Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring

Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions

Advanced extravehicular activity systems requirements definition study. Phase 2: Extravehicular activity at a lunar base

The focus is on Extravehicular Activity (EVA) systems requirements definition for an advanced space mission: remote-from-main base EVA on the Moon. The lunar environment, biomedical considerations, appropriate hardware design criteria, hardware and interface requirements, and key technical issues for advanced lunar EVA were examined. Six remote EVA scenarios (three nominal operations and three contingency situations) were developed in considerable detail

Differences in Quadriceps Muscle Layer Thickness (QMLT) and contributing risk factors to muscle mass in community-dwelling and institutionalized older adults

Sarcopenia, a major concern in the older adult population, is defined as age-related loss of muscle mass and strength. Quadriceps muscle layer thickness (QMLT) measured using ultrasonography (US) is a newly-validated tool to measure muscle mass, which can be used to identify sarcopenic individuals. Our objective was to determine the association of factors such as handgrip strength (HGS), protein intake, nutritional status (via Subjective Global Assessment-SGA) and fat mass (FM) percentage with QMLT size (measured by US) in community-dwelling and institutionalized older adults. Additionally, we aimed to understand how perceived food intake of protein-rich foods could have an impact actual food intake. Sixty-three older adults ≥65 years (23 community-dwelling and 40 institutionalized older adults) took part in a cross-sectional study measuring differences in QMLT size, HGS, protein intake, SGA scores, and FM percentage between groups. Additionally, focus groups and individual interviews provided qualitative perspectives on protein intake. QMLT size was not significant between groups (p=0.358); however, HGS was significantly higher in community-dwelling older adults (pr=0.432, pr=-0.361, p=0.004). HGS was the best predictor of QMLT size (b=0.391, r(63)=0.432, p=0.014) and QMLT measurements were highly reproducible (

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics

An energy-efficient and secure data inference framework for internet of health things: A pilot study

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices

Changes in body composition and metabolic syndrome risk factors : response to energy-restriction, protein intake, and high intensity interval training

Metabolic syndrome (MetS) and abdominal obesity (AbOb) increase the risk of developing cardiovascular disease and diabetes. Energy restriction (ER), highprotein (PRO) intake and high-intensity interval training (HIT) can independently improve MetS and AbOb. However, ER reduces metabolically active lean body mass (LBM) in addition to body fat (BF). Purpose: To determine the effects of a 16-wk ER diet with 2 levels of PRO (15% or 25% of energy), plus HIT, on MetS risk factors, AbOb, and body composition in women. Methods: Sedentary, premenopausal women (age=35±10y) with AbOb (waist circumference [WC] ≥80cm) were randomized to a 16-wk ER diet (-300kcals/d) with 15% (15PRO; n=17) or 25% (25PRO; n=18) of energy from PRO, plus 45min/d, 3d/wk HIT and 45min/d, 2d/wk continuous moderate-intensity exercise (CME) (-200kcals/d). Diet and physical activity (PA) were assessed using 4-d weighed food and PA records, respectively; diet and exercise compliance were assessed monthly with multiple-pass 24-h recalls and weekly tracking logs. Body weight (BW), WC, DXA-assessed body composition (BF [%], BF [kg], trunk fat [kg], and LBM [kg]), blood lipids (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], triglycerides [TG]), glycemic markers (fasting plasma glucose [FPG], insulin, and homeostatic model assessment for insulin resistance [HOMA-IR], beta cell function [HOMA-%β] and insulin sensitivity [HOMA-%S]) and resting blood pressure (BP) (systolic BP [SBP]; diastolic BP [DBP]) were assessed pre/post-intervention. Repeated measures analysis of variance and two sample t-tests were used at analyze the date. Results are reported as means±standard deviations. Results: There were significant time, but not group, differences in BW (-5.1±2.6kg, p=0.0141), WC (- 7.3±3.6cm, p<0.0001), TC (-18.1±17.4mg/dL, p<0.0001), LDL-C (12.2± 16.2mg/dL, p<0.0001), TG (-25.3±56.2mg/dL, p=0.0064), insulin (-2.1±4.2mg/dL, p=0.0048), HOMA-IR (-0.2±0.5, p=0.0062), HOMA-%β (-12.1±35.2%, p=0.0497), HOMA-%S (28.5±78.4%, p=0.0357), and SBP (-3±9mmHg, p=0.214). There were significant group x time differences in DBP (15PRO=-5±8mmHg, 25PRO=- 2±8mmHg; p=0.0024). There were no time or group differences in FPG or HDLC. There were significant time, but not group, effects on changes in BW (-5.1kg± 2.6, p<0.0001), BF (-3.3±1.6%, p<0.0001), and LBM (-0.6kg±1.5, p=0.0283). The 15PRO group lost more absolute whole BF (-5.2kg vs. -3.9kg, p=0.0355) and trunk fat (-3.1kg vs. -2.2kg) vs. the 25PRO group. Conclusion: Both diets significantly improved BW, AbOb, MetS risk factors, glycemic control, and BF (%); LBM (kg) loss was similar in both groups. Compared to the 15PRO diet had significantly greater absolute BF-kg and trunk fat-kg losses. Increased PRO intake did not improve AbOb or MetS risk beyond ER and HIT/CME. The impact of HIT/CME and the greater (-1.3kg) changes in BW in the 15PRO group may have contributed significantly to the changes in absolute BF and trunk fat. More research is needed to separate the impact of HIT/CME and weight loss from the impact of PRO during ER

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces