The Health4Life e-health intervention for modifying lifestyle risk behaviours of adolescents: secondary outcomes of a cluster randomised controlled trial.

OBJECTIVES: To investigate the effectiveness of a school-based multiple health behaviour change e-health intervention for modifying risk factors for chronic disease (secondary outcomes). STUDY DESIGN: Cluster randomised controlled trial. SETTING, PARTICIPANTS: Students (at baseline [2019]: year 7, 11-14 years old) at 71 Australian public, independent, and Catholic schools. INTERVENTION: Health4Life: an e-health school-based multiple health behaviour change intervention for reducing increases in the six major behavioural risk factors for chronic disease: physical inactivity, poor diet, excessive recreational screen time, poor sleep, and use of alcohol and tobacco. It comprises six online video modules during health education class and a smartphone app. MAIN OUTCOME MEASURES: Comparison of Health4Life and usual health education with respect to their impact on changes in twelve secondary outcomes related to the six behavioural risk factors, assessed in surveys at baseline, immediately after the intervention, and 12 and 24 months after the intervention: binge drinking, discretionary food consumption risk, inadequate fruit and vegetable intake, difficulty falling asleep, and light physical activity frequency (categorical); tobacco smoking frequency, alcohol drinking frequency, alcohol-related harm, daytime sleepiness, and time spent watching television and using electronic devices (continuous). RESULTS: A total of 6640 year 7 students completed the baseline survey (Health4Life: 3610; control: 3030); 6454 (97.2%) completed at least one follow-up survey, 5698 (85.8%) two or more follow-up surveys. Health4Life was not statistically more effective than usual school health education for influencing changes in any of the twelve outcomes over 24 months; for example: fruit intake inadequate: odds ratio [OR], 1.08 (95% confidence interval [CI], 0.57-2.05); vegetable intake inadequate: OR, 0.97 (95% CI, 0.64-1.47); increased light physical activity: OR, 1.00 (95% CI, 0.72-1.38); tobacco use frequency: relative difference, 0.03 (95% CI, -0.58 to 0.64) days per 30 days; alcohol use frequency: relative difference, -0.34 (95% CI, -1.16 to 0.49) days per 30 days; device use time: relative difference, -0.07 (95% CI, -0.29 to 0.16) hours per day. CONCLUSIONS: Health4Life was not more effective than usual school year 7 health education for modifying adolescent risk factors for chronic disease. Future e-health multiple health behaviour change intervention research should examine the timing and length of the intervention, as well as increasing the number of engagement strategies (eg, goal setting) during the intervention. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12619000431123 (prospective).

What set some young adults apart during the COVID-19 pandemic? Mental health trajectories, risk and protective factors in an Australian longitudinal study.

OBJECTIVE: Evidence suggests that young adults (aged 18-34) were disproportionately impacted by the COVID-19 pandemic, but little is known about their longer-term mental health changes beyond the early pandemic period. This article investigates heterogeneous trajectories of mental health among Australian young adults across 2 years of the pandemic and identifies a broad range of associated risk and protective factors. METHOD: Young adults (N = 653, Mage = 27.8 years) from the longitudinal Alone Together Study were surveyed biannually between July 2020 and June 2022. Measures assessed anxiety (7-item Generalised Anxiety Disorder scale) and depression (9-item Patient Health Questionnaire) symptoms at Waves 1-4, as well as demographic, psychological, adversity and COVID-19 factors at baseline. RESULTS: Four and three distinct trajectories of anxiety and depressive symptoms, respectively, were identified through growth mixture modelling. The proportion of participants in each anxiety trajectory were Asymptomatic (45.9%), Mild Stable (17.9%), Moderate-Severe Stable (31.1%) and Initially Severe/Recovering (5.1%). For depression, Mild Stable (58.3%), Moderate-Severe Stable (30.5%) and Reactive/Recovering (11.2%). Baseline factors associated with severe symptom trajectories included a lifetime mental health disorder, pre-pandemic stressful events, identifying as LGBTQIA+ and/or female, and experiencing one or more infection-control measures. Higher household income was protective. CONCLUSION: Most young adults demonstrated stable trajectories of low or high symptoms during the pandemic, with smaller groups showing initially severe or reactive symptoms followed by marked improvements over time. Vulnerable subgroups (gender- or sexuality-diverse, those with prior adversity or pre-existing mental ill-health) may face ongoing impacts and require targeted psychosocial supports to assist their mental health recovery post-COVID-19 and in the event of future crises.

