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In a study, I measure BMI of a large sample of adult people.
Among them, there is some who have very low and very high height.
As I can read on the internet (like here or even in the wiki, for instance), BMI is not relevant for very short or very tall people.
Some math professor even wrote an article with a "better" calculation of BMI, but with a very low impact in science world (didn't find it on pubmed) so it's not really peer-reviewed.
Let's take some examples, assuming "normal BMI" is 21:
- for a 110cm tall adult, ideal weight is 25kg
- for a 130cm tall adult, ideal weight is 36kg
These weights appear really low to me. Note that dwarfism cutoff is 145cm in France.
My question is then the following: How could I choose a cutoff on height to exclude BMI values ?
NB: I didn't find anything on pubmed nor on google scholar, but maybe I missed an important article
The best procedure is going to depend on what exactly you're going to do with your data. Regardless, you should NOT exclude the BMI values. Just include the height, weight, and BMI. If you're simply reporting BMI as a characteristic of your study population, you can annotate that figure and let your reviewers and readers decide what to make of it. This follows the principles in Chapter 4 of Hulley's Designing Clinical Research. You want to get and maintain all the data.
If you're going run some further analysis on BMI, or if BMI is an outcome in your study, then you can evaluate the impact of a cutoff on your results, but you have to decide ahead of time how you'll do your primary analysis. I recommend using the entire data set (no height cutoff) for your primary analysis (because *there is no established practice for a BMI height cutoff in the general medical literature), and then running a secondary analysis that excludes those research subjects above some threshold (you can pick it, but depending on the size of your sample, 2 standard deviations could be reasonable). That secondary analysis would be a hypothesis generating analysis. If excluding very short and very tall people changed the results of your analysis, that's something to mention in your discussion and something to consider for the next study.
*If you're in a specialized field, then follow the practice of that field for your primary analysis.
Introduction to Correlation and Regression Analysis
In this section we will first discuss correlation analysis, which is used to quantify the association between two continuous variables (e.g., between an independent and a dependent variable or between two independent variables). Regression analysis is a related technique to assess the relationship between an outcome variable and one or more risk factors or confounding variables. The outcome variable is also called the response or dependent variable and the risk factors and confounders are called the predictors , or explanatory or independent variables . In regression analysis, the dependent variable is denoted "y" and the independent variables are denoted by "x".
[ NOTE: The term "predictor" can be misleading if it is interpreted as the ability to predict even beyond the limits of the data. Also, the term "explanatory variable" might give an impression of a causal effect in a situation in which inferences should be limited to identifying associations. The terms "independent" and "dependent" variable are less subject to these interpretations as they do not strongly imply cause and effect.
What is the standard error?
Standard error statistics are a class of statistics that are provided as output in many inferential statistics, but function as descriptive statistics. Specifically, the term standard error refers to a group of statistics that provide information about the dispersion of the values within a set. Use of the standard error statistic presupposes the user is familiar with the central limit theorem and the assumptions of the data set with which the researcher is working.
The central limit theorem is a foundation assumption of all parametric inferential statistics. Its application requires that the sample is a random sample, and that the observations on each subject are independent of the observations on any other subject. It states that regardless of the shape of the parent population, the sampling distribution of means derived from a large number of random samples drawn from that parent population will exhibit a normal distribution (1). Specifically, although a small number of samples may produce a non-normal distribution, as the number of samples increases (that is, as n increases), the shape of the distribution of sample means will rapidly approach the shape of the normal distribution. A second generalization from the central limit theorem is that as n increases, the variability of sample means decreases (2). This is important because the concept of sampling distributions forms the theoretical foundation for the mathematics that allows researchers to draw inferences about populations from samples.
