BFS Statistical Analysis - patrickbohan.elementfx.compatrickbohan.elementfx.com/A_Statistical_Analysis_of_… · Web viewA Statistical Analysis of Benign Fasciculation Syndrome

BFS Statistical Analysis

A Statistical Analysis of Benign Fasciculation Syndrome (BFS) to

Identify Multiple Forms of the Disorder

Patrick Bohan

PO Box 331

109 Raven Way

Buena Vista, Colorado, USA

719-966-5167

[email protected]

Mitra Wagner

Abstract:

Defining and understanding neurological disorders has been a medical

mystery. Benign Fasciculation Syndrome (BFS) is one such disorder.

BFS is sometimes referred to as Peripheral Nerve Hyperexcitability

(PNH). BFS or PNH is a neurological disorder and its cause is not

entirely understood, but it theorized that the cause may stem from an

imbalance between potassium and sodium at the nerve endings. This

imbalance is what causes involuntary impulses that consequently

stimulate the nerve endings causing them to fire and twitchi. Other BFS

symptoms include muscle fatigue, cramps, pins and needles, muscle

vibrations, headaches, itching, sensitivity to temperatures, numbness,

muscle stiffness, muscle soreness and painii iii iv. Like most neurological

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mailto:[email protected]


disorders, there is no cure for BFS. One purpose of this writing is to

better define and understand the relationship between BFS symptoms,

body parts affected by BFS, the potential causes of BFS, and potential

remedies for BFS. To accomplish this task, a survey was conducted and

data was obtained from 125 people who have been diagnosed with BFS

or have BFS like symptoms. The data was analyzed using a simple

statistical analysis to find the mean, median, mode, standard

deviation, variance, range, percentile rank, skewness, standard error,

and coefficient of variance for each symptom, body part affected, and

potential remedy. The data was also modeled using a linear regression

analysis to determine if there is correlation between symptoms,

potential causes or triggers, body parts affected by BFS, and potential

remedies. From this data it is possible to identify unique forms of BFS

that stem from a variety of triggers. Each BFS form has its own set of

symptoms, conditions that make symptoms worse, and unique

potential remedies. For this reason, it is very difficult to find a cure for

BFS – because there are many forms of the disorder causing each

individual to have unique symptoms.

User Groups:

There are two online user groups that people can use to gather more

information about the disorder:

Facebook: https://www.facebook.com/#!/groups/88467288815/

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https://www.facebook.com/#!/groups/88467288815/


Internet: http://www.nextination.com/aboutbfs/

Background:

BFS sufferers live in fear because similar symptoms can be found in

other crippling and deadly disorders such as Parkinson Disease,

Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), and even

brain tumors. Because of this, many BFS patients have been forced to

undergo advanced medical testing including Magnetic Resonance

Imaging (MRI) performed on the brain as well as an Electromyography

(EMG) to rule out other neurological disordersv. Anyone with BFS, or

doctors that have studied BFS, will tell you that “benign” is a bad word

to describe the disorder. People may not die from BFS, but it can be

debilitatingvi. In fact, many BFS sufferers have similar symptoms to

other neurological disorders including Nueromyotonia (NMT), Benign

Cramp Fasciculation Syndrome (BCFS), fibromyalgia, Reflex

Sympathetic Dystrophy (RSD), stiff person syndrome, continuous

muscle fiber activity, continuous motor nerve discharges, and Isaac

Syndrome (an EMG can help determine the type of neurological

disorder)vii. Many remedies attempted to relieve BFS symptoms are

exactly the same as those remedies used for NMT, BCFS, RSD and

other neurological disordersviii. At this time there is no evidence that

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http://www.nextination.com/aboutbfs/


BFS sufferers are any more likely to acquire other more serious

neurological disorders, such as ALS or MS, than any “normal” personix.

Purpose:

Most BFS sufferers have been to multiple general practitioners and

neurologists looking for answers but have failed to receive any logical

explanations. Since BFS is benign there are no or very few studies on

the disorder and therefore, doctors do not have any answers. After

each doctor visit the medical files of BFS sufferers are put in a file

cabinet and locked away. How is this going to find a cure for BFS? It

will not! I have also come to realization that doctors are not necessarily

the best mathematicians to find solutions by comparing and reviewing

data (conversely, I do not understand medicine as well as doctors). In

fact, since doctors do not compare the medical records of people with

similar ailments (I am not blaming doctors because I realize they may

not have the tools to accomplish this task), they treat each patient like

a guinea pig using a trial error approach to find a drug regimen that

may work to alleviate some symptoms. And yes, what works for one

person afflicted with BFS may not necessarily work for another person

afflicted with BFS, so it is hard to pin point a treatment regimen for BFS

sufferers. If, on the other hand, doctors were supplied the results of

this study, they would better understand a starting point to treat their

patients since there are many different forms of BFS. For instance, the

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results indicate that people whose BFS symptoms get worse due to a

sickness will have more success using benzodiazepine drugs to

alleviate symptoms than other drug classifications. However,

benzodiazepine drugs are not as helpful in patients who believe their

symptoms get worse due to stress – anti-convulsants may work better.

We live in a verbal society, but numeric analysis is needed to help

solve the complex problems and mysteries of life. Having doctors

understand these differences for treating BFS would be one purpose

for writing this paper.

Therefore, I created a survey to anonymously obtain the medical

records of BFS sufferers into one location so we can statistically

analyze the data to better define and understand the ailment. I

completely understand that scientists, doctors, and researchers are

spending most of their time trying to solve Parkinson’s disease, ALS,

and MS since these diseases are, without question, much worse. Once

there are cures for these diseases, then it is possible that cures for BFS

could follow shortly afterwards. However, it is debatable as to whether

or not this approach to solving the mysteries of neurological disorders

is the best or most logical. As an engineer, I saw many projects fail

because we tried to design products that incorporated too many

features. This created many design complications and ultimately these

projects failed. On the other hand, multiple products that focused on

particular features were more successful, and over the course of time

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the features can eventually be incorporated into one product (for

example - the phone camera). This approach to problem solving saved

the company both in cost and time to market. The same can be said of

medicine – maybe it makes more sense to focus on less complicated

disorders such as BFS or RSD and apply what is learned to more

complicated neurological disorders such as ALS and MS. This seems to

be a fundamental issue when trying to solve problems (in my opinion)

– everyone wants to hit a home run instead of making small

incremental advances, regardless of the profession. Hopefully, the

analysis included in this paper will provide one of those small

incremental advances in not only understanding BFS, but the mysteries

of all neurological disorders.

The Survey:

A survey was created in Google Docs and can be found at the following

link: https://spreadsheets.google.com/spreadsheet/viewform?

hl=en_US&authkey=CJvBgaQM&formkey=dElCQkFBRWlvY1ZSTThKTm

NsbEg4d0E6MQ#gid=0

I will keep the survey open indefinitely with the hope that we can

continue to grow the sample size and therefore, better understand the

disorder. I will periodically update the data on my website (links to

specific types of data are listed throughout this paper).

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https://spreadsheets.google.com/spreadsheet/viewform?hl=en_US&authkey=CJvBgaQM&formkey=dElCQkFBRWlvY1ZSTThKTmNsbEg4d0E6MQ#gid=0




The Survey can also be reached from my BFS webpage:

http://patrickbohan.home.bresnan.net/BFS.htm. Click on the link “BFS

Survey”.

Data:

The excel data file for all 125 responses can be found on my BFS

website: http://patrickbohan.home.bresnan.net/BFS.htm. Click on the

link “Survey Data” and open the first tab titled “BFS”. This is the data

file that will be statistically analyzed except when remedy or treatment

variables are being analyzed. I use the data on the “BFS No Zero” tab

to statistically analyze remedy or treatment variables (this will be

explained in this text).

Data points with brackets “[]” around them were identified as outliers

because these responses were outside plus or minus 3 standard

deviations from the mean for all tested variables. Most outliers were

determined from the parameters: Symptom Averages, Body Part

Averages, and or Remedy Averages. A handful of other outliers were

determined by running a statistical analysis on each variable. Outliers

are omitted from any statistical analysis.

Data Summary:

The statistical analysis data for each parameter can be found at:

http://patrickbohan.home.bresnan.net/BFS.htm. Click on the link

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http://patrickbohan.home.bresnan.net/BFS.htm




“Survey Data” and view the excel file tab titled “Data Summary” to

find a statistical summary of all parameters in the survey. The tab

“Calculate” contains the averages for all parameters in the survey.

A lot of the statistics on the “Data Summary” tab are irrelevant. For

instance, statistical data for variables that had yes or no responses (1

or 0 answers respectively) are for the most part irrelevant. Variables

such as EMG, MRI, Sickness, Flu Shot, Chemicals, Exercise, Altitude,

Stress, History, Spine Injury, Sex, Remedies, and Missing had yes / no

responses – meaning other than the statistical average, most of the

other statistical results have very little meaning. Even statistical results

for variables that had multiple response options such as variables

Region or Day are for the most part irrelevant. More relevant results

for these parameters can be found on the “Calculate” tab, which

merely computes statistical averages. On the “Calculate” tab the

results to these questions are sorted to determine for instance, how

many people in the survey where from Europe or North America. The

“Calculate” tab results are shown below in Table I below (the

classification of variables, ie General (G), will be defined later and are

color coded on the “Data Summary” and “Calculate” tabs):

Table I: “Calculate” Tab Results

________________________________________________________________________

_______________________________________________

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General (G):

Age: 38.74

Sex: 64.5% Male

Region: 68% North America; 1.6% South America; 26.4% Europe; 0.8%

Europe; 3.2% Oceania

MRI: 56.8% Yes

EMG: 66.9% Yes

Years with symptoms: 3.49 years

Years diagnosed: 2.23 years

Causes / Triggers (CA):

Flu shot: 10.4%

Chemicals: 4.8%

Prescription drugs: 20.8%

Neck/Spine injury: 13.6%

Sickness: 28.8%

Exercise: 20%

Stress: 72.6%

History: 19.2%

Other: 20%

The sum of causes adds to more than 100% because people selected

multiple potential causes (this is okay).

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Stressers (ST):

Sickness 3.86 (out of 10)

Exercise: 5.59

Stress: 6.83

Symptoms (S):

Twitching: 7.64 (out of 10)

Pins and Needles: 3.72

Cramps: 3.34

Muscle Fatigue: 3.97

Headaches: 2.85

Itching: 2.13

Numbness: 2.79

Muscle Stiffness: 3.98

Muscle Vibrations/Buzzing: 4.7

Muscle Pain/Soreness: 4.52

Sensitivity to Temperature: 3.06

Symptom Average: 3.87

Body Part (B):

Feet: 5.68 (out of 10)

Lower Leg: 7.28

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Upper Leg: 5.15

Hip/Butt: 3.83

Back: 3.29

Abdomen: 2.88

Chest: 2.43

Head/Neck: 3.53

Hands: 4.4

Arms / Shoulders: 4.83

Generalized: 1.34 (the lower the number the more random and

generalized they symptoms)

Body Average: 4.32

Remedies (RE):

Benzodiazepine: 3.91 (out of 10, for those that tried the treatment);

54.5% did not try the method

Anti-Convulsant: 2.56; 57.7%

Anti-Depressant: 2.11; 54.1%

Potassium Channel: 1.4; 87.8%

Sleeping Pills: 3.02; 66.7%

Muscle Relaxant: 2.2; 66.4%

Homeopathic: 2.2; 63.4%

Supplements: 2.27; 25.2%

Diet: 2.03; 48%

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Acupuncture: 2.14; 82.1%

Massage: 2.35; 50%

Yoga: 2.46; 71.5%

Remedy Average: 2.39; 6.5%

Various (V):

Time: 4.91 (People feel their symptoms are slightly improving over

time since a 5 means that symptoms have stayed the same)

Day: 32% Morning; 29.6% Day/Evening; 38.4% Night (Time when

symptoms are worse)

Remedies: 15.2% of people said that certain remedy treatments made

their symptoms worse.

Missing: 9.6% of people said that a remedy solution that worked for

them was not included in the survey.

Altitude: 5.6% of people said their symptoms got worse at altitude.

________________________________________________________________________

__________________________________________________________

However, for many parameters, just knowing the mean (average) does

not really describe the variable without knowing more about the result

such as its standard deviation. For instance, if a child scored 5 points

below the average on a test, this does not tell us much statistically

without understanding how the class did as a whole. If the child’s test

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result was within one standard deviation of the class average, than the

child’s result would still rank in the middle of the class (a C grade – see

Figure 1). If, on the other hand, the result was over 2 standard

deviations away from the mean, than the child’s result would rank in

the bottom of the class (a D or F grade). Thus, understanding the

standard deviation, variance, and standard error of the class

distribution would be extremely helpful.

Let’s examine the results of one parameter on the “Data Summary”

tab, Twitching (Figures 2 and 3 below summarize the data results for

the variable twitching). The question in the survey for the variable

twitching specifically reads “Enter a number from 1 to 10 on how much

the symptom twitching affects you? A 1 means the symptom does not

affect you at all, and a 10 means the symptom occurs 24/7”. For the

rest of this writing I will refer to this question simply as “twitching”.

The results of the variable twitching are summarized below in Table II:

Table II: “Data Summary” Tab Results for Twitching

________________________________________________________________________

_______________________________________________

Mean – 7.64 x

The mean is the arithmetic average. In the case of twitching the

mean is 7.64. Hence, BFS sufferers, on average, feel twitching in

their body’s 76.4% of the time. The mean is also illustrated in Figure 13


2; it is the point at which the red bell cure line is at its maximum

point.

Median – 8 xi

The median is the result at which 50 percent of the survey

responses are above the result and 50 percent are below the result.

