OBJECTIVE:Perimenopause is the time leading up to and including the first 12 months after the final menstrual period. This study examined how social determinants of health (SDOH) including race/ethnicity, income, education and healthcare access influence perimenopause symptom burden. METHOD:A cross-sectional survey was distributed to English, Spanish, Portuguese and French-speaking Flo app users aged 35 years and above. Participants completed the validated Menopause Rating Scale (MRS) and provided data on key SDOH. Associations between SDOH and total MRS and domain scores (psychological, somatic, urogenital) were evaluated using multivariable linear regression. RESULTS:A total of 12,382 women completed the survey. All examined SDOH were significantly associated with total and domain MRS scores (p < 0.001). Healthcare access had the strongest association, followed by income sufficiency and education level. Asian, Black and Hispanic/Latino race/ethnicities were significantly associated with MRS scores (p < 0.001) compared with White race/ethnicity. In a subset of 2618 women who reported being in perimenopause, SDOH remained significantly associated with MRS scores, with income sufficiency showing the strongest association (p < 0.001). CONCLUSIONS:SDOH, particularly healthcare access and income, are strongly associated with perimenopause symptom burden. Addressing health inequities is essential to improve perimenopause care and reduce symptom burden.

OBJECTIVE:Perimenopause is the time leading up to a woman's last menstrual cycle and includes the 12 months afterward. Studies that systematically compare perimenopause symptoms across diverse cultural and geographic settings are lacking. This study, utilizing data from Flo, an international mobile health application, aimed to assess global knowledge and symptom experiences related to perimenopause. METHODS:This cross-sectional survey was conducted via the Flo application, offered to users aged 18 years and above. The primary endpoints were knowledge of perimenopause symptoms from all survey participants, and self-reported perimenopause symptoms for survey participants aged 35 years and above. Secondary analyses compared knowledge scores and symptoms across geographic regions. RESULTS:A total of 17,494 women from 158 countries were included. Commonly recognized perimenopause symptoms included hot flashes (71%), sleep problems (68%), and weight gain (65%). Of the participants, 12,681 were aged 35 years or above, with the most common self-reported symptoms being fatigue (83%), physical and mental exhaustion (83%), irritability (80%), depressive mood (77%), sleep problems (76%), digestive issues (76%), and anxiety (75%). This pattern of symptoms was similar among those who self-reported being in perimenopause, though higher than in those not in perimenopause. International variation in perimenopause symptom knowledge and symptoms experienced was noted ( P <0.001). CONCLUSIONS:This survey highlights a discordance between perimenopause knowledge and actual symptoms experienced across diverse global populations. While hot flashes were the most widely recognized symptom, respondents aged 35 years or above most commonly reported experiencing fatigue, physical and mental exhaustion, and irritability.

OBJECTIVE:Perimenopause is an under-recognized life stage that is often accompanied by complex and fluctuating symptoms. We aimed to quantify the prevalence of perimenopause uncertainty and explore the underlying drivers. METHODS:We conducted a mixed-methods study using a cross-sectional survey of US women aged 35 years and above (N=7,640). We estimated the prevalence of perimenopause uncertainty and examined subgroup differences by age and symptom severity. Content analysis of free-text responses (n=409) identified key uncertainty drivers. RESULTS:Overall, 34% (95% CI: 33%-35%) of participants reported being unsure of their reproductive stage. Uncertainty varied by age and symptom severity (P < 0.001), peaking at 42% (95% CI: 41%-43%) among those aged 40-44 and 37% (95% CI: 36%-38%) among those with severe symptom burden. Content analysis revealed three main uncertainty drivers. Symptom confusion and attribution were the most common (56%), reflecting difficulties interpreting bodily changes and distinguishing perimenopause from other causes. Knowledge gaps and information seeking accounted for 28% of responses, highlighting limited health literacy, age-based assumptions, and active searches for evidence. Barriers to confirmation and care (16%) described dismissive health care encounters and reluctance to acknowledge perimenopause. Younger women (35-39 y) were more likely to cite knowledge gaps, while health care barriers peaked in the 40-44 age group (P = 0.05). CONCLUSION:Perimenopause uncertainty is a prevalent and clinically meaningful phenomenon. Conceptually distinct from illness-focused uncertainty, this universal transition frequently involves ambiguity and limited validation. Clarifying symptom patterns, addressing knowledge gaps, and reducing barriers to confirmation may support women during perimenopause.