Researchers typically draw only one sample. It is not possible for them to take measurements on the entire population. They have neither the time nor the money. For the same reasons, researchers cannot draw many samples from the population of interest. Therefore, it is essential for them to be able to determine the probability that their sample measures are a reliable representation of the full population, so that they can make predictions about the population. The determination of the representativeness of a particular sample is based on the theoretical sampling distribution the behavior of which is described by the central limit theorem. The standard error statistics are estimates of the interval in which the population parameters may be found, and represent the degree of precision with which the sample statistic represents the population parameter. The smaller the standard error, the closer the sample statistic is to the population parameter. The standard error of a statistic is therefore the standard deviation of the sampling distribution for that statistic (3)
How, one might ask, does the standard error differ from the standard deviation? The two concepts would appear to be very similar. They are quite similar, but are used differently. The standard deviation is a measure of the variability of the sample. The standard error is a measure of the variability of the sampling distribution. Just as the standard deviation is a measure of the dispersion of values in the sample, the standard error is a measure of the dispersion of values in the sampling distribution. That is, of the dispersion of means of samples if a large number of different samples had been drawn from the population.
References and formulas used by the Body Surface Area Calculator.
The Body Surface Area formula.
Below are the body surface area formula by Dr’s Mosteller, DuBois and DuBois, Haycock and Boyd.
The Mosteller formula 1
BSA (m 2 ) = SQRT( [Height(cm) x Weight(kg) ]/ 3600 ) e.g. BSA = SQRT( (cm*kg)/3600 )
or in inches and pounds: BSA (m 2 ) = SQRT( [Height(in) x Weight(lbs)]/ 3131 )
The DuBois and DuBois formula 2
BSA (m 2 ) = 0.20247 x Height(m) 0.725 x Weight(kg) 0.425
A variation of DuBois and DuBois 15 that gives virtually identical results is:
BSA (m 2 ) = 0.007184 x Height(cm) 0.725 x Weight(kg) 0.425
This chart shows 33 adult cancer patients, comparing their BSA calculated with the methods of Mosteller and DuBois. The patients were pre-sorted into ascending order of BSA. The data points are so close to each other, the lines overlap each other.
The Haycock formula 3
BSA (m 2 ) = 0.024265 x Height(cm) 0.3964 x Weight(kg) 0.5378
The Gehan and George formula 4
BSA (m 2 ) = 0.0235 x Height(cm) 0.42246 x Weight(kg) 0.51456
The Boyd formula 5
BSA (m 2 ) = 0.0003207 x Height(cm) 0.3 x Weight(grams) (0.7285 – ( 0.0188 x LOG(grams) )
The Mosteller formula is recommended 6 . To learn more about the issues and differences in BSA formulas, read the BSA standards report by Thanh Vu B.Sc(pharm). It explains why the Mosteller formula was adopted for use by the Pharmacy and Therapeutics Committee of the Cross Cancer Institute, Edmonton, Alberta, Canada.
Lean Body Weight formula & Ideal Body Weight formula
Lean Body Weight (men) = (1.10 x Weight(kg)) – 128 ( Weight 2 /(100 x Height(m)) 2 )
Lean Body Weight (women) = (1.07 x Weight(kg)) – 148 ( Weight 2 /(100 x Height(m)) 2 )
click here for more information about Lean Body Weight, or lean body mass.
Ideal Body Weight (men) = 50 + 2.3 ( Height(in) – 60 ) ( Devine formula)
Ideal Body Weight (women) = 45.5 + 2.3 ( Height(in) – 60 ) (Robinson formula)
click here for more information about Ideal Weight formulas.
Formula for Body Mass Index.
Body Mass Index = Weight(kg) / Height(m) 2
Definitions of "overweight".
A Body Mass Index between 25 and 29.9 is "overweight", greater than or equal to 30 is "obese "7 . However, some very muscular people can have high Body Mass Indexes and in adolescents, BMIs frequently result in overestimation of fatness. A better classification 8 of "overweight" is given in this table below. These numbers are used in the BSA calculator.
|very overweight or obese||>32.3||>31.1|
Weight Percentile, and Height Percentile, compared to the population.
An adult whose weight is at the 50 th percentile, is at the average weight of the population. An adult at the 90 th percentile is quite heavy.