In the case of twitching the median result is 8. Hence, 50% are the

responses to the survey question twitching were below 8 and 50%

of the responses were above 8. This is illustrated in Figure 2.

Mode – 10 xii

The mode is the most common response or the response with the

highest occurrence or frequency. The most common answer for

twitching was 10 (the symptom happens 24/7). This is also

illustrated in Figure 2.

Standard Deviation—SD – 2.52 xiii

The standard deviation is a measure of the variability of a set of

responses around their mean. If responses cluster tightly around the

mean score, the standard deviation is smaller than it would be with

a more diverse group of responses from the mean. Any results

outside of the mean plus or minus three standard deviations is

considered an outlier and discarded from the analysis. Figure 1 xiv

shows a common bell curve or what is sometimes referred to as a

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normal distribution curve, probability density function, or Gaussian

distribution (µ is the mean and σ is the standard deviation). Figure 2

is the bell curve for the variable twitching. For twitching, the

standard deviation is 2.52 (3 standard deviations is equal to 7.6).

Hence, the mean plus 3 standard deviations is equal to 15.2 and the

mean minus 3 standard deviations is equal to 0. Obviously, 100% of

the data responses for the twitching question lie within this range

since all answers had to be between 1 and 10 (no outliers).

Sample Size – n - 125

The sample size is equivalent to the number of people that

participated in the survey – 125. Remember, the sample size per

statistical test may be less than 125 because outliers were omitted

from the calculations. The exact sample size per variable is shown

on the “Data Summary” tab.

Standard Error – SE - .230 xv

Standard error is the standard deviation of the values of a given

function of the data (parameter), over all possible samples of the

same size. This is usually defined by the standard deviation (SD)

divided by the square root of the sample size (n). The smaller the

standard error the more tightly clustered the data results are

around the mean. And conversely, a high standard error means the

data distribution is widely dispersed around mean. One would

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expect to find a large portion of the population (answers to the

twitching question) be between the mean plus and minus 3 times

the standard error.

Variance – 6.37 xvi

The (population) variance of a random variable is a non-negative

number which gives an idea of how widely spread the values of the

random variable are likely to be; the larger the variance, the more

scattered the observations are on average. In other words, variance

is a measure of the 'spread' of a distribution about its average

(mean) value. The variance for twitching is fairly dispersed because

responses covered the entire range of possibilities (1 through 10).

This too can be observed by reviewing Figure 2.

Percentile Rank – 1 at 0%, 5.875 at 25%, 8 at 50%, 10 at

75%, and 10 at 100% xvii

A percentile rank is typically defined as the proportion of scores in a

distribution that a specific score is greater than or equal to. For

percentile rank at 25%, this statistic equals the response where the

first 25% (frequency of occurrences) of the sample size population

resides. In the case of twitching 25% of the people answered 5.875

or lower. Obviously, the inverse is also true, that 75% of the people

answered higher than 5.875 for the variable twitching. Also, for

twitching, the percentile rank at 0% is 1, at 50% it is 8, at 75% it is

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10, and at 100% it is also 10. This concept can be visualized in

Figure 2.

Inter – Quartile Range – IQR – 4.125 xviii

The inter-quartile range is a measure of the spread of dispersion

within a data set. It is calculated by taking the difference between

the upper and the lower quartiles. IQR is generally defined as the

middle 50% of the data equal to percentile rank at 75% minus

percentile rank at 25%. In the case of the variable twitching

percentile rank at 75% = 10 and percentile rank at 25% = 5.75.

Hence, IQR equals 10 minus 5.875, which equals 4.25.

Range – 9 xix

The range of a sample (or a data set) is a measure of the spread or

the dispersion of the observations. It is the difference between the

largest and the smallest observed value of some quantitative

characteristic and is very easy to calculate. A great deal of

information is ignored when computing the range since only the

largest and the smallest data values are considered; the remaining

data are ignored. The range value of a data set is greatly influenced

by the presence of just one unusually large or small value in the

sample (outlier). In the twitching example the range of responses

were between 1 and 10. Hence, the range is equal to 10 minus 1,

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which of course equals 9. Once again, this can be seen by viewing

the histogram for twitching in Figure 2.

Coefficient of Variation – CV – 33.03% xx

The coefficient of variation (CV) measures the spread of a set of

data as a proportion of its mean. It is often expressed as a

percentage. It is the ratio of the sample standard deviation to the

sample mean. The smaller the coefficient of variance percentage

the more tightly clustered the result distribution is around the

mean. Conversely, the more dispersed a distribution is around the

mean equates to a larger coefficient of variance percentage. For

twitching the coefficient of variance result was 33.03%.

Skewness - -.773 xxi

Qualitatively, a negative skew indicates that the tail on the left side

of the probability density function (Bell Curve) is longer than the

right side and the bulk of the values (possibly including the median)

lie to the right of the mean. A positive skew indicates that the tail

on the right side is longer than the left side and the bulk of the

values lie to the left of the mean. A zero value indicates that the

values are relatively evenly distributed on both sides of the mean,

typically but not necessarily implying a symmetric distribution. The

larger the absolute value of the skewness magnitude, the more

skewed the data is to the right or left (depending on the polarity) in

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the bell curve. The twitching variable is skewed to the right as

shown by Figure 2. The example in Figure 1 has no skewness

because the bell curve is completely symmetrical.

_____________________________________________________________________

____________________________________________________________

Figure 1: Probability Density Function

1 2 3 4 5 6 7 8 9 10 110

2

4

6

8

10

12

14

16

18

20Histogram

Normal Fit(Mean=7.63, SD=2.53)

Twitching

Frequency

Figure 2: Probability Density Function (Bell Curve) for Twitching

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http://upload.wikimedia.org/wikipedia/commons/8/8c/Standard_deviation_diagram.svg


n 119 (cases excluded: 5 due to missing values)

Mean 7.63 Median 8.0095% CI 7.17 to 8.09 95.7% CI 8.00 to 9.00

SE 0.232Range 9.0

Variance 6.40 IQR 4.42SD 2.53

95% CI 2.24 to 2.90 Percentile0th 1.00 (minimum)

CV 33.2% 25th 5.58 (1st quartile)

50th 8.00 (median)

Skewness -0.76 75th 10.00 (3rd quartile)

Kurtosis -0.50 100th 10.00 (maximum)

Shapiro-Wilk W 0.85p <0.0001

Figure 3: Data Analysis for Twitching Variable

What can the data on the “Data Summary” tab tell us? It is very useful

to compare data for one parameter versus another parameter within

the same group or category of variables (ie symptoms, causes, body

parts, and remedies – these groups are color coded on the

spreadsheet). For symptom variables we can deduce the most

predictable parameter is Itching because it has the lowest standard

deviation, variance, and standard error. On the other hand, the

parameter Vibration/Buzzing Sensation has the lowest predictability

because it has the highest standard deviation, variance, and standard

error. This simply means that the responses to Itching are more tightly

distributed around the mean than the results to other parameters,

especially Vibration/Buzzing Sensation, which had sparsely distributed

results around the mean. For Body Part affected variables the Chest 20


parameter was the most predictable while the Feet parameter was the

least predictable. For Remedy variables the Massage parameter (I did

not consider potassium channel drugs because very few people tried

them) was the most predictable while the Benzodiazepine variable was

the least predictable. This simply means that while benzodiazepine

drugs helped many people alleviate their symptoms, but at the same

time benzodiazepine drugs did very little to help other people alleviate

BFS symptoms creating a large variance in the distribution around the

mean result. This does not make benzodiazepine drugs a bad choice to

treat BFS. In fact, the opposite may be true since other remedies had

lower variances only because they did not work as well (in other words,

most people answered lowered numbered results for other remedy

variables such as sleeping pills, muscle relaxants, homeopathic

treatments, supplements, and so forth. Hence, the results are

clustered more tightly around a lower mean value). Therefore,

benzodiazepine drugs had more success than any other treatment

types because it had a higher mean, but the results were mixed. In this

study, parameters with lower means tended to have lower variances,

standard deviations, and standard errors. The data on the “Data

Summary” tab was calculated from the data on “BFS” tab (for all data

except remedy variables) and the “BFS No Zero” tab (for remedy

variables).

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Correlation Data:

The correlation results for all 125 responses can be found at:

http://patrickbohan.home.bresnan.net/BFS.htm. Click on the link

“Survey Data” and look at the excel file tab titled “Correlation Results”

to find statistical correlation data between variables (t-statistic data). xxii

A linear regression model can have two purposes: one to predict future

results and or two, to find correlation. Most of the models generated

from the BFS survey have very low adjusted R² xxiii values (the results

are not linear) and are therefore, not very good models to predict

future outcomes. On the other hand, linear regression models can give

us an idea of which parameters have strong or even weak correlation,

and that can be useful information. This is why the t-statistic result for

each model simulation is important because the t-statistic is a measure

of correlation.

Each question in the survey: your age, your sex, how bad you get pins

and needles, how well yoga works for you, etc is a variable or

parameter (I use the two words interchangeably). When modeling

variables using a linear regression model, there are two sets of

variables - x and y. In the data result array (on the “Correlation

Results” tab) the horizontal axis is for y variables and the vertical axis

22



is for x variables (this is reversed from conventional algebra, but it was

easier for me to get the data into the table using this reversed format).

Only one variable is allowed for y in a linear regression analysis, but

multiple variables can be used for x (as long as there are more

equations than unknowns). For this study, I have grouped the x

variables into seven classifications – General (G), Causes / Triggers

(CA), Stressers (ST – those variables that can make BFS symptoms

worse), Symptoms (S), Body Parts affected (B), Remedies (RE), and

Various (V). I have used different color fonts to distinguish between

these groups on the “Correlation Results” tab for convenience. For

instance, the General (G) classification of variables consists of 7

parameters: age, sex, region, number of years with symptoms, years

diagnosed, EMG, and MRI.

The t-statistic is a good measure of correlation between corresponding

x and y variables. The higher the absolute value of the t-statistic result,

the better the correlation (lower standard error). A t-statistic value of

greater than 2 means very strong correlation (greater than a 94%

chance the variables are correlated); a t-statistic value between 1.6

and 2 means moderate correlation (86% to 94% chance); and a t-

statistic value below 0.5 means the correlation is very weak (less than

a 25% chance).

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The sign or polarity (+ or -) of the t-statistic result is also important. A

positive value means the x variable will tend to increase the value of

the y variable, whereas a negative value means the x variable will tend

to decrease the value of the y variable. For instance, a strong

correlation between twitching (y variable) and muscle relaxants (x

variable) can do one of two things: make the symptom better or worse

(- or + respectively). In this case, a positive value can increase the

twitching response (making it worse) whereas, a negative response

(would lower the twitching value – remember, twitching was rated on a

scale of 1 to 10 with 10 being the worse) would be beneficial and

something for people to try (as long as the magnitude of the t-statistic

showed strong correlation).

In essence, the “Correlation Results” tab is a matrix of t-statistic

results that is 57 long by 57 wide. T-statistic data was not obtained for

x variables within the same classification. For instance, Age as a y

variable was not modeled against other General (G) parameters such

as sex, region, years with symptoms etc. These results are designated

as “na” within the t-statistic matrix. Also, data in the matrix signified

with ND (No Data) indicates the data was not linear dependent so no

results were computed. T-statistic results with strong correlation and a

positive polarity are in a bold green font. T-statistic results with strong

correlation and an negative polarity are signified with a bold red font.

24


T-statistic results with moderate correlation and a positive polarity are

in a bold blue font. And finally, t-statistic results with moderate

correlation and a negative polarity are signified with a bold orange

font.

One final note, I used the data on the “BFS” tab to model all results

except for Remedies (RE). When Remedy parameters were the y

variable I used the excel file tab “BFS No Zero” data to model the

results. After all, it does not make much sense to find correlation to

remedies that people have not tried (a “0” response means people did

not try the remedy). Hence, the data within the “BFS No Zero” tab is

the same as the data on the “BFS” tab except “0” responses to

Remedy questions were omitted from the data. But, it is important to

keep in mind, the model results of RE parameters using the “BFS No

Zero” tab will result in fewer data points (smaller sample size, n) in the

model. For this reason, the results from these models may prove to be

less conclusive because the data size is in some cases significantly

smaller. Hence, when evaluating the data models for RE correlation

pay close attention to the sample size. When Remedies (RE) are

grouped together as the x variables, I use the data on the “BFS” tab to

run the models. Only a few people have tried all potential remedies,

hence the sample size would only be a single digit number if the “BFS

No Zero” tab data was used to model RE results as the x variable.

25


Let’s examine the results of one y parameter, twitching. Six twitching

models were run using twitching as the y variable and G, CA, ST, B, RE,

and V classification of parameters as x variables respectively (Figures

4 through Figure 9 respectively). The results listed below in Table III

provide t-statistic data for each parameter versus twitching. Table III

also contains a summary from the “Correlation” tab results to include

those parameters with the best correlation versus the listed variable

when it is modeled as the y variable:

Variable; t-statistic v. twitching; list of parameters with the best

correlation to the listed variable Green Font, (Red Font)

General Group (G)

Age; 1.17; cramps, acupuncture, (stress)

Sex; 0.98; (sensitivity to temperature), (yoga)

Region; -0.23; stress, prescription drugs, history

Years Diagnosed (YD); -0.34; massage, altitude

Years with Symptoms (YBFS); 0.78; benzodiazepine, remedies, (muscle

pain)

EMG; 1.37; exercise, back, arms, anti-convulsants, muscle relaxants,

remedies, (stress1), (hip), (yoga)

MRI; -2.33; (chemicals), (twitching), remedies

26



R2 0.07Adjusted R2 0.01

SE 2.51

Term Coefficient 95% CI SE t statistic DF pIntercept 6.369 4.382 to 8.356 1.0028 6.35 112 <0.0001

Age 0.02713 -0.01878 to 0.07303 0.023168 1.17 112 0.2441Sex 0.493 -0.499 to 1.485 0.5008 0.98 112 0.3271

Region -0.04869 -0.46746 to 0.37009 0.211356 -0.23 112 0.8182Years Diagnosed (YD) -0.02415 -0.16649 to 0.11820 0.071840 -0.34 112 0.7374

Years with BFS Symptoms (YBFS) 0.05059 -0.07735 to 0.17853 0.064572 0.78 112 0.4350

EMG 0.751 -0.335 to 1.837 0.5481 1.37 112 0.1734MRI -1.196 -2.215 to -0.178 0.5142 -2.33 112 0.0218

Figure 4: Linear Regression Model: Twitching V. General (G)

There is strong negative correlation between twitching and a MRI. This

suggests people are more apt to get an EMG than an MRI due to

twitching symptoms.

