Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

The Relationship between Patients' Demands and Workplace Violence among Healthcare Workers: A Multilevel Look Focusing on the Moderating Role of Psychosocial Working Conditions.

Workplace violence against healthcare workers is a widespread phenomenon with very severe consequences for the individuals affected and their organizations. The role played by psychosocial working conditions in healthcare workers' experiences of violence from patients and their family members has received relatively scant attention. In the present study, we investigated the idea that psychosocial working conditions (workload, job control, supervisor support, and team integration), by affecting the well-being and job performance of healthcare workers, play a critical role in the relationship between patients' demands and the escalation of workplace violence. Specifically, we tested the hypothesis that psychosocial working conditions moderate the relationship between patients' demands and workplace violence. Participants were 681 healthcare workers distributed in 55 work groups of three public healthcare facilities in Italy. Multilevel analysis showed significant interactions between patients' demands and each of the investigated psychosocial factors on workplace violence, which in all the cases were in the expected direction. The results suggest that improving the quality of the psychosocial work environment in which healthcare workers operate may be a critical aspect in the prevention of workplace violence.

Is it Time to Include Wearable Sleep Trackers in the Applied Psychologists' Toolbox?

Wearable sleep trackers are increasingly used in applied psychology. Particularly, the recent boom in the fitness tracking industry has resulted in a number of relatively inexpensive consumer-oriented devices that further enlarge the potential applications of ambulatory sleep monitoring. While being largely positioned as wellness tools, wearable sleep trackers could be considered useful health devices supported by a growing number of independent peer-reviewed studies evaluating their accuracy. The inclusion of sensors that monitor cardiorespiratory physiology, diurnal activity data, and other environmental signals allows for a comprehensive and multidimensional approach to sleep health and its impact on psychological well-being. Moreover, the increasingly common combination of wearable trackers and experience sampling methods has the potential to uncover within-individual processes linking sleep to daily experiences, behaviors, and other psychosocial factors. Here, we provide a concise overview of the state-of-the-art, challenges, and opportunities of using wearable sleep-tracking technology in applied psychology. Specifically, we review key device profiles, capabilities, and limitations. By providing representative examples, we highlight how scholars and practitioners can fully exploit the potential of wearable sleep trackers while being aware of the most critical pitfalls characterizing these devices. Overall, consumer wearable sleep trackers are increasingly recognized as a valuable method to investigate, assess, and improve sleep health. Incorporating such devices in research and professional practice might significantly improve the quantity and quality of the collected information while opening the possibility of involving large samples over representative time periods. However, a rigorous and informed approach to their use is necessary.

Navigating menopause at work: a preliminary study about challenges and support systems.

OBJECTIVE: Women's increasing workforce participation necessitates understanding unique life phases like menopause for enhanced workplace inclusivity. This research investigates the challenges and needs of peri-menopausal women in work settings, using the Job Demands-Resources model as a foundation. METHODS: A cross-sectional survey was administered to 351 working women aged 40 to 65 years in the United States. Hierarchical multiple regression models were employed to assess the relationship between the severity of menopausal symptoms, emotional exhaustion, work engagement, and turnover intentions. RESULTS: Most of the respondents reported moderate (38.46%) to severe (35.9%) menopausal symptoms. Notably, 54% of the women were caregivers for children or adults. About 77.7% of participants reported work-related challenges due to menopause, with a perceived reduction in productivity (56.8%) being the most common issue. The severity of menopausal symptoms was found to significantly predict more emotional exhaustion ( P < 0.001), less work engagement ( P < 0.001), and greater turnover intentions ( P = 0.03). Concerns about being perceived as less capable in the workplace due to menopausal symptoms were reported by 51.2% of respondents. A striking gap exists between the workplace measures desired by women, such as formal menopause policies and managerial training (65.4%-68%), and their actual implementation (2%-6.3%). CONCLUSIONS: This study reveals an exigent need for increased awareness and structural changes to support working women going through menopause. The findings have far-reaching implications for not just promoting gender equity and well-being but are also pivotal for maintaining a diversified, engaged, and effective workforce.

