Smartphone vs Manual Interpretation of Biomarkers for Ovulation and Luteal Phase Detection (SMOM Study)

NCT ID: NCT07248046

Last Updated: 2026-01-13

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

30 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-15

Study Completion Date

2026-11-15

Brief Summary

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This study will compare different combinations of fertility signs (cervical mucus (CM), luteinizing hormone \[LH\], pregnanediol glucuronide \[PDG\], and basal body temperature \[BBT\]) to determine which are most reliable for identifying ovulation and luteal phase length. Thirty existing Premom App users will track daily observations for three menstrual cycles. Participants will record mucus, perform urine tests, upload test strip photos to the Premom App, and measure BBT. Both participant readings and AI-assisted app readings will be analyzed. The main goal is to find which marker pairings give the most accurate picture of ovulation timing and luteal phase length. Secondary goals include understanding ease of use, the number of tests required, and whether the app improves accuracy.

Detailed Description

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This study examines how well different combinations of fertility signs-cervical mucus changes, hormone levels in urine, and body temperature-can help identify ovulation and luteal phase length. The study uses the Premom App, which allows users to record fertility signs and take photos of urine hormone test strips for AI-assisted interpretation. Four marker pairs will be compared: Mucus plus PDG, LH plus PDG, Mucus plus BBT, and LH plus BBT. Each participant will track three cycles, with both app-based and user-based readings analyzed. The study will assess which marker pairings are most accurate and user-friendly. Secondary endpoints include usability, test burden, and app vs. participant agreement. Results may support improvements in fertility awareness and digital health tools. Risks are minimal and involve time commitment for daily tracking. \*\*This is an observational study. Participants are not assigned to any intervention; all fertility signs collected are part of their existing self-tracking using the Premom App. The study analyzes these naturally occurring data to compare marker pairings for identifying ovulation and luteal phase length.\*\*

Conditions

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Fertility Mobile Applications Artifical Intelligence Cervical Mucus Body Temperature Luteinizing Hormone (LH) Ovulation Menstrual Cycle Progesterone

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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Premom App Users

Women aged 16 to 45 already using the Premom App, who will track daily fertility signs over 3 menstrual cycles.

Premom App-Assisted Fertility Tracking

Intervention Type OTHER

Participants use the Premom smartphone application (Easy Healthcare Corporation) to log daily fertility indicators - cervical mucus (CM), urinary luteinizing hormone (LH) strips, urinary pregnanediol glucuronide (PDG) strips, and basal body temperature (BBT) - over three menstrual cycles. Both participant-read and AI-interpreted app readings are compared to identify ovulation and luteal phase length. \*\*This is an observational study. Participants are not assigned to any intervention; all fertility signs collected are part of their existing self-tracking using the Premom App. The study analyzes these naturally occurring data to compare marker pairings for identifying ovulation and luteal phase length.\*\*

Interventions

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Premom App-Assisted Fertility Tracking

Participants use the Premom smartphone application (Easy Healthcare Corporation) to log daily fertility indicators - cervical mucus (CM), urinary luteinizing hormone (LH) strips, urinary pregnanediol glucuronide (PDG) strips, and basal body temperature (BBT) - over three menstrual cycles. Both participant-read and AI-interpreted app readings are compared to identify ovulation and luteal phase length. \*\*This is an observational study. Participants are not assigned to any intervention; all fertility signs collected are part of their existing self-tracking using the Premom App. The study analyzes these naturally occurring data to compare marker pairings for identifying ovulation and luteal phase length.\*\*

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Female, aged 16 to 45
* Natural menstrual cycles equal or less than 35 days
* Off hormonal contraception for more than 3 months
* Current user of the Premom App
* Willing to track cervical mucus, LH, PDG, and BBT for 3 full cycles
* Lives within 50 km of study site in the Ottawa region
* Able to provide informed consent

Exclusion Criteria

* Pregnant or breastfeeding
* Current hormonal therapy or contraception
* Known anovulatory disorders, e.g., Polycystic Ovary Syndrome, hypothalamic amenorrhea.
* Very irregular or absent cycles
* Not using the Premom App
* Unable or unwilling to complete tracking or provide consent
Minimum Eligible Age

16 Years

Maximum Eligible Age

45 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

Yes

Sponsors

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Bruyère Health Research Institute.

OTHER

Sponsor Role lead

Responsible Party

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Rene Leiva

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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St. Laurent Ideal Clinic

Ottawa, Ontario, Canada

Site Status RECRUITING

Countries

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Canada

Central Contacts

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Rene A Leiva, MD

Role: CONTACT

6135626262 ext. 1398

Facility Contacts

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Rene Leiva, MD

Role: primary

6135626262 ext. 1398

References

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Youngster M, Luz A, Baum M, Hourvitz R, Reuvenny S, Maman E, Hourvitz A. Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles. Fertil Steril. 2023 Nov;120(5):1004-1012. doi: 10.1016/j.fertnstert.2023.07.008. Epub 2023 Jul 23.

Reference Type BACKGROUND
PMID: 37490977 (View on PubMed)

• Wang L, He L, Li J, Zeng D, Wen Y. T Line and C Line Detection and Ratio Reading of the Ovulation Test Strip Based on Deep Learning. In: Intelligent Data Engineering and Automated Learning - IDEAL 2021. Springer, Cham. Lecture Notes in Computer Science. 2021 Nov 23;(13113):625-636. https://www.researchgate.net/publication/356488630_T_Line_and_C_Line_Detection_and_Ratio_Reading_of_the_Ovulation_Test_Strip_Based_on_Deep_Learning

Reference Type BACKGROUND

Potluri V, Kathiresan PS, Kandula H, Thirumalaraju P, Kanakasabapathy MK, Kota Sai Pavan S, Yarravarapu D, Soundararajan A, Baskar K, Gupta R, Gudipati N, C Petrozza J, Shafiee H. An inexpensive smartphone-based device for point-of-care ovulation testing. Lab Chip. 2018 Dec 18;19(1):59-67. doi: 10.1039/c8lc00792f.