Cause / Trigger Group (CA)

Flu Shot; -0.52; potassium channel

Chemicals; 2; (MRI), twitching, (hip), missing

Prescription Drugs (PD); 1.21; (region), vibration, (hands), anti-

depressants, missing

Spine or Neck Injury (SNI); 0.34; no correlation

27


Sickness; -1.34; (years with symptoms), sickness1, (headaches),

hands, diet

Exercise; 0.31; exercise1

Stress / Anxiety (SA); -1.44; (EMG), (exercise1), stress1, itching, head,

benzodiazepine, (time)

History; 0.19; (region), years with symptoms

Other; 0.83; (abdomen), anti-convulsants, (potassium channel),

homeopathic, remedies



SE 2.50


Flu Shot (FS) -0.4126 -1.9880 to 1.1628 0.79496 -0.52 110 0.6048Chemicals 2.218 0.019 to 4.417 1.1095 2.00 110 0.0481

Prescription Drugs (PD) 0.7057 -0.4523 to 1.8636 0.58430 1.21 110 0.2297Spine or Neck Injury

(SNI) 0.2437 -1.1709 to 1.6582 0.71379 0.34 110 0.7335Sickness -0.6925 -1.7146 to 0.3296 0.51575 -1.34 110 0.1822Exercise 0.1821 -0.9658 to 1.3301 0.57925 0.31 110 0.7538

Stress / Anxiety (SA) -0.7645 -1.8178 to 0.2888 0.53151 -1.44 110 0.1532History 0.1175 -1.0958 to 1.3308 0.61224 0.19 110 0.8482

Other 0.5219 -0.7309 to 1.7748 0.63219 0.83 110 0.4108

Figure 5: Linear Regression Model: Twitching V. Causes (CA)

There is strong positive correlation between twitching and exposure to

chemicals as a trigger for BFS. In other words, if exposure to chemicals

28


were the cause or trigger of the BFS ailment, expect twitching to be a

primary symptom.

Stressers Group (ST)

Stress Anxiety1 (SA1); -0.47; (EMG), prescription drugs, history, stress,

head, (anti-convulsants), (potassium channel), homeopathic,

benzodiazepine

Exercise1; 0.58; exercise, sensitivity to temperatures, (acupuncture)

Sickness1; 1.07; vibration, sleeping pills, muscle relaxants,

(acupuncture), (benzodiazepine), missing


R2 0.02Adjusted R2 -0.01

SE 2.54


Sickness1 0.08613 -0.07269 to 0.24495 0.080187 1.07 116 0.2850Exercise1 0.04567 -0.10983 to 0.20116 0.078507 0.58 116 0.5619

Stress / Anxiety 1 (SA1) -0.03896 -0.20454 to 0.12662 0.083602 -0.47 116 0.6421

Figure 6: Linear Regression Model: Twitching V. Stressers (ST)

There is no moderate or strong correlation between twitching and

stressers.

Symptoms Group (S)

29


Twitching – na; (MRI), chemicals, lower leg, arms, time, day

Pins and Needles (PN) – na; time, feet, sickness1,

Cramps – na; age, exercise, muscle relaxants, remedies, time

Muscle Fatigue and Weakness (MFW) – na; back, (yoga), time

Headaches – na; (exercise), (chest), head, (yoga)

Itching – na; lower leg, head

Numbness – na; anti-depressants

Muscle Stiffness (MS) – na; sickness, head

Vibration / Buzzing Sensation (VBS) – na; prescription drugs, sickness1,

abdomen

Muscle Pain / Soreness (MPS) – na; EMG, arms, time

Sensitivity to Temperatures (STT) – na; exercise1, (day)

Body Part Group (B)

Feet; 1.47; (years diagnosed), twitching, pins and needles, sleeping

pills

Lower Leg (LL); 5.68; (years diagnosed), years with symptoms,

twitching, cramps, diet, time

Upper Leg (UL); -0.58; exercise1, muscle fatigue, (potassium channel),

time

Hip / Buttock (HBR); 0.78; prescription drugs

Back; -0.53; muscle fatigue, altitude

Abdomen; 0.23; (region), (age)

30


Chest; -0.47; (potassium channel), muscle relaxants

Neck / Head (NH); -0.63; headaches, (day)

Hands; -1.1; (age), muscle relaxants

Arms / Shoulders (AS); 2.12; (potassium channel), (sleeping pills), EMG



SE 2.10

Term Coefficient 95% CI SE t statistic DF pIntercept 3.07 1.33 to 4.81 0.877 3.50 102 0.0007

Feet 0.1225 -0.0429 to 0.2879 0.08340 1.47 102 0.1449Lower Leg (LL) 0.4831 0.3144 to 0.6517 0.08504 5.68 102 <0.0001Upper Leg (UL) -0.06082 -0.26952 to 0.14788 0.105217 -0.58 102 0.5645

Hip / Buttock Region (HBR) 0.09856 -0.15053 to 0.34765 0.125580 0.78 102 0.4344

Back -0.06282 -0.29603 to 0.17039 0.117576 -0.53 102 0.5943Abdomen 0.0423 -0.3158 to 0.4004 0.18056 0.23 102 0.8153

Chest -0.0869 -0.4501 to 0.2763 0.18311 -0.47 102 0.6361Neck / Head (NH) -0.06607 -0.27538 to 0.14323 0.105522 -0.63 102 0.5326

Hands -0.1052 -0.2954 to 0.0850 0.09590 -1.10 102 0.2753Arms / Shoulder (AS) 0.2446 0.0160 to 0.4731 0.11524 2.12 102 0.0362

Figure 7: Linear Regression Model: Twitching V. Body Part (B)

There is super strong positive correlation between twitching and the

lower leg as well as the arms and shoulders. This should not come as

much of surprise since the lower leg and the arms are two of the most

affected body parts from BFS symptoms. The bottom line is that BFS

sufferers will tend to have strong twitching symptoms in their lower

legs and arms.