"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research.

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Potential Mediators of a School-Based Digital Intervention Targeting Six Lifestyle Risk Behaviours in a Cluster Randomised Controlled Trial of Australian Adolescents.

Lifestyle risk behaviours-physical inactivity, poor diet, poor sleep, recreational screen time, and alcohol and tobacco use-collectively known as the "Big 6" emerge during adolescence and significantly contribute to chronic disease development into adulthood. To address this issue, the Health4Life program targeted the Big 6 risk behaviours simultaneously via a co-designed eHealth school-based multiple health behaviour change (MHBC) intervention. This study used multiple causal mediation analysis to investigate some potential mediators of Health4Life's effects on the Big 6 primary outcomes from a cluster randomised controlled trial of Health4Life among Australian school children. Mediators of knowledge, behavioural intentions, self-efficacy, and self-control were assessed. The results revealed a complex pattern of mediation effects across different outcomes. Whilst there was a direct effect of the intervention on reducing moderate-to-vigorous physical activity risk, the impact on sleep duration appeared to occur indirectly through the hypothesised mediators. Conversely, for alcohol and tobacco use, both direct and indirect effects were observed in opposite directions cancelling out the total effect (competitive partial mediation). The intervention's effects on alcohol and tobacco use highlighted complexities, suggesting the involvement of additional undetected mediators. However, little evidence supported mediation for screen time and sugar-sweetened beverage intake risk. These findings emphasise the need for tailored approaches when addressing different risk behaviours and designing effective interventions to target multiple health risk behaviours. The trial was pre-registered with the Australian and New Zealand Clinical Trials Registry: ACTRN12619000431123.

Study protocol of the Our Futures Vaping Trial: a cluster randomised controlled trial of a school-based eHealth intervention to prevent e-cigarette use among adolescents.

BACKGROUND: Effective and scalable prevention approaches are urgently needed to address the rapidly increasing rates of e-cigarette use among adolescents. School-based eHealth interventions can be an efficient, effective, and economical approach, yet there are none targeting e-cigarettes within Australia. This paper describes the protocol of the OurFutures Vaping Trial which aims to evaluate the efficacy and cost-effectiveness of the first school-based eHealth intervention targeting e-cigarettes in Australia. METHODS: A two-arm cluster randomised controlled trial will be conducted among Year 7 and 8 students (aged 12-14 years) in 42 secondary schools across New South Wales, Western Australia and Queensland, Australia. Using stratified block randomisation, schools will be assigned to either the OurFutures Vaping Program intervention group or an active control group (health education as usual). The intervention consists of four web-based cartoon lessons and accompanying activities delivered during health education over a four-week period. Whilst primarily focused on e-cigarette use, the program simultaneously addresses tobacco cigarette use. Students will complete online self-report surveys at baseline, post-intervention, 6-, 12-, 24-, and 36-months after baseline. The primary outcome is the uptake of e-cigarette use at 12-month follow-up. Secondary outcomes include the uptake of tobacco smoking, frequency/quantity of e-cigarettes use and tobacco smoking, intentions to use e-cigarettes/tobacco cigarettes, knowledge about e-cigarettes/tobacco cigarettes, motives and attitudes relating to e-cigarettes, self-efficacy to resist peer pressure and refuse e-cigarettes, mental health, quality of life, and resource utilisation. Generalized mixed effects regression will investigate whether receiving the intervention reduces the likelihood of primary and secondary outcomes. Cost-effectiveness and the effect on primary and secondary outcomes will also be examined over the longer-term. DISCUSSION: If effective, the intervention will be readily accessible to schools via the OurFutures platform and has the potential to make substantial health and economic impact. Without such intervention, young Australians will be the first generation to use nicotine at higher rates than previous generations, thereby undoing decades of effective tobacco control. TRIAL REGISTRATION: The trial has been prospectively registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12623000022662; date registered: 10/01/2023).

