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Traditionally, most cleaning activities take place in the evening or during nighttime.In the Netherlands, day-time cleaning is becoming increasingly popular. It is however unknown how day-time cleaning affects perceptions and satisfaction of end-users. An experimental field study was conducted on trains of Netherlands Railways (NS) to determine how the presence of cleaning staff affects perceptions and satisfaction of train passengers.
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Artikel gaat over de inzet van virtual reality bij patiënten met pijn.
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Clima2025 paper
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In this document, we provide the methodological background for the Safety atWork project. This document combines several project deliverables as defined inthe overall project plan: validation techniques and methods (D5.1.1), performanceindicators for safety at work (D5.1.2), personal protection equipment methods(D2.1.2), situational awareness methods (D3.1.2), and persuasive technology methods(D4.1.2).
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The municipality of Amsterdam wants to have an emission free taxi sector by 2025. In order to reach that goal, the city has taken a number of measures which favour clean taxis above conventional taxis. One of these measures is an innovative priority privilege scheme at the Amsterdam Central Station taxi stand, which should lead to shorter waiting times and more trips for clean taxis. The municipality wants to know if the measure is effective. In this study, we present an analysis of visiting behaviour of clean and regular (diesel) taxis in order to assess the effectiveness of the privilege scheme to attract more clean taxis. As such it aims to contribute to a better understanding of the effect of the priority measure at the Amsterdam Central Station and to provide input for policy makers to introduce incentive schemes to stimulate clean taxis in cities. Analysed data covers a timespan from one year, starting October 2015 when the privilege scheme started with a call rate of 1 clean taxi to each 4 taxis called for a ride. The analysis shows the number of arriving clean taxis to shift from 1:6 to 1:4 during the observation period. Based on this analysis the municipality decided to modify the preference ratio beginning 2017. This study contributes to a better understanding of the effect of the privilege measures and provides input for policy makers introducing incentives to stimulate clean taxis in cities.
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Physical activity (PA) is important for healthy ageing. Better insight into objectively measured PA levels in older adults is needed, since most previous studies employed self-report measures for PA assessment, which are associated with overestimation of PA. This study aimed to provide insight in objectively measured indoor and outdoor PA of older adults, and in PA differences by frailty levels. Data were collected among non-frail (N = 74) and frail (N = 10) subjects, aged 65 to 89 years. PA, measured for seven days with accelerometers and GPS-devices, was categorized into three levels of intensity (sedentary, light, and moderate-to-vigorous PA). Older adults spent most time in sedentary and light PA. Subjects spent 84.7%, 15.1% and 0.2%per day in sedentary, light and moderate-to-vigorous PA respectively. On average, older adults spent 9.8 (SD 23.7) minutes per week in moderate-to-vigorous activity, and 747.0 (SD 389.6) minutes per week in light activity. None of the subjects met the WHO recommendations of 150 weekly minutes of moderate-to-vigorous PA. Age-, sex- and health status-adjusted results revealed no differences in PA between non-frail and frail older adults. Subjects spent significantly more sedentary time at home, than not at home. Non-frail subjects spent significantly more time not at home during moderate-to-vigorous activities, than at home. Objective assessment of PA in older adults revealed that most PA was of light intensity, and time spent in moderate-to-vigorous PA was very low. None of the older adults met the World Health Organization recommendations for PA. These levels of MVPA are much lower than generally reported based on self-reported PA. Future studies should employ objective methods, and age specific thresholds for healthy PA levels in older adults are needed. These results emphasize the need for effective strategies for healthy PA levels for the growing proportion of older adults. https://doi.org/10.1371/journal.pone.0123168
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To compare comfort‐related outcomes when wearing rigid gas permeable (RGP) contact lenses made of two different materials and using two cleaning regimes. In a double‐masked lens material cross‐over study, subjects (n = 28 who completed the study) were refitted with new lenses made from (A) Boston XO material in one eye and made from (B) ONSI‐56 material in the other eye. The lenses made from materials A and B were worn on the right eye and the left eye following the pattern AB–BA–AB (or vice versa) during the first, second, and third 5 week trial periods respectively. Miraflow cleaner (1st and 2nd period) was replaced by Boston Advance cleaner in the 3rd period. Comfort‐related outcomes were assessed by a numerical rating scale (NRS) after each period. Subjects rated six comfort‐related factors: satisfaction, sharpness of vision, end of day comfort, maximum comfortable wearing time, maximum wearing time and foreign body feeling. Additionally we obtained subjects’ preferences for type of lens and lens cleaner during an exit interview. The sessile drop method was used to measure static contact angles.
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PurposeTo determine which factors are associated with physical inactivity in hospitalized adults of all ages.MethodsA cross-sectional sample of 114 adults admitted to a gastrointestinal surgery, internal medicine or cardiology hospital ward (median age 60, length of stay 13 days) were observed during one random day from 8 am to 8 pm using wireless accelerometers and behavioral mapping protocols. Factors (e.g., comorbidities, self-efficacy, independence in mobility, functional restraints) were collected from medical records, surveys, and observations.ResultsPatients were physically active for median(IQR) 26 (13–52.3) min and were observed to lie in bed for 67.3%, sit for 25.2%, stand for 2.5%, and walk for 5.0% of the time. Multivariable regression analysis revealed that physical inactivity was 159.87% (CI = 89.84; 255.73) higher in patients dependent in basic mobility, and 58.88% (CI = 10.08; 129.33) higher in patients with a urinary catheter (adjusted R2 = 0.52). The fit of our multivariable regression analysis did not improve after adding hospital ward to the analysis (p > 0.05).ConclusionsIndependence in mobility and urine catheter presence are two important factors associated with physical inactivity in hospitalized adults of all ages, and these associations do not differ between hospital wards. Routine assessments of both factors may therefore help to identify physically inactive patients throughout the hospital.IMPLICATIONS FOR REHABILITATIONHealthcare professionals should be aware that physical inactivity during hospital stay may result into functional decline.Regardless of which hospital ward patients are admitted to, once patients require assistance in basic mobility or have a urinary catheter they are at risk of physical inactivity during hospital stay.Implementing routine assessments on the independence of basic mobility and urine catheter presence may therefore assist healthcare professionals in identifying physically inactive patients before they experience functional decline.
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Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
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