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BackgroundReproductive health conditions such as endometriosis, uterine fibroids and polycystic ovary syndrome affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5-40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased healthcare costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions.ObjectiveThis study aims to evaluate the accuracy of three symptom checkers developed by Flo Health assessing symptoms of endometriosis, uterine fibroids and polycystic ovary syndrome (PCOS) against current medical guidelines.MethodsIndependent general practitioners were recruited to create clinical case vignettes of simulated users with and without the conditions of interest. Vignettes were reviewed, modified and approved by separate general practitioners. A further independent panel of general practitioners reviewed the cases and designated a final classification. Vignettes were entered into the symptom checkers and the outcomes were compared with the final classification from the panel using accuracy metrics including percent agreement, sensitivity and specificity.ResultsA total of 24 cases were created per condition. Overall, exact matches between the vignette classification and the symptom checker outcome was 83.3% for endometriosis and uterine fibroids, and 87.5% for PCOS. While sensitivity was high for all conditions (>81%) and very high (100%) for PCOS, specificity was >81% for endometriosis and uterine fibroids and 75% for PCOS.ConclusionThe single condition symptom checkers have high levels of accuracy for endometriosis, uterine fibroids and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and healthcare providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

BACKGROUND Reproductive health conditions such as endometriosis, uterine fibroids and polycystic ovary syndrome affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5-40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased healthcare costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions. OBJECTIVE This study aims to evaluate the accuracy of three symptom checkers developed by Flo Health assessing symptoms of endometriosis, uterine fibroids and polycystic ovary syndrome (PCOS) against current medical guidelines. METHODS Independent general practitioners were recruited to create clinical case vignettes of simulated users with and without the conditions of interest. Vignettes were reviewed, modified and approved by separate general practitioners. A further independent panel of general practitioners reviewed the cases and designated a final classification. Vignettes were entered into the symptom checkers and the outcomes were compared with the final classification from the panel using accuracy metrics including percent agreement, sensitivity and specificity. RESULTS A total of 24 cases were created per condition. Overall, exact matches between the vignette classification and the symptom checker outcome was 83.3% for endometriosis and uterine fibroids, and 87.5% for PCOS. While sensitivity was high for all conditions (>81%) and very high (100%) for PCOS, specificity was >81% for endometriosis and uterine fibroids and 75% for PCOS. CONCLUSIONS The single condition symptom checkers have high levels of accuracy for endometriosis, uterine fibroids and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and healthcare providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

ObjectiveTo investigate symptomatology throughout the menstrual cycle, including the frequency of symptoms reported for each phase of the cycle and the relationship between symptoms and individual characteristics.MethodsThis study included self-reported cycle information and symptoms during ovulatory menstrual cycles in mobile application users. Symptom frequency was described overall, by phase, and by day. Relationships between symptoms and individual characteristics (age, body mass index) were tested with logistic regression.Results224,676 unique users and 498,126 unique cycles were included in the analysis. Somatic symptoms were most frequently reported, logged in 85.3% of cycles. Of total symptoms logged, somatic, gastrointestinal, and negative mood symptoms were most prevalent in the late luteal and bleeding days, while positive mood and discharge were most common in the fertile window. Symptom experience differed by age.ConclusionsMenstrual cycle-related symptoms are very common and vary throughout the cycle phases. While most women report negative symptoms including pain, bloating, and negative mood, women also report positive symptoms such as energetic and happy mood. Self-reported data from cycle tracking and health apps can improve our understanding of the prevalence and variability of menstrual-related symptoms.Funding SourceFlo Health UK LimitedPrecisMenstrual symptoms include both positive and negative physical and mood symptoms and differ by age.

The chronic and acute effects of stress can have divergent effects on health; long-termeffects are associated with detrimental physical and mental health sequelae, while acute effectsmay be advantageous in the short-term. Stress-induced analgesia, the attenuation of painperception due to stress, is a well-known phenomenon that has yet to be systematicallyinvestigated under ecological conditions. Using Flo, a women’s health app with a world-widemonthly active usership of more than 48 million, women in Ukraine were monitored for theirreporting of stress, pain and affective symptoms before, and immediately after, the onset of theRussian-Ukrainian conflict. To avoid potential selection (attrition) or collider bias, we rely on asample of 87,315 users who were actively logging multiple symptoms before and after the startof the war. We found an inverse relationship between stress and pain, whereby higher reports ofstress predicted lower rates of pain. Stress didn’t influence any other physiological symptomswith a similar magnitude, nor any other symptom had a similar effect on pain. This relationshipgeneralised with decreasing magnitude to countries neighbouring Ukraine, with Ukraine servingas the epicentre. These findings help characterise the relationship between stress and health in areal-world setting.