OBJECTIVES:Psychological health changes are known to occur during perimenopause, but the extent to which these differ from premenopause remains unclear. This study aimed to assess psychological symptoms during perimenopause compared with premenopause and evaluate differences by country. METHODS:Flo app users aged 35 years or above not taking hormonal contraception or hormone therapy were included. The psychological domain of the Menopause Rating Scale (range 0-16) and symptoms within this domain of depressive mood, irritability, anxiety, and exhaustion (range 0-4 each) were evaluated. Participants in perimenopause were compared with those in premenopause (self-reported not in perimenopause or postmenopause). RESULTS:A total of 7,975 women were included, with 2,353 in perimenopause (mean age 45 years [SD±4.5]) and 5,622 in premenopause (39 y [SD±4.0]). Psychological Menopause Rating Scale scores in the perimenopause group were significantly higher (7.3 [SD±3.56]) than in the premenopause group (4.8 [SD±3.56]; P<0.001). The individual symptoms of depressive mood, irritability, anxiety, and exhaustion were also higher in the perimenopause group (P<0.001, for all respectively), with the largest difference being exhaustion. These findings remained after adjusting for known confounders, including an existing diagnosis of anxiety or depression. Geographic analysis included 20 countries across 6 continents, with significant differences identified between countries for the overall psychological symptom domain score and all individual symptoms. CONCLUSION:Significantly greater psychological symptom burden was identified in the perimenopause group compared with the premenopause group, with the greatest difference noted for the individual symptom of exhaustion. Differences in symptom burden were also identified by country.

Orgasm frequency during partnered sexual activities is thought to be a predictor of sexual satisfaction. Less is known about the relationship between orgasms from solo sexual activities and satisfaction. The current study examined the interplay between orgasms and sexual satisfaction in partnered and solo contexts, the role of sexual variety, and distinct sexual activities in a sample of 27,931 Flo app users (99% female, 96% women). Participants (M age, 28.14 years) reported engaging in an average of 7.79/13 partnered and 6.45/13 solo sexual activities in the past six months. Clitoral stimulation by a partner was the most frequent partnered activity (90.2%), and clitoral stimulation by hand was the most frequent solo activity (84.6%). Orgasm frequency was associated with satisfaction with a moderate effect size. Solo orgasm frequency had a weak, negative association with satisfaction. Solo sexual variety was not a significant predictor of overall satisfaction nor orgasm frequency. Partnered orgasm frequency was a significant, positive predictor of satisfaction. Greater variety in partnered sexual activities was associated with higher satisfaction and higher orgasm frequency. During solo activities, the highest orgasm frequencies were associated with clitoral stimulation using a vibrator/dildo, watching porn/erotica, and clitoral stimulation by hand. During partnered activities, using sex toys, vaginal penetration with clitoral stimulation, and masturbation with partner present had the highest orgasm frequencies. When comparing similar solo and partnered activities, orgasm frequency was consistently higher when solo. Our findings offer insights into the nuanced interplay between solo and partnered sexual experiences, the importance of orgasms, and sexual satisfaction.

BACKGROUND: Copy number variants (CNVs) may increase the risk for neurodevelopmental conditions. The neurobiological mechanisms that link these high-risk genetic variants to clinical phenotypes are largely unknown. An important question is whether brain abnormalities in individuals who carry CNVs are associated with their degree of penetrance. METHODS: We investigated whether increased CNV penetrance for schizophrenia and other developmental disorders was associated with variations in cortical and subcortical morphology. We pooled T1-weighted brain magnetic resonance imaging and genetic data from 22 cohorts from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-CNV consortium. In the main analyses, we included 9268 individuals (aged 7-90 years, 54% female), from which we identified 398 carriers of 36 neurodevelopmental CNVs at 20 distinct loci. A secondary analysis was performed including additional neuroimaging data from the ENIGMA-22q consortium, including 274 carriers of the 22q11.2 deletion and 291 noncarriers. CNV penetrance was estimated through penetrance scores that were previously generated from large cohorts of patients and controls. These scores represent the probability risk of developing either schizophrenia or other developmental disorders (including developmental delay, autism spectrum disorder, and congenital malformations). RESULTS: For both schizophrenia and developmental disorders, increased penetrance scores were associated with lower surface area in the cerebral cortex and lower intracranial volume. For both conditions, associations between CNV-penetrance scores and cortical surface area were strongest in regions of the occipital lobes, specifically in the cuneus and lingual gyrus. CONCLUSIONS: Our findings link global and regional cortical morphometric features with CNV penetrance, providing new insights into neurobiological mechanisms of genetic risk for schizophrenia and other developmental disorders.

