Sensor-Based Smart Clothing for Women's Menopause Transition Monitoring

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

Background

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.

Methods

We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a daily estimation of days until labor onset.

Results

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

Conclusion

Continuous skin temperature reflects progression toward labor and hormonal status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Effectiveness of a 12-week tele-exercise training program on cardiorespiratory fitness and heart rate recovery in patients with cardiometabolic multimorbidity

BACKGROUND

Exercise has positive impacts on cardiometabolic health. However, evidence regarding the effectiveness of tele-exercise training on cardiorespiratory fitness and heart rate recovery in patients with cardiometabolic multimorbidity remains limited.

AIMS

The aim of this study was to assess whether an assumed increase in physical activity (PA) after a 12-week tele-exercise training program improved cardiorespiratory fitness and heart rate recovery of patients with cardiometabolic multimorbidity.

METHODS

A parallel-group randomized controlled trial was conducted. Eligible patients with cardiometabolic multimorbidity (n = 83) were randomized 1:1 to either an experimental group (EG, received a 12-week tele-exercise training program with 3 sessions/week and 30 min/session and weekly remote monitoring for maintenance of exercise) or a control group (CG, usual care only). PA, cardiorespiratory fitness, and heart rate recovery were assessed at baseline and 12 weeks. Generalized estimating equations were used to examine the intervention effects via the interaction of time and group.

RESULTS

Sixty-eight participants (81.9%) completed the study, and 83 were included in the intention-to-treat analysis. The EG with higher vigorous-intensity PA (β = 714, p = .037), walking behavior (β = 750, p = .0007), and total amount of PA (β = 1748, p = .001) after the intervention had significantly elevated cardiorespiratory fitness, including VO2peak (β = 3.9, p = .042), workload (β = 17.9, p = .034), and anaerobic threshold (β = 2.1, p = .041), and increased one-min heart rate recovery (β = 5.3, p = .025), compared with the CG.

LINKING EVIDENCE TO ACTION

A 12-week tele-exercise training program was effective for increased PA, elevated cardiorespiratory fitness, and improved heart rate recovery for patients with cardiometabolic multimorbidity. These findings highlight the feasibility of better delivering lifestyle interventions for cardiometabolic health management.

IoT garment for remote elderly care network

The elderly is a continuous growth sector thanks to the life expectancy increase in Western society. This sector is especially at risk from the appearance of respiratory diseases and, therefore, is the most affected sector in the COVID-19 epidemic. Many of these elderly require continuous care in residences or by specialized caregivers, but these personal contacts put this sector at risk. In this work, an IoT system for elderly remote monitoring is studied, designed, developed and tested. This system is composed by a smart garment that records information from various physiological sensors in order to detect falls, sudden changes in body temperature, heart problems and heat stroke; This information is sent to a cloud server through a gateway located in the patient's residence, allowing to real-time monitor remotely patient's activity using a customized App, as well as receiving alerts in dangerous situations. This system has been tested with professional caregivers, obtaining usability and functionality surveys; and, in addition, a detailed power-consumption study has been carried out. The results, compared with other similar systems, demonstrate that the proposed one is useful, usable, works in real time and has a decent power consumption that allows the patient to carry it during all day without charging the battery.