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Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes. Methods: This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one's demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state. Results: Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models' ability to accurately estimate healthcare outcomes for the sensitive groups. Discussion: These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.
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OBJECTIVE: To characterize cognitive workload (CWL) of cardiac surgery team members in a real-world setting during coronary artery bypass grafting (CABG) surgery using providers' heart rate variability (HRV) data as a surrogate measure of CWL. METHODS: HRV was collected from the surgeon, anesthesiologist, perfusionist, and scrub nurse, and audio/video recordings were made during isolated, nonemergency CABG surgeries (n = 27). Eight surgical phases were annotated by trained researchers, and HRV was calculated for each phase. RESULTS: Significant differences in CWL were observed within a given role across surgical phases. Results are reported as predicted probability (95% confidence interval [CI]). CWL was significantly higher for anesthesiologists during "preparation and induction" (0.57; 95% CI, 0.42-0.71) and "anastomoses" (0.44; 95% CI, 0.30-0.58) compared to other phases, and the same held for nurses during the "opening" (0.51; 95% CI, 0.37-0.65) and "postoperative" (0.68; 95% CI, 0.42-0.86) phases. Additional significant differences were observed between roles within a given surgical phase. For example, surgeons had significantly higher CWL during "anastomoses" (0.81; 95% CI, 0.69-0.89) compared to all other phases, and the same was true of perfusionists during the "opening" (0.79; 95% CI, 0.66-0.88) and "prebypass preparation" (0.50; 95% CI, 0.36-0.64) phases. CONCLUSIONS: Our innovative analysis demonstrates that CWL fluctuates across surgical procedures by role and phase, which may reflect the distribution of primary tasks. This corroborates earlier findings from self-report measures. The data suggest that team-wide, peak CWL during a phase decreases from early phases of surgery through initiation of cardiopumonary bypass (CPB), rises during anastomosis, and decreases after termination of CPB. Knowledge of these trends could encourage the adoption of behaviors to enhance team dynamics and performance.
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Like all job applicants, veterans have to face the ubiquitous employment interview and pass this potential hurdle to civilian sector employment. So, because of the uniqueness of transitioning from the military to civilian employment, the present paper sought to identify perceived interviewing strengths and weaknesses of veteran interviewees from (a) the perspective of civilian sector human resource professionals (i.e. hiring personnel) with experience interviewing veterans (Study 1, five focus groups, N = 14), and (b) veterans (Study 2, N = 93). Qualitative analysis of the focus group transcripts resulted in the emergence of two theme categories: (1) veteran interviewee strengths and (2) veteran interviewee weaknesses. This information guided the development of a 10-item survey that was completed by 93 veterans (Study 2). In its totality, the results (from both Study 1 and Study 2) indicated that communication of soft skills, confidence, and professionalism were perceived to be strengths that veterans displayed during civilian employment interviews, and conversely, the ineffective translation and communication of relevant technical skills acquired in the military, use of military jargon, and nervousness were considered to be weaknesses. Recommendations to capitalize on the strengths and mitigate the weaknesses are presented.
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Understanding how communication processes contribute to well-functioning versus distressed couple relationships has relied largely on brief, laboratory-based conversations. Harnessing technological advancements, the present study extends the literature by capturing couples' naturalistic communication over one full day at Time 1 (T1). This study tested associations between data-driven categories of couple communication behaviors and relationship outcomes (i.e., relationship aggression, satisfaction, and dissolution) at Time 2 (T2), approximately 1 year later. Emerging adults in different-gender dating couples (n = 106 couples; 212 individuals; Mage = 22.57 ± 2.44; M relationship length = 30.49 months ± 24.05; 72.2% non-White) were each provided a smartphone programmed to audio record approximately 50% of a typical day. Interactions between partners were transcribed and coded for location, activity, affect, and a range of positive and negative communication behaviors for each partner. Even after controlling for T1 assessments of the relevant outcome, one's own hostility and one's partner's hostility at T1 were each positively associated with T2 relationship aggression and negatively associated with T2 relationship satisfaction. One's own withdrawal at T1 was positively associated with T2 relationship aggression perpetration, whereas one's partner's withdrawal was negatively linked to relationship satisfaction at T2. One's own playfulness, unexpectedly, was linked to lower subsequent relationship satisfaction. Withdrawal increased the likelihood of relationship dissolution, whereas warmth and playfulness decreased the likelihood of dissolution. The relevance of couples' ordinary, everyday communication for meaningful relationship outcomes is discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Agressão , Relações Interpessoais , Adulto , Humanos , Adulto Jovem , Emoções , Satisfação Pessoal , Inquéritos e Questionários , Parceiros Sexuais/psicologia , ComunicaçãoRESUMO
Introduction: Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct. Methods: The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group. Results: The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect. Discussion: This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions.
