Impacts Of Technology On The Rate Of Obesity In Children Essay Example

technology alters with natural sleep due to prolonged exposure to intriguing aspects (Al-Agha, Al-Ghamdi, and Halabi, 2016). Furthermore, prolonged subjection to television, computers and other gaming devices amongst children reduce engagement in physical exercises and encourages consumption of unhealthy fast foods that attribute obesity (World Obesity Federation 2014). The study depicts the relationship between technological advancement and corresponding increase in obesity amongst childhood by evaluating the influence of contemporary technological devices such as hover-boards and video games (Wang, Bleich Weston, 2013). Furthermore, competent methods of reducing prevalence of the health condition amongst children can be constituted from analysis of the various altercations attributed by technology and their respective implications.

The study consists of analytical approach in evaluating the rate of childhood obesity before significant technological advancement and contrasting it with childhood obesity in the modern society. Relatively, analysis shall be implemented in determining effect of technology on physical performance by conducting interviews and providing questionnaires to parents and teachers

By evaluating the impact of modern technologies, such as video games, on the physical habits and eating patterns of children, parents might be able to ensure their children maintain a healthy body mass index (Rowan, 2012). The hazardous health condition has enormous mortality rates which are determined to increase in the future if circumstances do not change. In Australia, obesity was considered as the most avertable cause of death in 2009, attributing a death rate of 0.1 per 100, 000 citizens (1,043 deaths). This was a significant increase from the prior studies conducted in 2000 which revealed a death rate of 0.6 per 100,000 populations (Gray & Holman, 2013). Therefore, enabling reduction of childhood obesity by acknowledging technology

as a primal attribute and administering strategies to combat its impact would significantly lower the increasing mortality rate of obesity.

Obesity refers to a hazardous health condition facilitated by accumulation and disposition of fats in the body which eradicate normal functioning of organs. The severity of the health condition has escalated to enormous levels facilitating major concern in various nations. Evidently, nations within the gulf region are determined to incur most cases of obesity amongst individuals of all ages. According to findings depicted by Forbes magazine, 68 percent of the citizens in Saudi Arabia were overweight or obese in 2009 (Dossary, Sarkis, Hassan, El Regal, & Fouda, 2009). As a result, numerous health studies and researches have been conducted to identify the attributors of obesity in order to facilitate prevention and eradication. This excerpt aims at determining if contemporary advancement in technology attributes increase of childhood obesity.

Obesity in the modern world is created typically as a problem of personal attractiveness or public health. It is however more of an economic phenomenon than the two other reasons. Obesity, unlike many other physical conditions is avoidable by a simple behavioral change. Economists expect such behavioral changes to be undertaken especially if the benefits exceed the costs by any values. Obesity is a pejorative term related to the term overweight, which makes it clearer. In a model of rational choice, being overweight is not considered an issue of concern, weight is seen as a result of personal life choices along with other dimensions such as leisure time inactivity or activity, occupation, residence, and of course without forgetting food intake. Since the 1960s, it has been well known to the

public and medical profession that obesity impairs longevity and health altogether (Arora et al. 2012). Currently it is estimated that the mortality rate due to food intake and non-optimal levels of exercise comes second to tobacco consumption if they are rated according to the rate of death that could be evaded by the change in behavior (Philipson, 1999). Research shows that twice the number of children and three times the number adolescents suffer from obesity, which is an increase from 30 years ago, based on body mass increase scores. Particularly, over the same 30- year period, the CDC (Center for disease control and prevention presented a report that showed an increase in the percentage 6- to 11 – year-olds from about 7% to 18%, while the percentage of 12- to 19-years-olds similarly increased from about 5% to 18% ( CDC, 2013). Screen time has further been linked to obesity increase among children (Pagani et al., 2010) and also in the adolescents (Arora et al. 2012), as well as an immense reduction in exercise, which is shown by research to be predicted by the high increase in levels of media consumption ( Cox et al. 2012). It is however not just simply about displacement of time, since study reviews argue that a reduction in screen time does not guarantee or necessarily promote an increase in physical exercise. (Stansbury, Simpson & Martin, (2011).This study will also look at different relationships related to technology and obesity as follows.