What Does It Mean to Be Responsive to a Partner's Sexual Needs? Toward a Definition of Sexual Need Responsiveness.

Being responsive to a partner's sexual needs in relationships (i.e., sexual need responsiveness) is associated with higher sexual and relationship satisfaction. Previous research has focused on researcher-led definitions of sexual need responsiveness. The purpose of the present study was to develop a participant-informed definition of sexual need responsiveness. A total of 305 individuals responded to five open-ended questions concerning the definition and behaviors pertaining to sexual need responsiveness. The content analysis results showed that the most common elements in participants' definition included listening to and accommodating each other's sexual needs and wishes whenever possible, while respecting both partners' safety and boundaries. Both verbal and non-verbal communication in various forms was used to express and share needs with partners and participants often evaluated responsiveness in terms of their sexual satisfaction. The results provide a participant-informed definition of sexual need responsiveness and can be used to inform research and clinical practice.

Misogynistic Tweets Correlate With Violence Against Women.

How online social behavior covaries with real-world outcomes remains poorly understood. We examined the relationship between the frequency of misogynistic attitudes expressed on Twitter and incidents of domestic and family violence that were reported to the Federal Bureau of Investigation. We tracked misogynistic tweets in more than 400 areas across 47 American states from 2013 to 2014. Correlation and regression analyses found that misogynistic tweets were related to domestic- and family-violence incidents in those areas. A cross-lagged model showed that misogynistic tweets positively predicted domestic and family violence 1 year later; however, this effect was small. Results were robust to several known predictors of domestic violence. Our findings identify geolocated online misogyny as co-occurring with domestic and family violence. Because the longitudinal relationship between misogynistic tweets and domestic and family violence was small and conducted at the societal level, more research with multilevel data might be useful in the prediction of future violence.

No compelling positive association between ovarian hormones and wearing red clothing when using multinomial analyses.

Several studies report that wearing red clothing enhances women's attractiveness and signals sexual proceptivity to men. The associated hypothesis that women will choose to wear red clothing when fertility is highest, however, has received mixed support from empirical studies. One possible cause of these mixed findings may be methodological. The current study aimed to replicate recent findings suggesting a positive association between hormonal profiles associated with high fertility (high estradiol to progesterone ratios) and the likelihood of wearing red. We compared the effect of the estradiol to progesterone ratio on the probability of wearing: red versus non-red (binary logistic regression); red versus neutral, black, blue, green, orange, multi-color, and gray (multinomial logistic regression); and each of these same colors in separate binary models (e.g., green versus non-green). Red versus non-red analyses showed a positive trend between a high estradiol to progesterone ratio and wearing red, but the effect only arose for younger women and was not robust across samples. We found no compelling evidence for ovarian hormones increasing the probability of wearing red in the other analyses. However, we did find that the probability of wearing neutral was positively associated with the estradiol to progesterone ratio, though the effect did not reach conventional levels of statistical significance. Findings suggest that although ovarian hormones may affect younger women's preference for red clothing under some conditions, the effect is not robust when differentiating amongst other colors of clothing. In addition, the effect of ovarian hormones on clothing color preference may not be specific to the color red.

Standardized protocols for characterizing women's fertility: A data-driven approach.

Experts are divided on whether women's cognition and behavior differs between fertile and non-fertile phases of the menstrual cycle. One of the biggest criticisms of this literature concerns the use of indirect, imprecise, and flexible methodologies between studies to characterize women's fertility. To resolve this problem, we provide a data-driven method of best practices for characterizing women's fertile phase. We compared the accuracy of self-reported methods and counting procedures (i.e., the forward- and backward-counting methods) in estimating ovulation using data from 140 women whose fertility was verified with luteinizing hormone tests. Results revealed that no counting method was associated with ovulation with >30% accuracy. A minimum of 39.5% of the days in the six-day fertile window predicted by the counting methods were non-fertile, and correlations between counting method conception probabilities and actual conception probability were weak to moderate, rs=0.11-0.30. Poor results persisted when using a lenient window for predicting ovulation, across alternative estimators of the onset of the next cycle, and when removing outliers to increase the homogeneity of the sample. By contrast, combining counting methods with a relatively inexpensive test of luteinizing hormone predicted fertility with accuracy >95%, but only when specific guidelines were followed. To this end, herein we provide a cost-effective, pragmatic, and standardized protocol that will allow researchers to test whether fertility effects exist or not.

