Objectives: Promoting unstructured outside play is a promising vehicle to increase children’s physical activity (PA). This study investigates if factors of the social environment moderate the relationship between the perceived physical environment and outside play. Study design: 1875 parents from the KOALA Birth Cohort Study reported on their child’s outside play around age five years, and 1516 parents around age seven years. Linear mixed model analyses were performed to evaluate (moderating) relationships among factors of the social environment (parenting influences and social capital), the perceived physical environment, and outside play at age five and seven. Season was entered as a random factor in these analyses. Results: Accessibility of PA facilities, positive parental attitude towards PA and social capital were associated with more outside play, while parental concern and restriction of screen time were related with less outside play. We found two significant interactions; both involving parent perceived responsibility towards child PA participation. Conclusion: Although we found a limited number of interactions, this study demonstrated that the impact of the perceived physical environment may differ across levels of parent responsibility.
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BackgroundLittle is known about the association between fear of movement (kinesiophobia) and objectively measured physical activity (PA), the first 12 weeks after cardiac hospitalization.PurposeTo assess the longitudinal association between kinesiophobia and objectively measured PA and to assess the factor structure of kinesiophobia.MethodsWe performed a longitudinal observational study. PA was continuously measured from hospital discharge to 12 weeks using the Personal Activity Monitor. The PAM measures time spent per day in PA-intensity categories: light, moderate and heavy. Kinesiophobia was assessed with the Tampa Scale for Kinesiophobia (TSK) at four time points (hospital discharge, 3, 6 and 12 weeks). The longitudinal association between PA-intensity and kinesiophobia was studied with a random intercept cross lagged panel model (RI-CLPM). A RI-CLPM estimates effects from kinesiophobia on objectively measured PA and vice versa (cross-over effects), and autoregressive effects (e.g. kinesiophobia from one occasion to the next).ResultsIn total, 116 patients (83.6% male) with a median age of 65.5 were included in this study. On no occasion did we find an effect of kinesiophobia on PA and vice versa. Model fit for the original model was poor (X2: = 44.646 P<0.001). Best model fit was found for a model were kinesiophobia was modelled as a stable between factor (latent variable) and PA as autoregressive component (dynamic process) (X2 = 27.541 P<0.12).ConclusionKinesiophobia and objectively measured PA are not associated in the first 12 weeks after hospital discharge. This study shows that kinesiophobia remained relatively stable, 12 weeks after hospital discharge, despite fluctuations in light to moderate PA-intensity.
Background and objectives: Although Interdisciplinary Multimodal Pain Treatment (IMPT) programmes share a biopsychosocial approach to increase the wellbeing of patients with chronic pain, substantial variation in content and duration have been reported. In addition, it is unclear to what extent any favourable health outcomes are maintained over time. Therefore, our first aim was to identify and analyse the change over time of patient-related outcome measures in cohorts of patients who participated in IMPT programmes. Our second aim was to acquire insight into the heterogeneity of IMPT programmes. Databases and data treatment: The study protocol was registered in Prospero under CRD42018076093. We searched Medline, Embase, PsycInfo and Cinahl from inception to May 2020. All study selection, data extraction and risk of bias assessments were independently performed by two researchers. Study cohorts were eligible if they included adult patients with chronic primary musculoskeletal pain for at least 3 months. We assessed the change over time, by calculating pre-post, post-follow-up and pre-follow-up contrasts for seven different patient-reported outcome domains. To explore the variability between the IMPT programmes, we summarized the patient characteristics and treatment programmes using the intervention description and replication checklist. Results: The majority of the 72 included patient cohorts significantly improved during treatment. Importantly, this improvement was generally maintained at follow-up. In line with our expectations and with previous studies, we observed substantial methodological and statistical heterogeneity. Conclusions: This study shows that participation in an IMPT programme is associated with considerable improvements in wellbeing that are generally maintained at follow-up. The current study also found substantial heterogeneity in dose and treatment content, which suggests different viewpoints on how to optimally design an IMPT programme. Significance: The current study provides insight into the different existing approaches regarding the dose and content of IMPT programs. This analysis contributes to an increased understanding of the various approaches by which a biopsychosocial perspective on chronic pain can be translated to treatment programs. Furthermore, despite theoretical and empirical assertions regarding the difficulty to maintain newly learned health behaviors over time, the longitudinal analysis of health outcomes did not find a relapse pattern for patients who participated in IMPT programs.
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Vacation travel is an essential ingredient in quality of life. However, the contriubtion of vacations to quality of life could be improved in two ways: by optimizing the decisions people make when planning and undertaking their vacations, and by travel industry testing and implementing––based on evidence––innovative experience products which touch customers' emotions. Secondary analysis of two longitudinal panel datasets will address the impact of people's decisions in planning and undertaking their vacations, on their quality of life. Field experiments in cooperation with travel industry partners will address the effects of innovative experience products, such as apps designed to help vacationers meet fellow travelers, or personalized memory books designed to help people relive their vacations after return home. Experience data in these field experiments will be collected using technology of the Breda University of Applied Sciences' Experience Measurement Lab, a unique facility for measuring emotions continuously from research participants' body and mind. Thus, the project will contribute to general understanding of quality of life, will feed valuable knowledge about experience design, measurement, and implementation to the Dutch travel industry, and will support the Breda University of Applied Sciences' key research theme of Designing, Measuring, and Managing Experiences. Inspiring examples from the project will reinforce research methods courses in the academic Bachelor of Science in Tourism, the HBO Master in Tourism Destination Management, and the academic Master of Science in Leisure Studies. Wearable emotion measurement from the field experiment will be a cornerstone of the fourth-year HBO-bachelor module Business Intelligence, where students will conduct their own research projects on experience measurement using consumer wearables, based on knowledge from this postdoc project. Finally, a number of methodological and content questions within the project will serve as suitable thesis assignments for graduation students in the above educational tracks.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