KEY POINTS Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder of motor neurons, carrying a short survival. High-density motor unit recordings permit analysis of motor unit size (amplitude) and firing behaviour (afterhyperpolarization duration and muscle fibre conduction velocity). Serial recordings from biceps brachii indicated that motor units fired faster and with greater amplitude as disease progressed. First-recruited motor units in the latter stages of ALS developed characteristics akin to fast-twitch motor units, possibly as a compensatory mechanism for the selective loss of this motor unit subset. This process may become maladaptive, highlighting a novel therapeutic target to reduce motor unit vulnerability. ABSTRACT Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with a median survival of 3 years. We employed serial high-density surface electromyography (HDSEMG) to characterize voluntary and ectopic patterns of motor unit (MU) firing at different stages of disease. By distinguishing MU subtypes with variable vulnerability to disease, we aimed to evaluate compensatory neuronal adaptations that accompany disease progression. Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent 1-7 assessments each. HDSEMG measurements comprised 30 min of resting muscle and 1 min of light voluntary activity from biceps brachii bilaterally. MU decomposition was performed by the progressive FastICA peel-off technique. Inter-spike interval, firing pattern, MU potential area, afterhyperpolarization duration and muscle fibre conduction velocity were determined. In total, 373 MUs (ALS = 287; BFS = 86) were identified from 182 recordings. Weak ALS muscles demonstrated a lower mean inter-spike interval (82.7 ms) than strong ALS muscles (96.0 ms; P = 0.00919) and BFS muscles (95.3 ms; P = 0.0039). Mean MU potential area (area under the curve: 487.5 vs. 98.7 μV ms; P

BACKGROUND Research shows that poor knowledge and awareness of menstrual and pregnancy health among women are associated with adverse reproductive health and pregnancy outcomes. Menstrual cycle– and pregnancy-tracking mobile apps are promising tools for improving women’s awareness of and attitudes toward their reproductive health; however, there is little information about subscribers’ perceptions of app functionality and its impact on their knowledge and health. OBJECTIVE This study aimed to explore knowledge and health improvements related to menstrual cycle and pregnancy, as well as improvements in general health among Flo app users. We also investigated what components of the Flo app were associated with the abovementioned improvements and evaluated whether those improvements differed based on education level, country of residence (low- and middle-income vs high-income countries), free or premium subscription to the app, short- or long-term use of the app, and frequency of use. METHODS Flo subscribers who had been using the app for no less than 30 days, completed a web-based survey. A total of 2212 complete survey responses were collected. The survey included demographic questions and questions about motivations guiding the use of the Flo app and which components of the app improved their knowledge and health, as well as to what extent. RESULTS Most study participants reported improvements in menstrual cycle (1292/1452, 88.98%) and pregnancy (698/824, 84.7%) knowledge from Flo app use. Participants with higher levels of education and those from high-income countries reported using the app predominantly for getting pregnant (χ21=4.2, P=.04; χ21=52.3, Pχ21=19.3, Pχ21=20.9, P=.001, respectively). Participants with less education reported using the app to avoid pregnancy (χ21=4.2; P=.04) and to learn more about their body (χ21=10.8; P=.001) and sexual health (χ21=6.3; P=.01), while participants from low- and middle-income countries intended to mainly learn more about their sexual health (χ21=18.2; P CONCLUSIONS This study suggests that menstrual health apps, such as Flo, could present revolutionary tools to promote consumer health education and empowerment on a global scale.

