The Netherlands is known globally for its widespread use of bicycles and some call it a “cycling nation”. Indeed, many Dutch inhabitants own a bike and cycle frequently. Numbers show that 84% of the Dutch inhabitants from age 4 years and older own a bike. Those owners have an average of 1.3 bikes per person. This results in 18 million bikes in the Netherlands and 13.5 million bike owners.6 The Dutch use their bike as a means of transportation, but also for sports and exercise. Bike-use fits well in an active lifestyle and it is highly plausible that cycling is responsible for a large part of the daily physical activity in Dutch youth. It is estimated that Dutch people have on average a 6 months longer life expectancy attributable to bicycle use.7 It seems that the nation itself is well shaped to cycle: no large mountains, only a few small hills, and an extensive layout of cycle paths and routes in every city and village. In many urban areas separate cycle paths are very common. Our results show that many Dutch children use the bike as their way of transportation. It was demonstrated that active transportation is responsible for a large part of schoolrelated physical activity in Dutch youth.8 80% of 12-17 year-old children cycled three or more days to or from school/work.9 This resulted in an ‘A’ for the indicator active transportation (walking is included in the grade as well). Active transport is associated with increased total physical activity among youth.10,11 Also evidence is reported for an association between active transport and a healthier body composition and healthier level of cardiorespiratory fitness among youth. Although Dutch children accumulate a lot of daily physical activity through cycling, it is not enough to meet the current national physical activity guidelines of 60 minutes of moderate-to-vigorous physical activity per day. Even though cycling is an important component to the amount of daily physical activity, Dutch youth are not cycling to health
This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types—in terms of their attitudes, interests, and opinions (AIOs) with regard to running—and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed the questionnaire. Principal component analysis and cluster analysis were used to identify the different running types, and crosstabs obtained insights into the use of technology between different typologies. Based on the AIOs, four distinct runner types were identified: casual individual, social competitive, individual competitive, and devoted runners. Subsequently, we related the types to their use of sports watches and apps. Our results show a difference in the kinds of technology used by different runner types. Differentiation between types of runners can be useful for health professionals, policymakers involved in public health, engineers, and trainers or coaches to adapt their services to specific segments, in order to make use of the full potential of running-related systems to support runners to stay active and injury-free and contribute to a healthy lifestyle.
BACKGROUND: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