For calculating percentiles, the oldest version of the calculator used weight data from a study 9 of 3992 consecutive adult cancer patients who underwent CT scanning. That study did not stratify males versus females, and it did not include pediatrics. By using the variance distribution from that study, and then adjusting the means to 79.4kg for males and 64.5kg for females, it was able to estimate the weight percentile. The height data came from a study 10 of 600 randomly selected cancer patients. That study also did not stratify males versus females, and it did not include pediatrics. By using the variance distribution from that study, and then adjusting the means to 69 inches for males and 64 inches for females, the older calculator was able to estimate the height percentile.
The next version of the calculator used better data. Pediatric data was derived from standard pediatric growth charts, based on American 1979 data 13 . Adult data for median height and weight came from Canadian 1997 data 11 . The variances for height and weight and the weight for height data comes from Canadian 1971 data 12 . Americans tend to be a little taller and heavier than Canadians.
The most recent version of the calculator has switched to using exclusively American data from the NHANES III survey 14 of 1988 to 1994. This data is used for the height and weight percentiles algorithm. It incorporates both adults and children’s data of heights and weights, the median (average) values and the variances (the range of fatness – thinness). Keep this in mind, that American’s tend to be taller and heavier than many other nationalities.
Patient and treatment characteristics
Data on educational level, comorbidities, and lifestyle characteristics are represented in Table 2.
Mean age at start of L and L+T was 15 years 10 months, and 17 years 5 months, respectively. Mean treatment duration for L was 12.6 months and for L+T 11.4 months. No patients stopped treatment because they no longer wished to pursue gender reassignment.
Reported side effect is shown in Table 2. Headaches and hot flushes were reported during L monotherapy, whereas fatigue was a complaint during both L and L + T. One of the four patients with hot flushes stopped treatment because of this side effect. During L, there was a non-significant increase in acne (P = 0.125) however, the prevalence of acne significantly increased in the first 6 months of L+T (P = 0.021), requiring treatment with oral retinoic acid in three out of 13 individuals. Metrorrhagia was mainly reported in the first 6 months of L but significantly dropped in the next 6 months (P = 0.004). During L+T, the prevalence of metrorrhagia increased slightly over the course of treatment.
Mean height at start of L was 164.6 cm, and at start of L+T, it was 167.6 cm. Weight and body mass index (BMI) significantly increased in the first 6 months (P = 0.004 and P = 0.031, respectively), but had turned back to baseline after 12 months of L (P = 0.538 and P = 0.918, respectively). L+T was associated with a significant and continuous weight gain after 6 months (P = 0.023 and P = 0.003) and 12 months (P = 0.002 and P = 0.015, respectively). This increase in weight and BMI was significantly different from weight changes in age-matched same biological sex peers, based on standard deviation (SD) scores . Evolution of weight and BMI are represented in Table 3.
Safety and metabolic parameters
Mean hemoglobin (Hb) and hematocrit (Hct) levels increased significantly in the first 6 months of L and of L+T but remained stable in the next 6 months. None of the individual Hb values rose above the upper adult male reference (Fig. 1a, b).
Box-and-whisker plots of biochemical parameters. L0 baseline values, L6m after 6 months of L, L12m after 12 m of L, L+T0 before start of L+T, L+T6m after 6 months of L+T, L+T12m after 12 months of L+T. a Hemoglobin (g/dL, multiply by 10 for SI units: g/L) b hematocrit (%, multiply by 0,01 for SI units: proportion of 1.0) c AST (U/L, multiply by 0.0167 for SI units: μkat/L) d ALT (U/L, multiply by 0.0167 for SI units: μkat/L) e HDL (mg/dL, multiply by 0.0259 for SI units: mmol/L) f LDL (mg/dL, multiply by 0.0259 for SI units: mmol/L) g creatinine (mg/dL, multiply by 88.4 for SI units: μmol/L) h sex hormone-binding globulin (nmol/L) i luteinizing hormone (U/L) j estradiol (ng/L, multiply by 3.671 for SI units: pmol/L) k testosterone (ng/dL, multiply by 0.0347 for SI units: nmol/L) l free testosterone (ng/dL, multiply by 34.7 for SI units: pmol/L). L lynestrenol monotherapy L+T lynestrenol and testosterone esters combination therapy, AST/ALT aspartate/alanine amino transferase, HDL/LDL high/low density lipoprotein
Only alanine amino transferase (ALT) but not aspartate amino transferase (AST) showed a statistically significant, although not clinically relevant rise after 12 months of L. In one patient, ALT levels transiently increased above the upper male reference to 57 U/L after 12 months of L but normalized after the start of L+T. Both ALT and AST further increased under L+T treatment but remained well within the male reference range. None of the patients reached the threshold of three times the upper reference limit which we considered the cutoff to stop treatment (Fig. 1c, d). Creatinine significantly increased during the first 6 months of L and during the first 6 months of L+T but remained stable in the following 6 months (Fig. 1g).
