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Background Menstrual apps facilitate observation and analysis of menstrual cycles and associated factors through the collection and interpretation of data entered by users. As a subgroup of health-related apps, menstrual apps form part of one of the most dynamic and rapidly growing developments in biomedicine and health care. However, despite their popularity, qualitative research on how people engaging in period-tracking use and experience these apps remains scarce. Methods Between June 2016 and March 2017, we conducted 26 qualitative interviews with menstrual app users living in Austria and Spain. The participants were asked about their practices and experiences regarding app-supported menstrual tracking. The interviews were audio recorded, transcribed verbatim, and coded using the software NVivo. Results An inductive content analysis was performed and eight characteristics of app-supported menstrual tracking were identified: 1) tracking menstrual cycle dates and regularities, 2) preparing for upcoming periods, 3) getting to know menstrual cycles and bodies, 4) verifying menstrual experiences and sensations, 5) informing healthcare professionals, 6) tracking health, 7) contraception and seeking pregnancy, and 8) changes in tracking. Our study finds that period-tracking via apps has the potential to be an empowering practice as it helps users to be more aware of their menstrual cycles and health and to gain new knowledge. However, we also show that menstrual tracking can have negative consequences as it leads to distress in some cases, to privacy issues, and the work it requires can result in cessation. Finally, we present practical implications for healthcare providers and app developers. Conclusions This qualitative study gives insight into users’ practices and experiences of app-supported menstrual tracking. The results provide information for researchers, health care providers and app designers about the implications of app-supported period-tracking and describe opportunities for patient-doctor interactions as well as for further development of menstrual apps.

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2–120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38–100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26–1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

Surveys can be a critical tool in monitoring public health during emergencies. Existing surveillance systems may provide timely reporting of cases and deaths associated with diseases. However, during COVID-19 they did not provide accurate information on the number of cases with virus-related symptoms, testing and treatment seeking. In fact, the surge of potentially infected individuals seeking diagnosis, testing and treatment represented a serious but largely unmeasured dimension of the crisis. This study aimed to evaluate the potential value of monitoring health, attitudinal, and behavioral dimensions that are not included in current U.S. disease surveillance systems during population health emergencies. Additionally, it seeks to demonstrate the feasibility of designing and implementing a low-cost, rapid-turnaround health and behavioral monitoring system when comparable data from existing surveillance systems are unavailable. From March through November 2020, we conducted national surveys with approximately 1,000 interviews each month with Census-balanced samples from a large national commercial panel. These surveys employed replicate national samples drawn from all 50 US states and the District of Columbia. A total of 9,200 interviews, averaging about 20 min in length, were completed over the course of the nine months of fielding. Nearly a quarter of respondents (22%) reported they had been sick for three days or longer since January with what might be COVID. Respondents were questioned about their symptoms, whether they had seen a doctor, had a confirmatory test for the COVID virus, and test results. Approximately one in ten respondents were currently experiencing COVID-like symptoms each month (95% CI: 10.7–12.0%). These numbers dwarf the 0.3% in April and 3.6% in November who had ever had a COVID positive test result. Moreover, 42% of these symptomatic adults sought medical care or testing, increasing strains on the health care system,. Although surveys may not be needed to estimate diagnosed cases, hospitalizations, or deaths, they can provide the missing data on symptomatic cases in the population, the proportion seeking medical care, ability to obtain a confirmatory test, and reasons for not seeking care or testing. This study demonstrates the ability of surveys to provide such information in a timely fashion, which could be replicated in other countries.

In the Republic of Georgia, a 2018 national survey estimated that more than 40% of children aged 2–7 years had a blood lead concentration (BLC) of more than 5 µg/dL. The objective of this study was to document the feasibility of employing lead isotope ratios (LIRs) to identify and rank the Pb (lead) exposure sources most relevant to children across Georgia. A cross-sectional survey between November 2019 and February 2020 of 36 children previously identified as having BLCs > 5 µg/dL from seven regions of Georgia involved the collection of blood and 528 environmental samples, a questionnaire on behaviours and potential exposures. The LIRs in blood and environmental samples were analysed in individual children and across the whole group to ascertain clustering. A fitted statistical mixed-effect model to LIR data first found that the blood samples clustered with spices, tea, and paint, then, further isotopically distinct from blood were sand, dust, and soil, and lastly, milk, toys, pens, flour, and water. Analysis of the LIRs provided an indication and ranking of the importance of Pb environmental sources as explanatory factors of BLCs across the group of children. The findings support the deployment of interventions aimed at managing the priority sources of exposure in this population.

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications.

Over the past 15 years, the National Environmental Public Health Tracking Program (Tracking Program) has advanced technologically and programmatically, evolving from an abstract concept to a mature program. The Tracking Program, in collaboration with national, state, and local partners, uses data and expertise to identify and address environmental public health needs and improve public health capacity across the United States. Examples of the successful application of environmental public health tracking include informing health impact assessments and filling data gaps. The Tracking Program plans to continue working to direct innovative programs and solutions that protect and improve community health in years to come. With support from the Tracking Program, health departments can enhance their abilities to plan and conduct environmental public health activities.

Research on the use of digital health interventions for the management of infertility is still emerging and remains understudied. This review syntheses cross-domain qualitative research on the use of digital fertility trackers. We identified 29 papers and thematic analysis found that these tools are most frequently used alongside, but also sometimes in place of clinical care. The research shows that they pose significant disruption to patient-provider relationships and the broader fertility industry and may place patients at risk when developed without a strong research or medical base, or if used incorrectly. More work is needed on the impact of these tools on care pathways, and to provide guidance on differentiating evidence-based platforms from low quality trackers to safeguard patients and improve fertility treatment outcomes. Plain English Summary Research on using digital technologies such as apps to manage infertility is still new and not well studied. This literature review looked at 29 studies and articles on how people use digital fertility trackers either alongside or instead of fertility care in clinic or hospital settings. While these tools are often used by people in tandem with care from a healthcare professional, authors suggest they disrupt the relationship between patients and providers and may be changing the fertility industry itself. If these tools are poorly designed or used incorrectly, they can put patients at risk. More research is needed to understand how these tools affect fertility care and to help people and healthcare professionals identify high-quality trackers that are safe and effective.