31


Remedies Group (RE)

Anti-Convulsants (AC); 0; EMG, sickness1, muscle pain, (chest)

Anti-Depressants (AD); -1.11, numbness

Potassium Channel Drugs (PCD); -0.95; (age), (head), (upper leg),

(stress1), (exercise1), exercise

Sleeping Pills (SP); 0.18; region, hip, (remedies)

Muscle Relaxants (MR); -0.81; (chemicals), sickness, (muscle fatigue)

Homeopathic Treatments (HT); 0.99; numbness

Supplements; -0.83; exercise

Diet; 1.2; sickness, vibration

Acupuncture; -0.48; (sex); (time)

Massage; 0.1; no correlation

Yoga; 0.13; (sex); (EMG)

Benzodiazepine Drugs (BD); 0.39; age, exercise, stress1, (headaches)


R2 0.06Adjusted R2 -0.05

SE 2.54


Anti-Convulsants (AC) 0.0006226-

0.2734014 to 0.2746467 0.13816823 0.00 103 0.9964Anti-Depressants (AD) -0.1761 -0.4897 to 0.1376 0.15814 -1.11 103 0.2681

Potassium Channel Drugs (PCD) -0.6713 -2.0695 to 0.7270 0.70503 -0.95 103 0.3433

Sleeping Pills (SP) 0.02569 -0.25187 to 0.30325 0.139951 0.18 103 0.8547Muscle Relaxants (MR) -0.1367 -0.4700 to 0.1967 0.16808 -0.81 103 0.4181

Homeopathic Treatments (HT) 0.2113 -0.2121 to 0.6347 0.21347 0.99 103 0.3246Supplements -0.1376 -0.4673 to 0.1921 0.16623 -0.83 103 0.4098

32


Diet 0.2273 -0.1493 to 0.6039 0.18989 1.20 103 0.2341Acupuncture -0.1221 -0.6235 to 0.3794 0.25282 -0.48 103 0.6303

Massage 0.01742 -0.31329 to 0.34814 0.166753 0.10 103 0.9170Yoga 0.02319 -0.33952 to 0.38589 0.182884 0.13 103 0.8994

Benzodiazepine Drugs (BD) 0.04001 -0.16197 to 0.24199 0.101842 0.39 103 0.6952

Figure 8: Linear Regression Model: Twitching V. Remedies (RE)

There is no moderate or strong correlation to suggest any remedies

work very well to alleviate the twitching symptom.

Various Group (V)

Remedies; -0.4; EMG, sickness1, cramps, back, massage

Day; 2.27; twitching, (sensitivity to temperatures), (head)

Time; 4.86; (stress), exercise1, twitching, cramps, lower leg

Missing; 1.91; prescription drugs, history, sickness1, back, anti-

convulsants

Altitude; 0.05; years diagnosed



SE 2.28


Remedies -0.2534 -1.5071 to 1.0004 0.63289 -0.40 114 0.6897Time 0.5327 0.3154 to 0.7500 0.10972 4.86 114 <0.0001Day 0.5712 0.0724 to 1.0701 0.25182 2.27 114 0.0252

Missing 1.379 -0.053 to 2.811 0.7228 1.91 114 0.0590Altitude 0.04856 -1.77122 to 1.86835 0.918623 0.05 114 0.9579

33


Figure 9: Linear Regression Model: Twitching V. Various (V)

There is super strong positive correlation between twitching and how it

affects us during the day and over time. Once again, this should come

as no surprise since BFS sufferers feel twitching 76.4% of the time.

Oddly, there is also moderate correlation between missing remedies

from the survey and making twitching symptoms worse. I cannot

explain this result.

Defining Unique Groups of BFS

From the above correlation data, we can define unique forms or groups

of BFS that stem from or are triggered by different ailments or

conditions (listed in order from most common type to least common

type). Please note that symptoms or remedies that are not mentioned

merely means there is no strong or moderate correlation, it does not

mean that symptoms do or do not exist or remedies will or will not

work:

1. Stress BFS xxiv– This is the most common classification and it is

the one group that can see its symptoms reduce over time by

managing their anxiety levels. People with stress induced BFS

can also limit the symptoms over certain parts their body. The

data shows exercise works well to reduce stress levels. Stress

34


BFS is more likely to afflict younger people in Europe and stress

will exasperate symptoms. Symptoms include itching while the

neck and head region of the body are most likely to be affected.

Symptoms rarely consist of muscle soreness or cramps

meanwhile, the abdomen and hip and buttock region are rarely

affected. Muscle relaxants, benzodiazepine, and anti-depressants

do not work very well to combat the symptoms.

2. Sickness BFS xxv – The characteristics of this BFS group is that

symptoms generally affect the hands and symptoms are

exasperated by an illness. A person inflicted with a sickness is

more likely to have muscle stiffness, be more sensitive to

temperature, but less likely to get headaches. Diet and Muscle

relaxants are less likely to work to alleviate symptoms, and

people are more apt to get an EMG to rule out ALS and MRI to

rule out MS. But people in this classification of BFS are more

likely to find a remedy that works for them.

3. Prescription Drug BFS xxvi – This category of BFS sufferers

believe their symptoms started following the use of a

prescription drug, most commonly the use of drugs used to

combat infections, attention deficit disorder, and or allergies.

People with this form of the BFS disorder are more likely to

35


reside in the U.S. and stress will make their conditions worse.

Their symptoms will likely include a vibration or buzzing

sensation and it can affect the hip and buttock region as well as

the back (areas where symptoms are generally rare). Symptoms

are less likely to occur in the hands and anti-depressants do not

work well to alleviate symptoms. However, some people have

had success using supplements. This classification has the most

generalized symptoms (over most of their body). People in this

group also feel a potential remedy that works well for them was

not included in the survey. For instance, some writings list beta-

blockers, oxycodone, and cannabis as potential symptoms that

may alleviate symptoms in some BFS sufferers. Since these

remedies were not included in this study there is no data to

suggest what other remedies that BFS sufferers have tried. They

were omitted from the study since first, cannabis is illegal in

most states (did not want to scare away participants) two; beta

blockers are commonly used drugs and three; oxycodone is

extremely addictive and did not want to suggest people try

remedies that they can become addicted to.

4. Exercise BFS xxvii – People with this ailment of BFS believe their

symptoms started due to hard and or strenuous exercise. This

condition is more likely to occur in young men located in the U.S.

36


Exercise will exasperate symptoms. Hence, people classified in

this group can control their symptoms by cutting back on

exercise. Symptoms will be worse in the upper leg and will less

likely consist of headaches and numbness. Anti-seizure,

benzodiazepine, diet, and supplements do not work very well to

combat the disorder.

