Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

Evaluating the role of heart rate variability in monitoring stress and sleep quality among nurses in the aftermath of the COVID-19 pandemic

AIM

To assess heart rate variability (HRV) as a measure to assess job stress and sleep quality among nurses in the post-COVID-19 period.

BACKGROUND

The COVID-19 pandemic significantly affected nurses, with heightened job stress and impaired sleep quality impacting their well-being and effectiveness in patient care. HRV could offer insights for supporting strategies in the pandemic aftermath.

DESIGN

A quantitative cross-sectional study.

METHODS

This study involved 403 clinical nurses recruited from a teaching hospital in Taiwan. Data on job stress, work frustration, sleep quality and HRV were collected and analysed.

RESULTS

Among the nurses surveyed during the COVID-19 pandemic, 72.7% reported poor sleep quality (PSQI = 9.369). Job stress emerged as a strong predictor of work frustration. High stress levels and poor sleep quality were correlated with significantly decreased HRV, indicating a potential physiological impact of stress on the nurses' health and well-being.

CONCLUSIONS

HRV is a valuable and cost-effective measure for monitoring and managing nurses' well-being in the post-COVID-19 era. Targeted interventions can be implemented to support nurses' overall performance and promote their well-being by identifying those at high risk of job stress and poor sleep quality.

Does stress induction affect cognitive performance or avoidance of cognitive effort?

Previous research has shown that acute psychosocial stress impairs cognitive abilities, but recent studies suggest that this may be due to a decrease in willingness to engage in cognitive effort rather than a direct effect on performance. The aim of the present study was to replicate this last research and verify the influence of acute stress on avoidance of cognitive effort and cognitive performance. Fifty young, healthy individuals (26 females, 24 males) aged between 18 and 40 years were randomly assigned to two groups: a stress condition and a control condition. We used a Demand Selection Task paradigm (DST), in which participants chose between performing tasks that required either high or low cognitive effort. Stress was induced through the Trier Social Stress Test (TSST) and measured with both subjective and psychophysiological measurements. The results indicated that acute stress significantly increased participants' preference for less demanding behaviors, while no significant alterations in cognitive performance in task change activities were found. This study offers new perspectives on how stress affects behavior and decision-making in everyday life.

Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study

BACKGROUND

Early identification of mental disorder symptoms is crucial for timely treatment and reduction of recurring symptoms and disabilities. A tool to help individuals recognize warning signs is important. We posit that such a tool would have to rely on longitudinal analysis of patterns and trends in the individual's daily activities and mood, which can now be captured through data from wearable activity trackers, speech recordings from mobile devices, and the individual's own description of their mental state. In this paper, we describe such a tool developed by our team to detect early signs of depression, anxiety, and stress.

OBJECTIVE

This study aims to examine three questions about the effectiveness of machine learning models constructed based on multimodal data from wearables, speech, and self-reports: (1) How does speech about issues of personal context differ from speech while reading a neutral text, what type of speech data are more helpful in detecting mental health indicators, and how is the quality of the machine learning models influenced by multilanguage data? (2) Does accuracy improve with longitudinal data collection and how, and what are the most important features? and (3) How do personalized machine learning models compare against population-level models?

METHODS

We collect longitudinal data to aid machine learning in accurately identifying patterns of mental disorder symptoms. We developed an app that collects voice, physiological, and activity data. Physiological and activity data are provided by a variety of off-the-shelf fitness trackers, that record steps, active minutes, duration of sleeping stages (rapid eye movement, deep, and light sleep), calories consumed, distance walked, heart rate, and speed. We also collect voice recordings of users reading specific texts and answering open-ended questions chosen randomly from a set of questions without repetition. Finally, the app collects users' answers to the Depression, Anxiety, and Stress Scale. The collected data from wearable devices and voice recordings will be used to train machine learning models to predict the levels of anxiety, stress, and depression in participants.

RESULTS

The study is ongoing, and data collection will be completed by November 2023. We expect to recruit at least 50 participants attending 2 major universities (in Canada and Mexico) fluent in English or Spanish. The study will include participants aged between 18 and 35 years, with no communication disorders, acute neurological diseases, or history of brain damage. Data collection complied with ethical and privacy requirements.

CONCLUSIONS

The study aims to advance personalized machine learning for mental health; generate a data set to predict Depression, Anxiety, and Stress Scale results; and deploy a framework for early detection of depression, anxiety, and stress. Our long-term goal is to develop a noninvasive and objective method for collecting mental health data and promptly detecting mental disorder symptoms.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48210.

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

Games in Times of a Pandemic: Structured Overview of COVID-19 Serious Games

BACKGROUND

The COVID-19 pandemic introduced an urgent need for effective strategies to disseminate crucial knowledge and improve people's subjective well-being. Complementing more conventional approaches to knowledge dissemination, game-based interventions were developed to create awareness and educate people about the pandemic, hoping to change their attitudes and behavior.