State of the science and recommendations for using wearable technology in sleep and circadian research.

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Uncovering the main and interacting impact of workaholism on momentary hedonic tone at work: An experience sampling approach.

Workaholism is a current issue in modern organizations with well-characterized implications for individual health and well-being. Yet, the affective experience of workaholics at work and their emotional reactivity to job stressors have been poorly investigated, with the few available studies being cross-sectional or based on retrospective reports obtained outside the working time. Here, we conducted an experience sampling study to characterize workaholics' affective experience during work and their emotional reactivity to workday accumulation and momentary workload. A total of 139 full-time back-office workers participated in a 3-day protocol by reporting on their hedonic tone and momentary workload up to six times per workday. Multilevel modeling was used to investigate the relationship between trait workaholism and job-related hedonic tone as well as the cross-level interactions between workaholism and both workday accumulation and momentary workload. As expected, our results showed lower job-related hedonic tone for individuals with higher workaholic tendencies, with workaholism interacting with the two investigated job stressors. However, contrary to our hypotheses, we found that workaholism weakened, rather than strengthening, the negative trend of hedonic tone over the levels of both predictors, a result possibly explained by a blunted reactivity condition due to chronic job strain. Moreover, we corroborated previous findings suggesting worse outcomes in workaholic women compared to workaholic men. Organizations should consider taking action to monitor and manage the workaholism levels in the workforce and to create a psychosocial work environment that discourages the enactment of workaholic tendencies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Wearable and mobile technology to characterize daily patterns of sleep, stress, presleep worry, and mood in adolescent insomnia.

OBJECTIVES: Characterizing daily patterns of sleep, stress, presleep worry, and mood in adolescents with and without insomnia symptomatology. DESIGN: Two months of continuous wearable tracking and daily diary ratings. SETTING: Free-living conditions. PARTICIPANTS: Ninety-three adolescents (59 girls; 16-19 years old) with (N = 47; 26 with clinical and 21 with sub-clinical) and without (N = 46; control) DSM-5 insomnia symptomatology. MEASUREMENTS: Fitbit Charge 3 tracked sleep, heart rate, and steps. Evening electronic diaries collected ratings of daily stress, presleep worry, and mood. RESULTS: While sleep duration (control: 6.88 ± 1.41 hours; insomnia: 6.92 ± 1.28 hours), architecture, timing, and night-to-night variability were similar between groups, the insomnia group reported higher levels of stress and worry, being mainly related to "school". At the intraindividual level, stress and worry predicted shorter sleep duration and earlier wake up times, which, in turn, predicted higher stress the following day. Moreover, higher-than-usual stress predicted higher sleep-time heart rate, with a more consistent effect in adolescents with insomnia. Results were overall consistent after controlling for covariates and several robustness checks. CONCLUSIONS: There is a bidirectional relationship between daily stress and sleep, with daily stress negatively impacting sleep, which in turn leads to more stress in adolescents with and without insomnia symptoms. Findings also highlight the complexity of insomnia in adolescence, in which the core clinical features (perceived sleep difficulties) and the critical factors (stress/worry) implicated in the pathophysiology of the disorder are not necessarily reflected in objective sleep indicators.

Workaholism and the Enactment of Bullying Behavior at Work: A Prospective Analysis.