Female sexual response has historically been understudied. Using the female sexual function index (FSFI-6), this study estimated female sexual response and its associations with sociodemographic characteristics, self-reported health conditions, and logging of sex events. Participants included a large and demographically diverse sample of 2392 users of the Flo Health mobile application (app) in the United States. Participants scored higher on FSFI-6, suggesting higher sexual function, if they were actively trying to conceive, met the criteria for obesity (body mass index (BMI) ≥ 30 kg/m2), or engaged in higher-than-average in-app sex event logging. Conversely, being underweight (BMI < 18.5 kg/m2), or reporting at least one of the queried health conditions, was associated with lower FSFI-6 scores. This study demonstrates that many individual factors are relevant to female sexual response and is the first to report a positive association between trying to conceive, obesity, and sexual event logging with female sexual function.

ABSTRACT: Introduction:Over two billion people menstruate worldwide. Many lack the resources and knowledge to manage their menstruation, which can lead to reproductive health issues and stigma. Methods:This longitudinal study set out to describe menstrual health and hygiene (MHH) knowledge levels among adult women across global regions and estimate changes in knowledge from exposure to health information through the mobile application (app) Flo Health. Furthermore, the study quantified changes for psychosocial, menstrual and quality of life outcomes and explored whether these were mediated by improvements in MHH knowledge. At installation of the Flo app, 6165 participants across 52 countries were recruited for a baseline assessment. Follow-up data collection was conducted after at least 3 months of app access. Two study designs were used, following 513 respondents in a pre-post design and recruiting an additional 1346 respondents to match to baseline participants lost to follow-up in a repeated cross-sectional design. Results:MHH knowledge was low at baseline, with on average only one-third of knowledge quiz questions answered correctly. Compared with the baseline, MHH knowledge was 18.7% higher in the matched sample, while it increased by 8.1% in the pre-post sample after 3 or more months of access to Flo. Other changes included higher menstrual awareness (matched and pre-post: 9.0%), sexually transmitted infection awareness (matched: 1.7%; pre-post: +3.1%), quality of life (matched: +1.8%; pre-post: +3.5%) and lower menstrual stigma (matched: -8.1%) and menstrual impact on daily life (pre-post: -6.7%). In the matched sample between 23 and 66% of associations between app access and select outcomes were mediated by MHH knowledge. Conclusions:The poor MHH knowledge found in this study highlights the opportunities for improvement, which in turn could lead to better psychosocial, menstrual and quality of life outcomes. Mobile apps may represent an important tool for better MHH knowledge and associated benefits at scale.

Perimenopause is the transition through menopause and is associated with many symptoms, which can impact daily functioning. Many people who menstruate feel unprepared as they approach menopause, highlighting the need for research into the experience of individuals at this time. Here, we report the results of a survey of 4432 U.S. women about clinical help-seeking and the presence and severity of perimenopause symptoms. By assessing the reported frequencies of consultations and symptoms, we found high rates of consultation with doctors about perimenopause and significant symptom burden, even in individuals aged 30–45 years. Additionally, using logistic regression, we identified key cycle, vasomotor, and urinary symptoms that are most associated with a perimenopause diagnosis. These results suggest that although rates of clinical help-seeking, perimenopause diagnosis, and symptom severity increase as age approaches the typical age of the final menstrual period, a significant number of individuals aged 30–45 years experience perimenopause-related symptoms.

