The Apple Watch for monitoring mental health–related physiological symptoms : literature review

Background: An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts. Objective: This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment. Methods: A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch. Results: A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep. Conclusions: The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake

Personal Healthcare Agents for Monitoring and Predicting Stress and Hypertension from Biosignals

We live in exciting times. The fast paced growth in mobile computers has put powerful computational devices in the palm of our hands. Blazing fast connectivity has made human-human, human-machine, and machine-machine communication effortless. Wearable devices and the internet of things have made monitoring every aspect of our lives easier. This has given rise to the domain of quantified self where we can continuous record and quantify the various signals generated in everyday life. Sensors on smartphones can continuously record our location and motion profile. Sensors on wearable devices can track changes in our bodies’ physiological responses. This monitoring also has the capability to revolutionise the health care domain by creating more informed and involved patients. This has the potential to shift care-management from a physician-centric approach to a patient-centric approach allowing individuals to create more empowered patients and individuals who are in better control of their health. However, the data deluge from all these sources can sometimes be overwhelming. There is a need for intelligent technology that can help us navigate the data and take informed decisions. The goal of this work is to develop a mobile, personal intelligent agent platform that can become a digital companion to live with the user. It can monitor the covert and overt signal streams of the user, identify activity and stress levels to help the users’ make healthy choices regarding their lives. This thesis particularly targets patients suffering from or at-risk of essential hypertension since its a difficult condition to detect and manage. This thesis delivers the following contributions: 1) An intelligent personal agent platform for on-the-go continuous monitoring of covert and overt signals. 2) A machine learning algorithm for accurate recognition of activities using smartphone signals recorded from in-the-wild scenarios. 3) A machine learning pipeline to combine various physiological signal streams, motion profiles, and user annotations for on-the-go stress recognition. 4) We design and train a complete signal processing and classification system for hypertension prediction. 5) Through a small pilot study we demonstrate that this system can distinguish between hypertensive and normotensive subjects with high accuracy

Wearable Biosensors to Understand Construction Workers' Mental and Physical Stress

Occupational stress is defined as harmful physical and mental responses when job requirements are greater than a worker's capacity. Construction is one of the most stressful occupations because it involves physiologically and psychologically demanding tasks performed in a hazardous environment this stress can jeopardize construction safety, health, and productivity. Various instruments, such as surveys and interviews, have been used for measuring workers’ perceived mental and physical stress. However valuable, such instruments are limited by their invasiveness, which prevents them from being used for continuous stress monitoring. The recent advancement of wearable biosensors has opened a new door toward the non-invasive collection of a field worker’s physiological signals that can be used to assess their mental and physical status. Despite these advancements, challenges remain: acquiring physiological signals from wearable biosensors can be easily contaminated from diverse sources of signal noise. Further, the potential of these devices to assess field workers’ mental and physical status has not been examined in the naturalistic work environment. To address these issues, this research aims to propose and validate a comprehensive and efficient stress-measurement framework that recognizes workers mental and physical stress in a naturalistic environment. The focus of this research is on two wearable biosensors. First, a wearable EEG headset, which is a direct measurement of brain waves with the minimal time lag, but it is highly vulnerable to various artifacts. Second, a very convenient wristband-type biosensor, which may be used as a means for assessing both mental and physical stress, but there is a time lag between when subjects are exposed to stressors and when their physiological signals change. To achieve this goal, five interrelated and interdisciplinary studies were performed to; 1) acquire high-quality EEG signals from the job site; 2) assess construction workers’ emotion by measuring the valence and arousal level by analyzing the patterns of construction workers’ brainwaves; 3) recognize mental stress in the field based on brain activities by applying supervised-learning algorithms;4) recognize real-time mental stress by applying Online Multi-Task Learning (OMTL) algorithms; and 5) assess workers’ mental and physical stress using signals collected from a wristband biosensor. To examine the performance of the proposed framework, we collected physiological signals from 21 workers at five job sites. Results yielded a high of 80.13% mental stress-recognition accuracy using an EEG headset and 90.00% physical stress-recognition accuracy using a wristband sensor. These results are promising given that stress recognition with wired physiological devices within a controlled lab setting in the clinical domain has, at best, a similar level of accuracy. The proposed wearable biosensor-based, stress-recognition framework is expected to help us better understand workplace stressors and improve worker safety, health, and productivity through early detection and mitigation of stress at human-centered, smart and connected construction sites.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149965/1/hjebelli_1.pd