OBJECTIVE

This study provided an overview and analysis of digital and analog game-based interventions in the context of the COVID-19 pandemic. As major pandemics and other large-scale disruptive events are expected to increase in frequency in the coming decades, this analysis aimed to inform the design, uptake, and effects of similar future interventions.

METHODS

From November 2021 to April 2022, Scopus, Google, and YouTube were searched for articles and videos describing COVID-19-themed game-based interventions. Information regarding authorship, year of development or launch, country of origin, license, deployment, genre or type, target audience, player interaction, in-game goal, and intended transfer effects was extracted. Information regarding intervention effectiveness was retrieved where possible.

RESULTS

A diverse assortment of 23 analog and 43 digital serious games was identified, approximately one-third of them (25/66, 38%) through scientific articles. Most of these games were developed by research institutions in 2020 (13/66, 20%) and originated in Europe and North America (38/66, 58%). A limited number (20/66, 30%) were tested on relatively small samples, using a diversity of research methods to assess the potential changes in participants' knowledge, attitudes, and behaviors as well as their gameplay experience. Although most of the evaluated games (11/20, 55%) effectively engaged and motivated the players, increased awareness, and improved their understanding of COVID-19-related issues, the games' success in influencing people's behavior was often unclear or limited.

CONCLUSIONS

To increase the impact of similar future interventions aimed at disseminating knowledge and influencing people's attitudes and behaviors during a large-scale crisis, some considerations are suggested. On the basis of the study results and informed by existing game theories, recommendations are made in relation to game development, deployment, and distribution; game users, design, and use; game design terminology; and effectiveness testing for serious games.

The Moderation Role of AI-Enabled Service Quality on the Attitude Toward Fitness Apps

Artificial intelligent technology is rapidly transforming the fitness apps landscape by applying data mining technologies within given parameters. These wide-ranging AI-enabled services improve user interface and enhance customers' experience when exercising with the fitness apps. The current study integrated the four antecedents—technological functions, intrinsic information quality, perceived enjoyment, and social connection—to investigate the moderating influence of AI-enabled service quality on users' attitude toward physical activity. PLS-SEM was used to analyze and validate a sample of 170 participants. The findings posited that individuals' attitude toward physical activity is encouraged by the (1) technological functions (2) intrinsic information quality, and (3) perceived enjoyment. Further, the moderating role of AI-enabled service positively influencing the attitude toward physical activity with technological functions was also established.

Peripheral blood flow estimated by laser doppler flowmetry provides additional information about sleep state beyond that provided by pulse rate variability

Pulse rate variability (PRV), derived from Laser Doppler flowmetry (LDF) or photoplethysmography, has recently become widely used for sleep state assessment, although it cannot identify all the sleep stages. Peripheral blood flow (BF), also estimated by LDF, may be modulated by sleep stages; however, few studies have explored its potential for assessing sleep state. Thus, we aimed to investigate whether peripheral BF could provide information about sleep stages, and thus improve sleep state assessment. We performed electrocardiography and simultaneously recorded BF signals by LDF from the right-index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of polysomnographic recording. Time- and frequency-domain parameters of peripheral BF, and time-domain, frequency-domain, and non-linear indices of PRV and heart rate variability (HRV) were calculated. Finger-BF parameters in the time and frequency domains provided information about different sleep stages, some of which (such as the difference between N1 and rapid eye movement sleep) were not revealed by finger-PRV. In addition, finger-PRV patterns and HRV patterns were similar for most parameters. Further, both finger- and ear-BF results showed 0.2-0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation of respiration within this frequency band. These results showed that peripheral BF could provide information for different sleep stages, some of which was complementary to the information provided by PRV. Furthermore, the combination of peripheral BF and PRV may be more advantageous than HRV alone in assessing sleep states and related autonomic nervous activity.

Photoplethysmography Enabled Wearable Devices and Stress Detection: A Scoping Review

BACKGROUND

Mental and physical health are both important for overall health. Mental health includes emotional, psychological, and social well-being; however, it is often difficult to monitor remotely. The objective of this scoping review is to investigate studies that focus on mental health and stress detection and monitoring using PPG-based wearable sensors.

METHODS

A literature review for this scoping review was conducted using the PRISMA (Preferred Reporting Items for the Systematic Reviews and Meta-analyses) framework. A total of 290 studies were found in five medical databases (PubMed, Medline, Embase, CINAHL, and Web of Science). Studies were deemed eligible if non-invasive PPG-based wearables were worn on the wrist or ear to measure vital signs of the heart (heart rate, pulse transit time, pulse waves, blood pressure, and blood volume pressure) and analyzed the data qualitatively.

