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Open Access Publications from the University of California

Scholarly Works (10 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2020)

Parent emotion socialization behaviors (i.e., parents’ emotion expressions, responsiveness to, and regulation of their children’s emotions) are associated with children’s regulation of their own emotions. The link between socialization behaviors and child emotion regulation may have developmental implications, as children who exhibit greater difficulties regulating emotions also have worse social and mental health outcomes concurrently and predictively. Despite the vast body of research on parent emotion socialization, few studies have examined whether a mother’s own regulation of emotions is associated with emotion socialization or toddler emotion regulation (16-24 months). Our study examined the association of self-reported and physiological indices of mothers’ emotion regulation (ER) with self-reported parenting styles and child ER as observed in a behavioral task meant to elicit negative affect in the child (N = 153). Findings show mothers’ self-reported ER difficulties was positively associated with more negative parenting attitudes and conditional regard (e.g., restricting parental affection based on expectations towards child’s displays of anger and good behavior). Physiological indices of mothers’ ER were associated with parenting sensitivity. However, both self-reported and physiological indices of mothers’ ER difficulties were not associated with child distress or emotion regulatory behaviors. Mothers’ conditional regard toward child behavior and anger were associated with greater displays of child aggression. There may be unique differences in how subjective and objective indices of maternal ER are linked to parenting behaviors. Implications and limitations of these findings are discussed.

    Cover page: Parent Difficulties in Emotion Regulation: Associations with Parent Emotion Socialization and Child Emotion RegulationCreative Commons 'BY-NC-ND' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC Irvine Electronic Theses and Dissertations (2023)

    The ability to regulate or manage affect is crucial for well-being, and deficits in affect regulation are common in psychopathology. Factors such as sleep or cognitive load are linked to difficulties in regulating; however, little is known about factors that may support successful affect regulation. A presumed individual difference and necessary step of affect regulation is emotional awareness. Although facets of emotional awareness are theoretically a precursor for affect regulation, findings regarding their associations with habitual use of regulation strategies are mixed. It may be that while low emotional awareness is linked to greater difficulties in affect regulation, heightened emotional awareness may not be directly linked to how often regulation strategies are used. Furthermore, intensity of affect may be an important, relevant factor to consider in investigating the link between emotional awareness and affect regulation. The current dissertation systematically examined how facets of emotional awareness, as well as affect intensity, relate to affect regulation ability and success. The dissertation is comprised of three studies using distinct methodological approaches. Study 1 is a large cross-sectional study that (1) investigated links between trait-level emotional awareness and subjective affect regulation ability and (2) tested whether affect intensity moderated these potential links. Study 2 builds upon Study 1 by using an idiographic, temporal, and more ecologically valid approach (i.e., a longitudinal daily diary study) and tested the relations among emotional awareness, affect intensity, and successful affect regulation in daily life. Lastly, Study 3 is an electroencephalography experimental study that assessed links between trait-level emotional awareness and objectively measured state affect regulation success. Altogether, the current dissertation offers empirical contributions to the field of affective science that may inform future efforts aimed at supporting affective well-being.

      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2021)

      Background

      Supporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them.

      Objective

      To address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts.

      Methods

      We interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short.

      Results

      Users of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood.

      Conclusions

      Although people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations.
        Cover page: Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study.
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2023)

        Background

        Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time.

        Objective

        Previous attempts to model an individual's mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones). This study aims to investigate the capacity of monitoring affect using fully objective measurements. We conducted a comparatively long-term (12-month) study with a holistic sampling of participants' moods, including 20 affective states.

        Methods

        Longitudinal physiological data (eg, sleep and heart rate), as well as daily assessments of affect, were collected using 3 modalities (ie, smartphone, watch, and ring) from 20 college students over a year. We examined the difference between the distributions of data collected from each modality along with the differences between their rates of missingness. Out of the 20 participants, 7 provided us with 200 or more days' worth of data, and we used this for our predictive modeling setup. Distributions of positive affect (PA) and negative affect (NA) among the 7 selected participants were observed. For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). We also investigated the capability of each modality in predicting mood and the most important features of PA and NA RF models.

        Results

        RF was the best-performing model in our analysis and performed mood and stress (nervousness) prediction with ~81% and ~72% accuracy, respectively. PA models resulted in better performance compared to NA. The order of the most important modalities in predicting PA and NA was the smart ring, phone, and watch, respectively. SHAP (Shapley Additive Explanations) analysis showed that sleep and activity-related features were the most impactful in predicting PA and NA.