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Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.
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Ambiente Construído , Marcha , Humanos , Reprodutibilidade dos Testes , Inquéritos e Questionários , AutorrelatoRESUMO
Physiological linkage refers to moment-to-moment, time-linked coordination in physiological responses among people in close relationships. Although people in romantic relationships have been shown to evidence linkage in their physiological responses over time, it is still unclear how patterns of covariation relate to in-the-moment, as well as general levels of, relationship functioning. In the present study with data collected between 2014 and 2017, we capture linkage in electrodermal activity (EDA) in a diverse sample of young-adult couples, generally representative and generalizable to the Los Angeles community from which we sampled. We test how naturally occurring, shifting feelings of closeness with and annoyance toward one's partner relate to concurrent changes in levels of physiological linkage over the course of 1 day. Additionally, we examine how linkage relates to overall relationship satisfaction. Results showed that couples evidenced significant covariation in their levels of physiological arousal in daily life. Further, physiological linkage increased during hours that participants felt close to their romantic partners but not during hours that participants felt annoyed with their partners. Finally, those participants with overall higher levels of relationship satisfaction showed lower levels of linkage over the day of data collection. These findings highlight how individuals respond in sync with their romantic partners and how this process ebbs and flows in conjunction with the shifting emotional tone of their relationships. The discussion focuses on how linkage might enhance closeness or, alternatively, contribute to conflict escalation and the potential of linkage processes to promote positive interpersonal relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Resposta Galvânica da Pele , Relações Interpessoais , Adulto , Humanos , Parceiros Sexuais/psicologia , Emoções , Satisfação PessoalRESUMO
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.
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Inteligência Artificial , Saúde Mental , Humanos , Conscientização , Viés , TecnologiaRESUMO
Sensor-based assessment in combination with machine learning algorithms provide the potential to augment current practices of the (early) diagnosis of cognitive impairment. The goal of this paper is to detect cognitive impairment in elderly adults using sensor-based measures installed in the home. Longitudinal time-series data of sensor signals are analyzed with Poisson process (PP) models and supervised machine learning algorithms to identify individuals with mild cognitive impairment (MCI) and dementia. We examine two types of PP models: a homogeneous PP which assumes a constant rate of change for each sensor, and a non-homogeneous PP which incorporates contextual information by separately estimating the arrival rate for each task. Our results indicate that the proposed approach can effectively distinguish between patients with dementia and healthy individuals, as well as patients with MCI and healthy individuals based on the sensor-based PP features. Sensor-based assessment that relies on the non-homogeneous PP is further found to be more effective for the task of interest compared to homogeneous PP, as well as expert-based assessment. Findings from this research have the potential to help detect the early onset of cognitive impairment in elderly adults, and demonstrate the ability of computational models and machine learning to predict cognitive health, thus, contributing toward advancing aging-in-place. Clinical Relevance-This examines a computational method to quantify cognitive decline for elderly adults using home-based sensors. eventually contributing to ambulatory clinical biomarkers for dementia.