Global environmental change has degraded ecosystems. Challenges such as climate change, resource depletion (with its huge implications for human health and wellbeing), and persistent social inequalities in health have been identified as global public health issues with implications for both communicable and noncommunicable diseases. This contributes to pressure on healthcare systems, as well as societal systems that affect health. A novel strategy to tackle these multiple, interacting and interdependent drivers of change is required to protect the population’s health. Public health professionals have found that building strong, enduring interdisciplinary partnerships across disciplines can address environment and health complexities, and that developing Environmental and Public Health Tracking (EPHT) systems has been an effective tool. EPHT aims to merge, integrate, analyse and interpret environmental hazards, exposure and health data. In this article, we explain that public health decision-makers can use EPHT insights to drive public health actions, reduce exposure and prevent the occurrence of disease more precisely in efficient and cost-effective ways. An international network exists for practitioners and researchers to monitor and use environmental health intelligence, and to support countries and local areas toward sustainable and healthy development. A global network of EPHT programs and professionals has the potential to advance global health by implementing and sharing experience, to magnify the impact of local efforts and to pursue data knowledge improvement strategies, aiming to recognise and support best practices. EPHT can help increase the understanding of environmental public health and global health, improve comparability of risks between different areas of the world including Low and Middle-Income Countries (LMICs), enable transparency and trust among citizens, institutions and the private sector, and inform preventive decision making consistent with sustainable and healthy development. This shows how EPHT advances global health efforts by sharing recent global EPHT activities and resources with those working in this field. Experiences from the US, Europe, Asia and Australasia are outlined for operating successful tracking systems to advance global health.

Menstrual cycle tracking apps (MCTAs) have potential in epidemiological studies of women's health, facilitating real-time tracking of bleeding days and menstrual-associated signs and symptoms. However, information regarding the characteristics of MCTA users versus cycle nontrackers is limited, which may inform generalizability. We compared characteristics among individuals using MCTAs (app users), individuals who do not track their cycles (nontrackers), and those who used other forms of menstrual tracking (other trackers). The Ovulation and Menstruation Health Pilot Study tested the feasibility of a digitally enabled evaluation of menstrual health. Recruitment occurred between September 2017 and March 2018. Menstrual cycle tracking behavior, demographic, and general and reproductive health history data were collected from eligible individuals (females aged 18-45 years, comfortable communicating in English). Menstrual cycle tracking behavior was categorized in 3 ways: menstrual cycle tracking via app usage, that via other methods, and nontracking. Demographic factors, health conditions, and menstrual cycle characteristics were compared across the menstrual tracking method (app users vs nontrackers, app users vs other trackers, and other trackers vs nontrackers) were assessed using chi-square or Fisher exact tests. In total, 263 participants met the eligibility criteria and completed the digital survey. Most of the cohort (n=191, 72.6%) was 18-29 years old, predominantly White (n=170, 64.6%), had attained 4 years of college education or higher (n= 209, 79.5%), and had a household income below US $50,000 (n=123, 46.8%). Among all participants, 103 (39%) were MCTA users (app users), 97 (37%) did not engage in any tracking (nontrackers), and 63 (24%) used other forms of tracking (other trackers). Across all groups, no meaningful differences existed in race and ethnicity, household income, and education level. The proportion of ever-use of hormonal contraceptives was lower (n=74, 71.8% vs n=87, 90%, P=.001), lifetime smoking status was lower (n=6, 6% vs n=15, 17%, P=.04), and diagnosis rate of gastrointestinal reflux disease (GERD) was higher (n=25, 24.3% vs n=12, 12.4%, P=.04) in app users than in nontrackers. The proportions of hormonal contraceptives ever used and lifetime smoking status were both lower (n=74, 71.8% vs n=56, 88.9%, P=.01; n=6, 6% vs n=11, 17.5%, P=.02) in app users than in other trackers. Other trackers had lower proportions of ever-use of hormonal contraceptives (n=130, 78.3% vs n=87, 89.7%, P=.02) and higher diagnostic rates of heartburn or GERD (n=39, 23.5% vs n=12, 12.4%, P.03) and anxiety or panic disorder (n=64, 38.6% vs n=25, 25.8%, P=.04) than nontrackers. Menstrual cycle characteristics did not differ across all groups. Our results suggest that app users, other trackers, and nontrackers are largely comparable in demographic and menstrual cycle characteristics. Future studies should determine reasons for tracking and tracking-related behaviors to further understand whether individuals who use MCTAs are comparable to nontrackers.

The rapid development of digital mental health services can be attributed to the confluence of factors such as the rise of digitalization, individuals’ increasing agency within medical systems across the globe, and the broader trend of fostering neoliberal subjects who assume responsibility for their own health. However, the relationship between agency in addressing mental health issues and adhering to the conventional paradigm of medicalized cognition remains unclear. In this paper, we investigate the practices and motivations of young Russians using mental health apps through the conceptual frameworks of Foucault and Giddens. Our sample consisted of 22 in-depth interviews with Russians aged between 18 and 29. Findings suggest that mental health narratives are highly individualized and correspond with a neoliberal conceptualization of agency. Trackers serve as tools of self-reflection that eschew intentions to change oneself due to their reliance on manual data input, which undermines Foucault’s analysis of self-tracking.