Objectives: There is a lack of data on salivary sex hormones across the menstrual cycle (MC) or hormonal contraceptive (HC) cycle of elite athletes. We aimed to provide original data on salivary sex hormones (17β-estradiol, progesterone and free testosterone) in naturally menstruating female athletes with a regular cycle or irregular cycle and using combined HC. A secondary purpose was to compare these data with published data from the general population according to the menstrual status (MC or HC or irregularly menstruating). Methods: 367 saliva tests were performed on 44 elite athletes during 6 months of follow-up to certify for cycle regularity. Athletes were grouped into regular MC, n=13; irregular MC, n=5; and HC, n=26. We compared salivary data of regular MC across six cycle phases (menses, mid-follicular, late follicular, early luteal, mid-luteal and late luteal phases) with published data from women with a similar MC or HC status from the general population. Results: We provided salivary original data according to six sub-phases among elite athletes with regular MC. HC athletes showed lower salivary sex hormonal levels, markedly after the first week of active HC compared with regular MC. Athletes with irregular cycles do not show a progesterone rise from the first half to the second half of the cycle (Δirregular=0.38 (1.90), a rise detectable within regular MC group ΔregularMC=2.86 (2.88)). Conclusions: We provided original data for salivary sex hormone levels in elite female athletes. These references may be valuable for research investigating MC or combined HC data, particularly in longitudinal follow-ups requiring repeated measurements.

The purpose of the study is to identify variations in wellness parameters among elite female athletes, considering simultaneously the influence of natural menstrual cycle (MC) or hormonal contraception (HC) phases and weekly and seasonal variations. A total of 54 elite female athletes were followed up with through a 6-month daily monitoring of their reported fitness, sleep quality, mood, and menstrual symptoms. Estimated cycle phases, days of the week, and seasons were all integrated into a single model to study cyclicities concomitantly. A total of 263 cycles were analyzed, totaling 7370 pieces of daily self-reported data. We observed more frequent symptoms during the menstruation and the pause phase. Reported fitness was greater in the mid-cycle and mood was lower at the end of the cycle for the MC group. Wellness parameters were rather stable for HC users. For both groups, symptoms were more frequent in summer. Reported fitness levels were lower on Monday, while mood scores were highest on Saturday and Sunday. In summer and autumn, fitness, sleep quality, and mood were greater. A cyclical wellness rhythm was identified in the menstrual cycle and persists even when considering weekdays and seasons cyclicities. This comprehensive approach helps to identify physiological influence on human behaviors, considering the specificities of women.

Little is known about the relationship between sleep quality and quantity associated with mood, fitness, and menstrual cycle status in elite athletes. This longitudinal prospective cohort study explores these relationships in a French cohort of elite athletes in preparation for the Olympic Games. Self-reported sleep patterns, quality, mood, fitness and menstrual cycles were monitored daily via smartphone applications from February 2021 to February 2024, involving 108 athletes and 13 852 observations. Generalized Estimating Equations were used to evaluate associations between these variables across three menstrual cycle status groups (i.e regular, irregular, or under hormonal contraception). Sleep duration significantly correlated with self-reported sleep quality (β = 0.179), fitness (β = 0.241), and mood (β = 0.097) across all menstrual cycle status groups. Sleeping less than 8 h led to below-average scores in these parameters. Late bedtimes, especially after 11 PM for athletes with irregular cycles and after midnight for all groups, negatively impacted sleep quality, fitness, and mood. Athletes using hormonal contraception reported slightly longer sleep durations than those with regular (p < 0.04) or irregular cycles (p < 0.02). Despite individual variations, the cohort's average sleep duration (8.5 ± 1.2 h) aligned with recommended guidelines. Sleep patterns significantly influence mood and fitness similarly among elite athletes with a regular or irregular menstrual cycle or under hormonal contraception. Further studies are needed to clarify the role of hormonal status in sleep and its long-term implications for athlete health.