        Conclusions

        Generic machine learning-based affect prediction models, trained with population data, outperform existing methods, which use the individual's historical information. Our findings indicated that our mood prediction method outperformed the existing methods. Additionally, we found that sleep and activity level were the most important features for predicting next-day PA and NA, respectively.
          Cover page: Objective Prediction of Next-Day's Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study.
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2020)
          Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits. Recent advances in wearable and mobile technology have generated increased interest in digital mental healthcare that enables individuals to address episodic mental health symptoms. However, these efforts are typically reactive and symptom-focused and do not provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds over time. Recognizing that each individual is unique, we present the notion of Personalized Mental Health Navigation (MHN): a therapist-in-the-loop, cybernetic goal-based system that deploys a continuous cyclic loop of measurement, estimation, guidance, to steer the individual's mental health state towards a healthy zone. We outline the major components of MHN that is premised on the development of an individual's personal mental health state, holistically represented by a high-dimensional cover of multiple knowledge layers such as emotion, biological patterns, sociology, behavior, and cognition. We demonstrate the feasibility of the personalized MHN approach via a 12-month pilot case study for holistic stress management in college students and highlight an instance of a therapist-in-the-loop intervention using MHN for monitoring, estimating, and proactively addressing moderately severe depression over a sustained period of time. We believe MHN paves the way to transform mental healthcare from the current passive, episodic, reactive process (where individuals seek help to address symptoms that have already manifested) to a continuous and navigational paradigm that leverages a personalized model of the individual, promising to deliver timely interventions to individuals in a holistic manner.
            Cover page: Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-beingCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2021)

            Background

            The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things.

            Objective

            To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September.

            Methods

            The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual's psychological well-being across the pandemic.

            Results

            Over the course of the pandemic, Lee's depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee's positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=-0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=-0.58, P=.03) and diurnal beats per minute (r=-0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors.

            Conclusions

            We observed more pandemic concerns in April and noticed other contextual events relating to this individual's well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health.
              Cover page: Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study.
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2022)
              Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual's personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.
                Cover page: Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being.Creative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2024)
                Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individuals loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.
                  Cover page: Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications.
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2022)

                  Background

                  Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.

                  Objective

                  In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.

                  Methods

                  College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.

                  Results

                  Participants with a higher sleep onset latency (b=-1.09, SE 0.36; P=.006) and TST (b=-0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=-0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04).

                  Conclusions

                  Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.
                    Cover page: Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study
                    • Article
                    • Peer Reviewed
                    UC Berkeley Previously Published Works (2022)
                    The Positivity Resonance Theory of coexperienced positive affect describes moments of interpersonal connection characterized by shared positive affect, caring nonverbal synchrony, and biological synchrony. The construct validity of positivity resonance and its longitudinal associations with health have not been tested. The current longitudinal study examined whether positivity resonance in conflict interactions between 154 married couples predicts health trajectories over 13 years and longevity over 30 years. We used couples' continuous ratings of affect during the interactions to capture coexperienced positive affect and continuous physiological responses to capture biological synchrony between spouses. Video recordings were behaviorally coded for coexpressed positive affect, synchronous nonverbal affiliation cues (SNAC), and behavioral indicators of positivity resonance (BIPR). To evaluate construct validity, we conducted a confirmatory factor analysis to test a latent factor of positivity resonance encompassing coexperienced positive affect, coexpressed positive affect, physiological linkage of interbeat heart intervals, SNAC, and BIPR. The model showed excellent fit. To evaluate associations with health and longevity, we used dyadic latent growth curve modeling and Cox proportional hazards modeling, respectively, and found that greater latent positivity resonance predicted less steep declines in health and increased longevity. Associations were robust when accounting for initial health symptoms, sociodemographic characteristics, health-related behaviors, and individually experienced positive affect. We repeated health and longevity analyses, replacing latent positivity resonance with BIPR, and found consistent results. Findings validate positivity resonance as a multimodal construct, support the utility of the BIPR measure, and provide initial evidence for the characterization of positivity resonance as a positive health behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
                      Cover page: Positivity Resonance in Long-Term Married Couples: Multimodal Characteristics and Consequences for Health and Longevity
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