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 twitch[i]. Other BFS symptoms include muscle fatigue, cramps, pins and needles, muscle vibrations, headaches, itching, sensitivity to temperatures, numbness, muscle stiffness, muscle soreness and pain[ii] [iii] [iv]. Like most neurological 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:
Internet:
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 disorders[v]. 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 debilitating[vi]. 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 disorders[viii]. At this time there is no evidence that BFS sufferers are any more likely to acquire other more serious neurological disorders, such as ALS or MS, than any “normal” person[ix].
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 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 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:
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).
The Survey can also be reached from my BFS webpage: Click on the link “BFS Survey”.
Data:
The excel data file for all 125 responses can be found on my BFS website: 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: Click on the link “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
______
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).
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
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%
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 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 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 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 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 10, and at 100% it is also 10. This concept can be visualized in Figure 2.