Correlation of Screen Time to Obesity in Adults

Introduction

The following study centers around the effects on the bodies of those with increased screen time due to various reasons such as working in front of a computer for long hours daily. The data was taken from a study done by Banks et al. (2011) to discuss the correlation among various factors such as obesity and screen time, which are to be examined.

Studying this correlation in particular is relevant to the current situation where students currently in a pandemic face the long hours in front of their devices and are primarily at home with limited movement the whole day. This is as compared to face to face classes where students are able to get breaks from their screens in the typical setups and fit light exercise in, such as walking around their school’s campus. Also, it is especially important for students in screen time heavy career paths to be knowledgeable of the possible correlation so they may implement habits into their daily routines to help avoid this.

The data in this study was divided into four groups according to their range of screen time, and this analysis studies the average value for the aforementioned range. It will be analyzed using statistical methods, namely linear regression, to find the correlation of the two variables.

Methodology

Banks et. al utilized a large-scale study of healthy men and women aged 45 and above from the general population of New South Wales, Australia. These individuals were randomly sampled (with consent) from the Medicare Australia database which already provides complete coverage of the general population. The recruitment began in February 2006 and ended in April 2008 with a total of 91,266 participants.

In terms of data, all the necessary variables were derived from the 45 and Up Study questionnaire, along with the measure of the remoteness of residence assigned according to the mean Accessibility Remoteness Index of Australia score. First, the participant’s overall level of physical activity was classified based on their responses from the Active Australia Questionnaire–a record of the physical activities of a person in a certain duration. From there, “a weighted weekly average number of sessions was calculated for each participant by adding the total number of sessions, with vigorous activity sessions receiving twice the weighting of moderate activity or walking sessions, and was categorized as zero to three, four to nine, ten to seventeen and eighteen or more sessions per week” (35). Also, the predictive value of total weekly sessions for meeting the current physical activity recommendations–a total of 150 minutes of physical activity in five or more sessions per week–was tested through a receiver operating characteristic curve. Given that the area under the curve was 85.6%, the weekly sessions were perceived to have a relatively good predictive value for sufficient physical activity in the study.

Afterward, the total daily screen-time, sitting time, and standing time were all classified according to the participant’s response to the following question: “About how many hours in each 24-h day do you usually spend doing the following: watching television or using a computer; sitting; sleeping; standing.” These were categorized on a scale of 0–1, 2–3, 4–5, 6–7, and < 8 hours/day, with the exception of sleeping as it was categorized with 0–5, 6–7, 8, 9–10 and < 11 hours/day. On the other hand, functional capacity was determined through their Medical Outcomes Score, with those scoring 100 being considered to have no functional limitations and those with scores of 90–99, 60–89, and 0–59 having minor, moderate, and severe limitations, respectively. This left the overall study with 91,266 participants since individuals with missing data were excluded from the analyses.

With that, the study aimed to test the relationship between overall physical activity, screen time, sitting time, and standing time, as well as the correlation between the individual measures of physical activity and inactivity. The specifics are as follows:

“Relative risks (RR; prevalence ratios) and 95% CI for obesity according to screen-time, sitting time, and standing time were estimated by generalized linear models, with a log link and binomial distribution(18), adjusting were appropriate for age (in 5-year age groups), sex (male and female), income (pre-tax total annual household income $20000, $20000–$49999 and <$50000 Australian dollars) and educational attainment (less than secondary school graduation, secondary school graduation, post-secondary school certificate or diploma, and tertiary graduate). Sensitivity analyses were conducted examining the effect on the screen-time model of additional adjustment for smoking (current, past and never) alcohol consumption (zero to four, five to eleven, twelve to twenty, and twenty-one or more alcoholic drinks per week), fruit (less than two and two or more servings per day) and vegetable consumption (less than five and five or more servings per day), functional capacity (as categorized above) and disability (assistance required for daily tasks, no assistance required for daily tasks). Where appropriate, categories were included for missing values.”

To test for the linear trend of each variable, selected ordinal variables were treated as continuous. Consequently, to test for interaction using multiple screen time categories, the researchers utilized a likelihood ratio test which compared the model with and without the interaction term. And last, the weighted least-squares method was used to compare the RR of obesity for every 2 hours of additional daily screen-time in different study subgroups. As such, the effect of a specific sedentary behavior on obesity was examined according to its attributable differences in total physical activity level. This was added to the regression model and reported with the relationship between screen-time and obesity in different categories of overall physical activity. As a result, Banks et, al. hypothesized that the effects of screen-time and other sedentary behaviors on obesity vary based on an individual’s paid work status. Initially, analyses aimed to examine the variables as a whole. Unfortunately, with the lack of elderly people in paid work, the study was eventually restricted to those not in paid work–allows the relationship between obesity and physical activity, screen-time, sitting time, and sleeping time in the whole cohort to be examined still. And with that, all analyses were carried out through the SAS statistical software package version 9.13 (SAS Institute, Cary, NC, USA). The researchers also noted that all statistical tests were two-sided and used a significance level of P < 0.05.

