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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
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
1
BFS Statistical Analysis
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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BFS Statistical Analysis
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
3
BFS Statistical Analysis
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
4
BFS Statistical Analysis
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
5
BFS Statistical Analysis
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).
6
BFS Statistical Analysis
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
7
BFS Statistical Analysis
“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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BFS Statistical Analysis
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).
9
BFS Statistical Analysis
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
10
BFS Statistical Analysis
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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BFS Statistical Analysis
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
12
BFS Statistical Analysis
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
BFS Statistical Analysis
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
14
BFS Statistical Analysis
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
15
BFS Statistical Analysis
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
16
BFS Statistical Analysis
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,
17
BFS Statistical Analysis
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
18
BFS Statistical Analysis
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
19
BFS Statistical Analysis
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
BFS Statistical Analysis
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).
21
BFS Statistical Analysis
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
BFS Statistical Analysis
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).
23
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
n 120 (cases excluded: 5 due to missing values)
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
BFS Statistical Analysis
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
n 120 (cases excluded: 5 due to missing values)
R2 0.09Adjusted R2 0.02
SE 2.50
Term Coefficient 95% CI SE t statistic DF pIntercept 7.997 6.923 to 9.072 0.5422 14.75 110 <0.0001
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
BFS Statistical Analysis
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
n 120 (cases excluded: 5 due to missing values)
R2 0.02Adjusted R2 -0.01
SE 2.54
Term Coefficient 95% CI SE t statistic DF pIntercept 7.325 5.965 to 8.684 0.6864 10.67 116 <0.0001
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
BFS Statistical Analysis
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)
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BFS Statistical Analysis
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
n 113 (cases excluded: 12 due to missing values)
R2 0.38Adjusted R2 0.32
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.
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BFS Statistical Analysis
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)
n 116 (cases excluded: 9 due to missing values)
R2 0.06Adjusted R2 -0.05
SE 2.54
Term Coefficient 95% CI SE t statistic DF pIntercept 7.831 6.951 to 8.712 0.4439 17.64 103 <0.0001
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
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BFS Statistical Analysis
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
n 120 (cases excluded: 5 due to missing values)
R2 0.22Adjusted R2 0.18
SE 2.28
Term Coefficient 95% CI SE t statistic DF pIntercept 3.753 2.169 to 5.337 0.7996 4.69 114 <0.0001
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
BFS Statistical Analysis
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 Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
(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
BFS Statistical Analysis
(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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
BFS Statistical Analysis
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
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