Video gaming, internet use, and television are some of the technological and media activities that have been looked at through most studies conducted in Saudi Arabia. Several other studies have investigated

the impact of screen time on the health of both adolescents and children.
A study carried out on Australian adolescents, proved that excessive amounts of screen time predicted results that included, anxiety, withdrawal, loneliness, attention problems, aggression and depression (Stansbury, Simpson & Martin, (2011). A different study conducted suggested that excess amounts of screen time, video gaming and internet activity in particular, predicted the highest levels of sadness suicide planning and suicide ideation among teen in Australia and Saudi Arabia (Messias, Saini, Usman and Peeples, 2011).

Some researchers have examined the effects and impact of viewing television at a young age an individual’s health later in life. For instance, one study found that watching television at 29 months and 53 months of age predicted an increase in more attention issues and victimization problems at 10 years of age (Christakis, and Fowler, 2007). Plenty of television viewing at 30 to 33 months also predicted more behavior problems later at 5 years of age (Mistry, Minkovitz, Strobino & Borzekowski, 2007). Several other studies have qualified these findings showing that the television contents perhaps, (violent and non-violent entertainment shows) may be the cause, rather than the whole television time (Christakis, and Fowler, 2007).

Many study results have been consistent on the effects video gaming on health. For instance, one showed that the heavy use of video games regardless of the content predicted the more depression cases among young children and adolescents in general. Internet use irrespective of the content being viewed increases the signs of depression, impulsivity, social anxiety, hostility and compulsive disorder.

International body mass index statistics show that obesity among adults is a serious issue that is ranked at an

epidemic rate. Studies found out that obesity rates in these people are as high as 38% with 78% of these being considered as overweight (Taylor, 2011). Many arguments have come to a conclusion that screen time promotes or contributes to obesity prevalence through two ways; lack of proper exercise, and poor eating habits. Too much screen time subsequently increases the intake of food and also prevents one from physical exercise hence leading to possible obesity.

Physical activity in this case can be defined as movement of skeletal muscles to produce bodily movement and results to expenditure of body energy. The ability to relate or compare health indicators to physical activities depends on precise, dependable and accurate measures. Physical activity in most cases is measured physiological, electronic or mechanical measurements. Health advisors claim that for normal growth and development, and maintenance of proper fitness and health in children and the youth, regular physical activity is constantly needed. Major consensus statements concerning physical activity both adolescent’s and children health have recommended a regular and sustained physical activity of moderate to vigorous intensity in most of the days.

Over the last decade a series of research studies aimed at assessing the physical activity level among the children and adolescents of Saudi Arabia with reference to fitness and cardiovascular health (Al-Hazzaa, 2002). All-day heart rate telemetry measurements were employed in the examination of the physical activity. The findings were stored and later analyzed as follows, daily heart rate had an average of 105 beats per minute, the heart rate rarely exceeded 140 beats per minute in that particular boy which is a corresponding figure to the moderate level of intensity if

the children’s physical activity. The heart rate telemetry results showed that Saudi Arabia boys spent an average limited time on heart rate raising activities (about 159 beats per minute) or above. These two kinds of activities are considered as vigorous. Further results show that 16 percent of the children in Saudi Arabia never had their heart rate exceeding 159 beats per minute during the full day period and an approximate 15% of the children in other countries.

Worldwide, the prevalence of obesity in pediatrics is rapidly increasing. Being overweight in childhood is associated with being overweight in adulthood. Childhood obesity is a serious issue that should be closely monitored because of public health importance. The growth of obesity in a specific population is mostly caused by consumption of calories that exceeds the expenditure of the same. Once the reduction of physical activity or exercise exceeds the fall in calorie consumption, the growth rates of obesity and calorie expenditure may both be subsequently negative. In the economic sense, neither the objective nor subjective weight is the preferred weight especially with physical activity held fixed; the most preferred weight depends on the opportunities and preferences. The conditions for being either overweight or underweight rationally, are rather weak; whether with respect to one’s own subjective ideal weight or an objective ideal weight, set by a third person or party such as the public health community. This helps to explain the divergence between objective and subjective obesity.
As technology changes, it lowers the food prices and in turn lowers the prices of food, thereby freeing up time to raise income by other production forms, in this case weight will not continue

to grow indefinitely. The growth in obesity that is induced by price reductions or income gains is limited by non-monotonic impact of weight on utility.