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Disfunção Cognitiva , Demência , Adulto , Idoso , Disfunção Cognitiva/diagnóstico , Demência/diagnóstico , Diagnóstico Precoce , Humanos , Vida Independente , Aprendizado de MáquinaRESUMO
Does talking about loss with a romantic partner have salutary personal and relationship effects? Prior evidence reveals the benefits of emotional disclosure in couple relationships, yet disclosure about loss has been overlooked in research on couple communication. Using a novel communication paradigm with young-adult heterosexual romantic partners (N = 114 couples), we investigated emotions, physiological arousal (skin conductance responses [SCR]), and relationship closeness when narrating a personal loss and listening to the partner's loss, and compared these loss discussions to discussions about desired relationship changes. Based on partners' self-reports, narrating loss elicited more vulnerable and, unexpectedly, more antagonistic emotions. Both narrating and listening to loss produced higher self-reported partner closeness, compared to discussing change. In support of the physiological benefits of disclosure, women's SCRs decreased over the discussion when they narrated their own loss. However, both women and men as listeners show a general trend of increasing SCRs over the discussion, suggesting the challenges of being a responsive partner. Moreover, in line with the putative protective effects of partners' biological interdependencies, partner closeness also was higher when both partners showed synchronous decreasing SCR as women narrated their loss. Although limited to young couples in relatively short relationships, these findings reveal some potential benefits of talking about loss in the context of romantic relationships. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Relações Interpessoais , Parceiros Sexuais , Adulto , Comunicação , Emoções , Feminino , Heterossexualidade , Humanos , Masculino , Parceiros Sexuais/psicologiaRESUMO
Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.
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Diabetes Mellitus Tipo 2 , Carboidratos da Dieta , Biomarcadores , Glicemia/metabolismo , Dieta , Gorduras na Dieta/metabolismo , Proteínas Alimentares/metabolismo , Glucose , Humanos , Insulina , Refeições/fisiologia , NutrientesRESUMO
Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics. Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability. Methods: A multidisciplinary overview and literature synthesis. Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability. Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.
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Pessoas com Deficiência , Planejamento Ambiental , Idoso , Ambiente Construído , Cidades , Humanos , Características de ResidênciaRESUMO
The present study investigated whether the presence of a romantic partner in daily life is associated with attenuated sympathetic nervous system responses. Additionally, romantic attachment style was tested as a moderator. For one day, 106 heterosexual young adult dating couples wore ambulatory sensors that monitored electrodermal activity (EDA) - an index of sympathetic arousal. Couples reported whether they were together or apart for every hour of the data collection day. Men and women exhibited lower EDA during hours in which their partner was present compared to hours in which they were absent. Additionally, romantic attachment style moderated this association; those who had low anxious attachment showed a stronger attenuating effect of partner presence compared to those with higher anxious attachment. Similarly, those who had low avoidant attachment showed heightened effects of partner presence compared to those with higher avoidant attachment. Romantic partner presence may facilitate everyday health-promoting physiological processes.
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Relações Interpessoais , Apego ao Objeto , Ansiedade , Feminino , Heterossexualidade , Humanos , Masculino , Parceiros Sexuais , Adulto JovemRESUMO
The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.
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Atividades Cotidianas , Algoritmos , Idoso , Análise por Conglomerados , Humanos , Movimento (Física)RESUMO
As hospital workers face a growing number of patients and have to meet increasingly rigorous standards of care, their ability to successfully modulate their emotional reactions and flexibly handle stress presents a significant challenge. This paper examines a multimodal signal-driven way to quantify emotion self-regulation and stress spillover through a dynamical systems model (DSM). The proposed DSM models day-to-day changes of emotional arousal, captured through speech, physiology, and daily activity measures, and its interplay with daily stress. The parameters of the DSM quantify the degree of self-regulation and stress spillover, and are associated with work performance and cognitive ability in a multimodal dataset of 130 full-time hospital workers recorded over a 10-week period. Linear regression experiments indicate the effectiveness of the proposed features to reliably estimate individuals' work performance and cognitive ability, providing significantly higher Pearson's correlations compared to aggregate measures of emotional arousal. Results from this study demonstrate the importance of quantifying oscillatory behaviors from longitudinal ambulatory signals and can potentially deepen our understanding of emotion self-regulation and stress spillover using signal-driven measurements, which complement self-reports and provide estimates of the psychological constructs of interest in a fine-grained time resolution.