The key role of specific DSM-5 diagnostic criteria in the early development of alcohol use disorder: Findings from the RADAR prospective cohort study.

BACKGROUND: Prevention and early intervention of alcohol use disorder (AUD) is a public health priority, yet there are gaps in our understanding of how AUD emerges, which symptoms of AUD come first, and whether there are modifiable risk factors that forecast the development of the disorder. This study investigated potential early-warning-sign symptoms for the development of AUD. METHODS: Data were from the RADAR study, a prospective cohort study of contemporary emerging adults across Australia (n = 565, mean age = 18.9, range = 18-21 at baseline, 48% female). Participants were interviewed five times across a 2.5-year period. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) AUD criteria and diagnoses were assessed by clinical psychologists using the Structured Clinical Interview for DSM-IV (SCID-IV), modified to cover DSM-5 criteria. Hazard analyses modeled the time from first alcoholic drink to the emergence of any AUD criteria and determined which first-emergent AUD criteria were associated with a faster transition to disorder. RESULTS: By the final time point, 54.8% of the sample had experienced at least one DSM-5 AUD criterion and 26.1% met criteria for DSM-5 AUD. The median time from first AUD criterion to a diagnosis of AUD was 4 years. Social problems from drinking (hazard ratio [HR] = 3.24, CI95 = 2.14, 4.92, p < 0.001), major role (HR = 2.53, CI95 = 1.58, 4.06, p < 0.001), and drinking larger amounts/for longer than intended (HR = 2.04, CI95 = 1.20, 3.46, p = 0.008) were first-onset criteria associated with a faster transition to AUD. CONCLUSION: In the context of a prospective general population cohort study of the temporal development of AUD, alcohol-related social problems, major role problems, and using more or for longer than intended are key risk factors that may be targeted for early intervention.

Is the association between psychological distress and risky alcohol consumption shifting over time? An age-period-cohort analysis of the Australian population.

This study explored age, period, and cohort effects associated with trends in psychological distress and risky alcohol consumption. Data came from 108,536 Australians aged 14-79 years old from birth cohorts between 1925-2005, endorsing past year alcohol use in the 2004-2019 Australian National Drug Strategy Household Surveys. Risky alcohol consumption was split into exceeding weekly national drinking limits (>10 drinks per week) or daily limits (>4 drinks per day). An extended hierarchical age-period-cohort model was used to investigate differential effects on trends in psychological distress. Psychological distress showed an inverse U-shape throughout the lifespan with a peak in distress at age 60. Exceeding weekly alcohol limits was positively related to psychological distress prior to age 40 while exceeding daily alcohol limits remained positively related across the lifespan. There were relatively flat period effects, with no alcohol-related changes in psychological distress across years. Lastly, psychological distress gradually increased across birth cohorts until a notable spike among Australians born from 1980-2005 alongside weakening alcohol-related cohort effects. Overall, the recent increases in psychological distress did not appear to be meaningfully explained by risky alcohol consumption though risky alcohol consumption remained an important factor to consider alongside psychological distress.

A hidden pandemic? An umbrella review of global evidence on mental health in the time of COVID-19.