Total cholesterol and triglyceride levels did not change during treatment however, mean HDL decreased significantly and mean low-density lipoprotein (LDL) levels increased significantly in the first 6 months of L. During L+T, mean LDL levels did not change significantly (Fig. 1e, f). No significant changes in hemoglobin A1c (HbA1c), glucose levels, insulin levels, or homeostasis model assessment (HOMA) index were noticed during either L or L+T treatment.
Although no significant changes in mean TSH levels were observed, fT4 levels increased significantly both in the first and second half year of L. In the first 6 months of L+T, there was a decrease in TSH accompanied with a significant and consecutive decrease in fT4 in the first and next 6 months of treatment. However, in all patients, serum levels for both TSH and fT4 remained well within the reference range (Table 4). The eight patients who were using OC before L was started were excluded for baseline analysis of LH, FSH, E2, A, and SHBG. Mean AMH, T, and fT levels were not different in OC users as compared to non-OC users and were included in baseline analyses.
Mean SHBG, LH, but not FSH levels decreased sharply during the first 6 months of L and remained unchanged in the next 6 months (Fig. 1h). Only after L+T, LH and FSH were both fully suppressed (Fig. 1i). L caused a significant decrease in mean E2 levels at 6 months with no significant changes anymore thereafter (Fig. 1j). Mean AMH levels did not change during the course of treatment.
The significant decrease in T levels in the first 6 months of L was accompanied by a non-significant increase in fT . Both T and fT did not change in the next 6 months. As expected, mean T increased significantly in the first months of L+T, already at the lowest dose (50 mg/2 weeks) and further increased in the next months to reach T values well within the male reference range. This was accompanied by a similar increase in fT levels. Some patients exceeded the male upper reference of 25 ng/dL, due to blood sampling close to the last TE injection (Table 4 and Fig. 1k,l).
Symptoms of Abnormal Mono Levels
Although having high or low monocyte levels show up in a blood test won’t produce any symptoms themselves, you may show signs of the cause of abnormal mono levels. Let’s look briefly at what some of these could be.
Bacterial or viral infections are one of the most common symptoms of abnormal mono levels. For example, the Journal of Dental and Medical Sciences reported that common reasons for monocytosis (high monocyte count) include: 4
Inflamed and stiff joints
Another symptom of abnormal monocytes in lab blood test results is inflamed joints caused by arthritic conditions. According to the Journal of Leukocyte Biology, high mono levels can be a sign of chronic inflammatory diseases like rheumatoid arthritis or lupus. 5
Chronic low mono levels may result in a person bleeding easily or having unexplained bruising. The journal Mayo Clinic Proceeding reports that monocytopenia (low monocyte count) is a feature of some types of leukemia. Because leukemia can also cause low platelet count, easy bleeding is often a sign. 6
Losing weight without trying
Low monocytes in a blood test result may be accompanied by unexplained weight loss. This is also often a symptom of leukemia, and doctors will need to perform additional tests to find the cause of low mono count along with weight loss.