5. Other BFS – This is a group of people who feel their symptoms

of BFS were triggered by something other than those

classifications previously defined. People in this group are more

likely to experience muscle fatigue and weakness, headaches,

and numbness. People are more likely to get an EMG to rule out

ALS and symptoms will be exasperated by a sickness. Symptoms

are less likely to occur in the abdomen while anti-seizure and

homeopathic treatments do not work well to alleviate symptoms

and in fact may make them worse, but there has been some

success using diet to control symptoms. Symptoms are more

likely to occur or be worse earlier in the day. Symptoms for this

group of BFS sufferers are more likely to be localized (symptoms

happen in the same locations). Other causes of BFS have been

theorized and may include drug addiction, alcohol abuse, or even

gluten sensitivity. xxviii Drug and alcohol addiction were removed

37


from the study because we did not want to influence people from

participating in the survey.

6. History BFS xxix – There is evidence that BFS can be hereditary.

People in this group are more likely to come from the U.S. and

have suffered from symptoms longer than others. Symptoms are

less likely to occur in the arms and shoulders while muscle

relaxants and acupuncture do not work very well to alleviate

symptoms. People in this group feel a remedy that has worked

well for them is missing from the survey.

7. Spine Injury BFS xxx – This is the hardest class to describe

because it does not have very good correlation with other

parameters, but it is easy to identify. People with a spine injury

are more likely to see symptoms in their feet than in their upper

legs and arms. People with a spine injury are also less likely to

find a remedy to work for them probably since they have

permanent damage whereas other classifications do not

necessarily have body damage. Also, people with spine injuries

are more likely to have localized BFS symptoms.

8. Vaccine BFS xxxi – This group of people believe their symptoms

started after a vaccine, most notably a flu shot. This

38


classification of BFS has very little correlation to other

parameters. The symptoms include pins and needles and they

are less likely to have symptoms in the upper leg. Most people in

this group use a remedy that is not mentioned in the survey.

9. Chemical BFS xxxii – This is the rarest classification of people

who believe their symptoms started after being exposed to

chemicals, most notably organophosphates used in pesticides

and herbicides. People in this group are less likely to get an MRI

even though their symptoms will get worse over time. Twitching

is the primary symptom and will most likely occur in the arms.

Symptoms will generally get worse over the course of the day.

Finally, symptoms are less likely to occur in the hip and buttock

region. Symptoms for this classification of BFS sufferers are more

localized and finding a workable remedy is unlikely.

Data Naming Convention:

Since it is impossible to input all the statistical data and graphs (Figure

2 and Figure 3) as well as all the modeling data (Figures 4 through

Figure 9) into this paper for all parameters (there are nearly 400 data

summaries and models), the information can be obtained from my BFS

website or by email. I will keep the survey open and update the

information periodically. The data responses for each parameter

39


(similar to Figure 2 and Figure 3) can be found at:

http://patrickbohan.home.bresnan.net/BFS.htm and click on the link

“BFS Data Summary”. Each tab on this excel file has a unique name

and represents the statistical data summary of one variable or one

model simulation. The tab naming convention used on the “BFS Data

Summary” link is the parameter name and the extension DA (short for

Data). Hence, the tab name Age-DA will contain a statistical data

summary of the Age parameter. In some cases, the name is

abbreviated such as YD-DA is short for Years Diagnosed or YBFS-DA is

short for years with BFS Symptoms. For Remedy (RE) parameters I

used the “BFS No Zero” tab to compute the data summary results (to

eliminate “0” responses where people never tried the remedy). The

naming convention on the excel file tabs is, for example, Diet-NZDA

(NZDA stands for No Zero Data). If the –DA and –NZDA results do not

equal the results on the “Data Summary” tab of the “Survey Data”

excel file, it is because the information on the “Survey Data” is

updated immediately when the data is downloaded. The –DA and –

NZDA data pages are not updated as often. The excel file “BFS

Correlation Summary” (model data similar to figures 4 through 9) can

be obtained via an email request (the file is too big for my website).

The tab naming convention for the “BFS Correlation Summary” link is:

y parameter–x parameter group. For this study, the x variables are

grouped into seven classifications – General (G), Causes / Triggers

40



(CA), Stressers (ST – those variables that can make BFS symptoms

worse), Symptoms (S), Body (B), Remedies (RE), and Various (V). For

instance, the General (G) variables consist of 7 parameters: age, sex,

region, number of years with symptoms (YBFS), years diagnosed (YD),

EMG, and MRI. Hence, the tab names for Age models (when Age is the

y variable) are Age-CA, Age-ST, Age-S, Age-B, Age-RE, and Age-V. In

some cases I abbreviated the y variable names such as YD-CA, YD-ST,

YD-S, YD-B, YD-RE, and YD-V (YD is for Years Diagnosed).

Data Results:

Do the data results make sense? This is a difficult question to answer,

but it is one that scientists, engineers, and mathematicians must try to

address. An eyeball test of some results entered by participants does

not make sense to me. For instance, some people scored their

symptoms and body parts affected by BFS very high (well above

average), but at the same time they claimed that certain remedies

were very helpful. People that scored remedies usefulness for

example, an 8, on a scale of 10, I would have expected that their

symptom average to be no higher than 2. In other words, I would

expect these two categories to be inversely proportional, but that was

not necessarily the case for all respondents. That is the problem with a

subjective questionnaire; each person has its own interpretation of the

41


questions. After all, there are no right or wrong answers, but some of

these responses were omitted as outliers.

There were a few models where one would expect to find strong

correlation between certain x and y parameters. For instance, people

who felt the cause / trigger of their BFS symptoms were stress,

exercise, or sickness, I would therefore naturally hypothesize stress,

exercise, and sickness would make their symptoms worse once they

were stricken with the BFS disorder. So, let’s examine the correlation

models between Causes (CA) and Stressers (ST) to see if this

hypothesis is true. To do so, there are six models we need to evaluate:

Exercise-ST, SA-ST, Sickness-ST, Exercise1-CA, SA1-CA, and Sickness1-

CA (SA is short for Stress / Anxiety). The ST model results are what one

would suspect. Exercise-ST showed strong correlation between people

who felt their illness was caused / triggered by exercise and therefore,

exercise makes their BFS symptoms worse. Conversely, SA-ST and

Sickness-ST showed strong correlation between people who felt their

BFS illness was caused / triggered by stress and or a sickness did

indeed show that stress and or a sickness made their symptoms worse,

respectively. The SA1-CA and Exercise1-CA models were also

predictable. They showed very strong correlation between stress

making their symptoms worse and stress and exercise causing /

triggering the onset of their BFS illness respectively. However, the

42


Sickness1-CA model only showed moderate correlation between

sickness making their symptoms worse and sickness causing /

triggering the onset of their illness (but the sickness variable had the

strongest correlation of all cause (CA) variables. It is also important to

note that the model results for people where stress makes their

symptoms worse also indicates that exercise could help relieve their

symptoms.

Here are a few other correlation model results that make some sense:

only women have primarily used yoga as a remedy; cramping tends to

get worse the older we become; older people primarily have tried

acupuncture; pins and needles primarily occur in our feet; and people

with strong BFS symptoms in their head tend to have headaches. This

would lead me to believe that the results in the survey are fairly

accurate.

