research

1. research Charles E. Henley, D.O. Associate Dean for Clinical Affairs Marian University College of Osteopathic Medicine

2. Clinic Project • You are a partner in a family medicine practice and would like to improve the glycemic control of your type ll diabetic patients. You have heard about the use of patient report cards and wonder if it would work in your practice. You are also wondering how to do a study to find the answer, so you use the planning algorithm: P= population ( all type ll diabetic patients in the practice) I = Intervention ( patients receive report cards about their progress, on A1C, Weight, Blood Pressure, group A) C= control group ( Usual care. Patients do not receive report cards, ( group B) O= outcomes ( which group shows the most improvement over time in markers for improved control).

3. Clinic Project • Patients were randomized into groups A or B based on the last digit of their SS # either an odd or even. At the end of 4 and 6 months the data was assessed to see which group had better outcomes. • Group A showed a significant lowering of A1C as well as BP control and weight loss • Group B showed some improvement but not as much and BP and weight stayed the same. • You conclude that patient report cards are a valuable method for maintaining glucose control

4. Clinic Project • This was a prospective clinical trial. • The independent variable was report cards +/- and dependent variables were BP, Weight and A1C. • The research question was whether or not report cards would improve clinical outcomes and the hypothesis was yes , they could. • Because the variables were both bivariate and continuous, the hypothesis testing would consist of a simple t test, done by computer after the data was entered. • This was research, and it started with a simple observation and idea for helping patients.

5. Where do research ideas come from? • Things you see every day in your clinical practice and have questions about. • Things you read about. ( the use of report cards) • Things you hear about in conferences and meetings. • Questions that students or residents ask • Things from the lay press or news stories. • Questions from patients. • Conversations with colleagues. • Quality improvement activities in the hospital. • From your own curiosity.

6. What are the barriers to research? • 1. Too time consuming, I need to see patients to make money • 2. Too costly, I would need a grant. • 3. I don’t have the training in statistics • 4. I would need to hire a consultant ( see #2) • 5. I don’t know what I could do

7. Barriers can be overcome • Time: In the report card study, the nursing staff and clerks handled the report card logistics and patient education about the report cards. They also put the data into the computer. the physician just saw the patients as they would normally. • Cost: minimal to design a report card and print them out in the clinic. • Training:simple data entry on a spread sheet and rudimentary knowledge of how to think about study methods. Computers do most of the work. • Ideas: we just listed all the places good ideas come from.

8. Why do research • To answer a clinical question from practice • Intellectual curiosity • Interest in the topic • Intellectual challenge • Enjoy asking questions and getting answers • Break from routine of practice • To be part of some other group or organization (PBN, Med School).

9. MUCOM • Clinical faculty interests

10. Mike Kuchera • Doctor of osteopathic medicine (DO) degree from the Kirksville • College of Osteopathic Medicine (KCOM) • He is studying the effects of exercise in multiple sclerosis, and the role of osteopathic manipulative treatment (OMT) and other biomechanical approaches in improving function and quality of life in Parkinson's Disease. He has also done research on the efficacy of OMT in carpal tunnel syndrome and ankle sprains.

11. Thad Wilson PhD U Utah • Research interests are in thermal physiology, eccrine sweat glands, and the interaction between the environment and health / disease. He also has a special interest in sympathetic nervous system responses to manual therapies. • Charles Henley, DO, MPH • Research interests in heart rate variability as a surrogate for autonomic nervous system activity and how this is influenced through OMM.

12. Research Training • The more you know about the rules of science and methodology and various options for testing a hypothesis the more enjoyment you get out of asking questions. • No special math skills are required; computers do the heavy lifting. • Must have an innate curiosity about what we do and why we do it that way. (HRT and the woman’s health initiative) • There are tools that can help • You will all receive a copy of the research workbook, “Every Doc can do Research” Just follow the directions.

13. Getting started • Asking the research question. • An essential ingredient in any quality research project is a well-defined research question. • The question should be simple and specific - a small, clearly defined project is always preferable to one that is large and vague. • (e.g. Should we treat pregnant women who are carriers for Group B strep (GBS)?) • Search the Literature; OVID or PUBMED. • When you are done reading these articles, you should have a good idea of what has been accomplished, how it was done, the problems that others have had in their studies, and how the results of your study can add to the existing field of knowledge. • write a summary of your literature search,

14. Organizing Data • There are many good reasons to organize data • 1. to make sense of it. • 2. To see it clearly • 3. To understand it better • 4. To be able to manipulate it • 5. To test it • Some data is descriptive or observational and is meant to show or describe something • Some data is trying to answer a question in a structured way.

