Dataset Information

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.

ABSTRACT: BACKGROUND:Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ?C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. OBJECTIVE:In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. METHODS:We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. RESULTS:We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). CONCLUSIONS:Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

SUBMITTER: Goodale BM

PROVIDER: S-EPMC6495289 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.

Goodale Brianna Mae BM Shilaih Mohaned M Falco Lisa L Dammeier Franziska F Hamvas Györgyi G Leeners Brigitte B

Journal of medical Internet research 20190418 4

<h4>Background</h4>Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significant ...[more]

PMID: 30998226

Similar Datasets

Project description:INTRODUCTION:Smoking prevalence continues to be high over the world and smoking-induced diseases impose a heavy burden on the medical care system. As believed by many researchers, a promising way to promote healthcare and well-being at low cost for the large vulnerable smoking population is through eHealth solutions by providing self-help information about smoking cessation. But in the absence of first-hand knowledge about smoking habits in daily life settings, systems built on these methods often fail to deliver proactive and tailored interventions for different users and situations over time, thus resulting in low efficacy. To fill the gap, an observational study has been developed on the theme of objective and non-biased monitoring of smoking habits in a longitudinal and ambulatory mode. This paper presents the study protocol. The primary objective of the study is to reveal the contextual and physiological pattern of different smoking behaviours using wearable sensors and mobile phones. The secondary objectives are to (1) analyse cue factors and contextual situations of smoking events; (2) describe smoking types with regard to users' characteristics and (3) compare smoking types between and within subjects. METHODS AND ANALYSES:This is an observational study aimed at reaching 100 participants. Inclusion criteria are adults aged between 18 and 65 years, current smoker and office worker. The primary outcome is a collection of a diverse and inclusive data set representing the daily smoking habits of the general smoking population from similar social context. Data analysation will revolve around our primary and secondary objectives. First, linear regression and linear mixed model will be used to estimate whether a factor or pattern have consistent (p value<0.05) correlation with smoking. Furthermore, multivariate multilevel analysis will be used to examine the influence of smokers' characteristics (sex, age, education, socioeconomic status, nicotine dependence, attitudes towards smoking, quit attempts, etc), contextual factors, and physical and emotional statuses on their smoking habits. Most recent machine learning techniques will also be explored to combine heterogeneous data for classification of smoking events and prediction of craving. ETHICS AND DISSEMINATION:The study was designed together by an interdisciplinary group of researchers, including psychologist, psychiatrist, engineer and user involvement coordinator. The protocol was reviewed and approved by the ethical review board of UZ Leuven on 18 April 2016, with an approval number S60078. The study will allow us to characterise the types of smokers and triggering events. These findings will be disseminated through peer-reviewed articles.

Project description:BackgroundRelapse in schizophrenia may be preceded by early warning signs of biological, sensory, and clinical status. Early detection of warning signs may facilitate intervention and prevent relapses.ObjectiveThis study aims to investigate the feasibility of using wearable devices and self-reported technologies to identify symptom exacerbation correlates and relapse in patients with schizophrenia.MethodsIn this observational study, patients with schizophrenia were provided with remote sensing devices to continuously monitor activity (Garmin vivofit) and sleep (Philips Actiwatch), and smartphones were used to record patient-reported outcomes. Clinical assessments of symptoms (Positive and Negative Syndrome Scale and Brief Psychiatric Rating Scale) were performed biweekly, and other clinical scales on symptoms (Clinical Global Impression-Schizophrenia, Calgary Depression Scale), psychosocial functioning, physical activity (Yale Physical Activity Survey), and sleep (Pittsburgh Sleep Quality Index) were assessed every 4 weeks. Patients were observed for 4 months, and correlations between clinical assessments and aggregated device metrics data were assessed using a mixed-effect model. An elastic net model was used to predict the clinical symptoms based on the device features.ResultsOf the 40 patients enrolled, 1 patient relapsed after being stable with evaluable postbaseline data. Weekly patient-reported outcomes were moderately correlated with psychiatric symptoms (Brief Psychiatric Rating Scale total score, r=0.29; Calgary Depression Scale total score, r=0.37; and Positive and Negative Syndrome Scale total score, r=0.3). In the elastic net model, sleep and activity features derived from Philips Actigraph and Garmin vivofit were predictive of the sitting index of the Yale Physical Activity Survey and sleep duration component of the Pittsburgh Sleep Quality Index. On the basis of the combined patient data, a high percentage of data coverage and compliance (>80%) was observed for each device.ConclusionsThis study demonstrated that wearable devices and smartphones could be effectively deployed and potentially used to monitor patients with schizophrenia. Furthermore, metrics-based prediction models can assist in detecting earlier signs of symptom changes. The operational learnings from this study may provide insights to conduct future studies.Trial registrationClinicalTrials.gov NCT02224430; https://www.clinicaltrials.gov/ct2/show/NCT02224430.

Project description:BACKGROUND:Wearable devices are valuable assessment tools for patient outcomes in contexts such as clinical trials. To be successfully deployed, however, participants must be willing to wear them. Another concern is that usability studies are rarely published, often fail to test devices beyond 24 hours, and need to be repeated frequently to ensure that contemporary devices are assessed. OBJECTIVE:This study aimed to compare multiple wearable sensors in a real-world context to establish their usability within an older adult (>50 years) population. METHODS:Eight older adults wore seven devices for a minimum of 1 week each: Actigraph GT9x, Actibelt, Actiwatch, Biovotion, Hexoskin, Mc10 Biostamp_RC, and Wavelet. Usability was established through mixed methods using semistructured interviews and three questionnaires, namely, the Intrinsic Motivation Inventory (IMI), the System Usability Scale (SUS), and an acceptability questionnaire. Quantitative data were reported descriptively and qualitative data were analyzed using deductive content analysis. Data were then integrated using triangulation. RESULTS:Results demonstrated that no device was considered optimal as all scored below average in the SUS (median, IQR; min-max=57.5, 12.5; 47.5-63.8). Hexoskin was the lowest scored device based on the IMI (3.6; 3.4-4.5), while Biovotion, Actibelt, and Mc10 Biostamp_RC achieved the highest median results on the acceptability questionnaire (3.6 on a 6-point Likert scale). Qualitatively, participants were willing to accept less comfort, less device discretion, and high charging burdens if the devices were perceived as useful, namely through the provision of feedback for the user. Participants agreed that the purpose of use is a key enabler for long-term compliance. These views were particularly noted by those not currently wearing an activity-tracking device. Participants believed that wrist-worn sensors were the most versatile and easy to use, and therefore, the most suitable for long-term use. In particular, Actiwatch and Wavelet stood out for their comfort. The convergence of quantitative and qualitative data was demonstrated in the study. CONCLUSIONS:Based on the results, the following context-specific recommendations can be made: (1) researchers should consider their device selection in relation to both individual and environmental factors, and not simply the primary outcome of the research study; (2) if researchers do not wish their participants to have access to feedback from the devices, then a simple, wrist-worn device that acts as a watch is preferable; (3) if feedback is allowed, then it should be made available to help participants remain engaged; this is likely to apply only to people without cognitive impairments; (4) battery life of 1 week should be considered as a necessary feature to enhance data capture; (5) researchers should consider providing additional information about the purpose of devices to participants to support their continued use.

Dataset Information

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.

Publications

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets