Eating healthier at work can substantially promote health for office workers. However, little has been investigated on designing pervasive health interventions specialized in improving workday eating patterns. This paper presents a design study of an mHealth app called EAT@WORK, which was designed to support office workers in the Netherlands in developing healthy eating behaviors in work routines. Based on semi-structured interviews with 12 office workers from a variety of occupations, we synthesized four key features for EAT@WORK, including supporting easy access to relevant knowledge, assisting goal setting, integrating with health programs, and facilitating peer supports. The user acceptance of EAT@WORK was examined through a within-subject study with 14 office workers, followed by a qualitative study on the applicability of app features to different working contexts. Quantitative results showed that EAT@WORK was experienced more useful than a benchmark app (p < 0.01) and EAT@WORK was also perceived easier to use than the benchmark app (p < 0.01). The qualitative analysis suggested that the goal assistant feature could be valuable for different working contexts, while the integrated health program was considered more suitable for office work than telework. The social and knowledge support were expected to be on-demand features that should loosely be bonded with the working contexts. Based on these findings, we discuss design implications for the future development of such mHealth technologies to promote healthy eating routines among office workers.
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Chapter 6 in Consumption culture in Europe. The chapter focuses on cultural differences in consumption across Europe and describes general attitudes towards consumption and brands, the significance of shopping, and how these are linked to the motives of consumption of alcoholic and non-alcoholic drinks. These topics have been analysed using the Hofstede dimensions, and the evaluation also considers regional differences within the European Union. The main objective of this research is to attempt to understand consumption patterns and national cultural dimensions, general consumption values, and what their connections are to alcoholic and non-alcoholic drinking patterns. The main research question is how cultural styles influence consumption styles within Europe. This analysis concluded that some European societies are more adaptable to cross-cultural influence than others in relation to beverage consumption. The authors’ findings suggest that the cultural dimensions identified by Hofstede supported the understanding of cultural differences related to purchasing, brands and beverage consumption both at national and individual levels. However, there is an overlap between some countries in their drinking behaviour, which supports the claim that existing cultural patterns cannot fully explain the new beverage trends, especially in alcohol consumption. This indicates the necessity of a shift toward new dimensions with regard to beverage consumption and/or eventually consumer behaviour.
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National forestry Commission (SBB) and National Park De Biesbosch. Subcontractor through NRITNational parks with large flows of visitors have to manage these flows carefully. Methods of data collection and analysis can be of help to support decision making. The case of the Biesbosch National Park is used to find innovative ways to figure flows of yachts, being the most important component of water traffic, and to create a model that allows the estimation of changes in yachting patterns resulting from policy measures. Recent policies oriented at building additional waterways, nature development areas and recreational concentrations in the park to manage the demands of recreation and nature conservation offer a good opportunity to apply this model. With a geographical information system (GIS), data obtained from aerial photographs and satellite images can be analyzed. The method of space syntax is used to determine and visualize characteristics of the network of leisure routes in the park and to evaluate impacts resulting from expected changes in the network that accompany the restructuring of waterways.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