Despite the fact that workaholism and workplace aggressive behavior share many correlates, such as neuroticism, hostility, and negative affectivity, little is known about their relationship, with most evidence on both phenomena coming from cross-sectional studies. In the present study, we contributed to a better understanding of the antecedents of enacted workplace bullying behavior (i.e., perpetration of bullying), and the potential interpersonal implications of workaholism, by investigating their cross-lagged relationship. Data from a two-wave one-year panel study conducted with 235 employees in a national healthcare service organization showed substantial cross-sectional and cross-lagged positive relationships between workaholism and enacted workplace bullying. Whereas Time 1 workaholism was a significant predictor of Time 2 enacted workplace bullying, reversed causation was not supported. To shed light on the role of a potential mechanism explaining the link between workaholism and enactment of bullying, we examined whether job-related negative affect (e.g., anger) mediated their longitudinal relationship. However, whereas increased negative affect from T1 to T2 was positively associated with T2 enacted workplace bullying, the relationship between T1 workaholism and increased job-related negative affect was not significant, contrary to the hypothesized mediation. Taken together, our findings suggest that workaholism may be an important antecedent of enacted workplace bullying. Study limitations and future perspectives are discussed.

Age Diversity Climate Affecting Individual-Level Work-Related Outcomes.

The present study answers the call for more studies to investigate the age diversity climate's effect on individual-level outcomes. Building on the social identity approach and social exchange theory, we surveyed 110 Italian employees aged between 18 and 61 years old (M = 46.10, SD = 10.02) and investigated the role of age diversity climate in predicting intentions to quit (H1), job-related wellbeing (H2), and work engagement (H3). Our findings confirmed the hypotheses (H1 and H2), showing the added effect of age diversity climate over and above age, job tenure, role clarity, job demands, job control, perceived support, and perceived job and organizational fit. In fact, age diversity climate accounted for a significant increase in the variance explained for two of the three hypothesized models (i.e., intentions to quit and job-related wellbeing, but not work engagement). To conclude, this study contributes to the existing literature by showing the age diversity climate's predictive value for turnover intentions and job-related wellbeing, and corroborating the importance of supporting age diversity through a variety of Human Resources Management strategies.

Rigorous performance evaluation (previously, "validation") for informed use of new technologies for sleep health measurement.

New sleep technologies have become pervasive in the consumer space, and are becoming highly common in research and clinical sleep settings. The rapid, widespread use of largely unregulated and unstandardized technology has enabled the quantification of many different facets of sleep health, driving scientific discovery. As sleep scientists, it is our responsibility to inform principles and practices for proper evaluation of any new technology used in the clinical and research settings, and by consumers. A current lack of standardized methods for evaluating technology performance challenges the rigor of our scientific methods for accurate representation of the sleep health facets of interest. This special article describes the rationale and priorities of an interdisciplinary effort for rigorous, standardized, and rapid performance evaluation (previously, "validation") of new sleep and sleep disorders related technologies of all kinds (eg, devices or algorithms), including an associated article template for a new initiative for publication in Sleep Health of empirical studies systematically evaluating the performance of new sleep technologies. A structured article type should streamline manuscript development and enable more rapid writing, review, and publication. The goal is to promote rapid and rigorous evaluation and dissemination of new sleep technology, to enhance sleep research integrity, and to standardize terminology used in Rigorous Performance Evaluation papers to prevent misinterpretation while facilitating comparisons across technologies.

The Importance of Contextualized Psychosocial Risk Indicators in Workplace Stress Assessment: Evidence from the Healthcare Sector.

The routine assessment of workplace stress is mostly based on standardized self-report tools, including generic psychosocial risk indicators (G-PRIs) designed to fit very heterogeneous occupational sectors. However, the use "by default" of such indicators might be inadequate when they fail to characterize the specificity of the work environment; hence, the inclusion of more contextualized indicators (C-PRIs) has been recommended. We aimed at evaluating the additional contribution of three C-PRIs (Work-Family Conflict, Emotional Demands, and Excessive Demands from Patients) in predicting individual outcomes (Emotional Exhaustion, Turnover Intentions) compared to commonly used G-PRIs (e.g., Demand, Control, Support), in a sample of 787 healthcare workers involved in a routine workplace stress assessment. Multilevel hierarchical regression supported the additional contributions of C-PRIs in predicting both outcomes over G-PRIs, sex, age and shift work. More robust and consistent evidence emerged for Emotional Exhaustion, which was significantly predicted by all C-PRIs, whereas Turnover Intentions was only predicted by the C-PRI Emotional Demands. Importantly, not all G-PRIs showed a relationship with the two outcomes. Taken together, our results support the importance of including C-PRIs in workplace stress assessment carried out by organizations, which should be selected based on literature search and discussion with the stakeholders.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls.

