The Link Between Age and Partner Preferences in a Large, International Sample of Single Women.

Women's capacity to reproduce varies over the life span, and developmental goals such as family formation are age-graded and shaped by social norms about the appropriate age for completing specific developmental tasks. Thus, a woman's age may be linked to her ideas about what an ideal partner should be like. With the goals of replicating and extending prior research, in this study we examined the role of age in women's partner preferences across the globe. We investigated associations of age with ideal long-term partner preferences in a cross-cultural sample of 17,254 single (i.e., unpartnered) heterosexual women, ages 18 to 67, from 147 countries. Data were collected via an online questionnaire, the Ideal Partner Survey. Confirming our preregistered hypotheses, we found no or only negligible age effects on preferences for kindness-supportiveness, attractiveness, financial security-successfulness, or education-intelligence. Age was, however, positively associated with preferences for confidence-assertiveness. Consistent with family formation goals, age was associated with an ideal partner's parenting intentions (high until approximately age 30, then decreasing afterward). Age range deemed acceptable (and in particular, the discrepancy between one's own age and the minimum ideal age of a partner) increased with age. This latter pattern also replicated in exploratory analyses based on subsamples of lesbian and bisexual women. In summary, age has a limited impact on partner preferences. Of the attributes investigated, only preference for confidence-assertiveness was linked with age. However, age range deemed acceptable and an ideal partner's parenting intention, a dimension mostly neglected in earlier research, substantially vary with age.

Mobile sex-tech apps: How use differs across global areas of high and low gender equality.

Digital technologies are increasingly intertwined into people's sexual lives, with growing scholarly interest in the intersection of sex and technology (sex-tech). However, much of the literature is limited by its over emphasis on negative outcomes and the predominance of work by and about North Americans, creating the impression that sex-tech is largely a Western phenomenon. Based on responses from 130,885 women in 191 countries, we assessed how women around the world interact with mobile technology for sex-related purposes, and whether in areas of greater gender inequality, technological accessibility may be empowering women with knowledge about sexuality. We investigated women's use of technology to find sexual partners, learn about sex and improve their sexual relationships, and track their own sexual health. About one-fifth reported using mobile apps to find sexual partners. This use varied by region: about one-third in Oceania, one-fourth in Europe and the Americas, and one-fifth in Asia and Africa. Staying connected when apart was the most commonly selected reason for app use with a sexual partner. About one-third had used an app to track their own sexual activity. Very few reported that the app they used to improve their sexual relationships was detrimental (0.2%) or not useful (0.6%). Women in countries with greater gender inequality were less likely to have used mobile apps to find a sexual partner, but nearly four times more likely to have engaged in sending and receiving sexts. To our knowledge, this study provides the most comprehensive global data on sex-tech use thus far, demonstrates significant regional variations in sex-tech use, and is the first to examine women's engagement in sex-related mobile technology in locations with greater gender disparities. These findings may inform large-scale targeted studies, interventions, and sex education to improve the lives of women around the world.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.

Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment.

BACKGROUND: Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. OBJECTIVE: The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature's risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. METHODS: A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature's questions. Upon completion of the questionnaire, a Result Screen and Doctor's Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature's probability capabilities. RESULTS: The irregular cycle feature's first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects' risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; P=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (P=.03). CONCLUSIONS: The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.