Introduction: Low health literacy among girls, women, and people who menstruate is associated with worse reproductive health outcomes. Mobile apps promise tools for improving women’s health literacy, however, there is little information about subscribers’ perceptions of app functionality and its impact on their knowledge and health. Objective: This study aimed to explore knowledge and health improvements related to menstrual cycle and pregnancy, as well as improvements in general health in Flo app users. We also investigated what components of the Flo app were associated with the above mentioned improvements, and evaluated whether those improvements differed based on education level, free or a premium subscription to the app, short or long term use of the app, and frequency of use. Methods: Flo subscribers who had been using the app for no less than 30 days completed an online survey. A total of 2214 of complete survey responses were collected. The survey included demographic questions, questions about motivations guiding usage of the Flo app, to what extent and what components of the app may have helped improve their knowledge and health (i.e., reproductive, sexual, physical, mental health, health behaviours, and communication with healthcare providers).Results: Most study participants reported menstrual cycle (89.0%) and pregnancy (84.6%) knowledge improvements from Flo app use. 62.3% of Flo users reported ‘period predictions’ and 72.2% reported ‘reading and/or watching articles and video sources in the app’ helped them feel the most informed about their cycle and pregnancy health. Higher educated, Flo Premium, frequent, and short term app users reported using the app mainly for getting pregnant and pregnancy tracking. Lower educated and long time users reported using the app to learn more about their body, sexual health, irregular cycles and related conditions, and to avoid pregnancy. Importantly, intended uses of the app for both lower and higher educated users matched areas in which they have gained knowledge and achieved their health goals upon the Flo app use. Flo Premium, frequent, and long term users were more likely to report improvements in knowledge and health in all areas they chose to use the app for and even beyond.Conclusion: There is a need to increase reproductive health literacy in the global female population. This study has shown that the use of the period tracking app, Flo, as self-reported by its users, improved their menstrual cycle and pregnancy knowledge and health. Additionally, more frequent, long-term, and Flo Premium users were more likely to report knowledge and health improvements across multiple health areas. Thus, the use of digital health apps, such as the Flo app, should be considered for integration into healthcare systems to increase women’s health literacy in the wider population.

Introduction Prognostic uncertainty in amyotrophic lateral sclerosis (ALS) confounds clinical management planning, patient counseling, and trial stratification. Fasciculations are an early clinical hallmark of disease and can be quantified noninvasively. Using an innovative analytical method, we correlated novel fasciculation parameters with a predictive survival model. Methods Using high-density surface electromyography, we collected biceps recordings from ALS patients on their first research visit. By accessing an online survival prediction tool, we provided eight clinical and genetic parameters to estimate individual patient survival. Fasciculation analysis was performed using an automated algorithm (Surface Potential Quantification Engine), with a Cox proportional hazards model to calculate hazard ratios. Results The median predicted survival for 31 patients was 41 (interquartile range, 31.5-57) months. Univariate hazard ratios were 1.09 (95% confidence interval [CI], 1.03-1.16) for the rate of change of fasciculation frequency (RoCoFF) and 1.10 (95% CI, 1.01-1.19) for the amplitude dispersion rate. Only the RoCoFF remained significant (P = .04) in a multivariate model. Discussion Noninvasive measurement of fasciculations at a single time-point could enhance prognostic models in ALS, where higher RoCoFF values indicate shorter survival.

Delayed diagnosis of amyotrophic lateral sclerosis prevents early entry into clinical trials at a time when neuroprotective therapies would be most effective. Fasciculations are an early hallmark of amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface electromyography, we carried out 30-min biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, 7 patients with benign fasciculation syndrome, 1 patient with multifocal motor neuropathy and 17 healthy individuals. We employed the surface potential quantification engine to compute fasciculation frequency, fasciculation amplitude and inter-fasciculation interval. Inter-group comparison was assessed by Welch’s analysis of variance. Logistic regression, receiver operating characteristic curves and decision trees discerned the diagnostic performance of these measures. Fasciculation frequency, median fasciculation amplitude and proportion of inter-fasciculation intervals, Fasciculations are an early hallmark of amyotrophic lateral sclerosis and can be studied noninvasively using high-density surface electromyography. Here we show that fasciculation frequency measured in a 30-minute-long biceps brachii recording differentiates patients with amyotrophic lateral sclerosis from healthy and neurological controls with 80% sensitivity and 96% specificity., Graphical Abstract Graphical Abstract

