Literature Map

Literature Map

This is not a flat reading list. It is a reading map: start from your background, read the minimum set first, and understand why each paper matters before you expand.

Read by background

Which stack should you read first?

AI / DS readers first

Start with real-world variability, ovulation timing, label differences, and the structure of self-tracked data before you jump to model papers.

Medical / clinical readers first

Start with task framing, wearable sensing papers, and evaluation design so you can see what the AI literature is actually formalizing.

App developers first

Start with real-world variability, app-accuracy papers, and wearable evidence to understand product boundaries before model detail.

Minimum must-read set

Paper	Who should read it first	Why it matters	Link
Wilcox et al. 2000	Everyone	Explains why ovulation is not reliably tied to the midpoint.	PubMed
Bull et al. 2019	App / AI	Shows real-world cycle-length and variability distributions.	PubMed
Li et al. 2020	AI / medical	Shows heterogeneity in large-scale self-tracked data.	PubMed
Su et al. 2017	Medical / AI	Quickest route to understanding ovulation detection methods and their limits as reference labels.	PubMed
Goodale et al. 2019	App / AI / medical	Shows how multimodal wearable evidence is assembled in practice.	PubMed

Foundations: build the right problem view first

Year	Paper	Reading purpose
2000	Wilcox AJ, Dunson D, Baird DD. The timing of the fertile window in the menstrual cycle.	Understand why midpoint heuristics fail.
2016	Moglia ML, Nguyen HV, Chyjek K, et al. Evaluation of Smartphone Menstrual Cycle Tracking Applications.	Understand the gap between app claims and evidence.
2019	Bull JR, Rowland SP, Berglund Scherwitzl E, et al. Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles.	Understand real-world distribution and large-cohort variability.
2020	Li K, Urteaga I, Wiggins CH, et al. Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.	Understand heterogeneity in self-tracked data.

Clinical context: understand labels and medical framing first

Year	Paper	Reading purpose
2017	Su HW, Yi YC, Wei TY, Chang TC, Cheng CM. Detection of ovulation, a review of currently available methods.	Understand why ovulation detection methods do not form one clean ground truth.
2021	ASRM / SREI committee opinion on luteal phase deficiency.	Understand that even clinical labels may remain contested.
2025	Rosen Vollmar AK, Mahalingaiah S, Jukic AM. The Menstrual Cycle as a Vital Sign: a comprehensive review.	Re-anchor the area in recent medical framing.

Wearables: read how signals become evidence

Year	Paper	Reading purpose
2018	Shilaih M, Degroote L, Falco L, et al. Modern fertility awareness methods: wrist wearables capture temperature changes associated with the menstrual cycle.	Understand why wrist temperature entered the field.
2019	Goodale BM, Shilaih M, Falco L, et al. Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window.	Understand the multimodal wearable evidence chain.
2019	Maijala A, Kinnunen H, Koskimäki H, et al. Nocturnal finger skin temperature in menstrual cycle tracking.	Understand ring-based distal temperature value and limits.
2024	Jasinski SR, Presby DM, Grosicki GJ, et al. A novel method for quantifying fluctuations in wearable derived daily cardiovascular parameters across the menstrual cycle.	Understand recent work on wearable cardiovascular fluctuation quantification.

Methods & data: read structure before model names

Year	Paper	Reading purpose
2021	Li K, Urteaga I, Wiggins CH, Elhadad N. A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.	Understand why user behavior and biological behavior should not be conflated.
2022	Symul L, Holmes S. Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models.	Understand how latent-state models handle noisy self-tracked records.
2025	Luo C, Su YF, Ren YY, et al. Prediction of the fertile window and menstruation with a wearable device via machine-learning algorithms.	Understand how recent wearable + ML results should be interpreted cautiously.

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