Zhu, T. Y., Rothenbühler, M., Hamvas, G., Hofmann, A., Welter, J., Kahr, M., Kimmich, N., Shilaih, M., & Leeners, B. (2021). The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study. Journal of medical Internet research, 23(6), e20710. https://doi.org/10.2196/20710
Zhu TY, Rothenbühler M, Hamvas G, Hofmann A, Welter J, Kahr M, Kimmich N, Shilaih M, Leeners B. The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study. Journal of medical Internet research. 2021;23(6):e20710. doi:10.2196/20710
Zhu, T. Y., et al. "The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study." Journal of medical Internet research, vol. 23, no. 6, 2021, pp. e20710.
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Wrist skin temperature was more sensitive than BBT for ovulation
In a Swiss diagnostic accuracy study of 57 healthy women, wrist skin temperature worn overnight showed a temperature shift in about 62 of 100 ovulatory cycles. Basal body temperature (BBT), taken by mouth on waking, did so in about 23 of 100. Home LH tests served as the check.
Key Findings
Among 170 ovulatory cycles, 106 (62.4%) showed at least one temperature shift on wrist skin temperature, against 39 (22.9%) on BBT (P<.001).
Sensitivity was 0.62 (95% CI 0.55-0.70) for wrist skin temperature and 0.23 (0.17-0.30) for BBT. Specificity was 0.26 (0.10-0.48) and 0.70 (0.47-0.87).
Among 23 cycles with no LH surge, 17 (74%) showed a wrist temperature shift and 7 (30%) showed a BBT shift (P=.004).
Ovulation had occurred in 86.2% of cycles with a wrist temperature shift and 84.8% of cycles with a BBT shift.
Both temperatures rose after ovulation, and the wrist rise was larger: 0.50 °C against 0.20 °C.
Interpretation
Participants ranged from 18 to 35 years old and came from a convenience sample. Of the 57, 43 were White. Results describe that group. The study counted the day after a positive LH test as ovulation and used no ultrasound. Participants were not planning pregnancy, so no pregnancy outcomes were measured. Researchers excluded 73 of 266 collected cycles for missing data. One author works for the wrist device maker, and another sits on its advisory board. The authors advise against relying on either temperature alone to avoid pregnancy.
RRM Context
Restorative reproductive medicine uses cycle charting, so each sign of ovulation matters. A temperature shift and an LH surge are both indirect signs. The study compared one with the other.
Our editorial summary of this paper, not the article's abstract.
Abstract
Background
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.
Objective
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.
Methods
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.
Results
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.
Conclusions
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.
Duffy JMN et al., 2020·Human Reproduction·Free full text on PubMed Central
Can a core outcome set to standardize outcome selection, collection and reporting across future infertility research be developed? A minimum data set, known as a core outcome set, has been developed for randomized controlled trials (RCTs) and systematic reviews evaluating potential treatments for infertility. Complex issues, including a failure to consider the perspectives of people with fertility problems when selecting outcomes, variations in outcome definitions and the selective reporting of outcomes on the basis of statistical analysis, make the results of infertility research difficult to interpret. STUDY DESIGN, SIZE, A three-round Delphi survey (372 participants from 41 countries) and consensus development workshop (30 participants from 27 countries). MAIN The core outcome set consists of: viable intrauterine pregnancy confirmed by ultrasound (accounting for singleton, twin and higher multiple pregnancy); pregnancy loss (accounting for ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy); live birth; gestational age at delivery; birthweight; neonatal mortality; and major congenital anomaly. Time to pregnancy leading to live birth should be reported when applicable. Embedding the core outcome set within RCTs and systematic reviews should ensure the comprehensive selection, collection and reporting of core outcomes. Research funding bodies, the SPIRIT statement, and over 80 specialty journals, including Cochrane Gynaecology and Fertility Group, Fertility and Sterility and Human Reproduction, have committed to implementing this core outcome set.
Goodale BM et al., 2019·J Med Internet Res·Free full text on PubMed Central
Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.