OBJECTIVE:Pediatric athletes with concussion present with a variety of impairments on clinical assessment and require individualized treatment. The Buffalo Concussion Physical Examination is a brief, pertinent clinical assessment for individuals with concussion. The purpose of this study was to identify physical examination subtypes in pediatric athletes with concussion within 2 weeks of injury that are relevant to diagnosis and treatment. DESIGN:Secondary analysis of a published cohort study and clinician consensus. SETTING:Three university-affiliated sports medicine centers. PARTICIPANTS:Two hundred seventy children (14.9 ± 1.9 years). INDEPENDENT VARIABLES:Orthostatic intolerance, horizontal and vertical saccades, smooth pursuits, vestibulo-ocular reflex, near-point convergence, complex tandem gait, neck range of motion, neck tenderness, and neck spasm. MAIN OUTCOME MEASURES:Correlations between independent variables were calculated, and network graphs were made. k -means and hierarchical clustering were used to identify clusters of impairments. Optimal number of clusters was assessed. Results were reviewed by experienced clinicians and consensus was reached on proposed subtypes. RESULTS:Physical examination clusters overlapped with each other, and no optimal number of clusters was identified. Clinician consensus suggested 3 possible subtypes: (1) visio-vestibular (horizontal and vertical saccades, smooth pursuits, and vestibulo-ocular reflex), (2) cervicogenic (neck range of motion and spasm), and (3) autonomic/balance (orthostatic intolerance and complex tandem gait). CONCLUSIONS:Although we identified 3 physical examination subtypes, it seemed that physical examination findings alone are not enough to define subtypes that are both statistically supported and clinically relevant, likely because they do not include symptoms, assessment of mood or cognitive problems, or graded exertion testing.

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism. Using a novel source-based morphometry method called SS-Detect, we identified 12 structural brain patterns (SBPs) that discriminated individuals with 22q11.2 deletion syndrome from healthy controls. We further demonstrated that deletion size was related to structural covariance patterns; however, history of psychosis did not show a strong relationship with these covariance patterns.image

Abstract The intricate hormonal and physiological changes of the menstrual cycle can influence health on a daily basis. Although prior studies have helped improve our understanding of the menstrual cycle, they often lack diversity in the populations included, sample size, and the span of reproductive and life stages. This paper aims to describe the dynamic differences in menstrual cycle characteristics and associated symptoms by age in a large global cohort of period-tracking application users. This work aims to contribute to our knowledge and understanding of female physiology at varying stages of reproductive aging. This cohort study included self-reported menstrual cycle and symptom information in a sample of Flo application users aged 18–55. Cycle and period length and their variability, and frequency of menstrual cycle symptom logs are described by the age of the user. Based on data logged by over 19 million global users of the Flo app, the length of the menstrual cycle and period show clear age-associated patterns. With higher age, cycles tend to get shorter (Cycle length: $${\overline{\text{D}}}$$ D ¯ = 1.85 days, Cohen’s D = 0.59) and more variable (Cycle length SD: $${\overline{\text{D}}}$$ D ¯ = 0.42 days, Cohen’s D = 0.09), until close to the chronological age (40–44) suggesting menopausal transition, when both cycles and periods become longer (Cycle length: $${\overline{\text{D}}}$$ D ¯ = 0.86 days, t = 48.85, Cohen’s D = 0.26; Period length: $${\overline{\text{D}}}$$ D ¯ = 0.08, t = 15.6, Cohen’s D = 0.07) and more variable (Cycle length SD: $${\overline{\text{D}}}$$ D ¯ = 2.80 days, t = 111.43, d = 0.51; Period length SD: $${\overline{\text{D}}}$$ D ¯ = 0.23 days, t = 67.81, Cohen’s D = 0.31). The proportion of individuals with irregular cycles was highest in participants aged 51–55 (44.7%), and lowest in the 36–40 age group (28.3%). The spectrum of common menstrual cycle-related symptoms also varies with age. The frequency of logging of cramps and acne is lower in older participants, while logs of headache, backache, stress, and insomnia are higher in older users. Other symptoms show different patterns, such as breast tenderness and fatigue peaking between the ages of 20–40, or mood swings being most frequently logged in the youngest and oldest users. The menstrual cycle and related symptoms are not static throughout the lifespan. Understanding these age-related differences in cycle characteristics and symptoms is essential in understanding how best to care for and improve the daily experience for menstruators across the reproductive life span.