15. Data Reductionfrequency distribution Random numbers ( test scores, pulse rates, systolic BP, etc.) 95.00, 100.75, 126.00,114.00.134.25, 116.75,.97.5,102.25, 110.00, 125.00, 144.00, 112.00, 82.5, 135.00, 115.25, 132.00, 105.00, 118.25, 121.75, 109.25, 136.00, 124.00, 91.00, 148.00, 125.25, 88.5, 133.25, 95.00, 104.5, 135.00, 108.25, 122.25, 107.25, 137.00, 114.00 124.00, 118.00, 119.00, 117.25, 03.25, 115.00, 116.5, 99.5, 106.00, 103.75, 115.25, 128.5, 105.00 Of questionable value, no trends, cannot tell much from this data

16. Data Reduction • Array in ascending order; lowest to highest • 82.5, 88.5, 91.0, 93.25, 95.0, 99,5, 100.75, 102.25, 105.00, 106, 107.25, 108.25, 109.25, 110.0, 112.0, 113.25, 114.0, 115.0 115.5, 115.75, 116.5, 116.75, 118.0, 119.0, 121.75, 122.5, 124.0, 124.5, 125.0, 125.25, 126.0, 128.5, 128.75, 132.0, 133.25, 134.25, 135.0, 135.5, 136.0, 137.0, 144.0, 148.0 • Orders the data, can now see presence of large concentrations of groups, upper and lower 50% tiles,

17. Data Reduction Now group the data, by intervals of 10 ( or whatever you want) and count the number of variables in each class. This is the frequency distribution of the variables. • 82-92……..3 • 93-103……8 • 104-114….12 • 115-125….14 • 126-136….10 • 137-147…..2 • 148-158…..1 • Graphically Represent the data, (seven classes, intervals of 10,)

18. Frequency Histogram (Frequency distribution) 14 10 12 8 2 2 1 82-92 93-103 104-114 115-125 126-136 137-147 148-158

19. Research Methods • Hypothesis: An educated guess about an association that is testable, i.e. group A is better than group B • Null hypothesis: There is no difference between the groups. • Hypothesis testing: A test statistic is computed based on type of data and compared with a critical value from a statistical table. When the test statistic exceeds the critical value the null hypothesis is rejected and the difference is statistically significant.

20. Study design • P: Cohort of (hypertensive patients) • I: Group A (continuity clinic) • C: Group B (random care) • O: Better BP control yes/no

21. Types of Studies • Heart disease and serum cholesterol • A review of patients records who have had heart attacks reveals that several had elevated serum cholesterol levels • This constitutes a case series.

22. Types of Studies • In the case series you cannot determine whether or not the elevated serum cholesterol is associated with heart attacks unless you compare serum cholesterol in patients with heart attacks to serum cholesterol in patients who did not have heart attacks. This constitutes a prevalence or cross-sectional study

23. Types of Studies • If we were to select persons who had actual heart attacks (cases) and compare them to persons without heart attacks (controls) with respect to a history of elevated serum cholesterol levels, this would be a case-control study. But it does not tell you if the elevated serum cholesterol caused the heart attack.

24. Inference (sampling) • Since we can never test the entire population for a specific characteristic, we must measure it in a sample and infer that this is representative of the larger population. • Is the outcome we see from testing the sample the result of chance? Or does it really exist in the population studied. • If the difference in outcomes between groups is small we need more subjects to show a significance. If the difference is larger, we need fewer subjects.

25. Statistical Significance P values • If the computed statistic is larger than > 0.05 it is outside the limits of the normally distributed parameters and the null hypothesis is correct. When it is < 0.05 we reject the null hypothesis and the observation made from the study is considered statistically correct.