Background: The mental health impacts of the COVID-19 pandemic remain a public health concern. High quality synthesis of extensive global literature is needed to quantify this impact and identify factors associated with adverse outcomes. Methods: We conducted a rigorous umbrella review with meta-review and present (a) pooled prevalence of probable depression, anxiety, stress, psychological distress, and post-traumatic stress, (b) standardised mean difference in probable depression and anxiety pre-versus-during the pandemic period, and (c) comprehensive narrative synthesis of factors associated with poorer outcomes. Databases searched included Scopus, Embase, PsycINFO, and MEDLINE dated to March 2022. Eligibility criteria included systematic reviews and/or meta-analyses, published post-November 2019, reporting data in English on mental health outcomes during the COVID-19 pandemic. Findings: Three hundred and thirty-eight systematic reviews were included, 158 of which incorporated meta-analyses. Meta-review prevalence of anxiety symptoms ranged from 24.4% (95%CI: 18-31%, I 2: 99.98%) for general populations to 41.1% (95%CI: 23-61%, I 2: 99.65%) in vulnerable populations. Prevalence of depressive symptoms ranged from 22.9% (95%CI: 17-30%, I 2: 99.99%) for general populations to 32.5% (95%CI: 17-52%, I 2: 99.35) in vulnerable populations. Prevalence of stress, psychological distress and PTSD/PTSS symptoms were 39.1% (95%CI: 34-44%; I 2: 99.91%), 44.2% (95%CI: 32-58%; I 2: 99.95%), and 18.8% (95%CI: 15-23%; I 2: 99.87%), respectively. Meta-review comparing pre-COVID-19 to during COVID-19 prevalence of probable depression and probable anxiety revealed standard mean differences of 0.20 (95%CI = 0.07-0.33) and 0.29 (95%CI = 0.12-0.45), respectively. Conclusion: This is the first meta-review to synthesise the longitudinal mental health impacts of the pandemic. Findings show that probable depression and anxiety were significantly higher than pre-COVID-19, and provide some evidence that that adolescents, pregnant and postpartum people, and those hospitalised with COVID-19 experienced heightened adverse mental health. Policymakers can modify future pandemic responses accordingly to mitigate the impact of such measures on public mental health.

Health4Life eHealth intervention to modify multiple lifestyle risk behaviours among adolescent students in Australia: a cluster-randomised controlled trial.

BACKGROUND: Lifestyle risk behaviours are prevalent among adolescents and commonly co-occur, but current intervention approaches tend to focus on single risk behaviours. This study aimed to evaluate the efficacy of the eHealth intervention Health4Life in modifying six key lifestyle risk behaviours (ie, alcohol use, tobacco smoking, recreational screen time, physical inactivity, poor diet, and poor sleep, known as the Big 6) among adolescents. METHODS: We conducted a cluster-randomised controlled trial in secondary schools that had a minimum of 30 year 7 students, in three Australian states. A biostatistician randomly allocated schools (1:1) to Health4Life (a six-module, web-based programme and accompanying smartphone app) or an active control group (usual health education) with the Blockrand function in R, stratified by site and school gender composition. All students aged 11-13 years who were fluent in English and attended participating schools were eligible. Teachers, students, and researchers were not masked to allocation. Primary outcomes were alcohol use, tobacco use, recreational screen time, moderate to vigorous physical activity (MVPA), sugar-sweetened beverage intake, and sleep duration at 24 months, measured by self-report surveys, and analysed in all students who were eligible at baseline. Latent growth models estimated between-group change over time. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000431123). FINDINGS: Between April 1, 2019, and Sept 27, 2019, we recruited 85 schools (9280 students), of which 71 schools with 6640 eligible students (36 schools [3610 students] assigned to the intervention and 35 [3030 students] to the control) completed the baseline survey. 14 schools were excluded from the final analysis or withdrew, mostly due to a lack of time. We found no between-group differences for alcohol use (odds ratio 1·24, 95% CI 0·58-2·64), smoking (1·68, 0·76-3·72), screen time (0·79, 0·59-1·06), MVPA (0·82, 0·62-1·09), sugar-sweetened beverage intake (1·02, 0·82-1·26), or sleep (0·91, 0·72-1·14) at 24 months. No adverse events were reported during this trial. INTERPRETATION: Health4Life was not effective in modifying risk behaviours. Our results provide new knowledge about eHealth multiple health behaviour change interventions. However, further research is needed to improve efficacy. FUNDING: Paul Ramsay Foundation, the Australian National Health and Medical Research Council, the Australian Government Department of Health and Aged Care, and the US National Institutes of Health.

Definition matters: assessment of tolerance to the effects of alcohol in a prospective cohort study of emerging adults.