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code.

Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

Sleep quality and quantity in Italian University students: an actigraphic study.

According to international recommendations, young adults should sleep at least 7 h per night and experience good sleep quality to avoid physical and mental health problems. University students are particularly exposed to the risk of sleep issues due to their tendency to go to bed late at night and other social and environmental factors. Here, we aimed to objectively characterize the weekly sleep habits using actigraphy (in terms of quantity, quality, and timing) in 82 Italian university students (mean age = 23.89, SD = 2.51 yrs, 44 women). Exploratory analyses were performed using linear mixed-effect regression to account for several factors (e.g., gender, circadian preferences, depressive symptomatology). We showed that participants spent in bed (TIB) about 7 h and 31 min during weekdays and 7 h and 46 min during the weekend, with 76.83% of the sample showing a TIB longer than 7 h. Women students spent more time in bed than men (~25 min) and went to bed earlier (~29 min). One-third of the sample showed a sleep efficiency <85%, whereas 70.73% of the sample showed a wake after sleep onset >40 min, with no differences between men and women. Depressive symptoms, alcohol and coffee consumption affected sleep onset latency, whereas circadian preference was strongly associated with bed and waketime. Moreover, most of the students did not take daytime naps to catch-up with sleep loss. Overall, our sample spent adequate time in bed, but they experienced a moderate low sleep quality, mainly due to high wake after sleep onset.

Sensors Capabilities, Performance, and Use of Consumer Sleep Technology.

Sleep is crucial for the proper functioning of bodily systems and for cognitive and emotional processing. Evidence indicates that sleep is vital for health, well-being, mood, and performance. Consumer sleep technologies (CSTs), such as multisensory wearable devices, have brought attention to sleep and there is growing interest in using CSTs in research and clinical applications. This article reviews how CSTs can process information about sleep, physiology, and environment. The growing number of sensors in wearable devices and the meaning of the data collected are reviewed. CSTs have the potential to provide opportunities to measure sleep and sleep-related physiology on a large scale.

Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions.

Wearable sensors are promising instruments for conducting both laboratory and ambulatory research in psychophysiology. However, scholars should be aware of their measurement error and the conditions in which accuracy is achieved. This study aimed to assess the accuracy of a wearable sensor designed for research purposes, the E4 wristband (Empatica, Milan, Italy), in measuring heart rate (HR), heart rate variability (HRV), and skin conductance (SC) over five laboratory conditions widely used in stress reactivity research (seated rest, paced breathing, orthostatic, Stroop, speech task) and two ecological conditions (slow walking, keyboard typing). Forty healthy participants concurrently wore the wristband and two gold standard measurement systems (i.e., electrocardiography and finger SC sensor). The wristband accuracy was determined by evaluating the signal quality and the correlations with and the Bland-Altman plots against gold standard-derived measurements. Moreover, exploratory analyses were performed to assess predictors of measurement error. Mean HR measures showed the best accuracy over all conditions. HRV measures showed satisfactory accuracy in seated rest, paced breathing, and recovery conditions but not in dynamic conditions, including speaking. Accuracy was diminished by wrist movements, cognitive and emotional stress, nonstationarity, and larger wrist circumferences. Wrist SC measures showed neither correlation nor visual resemblance with finger SC signal, suggesting that the two sites may reflect different phenomena. Future studies are needed to assess the responsivity of wrist SC to emotional and cognitive stress. Limitations and implications for laboratory and ambulatory research are discussed.