Prior, J. C., Vigna, Y. M., Schulzer, M., Hall, J. E., & Bonen, A. (1990). Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak. Clinical and investigative medicine. Medecine clinique et experimentale, 13(3), 123-131.
Prior JC, Vigna YM, Schulzer M, Hall JE, Bonen A. Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak. Clin Invest Med. 1990;13(3):123-131.
Prior, J. C., et al. "Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak." Clinical and investigative medicine. Medecine clinique et experimentale, vol. 13, no. 3, 1990, pp. 123-131.
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Basal temperature data are known to provide unreliable assessments of luteal phase length when they are evaluated by qualitative, visual-pattern methods. This study of 24 cycles in 24 women compared the serum LH peak day with the luteal phase onset day determined by three quantitative basal temperature methods: a) a new computerized least mean square method developed by the authors; b) the mean temperature method reported by Vollman; and c) a computerized version of the World Health Organization cumulative sum method of Royston. The luteal phase onset day determined by the three quantitative basal temperature methods, (a, b, and c) correlated well with the midcycle LH peak (r = 0.879, 0.891, and 0.791, respectively, all p less than 0.001). The cumulative sum method, however, was only able to analyze 19/24 cycles. The mean delay between the LH peak day and the luteal phase onset day determined by thermal shift was 2.4 +/- 1.5, 2.7 +/- 1.4, and 4.1 +/- 2.0 d (mean +/- SD), respectively. The mean temperature method, but not the other two methods, showed an increasing delay between the LH peak day and the thermal shift day with longer follicular phase lengths. Rectal and oral temperature data from the same cycle give identical luteal onset days when analyzed by the least mean square and mean temperature methods, but discrepant days by the cumulative sum analysis. The least mean square technique is a reliable and precise method for population documentation of luteal phase lengths.
Guthardt Y et al., 2026·Sci Rep·Free full text on PubMed Central
This systematic review and meta-analysis examined the relationship between menstrual cycle phases and the incidence of muscle injuries in female team sport athletes, following PRISMA 2020 and PERSiST guidelines. A comprehensive search was conducted in PubMed, Scopus, and SPORTDiscus from inception to mid-January 2024. Studies were included if they examined female team sport athletes of reproductive age with regular menstrual cycles and compared the occurrence of muscle injuries across at least two menstrual phases. Studies involving hormonal contraceptive use, medications affecting the menstrual cycle or musculoskeletal system, or menstrual dysfunction were excluded. Three studies met the inclusion criteria, involving 318 participants. Meta-analysis yielded a pooled Risk Ratio of 1.18 (95% CI: 0.75 to 1.86, p = 0.46) for injury risk between the luteal and follicular phases, suggesting no statistically significant association. However, the certainty of the cumulative evidence was rated as very low due to methodological limitations, including inconsistent phase classifications and reliance on imprecise methods for identifying menstrual phases. Consequently, no practical or clinical recommendations can be made at this time. Future research employing standardised, physiologically accurate methods for classifying and detecting menstrual cycle phases is necessary to better understand the potential links between hormonal fluctuations and injury risk.
To assess the reliability of the most widely used clinical methods for predicting or confirming ovulation. We monitored spontaneous cycles in 101 infertile women using basal body temperature (BBT), transvaginal ultrasound, a urinary stick system for LH surge, and three serum progesterone measurements in the midluteal phase. Transvaginal ultrasound monitoring was standard for ovulation detection and sensitivity. We calculated specificity and accuracy of each method compared with that standard. Follicular development and ultrasound evidence of ovulation were confirmed in 97 of 101 cycles (96%). Urinary LH surge preceded follicular rupture assessed by ultrasonography in all cycles and showed concordance with ultrasound-evidenced ovulation in 98 of 101 cases. The timing of BBT nadir had wide variability, and BBT and ultrasonography agreed in a similar percentage of cases (74%). Midluteal serum progesterone assessments showed ovulatory values in 93 subjects, and ovulation was concordant with ultrasonography in 90 subjects. Urinary LH was accurate in predicting ovulation with ultrasonography as the standard for detection, but time varied widely. The nadir of BBT predicted ovulation poorly. The BBT chart was less accurate for confirming ovulation, whereas a single serum progesterone assessment in midluteal phase seemed as effective as repeated serum progesterone measures.