Objective Vestibulo-oculomotor impairments are common after concussion. The Buffalo Concussion Physical Exam (BCPE) is a brief, pertinent physical examination for helping to diagnose concussion. We derived a scoring algorithm to identify adolescents who may take longer to recover (>30 days) and may benefit from early specific therapy. Design Retrospective cohort. Setting University clinics. Participants 263 adolescents diagnosed with concussion using international guidelines (14.92±1.9 years, 62% male, 90% sport-related, 6.0±4 days from injury). Interventions (or Assessment of Risk Factors) BCPE was performed at the initial visit and weekly until recovery. We used a binomial generalized linear model with complementary log-log error function. Significant predictor variables were stepwise selected using Akaike Information Criterion, and k-fold cross-validation was used for internal validity. Outcome Measures Days since injury (1-point/day), high-velocity/multiple impacts (2-points), >2 previous concussion (4-points), dizziness on postural change (5-points), abnormal vestibulo-ocular reflex (VOR, 5-points), abnormal tandem gait (1-point), high-velocity/multiple impact plus abnormal VOR (5-points), and dizziness on postural change plus abnormal VOR (minus 4-points) were used to categorize low (0–10), medium (11–15) and high (>15 points) risk. Main Results The algorithm correctly identified 73% of normal recoverers as low-risk, 23% as medium-risk, and 4% as high-risk. It correctly identified 28% of delayed recoverers as low-risk, 28% as medium-risk, and 44% as high-risk. Medium- to high-risk score within one week of injury was 72% sensitive and 74% specific for delayed recovery. Conclusions An easily calculable score using 3 questions and 3 physical exam findings within one week of injury identified concussed adolescents who may benefit from early therapeutic intervention.

Background: Previous research suggests that the processing of internal body sensations (interoception) affects how we experience pain. There is some evidence that people with fibromyalgia syndrome (FMS), which is a condition characterised by chronic pain and fatigue, may have altered interoceptive processing. However, extant findings are inconclusive, and some tasks previously used to measure interoception are of questionable validity. We used a task which overcomes problems with previous tasks – the Phase Adjustment Task (PAT) – to measure interoception in adults with FMS. Methods: We examined: (i) the tolerability of the PAT in an FMS sample (N = 154); (ii) if there are differences in facets of interoception (PAT performance, PAT-related confidence, and scores on the Private Body Consciousness Scale) between an FMS sample and an age- and gender-matched pain-free control sample (N = 94); and (iii) if subgroups of participants with FMS could be identified according to interoceptive accuracy levels. Results: After including additional task breaks and a recommended hand posture, we found that the PAT was tolerable in the FMS sample. Participants in the FMS sample were more likely to be classified as ‘interoceptive’ on the PAT, and had significantly higher self-reported interoception and interoceptive beliefs compared to participants in the pain-free sample. Within the FMS sample, participants who were classified as interoceptive on the PAT had significantly lower symptom impact than the unclassified participants. Conversely, self-reported interoception was positively correlated with FMS symptom severity and impact. Conclusions: The present findings suggest that interoception may be an important factor to consider in understanding and managing FMS symptoms, and that the PAT is a useful tool for assessing interoception in this population. We recommend future longitudinal work to better understand associations between fluctuating FMS symptoms and interoceptive processing.