Severe mood swings
Interestingly, severe mood swings and depression could mean that blood test results show high monocyte levels. According to the Journal of Psychiatric Research, elevated white blood cell count, including monocytes, is often seen in lab test results of people who suffer from depression. 7
Signs of liver disease
The journal Blood reported that symptoms of liver disease are sometimes associated with increased levels of monocytes. The elevated monocyte count is due to inflammation in the liver and can be used to diagnose the severity of the condition. 8
44 Types of Graphs and Charts
Line charts, or line graphs, are powerful visual tools that illustrate trends in data over a period of time or a particular correlation. For example, one axis of the graph might represent a variable value, while the other axis often displays a timeline.
Each value is plotted on the chart, then the points are connected to display a trend over the compared time span. Multiple trends can be compared by plotting lines of various colors.
For example, the interest of digital marketing over time can be visually shown with ease through the use of a line graph. Simply plot each number of searches along the timeline to view the trend.
The simplest and and most straightforward way to compare various categories is the classic bar graph. The universally-recognized graph features a series of bars of varying lengths.
One axis of a bar graph features the categories being compared, while the other axis represents the value of each. The length of each bar is proportionate to the numerical value or percentage that it represents.
For example, $4 could be represented by a rectangular bar four units long, while $5 would equate to a five-unit long bar. With one quick glance, audiences learn exactly how the various items size up against one another.
Bar graphs work great for visually presenting nearly any type of data, but they hold particular power in the marketing industry. The graphs are ideal for comparing any sort of numeric value, including group sizes, inventories, ratings and survey responses.
Pie charts are the simplest and most efficient visual tool for comparing parts of a whole. For example, a pie chart can quickly and effectively compare various budget allocations, population segments or market-research question responses.
Marketing content designers frequently rely on pie charts to compare the size of market segments. For example, a simple pie graph can clearly illustrate how the most popular mobile-phone manufacturers compare based on the sizes of their user-bases.
Audiences are able to quickly understand that stock photography is the most-used visual in marketing, with original graphics – like those that can be created with Visme – coming in as a close second.
Mosaic or Mekko Charts
Basic line, bar and pie charts are excellent tools for comparing one or two variables in few categories, but what happens when you need to compare multiple variables or multiple categories at the same time?
What if all those variables aren’t numeric even? A mosaic – or Mekko – chart plot might be the better choice.
Perhaps a market analyst, for example, wants to compare more than the size of various mobile-phone markets. What if, instead, he or she needs to compare the size of the user bases, as well as the age groups within each group?
A mosaic chart would allow said marketer to illustrate all the variables in a clear and straightforward manner.
In the above example, one axis of the chart represents the categories being compared – mobile phone manufacturers – while the other axis lists various age ranges.
The size and color of each cross-section of the chart corresponds with the market segment it represents, as depicted in the chart's legend.
Market segments are often divided based on age and gender, and a population pyramid is an ideal visual representation of the two groups.
The graph classically takes on the shape of a pyramid when a population is healthy and growing -- the largest groups are the youngest, and each gender dwindles somewhat equally as the population ages, leaving the smallest groups at the top of the graph.
A population pyramid that veers away from its classic shape might indicate an irregularity in a population during a particular period, such as a famine or an economic boom that led to an increase in deaths or births.
Of course, population pyramids aren’t always used to compare populations by age, and therefore don’t always take on the graph’s namesake shape.
A marketer, for example, might use the design to compare a population by income, weight or IQ, in which the smallest groups will often be at both the top and bottom. Regardless, the graph clearly depicts population trends, while it compares the sizes of two related groups.
When a statistician needs to visually compare three or more quantitative variables, he or she might choose to use a radar chart , also known as a spider or star chart.
The chart usually consists of a series of radii, each representing a different category, that splay out from a center point like spokes.
The length of each “spoke” is proportionate to the value being compared. For each category, the spokes are then connected with a line of a designated pattern or color, forming a star-like shape with points equal to the number of categories.
The result is a graphic representation that can reveal trends and compare categories all at the same time.