Reference Type BACKGROUND
PMID: 30534677 (View on PubMed)

Pattnaik S, Das D, Venkatesan VA. Validation of urinary reproductive hormone measurements using a novel smartphone connected reader. Sci Rep. 2023 Jun 7;13(1):9227. doi: 10.1038/s41598-023-36539-w.

Reference Type BACKGROUND
PMID: 37286704 (View on PubMed)

Mendizabal-Ruiz G, Paredes O, Alvarez A, Acosta-Gomez F, Hernandez-Morales E, Gonzalez-Sandoval J, Mendez-Zavala C, Borrayo E, Chavez-Badiola A. Artificial Intelligence in Human Reproduction. Arch Med Res. 2024 Dec;55(8):103131. doi: 10.1016/j.arcmed.2024.103131. Epub 2024 Nov 29.

Reference Type BACKGROUND
PMID: 39615376 (View on PubMed)

Leiva RA, Bouchard TP, Abdullah SH, Ecochard R. Urinary Luteinizing Hormone Tests: Which Concentration Threshold Best Predicts Ovulation? Front Public Health. 2017 Nov 28;5:320. doi: 10.3389/fpubh.2017.00320. eCollection 2017.

Reference Type BACKGROUND
PMID: 29234665 (View on PubMed)

Leiva R, Ecochard R. Helping Patients to Predict and Confirm Ovulation with the Use of Combined Urinary Hormonal and Smartphone Technology: A Proof-of-Concept Retrospective Descriptive Case Series. Semin Reprod Med. 2024 Jun;42(2):90-99. doi: 10.1055/s-0044-1791702. Epub 2024 Oct 8.

Reference Type BACKGROUND
PMID: 39379045 (View on PubMed)

Leiva R, McNamara-Kilian M, Niezgoda H, Ecochard R, Bouchard T. Pilot observational prospective cohort study on the use of a novel home-based urinary pregnanediol 3-glucuronide (PDG) test to confirm ovulation when used as adjunct to fertility awareness methods (FAMs) stage 1. BMJ Open. 2019 May 27;9(5):e028496. doi: 10.1136/bmjopen-2018-028496.

Reference Type BACKGROUND
PMID: 31133596 (View on PubMed)

Gibbons T, Reavey J, Georgiou EX, Becker CM. Timed intercourse for couples trying to conceive. Cochrane Database Syst Rev. 2023 Sep 15;9(9):CD011345. doi: 10.1002/14651858.CD011345.pub3.

Reference Type BACKGROUND
PMID: 37709293 (View on PubMed)

Ecochard R, Leiva R, Bouchard T, Boehringer H, Iwaz J, Plotton I. Descriptive analysis of the relationship between progesterone and basal body temperature across the menstrual cycle. Steroids. 2022 Feb;178:108964. doi: 10.1016/j.steroids.2022.108964. Epub 2022 Jan 20.

Reference Type BACKGROUND
PMID: 35065994 (View on PubMed)

Ecochard R, Leiva R, Bouchard T, Boehringer H, Direito A, Mariani A, Fehring R. Use of urinary pregnanediol 3-glucuronide to confirm ovulation. Steroids. 2013 Oct;78(10):1035-40. doi: 10.1016/j.steroids.2013.06.006. Epub 2013 Jul 4.

Reference Type BACKGROUND
PMID: 23831784 (View on PubMed)

Ecochard R, Duterque O, Leiva R, Bouchard T, Vigil P. Self-identification of the clinical fertile window and the ovulation period. Fertil Steril. 2015 May;103(5):1319-25.e3. doi: 10.1016/j.fertnstert.2015.01.031. Epub 2015 Feb 24.

Reference Type BACKGROUND
PMID: 25724738 (View on PubMed)

Duane M, Stanford JB, Porucznik CA, Vigil P. Fertility Awareness-Based Methods for Women's Health and Family Planning. Front Med (Lausanne). 2022 May 24;9:858977. doi: 10.3389/fmed.2022.858977. eCollection 2022.

Reference Type BACKGROUND
PMID: 35685421 (View on PubMed)

Bouchard TP, Fehring RJ, Schneider M. Pilot Evaluation of a New Urine Progesterone Test to Confirm Ovulation in Women Using a Fertility Monitor. Front Public Health. 2019 Jul 2;7:184. doi: 10.3389/fpubh.2019.00184. eCollection 2019.

Reference Type BACKGROUND
PMID: 31312631 (View on PubMed)

Bouchard TP. Using Quantitative Hormonal Fertility Monitors to Evaluate the Luteal Phase: Proof of Concept Case Study. Medicina (Kaunas). 2023 Jan 10;59(1):140. doi: 10.3390/medicina59010140.

Reference Type BACKGROUND
PMID: 36676764 (View on PubMed)

Related Links

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https://doi.org/10.12688/f1000research.138006.1

• G. Maroju R, G. Choudhari S, Shaikh MK et al. Application of artificial intelligence-based strategies for promotion of family planning in India: a scoping review \[version 1; peer review: awaiting peer review\]. F1000Research 2023, 12:1447

https://www.ncbi.nlm.nih.gov/books/NBK546686/

• Steward K, Raja A. Physiology, Ovulation And Basal Body Temperature. \[Updated 2023 Jul 17\]. In: StatPearls \[Internet\]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-.

Other Identifiers

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REB-2026-56

Identifier Type: -

Identifier Source: org_study_id

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