BACKGROUND AND AIMS: Tolerance to the effects of alcohol is an important element in the diagnosis of alcohol use disorders (AUD); however, there is ongoing debate about its utility in the diagnosis AUD in adolescents and young adults. This study aimed to refine the assessment of tolerance in young adults by testing different definitions of tolerance and their associations with longitudinal AUD outcomes. DESIGN: Prospective cohort study. SETTINGS: Australia. PARTICIPANTS: A contemporary cohort of emerging adults across Australia (n = 565, mean age = 18.9, range = 18-21 at baseline). MEASUREMENTS: Clinician-administered Structured Clinical Interview for DSM-IV Research Version (SCID-IV-RV) assessed for AUD criteria across five interviews, at 6-month intervals over 2.5 years. Tolerance definitions were operationalized using survey-type response (yes/no), clinician judgement (SCID-IV-RV), different initial drinking quantity and percentage increase thresholds and average heavy consumption metrics. AUD persistence was operationalized by the number of times AUD was present across the 2.5-year study period (n = 491), and new-onset AUD was operationalized as any new incidence of AUD during the follow-up period (n = 461). FINDINGS: The (i) SCID-IV-RV clinician judgement [odds ratio (OR) = 2.50, P = 0.005], (ii) an initial drinking quantity threshold of four to five drinks and 50% minimum increase (OR = 2.48, P = 0.007) and (iii) 50% increase only (OR = 2.40, P = 0.005) were the tolerance definitions more strongly associated with any new onset of AUD throughout the four follow-up time-points than other definitions. However, these definitions were not associated with persistent AUD (Ps > 0.05). Average heavy consumption definitions of tolerance were most strongly associated with persistent AUD (OR = 6.66, P = 0.001; OR = 4.65, P = 0.004) but not associated with new-onset AUD (Ps > 0.05). CONCLUSIONS: Initial drink and percentage change thresholds appear to improve the efficacy of change-based tolerance as an indicator for new-onset alcohol use disorder diagnosis in self-report surveys of young adults. When predicting persistent alcohol use disorder, average heavy consumption-based indicators appear to be a better way to measure tolerance than self-reported change-based definitions.

DSM-5 and ICD-11 alcohol use disorder criteria in young adult regular drinkers: Lifetime prevalence and age of onset.

BACKGROUND: Alcohol Use Disorder (AUD) is a significant contributor to global disease burden. AUD has a relatively early onset during young adulthood (Teesson et al., 2010). However, compared to AUD in adults, we have relatively little understanding of AUD in adolescents and emerging adults. METHODS: The RADAR study is a prospective cohort study designed to investigate the emergence of AUD in community-dwelling adolescents and emerging adults across Australia (age range = 18-21 at baseline). At 6 monthly intervals over 2.5 years, participants were interviewed regarding alcohol consumption and alcohol use disorder criteria by clinical psychologists using the SCID-IV-RV. This paper reports the baseline findings of the RADAR cohort. RESULTS: Proportions of lifetime criteria endorsement among regular drinkers varied considerably. Tolerance was the most endorsed criterion (50.3%), followed by Social Problems (10.4%) and Larger/Longer (9.0%). The median age of onset for most individual AUD criteria was 18 years of age. 18.4% of our cohort met DSM-5 AUD diagnosis in their lifetime to date, and 16.8% met ICD-11 dependence. When removing Tolerance from the AUD criteria, DSM-5 AUD lifetime prevalence reduced to 11.0%, and ICD-11 AUD lifetime prevalence fell to 7.1% in our cohort. CONCLUSIONS: Variable rates of criteria endorsement likely reflect both true differences in the experience of AUD criteria and methodological challenges in the assessment of AUD in an emerging adult age group. High rates of tolerance to the effects of alcohol, and relatively low rates of drinking larger/longer than intended are discussed considering methodological challenges in assessing these criteria in young adults.

Aggression in Women: Behavior, Brain and Hormones.

