Moreover, we recommend that the rider’s upper leg angle should be within 23 of parallel to the ground as long as the rider is able to pass criteria 3 , 8 , and 9 . These new thresholds were selected based on the empirical results of our validation experiments and the angle values reported in the previously mentioned survey . Lastly, we stress that the fit guidelines are essential to assess whether the machine is suitable to the rider. We strongly recommend that stakeholders consider the fit criteria when evaluating youth’s readiness to ride a utility ATV.Current guidelines for ATV-youth fit are mainly based on the rider’s age and vehicle’s engine size and maximum speed . However, these recommendations are not supported by the present findings, which clearly showed that some fit criteria favor smaller youth while some benefit taller youth, regardless of the rider’s age and vehicle’s engine size or maximum speed. Furthermore, previous studies have also demonstrated that only rider’s age and ATV characteristics are insufficient to evaluate youth-ATV fit . As such, we strongly recommend that parents, dealerships, youth occupational health professionals, flood table and policy makers adopt fit guidelines based on the reach ability of youth for the assessment of youth-ATV fit.
There are noteworthy limitations of this study that need to be considered when interpreting the results. First, one may argue that the database selected for this study is outdated. Nevertheless, to the best of our knowledge, this is the only available source that includes enough parameters to create youth mockups on SAMMIE CAD. In addition, there is no clear evidence of the secular trend in anthropometry over U.S. youth over the past 40 years . For instance, when investigating other sources, we did not observe any significant differences in the mean values of shoulder breadth and hand length for youth aged 5 or 10 years old. However, it is reasonable to assume that there might be differences in the sizes of the youth population of 2022 and their counterparts of 1977. This potential difference should be considered in the interpretation and generalizability of the present findings. Second, although we used a systematic approach to identify common ATVs used in the United States, the sample is subject to sampling error and may not be necessarily representative of the models ridden specifically by youth. Moreover, safe and effective riding of utility ATVs involves consideration of factors other than the ability of youth to reach its controls or attain a specific posture. ATVs are rider-active vehicles, which means that riders must be able to shift their body weight to safely perform maneuvers such as turning, negotiating hills, and crossing obstacles . These circumstances warrant further investigation. Third, we had to determine the absolute location of each control due to feasibility issues.
The further-most position was used as the standard position for all controls with gradual adjustment such as the hand gearshift, while pedals were set to resting position. Fourth, all the human mockups were placed at the ATVs’ seat reference point . This may not be the ‘‘best-case” scenario from a reach standpoint since many riders, especially small youth, tend to sit closer to the handlebars to allow control reaching. However, the SRP is a standardized expected seat position, which allowed for a consistent evaluation approach among the various conditions. Finally, the reach simulations were performed with static mockups . In real riding situations, riders may shift their hips forward and/or bend their trunks to reach an otherwise unreachable control and perform active riding. However, while active riding can increase the ATV’s stability by 10–30 % , there is no clear evidence that active riding and rider separation reduces the risk of rollover for agricultural ATVs specifically .This warrants further investigation.Human movement or travel is important with regard to infectious disease epidemiology and ecology. Infectious diseases are heterogeneously distributed across landscapes. Individuals may be exposed to greater risk of acquiring infection if they move through transmission hotspots. Infectious individuals who travel may disperse pathogens across the landscape. Healthcare facilities are also heterogeneously distributed across landscapes, with ramifications for individual, household, and community access to diagnosis and treatment. Generally speaking, individuals who must travel long distances or through difficult terrain in order to seek diagnosis or treatment are less likely to receive adequate treatment.
As early as the 1950s, the human movement was recognized as one of the most important factors for disease elimination and eradication. A growing number of research projects, some focused on health, are recording human movement patterns. There have been attempts to map human movement in the rural Thailand border areas to delineate and intervene the risks of malaria. These projects can be broadly divided into those that are based on questionnaires/interviews and those that are based on empirical measurements All approaches have strengths and weaknesses. Interview/questionnaire-based approaches are prone to recollection bias and some movements may be unreported because of their nature . Mobile phone records provide a source of movement information across broad swaths of many populations. However, the movement data are limited to the resolution of mobile tower density, and mobile phone towers are not evenly distributed across landscapes . There is bias in who owns and uses mobile phones as well and mobile phone records will not allow for fine-scale mapping of the routes travelled in between locations. Wearable GPS devices offer extremely detailed data, but are labor intensive and dependent on volunteer cohort members. However, as the devices have become more compact and have become more affordable, their use is increasingly common. The main goals of this pilot project were to: I.) assess the feasibility of using GPS loggers to track human movement patterns among people living on the Thailand-Myanmar border, and II.) measure human movement patterns, 4×8 flood tray including how they vary seasonally, among a cohort of participants. The results of this work have implications for further research in this region with regard to targeted public health interventions, normal travel patterns and related exposure to different environments, for individual risk of infection by various diseases , and with regard to human disease ecology. The resulting data can also be useful for calibrating human movement patterns of individuals in an individual based modelling system.The study area is on the Thailand-Myanmar border. Participants were recruited from clinics that serve rural, mostly underdeveloped, and low population density communities. Most participants were of the Karen ethnic group. Villages were made up of a few dozen of mostly multi-generation families living in stilt houses made of wood and thatched roof. Villages didn’t normally have schools, clinics, or sanatory toilets. The houses are normally located along the main dirt road of the village. The dirt roads then continued to connect to other villages and small towns through a hilly and rugged terrain with occasional watersheds and river basins which made traversing difficult, especially during the rainy season. Villagers made their living mostly though agriculture, but they have to undertake various types of jobs throughout the year for their subsistence.