Results

##   Hours  BMI
## 1     1 25.5
## 2     3 26.4
## 3     5 27.1
## 4     7 27.7
## 
## Call:
## lm(formula = BMI ~ Hours, data = data)
## 
## Residuals:
##     1     2     3     4 
## -0.08  0.09  0.06 -0.07 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 25.21500    0.10989  229.46  1.9e-05 ***
## Hours        0.36500    0.02398   15.22  0.00429 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1072 on 2 degrees of freedom
## Multiple R-squared:  0.9914, Adjusted R-squared:  0.9872 
## F-statistic: 231.7 on 1 and 2 DF,  p-value: 0.004288

\[\hat{y}=0.365x+25.215\]

The values above were simplified to an integer instead of a range. The actual values in this study are as follows:
\[0-1.9 hrs = 25.5 BMI\] \[2-3.9 hrs = 26.4 BM\] \[4-5.9 hrs = 27.1 BM\] \[6-7.9 hrs = 27.7 BM\]

Assuming that the normal student spends the same amount of time on their screens as a regular school day, then that would amount to about 7 hours of screen time a day. An extra hour or two can be added on to account for the leisure activities. This means that there is a high chance of eventually developing into having a BMI of greater than 27.7, which is middling the overweight range. Using a conservative estimate of 8 hours a day, we end up with the following value: \[\hat{y}=0.365(8)+25.215=28.135\]

Due to the current pandemic, it is very likely that the students of this generation will become overweight based on the data pointing towards it.

We can get the unbiased estimate \(\sigma^2\) by using the following formula:

\[σ^2=\frac{\Sigma e^2}{n-p}=\frac{SS_e}{n-2}\]

\[SS_e=\Sigma (y-\hat{y})^2\]

Pulling these values from the function above, we get:

## [1] 0.0115

The unbiased estimator, \(\sigma^2\), is 0.0115.

Discussion

Based on the regression plot given in the results, the correlation between screen time and BMI seem to be directly proportional. This study involves a very large sample size of 91,266 participants, which points toward a more accurate data set.

The collected data shows that the average BMI of adults increases in relation to their screen-time usage.The test statistics also show that there is only a 0.1072 residual standard error which results to highly accurate and closely-related values between the two variables. The results clearly show that there is some sort of correlation between screen-time and BMI, however, this does not mean that screen-time is the only indicator of a higher or lower BMI among adults or if having a lower BMI is due to the low screen-time an adult uses in a day. There are many other factors that affect BMI. These can range from diet and exercise, to genetics and metabolism. Although these can affect BMI in an adult, this is not the focus of this study.

There is undoubtedly a correlation between screen-time and BMI because screen-time typically pertains to an indoor activity that does not require much physical movement other than the hands and arms. Having a higher screen-time could very much lead to less physical exercise, which causes the typical adult to put on a few extra pounds. A higher screen-time also generally means having a more sedentary lifestyle because of the appeal of electronic gadgets. In the case of students that would normally dedicate majority of their exercise to getting around school and physical education, they would lose that opportunity for exercise and end up with more time in front of computer screens, which based on this study, will eventually lead to an increase in BMI.

There are a few things that this study proves. The first of which is that there is a very strong correlation between screen-time and BMI among adults. This can be confirmed through a more logical approach by analyzing it directly. Sitting down in front of a computer or television screen all day means that the person is barely burning any calories. The second of which is that the online learning system has a major flaw. Not only is it detrimental to the students’ mental health and well being, it is also a burden to their everyday physical requirements which can be easily attainable in face-to-face classes.

Even though the body mass index is a good starting point when it comes to predicting obesity and allows people to adjust their lifestyle early on, it has its limitations which prove that it is not the most accurate when identifying the health of a person. In an article by Richard V. Burkhauser and John Cawley back in 2008, it was mentioned that, “BMI is seriously flawed because it does not distinguish fat from fat-free mass such as muscle and bone.” Other alternatives in determining the health or obesity of an individual include body fat percentage, and waist-to-height ratio. Body fat percentage takes into consideration your muscle mass and bone mass to accurately obtain the body fat of a person. Waist-to-height ratio benefits in the way that it estimates the body fat surrounding one’s vital organs around the waist, which may cause cardiovascular diseases (Ashwell et al. 2011).

Conclusion

From this study it could be concluded that the correlation between screen time and BMI is proportional. As stated previously, due to our current circumstances this knowledge would not only be helpful to know but could also serve as a warning to the people who now have to work and study at home. Though as said previously screen time is not the only factor to the rise in BMI but it could also imply many things that could also be useful to know. Perhaps this could be what the study can improve on. This study could be recommended to fellow students, teachers, and even institutions so that it could be shared within their respective communities. Due to how fast are normal lives changed many of us are still unaware of how this new normal has affected us. With research like this shared, we may be able to know more about our new lives and take measures to make sure we can avoid the negative aspects of this new normal.

References

Ashwell, M., Gunn, P., & Gibson, S. (2011). Waist-to-height ratio is a better screening tool than waist circumference and BMI for Adult cardiometabolic risk FACTORS: Systematic review and meta-analysis. Obesity Reviews, 13(3), 275–286. https://doi.org/10.1111/j.1467-789x.2011.00952.x

Banks, E., Jorm, L., Rogers, K., Clements, M., & Bauman, A. (2011). Screen-time, obesity, ageing and disability: Findings from 91 266 participants in the 45 and Up Study. Public Health Nutrition, 14(1), 34-43. doi:10.1017/S1368980010000674

Cawley, J., & Burkhauser, R. (2008). Beyond BMI: The value of more accurate measures of fatness and obesity in social science research. Journal of Health Economics, 27(2), 519–529. https://doi.org/10.3386/w12291