26. Confidence Intervals • Based on a normal distribution with 95% of the curve represented by -1.96 and + 1.96 and a mean of zero. • Any observation (x) falls between -1.96 and +1.96 • X (observed) = (SD)(+/- 2) (2 =1.96 rounded up) • Example: Patient W has BP observation of x=100mmHg. There is an estimated SD =7mmHg. The confidence limits for the mean (m) are: 100- (2)(7)=86 and100+2(7)=114 The 95% confidence interval for (m) is between 86 and 114

27. Normally Distributed Data • Normal distribution of data • 1 SD is 68% of data points • 2 SD is 95% of all data • 3 SD is 99.7 % of data

28. Understanding Data Variables • In experimental research, variables are usually described in terms of being an independent variable or a dependent variable. They can also be confounding variables. • In a study of pancreatic cancer and coffee drinking, the presence of smoking was a confounding variable. • The Independent variable would be coffee drinking

29. Understanding Data Independent variable: • In experimental research the Independent Variable is the variable controlled by the investigator. Example: in a clinical trial for acute bronchitis, the independent variable is the treatment; (antibiotics vs. placebo) • In observational studies, the independent variable is the cause in a cause and effect relationship Example: smoking and lung cancer; smoking is independent variable.

30. Understanding Data • In epidemiologic studies, the independent variable is the exposure Example: H1N1 and the flu, The exposure to virus is the independent variable. • The Dependent variable is the outcome variable, and will be affected by the independent variable in some way. In this example the presence or absence of the flu is the dependent variable.

31. Scales Scales are classified as: • Nominal or Categorical • Ordinal • Ratio or Interval • Continuous or Parametric

32. Scales • Nominal or categorical scales allow classification by name only such as sex, religion, race and the dichotomous scale of yes no. • Ordinal scales have an order but the distance between may not be the same. i.e., The distance between fair and good may not be the same as the distance between good and great.

33. Scales • Ratio or Interval scales are ordered like ordinal scales but the distance between scales is the same. Ratio scales also have an absolute zero. • Continuous or parametric scales are just that… continuous. age, BP, test scores, are all continuous and have a Gaussian distribution of normality. The data is evenly distributed across a standard bell shaped curve and treatment of data is by parametric analysis.

34. Selecting a statistical test(test of the hypothesis) • Identify the independent, dependent and confounding variables and the type of scale or data being used.(example). • If the independent and dependent variables are both nominal (binomial or two values), then the selection of a test will be one of the non parametric tests such as chi-square. • Both variables are nominal: Rogaine vs. placebo and hair growth (yes/no) = chi-square

35. Bivariate Analysis

36. Using the information in a study • Study question (hypothesis): Does continuity of care improve BP control? • Independent variable: continuity of care • Dependent variable: BP control: yes/no • Scale: Nominalbivariate(dependent variable) • Scale: Nominalbivariate (independent Variable) • Statistic (Hypothesis testing): chi-square

37. Article on heart rate variability • Osteopathic Medicine and Primary Care • BioMedCentral • Research • Open Access • Osteopathic manipulative treatment and its relationship to autonomic nervous system activity as demonstrated by heart rate variability: a repeated measures studyCharles E Henley1, Douglas Ivins1, Miriam Mills2, Frances K Wen1 and Bruce A Benjamin*3

38. HRV Study • A theoretical basis for the action of OMT and its effect in the body has been advanced based on autonomic activation causing concomitant vasodilatation, smooth muscle relaxation, and increased blood flow, resulting in improved range of motion, decrease in pain perception, and/or change in tissue.

39. HRV Study • Indirect methods have been developed and refined to provide noninvasive markers of autonomic balance, with heart rate variability (HRV) being commonly used. HRV is based on the inherent variation of the R to R intervals of a standard electro- cardiogram (ECG), with these variations largely due to changes in autonomic balance at the sinus node.

40. Methods • Patients were first placed in the supine position on a tilt table and measurements of sympathetic activity and vagal tone were made using the LF/HF ratios. • Patient were then placed at a 50 degree angle which put them into a strong sympathetic environment. • Muscle energy OMM was then performed in this upright position and a strong parasympathetic response was observed.

41. Conclusion The vagal response produced by the myo- fascialrelease procedure in the maximally stimulated sympathetic environment could only have come from the application of the OMT. This demonstrates the association between OMT and the autonomic nervous system. The lack of significance between control and sham in all positions indicates that HRV may be a useful method of developing sham controls in future studies of OMT.

42. The End