Your test results: A preview
Your test results will show your cholesterol levels in milligrams per deciliter of blood (mg/dL). Your total cholesterol and HDL (good) cholesterol are among numerous factors your doctor can use to predict your lifetime or 10-year risk for a heart attack or stroke. Your doctor will also consider other risk factors, such as age, family history, smoking status, diabetes and high blood pressure.
Lipid profile or lipid panel is a blood test that will give you results for your HDL (good) cholesterol, LDL (bad) cholesterol, triglycerides and total blood (or serum) cholesterol.
HDL (good) cholesterol
HDL cholesterol is called &ldquogood&rdquo cholesterol. A healthy HDL-cholesterol level may protect against heart attack and stroke. Your doctor will evaluate your HDL and other cholesterol levels and other factors to assess your risk for heart attack or stroke.
People with high blood triglycerides usually also have lower levels of HDL. Genetic factors, Type 2 diabetes, smoking, being overweight and being sedentary can all lower HDL cholesterol. Women tend to have higher levels of HDL cholesterol than men do, but this can change after menopause.
LDL (bad) cholesterol
Since LDL is the bad kind of cholesterol, a low LDL level is considered good for your heart health.
LDL levels are one factor among many to consider when evaluating cardiovascular risk. Talk to your doctor about your LDL cholesterol level as well as other factors that impact your cardiovascular health.
A diet high in saturated and trans fat is unhealthy because it tends to raise LDL cholesterol levels.
Triglycerides are the most common type of fat in your body. They come from food, and your body also makes them.
Normal triglyceride levels vary by age and sex. People with high triglycerides often have a high total cholesterol level, including a high LDL (bad) cholesterol level and a low HDL (good) cholesterol level. Many people with metabolic syndrome or diabetes also have high triglyceride levels.
Factors that can contribute to elevated triglyceride levels:
- Overweight or obesity
- Insulin resistance or metabolic syndrome
- Diabetes mellitus, especially with poor glucose control
- Alcohol consumption, especially in excess
- Excess sugar intake, especially from processed foods
- High saturated fat intake
- Chronic kidney disease
- Physical inactivity
- Pregnancy (especially in the third trimester)
- Inflammatory diseases (such as rheumatoid arthritis, systemic lupus erythematosus
Some medications may also increase triglycerides.
Total blood (or serum) cholesterol
This part of your test results is a composite of different measurements. Your total blood cholesterol is calculated by adding your HDL and LDL cholesterol levels, plus 20% of your triglyceride level.
&ldquoNormal ranges&rdquo are less important than your overall cardiovascular risk. Like HDL and LDL cholesterol levels, your total blood cholesterol level should be considered in context with your other known risk factors.
Your doctor can recommend treatment approaches accordingly.Play without Auto-Play Play Video Text
Written by American Heart Association editorial staff and reviewed by science and medicine advisers. See our editorial policies and staff.
A Few Quick Notes Before We Start…
- If you’re looking for signs other than testing your actual body ketone levels as to whether you’re in ketosis or not, then please check out this article instead that provides you with signs you’re in ketosis.
- If you’re a type 1 diabetic, then this article is not for you and the optimal levels suggested below are not applicable to you. Please check out the tons of other ketone level articles on the web to ensure your ketone levels do not reach dangerous levels.
- And lastly, while the levels of ketones in your body is important, it’s not all that you should be thinking or worrying about. For example, while you may be able to raise your levels by taking exogenous ketone supplements like KETO//OS®, this artificially induced higher ketone levels may not offer the same benefits as when you produce your own. As Marty Kendall put it:
“The real ketone magic…[occurs when] we deplete glucose [and] we train our body to produce ketones.”