Bedford JL et al., 2009·Eur J Obstet Gynecol Reprod Biol
To assess computerised least-squares analysis of quantitative basal temperature (LS-BT) against urinary pregnanediol glucuronide (PdG) as an indirect measure of ovulation, and to evaluate the stability of LS-QBT to wake-time variation. Cross-sectional study of 40 healthy, normal-weight, regularly menstruating women aged 19-34. Participants recorded basal temperature and collected first void urine daily for one complete menstrual cycle. Evidence of luteal activity (ELA), an indirect ovulation indicator, was assessed using Kassam's PdG algorithm, which identifies a sustained 3-day PdG rise, and the LS-QBT algorithm, by determining whether the temperature curve is significantly biphasic. Cycles were classified as ELA(+) or ELA(-). We explored the need to pre-screen for wake-time variations by repeating the analysis using: (A) all recorded temperatures, (B) wake-time adjusted temperatures, (C) temperatures within 2h of average wake-time, and (D) expert reviewed temperatures. Relative to PdG, classification of cycles as ELA(+) was 35 of 36 for LS-QBT methods A and B, 33 of 34 (method C) and 30 of 31 (method D). Classification of cycles as ELA(-) was 1 of 4 (methods A and B) and 0 of 3 (methods C and D). Positive predictive value was 92% for methods A-C and 91% for method D. Negative predictive value was 50% for methods A and B and 0% for methods C and D. Overall accuracy was 90% for methods A and B, 89% for method C and 88% for method D. The day of a significant temperature increase by LS-QBT and the first day of a sustained PdG rise were correlated (r=0.803, 0.741, 0.651, 0.747 for methods A-D, respectively, all p<0.001). LS-QBT showed excellent detection of ELA(+) cycles (sensitivity, positive predictive value) but poor detection of ELA(-) cycles (specificity, negative predictive value) relative to urinary PdG. Correlations between the methods and overall accuracy were good and similar for all analyses. Findings suggest that LS-QBT is robust to wake-time variability and that expert interpretation is unnecessary. This method shows promise for use as an epidemiological tool to document cyclic progesterone increase. Further validation relative to daily transvaginal ultrasound is required.
To determine the ability of luteal phase length determined by basal body temperature (BBT) pattern and a midluteal serum progesterone level to predict the result of an endometrial biopsy in a subsequent cycle. We performed a retrospective analysis of 141 women with a history of infertility who were being evaluated for luteal function. The luteal phase length determined from a BBT chart of one menstrual cycle was compared to a single midluteal serum progesterone level from a second menstrual cycle. These findings were compared to a luteal phase endometrial biopsy performed in a third menstrual cycle. Subjects were divided into four groups depending upon luteal phase length (normal 11 or more days) and serum progesterone level (normal at least 10 ng/mL). The four groups were designated "normal," "short luteal phase," "low progesterone," and "abnormal," depending upon the results of the two tests. The frequency of in- and out-of-phase endometrial biopsy results in the four groups was compared. There was no difference in the occurrence of an in- or out-of-phase endometrial biopsy when the four groups were compared. Neither luteal phase length nor a single midluteal serum progesterone level was predictive of subsequent in-phase or out-of-phase endometrial biopsy.
Zhu TY et al., 2021·Journal of medical Internet research·Free full text on PubMed Central
As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation. This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles. This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases. Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P<.001) and had a higher true-positive rate (54.9% vs 20.2%) for detecting ovulation; however, it also had a higher false-positive rate (8.8% vs 3.6%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2% for wrist skin temperature and 84.8% for BBT. Both temperatures had low negative predictive values (8.8% for wrist skin temperature and 10.9% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves. For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures.
Research Methods › Measurement and Statistics › Instrument Development and Validation · Menstrual Cycle › Cycle Biomarkers › Basal Body Temperature · Fertility Awareness › Methods › Sympto-Thermal Method
Jerilynn C Prior
J Prior
PMID 2364587 2364587 Prior et al. 1990, Prior 1990
Cite this article
Prior, J. C., Vigna, Y. M., Schulzer, M., Hall, J. E., & Bonen, A. (1990). Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak. Clinical and investigative medicine. Medecine clinique et experimentale, 13(3), 123-131.
Prior JC, Vigna YM, Schulzer M, Hall JE, Bonen A. Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak. Clin Invest Med. 1990;13(3):123-131.
Prior, J. C., et al. "Determination of luteal phase length by quantitative basal temperature methods: validation against the midcycle LH peak." Clinical and investigative medicine. Medecine clinique et experimentale, vol. 13, no. 3, 1990, pp. 123-131.