Shilaih M et al., 2017·Biosci Rep·Free full text on PubMed Central
Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests that taking one's daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone (LH) test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82%), with the lowest cycle temperature occurring in the fertile window 41% of the time. Most temporal shifts (307/357, 86%) occurred on ovulation day (OV) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, the present study suggests that WST could be a promising, convenient parameter for future multiparameter fertility awareness methods.
Fertility and Outcomes · Conception After Excision
To investigate the prevalence of miscarriage in women with endometriosis (WwE) compared with disease-free control women (CW).
Cross-sectional analysis nested in a retrospective observational study (n = 940).
Hospitals and associated private practices. PATIENT(S): Previously pregnant women (n = 268) within reproductive age in matched pairs. INTERVENTION(S): Retrospective analysis of surgical reports and self-administered questionnaires. MAIN OUTCOME MEASURE(S): Rate of miscarriage, subanalysis for fertility status (≤12 vs. >12 months' time to conception), endometriosis stages (revised American Society of Reproductive Medicine classification [rASRM] I/II vs. III/IV) and phenotypic localizations (superficial peritoneal, ovarian, and deep infiltrating endometriosis). RESULT(S): The miscarriage rate was higher in WwE (35.8% [95% confidence interval 29.6%-42.0%]) compared with CW (22.0% [16.7%-27.0%]); adjusted incidence risk ratio of 1.97 (95% CI 1.41-2.75). This remained significant in subfertile WwE (50.0% [40.7%-59.4%]) vs. CW (25.8% [8.5%-41.2%]) but not in fertile WwE (24.5% [16.3%-31.6%]) vs. CW (21.5% [15.9%-26.8%]). The miscarriage rate was higher in women with milder forms (rASRM I/II 42.1% [32.6%-51.4%] vs. rASRM III/IV 30.8% [22.6%-38.7%], compared with 22.0% [16.7%-27.0%] in CW), and in women with superficial peritoneal endometriosis (42.0% [32.0%-53.9%]) compared with ovarian endometriosis (28.6% [17.7%-38.7%]) and deep infiltrating endometriosis (33.9% [21.2%-46.0%]) compared with CW (22.0% [16.7%-27.0%]). CONCLUSION(S): Mild endometriosis, as in superficial lesions, is related to a great extent of inflammatory disorder, possibly leading to defective folliculogenesis, fertilization, and/or implantation, presenting as increased risk of miscarriage. TRIAL NCT02511626.
Shpaichler Y et al., 2025·Sensors·Free full text on PubMed Central
Several studies have evaluated the reliability of using temperature sensors placed in different locations on the body to identify the day of ovulation. However, such demonstrations are lacking for axillary temperature wearable devices. This study aimed to evaluate the accuracy with which an axillary temperature armband sensor (Tempdrop) identifies the day of ovulation and the fertile window, using the Clearblue Connected Ovulation Test System as the reference method. A total of 194 cycles were analyzed from 125 women that participated in the study between April 2023 and June 2024. The performance parameters were high: the sensitivity (96.8% (95% CI 95.6; 97.7)), specificity (99.1% (98.8; 99.4)), accuracy (98.6% (98.2; 98.9)), positive predictive value (96.8% (95.6; 97.7)) and negative predictive value (99.1% (98.8; 99.4)). Furthermore, the results revealed a remarkably clear and better-than-expected change in temperature around the time of ovulation. This axillary temperature wearable sensor is an effective alternative to urine ovulation tests for determining the timing of ovulation. Another advantage is that it provides a clear temperature curve that can be used to evaluate the quality of the luteal phase.