BackgroundReproductive health conditions such as polycystic ovary syndrome (PCOS), endometriosis, and uterine fibroids pose a significant burden to people who menstruate, health care systems, and economies. Despite clinical guidelines for each condition, prolonged delays in diagnosis are commonplace, resulting in an increase to health care costs and risk of health complications. Symptom checker apps have the potential to significantly reduce time to diagnosis by providing users with health information and tools to better understand their symptoms. ObjectiveThis study aims to study the prevalence and predictive importance of self-reported symptoms of PCOS, endometriosis, and uterine fibroids, and to explore the efficacy of 3 symptom checkers (developed by Flo Health UK Limited) that use self-reported symptoms when screening for each condition. MethodsFlo’s symptom checkers were transcribed into separate web-based surveys for PCOS, endometriosis, and uterine fibroids, asking respondents their diagnostic history for each condition. Participants were aged 18 years or older, female, and living in the United States. Participants either had a confirmed diagnosis (condition-positive) and reported symptoms retrospectively as experienced at the time of diagnosis, or they had not been examined for the condition (condition-negative) and reported their current symptoms as experienced at the time of surveying. Symptom prevalence was calculated for each condition based on the surveys. Least absolute shrinkage and selection operator regression was used to identify key symptoms for predicting each condition. Participants’ symptoms were processed by Flo’s 3 single-condition symptom checkers, and accuracy was assessed by comparing the symptom checker output with the participant’s condition designation. ResultsA total of 1317 participants were included with 418, 476, and 423 in the PCOS, endometriosis, and uterine fibroids groups, respectively. The most prevalent symptoms for PCOS were fatigue (92%), feeling anxious (87%), BMI over 25 (84%); for endometriosis: very regular lower abdominal pain (89%), fatigue (85%), and referred lower back pain (80%); for uterine fibroids: fatigue (76%), bloating (69%), and changing sanitary protection often (68%). Symptoms of anovulation and amenorrhea (long periods, irregular cycles, and absent periods), and hyperandrogenism (excess hair on chin and abdomen, scalp hair loss, and BMI over 25) were identified as the most predictive symptoms for PCOS, while symptoms related to abdominal pain and the effect pain has on life, bleeding, and fertility complications were among the most predictive symptoms for both endometriosis and uterine fibroids. Symptom checker accuracy was 78%, 73%, and 75% for PCOS, endometriosis, and uterine fibroids, respectively. ConclusionsThis exploratory study characterizes self-reported symptomatology and identifies the key predictive symptoms for 3 reproductive conditions. The Flo symptom checkers were evaluated using real, self-reported symptoms and demonstrated high levels of accuracy.

BackgroundReproductive health literacy and menstrual health awareness play a crucial role in ensuring the health and well-being of women and people who menstruate. Further, awareness of one’s own menstrual cycle patterns and associated symptoms can help individuals identify and manage conditions of the menstrual cycle such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD). Digital health products, and specifically menstrual health apps, have the potential to effect positive change due to their scalability and ease of access. ObjectiveThe primary aim of this study was to measure the efficacy of a menstrual and reproductive health app, Flo, in improving health literacy and health and well-being outcomes in menstruating individuals with and without PMS and PMDD. Further, we explored the possibility that the use of the Flo app could positively influence feelings around reproductive health management and communication about health, menstrual cycle stigma, unplanned pregnancies, quality of life, work productivity, absenteeism, and body image. MethodsWe conducted 2 pilot, 3-month, unblinded, 2-armed, remote randomized controlled trials on the effects of using the Flo app in a sample of US-based (1) individuals who track their cycles (n=321) or (2) individuals who track their cycles and are affected by PMS or PMDD (n=117). ResultsThe findings revealed significant improvements at the end of the study period compared to baseline for our primary outcomes of health literacy (cycle tracking: D̄=1.11; t311=5.73, P<.001; PMS or PMDD: D̄=1.20; t115=3.76, P<.001) and menstrual health awareness (D̄=3.97; t311=7.71, P<.001), health and well-being (D̄=3.44; t311=5.94, P<.001), and PMS or PMDD symptoms burden (D̄=–7.08; t115=–5.44, P<.001). Improvements were also observed for our secondary outcomes of feelings of control and management over health (D̄=1.01; t311=5.08, P<.001), communication about health (D̄=0.93; t311=2.41, P=.002), menstrual cycle stigma (D̄=–0.61; t311=–2.73, P=.007), and fear of unplanned pregnancies (D̄=–0.22; t311=–2.11, P=.04) for those who track their cycles, as well as absenteeism from work and education due to PMS or PMDD (D̄=–1.67; t144=–2.49, P=.01). ConclusionsThese pilot randomized controlled trials demonstrate that the use of the Flo app improves menstrual health literacy and awareness, general health and well-being, and PMS or PMDD symptom burden. Considering the widespread use and affordability of the Flo app, these findings show promise for filling important gaps in current health care provisioning such as improving menstrual knowledge and health. Trial RegistrationOSF Registries osf.io/pcgw7; https://osf.io/pcgw7 ; OSF Registries osf.io/ry8vq; https://osf.io/ry8vq

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant’s vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

BackgroundGenomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question.MethodA total of 493 individuals were included: 389 with a ND-GC, mean age = 9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set.ResultsAll machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development.LimitationsThis study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application.ConclusionsIn this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment.

Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10–17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.

22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.