Background: Wearable and connected digital devices continuously generate large volumes of real-world behavioral, physiological, and environmental data, offering new opportunities for health monitoring and personalized care. Digital phenotyping has emerged as a promising paradigm; yet, there is limited consensus regarding how such data should be analyzed. Inconsistent analytical practices and insufficient methodological reporting may compromise reproducibility, comparability, and the validity of findings. Objective: This scoping review examined current analytical practices in digital phenotyping in health research, identified methodological gaps, and highlighted the need for transparency and standardized approaches. Methods: Literature searches were conducted in PubMed, Scopus, Embase, and Web of Science, with Google Scholar used as a complementary source. Eligible studies were published up to December 31, 2024, involved human populations, and used wearable digital devices in longitudinal health research. Studies were screened independently by multiple reviewers using predefined eligibility criteria. Data extraction focused on 6 methodological domains: sample size determination, variable selection and definition, data cleaning and preprocessing, digital phenotyping methods, predictive modeling, and statistical significance handling in large-scale data contexts. Results: A total of 162 studies were included. Most studies were published from 2018 onward (n=144, 89%) and were conducted primarily in North America (n=92, 57%). Activity trackers (n=81, 50%), smartphones (n=35, 22%), accelerometers (n=26, 16%), and smartwatches (n=24, 15%) were the most frequently used devices. The most common outcomes were activity level (n=67, 41%), sleep (n=65, 40%), step counts (n=63, 39%), and heart rate (n=46, 28%). Heterogeneity and limited reporting were observed across all methodological domains. Preprocessing was the most frequently reported component (n=88, 54%), although specific aspects such as missing-data handling remained inconsistently described (n=35, 22%). Sample size determination methods were reported in only 30% (n=48) of studies, and variable selection methods were described in 27% (n=43). Digital phenotyping approaches were identified in 30% (n=48) of studies and predominantly used regression-based models. Predictive modeling approaches were reported in 17% (n=28) of studies, with substantial diversity in algorithms and limited reporting of validation procedures. Only 4.3% (n=7) of studies explicitly discussed statistical challenges related to large or high-dimensional datasets. Practices varied across health fields, with no domain consistently demonstrating comprehensive reporting across all methodological components. Conclusions: Digital phenotyping research is expanding rapidly, but methodological practices remain heterogeneous and insufficiently standardized. By providing a cross-domain overview of how wearable-derived longitudinal data are currently processed and analyzed in health research, this review highlights recurring gaps in the reporting and justification of analytical choices, particularly regarding sample size determination, preprocessing, variable definition, predictive modeling, and statistical inference. These findings emphasize the need for clearer analytical frameworks and more consistent reporting practices to improve transparency, reproducibility, and methodological rigor in wearable-based digital health research. Trial Registration: PROSPERO CRD420251234000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251234000

BACKGROUND Wearable and connected digital devices continuously generate large volumes of real-world behavioral, physiological, and environmental data, offering new opportunities for health monitoring and personalized care. Digital phenotyping has emerged as a promising paradigm; yet, there is limited consensus regarding how such data should be analyzed. Inconsistent analytical practices and insufficient methodological reporting may compromise reproducibility, comparability, and the validity of findings. OBJECTIVE This scoping review examined current analytical practices in digital phenotyping in health research, identified methodological gaps, and highlighted the need for transparency and standardized approaches. METHODS Literature searches were conducted in PubMed, Scopus, Embase, and Web of Science, with Google Scholar used as a complementary source. Eligible studies were published up to December 31, 2024, involved human populations, and used wearable digital devices in longitudinal health research. Studies were screened independently by multiple reviewers using predefined eligibility criteria. Data extraction focused on 6 methodological domains: sample size determination, variable selection and definition, data cleaning and preprocessing, digital phenotyping methods, predictive modeling, and statistical significance handling in large-scale data contexts. RESULTS A total of 162 studies were included. Most studies were published from 2018 onward (n=144, 89%) and were conducted primarily in North America (n=92, 57%). Activity trackers (n=81, 50%), smartphones (n=35, 22%), accelerometers (n=26, 16%), and smartwatches (n=24, 15%) were the most frequently used devices. The most common outcomes were activity level (n=67, 41%), sleep (n=65, 40%), step counts (n=63, 39%), and heart rate (n=46, 28%). Heterogeneity and limited reporting were observed across all methodological domains. Preprocessing was the most frequently reported component (n=88, 54%), although specific aspects such as missing-data handling remained inconsistently described (n=35, 22%). Sample size determination methods were reported in only 30% (n=48) of studies, and variable selection methods were described in 27% (n=43). Digital phenotyping approaches were identified in 30% (n=48) of studies and predominantly used regression-based models. Predictive modeling approaches were reported in 17% (n=28) of studies, with substantial diversity in algorithms and limited reporting of validation procedures. Only 4.3% (n=7) of studies explicitly discussed statistical challenges related to large or high-dimensional datasets. Practices varied across health fields, with no domain consistently demonstrating comprehensive reporting across all methodological components. CONCLUSIONS Digital phenotyping research is expanding rapidly, but methodological practices remain heterogeneous and insufficiently standardized. By providing a cross-domain overview of how wearable-derived longitudinal data are currently processed and analyzed in health research, this review highlights recurring gaps in the reporting and justification of analytical choices, particularly regarding sample size determination, preprocessing, variable definition, predictive modeling, and statistical inference. These findings emphasize the need for clearer analytical frameworks and more consistent reporting practices to improve transparency, reproducibility, and methodological rigor in wearable-based digital health research. CLINICALTRIAL PROSPERO CRD420251234000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251234000