Maijala A et al., 2019·BMC Womens Health·Free full text on PubMed Central
Body temperature is a common method in menstrual cycle phase tracking because of its biphasic form. In ambulatory studies, different skin temperatures have proven to follow a similar pattern. The aim of this pilot study was to assess the applicability of nocturnal finger skin temperature based on a wearable Oura ring to monitor menstrual cycle and predict menstruations and ovulations in real life. Volunteer women (n = 22) wore the Oura ring, measured ovulation through urine tests, and kept diaries on menstruations at an average of 114.7 days (SD 20.6), of which oral temperature was measured immediately after wake-up at an average of 1.9 cycles (SD 1.2). Skin and oral temperatures were compared by assessing daily values using repeated measures correlation and phase mean values and differences between phases using dependent t-test. Developed algorithms using skin temperature were tested to predict the start of menstruation and ovulation. The performance of algorithms was assessed with sensitivity and positive predictive values (true positive defined with different windows around the reported day). Nocturnal skin temperatures and oral temperatures differed between follicular and luteal phases with higher temperatures in the luteal phase, with a difference of 0.30 °C (SD 0.12) for skin and 0.23 °C (SD 0.09) for oral temperature (p < 0.001). Correlation between skin and oral temperatures was found using daily temperatures (r = 0.563, p < 0.001) and differences between phases (r = 0.589, p = 0.004). Menstruations were detected with a sensitivity of 71.9-86.5% in window lengths of ±2 to ±4 days. Ovulations were detected with the best-performing algorithm with a sensitivity of 83.3% in fertile window from - 3 to + 2 days around the verified ovulation. Positive predictive values had similar percentages to those of sensitivities. The mean offset for estimations were 0.4 days (SD 1.8) for menstruations and 0.6 days (SD 1.5) for ovulations with the best-performing algorithm. Nocturnal skin temperature based on wearable ring showed potential for menstrual cycle monitoring in real life conditions.
Shilaih M et al., 2017·Sci Rep·Free full text on PubMed Central
An affordable, user-friendly fertility-monitoring tool remains an unmet need. We examine in this study the correlation between pulse rate (PR) and the menstrual phases using wrist-worn PR sensors. 91 healthy, non-pregnant women, between 22-42 years old, were recruited for a prospective-observational clinical trial. Participants measured PR during sleep using wrist-worn bracelets with photoplethysmographic sensors. Ovulation day was estimated with "Clearblue Digital-Ovulation-urine test". Potential behavioral and nutritional confounders were collected daily. 274 ovulatory cycles were recorded from 91 eligible women, with a mean cycle length of 27.3 days (±2.7). We observed a significant increase in PR during the fertile window compared to the menstrual phase (2.1 beat-per-minute, p < 0.01). Moreover, PR during the mid-luteal phase was also significantly elevated compared to the fertile window (1.8 beat-per-minute, p < 0.01), and the menstrual phase (3.8 beat-per-minute, p < 0.01). PR increase in the ovulatory and mid-luteal phase was robust to adjustment for the collected confounders. There is a significant increase of the fertile-window PR (collected during sleep) compared to the menstrual phase. The aforementioned association was robust to the inter- and intra-person variability of menstrual-cycle length, behavioral, and nutritional profiles. Hence, PR monitoring using wearable sensors could be used as one parameter within a multi-parameter fertility awareness-based method.
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.
Fertility Awareness › Effectiveness › Comparative Effectiveness · Menstrual Cycle › Cycle Biomarkers › Basal Body Temperature · Research Methods › Study Design › Cohort Studies
Györgyi Hamvas, Mohaned Shilaih, Brigitte Leeners
G Hamvas, M Shilaih, B Leeners
PMID 34100763 34100763 DOI 10.2196/20710 10.2196/20710 Zhu et al. 2021, Zhu 2021
Cite this article
Zhu, T. Y., Rothenbühler, M., Hamvas, G., Hofmann, A., Welter, J., Kahr, M., Kimmich, N., Shilaih, M., & Leeners, B. (2021). The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study. Journal of medical Internet research, 23(6), e20710. https://doi.org/10.2196/20710
Zhu TY, Rothenbühler M, Hamvas G, Hofmann A, Welter J, Kahr M, Kimmich N, Shilaih M, Leeners B. The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study. Journal of medical Internet research. 2021;23(6):e20710. doi:10.2196/20710
Zhu, T. Y., et al. "The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study." Journal of medical Internet research, vol. 23, no. 6, 2021, pp. e20710.
Keywords
BBT, basal body temperature, diagnostic accuracy, fertility, menstruation, mobile phone, oral temperature, ovulation, sensor, thermometer, wearable, wrist skin temperature
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