Theoretically-Based Emotion Regulation Strategies Using a Mobile App and Wearable Sensor Among Homeless Adolescent Mothers: Acceptability and Feasibility Study.

Background: Many adolescent mothers are parenting young children under highly stressful conditions as they are managing first-time parenthood, poverty, lack of housing, school and work, and challenging peer and familial relationships. Mobile health (mHealth) technology has the potential to intervene at various points in the emotion regulation process of adolescent mothers to provide them support for more adaptive emotional and behavioral regulation in the course of their daily life. Objective: The goal of this study was to examine the acceptability, feasibility, use patterns, and mechanisms by which a mobile technology used as an adjunct to in-person, provider-delivered sessions fostered adolescent mothers' adaptive emotion regulation strategies under real-life conditions. Methods: Participants (N=49) were enrolled in the intervention condition of a larger pilot study of homeless adolescent mothers living in group-based shelters. The mHealth technology. Calm Mom, consisted of a mobile app and a wrist-worn sensorband for the ambulatory measurement and alerting of increased electrodermal activity (EDA), a physiological measurement of stress. We examined logs of mobile app activity and conducted semistructured qualitative interviews with a subsample (N=10) of participants. Qualitative data analysis was guided by the theoretical frames of the intervention and a technology acceptance model and included an analysis of emerging themes and concepts. Results: Overall, participants indicated that one or more of the elements of Calm Mom supported their ability to effectively regulate their emotions in the course of their daily life in ways that were consonant with the intervention's theoretical model. For many adolescent mothers, the app became an integral tool for managing stress. Due to technical challenges, fewer participants received sensorband alerts; however, those who received alerts reported high levels of acceptability as the technology helped them to identify their emotions and supported them in engaging in more adaptive behaviors during real-life stressful situations with their children, peers, and family members. Conclusions: Calm Mom is a promising technology for providing theoretically driven behavioral intervention strategies during real-life stressful moments among a highly vulnerable population. Future research efforts will involve addressing technology challenges and refining tailoring algorithms for implementation in larger-scale studies.

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals.

Recent clinical studies show that the contour of the photoplethysmogram (PPG) wave contains valuable information for characterizing cardiovascular activity. However, analyzing the PPG wave contour is difficult; therefore, researchers have applied first or higher order derivatives to emphasize and conveniently quantify subtle changes in the filtered PPG contour. Our hypothesis is that analyzing the whole PPG recording rather than each PPG wave contour or on a beat-by-beat basis can detect heat-stressed subjects and that, consequently, we will be able to investigate the impact of global warming on human health. Here, we explore the most suitable derivative order for heat stress assessment based on the energy and entropy of the whole PPG recording. The results of our study indicate that the use Int. J. Environ. Res. Public Health 2015, 7 12777 of the entropy of the seventh derivative of the filtered PPG signal shows promising results in detecting heat stress using 20-second recordings, with an overall accuracy of 71.6%. Moreover, the combination of the entropy of the seventh derivative of the filtered PPG signal with the root mean square of successive differences, or RMSSD (a traditional heart rate variability index of heat stress), improved the detection of heat stress to 88.9% accuracy.

Frequency analysis of photoplethysmogram and its derivatives.

There are a limited number of studies on heat stress dynamics during exercise using the photoplethysmogram (PPG). We investigate the PPG signal and its derivatives for heat stress assessment using Welch (non-parametric) and autoregressive (parametric) spectral estimation methods. The preliminary results of this study indicate that applying the first and second derivatives to PPG waveforms is useful for determining heat stress level using 20-s recordings. Interestingly, Welch's and Yule-Walker's methods in agreement that the second derivative is an improved detector for heat stress. In fact, both spectral estimation methods showed a clear separation in the frequency domain between measurements before and after simulated heat-stress induction when the second derivative is applied. Moreover, the results demonstrate superior performance of the Welch's method over the Yule-Walker's method in separating before and after the three simulated heat-stress inductions.

Wearable sensors: opportunities and challenges for low-cost health care.

Wearable sensors enable long-term continuous physiological monitoring, which is important for the treatment and management of many chronic illnesses, neurological disorders, and mental health issues. Examples include: diabetes, autism spectrum disorder (ASD), depression, drug addition, and anxiety disorders. In this paper, we present a few mobile health technologies developed by our group and also discuss emerging opportunities as well as existing challenges. Technologies presented include wearable sensors for electrodermal activity (EDA) and mobile plethysmography as well as mobile phones and the supporting wireless network architecture.

Clip-on wireless wearable microwave sensor for ambulatory cardiac monitoring.

We present a new type of non-contact sensor for use in ambulatory cardiac monitoring. The sensor operation is based on a microwave Doppler technique; however, instead of detecting the heart activity from a distance, the sensor is placed on the patient's chest over the clothing. The microwave sensor directly measures heart movement rather than electrical activity, and is thus complementary to ECG. The primary advantages of the microwave sensor includes small size, light weight, low power, low-cost, and the ability to operate through clothing. We present a sample sensor design that incorporates a 2.4 GHz Doppler circuit, integrated microstrip patch antenna, and microntroller with 12-bit ADC data sampling. The prototype sensor also includes a wireless data link for sending data to a remote PC or mobile phone. Sample data is shown for several subjects and compared to data from a commercial portable ECG device. Data collected from the microwave sensor exhibits a significant amount of features, indicating possible use as a tool for monitoring heart mechanics and detection of abnormalities such as fibrillation and akinesia.