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Regulação Emocional , Fala , Atividades Cotidianas , Emoções , Ocupações em Saúde , HumanosRESUMO
Prolonged influence of negative emotions can result in clinical depression or anxiety, and while many prescribed techniques exist, music therapy approaches, coupled with psychotherapy, have shown to help lower depressive symptoms, supplementing traditional treatment approaches. Identifying the appropriate choice of music, therefore, is of utmost importance. Selecting appropriate playlists, however, are challenged by user feedback that may inadvertently select songs that amplify the negative effects. Therefore, this work uses electroencephalogram (EEG) that automatically identifies the emotional impact of music and trains a reinforcement-learning approach to identify an adaptive personalized playlist of music to lead to improved emotional states. This work uses data from 32 users, collected in the publicly available DEAP dataset, to select songs for users that guide them towards joyful emotional states. Using a domain-specific reward-shaping function, a Q-learning agent is able to correctly guide a majority of users to the target emotional states, represented in a common emotion wheel. The average angular error of all users is 57°, with a standard deviation of 2.8 and the target emotional state is achieved.Clinical relevance- Music therapy for improving clinical depression and anxiety can be supplemented by additional emotion-guided music decisions in remote and personal settings by using automated techniques to capture emotional state and identify music that best guides users to target joyful states.
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Música , Eletroencefalografia , Emoções , Humanos , Aprendizagem , Reforço PsicológicoRESUMO
BACKGROUND: Although past longitudinal research demonstrates that romantic partners affect one another's health outcomes, considerably less is known about how romantic experiences "get under the skin" in everyday life. PURPOSE: The current study investigated whether young couples' naturally occurring feelings of closeness to and annoyance with each other during waking hours were associated with their overnight cardiovascular activity. METHODS: Participants were 63 heterosexual young adult dating couples (Mage = 23.07). Using ecological momentary assessments, couples reported their hourly feelings of closeness to and annoyance with their partners across 1 day; subsequent overnight heart rate was captured through wearable electrocardiogram biosensors. Actor-partner interdependence models tested whether individuals' overnight heart rate varied as a function of (a) their own daytime feelings of closeness and annoyance (actor effects) and (b) their partner's daytime feelings of closeness and annoyance (partner effects) while controlling for daytime heart rate. RESULTS: Although young adults' feelings of romantic closeness and annoyance were unrelated to their own overnight heart rate (i.e., no actor effects), gender-specific partner effects emerged. Young men's nocturnal heart rate was uniquely predicted by their female partner's daytime relationship feelings. When women felt closer to their partners during the day, men exhibited lower overnight heart rate. When women felt more annoyed with their partners during the day, men exhibited heightened overnight heart rate. CONCLUSIONS: The findings illustrate gender-specific links between couple functioning and physiological arousal in the everyday lives of young dating couples, implicating physiological sensitivity to partner experiences as one potential pathway through which relationships affect health.
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Emoções/fisiologia , Frequência Cardíaca/fisiologia , Relações Interpessoais , Parceiros Sexuais , Avaliação Momentânea Ecológica , Feminino , Heterossexualidade , Humanos , Masculino , Modelos Psicológicos , Adulto JovemRESUMO
Individuals exposed to aggression and who perpetrate aggression against others show differences in their physiological activation during stress; the goal of the present study is to investigate physiological stress reactivity as a factor contributing to the intergenerational transmission of aggression. To test associations between family-of-origin aggression (FOA), physiological reactivity in daily life, and dating aggression perpetration, we used ecological momentary assessment to monitor fluctuations in young adult (Mageâ¯=â¯23.1â¯years) dating couples' electrodermal activity (EDA) over an entire day and examined how naturally-occurring bouts of annoyance between partners relate to EDA, FOA, and dating aggression perpetration. Dating perpetration was linked to lower general levels of EDA in both men and women, while FOA was linked to lower general levels of EDA in men only. For women, multi-group, multilevel models showed that FOA and dating aggression perpetration moderated the association between feeling annoyed and EDA, such that those with greater FOA and dating aggression perpetration showed greater EDA reactivity during naturally-occurring relationship stress. Furthermore, this pattern of EDA reactivity mediated the link between FOA and dating aggression perpetration in women. These results provide evidence that FOA and dating aggression perpetration are linked to patterns of physiological responsivity in everyday life and suggest that these patterns could be important factors contributing to the intergenerational transmission of aggression.