Selected parents were identified and asked to tell their age, gender, ethnic background, and educational level as well as their specific areas of residence, here they needed to give the zip codes of the areas they all resided. Using the Census Bureau data, this information was converted to a median income. The parents were also asked to indicate their child’s gender and age.

Parents were asked to appropriate the height and weight of both themselves and their children. The figures obtained here were converted to body mass index based on an online formula.

Parents were questioned concerning their health as well as the health of their children in the following areas; Symptomology of physical health (the number of days they had been sick in the last 12 months, general physical health, (the number of headaches and stomach aches from an eleven item symptomology checklist that was developed by the people conducting the experiment.

A survey that included an 18 item Attention Deficit Hyperactivity Disorder Rating Scale-IV-school version, (DuPaul et al, 1997) and each item was supposed to be answered on a four-point scale of sometimes, rarely, often and very often. In addition, a child and parent attention symptomology checklist (for example, difficulty in paying attention, antisocial behavior) was included (Rosen, 2015).

A series of about ten to twelve questions was asked to the parents concerning their usage of media and technology (e. g going online, chatting, playing video games, sending and receiving emails, computer usage for other reasons other than going online, and playing with

technological toys) on a scale of (not all, less than an hour to 10 hours and more than 10 hours per day (Cheever, Carrier, Rosen, Benitez & Chang, 2009). Parents were also asked about their children’s technological ownership (iPod, cell phone) and the use of technology when alone in their rooms (video games, computer etc.)

All relevant items were converted to z-scores, factor analyses and used to develop some scales which had the following ill-being factors for children included, attention problems, physical problems, behavior changes, and psychological problems. Four ill-being factors had their mean z-scores found after computing a total ill-being score. On the other hand, the total technology use was created by adding up the number of hours per day for all the ten forms of technology and media that had been queried.

Children, teens, preteens and parents were seen to be consuming sweets, junk food, fried foods, regular sodas, and fast food meals. There however maybe overlaps in the manner in which the parents answered the survey items, the children and parents consume these four kinds of food groupings on almost a daily basis. Furthermore, older children were seen to be consuming coffee, regular coffee, energy drinks, with the teens being found to be consuming more overall than children and preteens. On the other hand, parents were seen to consume the most amounts of diet drinks and coffee.

The height and weight of both parents and children were used to calculate or compute, a body mass index score that was later to a specific category according to CDC tables. The overall results were as follows, the children (66%), teens (38%), and preteens (52%) were classified as

either being obese or at risk for obesity. These figures are concurrent with the percentages presented by the National Center for Health statistics.

The minutes and hours for each technology or media with a sum of total media usage computed as the total of the nine individual items. For all technology or media forms, excluding technologies toys and video games, and also including the total usage, teens were seen to have spent significantly more amount of time than the preteens who exhibited more hours than the children. As for the case of video gaming, the teens and preteens showed no differences that were significant enough but played significantly more than the children. Children spent more hours significantly on technological toys than both the teens and preteens, this did not differ significantly.

This hypothesis predicted that in addition to factoring out the usage of media on a daily basis, after factoring out the demographic findings for both the children and the parents, eating unhealthy would still predict ill-being beyond the demographics and technology use predictability. A series of hierarchical regressions where demographic data was factored out first, followed by the use of technology, before determining whether unhealthy eating habits predicted ill-being, were used to test this hypothesis. 55 different regression models analyses were carried out with independent variables which included demographics, each separate technology use, plus the total use of technology.

The second hypothesis predicted that physical inactivity would bring about ill-being even after parent and child demographics, and the use of technology on a daily basis. Generally, the lack of physical activity predicted the total as well as two other components of ill-being (behavioral changes and psychological use)

after demographics, the total use of technology on a daily basis has been factored out. The lack of physical activity did not predict attention problems significantly in teens after issues such as technological use and demographics had been factored out. In other words, this second hypothesis was supported partially for teenagers.

The third hypothesis predicted the use of technology on a daily basis would still predict ill-being despite factors such as parent and parent demographics, unhealthy eating had been factored out. For total ill-being and problems with attention in this hypothesis, playing with technological toys and daily music use were also significant predictors, on the other hand, for physical problems, the daily use of TVs and DVDs and some other technological toys were significant predictors as well. No psychological issues were predicted by daily technological uses, while behavior change was only predicted by daily music. This third hypothesis was therefore partially supported for children. For the preteens that were (9 to 12-year-olds) technology use did predict significant physical and ill-being problems, and not behavioral problems, psychological issues, or attention issues. The total ill-being was significantly predicted by IM/Chat, video games, testing, and music. Psychological issues in contrast, were only predicted by the daily use of phones and e-mails altogether. Behavioral problems were only predicted by the use of technological toys on a daily basis. Playing of video games and the use of technological toys predicted attention problems. In general, this third hypothesis was supported for preteens partially.