We review the literature on aggression in women with an emphasis on laboratory experimentation and hormonal and brain mechanisms. Women tend to engage in more indirect forms of aggression (e.g., spreading rumors) than other types of aggression. In laboratory studies, women are less aggressive than men, but provocation attenuates this difference. In the real world, women are just as likely to aggress against their romantic partner as men are, but men cause more serious physical and psychological harm. A very small minority of women are also sexually violent. Women are susceptible to alcohol-related aggression, but this type of aggression may be limited to women high in trait aggression. Fear of being harmed is a robust inhibitor of direct aggression in women. There are too few studies and most are underpowered to detect unique neural mechanisms associated with aggression in women. Testosterone shows the same small, positive relationship with aggression in women as in men. The role of cortisol is unclear, although some evidence suggests that women who are high in testosterone and low in cortisol show heightened aggression. Under some circumstances, oxytocin may increase aggression by enhancing reactivity to provocation and simultaneously lowering perceptions of danger that normally inhibit many women from retaliating. There is some evidence that high levels of estradiol and progesterone are associated with low levels of aggression. We highlight that more gender-specific theory-driven hypothesis testing is needed with larger samples of women and aggression paradigms relevant to women.

The role of mating context and fecundability in women's preferences for men's facial masculinity and beardedness.

The ovulatory shift hypothesis proposes that women's preferences for masculine physical and behavioral traits are greater at the peri-ovulatory period than at other points of the menstrual cycle. However, many previous studies used self-reported menstrual cycle data to estimate fecundability rather than confirming the peri-ovulatory phase hormonally. Here we report two studies and three analyses revisiting the ovulatory shift hypothesis with respect to both facial masculinity and beardedness. In Study 1, a large sample of female participants (N = 2,161) self-reported their cycle phase and provided ratings for faces varying in beardedness (clean-shaven, light stubble, heavy stubble, full beards) and masculinity (-50%, -25%, natural, +25% and +50%) in a between-subjects design. In Study 2, 68 women provided the same ratings data, in a within-subjects design in which fertility was confirmed via luteinising hormone (LH) tests and analysed categorically. In Study 2, we also measured salivary estradiol (E) and progesterone (P) at the low and high fertility phases of the menstrual cycle among 36 of these women and tested whether shifts in E, P or E:P ratios predicted face preferences. Preferences for facial masculinity and beardedness did not vary as predicted with fecundability in Study 1, or with respect to fertility as confirmed via LH in Study 2. However, consistent with the ovulatory shift hypothesis, increasing E (associated with cyclical increases in fecundability) predicted increases in preferences for relatively more masculine faces; while high P (associated with cyclical decreases in fecundability) predicted increases in preferences for relatively more feminine faces. We also found an interaction between E and preferences for facial masculinity and beardedness, such that stubble was more attractive on un-manipulated than more masculine faces among women with high E. We consider discrepancies between our findings and those of other recent studies and suggest that closer scrutiny of the stimuli used to measure masculinity preferences across studies may help account for the many conflicting findings that have recently appeared regarding cycle phase preference shifts for facial masculinity.

High estradiol and low progesterone are associated with high assertiveness in women.

Sexual selection theory posits that women are more selective than men are when choosing a mate. This evolutionary theory suggests that "choosiness" increases during the fertile window because the costs and benefits of mate selection are highest when women are likely to conceive. Little research has directly investigated reproductive correlates of choice assertion. To address this gap, in the present research we investigated whether fertility, estradiol, and progesterone influenced general assertiveness in women. We recruited 98 naturally cycling, ethnically diverse women. Using a within-subjects design and ovarian hormone concentrations at fertile and non-fertile menstrual cycle phases, we measured implicit assertiveness and self-reported assertive behavior. To see if fertility-induced high assertiveness was related to increased sexual motivation, we also measured women's implicit sexual availability and interest in buying sexy clothes. Results showed that high estradiol and low progesterone predicted higher assertiveness. Sexual availability increased during periods of high fertility. Low progesterone combined with high estradiol predicted greater interest in buying sexy clothes. Results held when controlling for individual differences in mate value and sociosexual orientation. Our findings support the role of fluctuating ovarian hormones in the expression and magnitude of women's assertiveness. High assertiveness during the fertile window may be a psychological adaptation that promotes mate selectivity and safeguards against indiscriminate mate choice when conception risk is highest.