New Electrochemical Sensors for Decentralized Analysis

Nous sensors electroquímics per a analisis decentralitzats és una tesis que emmarca diferents aspectes del desenvolupament de sensors potenciomètrics, des de la seva fabricació, el diseny adequat, i finalment, la seva aplicabilitat en escenaris reals. En el context actual, l'evolució de la tecnologia, especialment l'aparició a nivell global d'internet, i la disponibilitat d'aquesta a baix cost han permès la creació d'eines que ens permeten connectar el món físic i, en el cas d'aquesta tesis, el món químic a la xarxa. Aquesta connexió aporta un nou grau dins l'escala de valor per a la societat actual. Concretament, aquesta aportació tecnològica va adreçada a superar els nous reptes de l'actualitat, com poden ser la sostenibilitat del sistema sanitari a causa de l'embelliment de la societat, el control medioambiental, així com també mantenir la seguretat per a la societat del benestar del futur. Així doncs, aquesta tesis presenta solucions efectives per al desenvolupament d'eines de captació d'informació que serviràn per nudrir a la societat de major coneixement. Conseqüentment, produint nous negocis al voltant, de la fabricació, processament i creació de valor entorn a aquestes dades. La recerca i desenvolupament de sensors potenciomètrics integrats a la roba per detectar els nivells d'electròlits i sensors senzills de paper per a la determinació de biomolècules, com la glucosa, són alguns dels objectius aconseguits en aquesta tesis. A més a més, sensors integrats en globus permeten l'estudi de les seves propietats mecàniques i electroquímiques, així com també, aporten noves solucions a problemes reals. Totes aquestes aplicacions serveixen de portals de captació d'informació química cap a la integració dins la nova societat de la informació.Nuevos sensores electroquímicos para analisis decentralizados es una tesis que enmarca diferentes aspectos del desarrollo de sensores potenciométricos, desde su fabricación, el diseño adecuado, i finalmente, su aplicabilidad en escenarios reales. En el contexto actual, la evolución de la tecnología, especialmente la aparición a nivel global de internet, y la disponibilidad de esta a bajo coste han permitido la creación de herramientas que nos permiten conectar el mundo físico y, en el caso de esta tesis, el mundo químico a la red. Esta conexión aporta un nuevo grado dentro la escala de valor para la sociedad actual. Concretamente, esta aportación tecnológica va dirigida a superar los nuevos retos de la actualidad, como pueden ser la sostenibilidad del sistema sanitario a causa del envejecimiento de la poblacion, el control medioambiental, así como también mantener la seguridad para la sociedad del bienestar del futuro. Entonces, esta tesis presenta soluciones efectivas para el desarrollo de herramientas de captación de información que servirán para nutrir a la sociedad de un mayor conocimiento. Por consiguiente, produciendo nuevos negocios alrededor, de la fabricación, procesado i creación de valor en los datos obtenidos. La investigación y desarrollo de sensores potenciométricos integrados en la ropa para detectar los niveles de electrolitos y sensores simples en papel para la determinación de biomoléculas, como la glucosa, son algunos de los objetivos conseguidos en esta tesis. Además, sensores integrados en globos permiten el estudio de sus propiedades mecánicas y electroquímicas, así como, aportando nuevas soluciones a problemas reales. Todas estas aplicaciones sirven de portales de captación de información química hacia la integración dentro de la nueva sociedad de la información.ew Electrochemical Sensors for Decentralized Analysis is a thesis that wisely discuss the developments of potentiometric sensors, from the fabrication step, the use of a suitable design, to the applicability in real scenarios. Nowadays, the evolution of technology, specially the creation of the global internet network, and the low-cost availability of such technology have allowed the development of tools that connect the physical world and, addressed in this thesis, the chemical world into the network. This connection adds a new level in the value chain for the present society. Precisely, this technology approach is focus on circumvent new present challenges of society. For instance, sustainability of the healthcare system caused by the population aging, environmental monitoring, as well as, keep security and safety to the welfare of society of the future. Therefore, this thesis presents successful solutions for the development of tools to gather chemical information. This information will nurture society with high-value knowledge. Accordingly, new business development from, sensing products, data treatment and information management are going to be created. Research and development of potentiometric sensors integrated into garments for electrolyte detection and simple sensors built in paper for biomolecules determination, such as glucose, and liquid monitoring, such as sweat, are some of the accomplished objectives from this thesis. Furthermore, balloon-embedded sensors allow the study of the mechanical and electrochemical properties of the electrodes, as well as, contributing with new solutions to real problems. All the applications developed in this thesis are utilized as gateways for chemical information acquisition towards the integration into the new information society

Enabling Thermally Adaptive and Sustainable Built Environments through Sensing and Modeling of Human-Building Interactions

Fundamental interactions between buildings and their occupants have a multitude of significant impacts. First, built environments critically affect occupants’ health and wellness, especially given that people spend more than 90% of time indoors. Among several environmental factors, the lack of thermal comfort is a common problem despite nearly half of the building energy being consumed by heating, ventilation, and air conditioning (HVAC) systems. Humans, in turn, closely influence the sustainable operation of buildings through various occupant energy-use behaviors. Recent studies indicate that actions performed or abstained by occupants have a major influence on building energy performance and can negate the benefits of investing in energy-efficient building systems. This dissertation focused on these two primary interplays of human-building interactions. First, uncertainties in occupants’ thermal comfort due to the varying human physiological, psychological, and behavioral factors lead to significant thermal dissatisfaction and often result in sick building syndrome. A potential solution is the human-in-the-loop approach to sense thermal comfort and provide more personalized environments. However, existing comfort assessing approaches have several key limitations including the need for continuous human input to adjust setpoints, lack of actionable human data in comfort prediction, intrusiveness and privacy concerns, and difficulty in integrating within HVAC operations. To address these issues, this research first investigated the integration of environmental data with human bio-signals collected from wristbands and smartphones for thermal comfort prediction and achieved 85% classification accuracy. This approach however required humans to provide their information from wearable devices and respond to a polling app. To address these limitations, the research further explored low-cost infrared thermal camera networks to non-intrusively collect facial skin temperature for real-time comfort assessment in both single and multi-occupancy spaces. Similar prediction accuracy is achieved without using any personal devices. Building on these comfort sensing approaches, this dissertation demonstrates how to bridge personal comfort models and physiological predictive models to determine optimum setpoints for improved overall satisfaction or reduced energy use while maintaining comfort. The proposed sensing and optimization methods can serve as a basis for automated environment control to improve human experience and well-being. The second part of this research addressed why behavior interventions result in different energy reduction rates and identified two important gaps: lack of fundamental understanding of behavioral determinants of occupants, and lack of methods to quantitatively describe the varying occupant characteristics which affect the effectiveness of interventions. To address these gaps, the research developed a conceptual framework which explains occupant behaviors with three determining factors - motivation, opportunity, and ability (MOA) incorporating insights from building science and social psychology. Based on MOA levels, clustering analysis and agent-based modeling were applied to classify occupancy characteristics and evaluate the effectiveness of a chosen intervention. The framework was improved by integrating MOA factors with two classical behavioral theories to address the challenges in defining and measuring MOA factors. The results showed an improved explanatory power over a single theory and suggested that favorable behaviors can be promoted by motivating occupants, removing environmental constraints, and improving occupants’ abilities. This framework enables decision-makers to develop effective and economical interventions to solicit behavioral change and achieve building efficiency. Building upon these two perspectives of human-building interactions, future studies can investigate how personalized thermal environments will improve occupant behaviors in interacting with HVAC systems and the corresponding impacts on building energy consumption.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153410/1/dliseren_1.pd

Step Count Reliability and Validity of Five Wearable Technology Devices While Walking and Jogging in both a Free Motion Setting and on a Treadmill

Wearable technology devices are used by millions of people who use daily step counts to promote healthy lifestyles. However, the accuracy of many of these devices has not been determined. The purpose was to determine reliability and validity of the Samsung Gear 2, FitBit Surge, Polar A360, Garmin Vivosmart HR+, and the Leaf Health Tracker when walking and jogging in free motion and treadmill conditions. Forty volunteers completed walking and jogging free motion and treadmill protocols of 5-minute intervals. The devices were worn simultaneously in randomized configurations. The mean of two manual steps counters was used as the criterion measure. Test-retest reliability was determined via Intraclass Correlation Coefficient (ICC). Validity was determined via a combination of Pearson’s Correlation Coefficient, mean absolute percent error (MAPE: free motion ≤ 10.0%, treadmill ≤ 5.00%), and Bland-Altman analysis (device bias and limits of agreement). Significance was set at p\u3c 0.05. The Samsung Gear 2 was deemed to be both reliable and valid for the jogging conditions, but not walking. The Fitbit Surge was reliable and valid for all conditions except for treadmill walking (deemed reliable, ICC = 0.76; but not valid). The Polar A360 was found to be reliable for one condition (treadmill jog ICC = 0.78), but not valid for any condition. The Garmin Vivosmart HR+ and Leaf Health Tracker were found to be both reliable and valid for all situations. While each device returned some level of consistency and accuracy during either free motion or treadmill exercises, the Garmin Vivosmart HR+ and the Leaf Health Tracker were deemed to be reliable and valid for all conditions tested

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies

Wearable Technology Devices: Heart Rate and Step Count Analysis

The overarching purpose of this dissertation was to evaluate and analyze heart rate and/or step count measurements for six popular wearable technology devices: the Samsung Gear 2, FitBit Surge, Polar A360, Garmin Vivosmart HR+, Leaf Health Tracker, and the Scosche Rhythm+ in four separate conditions: free motion walking, free motion jogging, treadmill walking, and treadmill jogging. The four studies presented here utilized one test design and data collection protocol in which many measurements could be addressed simultaneously. Currently, there is no accepted standardized protocol to evaluate wearable technology devices. The test design utilized for this research series was introduced as a potential foundation for the establishment of a common procedure. There were three purposes for the first study in this series of four research projects. First, this study looked at whether the tested devices that recorded heart rate were reliable and valid in each of the four stated conditions. Only the Garmin Vivosmart HR+ and the Scosche Rhythm+ were significantly acceptable for all four conditions. Secondly, while all the tested devices used photoplethysmography to record heart rate, this technique has not been thoroughly validated for this purpose. Limited research indicates that devices that use this method as a measurement technique and are worn on the forearm are more accurate than those worn elsewhere on the body. Results from our study supported this conclusion. The Scosche Rhythm+, being a fore arm worn device, did produce more significantly acceptable results than the wrist worn Garmin Vivosmart HR+. Third, a standardized heart rate testing protocol has been introduced by the Consumer Technology Association. However, their recommended measurement criteria (a measurement every 1-5 seconds which would require special software to record) can be viewed as financially prohibitive, restrictive, and over compensating. The protocol used in our research presented evidence that ours, which used an average of several minutes of heart rate values, was easier to implement and did not required a financial investment to perform. The second study had two purposes. First, this study looked at whether the tested devices that recorded step count were reliable and valid in each of the four conditions. Only the FitBit Surge, Garmin Vivosmart HR+ and the Leaf Health Tracker were significantly acceptable for all four conditions. Secondly, the Consumer Technology Association has recommended a standardized step count protocol which would require the videotaping of an activity with separate tape reviews by two persons at a future time. This protocol is not feasible in certain conditions such as outside testing. Additionally, both reviewers would need to produce the exact same step count. Our testing used two manual counters where the mean of the two were used as the criterion measure. We provided strong evidence that this is an acceptable criterion measure for step counting that does not require additional time or resources. The third study compared heart rate and step count values measured by the tested devices between the different conditions. Measurements taken during free motion walking were compared to treadmill walking and those taken during free motion jogging were compared to treadmill jogging. It is generally believed that most wearable technology device companies perform device testing on a treadmill in a laboratory. Our conclusion was that there was no significant interaction or main effects for walking heart rate value comparisons. Jogging heart rate values saw significant main effects from both the environment and between the devices. Walking step count values had a significant interaction between the devices and the environment. Jogging step count values had a significant main effect between the devices. When utilizing wearable technology devices for the measurement of heart rate during walking or jogging, the Garmin Vivosmart HR+ and Rhythm Scosche Rhythm+ provided acceptable measures both in the laboratory as well as in a free motion environment. The FitBit Surge, Garmin Vivo Smart HR+, and the Leaf Health Tracker produced similar results for step count. The fourth study evaluated whether there was a correlation between both body composition percentages and body mass index values and the percent error calculated between a manual step count and that recorded by the wearable technology devices. Our results gave evidence that there are no significant correlations between body mass index and the calculated percent error. For body composition, only two conditions for the wrist worn devices had a positive significant correlation; the Samsung Gear 2 when free motion walking and the Garmin Vivosmart HR+ when free motion walking. The waist worn Leaf Activity Tracker had positive significant correlations for both treadmill walking and treadmill jogging. Even though our study produced four conditions with significant correlations, all were low to moderate in value