The analysis of the above 6 models does, for the most part, illustrate

that data responses for these questions does in fact make sense. Other

model categories would be much harder to deduce if the data makes

sense. However, if the results for some questions make sense then one

can be fairly confident that other model category results are most

likely, fairly accurate. At least we can hope they are.

43


Future Studies:

It is possible to expand this study in the future. For instance, it was

learned that supplements were a good treatment for BFS sufferers

whose symptoms are caused by exercise. However, we do not know

what supplements that people used so this result can be vague and

misleading. However, a future study can pin point what supplements

people used (for example, magnesium, potassium, vitamin D, and

quinine are all common supplements tried by BFS sufferers).

Acknowledgments:

I would like to thank my fellow BFS suffers for taking part in this survey

and study. Without their cooperation we would have failed to obtain a

decent sample size to conduct this study and subsequently bring forth

pertinent statistical information about the BFS ailment.

Discussion:

The best way to characterize a person with BFS is as follows (those

parameters with an average above 65% on Table I): A North American

male about 39 years old who has had symptoms for about 3.5 years

and has been diagnosed with BFS for about 2.25 years. They had a MRI

to prove they do not have MS and a EMG to rule out ALS. This person

believes that stress caused or triggered the symptoms and stress will

44


most likely make symptoms worse. The primary symptom is twitching

in the lower leg and most remedies and drugs do very little to alleviate

the symptoms. The symptoms are not localized and generally occur

randomly throughout the body.

There was a pattern on the “Data Summary” tab for parameters whose

responses were between 1 and 10. Generally speaking, parameters

with high averages (7 or higher) or parameters with low averages (3 or

lower) tended to have lower standard deviations, variances, and

standard errors than parameters with medium averages (4 through 6).

This tells us that parameters with medium averages had mixed results

(some people answered 10 while others answered 1). This means

these parameters are less predictable and do not necessarily define

BFS symptoms. For instance, the results for Symptom (S) variables

illustrates that Twitching (high average) and Itching (low average) had

lower variances (tighter distribution – more predictable) than medium

average symptoms such as Muscle Stiffness (MS), Muscle Pain and

Soreness (MPS), Vibration/Buzzing Sensation (VBS), and Muscle Fatigue

and Weakness (MFW). Hence, BFS can be better defined by saying

sufferers are most likely to exhibit muscle twitching symptoms, but no

itching symptoms, whereas MS, MPS, VBS, and MFW may be a

symptom in some sufferers but not in others – less predictable.

45


The summary of the strong correlation results in Table III can tell us a

lot about the BFS disorder. One thing that sticks out is that 5 of the 11

symptoms showed strong positive correlation to the variable time

indicating the symptoms are becoming worse over time. In fact, BFS

symptoms can get worse over time unless the disorder was triggered

by stress and made worse by stress. Stress is the one thing we can

control to minimize symptoms even though no remedy seems to work

to alleviate stress. In fact, of the nine potential causes only exercise

(benzodiazepines and supplements) and sickness (diet and muscle

relaxants) show some success with remedies (but remember the

amount of relief from these remedies is usually very small – low

statistical averages). We may also be able to draw some other

conclusions from Table III: prescription drugs causing or triggering BFS

leads to the vibration / buzzing symptom; anti-depressants may be the

best remedy to treat the symptom numbness; the itching symptom

primarily occurs on the head; muscle fatigue primarily occurs on the

back; sensitivity to temperatures is primarily caused and triggered by

exercise; exercise may help alleviate headaches; and so forth.

The main premise or hypothesis of this writing is that people afflicted

with BFS have unique symptoms and therefore, there must be many

unique types of the BFS disorder. We have identified 9 types of BFS

including, vaccine, chemical, prescription drug, spine injury, sickness,

46


exercise, stress, history, and other. These forms of BFS are unique

because there is very little overlap between the correlation of

symptoms and remedies for these different BFS classifications. And to

complicate matters, many people believe they have had more than

one potential trigger – meaning they may have a combination of BFS

types. For instance, I believe there may have been a multitude of

triggers for my BFS symptoms – exercise (high altitude climbing and

mountaineering), history (grandmother with Parkinson’s disease),

sickness (had a gamma globulin deficiency that caused me to get

infectious boils), prescription drugs (have taken antibiotics regularly for

folliculitis, and allergy medications), and like many people have

experienced a great deal of stress. It is possible that once afflicted with

BFS that other triggers can make symptoms worse and introduce new

symptoms. I believe this has happened with me over the course of my

life – making my form of BFS unique and therefore, uniquely difficult to

cure and find solutions to alleviate symptoms.

I am not a doctor, but I am an expert on BFS because I am inflicted

with the disorder. I am not a PhD, but I have worked extensively on

data analysis and modeling over my 22 year career as an engineer. I

would offer my services to collect and analyze data for any disorder

free of charge (contact me at [email protected]), as long as

someone will generate the questionnaire or survey. The goal of this

47

mailto:[email protected]


paper is to not only better define and understand BFS, but to give it

the exposure it deserves. At a minimum, if the information in this study

can provide some sense of comfort to the people inflicted with BFS,

then it accomplishes its goal. Remember, stress is a big trigger and

can inflate symptoms – and having BFS creates unneeded stress –

People with BFS are always asking themselves morbid questions: Do I

have MS? Do I have ALS? Am I going to die? If we can alleviate these

fears by showing others are going through the same situation, then we

accomplished one major goal in this writing.

References

48

i Brain, Vol. 125, No. 8, August 2002, "Phenotypic Variants of Autoimmune Peripheral Nerve

Hyperexcitability," by Ian K. Hart, Paul Maddison, John Newsom-Davis, Angela Vincent, and Kerry

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xviii Boslaugh, Watter, Statistics in a Nutshell, page 370xix Boslaugh, Watter, Statistics in a Nutshell, page 59xx Boslaugh, Watter, Statistics in a Nutshell, page 62xxi Boslaugh, Watter, Statistics in a Nutshell, page 58xxii Boslaugh, Watter, Statistics in a Nutshell, page 179xxiii Boslaugh, Watter, Statistics in a Nutshell, page 182xxiv Blexrud, Windbank, Daube, Long Term Follow-up of 121 Patients with Benign Fasciculationsxxv ^ http://www.ailments.com/296/Benign_Fasciculation_Syndrome.htmlxxvi Blexrud, Windbank, Daube, Long Term Follow-up of 121 Patients with Benign Fasciculationsxxvii http://www.ailments.com/296/Benign_Fasciculation_Syndrome.htmlxxviii Blexrud, Windbank, Daube, Long Term Follow-up of 121 Patients with Benign Fasciculationsxxix Hart, Maddison, Dave, Vincent, Mills, Phenotypic Variants of Autoimmune Peripheral Nerve Hyperexcitabilityxxx http://www.ailments.com/296/Benign_Fasciculation_Syndrome.htmlxxxi Blexrud, Windbank, Daube, Long Term Follow-up of 121 Patients with Benign Fasciculationsxxxii http://www.ailments.com/296/Benign_Fasciculation_Syndrome.html

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