Conclusions and discussion
We wished to determine whether long-term workplace computer/VDT use could be associated with facial skin issues such as acne, hypersensitivity, and signs of accelerated aging by comparing women who were characterized by (a) computer use or (b) no computer use. We were able to recruit 100 Chinese women who fit the criteria between the ages of 35 and 45 and we measured both subjective and objective endpoints. Although there is public concern that extended computer use leads to general health risks (Baliatsas et al. 2015), substantiation of objectively measured effects on specific facial skin damage endpoints is lacking. Through this study, we wished to, first, show whether measurable adverse effects could be associated with daily extended office work-related computer/VDT use, and second, if these effects could be explained by factors other than computer/VDT use, such as sleep disruption, stress, exercise, or other lifestyle differences. In this study, subjects’ exposure to EMF was predicted to differ only with respect to computer use, if at all, as all subjects worked in the same building and live in the same area, such that the exposure to high-voltage overhead power lines was the same for all subjects and the survey did not reveal any differences in mobile phone use.
In summary, 8 or more hours per day of computer use was found to be statistically significantly associated with more severe acne and higher levels of sebum, but correlated with lower values for facial wrinkles and pigmented spots. In addition, subjects who were computer users were found to have a higher risk of sensory hypersensitivity when subjectively reported but not when tested with the “lactic acid sting test”. Other factors such as sleep quality/quantity and sun exposure, that could influence the occurrence and severity of acne, skin aging, and skin hypersensitivity, were assessed and not found to correlate. It should be noted that none of the other lifestyle, attitudinal, or behavioral characteristics measured in this study were different between the two groups. These included use of acne medication or cosmetic products for acne, and there was no difference between the groups in their use of cosmetic procedures, including botox injections, resurfacing procedures, laser treatments, or dermal fillers. There was no difference in reported job satisfaction or in diagnosed atopic dermatitis or rosacea. There was limited use of cosmetic anti-aging or whitening products, possibly due to the relatively young age of the subjects. As assessed by a written survey, exercise did not appear to differ significantly between the two groups however, individual wearable monitoring devices may have been able to detect a difference between subjects in the CG versus the NCG which then could be analyzed with regard to acne. Therefore, long periods of inactivity may be a possible confounder that could not be fully accounted for with the experimental protocol employed. Related to this, no difference was detected in BMI between the two groups.
It was particularly important to assess sleep in this study, as recent reports suggest that computer screen-generated light in the blue range can impact human health via several related pathways and in multiple organs (Tosini et al. 2016 Kayaba et al. 2014 Beaven and Ekstrom 2013) or may have no effect (O’Hagan et al. 2016). For example, one study showed that the blue light of LED-backlit computer screens significantly suppressed melatonin production in human subjects (Sroykham and Wongsawat 2013). In addition, a recent report suggested that there exists a relationship between sleep quality and facial sebum levels in women with acne vulgaris (Bilgic et al. 2016). In another study unrelated to computer use, Caucasian women who were good sleepers had significantly lower intrinsic skin aging scores that poor sleepers (Oyetakin-White et al. 2015). Because it has been suggested that high computer use can lead to sleep disturbances, in the current study questions related to sleep quality were given to all subjects. There was no difference in reported sleep quantity, quality, or in the perception of fatigue between the CG and NCG. In addition, the self-reported skin type (combination, oily, dry), the occurrence of flushing/blushing, the satisfaction with how the subject’s skin/face looks, or how healthy the subject’s face and complexion were self-perceived were not associated with computer use.
One skin characteristic that is often reported to be associated with VDT use is the “sensitive skin syndrome” in which subjects are more likely to react to lactic acid with itching, burning, and stinging. Complaints of tightness, stinging, burning, and itching sensations as well as erythema and facial dryness have been previously reported for video display workers (Eriksson et al. 1997) Overall, VDT workers report skin symptoms more frequently than non-VDT office employees, and the term “electromagnetic hypersensitivity” has been coined to describe people who experience health symptoms in the vicinity of electromagnetic fields (EMFs) and who regard them as causal for their complaints (Tuengler and von Klitzing 2013). The fraction of the population with electromagnetic hypersensitivity has been estimated to be as high as 9%, and projected to be up to 50% by the year 2017 (Hallberg and Oberfeld 2006). Previous reports that computer users to have relatively hypersensitive skin was supported here only in terms of the subjective survey results, not as assessed by the lactic acid sting test.