This study was designed to test several hypotheses to understand the causes of ill-being in children, preteens and young adults or teenagers. Among the things that were proposed was a

model that tested two paths, and suggested from the literature including a path from unhealthy feeding, to ill-being after daily media had been factored out (Rosen 2015). The second path from the lack of sufficient exercise to ill-being after technology use and daily media had been factored out. A final third hypothesis was tested and it factored out both the lack of physical activity and unhealthy feeding in order to determine if media or technology use alone could result or predict ill-being. 10 distinct forms if technology or media use were tested individually in each path model as well as the total media consumption. Additionally, four different types of ill-being were tested by each path model (physical problems, attention problems, behavior problems and psychological issues) as well the consumption of media on a daily basis. This as a result, led to the testing of 55 path models in total, for each age group, and for each hypothesis too. Hierarchical multiple regression was performed for each path model, by factoring out blocks of all child and parent demographics, including the age, education, gender, family median income, ethnicity etc. The body mass index and the child’s details e.g. age, and gender were also included.

The study showed that young children, preteens, and teenagers consumed foods that were unhealthy at a very high rate with an average participant or teenager consuming foods that are unhealthy, two to three times a week, to close to once a day. Furthermore, just as one would predict, body weight would obviously be impacted by body weight with plenty of junk, sweets, fast food meals, and fast food consumption. All these types of foods

contain large amounts of fats and calories. It was not surprising that a percentage of the children, 62% to be precise, 52% of the teens and 38% of the teenagers were either at a risk of being overweight or were obese already. A test on the model that showed a direct path from eating unhealthy foods to ill-being, after factoring out at least all the relevant child and parent demographics including the parent body mass index as well as the total use of technology on a daily basis and each and every individual type of technology usage. Each of the three age-groups produced differing results.

Concerns about this impending crisis, an international epidemic (obesity), have been raised by the general public community and many health economists as well. Education of the people on how to combat obesity and the importance of exercising through the publicly financed education is the most commonly recommended intervention. The question to be looked into here is whether obesity creates negative externalities that might warrant public intervention. As it is true for other health related behaviors, a potentially external effect of obesity is derived from financing of healthcare by the public. Since morbidity and mortality are some of the factors that are increased by obesity, it may seem obvious that medical care is somehow heavily subsidized, and taxing obesity would be bound to reduce a negative externality through a health care insurance that is tax-financed. This however ignores the fact that reducing mortality increases the elderly fraction of the population, and that the elderly consume not only a disproportionate fraction of the expenditures but a rather more subsidized.
A research by the American

Academy of Pediatrics advocates that toddlers below the age of two should get no screen time. From the data, there are no similar stipulations that appear, for teens and preteens to be using technology for many hours a day. Regardless of the demographic makeup of either children or parents, current studies indicate that more poor health cases may be potentially be caused by use of technology, whether it is defined as behavioral problems, attention issues, or physical problems. It is important that both parents and children should develop their own screen time play. This will help in modification of your home environment by coming up with specific screen time plans that are well scheduled and promote healthy living. It is also important to create time for other activities. In general, the results or outcome indicate that the effect of reducing screen time is small but still significant since it results to positive change in a population’s health status, considering the number of children who use screen media.

This study includes several limitations; one of the limitations is the broad categories that have been used to assess screen time. The fact the you have knowledge about someone spending long hours of time per day online does not necessarily mean you have an idea of the type of activity he or she is engaging in, these activities could be critical in impacting health.

This study has generally illuminated the screen activities that appear to be more predictive of poor health despite using a broad definition of screen time. The results of this study claim that technology appears to possess independent effects on health that differ between teenagers, preteens, and

children. These studies suggest that as much as children are helped to keep fit and to eat healthy, it is still not a sole solution that would help them attain or have good health. Parents need to be informed of the potentially harmful effects of technology and in turn implement good strategies that will help reduce their screen time by regulating their own usage.