Non-professional runners make extensive use of consumer-available wearable devices and smartphone apps to monitor training sessions, health, and physical performance. Despite the popularity of these products, they usually neglect subjective factors, such as psychosocial stress, unexpected daily physical (in)activity, sleep quality perception, and/or previous injuries. Consequently, the implementation of these products may lead to underperformance, reduced motivation, and running-related injuries. This paper investigates how the integration of subjective training, off-training, and contextual factors from a 24/7 perspective might lead to better individual screening and health protection methods for recreational runners. Using an online-based Ecological Momentary Assessment survey, a seven-day cohort study was conducted. Twenty participants answered daily surveys three times a day regarding subjective off-training and contextual data; e.g., health, sleep, stress, training, environment, physiology, and lifestyle factors. The results show that daily habits of people are unstructured, unlikely predictable, and influenced by factors, such as the demands of work, social life, leisure time, or sleep. By merging these factors with sensor-based data, running-related systems would be able to better assess the individual workload of recreational runners and support them to reduce their risk of suffering from running-related injuries
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The aim of this study was to investigate changes in heart rate during submaximal exercise as an index of cardiovascular function in older adults participating in the Groningen Active Living Model recreational sports programme who were sedentary or underactive at baseline. A repeated measurement design was conducted; 151 participants were included, providing 398 heart rate files over a period of 18 months. Multi-level analyses were conducted; growth and final models were developed. Significant decreases in mean heart rate over time were observed for all walking speeds. The covariates of sex and body mass index (BMI) were significantly related to mean heart rate at each walking speed, except for BMI at 7 km/h. No significant relationships were observed between energy expenditure for recreational sports activities and leisure-time physical activities and mean heart rate, except for energy expenditure for leisure-time physical activities at 7 km/h. From baseline to December 2002, decreases in predicted mean heart rate were 5.5, 6.0, 10.0, and 9.0 beats/min at walking speeds of 4, 5, 6, and 7 km/h; relative decreases ranged from 5.1 to 7.4%. Significant decreases in heart rate observed during submaximal exercise reflected a potential increase in cardiovascular function after 18 months of participation in the Groningen Active Living Model recreational sports programme.DOI:10.1080/02640410903008749
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Background: A large number of people participate in individual or unorganized sports on a recreational level. Furthermore, many participants drop out because of injury or lowered motivation. Potentially, physical activity–related apps could motivate people during sport participation and help them to follow and maintain a healthy active lifestyle. It remains unclear what the quality of running, cycling, and walking apps is and how it can be assessed. Quality of these apps was defined as having a positive influence on participation in recreational sports. This information will show which features need to be assessed when rating physical activity–related app quality. Objective: The aim of this study was to identify expert perception on which features are important for the effectiveness of physical activity–related apps for participation in individual, recreational sports. Methods: Data were gathered via an expert panel approach using the nominal group technique. Two expert panels were organized to identify and rank app features relevant for sport participation. Experts were researchers or professionals in the field of industrial design and information technology (technology expert panel) and in the field of behavior change, health, and human movement sciences who had affinity with physical activity–related apps (health science expert panel). Of the 24 experts who were approached, 11 (46%) agreed to participate. Each panel session consisted of three consultation rounds. The 10 most important features per expert were collected. We calculated the frequency of the top 10 features and the mean importance score per feature (0-100). The sessions were taped and transcribed verbatim; a thematic analysis was conducted on the qualitative data. Results: In the technology expert panel, applied feedback and feedforward (91.3) and fun (91.3) were found most important (scale 0-100). Together with flexibility and look and feel, these features were mentioned most often (all n=4 [number of experts]; importance scores=41.3 and 43.8, respectively). The experts in the health science expert panels a and b found instructional feedback (95.0), motivating or challenging (95.0), peer rating and use (92.0), motivating feedback (91.3), and monitoring or statistics (91.0) most important. Most often ranked features were monitoring or statistics, motivating feedback, works good technically, tailoring starting point, fun, usability anticipating or context awareness, and privacy (all n=3-4 [number of experts]; importance scores=16.7-95.0). The qualitative analysis resulted in four overarching themes: (1) combination behavior change, technical, and design features needed; (2) extended feedback and tailoring is advised; (3) theoretical or evidence base as standard; and (4) entry requirements related to app use. Conclusions: The results show that a variety of features, including design, technical, and behavior change, are considered important for the effectiveness of physical activity–related apps by experts from different fields of expertise. These insights may assist in the development of an improved app rating scale.
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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.
While several governmental and research efforts are set upon mobility-as-a-service (MaaS), most of them are driven by individual travel behavior and potential usage. Scholars argue that this is a too narrow perspective when evaluating government projects because choices individuals make in a private setting might not accurately reflect their preferences towards public policy. Participatory Value Evaluation (PVE) is a novel evaluation framework specifically designed to alleviate this issue by analyzing preferences on the allocation of public budgets. Thus, based on PVE, this project aims at assessing different features of MaaS-services (e.g. enhancing mobility of the elderly and the poor, complementing public transport, etc.) from a social desirability perspective and compare them with investments in alternative social projects. Specifically, it aims at establishing the citizen value of MaaS as compared to social investments in green/recreational areas or transport infrastructure (e.g. bike or bus lanes), and eliciting trade-offs between different features of them. The project includes the selection of different investment projects (and their features) that are politically relevant in Rotterdam. It also includes a qualitative assessment on the way individuals evaluate different social projects and their features and a quantitative assessment based on choice models that allow eliciting trade-offs between different attributes and projects. Finally, policy recommendations are provided based on these results. They allow conceiving investments projects to maximize the societal benefits as well as to construct optimal investment portfolios. This information is to be used as a complement of the evaluation of projects on the basis of individual preferences.