There is some evidence in previous studies to suggest that job-associated computer use is accompanied by a higher frequency of “skin disorders”, including rashes (Knave et al. 1985). For example, Liden and Wahlberg (1985a) reported that subjects with rosacea, seborrhoeic dermatitis, and acne were over-represented in a VDT-exposed group in one study, and in another published study they reported that there was a higher frequency of diagnosed seborrheic dermatitis, acne, rosacea, and perioral dermatitis among exposed subjects when compared with control subjects (Liden and Wahlberg 1985b). They further review additional studies in which dermatologic examination rules out contact allergy, increased photosensitivity, and in which symptoms were “clearly related to time at work”. Interestingly, even though the level of EMF and LFEMF emitted from VDTs is considered below harmful levels and often below the limits of detection, in one study with acute provocation (2–4 h) seated close to but facing away from an “ordinary PC”, investigators claimed that mast cells increased in the upper dermis within 24 h (Johansson et al. 2001). This phenomenon was found in 5 out of 13 subjects, leading to a hypothesis that some fraction of humans is hypersensitive to VDT and particularly prone to “screen dermatitis”. It should be noted that one study of university students that used a questionnaire to probe symptoms such as headache, fatigue, difficulties in concentration, vertigo/dizziness, attention disorders, nervousness, palpitation, low back pain, myalgia, and tinnitus found no significant differences in the prevalence of these symptoms between VDT users and those who did not use VDTs.
Some observed physiological sequelae of work with video display terminals are relatively easy to explain, such as eye and shoulder, neck and back musculoskeletal discomfort. Other perceived consequences of long hours at a computer are not as easily explained. The mechanism for increased sebum and acne, the perception of sensory hypersensitivity, lower wrinkling, and pigment spots is not clear. A more complete psychological or behavioral evaluation of stress might reveal differences which correlate with sebum and acne, but would be unlikely to also correlate with lower wrinkling and pigment spots. Furthermore, a psychological cause is also not supported by the post-hoc answers to questions about the subjects’ perceived dangers of computer use. A reasonable explanation for the lower wrinkling and spots would be a small but long-term difference in sun exposure, which may not have been accurately assessed by the survey used in this study. One study reported that Langerhans’ cells were depleted in “screen dermatitis” (Gangi and Johansson 1997), but we did not confirm this by biopsies or antigen sensitization assays in the present work. Another group suggested, after collecting data from 3877 subjects, that the evidence did not support a direct physiological impact of VDT work on the skin, but rather than any effects were due to psychological stimuli (Liden and Berg 1991). Bergqvist and Wahlberg (1994) reported no associations between EMF levels and skin disease or symptoms but indicated instead that there were other factors that did associate with skin symptoms, such as perceived work load, inability to take rest breaks, and a low relative humidity. We did not uncover explanatory mechanisms for the observations made, but strongly suggest that additional investigations should be made because of the current findings and the public’s perception that extended computer use adversely affects certain aspects of skin physiology. For example, a recent advice website, http://www.glamour.com/lipstick/blogs/girls-in-the-beauty-department/2013/03/change-your-life-beauty-tip-th-6 suggested that hair falling onto the face during computer use was a cause of increased risk of acne. In an editorial in the JAAD, Berg and Liden proposed the hypothesis (never verified) that an electrostatic field causes a deposition of volatile and particle-bound air pollutants on the skin, leading to toxic irritation (Berg and Liden 1987).
Properly designed double-blind provocation protocols may lead to a deeper understanding of acute effects—but have been difficult to achieve for many reasons, including the possibility that the percentage of the population susceptible to this is low. In addition, important endpoints measured such as acne, dark spots, and wrinkles are not “acute”-type phenomena. In contrast, skin sensory sensitivity may be more amenable to acute challenge protocols. The present study was designed to examine skin conditions in women who had worked for at least 10 years in office-related computer work. To our knowledge, this is the first observational report to suggest a link between extended office-related computer/VDT use and an increased risk of acne and perceived facial hypersensitivity. There is clearly the possibility that these associations are due to office environmental and work condition issues.