adapted for individual children. Some of the design and materials of the toolkit needed attention due to fragility and hygiene. Conclusion The WMY Toolkit is a promising and innovative way to integrate behavior change techniques into pediatric physical therapy practice.
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This chapter will be part of a monograph on social inclusion, the interface between leisure and work in relation to people with intellectual disabilities.
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Background: The built environment is increasingly recognized as a determinant for health and health behaviors. Existing evidence regarding the relationship between environment and health (behaviors) is varying in significance and magnitude, and more high-quality longitudinal studies are needed. The aim of this study was to evaluate the effects of a major urban redesign project on physical activity (PA), sedentary behavior (SB), active transport (AT), health-related quality of life (HRQOL), social activities (SA) and meaningfulness, at 29–39 months after opening of the reconstructed area. Methods: PA and AT were measured using accelerometers and GPS loggers. HRQOL and sociodemographic characteristics were assessed using questionnaires. In total, 241 participants provided valid data at baseline and follow-up. We distinguished three groups, based on proximity to the intervention area: maximal exposure group, minimal exposure group and no exposure group. Results: Both the maximal and minimal exposure groups showed significantly different trends regarding transportbased PA levels compared to the no exposure group. In the exposure groups SB decreased, while it increased in the no exposure group. Also, transport-based light intensity PA remained stable in the exposure groups, while it significantly decreased in the no exposure group. No intervention effects were found for total daily PA levels. Scores on SA and meaningfulness increased in the maximal exposure group and decreased in the minimal and no exposure group, but changes were not statistically significant. Conclusion: The results of this study emphasize the potential of the built environment in changing SB and highlights the relevance of longer-term follow-up measurements to explore the full potential of urban redesign projects.
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The aim of the current study was to examine the effectiveness of a school-centered multicomponent PA intervention, called ‘Active Living’, on children's daily PA levels. A quasi-experimental design was used including 9 intervention schools and 9 matched control schools located in the Netherlands. The baseline measurement took place between March–June 2013, and follow-up measurements were conducted 12 months afterwards. Accelerometer (ActiGraph, GT3X +) data of 520 children aged 8–11 years were collected and supplemented with demographics and weather conditions data. Implementation magnitude of the interventions was measured by keeping logbooks on the number of implemented physical environmental interventions (PEIs) and social environmental interventions (SEIs). Multilevel multivariate linear regression analyses were used to study changes in sedentary behavior (SB), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) between baseline and follow-up. Finally, effect sizes (ESs) were calculated using Cohen's d. No pooled effects on PA and SB were found between children exposed and not exposed to Active Living after 12 months. However, children attending Active Living schools that implemented larger numbers of both PEIs and SEIs engaged in 15 more minutes of LPA per weekday at follow-up than children in the control condition (ES = 0.41; p < .05). Moreover, children attending these schools spent less time in SB at follow-up (ES = 0.33), although this effect was non-significant. No significant effects were found on MVPA. A school-centered multicomponent PA intervention holds the potential to activate children, but a comprehensive set of intervention elements with a sufficient magnitude is necessary to achieve at least moderate effect sizes.
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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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