Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Abstract

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a ‘clock’ of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal ‘clock’ of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman’s), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

“…55 Among these outputs, temperature provided the greatest contribution to the model. Although we were not able to find recent other recent studies of human temperature in labor prediction, 52 a research group recently 68 demonstrated decreases in physical activity with advancing gestational age, and an 18-participant cohort study demonstrated decreases in HR and increases in HRV in the third trimester. 69 Although more human studies are needed, these results build on a wealth of animal literature demonstrating unique decreases in temperature prior to parturition, and numerous efforts to identify features indicative of imminent labor, ranging from simple thresholds 43 to machine learning approaches similar to the present study.…”

Evaluation of Sleep in Pregnant Inpatients Compared With Outpatients

“…26 Although less is known about the magnitude of perinatal risk, sleep disturbances in pregnancy have been associated with gestational diabetes, hypertensive disorders, preterm birth, and mental health conditions. 2,8,10,27–29 Whether the decrease in sleep that we identified contributes to worsening clinical status remains unknown.…”

“…4,6,7 Information about sleep and sleep quality is particularly important given the known associations between sleep disturbances and increases in complications such as gestational diabetes, hypertensive disorders, and preterm birth. 8–10 Therefore, we conducted a prospective pilot study to evaluate whether antepartum hospitalization was associated with differences in sleep duration and quality compared with gestational age–matched outpatients.…”

Sexualität und Schwangerschaft: Welchen Einfluss hat Vaginalverkehr auf die Schwangerschaftsdauer?

“…Gleiches gilt auch für das Zulassen des weiblichen Orgasmus [21,19,9]. Eine aktuelle Studie der Universität Stanford [18] untersuchte den Zusammenhang zwischen erholsamem Schlaf sowie körperlicher Aktivität und dem Risiko, eine Frühgeburt zu erleiden. Die Ergebnisse zeigen signifikante Zusammenhänge zwischen einem erhöhten Frühgeburtsrisiko durch gestörten Schlafrhythmus und mangelnder körperlicher Aktivität.…”