RESULTS

Twenty-three studies met the inclusion criteria, with four real-life studies, eighteen clinical studies, and one joint clinical and real-life study. Out of the twenty-three studies, seventeen were published as journal-based articles, and six were conference papers with full texts. Because most of the articles were concerned with physiological and psychological stress, we decided to only include those that focused on stress. In twelve of the twenty articles, a PPG-based sensor alone was used to monitor stress, while in the remaining eight papers, a PPG sensor was used in combination with other sensors.

CONCLUSION

The growing demand for wearable devices for mental health monitoring is evident. However, there is still a significant amount of research required before wearable devices can be used easily and effectively for such monitoring. Although the results of this review indicate that mental health monitoring and stress detection using PPG is possible, there are still many limitations within the current literature, such as a lack of large and diverse studies and ground-truth methods, that need to be addressed before wearable devices can be globally useful to patients.

Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey

Wireless networks have gained significant attention and importance in healthcare as various medical devices such as mobile devices, sensors, and remote monitoring equipment must be connected to communication networks. In order to provide advanced medical treatments to patients, high-performance technologies such as the emerging fifth generation/sixth generation (5G/6G) are required for transferring data to and from medical devices and in addition to their major components developed with improved optimization methods which are substantially needed and embedded in them. Providing intelligent system design is a challenging task in medical applications, as it affects the whole behaviors of medical devices. A critical review of the medical devices and the various optimization methods employed are presented in this paper, to pave the way for designers to develop an apparatus that is applicable in the healthcare industry under 5G technology and future 6G wireless networks.

Impact of the Moderating Effect of National Culture on Adoption Intention in Wearable Health Care Devices: Meta-analysis

Background

Wearable health care devices have not yet been commercialized on a large scale. Additionally, people in different countries have different utilization rates. Therefore, more in-depth studies on the moderating effect of national culture on adoption intention in wearable health care devices are necessary.

Objective

This study aims to explore the summary results of the relationships between perceived usefulness and perceived ease of use with adoption intention in wearable health care devices and the impact of the moderating effect of national culture on these two relationships.

Methods

We searched for studies published before September 2021 in the Web of Science, EBSCO, Engineering Village, China National Knowledge Infrastructure, IEEE Xplore, and Wiley Online Library databases. CMA (version 2.0, Biostat Inc) software was used to perform the meta-analysis. We conducted publication bias and heterogeneity tests on the data. The random-effects model was used to estimate the main effect size, and a sensitivity analysis was conducted. A meta-regression analysis was used to test the moderating effect of national culture.

Results

This meta-analysis included 20 publications with a total of 6128 participants. Perceived usefulness (r=0.612, P<.001) and perceived ease of use (r=0.462, P<.001) positively affect adoption intention. The relationship between perceived usefulness and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.008, P<.001) and indulgence/restraint (β=.005, P<.001), and negatively moderated by uncertainty avoidance (β=-.005, P<.001). The relationship between perceived ease of use and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.006, P<.001) and indulgence/restraint (β=.009, P<.001), and negatively moderated by uncertainty avoidance (β=-.004, P<.001).

Conclusions

This meta-analysis provided comprehensive evidence on the positive relationship between perceived usefulness and perceived ease of use with adoption intention and the moderating effect of national culture on these two relationships. Regarding the moderating effect, perceived usefulness and perceived ease of use have a greater impact on adoption intention for people in individualistic, masculine, low uncertainty avoidance, and indulgence cultures, respectively.

Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices' ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants' body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond

The new era of digitalized knowledge and information technology (IT) has improved efficiency in all medical fields, and digital health solutions are becoming the norm. There has also been an upsurge in utilizing digital solutions during the COVID-19 pandemic to address the unmet mental healthcare needs, especially for those unable to afford in-person office-based therapy sessions or those living in remote rural areas with limited access to mental healthcare providers. Despite these benefits, there are significant concerns regarding the widespread use of such technologies in the healthcare system. A few of those concerns are a potential breach in the patients' privacy, confidentiality, and the agency of patients being at risk of getting used for marketing or data harnessing purposes. Digital phenotyping aims to detect and categorize an individual's behavior, activities, interests, and psychological features to properly customize future communications or mental care for that individual. Neuromarketing seeks to investigate an individual's neuronal response(s) (cortical and subcortical autonomic) characteristics and uses this data to direct the person into purchasing merchandise of interest, or shaping individual's opinion in consumer, social or political decision making, etc. This commentary's primary concern is the intersection of these two concepts that would be an inevitable threat, more so, in the post-COVID era when disparities would be exaggerated globally. We also addressed the potential "dark web" applications in this intersection, worsening the crisis. We intend to raise attention toward this new threat, as the impacts might be more damming in low-income settings or/with vulnerable populations. Legal, health ethics, and government regulatory processes looking at broader impacts of digital marketing need to be in place.

The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.

When CVaR Meets With Bluetooth PAN: A Physical Distancing System for COVID-19 Proactive Safety

In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.