They developed land in and around their villages into farms to cultivate rice and vegetables. They farm poultry and pigs under their stilt houses. Some villagers go into the forests for hunting, or for foraging wood, and to collect wild edibles. They would go to the farms and forests overnight occasionally, and sleep without much protection from mosquitos and insects. We focused on farms and forests as places of interest in this study since apart from their homes, farms and forests might be the places the villagers spend significant amount of their time while being vulnerable to infectious diseases such as malaria.Participants were recruited from 10 villages near two clinics on the Thailand-Myanmar border: Wang Pah and Maw Ker Tai Clinics . These clinics primarily serve migrant and cross border populations and have connections to village health workers in nearby villages. We reached out to village health workers in the nearby villages to explain the project and to ask if they could help us recruit participants from their respective villages. Participants were recruited and interviewed at the respective clinics. There were no house visits and all data were collected at the clinics. The study targeted individuals who were 18 years of age and above from the Karen or Burmese ethnic groups, who stated that they would be able to keep track of the GPS device, who were capable of walking outside of village boundaries at recruitment, and who were willing to provide written consent to the study. As incentives, participants were provided with a waterproof handbag at the beginning of the study, a headlamp in the middle of the study, and a jacket at the end of the study. The total cost of incentives per person was less than 10 GBP. Upon recruitment, the age and gender of each participant was recorded following receipt of written informed consent . Participants were asked to carry i-gotU GT-600 mobile GPS devices for the study. They have a reported average location error of less than 10 meters. They were programmed to take a reading every 30 minutes. The devices are equipped with motion sensors and were set to go into a dormant mode if they sat still for longer than one hour, and to resume taking GPS readings upon detection of movement. Devices were also set to take readings at one-minute intervals if the device was moving ≥ 15km/hour . Field managers met with study participants each month. During these meetings participants were questioned about their continued willingness to participate in the study by the field managers, their general movement patterns during the previous month and with regard to any illnesses. A newly charged GPS device was given to each participant during each of these monthly meetings and the GPS logger that had been carried during the previous month was collected for re-charging. The GPS device batteries last roughly 1 to 1 ½ months. The data were transferred to a computer and stored in an encrypted folder with a unique code for each person to maintain security and anonymity. Separate data files were combined to obtain aggregated, longitudinal data for each participant. QGIS version 3.4.9 was used to generate study location maps and to visually explore the raw data. R statistical software version 4.0.3 was used for the data processing and analysis, using the “sp”, “rgdal”, “raster”, “proj4”, “reshape” and “ggplot2” R packages. GPS coordinates, which were originally recorded in 1984 World Geographical Coordinate System , were projected to UTM zone 47N to perform geographical calculations. Land cover types were classified manually using satellite imagery from Google Earth . Farms could be differentiated from forests by presence of human intervention on the vegetation cover e.g., vegetation cover in the farms were in more or less neatly arranged rows/columns. While formal ground truthing was not done after categorization, the locations of farms and forests do correspond to our experiences on the ground in these villages.Our analyses focused on quantifying daily movement ranges, multi-day trips, and time spent in farm or forest areas across population strata. The last GPS point of the day between 6pm to 12 midnight was considered to be the location where an individual spent the night. The median center of these points was assumed to be the individual’s home location. A buffer with 266 meters radius was created around each home to create a polygon for home area. Polygons for the farms and forests were manually classified using satellite imagery from Google Earth. As a proxy for how far people move each day, we calculated the maximum daily Euclidian distance, which is the furthest Euclidian distance a person was away from the location he or she slept the previous night. Multiday trips away from home were identified when the minimal daily Euclidian distances were more than 266 meters from the individual’s home location consecutively for two or more days. The Wilcoxon rank-sum test was used to compare the distributions of maximum daily Euclidian distances. A negative-binomial generalized linear mixed-effects model was used to investigate potential associations between the total number of nights spent in the farms or forests and other characteristics such as age group, gender, and season .