My first independent Vancouver research was with women runners in the late 1970s. There was a North American “aerobics” fad at the time, plus women were rallying to be allowed to run longer races than 10 kilometers. I had to create the tools I needed. I don’t remember why, except for a sense of completeness, that I knew
ovulation was important. Only after release of an egg was
progesterone produced. I wanted to understand not only about cycle length—that would give me an idea of
estrogen production—but also about
progesterone.
I needed to study ovulation. Other than a
progesterone blood test that was expensive and very difficult to time right, there was no other way than basal body temperature (BBT) at that time.
I was also upset that the current women runner published studies were short-term, in very young and stressed women, often college/university students living in dorms, staying up all night to cram, and going out partying the next. Results of study after similar study declared that longer distance running caused
amenorrhea (no flow for 3 or more months). These studies measured lots of
hormones but didn’t assess
ovulation or track the length of time
progesterone was high, the
luteal phase.
I confirmed by studying the early literature that progesterone caused the morning temperature to increase3. I also realized early on, by asking university kinesiology student volunteers to plot their first morning temperatures, that BBT was full of errors (even in this educated, science-studying group). And also there was already research showing that the BBT method was inaccurate4.
Development and Validation of the QBT© Method
If the temperature increase reflected
progesterone, I needed to make it into something accurate and reliable. Working with an MD statistician, Michael Schulzer, we figured women could simply record the temperature (on a list), and if we developed a mathematical way to interpret it, we might have the
ovulation tool we needed. The
Quantitative Basal Temperature© (QBT©) method took all the temperatures in a single cycle and created a valid statistical way to analyze them. When we found the day at which there was the most difference between two average within-cycles temperatures, we confirmed that these were significantly different. As we were working to develop our
ovulation test, I found the now-classical monograph by Rudolph Vollman
5 in which he reported on thousands of cycles of
rectal first-morning temperature data from about 600 women. He had also developed a quantitative way of analyzing a cycle’s data, called the “mean temperature method.”
I knew we needed to validate our new quantitative tests of
ovulation. At a Canadian sports medicine conference, I learned that a colleague in Halifax was studying running in young women and had funding to do daily mid-cycle
blood tests to measure the
luteinizing hormone (
LH) peak that triggers
ovulation. We agreed that they would provide their temperature data which we would analyze independently. We would then compare that quantitative temperature change information with the
LH peak6. We found a strong relationship between the
LH peak day and the temperature change day. We had validated our
QBT© test!
Research Findings and Further Developments
Runner friends of mine and I worked together keeping track of our time and miles of running, and our temperature data. We found that the longest runs were associated with shortening of the
luteal phases, although the lengths of cycles and the presence of
ovulation stayed the same
7. I was even able, with two women friends, one who was training to run her first marathon, and the other who was struggling to become pregnant again, to collect one-year records of changes in cycles and
ovulation alongside running distances. We wrote up the data with the two runners as co-authors. When we couldn’t get this study published, we sent it as a letter to the
Lancet8. This was “patient-reported data” long before that became a well-funded fad.
I also got frustrated with the many “disease-suggesting” questions on premenstrual symptom (
PMS) tools. I decided to make a simpler kind of daily
Menstrual Cycle Diary©. We needed to record more objective cycle things like flow (as soaked normal-sized pads or tampons each day) and
cramps on a 0-4 scale. This tool required writing something for each item for each day—a 0 was fine. We included other cycle-related things like fluid retention, frustration and breast tenderness also on a 0-4 scale. We also asked about other daily life experiences but that have no zero. These included changes in breast size, appetite, interest in sex, and feeling of self-worth and energy. Here we used a scale oriented around the woman’s usual (U) with letters higher and lower to show change. I also asked women to keep a record of how many minutes of running or other moderate-to-intense exercise each day plus minutes of things like walking.
Commitment to Participants
From the start I committed to share results with participants. Interestingly ethics boards still do not require it; for-profit drug studies never reveal anything to “subjects.” We not only always share each participant’s data with them, but also disclose the whole study’s final results. That means long before the study is published, but asking them not to share, or today, not to put it on social media. In the early days we would share study results through a party: one such was a pot-luck spaghetti supper! Later we would hold a gathering just for participants. Currently we use a password protected study-specific section on the CeMCOR website. This sharing of results creates a feeling of teamwork since CeMCOR’s participants are really co-researchers.
Publishing Challenges and Triumphs
As a recognition of the investment of participants, plus the public funding involved directly or indirectly,
I never quit trying to get a research study published. My longest effort so far lasted almost 13 years and more than 25 rejections! We had completed a randomized comparative 1-year study of
hot flushes in women who had undergone bilateral surgical removal of
ovaries plus their
uterus for non-cancer reasons when they were still menstruating. We compared treatment with
estrogen (then PremarinÒ) and
progestin (ProveraÒ) recording
night sweats and daytime flushes daily over one year.
Everyone then, and still, knew that
estrogen would be far superior. However, the results showed that
progestin was as effective at hot flush treatment as estrogen in these women at the very highest risk for severe symptoms.
I have learned that to become a good clinical researcher requires a lot more than conventional academic smarts. It requires curiosity, determination, and the courage to try out new ideas you think are good. It also needs a commitment to share results with the people you engage as participants by treating them as responsible co-researchers. Finally, a successful researcher, whose results change the current concepts9 must have a stubborn determination to publish and widely share the new and controversial information they have learned, no matter what the barriers10.
Thank you for being part of the CeMCOR community!
Dr. Jerilynn C. Prior
Reference List:
1. Copp DH, Cameron EC, Cheney BA, et al. Evidence for calcitonin--a new hormone from the parathyroid that lowers blood calcium. Endocrinology 1962;70:638-49.
2. Cameron EC, Boyd RM, Luk D. Cortical thickness measurements and photon absorptiometry for determination of bone quality. Can Med Assoc J 1977;116:145-49.
3. Magallon DT, Masters WH. Basal temperature studies in the aged female: influence of estrogen, progesterone, and androgen. J Clin Endocrinol Metab 1950;10(5):511-18. doi: https://doi.org/ 10.1210/jcem-10-5-511 [published Online First: 1950/05/01]
4. Johansson ED, Larsson-Cohn U, Gemzell C. Monophasic basal body temperature in ovulatory menstrual cycles. American Journal Obstetrics Gynecology 1972;113:933-37.
5. Vollman RF. The menstrual cycle. In: Friedman EA, ed. Major Problems in Obstetrics and Gynecology, Vol 7. Toronto: W.B. Saunders Company 1977:11-193.
6. Prior JC, Vigna YM, Schulzer M, et al. Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak. Clinical & Investigative Medicine 1990;13:123-31.
7. Prior JC, Cameron K, Ho Yeun B, et al. Menstrual cycle changes with marathon training: anovulation and short luteal phase. Canadian Journal of Applied Sport Science 1982;7:173-77.
8. Prior JC, Ho Yeun B, Clement P, et al. Reversible luteal phase changes and infertility associated with marathon training. Lancet1982;1:269-70.
9. Kuhn TS. The structure of scientific revolution. Chicago: University of Chicago Press 1970.
10. Kalidasan D, Goshtasebi A, Chrisler J, et al. Prospective analyses of sex/genderrelated publication decisions in general medical journals: editorial rejection of population- based women’s reproductive physiology. BMJ Open 2022 doi: 10.1136/bmjopen-2021-057854
