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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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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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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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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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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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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In Den Haag verblijven zo’n 3.000 Oekraïense ontheemden in de gemeentelijke en particuliere opvang. In dit onderzoek is onderzocht op welke wijze(n) Oekraïense vluchtelingen erin slagen hun weg te vinden in de Haagse samenleving, wat daarbij de rol is van (in-)formele netwerken en wat hun toekomstplannen en verwachtingen zijn. Hiervoor hebben we interviewgesprekken gevoerd met Oekraïense ontheemden en met Nederlandse gastgezinnen die Oekraïners in huis hebben (gehad). Het veldwerk vond plaats van januari tot mei 2023.
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Background: Recently, research focus has shifted to the combination of all 24-h movement behaviors (physical activity, sedentary behavior and sleep) instead of each behavior separately. Yet, no reliable and valid proxy-report tools exist to assess all these behaviors in 0–4-year-old children. By involving end-users (parents) and key stakeholders (researchers, professionals working with young children), this mixed-methods study aimed to 1) develop a mobile application (app)-based proxy-report tool to assess 24-h movement behaviors in 0–4-year-olds, and 2) examine its content validity. Methods: First, we used concept mapping to identify activities 0–4-year-olds engage in. Parents (n = 58) and professionals working with young children (n = 21) generated a list of activities, sorted related activities, and rated the frequency children perform these activities. Second, using multidimensional scaling and cluster analysis, we created activity categories based on the sorted activities of the participants. Third, we developed the My Little Moves app in collaboration with a software developer. Finally, we examined the content validity of the app with parents (n = 14) and researchers (n = 6) using focus groups and individual interviews. Results: The app has a time-use format in which parents proxy-report the activities of their child, using eight activity categories: personal care, eating/drinking, active transport, passive transport, playing, screen use, sitting/lying calmly, and sleeping. Categories are clarified by providing examples of children’s activities. Additionally, 1–4 follow-up questions collect information on intensity (e.g., active or calm), posture, and/or context (e.g., location) of the activity. Parents and researchers considered filling in the app as feasible, taking 10–30 min per day. The activity categories were considered comprehensive, but alternative examples for several activity categories were suggested to increase the comprehensibility and relevance. Some follow-up questions were considered less relevant. These suggestions were adopted in the second version of the My Little Moves app. Conclusions: Involving end-users and key stakeholders in the development of the My Little Moves app resulted in a tailored tool to assess 24-h movement behaviors in 0–4-year-olds with adequate content validity. Future studies are needed to evaluate other measurement properties of the app.
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