Amyotrophic lateral sclerosis is a devastating neurodegenerative disease with a median survival of 3 years from symptom onset. Accessible and reliable biomarkers of motor neuron decline are urgently needed to quicken the pace of drug discovery. Fasciculations represent an early pathophysiological hallmark of amyotrophic lateral sclerosis and can be reliably detected by high-density surface electromyography. We set out to quantify fasciculation potentials prospectively over 14 months, seeking comparisons with established markers of disease progression. Twenty patients with amyotrophic lateral sclerosis and five patients with benign fasciculation syndrome underwent up to seven assessments each. At each assessment, we performed the amyotrophic lateral sclerosis-functional rating scale, sum power score, slow vital capacity, 30-min high-density surface electromyography recordings from biceps and gastrocnemius and the motor unit number index. We employed the Surface Potential Quantification Engine, which is an automated analytical tool to detect and characterize fasciculations. Linear mixed-effect models were employed to account for the pseudoreplication of serial measurements. The amyotrophic lateral sclerosis-functional rating scale declined by 0.65 points per month (P 900 000 fasciculations revealed inter-fasciculation intervals, Employing high-density surface electromyography alongside a novel analytical algorithm, this longitudinal study explores the interactions between fasciculation firing, neuronal loss and disease progression in amyotrophic lateral sclerosis. Showing most promise as a potential biomarker, fasciculation frequency is postulated to rise steadily and then fall rapidly over the course of disease., Graphical Abstract Graphical Abstract

This review covers references found in the recent literature (from 2015) describing clinically relevant chemical monitoring that either gives continuous chemical information in real time or has the near prospect of doing so.

Highlights • SPiQE combines serial high-density surface EMG with an innovative signal-processing methodology. • SPiQE identifies fasciculations in ALS patients with high sensitivity and specificity. • The optimal noise-responsive model achieves an average classification accuracy of 88%., Objectives Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool. Methods Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model’s predictive ability. Results By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/− 0.2 SEM), specificity of 91.6% (+/− 0.1 SEM) and classification accuracy of 87.9% (+/− 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). Conclusion We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). Significance Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.

Objectives Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). The Surface Potential Quantification Engine (SPiQE) is a novel analytical tool to identify fasciculation potentials from high-density surface electromyography (HDSEMG). This method was accurate on relaxed recordings amidst fluctuating noise levels. To avoid time-consuming manual exclusion of voluntary muscle activity, we developed a method capable of rapidly excluding voluntary potentials and integrating with the established SPiQE pipeline. Methods Six ALS patients, one patient with benign fasciculation syndrome and one patient with multifocal motor neuropathy underwent monthly thirty-minute HDSEMG from biceps and gastrocnemius. In MATLAB, we developed and compared the performance of four Active Voluntary IDentification (AVID) strategies, producing a decision aid for optimal selection. Results Assessment of 601 one-minute recordings permitted the development of sensitive, specific and screening strategies to exclude voluntary potentials. Exclusion times (0.2–13.1 minutes), processing times (10.7–49.5 seconds) and fasciculation frequencies (27.4–71.1 per minute) for 165 thirty-minute recordings were compared. The overall median fasciculation frequency was 40.5 per minute (10.6–79.4 IQR). Conclusion We hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings. Significance Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.

Introduction: Fasciculations represent early neuronal hyperexcitability in amyotrophic lateral sclerosis (ALS). To aid calibration as a disease biomarker, we set out to characterize the daytime variability of fasciculation firing. Methods: Fasciculation awareness scores were compiled from 19 ALS patients. In addition, 10 ALS patients prospectively underwent high-density surface electromyographic (HDSEMG) recordings from biceps and gastrocnemius at three time-points during a single day. Results: Daytime fasciculation awareness scores were low (mean: 0.28 muscle groups), demonstrating significant variability (coefficient of variation: 303%). Biceps HDSEMG recordings were highly consistent for fasciculation potential frequency (intraclass correlation coefficient [ICC] = 95%, n = 19) and the interquartile range of fasciculation potential amplitude (ICC = 95%, n = 19). These parameters exhibited robustness to observed fluctuations in data quality parameters. Gastrocnemius demonstrated more modest levels of consistency overall (44% to 62%, n = 20). Discussion: There was remarkable daytime consistency of fasciculation firing in the biceps of ALS patients, despite sparse and intermittent awareness among patients’ accounts.

Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability.By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/- 0.2 SEM), specificity of 91.6% (+/- 0.1 SEM) and classification accuracy of 87.9% (+/- 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394).We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE).Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.